diff --git a/README.md b/README.md index 09e20cf70fe..15f71dad5fb 100644 --- a/README.md +++ b/README.md @@ -103,50 +103,16 @@ Apart from MMDetection, we also released [MMEngine](https://github.com/open-mmla ### Highlight -**v3.2.0** was released in 12/10/2023: +**v3.3.0** was released in 5/1/2024: -**1. Detection Transformer SOTA Model Collection** -(1) Supported four updated and stronger SOTA Transformer models: [DDQ](configs/ddq/README.md), [CO-DETR](projects/CO-DETR/README.md), [AlignDETR](projects/AlignDETR/README.md), and [H-DINO](projects/HDINO/README.md). -(2) Based on CO-DETR, MMDet released a model with a COCO performance of 64.1 mAP. -(3) Algorithms such as DINO support `AMP/Checkpoint/FrozenBN`, which can effectively reduce memory usage. +**[MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and Detection](https://arxiv.org/abs/2401.02361)** -**2. [Comprehensive Performance Comparison between CNN and Transformer](<(projects/RF100-Benchmark/README.md)>)** -RF100 consists of a dataset collection of 100 real-world datasets, including 7 domains. It can be used to assess the performance differences of Transformer models like DINO and CNN-based algorithms under different scenarios and data volumes. Users can utilize this benchmark to quickly evaluate the robustness of their algorithms in various scenarios. +Grounding DINO is a grounding pre-training model that unifies 2d open vocabulary object detection and phrase grounding, with wide applications. However, its training part has not been open sourced. Therefore, we propose MM-Grounding-DINO, which not only serves as an open source replication version of Grounding DINO, but also achieves significant performance improvement based on reconstructed data types, exploring different dataset combinations and initialization strategies. Moreover, we conduct evaluations from multiple dimensions, including OOD, REC, Phrase Grounding, OVD, and Fine-tune, to fully excavate the advantages and disadvantages of Grounding pre-training, hoping to provide inspiration for future work. -
- -
- -**3. Support for [GLIP](configs/glip/README.md) and [Grounding DINO](configs/grounding_dino/README.md) fine-tuning, the only algorithm library that supports Grounding DINO fine-tuning** -The Grounding DINO algorithm in MMDet is the only library that supports fine-tuning. Its performance is one point higher than the official version, and of course, GLIP also outperforms the official version. -We also provide a detailed process for training and evaluating Grounding DINO on custom datasets. Everyone is welcome to give it a try. - -| Model | Backbone | Style | COCO mAP | Official COCO mAP | -| :----------------: | :------: | :-------: | :--------: | :---------------: | -| Grounding DINO-T | Swin-T | Zero-shot | 48.5 | 48.4 | -| Grounding DINO-T | Swin-T | Finetune | 58.1(+0.9) | 57.2 | -| Grounding DINO-B | Swin-B | Zero-shot | 56.9 | 56.7 | -| Grounding DINO-B | Swin-B | Finetune | 59.7 | | -| Grounding DINO-R50 | R50 | Scratch | 48.9(+0.8) | 48.1 | - -**4. Support for the open-vocabulary detection algorithm [Detic](projects/Detic_new/README.md) and multi-dataset joint training.** -**5. Training detection models using [FSDP and DeepSpeed](<(projects/example_largemodel/README.md)>).** - -| ID | AMP | GC of Backbone | GC of Encoder | FSDP | Peak Mem (GB) | Iter Time (s) | -| :-: | :-: | :------------: | :-----------: | :--: | :-----------: | :-----------: | -| 1 | | | | | 49 (A100) | 0.9 | -| 2 | √ | | | | 39 (A100) | 1.2 | -| 3 | | √ | | | 33 (A100) | 1.1 | -| 4 | √ | √ | | | 25 (A100) | 1.3 | -| 5 | | √ | √ | | 18 | 2.2 | -| 6 | √ | √ | √ | | 13 | 1.6 | -| 7 | | √ | √ | √ | 14 | 2.9 | -| 8 | √ | √ | √ | √ | 8.5 | 2.4 | - -**6. Support for the [V3Det](configs/v3det/README.md) dataset, a large-scale detection dataset with over 13,000 categories.** +code: [mm_grounding_dino/README.md](configs/mm_grounding_dino/README.md)
- +
We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet). diff --git a/README_zh-CN.md b/README_zh-CN.md index ccf1cbf0082..885d1f22617 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -102,51 +102,18 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope ### 亮点 -**v3.2.0** 版本已经在 2023.10.12 发布： +**v3.3.0** 版本已经在 2024.1.5 发布： -**1. 检测 Transformer SOTA 模型大合集** -(1) 支持了 [DDQ](configs/ddq/README.md)、[CO-DETR](projects/CO-DETR/README.md)、[AlignDETR](projects/AlignDETR/README.md) 和 [H-DINO](projects/HDINO/README.md) 4 个更新更强的 SOTA Transformer 模型 -(2) 基于 CO-DETR, MMDet 中发布了 COCO 性能为 64.1 mAP 的模型 -(3) DINO 等算法支持 AMP/Checkpoint/FrozenBN，可以有效降低显存 +**MM-Grounding-DINO: 轻松涨点，数据到评测全面开源** -**2. [提供了全面的 CNN 和 Transformer 的性能对比](projects/RF100-Benchmark/README_zh-CN.md)** -RF100 是由 100 个现实收集的数据集组成，包括 7 个域，可以验证 DINO 等 Transformer 模型和 CNN 类算法在不同场景不同数据量下的性能差异。用户可以用这个 Benchmark 快速验证自己的算法在不同场景下的鲁棒性。 +Grounding DINO 是一个统一了 2d 开放词汇目标检测和 Phrase Grounding 的检测预训练模型，应用广泛，但是其训练部分并未开源，为此提出了 MM-Grounding-DINO。其不仅作为 Grounding DINO 的开源复现版，MM-Grounding-DINO 基于重新构建的数据类型出发，在探索了不同数据集组合和初始化策略基础上实现了 Grounding DINO 的性能极大提升，并且从多个维度包括 OOD、REC、Phrase Grounding、OVD 和 Finetune 等方面进行评测，充分挖掘 Grounding 预训练优缺点，希望能为后续工作提供启发。 -
- -
- -**3. 支持了 [GLIP](configs/glip/README.md) 和 [Grounding DINO](configs/grounding_dino/README.md) 微调，全网唯一支持 Grounding DINO 微调** -MMDet 中的 Grounding DINO 是全网唯一支持微调的算法库，且性能高于官方 1 个点，当然 GLIP 也比官方高。 -我们还提供了详细的 Grounding DINO 在自定义数据集上训练评估的流程，欢迎大家试用。 - -| Model | Backbone | Style | COCO mAP | Official COCO mAP | -| :----------------: | :------: | :-------: | :--------: | :---------------: | -| Grounding DINO-T | Swin-T | Zero-shot | 48.5 | 48.4 | -| Grounding DINO-T | Swin-T | Finetune | 58.1(+0.9) | 57.2 | -| Grounding DINO-B | Swin-B | Zero-shot | 56.9 | 56.7 | -| Grounding DINO-B | Swin-B | Finetune | 59.7 | | -| Grounding DINO-R50 | R50 | Scratch | 48.9(+0.8) | 48.1 | - -**4. 支持开放词汇检测算法 [Detic](projects/Detic_new/README.md) 并提供多数据集联合训练可能** - -**5. 轻松使用 [FSDP 和 DeepSpeed 训练检测模型](projects/example_largemodel/README_zh-CN.md)** - -| ID | AMP | GC of Backbone | GC of Encoder | FSDP | Peak Mem (GB) | Iter Time (s) | -| :-: | :-: | :------------: | :-----------: | :--: | :-----------: | :-----------: | -| 1 | | | | | 49 (A100) | 0.9 | -| 2 | √ | | | | 39 (A100) | 1.2 | -| 3 | | √ | | | 33 (A100) | 1.1 | -| 4 | √ | √ | | | 25 (A100) | 1.3 | -| 5 | | √ | √ | | 18 | 2.2 | -| 6 | √ | √ | √ | | 13 | 1.6 | -| 7 | | √ | √ | √ | 14 | 2.9 | -| 8 | √ | √ | √ | √ | 8.5 | 2.4 | +arxiv 技术报告：https://arxiv.org/abs/2401.02361 -**6. 支持了 [V3Det](configs/v3det/README.md) 1.3w+ 类别的超大词汇检测数据集** +代码地址: [mm_grounding_dino/README.md](configs/mm_grounding_dino/README.md)
- +
我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。 diff --git a/configs/faster_rcnn/README.md b/configs/faster_rcnn/README.md index 0d9912db29d..8bcdcf6d512 100644 --- a/configs/faster_rcnn/README.md +++ b/configs/faster_rcnn/README.md @@ -14,50 +14,50 @@ State-of-the-art object detection networks depend on region proposal algorithms ## Results and Models -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | 1x | - | - | 35.6 | [config](./faster-rcnn_r50-caffe_c4-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152.log.json) | -| R-50-DC5 | caffe | 1x | - | - | 37.2 | [config](./faster-rcnn_r50-caffe-dc5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json) | -| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](./faster-rcnn_r50-caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | -| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](./faster-rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN (FP16) | pytorch | 1x | 3.4 | 28.8 | 37.5 | [config](./faster-rcnn_r50_fpn_amp-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](./faster-rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | -| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](./faster-rcnn_r101-caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | -| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](./faster-rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](./faster-rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](./faster-rcnn_x101-32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](./faster-rcnn_x101-32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](./faster-rcnn_x101-64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](./faster-rcnn_x101-64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-C4 | caffe | 1x | - | - | 35.6 | [config](./faster-rcnn_r50-caffe_c4-1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50-caffe-c4_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152.log.json) | +| R-50-DC5 | caffe | 1x | - | - | 37.2 | [config](./faster-rcnn_r50-caffe-dc5_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50-caffe-dc5_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json) | +| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](./faster-rcnn_r50-caffe_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50-caffe_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](./faster-rcnn_r50_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN (FP16) | pytorch | 1x | 3.4 | 28.8 | 37.5 | [config](./faster-rcnn_r50_fpn_amp-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](./faster-rcnn_r50_fpn_2x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50_fpn_2x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | +| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](./faster-rcnn_r101-caffe_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101-caffe_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](./faster-rcnn_r101_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](./faster-rcnn_r101_fpn_2x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101_fpn_2x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](./faster-rcnn_x101-32x4d_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-32x4d_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](./faster-rcnn_x101-32x4d_fpn_2x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-32x4d_fpn_2x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](./faster-rcnn_x101-64x4d_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-64x4d_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](./faster-rcnn_x101-64x4d_fpn_2x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-64x4d_fpn_2x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | ## Different regression loss We trained with R-50-FPN pytorch style backbone for 1x schedule. -| Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Config | Download | -| :------: | :------------: | :------: | :------------: | :----: | :----------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](./faster-rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN | IoULoss | | | 37.9 | [config](./faster-rcnn_r50_fpn_iou_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954-938e81f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | -| R-50-FPN | GIoULoss | | | 37.6 | [config](./faster-rcnn_r50_fpn_giou_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | -| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](./faster-rcnn_r50_fpn_bounded-iou_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | +| Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :------------: | :------: | :------------: | :----: | :----------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](./faster-rcnn_r50_fpn_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50_fpn_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | IoULoss | | | 37.9 | [config](./faster-rcnn_r50_fpn_iou_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50_fpn_iou_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | +| R-50-FPN | GIoULoss | | | 37.6 | [config](./faster-rcnn_r50_fpn_giou_1x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50_fpn_giou_1x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | +| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](./faster-rcnn_r50_fpn_bounded-iou_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | ## Pre-trained Models We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | -| :-----------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| [R-50-C4](./faster-rcnn_r50-caffe-c4_ms-1x_coco.py) | caffe | 1x | - | | 35.9 | [config](./faster-rcnn_r50-caffe-c4_ms-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527.log.json) | -| [R-50-DC5](./faster-rcnn_r50-caffe-dc5_ms-1x_coco.py) | caffe | 1x | - | | 37.4 | [config](./faster-rcnn_r50-caffe-dc5_ms-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) | -| [R-50-DC5](./faster-rcnn_r50-caffe-dc5_ms-3x_coco.py) | caffe | 3x | - | | 38.7 | [config](./faster-rcnn_r50-caffe-dc5_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) | -| [R-50-FPN](./faster-rcnn_r50-caffe_fpn_ms-2x_coco.py) | caffe | 2x | 3.7 | | 39.7 | [config](./faster-rcnn_r50-caffe_fpn_ms-2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) | -| [R-50-FPN](./faster-rcnn_r50-caffe_fpn_ms-3x_coco.py) | caffe | 3x | 3.7 | | 39.9 | [config](./faster-rcnn_r50-caffe_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054.log.json) | -| [R-50-FPN](./faster-rcnn_r50_fpn_ms-3x_coco.py) | pytorch | 3x | 3.9 | | 40.3 | [config](./faster-rcnn_r50_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822.log.json) | -| [R-101-FPN](./faster-rcnn_r101-caffe_fpn_ms-3x_coco.py) | caffe | 3x | 5.6 | | 42.0 | [config](./faster-rcnn_r101-caffe_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742.log.json) | -| [R-101-FPN](./faster-rcnn_r101_fpn_ms-3x_coco.py) | pytorch | 3x | 5.8 | | 41.8 | [config](./faster-rcnn_r101_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822.log.json) | -| [X-101-32x4d-FPN](./faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py) | pytorch | 3x | 7.0 | | 42.5 | [config](./faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151.log.json) | -| [X-101-32x8d-FPN](./faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py) | pytorch | 3x | 10.1 | | 42.4 | [config](./faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954.log.json) | -| [X-101-64x4d-FPN](./faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py) | pytorch | 3x | 10.0 | | 43.1 | [config](./faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-----------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-C4](./faster-rcnn_r50-caffe-c4_ms-1x_coco.py) | caffe | 1x | - | | 35.9 | [config](./faster-rcnn_r50-caffe-c4_ms-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527.log.json) | +| [R-50-DC5](./faster-rcnn_r50-caffe-dc5_ms-1x_coco.py) | caffe | 1x | - | | 37.4 | [config](./faster-rcnn_r50-caffe-dc5_ms-1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) | +| [R-50-DC5](./faster-rcnn_r50-caffe-dc5_ms-3x_coco.py) | caffe | 3x | - | | 38.7 | [config](./faster-rcnn_r50-caffe-dc5_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) | +| [R-50-FPN](./faster-rcnn_r50-caffe_fpn_ms-2x_coco.py) | caffe | 2x | 3.7 | | 39.7 | [config](./faster-rcnn_r50-caffe_fpn_ms-2x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50-caffe_fpn_ms-2x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) | +| [R-50-FPN](./faster-rcnn_r50-caffe_fpn_ms-3x_coco.py) | caffe | 3x | 3.7 | | 39.9 | [config](./faster-rcnn_r50-caffe_fpn_ms-3x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r50-caffe_fpn_ms-3x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054.log.json) | +| [R-50-FPN](./faster-rcnn_r50_fpn_ms-3x_coco.py) | pytorch | 3x | 3.9 | | 40.3 | [config](./faster-rcnn_r50_fpn_ms-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [R-101-FPN](./faster-rcnn_r101-caffe_fpn_ms-3x_coco.py) | caffe | 3x | 5.6 | | 42.0 | [config](./faster-rcnn_r101-caffe_fpn_ms-3x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101-caffe_fpn_ms-3x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742.log.json) | +| [R-101-FPN](./faster-rcnn_r101_fpn_ms-3x_coco.py) | pytorch | 3x | 5.8 | | 41.8 | [config](./faster-rcnn_r101_fpn_ms-3x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101_fpn_ms-3x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [X-101-32x4d-FPN](./faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py) | pytorch | 3x | 7.0 | | 42.5 | [config](./faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-32x4d_fpn_ms-3x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151.log.json) | +| [X-101-32x8d-FPN](./faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py) | pytorch | 3x | 10.1 | | 42.4 | [config](./faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-32x8d_fpn_ms-3x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954.log.json) | +| [X-101-64x4d-FPN](./faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py) | pytorch | 3x | 10.0 | | 43.1 | [config](./faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py) | [model](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_x101-64x4d_fpn_ms-3x_coco) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528.log.json) | We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories. diff --git a/configs/glip/README.md b/configs/glip/README.md index 1252d922ac8..e74e98d1b57 100644 --- a/configs/glip/README.md +++ b/configs/glip/README.md @@ -56,7 +56,7 @@ model.save_pretrained("your path/bert-base-uncased") tokenizer.save_pretrained("your path/bert-base-uncased") ``` -## Results and Models +## COCO Results and Models | Model | Zero-shot or Finetune | COCO mAP | Official COCO mAP | Pre-Train Data | Config | Download | | :--------: | :-------------------: | :------: | ----------------: | :------------------------: | :---------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | @@ -78,3 +78,96 @@ Note: 3. Taking the GLIP-T(A) model as an example, I trained it twice using the official code, and the fine-tuning mAP were 52.5 and 52.6. Therefore, the mAP we achieved in our reproduction is higher than the official results. The main reason is that we modified the `weight_decay` parameter. 4. Our experiments revealed that training for 24 epochs leads to overfitting. Therefore, we chose the best-performing model. If users want to train on a custom dataset, it is advisable to shorten the number of epochs and save the best-performing model. 5. Due to the official absence of fine-tuning hyperparameters for the GLIP-L model, we have not yet reproduced the official accuracy. I have found that overfitting can also occur, so it may be necessary to consider custom modifications to data augmentation and model enhancement. Given the high cost of training, we have not conducted any research on this matter at the moment. + +## LVIS Results + +| Model | Official | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | Val1.0 APr | Val1.0 APc | Val1.0 APf | Val1.0 AP | Pre-Train Data | Config | Download | +| :--------: | :------: | :---------: | :---------: | :---------: | :--------: | :--------: | :--------: | :--------: | :-------: | :------------------------: | :---------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | +| GLIP-T (A) | ✔ | | | | | | | | | O365 | [config](lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_a_mmdet-b3654169.pth) | +| GLIP-T (A) | | 12.1 | 15.5 | 25.8 | 20.2 | 6.2 | 10.9 | 22.8 | 14.7 | O365 | [config](lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_a_mmdet-b3654169.pth) | +| GLIP-T (B) | ✔ | | | | | | | | | O365 | [config](lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_b_mmdet-6dfbd102.pth) | +| GLIP-T (B) | | 8.6 | 13.9 | 26.0 | 19.3 | 4.6 | 9.8 | 22.6 | 13.9 | O365 | [config](lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_b_mmdet-6dfbd102.pth) | +| GLIP-T (C) | ✔ | 14.3 | 19.4 | 31.1 | 24.6 | | | | | O365,GoldG | [config](lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_c_mmdet-2fc427dd.pth) | +| GLIP-T (C) | | 14.4 | 19.8 | 31.9 | 25.2 | 8.3 | 13.2 | 28.1 | 18.2 | O365,GoldG | [config](lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_c_mmdet-2fc427dd.pth) | +| GLIP-T | ✔ | | | | | | | | | O365,GoldG,CC3M,SBU | [config](lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_mmdet-c24ce662.pth) | +| GLIP-T | | 18.1 | 21.2 | 33.1 | 26.7 | 10.8 | 14.7 | 29.0 | 19.6 | O365,GoldG,CC3M,SBU | [config](lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_mmdet-c24ce662.pth) | +| GLIP-L | ✔ | 29.2 | 34.9 | 42.1 | 37.9 | | | | | FourODs,GoldG,CC3M+12M,SBU | [config](lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_l_mmdet-abfe026b.pth) | +| GLIP-L | | 27.9 | 33.7 | 39.7 | 36.1 | 20.2 | 25.8 | 35.3 | 28.5 | FourODs,GoldG,CC3M+12M,SBU | [config](lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/glip/glip_l_mmdet-abfe026b.pth) | + +Note: + +1. The above are zero-shot evaluation results. +2. The evaluation metric we used is LVIS FixAP. For specific details, please refer to [Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details](https://arxiv.org/pdf/2102.01066.pdf). +3. We found that the performance on small models is better than the official results, but it is lower on large models. This is mainly due to the incomplete alignment of the GLIP post-processing. + +## ODinW (Object Detection in the Wild) Results + +Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER 1 , the first benchmark and toolkit for evaluating (pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is platform for Computer Vision in the Wild (CVinW), and is publicly released at https://computer-vision-in-the-wild.github.io/ELEVATER/ + +### Results and models of ODinW13 + +| Method | GLIP-T(A) | Official | GLIP-T(B) | Official | GLIP-T(C) | Official | GroundingDINO-T | GroundingDINO-B | +| --------------------- | --------- | --------- | --------- | --------- | --------- | --------- | --------------- | --------------- | +| AerialMaritimeDrone | 0.123 | 0.122 | 0.110 | 0.110 | 0.130 | 0.130 | 0.173 | 0.281 | +| Aquarium | 0.175 | 0.174 | 0.173 | 0.169 | 0.191 | 0.190 | 0.195 | 0.445 | +| CottontailRabbits | 0.686 | 0.686 | 0.688 | 0.688 | 0.744 | 0.744 | 0.799 | 0.808 | +| EgoHands | 0.013 | 0.013 | 0.003 | 0.004 | 0.314 | 0.315 | 0.608 | 0.764 | +| NorthAmericaMushrooms | 0.502 | 0.502 | 0.367 | 0.367 | 0.297 | 0.296 | 0.507 | 0.675 | +| Packages | 0.589 | 0.589 | 0.083 | 0.083 | 0.699 | 0.699 | 0.687 | 0.670 | +| PascalVOC | 0.512 | 0.512 | 0.541 | 0.540 | 0.565 | 0.565 | 0.563 | 0.711 | +| pistols | 0.339 | 0.339 | 0.502 | 0.501 | 0.503 | 0.504 | 0.726 | 0.771 | +| pothole | 0.007 | 0.007 | 0.030 | 0.030 | 0.058 | 0.058 | 0.215 | 0.478 | +| Raccoon | 0.075 | 0.074 | 0.285 | 0.288 | 0.241 | 0.244 | 0.549 | 0.541 | +| ShellfishOpenImages | 0.253 | 0.253 | 0.337 | 0.338 | 0.300 | 0.302 | 0.393 | 0.650 | +| thermalDogsAndPeople | 0.372 | 0.372 | 0.475 | 0.475 | 0.510 | 0.510 | 0.657 | 0.633 | +| VehiclesOpenImages | 0.574 | 0.566 | 0.562 | 0.547 | 0.549 | 0.534 | 0.613 | 0.647 | +| Average | **0.325** | **0.324** | **0.320** | **0.318** | **0.392** | **0.392** | **0.514** | **0.621** | + +### Results and models of ODinW35 + +| Method | GLIP-T(A) | Official | GLIP-T(B) | Official | GLIP-T(C) | Official | GroundingDINO-T | GroundingDINO-B | +| --------------------------- | --------- | --------- | --------- | --------- | --------- | --------- | --------------- | --------------- | +| AerialMaritimeDrone_large | 0.123 | 0.122 | 0.110 | 0.110 | 0.130 | 0.130 | 0.173 | 0.281 | +| AerialMaritimeDrone_tiled | 0.174 | 0.174 | 0.172 | 0.172 | 0.172 | 0.172 | 0.206 | 0.364 | +| AmericanSignLanguageLetters | 0.001 | 0.001 | 0.003 | 0.003 | 0.009 | 0.009 | 0.002 | 0.096 | +| Aquarium | 0.175 | 0.175 | 0.173 | 0.171 | 0.192 | 0.182 | 0.195 | 0.445 | +| BCCD | 0.016 | 0.016 | 0.001 | 0.001 | 0.000 | 0.000 | 0.161 | 0.584 | +| boggleBoards | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.134 | +| brackishUnderwater | 0.016 | 0..013 | 0.021 | 0.027 | 0.020 | 0.022 | 0.021 | 0.454 | +| ChessPieces | 0.001 | 0.001 | 0.000 | 0.000 | 0.001 | 0.001 | 0.000 | 0.000 | +| CottontailRabbits | 0.710 | 0.709 | 0.683 | 0.683 | 0.752 | 0.752 | 0.806 | 0.797 | +| dice | 0.005 | 0.005 | 0.004 | 0.004 | 0.004 | 0.004 | 0.004 | 0.082 | +| DroneControl | 0.016 | 0.017 | 0.006 | 0.008 | 0.005 | 0.007 | 0.042 | 0.638 | +| EgoHands_generic | 0.009 | 0.010 | 0.005 | 0.006 | 0.510 | 0.508 | 0.608 | 0.764 | +| EgoHands_specific | 0.001 | 0.001 | 0.004 | 0.006 | 0.003 | 0.004 | 0.002 | 0.687 | +| HardHatWorkers | 0.029 | 0.029 | 0.023 | 0.023 | 0.033 | 0.033 | 0.046 | 0.439 | +| MaskWearing | 0.007 | 0.007 | 0.003 | 0.002 | 0.005 | 0.005 | 0.004 | 0.406 | +| MountainDewCommercial | 0.218 | 0.227 | 0.199 | 0.197 | 0.478 | 0.463 | 0.430 | 0.580 | +| NorthAmericaMushrooms | 0.502 | 0.502 | 0.450 | 0.450 | 0.497 | 0.497 | 0.471 | 0.501 | +| openPoetryVision | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.051 | +| OxfordPets_by_breed | 0.001 | 0.002 | 0.002 | 0.004 | 0.001 | 0.002 | 0.003 | 0.799 | +| OxfordPets_by_species | 0.016 | 0.011 | 0.012 | 0.009 | 0.013 | 0.009 | 0.011 | 0.872 | +| PKLot | 0.002 | 0.002 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.774 | +| Packages | 0.569 | 0.569 | 0.279 | 0.279 | 0.712 | 0.712 | 0.695 | 0.728 | +| PascalVOC | 0.512 | 0.512 | 0.541 | 0.540 | 0.565 | 0.565 | 0.563 | 0.711 | +| pistols | 0.339 | 0.339 | 0.502 | 0.501 | 0.503 | 0.504 | 0.726 | 0.771 | +| plantdoc | 0.002 | 0.002 | 0.007 | 0.007 | 0.009 | 0.009 | 0.005 | 0.376 | +| pothole | 0.007 | 0.010 | 0.024 | 0.025 | 0.085 | 0.101 | 0.215 | 0.478 | +| Raccoons | 0.075 | 0.074 | 0.285 | 0.288 | 0.241 | 0.244 | 0.549 | 0.541 | +| selfdrivingCar | 0.071 | 0.072 | 0.074 | 0.074 | 0.081 | 0.080 | 0.089 | 0.318 | +| ShellfishOpenImages | 0.253 | 0.253 | 0.337 | 0.338 | 0.300 | 0.302 | 0.393 | 0.650 | +| ThermalCheetah | 0.028 | 0.028 | 0.000 | 0.000 | 0.028 | 0.028 | 0.087 | 0.290 | +| thermalDogsAndPeople | 0.372 | 0.372 | 0.475 | 0.475 | 0.510 | 0.510 | 0.657 | 0.633 | +| UnoCards | 0.000 | 0.000 | 0.000 | 0.001 | 0.002 | 0.003 | 0.006 | 0.754 | +| VehiclesOpenImages | 0.574 | 0.566 | 0.562 | 0.547 | 0.549 | 0.534 | 0.613 | 0.647 | +| WildfireSmoke | 0.000 | 0.000 | 0.000 | 0.000 | 0.017 | 0.017 | 0.134 | 0.410 | +| websiteScreenshots | 0.003 | 0.004 | 0.003 | 0.005 | 0.005 | 0.006 | 0.012 | 0.175 | +| Average | **0.134** | **0.134** | **0.138** | **0.138** | **0.179** | **0.178** | **0.227** | **0.492** | + +### Results on Flickr30k + +| Model | Official | Pre-Train Data | Val R@1 | Val R@5 | Val R@10 | Test R@1 | Test R@5 | Test R@10 | +| ------------- | -------- | ------------------- | ------- | ------- | -------- | -------- | -------- | --------- | +| **GLIP-T(C)** | ✔ | O365, GoldG | 84.8 | 94.9 | 96.3 | 85.5 | 95.4 | 96.6 | +| **GLIP-T(C)** | | O365, GoldG | 84.9 | 94.9 | 96.3 | 85.6 | 95.4 | 96.7 | +| **GLIP-T** | | O365,GoldG,CC3M,SBU | 85.3 | 95.5 | 96.9 | 86.0 | 95.9 | 97.2 | diff --git a/configs/glip/flickr30k/glip_atss_swin-t_c_fpn_dyhead_pretrain_obj365-goldg_zeroshot_flickr30k.py b/configs/glip/flickr30k/glip_atss_swin-t_c_fpn_dyhead_pretrain_obj365-goldg_zeroshot_flickr30k.py new file mode 100644 index 00000000000..14d6e8aaa63 --- /dev/null +++ b/configs/glip/flickr30k/glip_atss_swin-t_c_fpn_dyhead_pretrain_obj365-goldg_zeroshot_flickr30k.py @@ -0,0 +1,61 @@ +_base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py' + +lang_model_name = 'bert-base-uncased' + +model = dict(bbox_head=dict(early_fuse=True)) + +dataset_type = 'Flickr30kDataset' +data_root = 'data/flickr30k_entities/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive', 'phrase_ids', 'phrases')) +] + +dataset_Flickr30k_val = dict( + type=dataset_type, + data_root=data_root, + ann_file='final_flickr_separateGT_val.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, +) + +dataset_Flickr30k_test = dict( + type=dataset_type, + data_root=data_root, + ann_file='final_flickr_separateGT_test.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, +) + +val_evaluator_Flickr30k = dict(type='Flickr30kMetric', ) + +test_evaluator_Flickr30k = dict(type='Flickr30kMetric', ) + +# ----------Config---------- # +dataset_prefixes = ['Flickr30kVal', 'Flickr30kTest'] +datasets = [dataset_Flickr30k_val, dataset_Flickr30k_test] +metrics = [val_evaluator_Flickr30k, test_evaluator_Flickr30k] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_lvis.py b/configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_lvis.py new file mode 100644 index 00000000000..1f79e447d3f --- /dev/null +++ b/configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_lvis.py @@ -0,0 +1,12 @@ +_base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py' + +model = dict( + backbone=dict( + embed_dims=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=12, + drop_path_rate=0.4, + ), + neck=dict(in_channels=[384, 768, 1536]), + bbox_head=dict(early_fuse=True, num_dyhead_blocks=8)) diff --git a/configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_mini-lvis.py b/configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_mini-lvis.py new file mode 100644 index 00000000000..13f1a69082b --- /dev/null +++ b/configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_mini-lvis.py @@ -0,0 +1,12 @@ +_base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py' + +model = dict( + backbone=dict( + embed_dims=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=12, + drop_path_rate=0.4, + ), + neck=dict(in_channels=[384, 768, 1536]), + bbox_head=dict(early_fuse=True, num_dyhead_blocks=8)) diff --git a/configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py b/configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py new file mode 100644 index 00000000000..4d526d59008 --- /dev/null +++ b/configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py @@ -0,0 +1,24 @@ +_base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +dataset_type = 'LVISV1Dataset' +data_root = 'data/coco/' + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type=dataset_type, + ann_file='annotations/lvis_od_val.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +# numpy < 1.24.0 +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + 'annotations/lvis_od_val.json') +test_evaluator = val_evaluator diff --git a/configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py b/configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py new file mode 100644 index 00000000000..70a57a3f581 --- /dev/null +++ b/configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py @@ -0,0 +1,25 @@ +_base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +dataset_type = 'LVISV1Dataset' +data_root = 'data/coco/' + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type=dataset_type, + ann_file='annotations/lvis_v1_minival_inserted_image_name.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +# numpy < 1.24.0 +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + + 'annotations/lvis_v1_minival_inserted_image_name.json') +test_evaluator = val_evaluator diff --git a/configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py b/configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py new file mode 100644 index 00000000000..6dc712b3bcb --- /dev/null +++ b/configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py @@ -0,0 +1,3 @@ +_base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py' + +model = dict(bbox_head=dict(early_fuse=True)) diff --git a/configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_mini-lvis.py b/configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_mini-lvis.py new file mode 100644 index 00000000000..3babb91101a --- /dev/null +++ b/configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_mini-lvis.py @@ -0,0 +1,3 @@ +_base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py' + +model = dict(bbox_head=dict(early_fuse=True)) diff --git a/configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw13.py b/configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw13.py new file mode 100644 index 00000000000..d38effba8c1 --- /dev/null +++ b/configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw13.py @@ -0,0 +1,338 @@ +_base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py' + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + test_mode=True, + pipeline=base_test_pipeline, + return_classes=True) +val_evaluator_AerialMaritimeDrone = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' + +caption_prompt = None +# caption_prompt = { +# 'penguin': { +# 'suffix': ', which is black and white' +# }, +# 'puffin': { +# 'suffix': ' with orange beaks' +# }, +# 'stingray': { +# 'suffix': ' which is flat and round' +# }, +# } +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 CottontailRabbits---------------------# +class_name = ('Cottontail-Rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' + +caption_prompt = None +# caption_prompt = {'Cottontail-Rabbit': {'name': 'rabbit'}} + +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 EgoHands---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' + +caption_prompt = None +# caption_prompt = {'hand': {'suffix': ' of a person'}} + +dataset_EgoHands = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 NorthAmericaMushrooms---------------------# +class_name = ('CoW', 'chanterelle') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + +caption_prompt = None +# caption_prompt = { +# 'CoW': { +# 'name': 'flat mushroom' +# }, +# 'chanterelle': { +# 'name': 'yellow mushroom' +# } +# } + +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' + +caption_prompt = None +# caption_prompt = { +# 'package': { +# 'prefix': 'there is a ', +# 'suffix': ' on the porch' +# } +# } + +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------7 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------9 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' + +caption_prompt = None +# caption_prompt = { +# 'pothole': { +# 'prefix': 'there are some ', +# 'name': 'holes', +# 'suffix': ' on the road' +# } +# } + +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------10 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------11 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# +dataset_prefixes = [ + 'AerialMaritimeDrone', 'Aquarium', 'CottontailRabbits', 'EgoHands', + 'NorthAmericaMushrooms', 'Packages', 'PascalVOC', 'pistols', 'pothole', + 'Raccoon', 'ShellfishOpenImages', 'thermalDogsAndPeople', + 'VehiclesOpenImages' +] +datasets = [ + dataset_AerialMaritimeDrone, dataset_Aquarium, dataset_CottontailRabbits, + dataset_EgoHands, dataset_NorthAmericaMushrooms, dataset_Packages, + dataset_PascalVOC, dataset_pistols, dataset_pothole, dataset_Raccoon, + dataset_ShellfishOpenImages, dataset_thermalDogsAndPeople, + dataset_VehiclesOpenImages +] +metrics = [ + val_evaluator_AerialMaritimeDrone, val_evaluator_Aquarium, + val_evaluator_CottontailRabbits, val_evaluator_EgoHands, + val_evaluator_NorthAmericaMushrooms, val_evaluator_Packages, + val_evaluator_PascalVOC, val_evaluator_pistols, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_ShellfishOpenImages, + val_evaluator_thermalDogsAndPeople, val_evaluator_VehiclesOpenImages +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw35.py b/configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw35.py new file mode 100644 index 00000000000..2eaf09ed771 --- /dev/null +++ b/configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw35.py @@ -0,0 +1,794 @@ +_base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py' + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone_large---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone_large = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_large = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 AerialMaritimeDrone_tiled---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/tiled/' +dataset_AerialMaritimeDrone_tiled = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_tiled = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 AmericanSignLanguageLetters---------------------# +class_name = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', + 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/' # noqa +dataset_AmericanSignLanguageLetters = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AmericanSignLanguageLetters = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 BCCD---------------------# +class_name = ('Platelets', 'RBC', 'WBC') +metainfo = dict(classes=class_name) +_data_root = data_root + 'BCCD/BCCD.v3-raw.coco/' +dataset_BCCD = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_BCCD = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 boggleBoards---------------------# +class_name = ('Q', 'a', 'an', 'b', 'c', 'd', 'e', 'er', 'f', 'g', 'h', 'he', + 'i', 'in', 'j', 'k', 'l', 'm', 'n', 'o', 'o ', 'p', 'q', 'qu', + 'r', 's', 't', 't\\', 'th', 'u', 'v', 'w', 'wild', 'x', 'y', 'z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'boggleBoards/416x416AutoOrient/export/' +dataset_boggleBoards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_boggleBoards = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------7 brackishUnderwater---------------------# +class_name = ('crab', 'fish', 'jellyfish', 'shrimp', 'small_fish', 'starfish') +metainfo = dict(classes=class_name) +_data_root = data_root + 'brackishUnderwater/960x540/' +dataset_brackishUnderwater = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_brackishUnderwater = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 ChessPieces---------------------# +class_name = (' ', 'black bishop', 'black king', 'black knight', 'black pawn', + 'black queen', 'black rook', 'white bishop', 'white king', + 'white knight', 'white pawn', 'white queen', 'white rook') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' +dataset_ChessPieces = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ChessPieces = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------9 CottontailRabbits---------------------# +class_name = ('rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------10 dice---------------------# +class_name = ('1', '2', '3', '4', '5', '6') +metainfo = dict(classes=class_name) +_data_root = data_root + 'dice/mediumColor/export/' +dataset_dice = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_dice = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------11 DroneControl---------------------# +class_name = ('follow', 'follow_hand', 'land', 'land_hand', 'null', 'object', + 'takeoff', 'takeoff-hand') +metainfo = dict(classes=class_name) +_data_root = data_root + 'DroneControl/Drone Control.v3-raw.coco/' +dataset_DroneControl = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_DroneControl = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 EgoHands_generic---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' +caption_prompt = {'hand': {'suffix': ' of a person'}} +dataset_EgoHands_generic = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_generic = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 EgoHands_specific---------------------# +class_name = ('myleft', 'myright', 'yourleft', 'yourright') +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/specific/' +dataset_EgoHands_specific = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_specific = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------14 HardHatWorkers---------------------# +class_name = ('head', 'helmet', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'HardHatWorkers/raw/' +dataset_HardHatWorkers = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_HardHatWorkers = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------15 MaskWearing---------------------# +class_name = ('mask', 'no-mask') +metainfo = dict(classes=class_name) +_data_root = data_root + 'MaskWearing/raw/' +dataset_MaskWearing = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MaskWearing = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------16 MountainDewCommercial---------------------# +class_name = ('bottle', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'MountainDewCommercial/' +dataset_MountainDewCommercial = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MountainDewCommercial = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------17 NorthAmericaMushrooms---------------------# +class_name = ('flat mushroom', 'yellow mushroom') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------18 openPoetryVision---------------------# +class_name = ('American Typewriter', 'Andale Mono', 'Apple Chancery', 'Arial', + 'Avenir', 'Baskerville', 'Big Caslon', 'Bradley Hand', + 'Brush Script MT', 'Chalkboard', 'Comic Sans MS', 'Copperplate', + 'Courier', 'Didot', 'Futura', 'Geneva', 'Georgia', 'Gill Sans', + 'Helvetica', 'Herculanum', 'Impact', 'Kefa', 'Lucida Grande', + 'Luminari', 'Marker Felt', 'Menlo', 'Monaco', 'Noteworthy', + 'Optima', 'PT Sans', 'PT Serif', 'Palatino', 'Papyrus', + 'Phosphate', 'Rockwell', 'SF Pro', 'SignPainter', 'Skia', + 'Snell Roundhand', 'Tahoma', 'Times New Roman', 'Trebuchet MS', + 'Verdana') +metainfo = dict(classes=class_name) +_data_root = data_root + 'openPoetryVision/512x512/' +dataset_openPoetryVision = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_openPoetryVision = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------19 OxfordPets_by_breed---------------------# +class_name = ('cat-Abyssinian', 'cat-Bengal', 'cat-Birman', 'cat-Bombay', + 'cat-British_Shorthair', 'cat-Egyptian_Mau', 'cat-Maine_Coon', + 'cat-Persian', 'cat-Ragdoll', 'cat-Russian_Blue', 'cat-Siamese', + 'cat-Sphynx', 'dog-american_bulldog', + 'dog-american_pit_bull_terrier', 'dog-basset_hound', + 'dog-beagle', 'dog-boxer', 'dog-chihuahua', + 'dog-english_cocker_spaniel', 'dog-english_setter', + 'dog-german_shorthaired', 'dog-great_pyrenees', 'dog-havanese', + 'dog-japanese_chin', 'dog-keeshond', 'dog-leonberger', + 'dog-miniature_pinscher', 'dog-newfoundland', 'dog-pomeranian', + 'dog-pug', 'dog-saint_bernard', 'dog-samoyed', + 'dog-scottish_terrier', 'dog-shiba_inu', + 'dog-staffordshire_bull_terrier', 'dog-wheaten_terrier', + 'dog-yorkshire_terrier') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-breed/' # noqa +dataset_OxfordPets_by_breed = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_breed = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------20 OxfordPets_by_species---------------------# +class_name = ('cat', 'dog') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-species/' # noqa +dataset_OxfordPets_by_species = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_species = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------21 PKLot---------------------# +class_name = ('space-empty', 'space-occupied') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PKLot/640/' # noqa +dataset_PKLot = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PKLot = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------22 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' +caption_prompt = { + 'package': { + 'prefix': 'there is a ', + 'suffix': ' on the porch' + } +} +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------23 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------24 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------25 plantdoc---------------------# +class_name = ('Apple Scab Leaf', 'Apple leaf', 'Apple rust leaf', + 'Bell_pepper leaf', 'Bell_pepper leaf spot', 'Blueberry leaf', + 'Cherry leaf', 'Corn Gray leaf spot', 'Corn leaf blight', + 'Corn rust leaf', 'Peach leaf', 'Potato leaf', + 'Potato leaf early blight', 'Potato leaf late blight', + 'Raspberry leaf', 'Soyabean leaf', 'Soybean leaf', + 'Squash Powdery mildew leaf', 'Strawberry leaf', + 'Tomato Early blight leaf', 'Tomato Septoria leaf spot', + 'Tomato leaf', 'Tomato leaf bacterial spot', + 'Tomato leaf late blight', 'Tomato leaf mosaic virus', + 'Tomato leaf yellow virus', 'Tomato mold leaf', + 'Tomato two spotted spider mites leaf', 'grape leaf', + 'grape leaf black rot') +metainfo = dict(classes=class_name) +_data_root = data_root + 'plantdoc/416x416/' +dataset_plantdoc = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_plantdoc = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------26 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' +caption_prompt = { + 'pothole': { + 'name': 'holes', + 'prefix': 'there are some ', + 'suffix': ' on the road' + } +} +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + caption_prompt=caption_prompt, + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------27 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------28 selfdrivingCar---------------------# +class_name = ('biker', 'car', 'pedestrian', 'trafficLight', + 'trafficLight-Green', 'trafficLight-GreenLeft', + 'trafficLight-Red', 'trafficLight-RedLeft', + 'trafficLight-Yellow', 'trafficLight-YellowLeft', 'truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'selfdrivingCar/fixedLarge/export/' +dataset_selfdrivingCar = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_selfdrivingCar = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------29 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------30 ThermalCheetah---------------------# +class_name = ('cheetah', 'human') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ThermalCheetah/' +dataset_ThermalCheetah = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ThermalCheetah = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------31 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------32 UnoCards---------------------# +class_name = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', + '12', '13', '14') +metainfo = dict(classes=class_name) +_data_root = data_root + 'UnoCards/raw/' +dataset_UnoCards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_UnoCards = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------33 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------34 WildfireSmoke---------------------# +class_name = ('smoke', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'WildfireSmoke/' +dataset_WildfireSmoke = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_WildfireSmoke = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------35 websiteScreenshots---------------------# +class_name = ('button', 'field', 'heading', 'iframe', 'image', 'label', 'link', + 'text') +metainfo = dict(classes=class_name) +_data_root = data_root + 'websiteScreenshots/' +dataset_websiteScreenshots = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_websiteScreenshots = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# + +dataset_prefixes = [ + 'AerialMaritimeDrone_large', + 'AerialMaritimeDrone_tiled', + 'AmericanSignLanguageLetters', + 'Aquarium', + 'BCCD', + 'boggleBoards', + 'brackishUnderwater', + 'ChessPieces', + 'CottontailRabbits', + 'dice', + 'DroneControl', + 'EgoHands_generic', + 'EgoHands_specific', + 'HardHatWorkers', + 'MaskWearing', + 'MountainDewCommercial', + 'NorthAmericaMushrooms', + 'openPoetryVision', + 'OxfordPets_by_breed', + 'OxfordPets_by_species', + 'PKLot', + 'Packages', + 'PascalVOC', + 'pistols', + 'plantdoc', + 'pothole', + 'Raccoons', + 'selfdrivingCar', + 'ShellfishOpenImages', + 'ThermalCheetah', + 'thermalDogsAndPeople', + 'UnoCards', + 'VehiclesOpenImages', + 'WildfireSmoke', + 'websiteScreenshots', +] + +datasets = [ + dataset_AerialMaritimeDrone_large, dataset_AerialMaritimeDrone_tiled, + dataset_AmericanSignLanguageLetters, dataset_Aquarium, dataset_BCCD, + dataset_boggleBoards, dataset_brackishUnderwater, dataset_ChessPieces, + dataset_CottontailRabbits, dataset_dice, dataset_DroneControl, + dataset_EgoHands_generic, dataset_EgoHands_specific, + dataset_HardHatWorkers, dataset_MaskWearing, dataset_MountainDewCommercial, + dataset_NorthAmericaMushrooms, dataset_openPoetryVision, + dataset_OxfordPets_by_breed, dataset_OxfordPets_by_species, dataset_PKLot, + dataset_Packages, dataset_PascalVOC, dataset_pistols, dataset_plantdoc, + dataset_pothole, dataset_Raccoon, dataset_selfdrivingCar, + dataset_ShellfishOpenImages, dataset_ThermalCheetah, + dataset_thermalDogsAndPeople, dataset_UnoCards, dataset_VehiclesOpenImages, + dataset_WildfireSmoke, dataset_websiteScreenshots +] + +metrics = [ + val_evaluator_AerialMaritimeDrone_large, + val_evaluator_AerialMaritimeDrone_tiled, + val_evaluator_AmericanSignLanguageLetters, val_evaluator_Aquarium, + val_evaluator_BCCD, val_evaluator_boggleBoards, + val_evaluator_brackishUnderwater, val_evaluator_ChessPieces, + val_evaluator_CottontailRabbits, val_evaluator_dice, + val_evaluator_DroneControl, val_evaluator_EgoHands_generic, + val_evaluator_EgoHands_specific, val_evaluator_HardHatWorkers, + val_evaluator_MaskWearing, val_evaluator_MountainDewCommercial, + val_evaluator_NorthAmericaMushrooms, val_evaluator_openPoetryVision, + val_evaluator_OxfordPets_by_breed, val_evaluator_OxfordPets_by_species, + val_evaluator_PKLot, val_evaluator_Packages, val_evaluator_PascalVOC, + val_evaluator_pistols, val_evaluator_plantdoc, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_selfdrivingCar, + val_evaluator_ShellfishOpenImages, val_evaluator_ThermalCheetah, + val_evaluator_thermalDogsAndPeople, val_evaluator_UnoCards, + val_evaluator_VehiclesOpenImages, val_evaluator_WildfireSmoke, + val_evaluator_websiteScreenshots +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw13.py b/configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw13.py new file mode 100644 index 00000000000..c3479b62b78 --- /dev/null +++ b/configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw13.py @@ -0,0 +1,3 @@ +_base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw13.py' + +model = dict(bbox_head=dict(early_fuse=True)) diff --git a/configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw35.py b/configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw35.py new file mode 100644 index 00000000000..182afc66c93 --- /dev/null +++ b/configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw35.py @@ -0,0 +1,3 @@ +_base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw35.py' + +model = dict(bbox_head=dict(early_fuse=True)) diff --git a/configs/glip/odinw/override_category.py b/configs/glip/odinw/override_category.py new file mode 100644 index 00000000000..9ff05fc6e5e --- /dev/null +++ b/configs/glip/odinw/override_category.py @@ -0,0 +1,109 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse + +import mmengine + + +def parse_args(): + parser = argparse.ArgumentParser(description='Override Category') + parser.add_argument('data_root') + return parser.parse_args() + + +def main(): + args = parse_args() + + ChessPieces = [{ + 'id': 1, + 'name': ' ', + 'supercategory': 'pieces' + }, { + 'id': 2, + 'name': 'black bishop', + 'supercategory': 'pieces' + }, { + 'id': 3, + 'name': 'black king', + 'supercategory': 'pieces' + }, { + 'id': 4, + 'name': 'black knight', + 'supercategory': 'pieces' + }, { + 'id': 5, + 'name': 'black pawn', + 'supercategory': 'pieces' + }, { + 'id': 6, + 'name': 'black queen', + 'supercategory': 'pieces' + }, { + 'id': 7, + 'name': 'black rook', + 'supercategory': 'pieces' + }, { + 'id': 8, + 'name': 'white bishop', + 'supercategory': 'pieces' + }, { + 'id': 9, + 'name': 'white king', + 'supercategory': 'pieces' + }, { + 'id': 10, + 'name': 'white knight', + 'supercategory': 'pieces' + }, { + 'id': 11, + 'name': 'white pawn', + 'supercategory': 'pieces' + }, { + 'id': 12, + 'name': 'white queen', + 'supercategory': 'pieces' + }, { + 'id': 13, + 'name': 'white rook', + 'supercategory': 'pieces' + }] + + _data_root = args.data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = ChessPieces + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + CottontailRabbits = [{ + 'id': 1, + 'name': 'rabbit', + 'supercategory': 'Cottontail-Rabbit' + }] + + _data_root = args.data_root + 'CottontailRabbits/' + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = CottontailRabbits + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + NorthAmericaMushrooms = [{ + 'id': 1, + 'name': 'flat mushroom', + 'supercategory': 'mushroom' + }, { + 'id': 2, + 'name': 'yellow mushroom', + 'supercategory': 'mushroom' + }] + + _data_root = args.data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = NorthAmericaMushrooms + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + +if __name__ == '__main__': + main() diff --git a/configs/grounding_dino/README.md b/configs/grounding_dino/README.md index 715b630cc79..2a527828a46 100644 --- a/configs/grounding_dino/README.md +++ b/configs/grounding_dino/README.md @@ -59,7 +59,7 @@ python demo/image_demo.py \ -## Results and Models +## COCO Results and Models | Model | Backbone | Style | COCO mAP | Official COCO mAP | Pre-Train Data | Config | Download | | :----------------: | :------: | :-------: | :--------: | :---------------: | :----------------------------------------------: | :------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | @@ -75,6 +75,151 @@ Note: 2. Finetune refers to fine-tuning on the COCO 2017 dataset. The R50 model is trained using 8 NVIDIA GeForce 3090 GPUs, while the remaining models are trained using 16 NVIDIA GeForce 3090 GPUs. The GPU memory usage is approximately 8.5GB. 3. Our performance is higher than the official model due to two reasons: we modified the initialization strategy and introduced a log scaler. +## LVIS Results + +| Model | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | Val1.0 APr | Val1.0 APc | Val1.0 APf | Val1.0 AP | Pre-Train Data | Config | Download | +| :--------------: | :---------: | :---------: | :---------: | :--------: | :--------: | :--------: | :--------: | :-------: | :----------------------------------------------: | :-----------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | +| Grounding DINO-T | 18.8 | 24.2 | 34.7 | 28.8 | 10.1 | 15.3 | 29.9 | 20.1 | O365,GoldG,Cap4M | [config](lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth) | +| Grounding DINO-B | 27.9 | 33.4 | 37.2 | 34.7 | 19.0 | 24.1 | 32.9 | 26.7 | COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO | [config](lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth) | + +Note: + +1. The above are zero-shot evaluation results. +2. The evaluation metric we used is LVIS FixAP. For specific details, please refer to [Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details](https://arxiv.org/pdf/2102.01066.pdf). + +## ODinW (Object Detection in the Wild) Results + +Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER 1 , the first benchmark and toolkit for evaluating (pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is platform for Computer Vision in the Wild (CVinW), and is publicly released at https://computer-vision-in-the-wild.github.io/ELEVATER/ + +### Results and models of ODinW13 + +| Method | GLIP-T(A) | Official | GLIP-T(B) | Official | GLIP-T(C) | Official | GroundingDINO-T | GroundingDINO-B | +| --------------------- | --------- | --------- | --------- | --------- | --------- | --------- | --------------- | --------------- | +| AerialMaritimeDrone | 0.123 | 0.122 | 0.110 | 0.110 | 0.130 | 0.130 | 0.173 | 0.281 | +| Aquarium | 0.175 | 0.174 | 0.173 | 0.169 | 0.191 | 0.190 | 0.195 | 0.445 | +| CottontailRabbits | 0.686 | 0.686 | 0.688 | 0.688 | 0.744 | 0.744 | 0.799 | 0.808 | +| EgoHands | 0.013 | 0.013 | 0.003 | 0.004 | 0.314 | 0.315 | 0.608 | 0.764 | +| NorthAmericaMushrooms | 0.502 | 0.502 | 0.367 | 0.367 | 0.297 | 0.296 | 0.507 | 0.675 | +| Packages | 0.589 | 0.589 | 0.083 | 0.083 | 0.699 | 0.699 | 0.687 | 0.670 | +| PascalVOC | 0.512 | 0.512 | 0.541 | 0.540 | 0.565 | 0.565 | 0.563 | 0.711 | +| pistols | 0.339 | 0.339 | 0.502 | 0.501 | 0.503 | 0.504 | 0.726 | 0.771 | +| pothole | 0.007 | 0.007 | 0.030 | 0.030 | 0.058 | 0.058 | 0.215 | 0.478 | +| Raccoon | 0.075 | 0.074 | 0.285 | 0.288 | 0.241 | 0.244 | 0.549 | 0.541 | +| ShellfishOpenImages | 0.253 | 0.253 | 0.337 | 0.338 | 0.300 | 0.302 | 0.393 | 0.650 | +| thermalDogsAndPeople | 0.372 | 0.372 | 0.475 | 0.475 | 0.510 | 0.510 | 0.657 | 0.633 | +| VehiclesOpenImages | 0.574 | 0.566 | 0.562 | 0.547 | 0.549 | 0.534 | 0.613 | 0.647 | +| Average | **0.325** | **0.324** | **0.320** | **0.318** | **0.392** | **0.392** | **0.514** | **0.621** | + +### Results and models of ODinW35 + +| Method | GLIP-T(A) | Official | GLIP-T(B) | Official | GLIP-T(C) | Official | GroundingDINO-T | GroundingDINO-B | +| --------------------------- | --------- | --------- | --------- | --------- | --------- | --------- | --------------- | --------------- | +| AerialMaritimeDrone_large | 0.123 | 0.122 | 0.110 | 0.110 | 0.130 | 0.130 | 0.173 | 0.281 | +| AerialMaritimeDrone_tiled | 0.174 | 0.174 | 0.172 | 0.172 | 0.172 | 0.172 | 0.206 | 0.364 | +| AmericanSignLanguageLetters | 0.001 | 0.001 | 0.003 | 0.003 | 0.009 | 0.009 | 0.002 | 0.096 | +| Aquarium | 0.175 | 0.175 | 0.173 | 0.171 | 0.192 | 0.182 | 0.195 | 0.445 | +| BCCD | 0.016 | 0.016 | 0.001 | 0.001 | 0.000 | 0.000 | 0.161 | 0.584 | +| boggleBoards | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.134 | +| brackishUnderwater | 0.016 | 0..013 | 0.021 | 0.027 | 0.020 | 0.022 | 0.021 | 0.454 | +| ChessPieces | 0.001 | 0.001 | 0.000 | 0.000 | 0.001 | 0.001 | 0.000 | 0.000 | +| CottontailRabbits | 0.710 | 0.709 | 0.683 | 0.683 | 0.752 | 0.752 | 0.806 | 0.797 | +| dice | 0.005 | 0.005 | 0.004 | 0.004 | 0.004 | 0.004 | 0.004 | 0.082 | +| DroneControl | 0.016 | 0.017 | 0.006 | 0.008 | 0.005 | 0.007 | 0.042 | 0.638 | +| EgoHands_generic | 0.009 | 0.010 | 0.005 | 0.006 | 0.510 | 0.508 | 0.608 | 0.764 | +| EgoHands_specific | 0.001 | 0.001 | 0.004 | 0.006 | 0.003 | 0.004 | 0.002 | 0.687 | +| HardHatWorkers | 0.029 | 0.029 | 0.023 | 0.023 | 0.033 | 0.033 | 0.046 | 0.439 | +| MaskWearing | 0.007 | 0.007 | 0.003 | 0.002 | 0.005 | 0.005 | 0.004 | 0.406 | +| MountainDewCommercial | 0.218 | 0.227 | 0.199 | 0.197 | 0.478 | 0.463 | 0.430 | 0.580 | +| NorthAmericaMushrooms | 0.502 | 0.502 | 0.450 | 0.450 | 0.497 | 0.497 | 0.471 | 0.501 | +| openPoetryVision | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.051 | +| OxfordPets_by_breed | 0.001 | 0.002 | 0.002 | 0.004 | 0.001 | 0.002 | 0.003 | 0.799 | +| OxfordPets_by_species | 0.016 | 0.011 | 0.012 | 0.009 | 0.013 | 0.009 | 0.011 | 0.872 | +| PKLot | 0.002 | 0.002 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.774 | +| Packages | 0.569 | 0.569 | 0.279 | 0.279 | 0.712 | 0.712 | 0.695 | 0.728 | +| PascalVOC | 0.512 | 0.512 | 0.541 | 0.540 | 0.565 | 0.565 | 0.563 | 0.711 | +| pistols | 0.339 | 0.339 | 0.502 | 0.501 | 0.503 | 0.504 | 0.726 | 0.771 | +| plantdoc | 0.002 | 0.002 | 0.007 | 0.007 | 0.009 | 0.009 | 0.005 | 0.376 | +| pothole | 0.007 | 0.010 | 0.024 | 0.025 | 0.085 | 0.101 | 0.215 | 0.478 | +| Raccoons | 0.075 | 0.074 | 0.285 | 0.288 | 0.241 | 0.244 | 0.549 | 0.541 | +| selfdrivingCar | 0.071 | 0.072 | 0.074 | 0.074 | 0.081 | 0.080 | 0.089 | 0.318 | +| ShellfishOpenImages | 0.253 | 0.253 | 0.337 | 0.338 | 0.300 | 0.302 | 0.393 | 0.650 | +| ThermalCheetah | 0.028 | 0.028 | 0.000 | 0.000 | 0.028 | 0.028 | 0.087 | 0.290 | +| thermalDogsAndPeople | 0.372 | 0.372 | 0.475 | 0.475 | 0.510 | 0.510 | 0.657 | 0.633 | +| UnoCards | 0.000 | 0.000 | 0.000 | 0.001 | 0.002 | 0.003 | 0.006 | 0.754 | +| VehiclesOpenImages | 0.574 | 0.566 | 0.562 | 0.547 | 0.549 | 0.534 | 0.613 | 0.647 | +| WildfireSmoke | 0.000 | 0.000 | 0.000 | 0.000 | 0.017 | 0.017 | 0.134 | 0.410 | +| websiteScreenshots | 0.003 | 0.004 | 0.003 | 0.005 | 0.005 | 0.006 | 0.012 | 0.175 | +| Average | **0.134** | **0.134** | **0.138** | **0.138** | **0.179** | **0.178** | **0.227** | **0.492** | + +## Flickr30k Results + +| Model | Pre-Train Data | Val R@1 | Val R@5 | Val R@10 | Tesst R@1 | Test R@5 | Test R@10 | Config | Download | +| :--------------: | :--------------: | ------- | ------- | -------- | --------- | -------- | --------- | :-------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Grounding DINO-T | O365,GoldG,Cap4M | 87.8 | 96.6 | 98.0 | 88.1 | 96.9 | 98.2 | [config](grounding_dino_swin-t_finetune_16xb2_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/grounding_dino_swin-t_finetune_16xb2_1x_coco/grounding_dino_swin-t_finetune_16xb2_1x_coco_20230921_152544-5f234b20.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/grounding_dino_swin-t_finetune_16xb2_1x_coco/grounding_dino_swin-t_finetune_16xb2_1x_coco_20230921_152544.log.json) | + +Note: + +1. `@1,5,10` refers to precision at the top 1, 5, and 10 positions in a predicted ranked list. +2. The pretraining data used by Grounding DINO-T is `O365,GoldG,Cap4M`, and the corresponding evaluation configuration is (grounding_dino_swin-t_pretrain_zeroshot_refcoco)\[refcoco/grounding_dino_swin-t_pretrain_zeroshot_refcoco.py\]. + +Test Command + +```shell +cd mmdetection +bash tools/dist_test.sh configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py checkpoints/groundingdino_swint_ogc_mmdet-822d7e9d.pth 8 +``` + +## Referring Expression Comprehension Results + +| Method | Grounding DINO-T
(O365,GoldG,Cap4M) | Grounding DINO-B
(COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO) | +| --------------------------------------- | ----------------------------------------- | ------------------------------------------------------------------------- | +| RefCOCO val @1,5,10 | 50.77/89.45/94.86 | 84.61/97.88/99.10 | +| RefCOCO testA @1,5,10 | 57.45/91.29/95.62 | 88.65/98.89/99.63 | +| RefCOCO testB @1,5,10 | 44.97/86.54/92.88 | 80.51/96.64/98.51 | +| RefCOCO+ val @1,5,10 | 51.64/86.35/92.57 | 73.67/96.60/98.65 | +| RefCOCO+ testA @1,5,10 | 57.25/86.74/92.65 | 82.19/97.92/99.09 | +| RefCOCO+ testB @1,5,10 | 46.35/84.05/90.67 | 64.10/94.25/97.46 | +| RefCOCOg val @1,5,10 | 60.42/92.10/96.18 | 78.33/97.28/98.57 | +| RefCOCOg test @1,5,10 | 59.74/92.08/96.28 | 78.11/97.06/98.65 | +| gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc | 41.32/91.82 | 46.18/81.44 | +| gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc | 27.23/90.24 | 38.60/76.06 | +| gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc | 29.70/93.49 | 35.87/80.58 | + +Note: + +1. `@1,5,10` refers to precision at the top 1, 5, and 10 positions in a predicted ranked list. +2. `Pr@(F1=1, IoU≥0.5),N-acc` from the paper [GREC: Generalized Referring Expression Comprehension](https://arxiv.org/pdf/2308.16182.pdf) +3. The pretraining data used by Grounding DINO-T is `O365,GoldG,Cap4M`, and the corresponding evaluation configuration is (grounding_dino_swin-t_pretrain_zeroshot_refcoco)\[refcoco/grounding_dino_swin-t_pretrain_zeroshot_refcoco.py\]. +4. The pretraining data used by Grounding DINO-B is `COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO`, and the corresponding evaluation configuration is (grounding_dino_swin-t_pretrain_zeroshot_refcoco)\[refcoco/grounding_dino_swin-b_pretrain_zeroshot_refcoco.py\]. + +Test Command + +```shell +cd mmdetection +./tools/dist_test.sh configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth 8 +./tools/dist_test.sh configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth 8 +``` + +## Description Detection Dataset + +```shell +pip install ddd-dataset +``` + +| Method | mode | Grounding DINO-T
(O365,GoldG,Cap4M) | Grounding DINO-B
(COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO) | +| -------------------------------- | -------- | ----------------------------------------- | ------------------------------------------------------------------------- | +| FULL/short/middle/long/very long | concat | 17.2/18.0/18.7/14.8/16.3 | 20.2/20.4/21.1/18.8/19.8 | +| FULL/short/middle/long/very long | parallel | 22.3/28.2/24.8/19.1/13.9 | 25.0/26.4/27.2/23.5/19.7 | +| PRES/short/middle/long/very long | concat | 17.8/18.3/19.2/15.2/17.3 | 20.7/21.7/21.4/19.1/20.3 | +| PRES/short/middle/long/very long | parallel | 21.0/27.0/22.8/17.5/12.5 | 23.7/25.8/25.1/21.9/19.3 | +| ABS/short/middle/long/very long | concat | 15.4/17.1/16.4/13.6/14.9 | 18.6/16.1/19.7/18.1/19.1 | +| ABS/short/middle/long/very long | parallel | 26.0/32.0/33.0/23.6/15.5 | 28.8/28.1/35.8/28.2/20.2 | + +Note: + +1. Considering that the evaluation time for Inter-scenario is very long and the performance is low, it is temporarily not supported. The mentioned metrics are for Intra-scenario. +2. `concat` is the default inference mode for Grounding DINO, where it concatenates multiple sub-sentences with "." to form a single sentence for inference. On the other hand, "parallel" performs inference on each sub-sentence in a for-loop. + ## Custom Dataset To facilitate fine-tuning on custom datasets, we use a simple cat dataset as an example, as shown in the following steps. diff --git a/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py b/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py new file mode 100644 index 00000000000..ac655b74aa6 --- /dev/null +++ b/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py @@ -0,0 +1,14 @@ +_base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py' + +model = dict( + type='GroundingDINO', + backbone=dict( + pretrain_img_size=384, + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + drop_path_rate=0.3, + patch_norm=True), + neck=dict(in_channels=[256, 512, 1024]), +) diff --git a/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_parallel_dod.py b/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_parallel_dod.py new file mode 100644 index 00000000000..9a1c8f2ac74 --- /dev/null +++ b/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_parallel_dod.py @@ -0,0 +1,3 @@ +_base_ = 'grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py' + +model = dict(test_cfg=dict(chunked_size=1)) diff --git a/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py b/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py new file mode 100644 index 00000000000..bb418011bf4 --- /dev/null +++ b/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py @@ -0,0 +1,78 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' + +data_root = 'data/d3/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', 'sent_ids')) +] + +# -------------------------------------------------# +val_dataset_full = dict( + type='DODDataset', + data_root=data_root, + ann_file='d3_json/d3_full_annotations.json', + data_prefix=dict(img='d3_images/', anno='d3_pkl'), + pipeline=test_pipeline, + test_mode=True, + backend_args=None, + return_classes=True) + +val_evaluator_full = dict( + type='DODCocoMetric', + ann_file=data_root + 'd3_json/d3_full_annotations.json') + +# -------------------------------------------------# +val_dataset_pres = dict( + type='DODDataset', + data_root=data_root, + ann_file='d3_json/d3_pres_annotations.json', + data_prefix=dict(img='d3_images/', anno='d3_pkl'), + pipeline=test_pipeline, + test_mode=True, + backend_args=None, + return_classes=True) +val_evaluator_pres = dict( + type='DODCocoMetric', + ann_file=data_root + 'd3_json/d3_pres_annotations.json') + +# -------------------------------------------------# +val_dataset_abs = dict( + type='DODDataset', + data_root=data_root, + ann_file='d3_json/d3_abs_annotations.json', + data_prefix=dict(img='d3_images/', anno='d3_pkl'), + pipeline=test_pipeline, + test_mode=True, + backend_args=None, + return_classes=True) +val_evaluator_abs = dict( + type='DODCocoMetric', + ann_file=data_root + 'd3_json/d3_abs_annotations.json') + +# -------------------------------------------------# +datasets = [val_dataset_full, val_dataset_pres, val_dataset_abs] +dataset_prefixes = ['FULL', 'PRES', 'ABS'] +metrics = [val_evaluator_full, val_evaluator_pres, val_evaluator_abs] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py b/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py new file mode 100644 index 00000000000..3d680091162 --- /dev/null +++ b/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py @@ -0,0 +1,3 @@ +_base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py' + +model = dict(test_cfg=dict(chunked_size=1)) diff --git a/configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py b/configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py new file mode 100644 index 00000000000..c1996567588 --- /dev/null +++ b/configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py @@ -0,0 +1,57 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' + +dataset_type = 'Flickr30kDataset' +data_root = 'data/flickr30k_entities/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive', 'phrase_ids', 'phrases')) +] + +dataset_Flickr30k_val = dict( + type=dataset_type, + data_root=data_root, + ann_file='final_flickr_separateGT_val.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, +) + +dataset_Flickr30k_test = dict( + type=dataset_type, + data_root=data_root, + ann_file='final_flickr_separateGT_test.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, +) + +val_evaluator_Flickr30k = dict(type='Flickr30kMetric') + +test_evaluator_Flickr30k = dict(type='Flickr30kMetric') + +# ----------Config---------- # +dataset_prefixes = ['Flickr30kVal', 'Flickr30kTest'] +datasets = [dataset_Flickr30k_val, dataset_Flickr30k_test] +metrics = [val_evaluator_Flickr30k, test_evaluator_Flickr30k] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py b/configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py index 1117cb06d39..7448764ef7e 100644 --- a/configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py +++ b/configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py @@ -119,7 +119,8 @@ dict( type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', - 'scale_factor', 'text', 'custom_entities')) + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) ] val_dataloader = dict( diff --git a/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_lvis.py b/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_lvis.py new file mode 100644 index 00000000000..6084159044e --- /dev/null +++ b/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_lvis.py @@ -0,0 +1,14 @@ +_base_ = './grounding_dino_swin-t_pretrain_zeroshot_lvis.py' + +model = dict( + type='GroundingDINO', + backbone=dict( + pretrain_img_size=384, + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + drop_path_rate=0.3, + patch_norm=True), + neck=dict(in_channels=[256, 512, 1024]), +) diff --git a/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py b/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py new file mode 100644 index 00000000000..68467a7237c --- /dev/null +++ b/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py @@ -0,0 +1,14 @@ +_base_ = './grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py' + +model = dict( + type='GroundingDINO', + backbone=dict( + pretrain_img_size=384, + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + drop_path_rate=0.3, + patch_norm=True), + neck=dict(in_channels=[256, 512, 1024]), +) diff --git a/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py b/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py new file mode 100644 index 00000000000..3d05f0ce1c0 --- /dev/null +++ b/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py @@ -0,0 +1,24 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +dataset_type = 'LVISV1Dataset' +data_root = 'data/coco/' + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type=dataset_type, + ann_file='annotations/lvis_od_val.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +# numpy < 1.24.0 +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + 'annotations/lvis_od_val.json') +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py b/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py new file mode 100644 index 00000000000..0aac6cf33a9 --- /dev/null +++ b/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py @@ -0,0 +1,25 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +dataset_type = 'LVISV1Dataset' +data_root = 'data/coco/' + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type=dataset_type, + ann_file='annotations/lvis_v1_minival_inserted_image_name.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +# numpy < 1.24.0 +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + + 'annotations/lvis_v1_minival_inserted_image_name.json') +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw13.py b/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw13.py new file mode 100644 index 00000000000..65a6bc2a078 --- /dev/null +++ b/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw13.py @@ -0,0 +1,338 @@ +_base_ = '../grounding_dino_swin-b_pretrain_mixeddata.py' + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + test_mode=True, + pipeline=base_test_pipeline, + return_classes=True) +val_evaluator_AerialMaritimeDrone = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' + +caption_prompt = None +# caption_prompt = { +# 'penguin': { +# 'suffix': ', which is black and white' +# }, +# 'puffin': { +# 'suffix': ' with orange beaks' +# }, +# 'stingray': { +# 'suffix': ' which is flat and round' +# }, +# } +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 CottontailRabbits---------------------# +class_name = ('Cottontail-Rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' + +caption_prompt = None +# caption_prompt = {'Cottontail-Rabbit': {'name': 'rabbit'}} + +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 EgoHands---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' + +caption_prompt = None +# caption_prompt = {'hand': {'suffix': ' of a person'}} + +dataset_EgoHands = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 NorthAmericaMushrooms---------------------# +class_name = ('CoW', 'chanterelle') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + +caption_prompt = None +# caption_prompt = { +# 'CoW': { +# 'name': 'flat mushroom' +# }, +# 'chanterelle': { +# 'name': 'yellow mushroom' +# } +# } + +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' + +caption_prompt = None +# caption_prompt = { +# 'package': { +# 'prefix': 'there is a ', +# 'suffix': ' on the porch' +# } +# } + +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------7 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------9 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' + +caption_prompt = None +# caption_prompt = { +# 'pothole': { +# 'prefix': 'there are some ', +# 'name': 'holes', +# 'suffix': ' on the road' +# } +# } + +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------10 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------11 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# +dataset_prefixes = [ + 'AerialMaritimeDrone', 'Aquarium', 'CottontailRabbits', 'EgoHands', + 'NorthAmericaMushrooms', 'Packages', 'PascalVOC', 'pistols', 'pothole', + 'Raccoon', 'ShellfishOpenImages', 'thermalDogsAndPeople', + 'VehiclesOpenImages' +] +datasets = [ + dataset_AerialMaritimeDrone, dataset_Aquarium, dataset_CottontailRabbits, + dataset_EgoHands, dataset_NorthAmericaMushrooms, dataset_Packages, + dataset_PascalVOC, dataset_pistols, dataset_pothole, dataset_Raccoon, + dataset_ShellfishOpenImages, dataset_thermalDogsAndPeople, + dataset_VehiclesOpenImages +] +metrics = [ + val_evaluator_AerialMaritimeDrone, val_evaluator_Aquarium, + val_evaluator_CottontailRabbits, val_evaluator_EgoHands, + val_evaluator_NorthAmericaMushrooms, val_evaluator_Packages, + val_evaluator_PascalVOC, val_evaluator_pistols, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_ShellfishOpenImages, + val_evaluator_thermalDogsAndPeople, val_evaluator_VehiclesOpenImages +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw35.py b/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw35.py new file mode 100644 index 00000000000..e73cd8e61ba --- /dev/null +++ b/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw35.py @@ -0,0 +1,796 @@ +_base_ = '../grounding_dino_swin-b_pretrain_mixeddata.py' + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone_large---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone_large = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_large = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 AerialMaritimeDrone_tiled---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/tiled/' +dataset_AerialMaritimeDrone_tiled = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_tiled = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 AmericanSignLanguageLetters---------------------# +class_name = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', + 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/' # noqa +dataset_AmericanSignLanguageLetters = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AmericanSignLanguageLetters = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 BCCD---------------------# +class_name = ('Platelets', 'RBC', 'WBC') +metainfo = dict(classes=class_name) +_data_root = data_root + 'BCCD/BCCD.v3-raw.coco/' +dataset_BCCD = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_BCCD = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 boggleBoards---------------------# +class_name = ('Q', 'a', 'an', 'b', 'c', 'd', 'e', 'er', 'f', 'g', 'h', 'he', + 'i', 'in', 'j', 'k', 'l', 'm', 'n', 'o', 'o ', 'p', 'q', 'qu', + 'r', 's', 't', 't\\', 'th', 'u', 'v', 'w', 'wild', 'x', 'y', 'z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'boggleBoards/416x416AutoOrient/export/' +dataset_boggleBoards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_boggleBoards = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------7 brackishUnderwater---------------------# +class_name = ('crab', 'fish', 'jellyfish', 'shrimp', 'small_fish', 'starfish') +metainfo = dict(classes=class_name) +_data_root = data_root + 'brackishUnderwater/960x540/' +dataset_brackishUnderwater = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_brackishUnderwater = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 ChessPieces---------------------# +class_name = (' ', 'black bishop', 'black king', 'black knight', 'black pawn', + 'black queen', 'black rook', 'white bishop', 'white king', + 'white knight', 'white pawn', 'white queen', 'white rook') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' +dataset_ChessPieces = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ChessPieces = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------9 CottontailRabbits---------------------# +class_name = ('rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------10 dice---------------------# +class_name = ('1', '2', '3', '4', '5', '6') +metainfo = dict(classes=class_name) +_data_root = data_root + 'dice/mediumColor/export/' +dataset_dice = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_dice = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------11 DroneControl---------------------# +class_name = ('follow', 'follow_hand', 'land', 'land_hand', 'null', 'object', + 'takeoff', 'takeoff-hand') +metainfo = dict(classes=class_name) +_data_root = data_root + 'DroneControl/Drone Control.v3-raw.coco/' +dataset_DroneControl = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_DroneControl = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 EgoHands_generic---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' +caption_prompt = {'hand': {'suffix': ' of a person'}} +dataset_EgoHands_generic = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + # NOTE w. prompt 0.548; wo. prompt 0.764 + # caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_generic = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 EgoHands_specific---------------------# +class_name = ('myleft', 'myright', 'yourleft', 'yourright') +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/specific/' +dataset_EgoHands_specific = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_specific = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------14 HardHatWorkers---------------------# +class_name = ('head', 'helmet', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'HardHatWorkers/raw/' +dataset_HardHatWorkers = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_HardHatWorkers = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------15 MaskWearing---------------------# +class_name = ('mask', 'no-mask') +metainfo = dict(classes=class_name) +_data_root = data_root + 'MaskWearing/raw/' +dataset_MaskWearing = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MaskWearing = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------16 MountainDewCommercial---------------------# +class_name = ('bottle', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'MountainDewCommercial/' +dataset_MountainDewCommercial = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MountainDewCommercial = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------17 NorthAmericaMushrooms---------------------# +class_name = ('flat mushroom', 'yellow mushroom') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------18 openPoetryVision---------------------# +class_name = ('American Typewriter', 'Andale Mono', 'Apple Chancery', 'Arial', + 'Avenir', 'Baskerville', 'Big Caslon', 'Bradley Hand', + 'Brush Script MT', 'Chalkboard', 'Comic Sans MS', 'Copperplate', + 'Courier', 'Didot', 'Futura', 'Geneva', 'Georgia', 'Gill Sans', + 'Helvetica', 'Herculanum', 'Impact', 'Kefa', 'Lucida Grande', + 'Luminari', 'Marker Felt', 'Menlo', 'Monaco', 'Noteworthy', + 'Optima', 'PT Sans', 'PT Serif', 'Palatino', 'Papyrus', + 'Phosphate', 'Rockwell', 'SF Pro', 'SignPainter', 'Skia', + 'Snell Roundhand', 'Tahoma', 'Times New Roman', 'Trebuchet MS', + 'Verdana') +metainfo = dict(classes=class_name) +_data_root = data_root + 'openPoetryVision/512x512/' +dataset_openPoetryVision = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_openPoetryVision = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------19 OxfordPets_by_breed---------------------# +class_name = ('cat-Abyssinian', 'cat-Bengal', 'cat-Birman', 'cat-Bombay', + 'cat-British_Shorthair', 'cat-Egyptian_Mau', 'cat-Maine_Coon', + 'cat-Persian', 'cat-Ragdoll', 'cat-Russian_Blue', 'cat-Siamese', + 'cat-Sphynx', 'dog-american_bulldog', + 'dog-american_pit_bull_terrier', 'dog-basset_hound', + 'dog-beagle', 'dog-boxer', 'dog-chihuahua', + 'dog-english_cocker_spaniel', 'dog-english_setter', + 'dog-german_shorthaired', 'dog-great_pyrenees', 'dog-havanese', + 'dog-japanese_chin', 'dog-keeshond', 'dog-leonberger', + 'dog-miniature_pinscher', 'dog-newfoundland', 'dog-pomeranian', + 'dog-pug', 'dog-saint_bernard', 'dog-samoyed', + 'dog-scottish_terrier', 'dog-shiba_inu', + 'dog-staffordshire_bull_terrier', 'dog-wheaten_terrier', + 'dog-yorkshire_terrier') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-breed/' # noqa +dataset_OxfordPets_by_breed = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_breed = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------20 OxfordPets_by_species---------------------# +class_name = ('cat', 'dog') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-species/' # noqa +dataset_OxfordPets_by_species = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_species = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------21 PKLot---------------------# +class_name = ('space-empty', 'space-occupied') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PKLot/640/' # noqa +dataset_PKLot = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PKLot = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------22 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' +caption_prompt = { + 'package': { + 'prefix': 'there is a ', + 'suffix': ' on the porch' + } +} +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, # NOTE w. prompt 0.728; wo. prompt 0.670 + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------23 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------24 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------25 plantdoc---------------------# +class_name = ('Apple Scab Leaf', 'Apple leaf', 'Apple rust leaf', + 'Bell_pepper leaf', 'Bell_pepper leaf spot', 'Blueberry leaf', + 'Cherry leaf', 'Corn Gray leaf spot', 'Corn leaf blight', + 'Corn rust leaf', 'Peach leaf', 'Potato leaf', + 'Potato leaf early blight', 'Potato leaf late blight', + 'Raspberry leaf', 'Soyabean leaf', 'Soybean leaf', + 'Squash Powdery mildew leaf', 'Strawberry leaf', + 'Tomato Early blight leaf', 'Tomato Septoria leaf spot', + 'Tomato leaf', 'Tomato leaf bacterial spot', + 'Tomato leaf late blight', 'Tomato leaf mosaic virus', + 'Tomato leaf yellow virus', 'Tomato mold leaf', + 'Tomato two spotted spider mites leaf', 'grape leaf', + 'grape leaf black rot') +metainfo = dict(classes=class_name) +_data_root = data_root + 'plantdoc/416x416/' +dataset_plantdoc = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_plantdoc = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------26 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' +caption_prompt = { + 'pothole': { + 'name': 'holes', + 'prefix': 'there are some ', + 'suffix': ' on the road' + } +} +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + # NOTE w. prompt 0.221; wo. prompt 0.478 + # caption_prompt=caption_prompt, + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------27 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------28 selfdrivingCar---------------------# +class_name = ('biker', 'car', 'pedestrian', 'trafficLight', + 'trafficLight-Green', 'trafficLight-GreenLeft', + 'trafficLight-Red', 'trafficLight-RedLeft', + 'trafficLight-Yellow', 'trafficLight-YellowLeft', 'truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'selfdrivingCar/fixedLarge/export/' +dataset_selfdrivingCar = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_selfdrivingCar = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------29 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------30 ThermalCheetah---------------------# +class_name = ('cheetah', 'human') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ThermalCheetah/' +dataset_ThermalCheetah = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ThermalCheetah = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------31 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------32 UnoCards---------------------# +class_name = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', + '12', '13', '14') +metainfo = dict(classes=class_name) +_data_root = data_root + 'UnoCards/raw/' +dataset_UnoCards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_UnoCards = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------33 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------34 WildfireSmoke---------------------# +class_name = ('smoke', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'WildfireSmoke/' +dataset_WildfireSmoke = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_WildfireSmoke = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------35 websiteScreenshots---------------------# +class_name = ('button', 'field', 'heading', 'iframe', 'image', 'label', 'link', + 'text') +metainfo = dict(classes=class_name) +_data_root = data_root + 'websiteScreenshots/' +dataset_websiteScreenshots = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_websiteScreenshots = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# + +dataset_prefixes = [ + 'AerialMaritimeDrone_large', + 'AerialMaritimeDrone_tiled', + 'AmericanSignLanguageLetters', + 'Aquarium', + 'BCCD', + 'boggleBoards', + 'brackishUnderwater', + 'ChessPieces', + 'CottontailRabbits', + 'dice', + 'DroneControl', + 'EgoHands_generic', + 'EgoHands_specific', + 'HardHatWorkers', + 'MaskWearing', + 'MountainDewCommercial', + 'NorthAmericaMushrooms', + 'openPoetryVision', + 'OxfordPets_by_breed', + 'OxfordPets_by_species', + 'PKLot', + 'Packages', + 'PascalVOC', + 'pistols', + 'plantdoc', + 'pothole', + 'Raccoons', + 'selfdrivingCar', + 'ShellfishOpenImages', + 'ThermalCheetah', + 'thermalDogsAndPeople', + 'UnoCards', + 'VehiclesOpenImages', + 'WildfireSmoke', + 'websiteScreenshots', +] + +datasets = [ + dataset_AerialMaritimeDrone_large, dataset_AerialMaritimeDrone_tiled, + dataset_AmericanSignLanguageLetters, dataset_Aquarium, dataset_BCCD, + dataset_boggleBoards, dataset_brackishUnderwater, dataset_ChessPieces, + dataset_CottontailRabbits, dataset_dice, dataset_DroneControl, + dataset_EgoHands_generic, dataset_EgoHands_specific, + dataset_HardHatWorkers, dataset_MaskWearing, dataset_MountainDewCommercial, + dataset_NorthAmericaMushrooms, dataset_openPoetryVision, + dataset_OxfordPets_by_breed, dataset_OxfordPets_by_species, dataset_PKLot, + dataset_Packages, dataset_PascalVOC, dataset_pistols, dataset_plantdoc, + dataset_pothole, dataset_Raccoon, dataset_selfdrivingCar, + dataset_ShellfishOpenImages, dataset_ThermalCheetah, + dataset_thermalDogsAndPeople, dataset_UnoCards, dataset_VehiclesOpenImages, + dataset_WildfireSmoke, dataset_websiteScreenshots +] + +metrics = [ + val_evaluator_AerialMaritimeDrone_large, + val_evaluator_AerialMaritimeDrone_tiled, + val_evaluator_AmericanSignLanguageLetters, val_evaluator_Aquarium, + val_evaluator_BCCD, val_evaluator_boggleBoards, + val_evaluator_brackishUnderwater, val_evaluator_ChessPieces, + val_evaluator_CottontailRabbits, val_evaluator_dice, + val_evaluator_DroneControl, val_evaluator_EgoHands_generic, + val_evaluator_EgoHands_specific, val_evaluator_HardHatWorkers, + val_evaluator_MaskWearing, val_evaluator_MountainDewCommercial, + val_evaluator_NorthAmericaMushrooms, val_evaluator_openPoetryVision, + val_evaluator_OxfordPets_by_breed, val_evaluator_OxfordPets_by_species, + val_evaluator_PKLot, val_evaluator_Packages, val_evaluator_PascalVOC, + val_evaluator_pistols, val_evaluator_plantdoc, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_selfdrivingCar, + val_evaluator_ShellfishOpenImages, val_evaluator_ThermalCheetah, + val_evaluator_thermalDogsAndPeople, val_evaluator_UnoCards, + val_evaluator_VehiclesOpenImages, val_evaluator_WildfireSmoke, + val_evaluator_websiteScreenshots +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py b/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py new file mode 100644 index 00000000000..216b8059726 --- /dev/null +++ b/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py @@ -0,0 +1,338 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' # noqa + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + test_mode=True, + pipeline=base_test_pipeline, + return_classes=True) +val_evaluator_AerialMaritimeDrone = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' + +caption_prompt = None +# caption_prompt = { +# 'penguin': { +# 'suffix': ', which is black and white' +# }, +# 'puffin': { +# 'suffix': ' with orange beaks' +# }, +# 'stingray': { +# 'suffix': ' which is flat and round' +# }, +# } +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 CottontailRabbits---------------------# +class_name = ('Cottontail-Rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' + +caption_prompt = None +# caption_prompt = {'Cottontail-Rabbit': {'name': 'rabbit'}} + +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 EgoHands---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' + +caption_prompt = None +# caption_prompt = {'hand': {'suffix': ' of a person'}} + +dataset_EgoHands = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 NorthAmericaMushrooms---------------------# +class_name = ('CoW', 'chanterelle') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + +caption_prompt = None +# caption_prompt = { +# 'CoW': { +# 'name': 'flat mushroom' +# }, +# 'chanterelle': { +# 'name': 'yellow mushroom' +# } +# } + +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' + +caption_prompt = None +# caption_prompt = { +# 'package': { +# 'prefix': 'there is a ', +# 'suffix': ' on the porch' +# } +# } + +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------7 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------9 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' + +caption_prompt = None +# caption_prompt = { +# 'pothole': { +# 'prefix': 'there are some ', +# 'name': 'holes', +# 'suffix': ' on the road' +# } +# } + +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------10 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------11 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# +dataset_prefixes = [ + 'AerialMaritimeDrone', 'Aquarium', 'CottontailRabbits', 'EgoHands', + 'NorthAmericaMushrooms', 'Packages', 'PascalVOC', 'pistols', 'pothole', + 'Raccoon', 'ShellfishOpenImages', 'thermalDogsAndPeople', + 'VehiclesOpenImages' +] +datasets = [ + dataset_AerialMaritimeDrone, dataset_Aquarium, dataset_CottontailRabbits, + dataset_EgoHands, dataset_NorthAmericaMushrooms, dataset_Packages, + dataset_PascalVOC, dataset_pistols, dataset_pothole, dataset_Raccoon, + dataset_ShellfishOpenImages, dataset_thermalDogsAndPeople, + dataset_VehiclesOpenImages +] +metrics = [ + val_evaluator_AerialMaritimeDrone, val_evaluator_Aquarium, + val_evaluator_CottontailRabbits, val_evaluator_EgoHands, + val_evaluator_NorthAmericaMushrooms, val_evaluator_Packages, + val_evaluator_PascalVOC, val_evaluator_pistols, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_ShellfishOpenImages, + val_evaluator_thermalDogsAndPeople, val_evaluator_VehiclesOpenImages +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py b/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py new file mode 100644 index 00000000000..3df0394a204 --- /dev/null +++ b/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py @@ -0,0 +1,796 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' # noqa + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone_large---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone_large = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_large = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 AerialMaritimeDrone_tiled---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/tiled/' +dataset_AerialMaritimeDrone_tiled = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_tiled = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 AmericanSignLanguageLetters---------------------# +class_name = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', + 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/' # noqa +dataset_AmericanSignLanguageLetters = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AmericanSignLanguageLetters = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 BCCD---------------------# +class_name = ('Platelets', 'RBC', 'WBC') +metainfo = dict(classes=class_name) +_data_root = data_root + 'BCCD/BCCD.v3-raw.coco/' +dataset_BCCD = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_BCCD = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 boggleBoards---------------------# +class_name = ('Q', 'a', 'an', 'b', 'c', 'd', 'e', 'er', 'f', 'g', 'h', 'he', + 'i', 'in', 'j', 'k', 'l', 'm', 'n', 'o', 'o ', 'p', 'q', 'qu', + 'r', 's', 't', 't\\', 'th', 'u', 'v', 'w', 'wild', 'x', 'y', 'z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'boggleBoards/416x416AutoOrient/export/' +dataset_boggleBoards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_boggleBoards = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------7 brackishUnderwater---------------------# +class_name = ('crab', 'fish', 'jellyfish', 'shrimp', 'small_fish', 'starfish') +metainfo = dict(classes=class_name) +_data_root = data_root + 'brackishUnderwater/960x540/' +dataset_brackishUnderwater = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_brackishUnderwater = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 ChessPieces---------------------# +class_name = (' ', 'black bishop', 'black king', 'black knight', 'black pawn', + 'black queen', 'black rook', 'white bishop', 'white king', + 'white knight', 'white pawn', 'white queen', 'white rook') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' +dataset_ChessPieces = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ChessPieces = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------9 CottontailRabbits---------------------# +class_name = ('rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------10 dice---------------------# +class_name = ('1', '2', '3', '4', '5', '6') +metainfo = dict(classes=class_name) +_data_root = data_root + 'dice/mediumColor/export/' +dataset_dice = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_dice = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------11 DroneControl---------------------# +class_name = ('follow', 'follow_hand', 'land', 'land_hand', 'null', 'object', + 'takeoff', 'takeoff-hand') +metainfo = dict(classes=class_name) +_data_root = data_root + 'DroneControl/Drone Control.v3-raw.coco/' +dataset_DroneControl = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_DroneControl = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 EgoHands_generic---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' +caption_prompt = {'hand': {'suffix': ' of a person'}} +dataset_EgoHands_generic = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + # NOTE w. prompt 0.526, wo. prompt 0.608 + # caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_generic = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 EgoHands_specific---------------------# +class_name = ('myleft', 'myright', 'yourleft', 'yourright') +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/specific/' +dataset_EgoHands_specific = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_specific = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------14 HardHatWorkers---------------------# +class_name = ('head', 'helmet', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'HardHatWorkers/raw/' +dataset_HardHatWorkers = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_HardHatWorkers = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------15 MaskWearing---------------------# +class_name = ('mask', 'no-mask') +metainfo = dict(classes=class_name) +_data_root = data_root + 'MaskWearing/raw/' +dataset_MaskWearing = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MaskWearing = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------16 MountainDewCommercial---------------------# +class_name = ('bottle', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'MountainDewCommercial/' +dataset_MountainDewCommercial = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MountainDewCommercial = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------17 NorthAmericaMushrooms---------------------# +class_name = ('flat mushroom', 'yellow mushroom') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------18 openPoetryVision---------------------# +class_name = ('American Typewriter', 'Andale Mono', 'Apple Chancery', 'Arial', + 'Avenir', 'Baskerville', 'Big Caslon', 'Bradley Hand', + 'Brush Script MT', 'Chalkboard', 'Comic Sans MS', 'Copperplate', + 'Courier', 'Didot', 'Futura', 'Geneva', 'Georgia', 'Gill Sans', + 'Helvetica', 'Herculanum', 'Impact', 'Kefa', 'Lucida Grande', + 'Luminari', 'Marker Felt', 'Menlo', 'Monaco', 'Noteworthy', + 'Optima', 'PT Sans', 'PT Serif', 'Palatino', 'Papyrus', + 'Phosphate', 'Rockwell', 'SF Pro', 'SignPainter', 'Skia', + 'Snell Roundhand', 'Tahoma', 'Times New Roman', 'Trebuchet MS', + 'Verdana') +metainfo = dict(classes=class_name) +_data_root = data_root + 'openPoetryVision/512x512/' +dataset_openPoetryVision = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_openPoetryVision = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------19 OxfordPets_by_breed---------------------# +class_name = ('cat-Abyssinian', 'cat-Bengal', 'cat-Birman', 'cat-Bombay', + 'cat-British_Shorthair', 'cat-Egyptian_Mau', 'cat-Maine_Coon', + 'cat-Persian', 'cat-Ragdoll', 'cat-Russian_Blue', 'cat-Siamese', + 'cat-Sphynx', 'dog-american_bulldog', + 'dog-american_pit_bull_terrier', 'dog-basset_hound', + 'dog-beagle', 'dog-boxer', 'dog-chihuahua', + 'dog-english_cocker_spaniel', 'dog-english_setter', + 'dog-german_shorthaired', 'dog-great_pyrenees', 'dog-havanese', + 'dog-japanese_chin', 'dog-keeshond', 'dog-leonberger', + 'dog-miniature_pinscher', 'dog-newfoundland', 'dog-pomeranian', + 'dog-pug', 'dog-saint_bernard', 'dog-samoyed', + 'dog-scottish_terrier', 'dog-shiba_inu', + 'dog-staffordshire_bull_terrier', 'dog-wheaten_terrier', + 'dog-yorkshire_terrier') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-breed/' # noqa +dataset_OxfordPets_by_breed = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_breed = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------20 OxfordPets_by_species---------------------# +class_name = ('cat', 'dog') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-species/' # noqa +dataset_OxfordPets_by_species = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_species = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------21 PKLot---------------------# +class_name = ('space-empty', 'space-occupied') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PKLot/640/' # noqa +dataset_PKLot = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PKLot = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------22 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' +caption_prompt = { + 'package': { + 'prefix': 'there is a ', + 'suffix': ' on the porch' + } +} +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, # NOTE w. prompt 0.695; wo. prompt 0.687 + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------23 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------24 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------25 plantdoc---------------------# +class_name = ('Apple Scab Leaf', 'Apple leaf', 'Apple rust leaf', + 'Bell_pepper leaf', 'Bell_pepper leaf spot', 'Blueberry leaf', + 'Cherry leaf', 'Corn Gray leaf spot', 'Corn leaf blight', + 'Corn rust leaf', 'Peach leaf', 'Potato leaf', + 'Potato leaf early blight', 'Potato leaf late blight', + 'Raspberry leaf', 'Soyabean leaf', 'Soybean leaf', + 'Squash Powdery mildew leaf', 'Strawberry leaf', + 'Tomato Early blight leaf', 'Tomato Septoria leaf spot', + 'Tomato leaf', 'Tomato leaf bacterial spot', + 'Tomato leaf late blight', 'Tomato leaf mosaic virus', + 'Tomato leaf yellow virus', 'Tomato mold leaf', + 'Tomato two spotted spider mites leaf', 'grape leaf', + 'grape leaf black rot') +metainfo = dict(classes=class_name) +_data_root = data_root + 'plantdoc/416x416/' +dataset_plantdoc = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_plantdoc = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------26 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' +caption_prompt = { + 'pothole': { + 'name': 'holes', + 'prefix': 'there are some ', + 'suffix': ' on the road' + } +} +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + # NOTE w. prompt 0.137; wo. prompt 0.215 + # caption_prompt=caption_prompt, + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------27 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------28 selfdrivingCar---------------------# +class_name = ('biker', 'car', 'pedestrian', 'trafficLight', + 'trafficLight-Green', 'trafficLight-GreenLeft', + 'trafficLight-Red', 'trafficLight-RedLeft', + 'trafficLight-Yellow', 'trafficLight-YellowLeft', 'truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'selfdrivingCar/fixedLarge/export/' +dataset_selfdrivingCar = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_selfdrivingCar = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------29 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------30 ThermalCheetah---------------------# +class_name = ('cheetah', 'human') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ThermalCheetah/' +dataset_ThermalCheetah = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ThermalCheetah = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------31 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------32 UnoCards---------------------# +class_name = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', + '12', '13', '14') +metainfo = dict(classes=class_name) +_data_root = data_root + 'UnoCards/raw/' +dataset_UnoCards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_UnoCards = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------33 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------34 WildfireSmoke---------------------# +class_name = ('smoke', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'WildfireSmoke/' +dataset_WildfireSmoke = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_WildfireSmoke = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------35 websiteScreenshots---------------------# +class_name = ('button', 'field', 'heading', 'iframe', 'image', 'label', 'link', + 'text') +metainfo = dict(classes=class_name) +_data_root = data_root + 'websiteScreenshots/' +dataset_websiteScreenshots = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_websiteScreenshots = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# + +dataset_prefixes = [ + 'AerialMaritimeDrone_large', + 'AerialMaritimeDrone_tiled', + 'AmericanSignLanguageLetters', + 'Aquarium', + 'BCCD', + 'boggleBoards', + 'brackishUnderwater', + 'ChessPieces', + 'CottontailRabbits', + 'dice', + 'DroneControl', + 'EgoHands_generic', + 'EgoHands_specific', + 'HardHatWorkers', + 'MaskWearing', + 'MountainDewCommercial', + 'NorthAmericaMushrooms', + 'openPoetryVision', + 'OxfordPets_by_breed', + 'OxfordPets_by_species', + 'PKLot', + 'Packages', + 'PascalVOC', + 'pistols', + 'plantdoc', + 'pothole', + 'Raccoons', + 'selfdrivingCar', + 'ShellfishOpenImages', + 'ThermalCheetah', + 'thermalDogsAndPeople', + 'UnoCards', + 'VehiclesOpenImages', + 'WildfireSmoke', + 'websiteScreenshots', +] + +datasets = [ + dataset_AerialMaritimeDrone_large, dataset_AerialMaritimeDrone_tiled, + dataset_AmericanSignLanguageLetters, dataset_Aquarium, dataset_BCCD, + dataset_boggleBoards, dataset_brackishUnderwater, dataset_ChessPieces, + dataset_CottontailRabbits, dataset_dice, dataset_DroneControl, + dataset_EgoHands_generic, dataset_EgoHands_specific, + dataset_HardHatWorkers, dataset_MaskWearing, dataset_MountainDewCommercial, + dataset_NorthAmericaMushrooms, dataset_openPoetryVision, + dataset_OxfordPets_by_breed, dataset_OxfordPets_by_species, dataset_PKLot, + dataset_Packages, dataset_PascalVOC, dataset_pistols, dataset_plantdoc, + dataset_pothole, dataset_Raccoon, dataset_selfdrivingCar, + dataset_ShellfishOpenImages, dataset_ThermalCheetah, + dataset_thermalDogsAndPeople, dataset_UnoCards, dataset_VehiclesOpenImages, + dataset_WildfireSmoke, dataset_websiteScreenshots +] + +metrics = [ + val_evaluator_AerialMaritimeDrone_large, + val_evaluator_AerialMaritimeDrone_tiled, + val_evaluator_AmericanSignLanguageLetters, val_evaluator_Aquarium, + val_evaluator_BCCD, val_evaluator_boggleBoards, + val_evaluator_brackishUnderwater, val_evaluator_ChessPieces, + val_evaluator_CottontailRabbits, val_evaluator_dice, + val_evaluator_DroneControl, val_evaluator_EgoHands_generic, + val_evaluator_EgoHands_specific, val_evaluator_HardHatWorkers, + val_evaluator_MaskWearing, val_evaluator_MountainDewCommercial, + val_evaluator_NorthAmericaMushrooms, val_evaluator_openPoetryVision, + val_evaluator_OxfordPets_by_breed, val_evaluator_OxfordPets_by_species, + val_evaluator_PKLot, val_evaluator_Packages, val_evaluator_PascalVOC, + val_evaluator_pistols, val_evaluator_plantdoc, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_selfdrivingCar, + val_evaluator_ShellfishOpenImages, val_evaluator_ThermalCheetah, + val_evaluator_thermalDogsAndPeople, val_evaluator_UnoCards, + val_evaluator_VehiclesOpenImages, val_evaluator_WildfireSmoke, + val_evaluator_websiteScreenshots +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/grounding_dino/odinw/override_category.py b/configs/grounding_dino/odinw/override_category.py new file mode 100644 index 00000000000..9ff05fc6e5e --- /dev/null +++ b/configs/grounding_dino/odinw/override_category.py @@ -0,0 +1,109 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse + +import mmengine + + +def parse_args(): + parser = argparse.ArgumentParser(description='Override Category') + parser.add_argument('data_root') + return parser.parse_args() + + +def main(): + args = parse_args() + + ChessPieces = [{ + 'id': 1, + 'name': ' ', + 'supercategory': 'pieces' + }, { + 'id': 2, + 'name': 'black bishop', + 'supercategory': 'pieces' + }, { + 'id': 3, + 'name': 'black king', + 'supercategory': 'pieces' + }, { + 'id': 4, + 'name': 'black knight', + 'supercategory': 'pieces' + }, { + 'id': 5, + 'name': 'black pawn', + 'supercategory': 'pieces' + }, { + 'id': 6, + 'name': 'black queen', + 'supercategory': 'pieces' + }, { + 'id': 7, + 'name': 'black rook', + 'supercategory': 'pieces' + }, { + 'id': 8, + 'name': 'white bishop', + 'supercategory': 'pieces' + }, { + 'id': 9, + 'name': 'white king', + 'supercategory': 'pieces' + }, { + 'id': 10, + 'name': 'white knight', + 'supercategory': 'pieces' + }, { + 'id': 11, + 'name': 'white pawn', + 'supercategory': 'pieces' + }, { + 'id': 12, + 'name': 'white queen', + 'supercategory': 'pieces' + }, { + 'id': 13, + 'name': 'white rook', + 'supercategory': 'pieces' + }] + + _data_root = args.data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = ChessPieces + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + CottontailRabbits = [{ + 'id': 1, + 'name': 'rabbit', + 'supercategory': 'Cottontail-Rabbit' + }] + + _data_root = args.data_root + 'CottontailRabbits/' + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = CottontailRabbits + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + NorthAmericaMushrooms = [{ + 'id': 1, + 'name': 'flat mushroom', + 'supercategory': 'mushroom' + }, { + 'id': 2, + 'name': 'yellow mushroom', + 'supercategory': 'mushroom' + }] + + _data_root = args.data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = NorthAmericaMushrooms + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + +if __name__ == '__main__': + main() diff --git a/configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py b/configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py new file mode 100644 index 00000000000..dea0bad08c0 --- /dev/null +++ b/configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py @@ -0,0 +1,14 @@ +_base_ = './grounding_dino_swin-t_pretrain_zeroshot_refexp.py' + +model = dict( + type='GroundingDINO', + backbone=dict( + pretrain_img_size=384, + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + drop_path_rate=0.3, + patch_norm=True), + neck=dict(in_channels=[256, 512, 1024]), +) diff --git a/configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py b/configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py new file mode 100644 index 00000000000..4b5c46574a3 --- /dev/null +++ b/configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py @@ -0,0 +1,228 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' + +# 30 is an empirical value, just set it to the maximum value +# without affecting the evaluation result +model = dict(test_cfg=dict(max_per_img=30)) + +data_root = 'data/coco/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) +] + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/final_refexp_val.json' +val_dataset_all_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) +val_evaluator_all_val = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_testA.json' +val_dataset_refcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testA = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_testB.json' +val_dataset_refcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testB = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_testA.json' +val_dataset_refcoco_plus_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_plus_testA = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_testB.json' +val_dataset_refcoco_plus_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_plus_testB = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcocog_test.json' +val_dataset_refcocog_test = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcocog_test = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_val.json' +val_dataset_grefcoco_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_grefcoco_val = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_testA.json' +val_dataset_grefcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_grefcoco_testA = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_testB.json' +val_dataset_grefcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_grefcoco_testB = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +datasets = [ + val_dataset_all_val, val_dataset_refcoco_testA, val_dataset_refcoco_testB, + val_dataset_refcoco_plus_testA, val_dataset_refcoco_plus_testB, + val_dataset_refcocog_test, val_dataset_grefcoco_val, + val_dataset_grefcoco_testA, val_dataset_grefcoco_testB +] +dataset_prefixes = [ + 'val', 'refcoco_testA', 'refcoco_testB', 'refcoco+_testA', + 'refcoco+_testB', 'refcocog_test', 'grefcoco_val', 'grefcoco_testA', + 'grefcoco_testB' +] +metrics = [ + val_evaluator_all_val, val_evaluator_refcoco_testA, + val_evaluator_refcoco_testB, val_evaluator_refcoco_plus_testA, + val_evaluator_refcoco_plus_testB, val_evaluator_refcocog_test, + val_evaluator_grefcoco_val, val_evaluator_grefcoco_testA, + val_evaluator_grefcoco_testB +] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/README.md b/configs/mm_grounding_dino/README.md new file mode 100644 index 00000000000..bcc913446dc --- /dev/null +++ b/configs/mm_grounding_dino/README.md @@ -0,0 +1,363 @@ +# MM Grounding DINO + +> [An Open and Comprehensive Pipeline for Unified Object Grounding and Detection](https://arxiv.org/abs/2401.02361) + + + +## Abstract + +Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness has led to its widespread adoption as a mainstream architecture for various downstream applications. However, despite its significance, the original Grounding-DINO model lacks comprehensive public technical details due to the unavailability of its training code. To bridge this gap, we present MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline, which is built with the MMDetection toolbox. It adopts abundant vision datasets for pre-training and various detection and grounding datasets for fine-tuning. We give a comprehensive analysis of each reported result and detailed settings for reproduction. The extensive experiments on the benchmarks mentioned demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny baseline. We release all our models to the research community. + +
+ +
+ +
+ +
+ +## Dataset Preparation + +Please refer to [dataset_prepare.md](dataset_prepare.md) or [中文版数据准备](dataset_prepare_zh-CN.md) + +## Usage + +Please refer to [usage.md](usage.md) or [中文版用法说明](usage_zh-CN.md) + +## Zero-Shot COCO Results and Models + +| Model | Backbone | Style | COCO mAP | Pre-Train Data | Config | Download | +| :--------: | :------: | :-------: | :--------: | :-------------------: | :------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| GDINO-T | Swin-T | Zero-shot | 46.7 | O365 | | | +| GDINO-T | Swin-T | Zero-shot | 48.1 | O365,GoldG | | | +| GDINO-T | Swin-T | Zero-shot | 48.4 | O365,GoldG,Cap4M | [config](../grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth) | +| MM-GDINO-T | Swin-T | Zero-shot | 48.5(+1.8) | O365 | [config](grounding_dino_swin-t_pretrain_obj365.py) | | +| MM-GDINO-T | Swin-T | Zero-shot | 50.4(+2.3) | O365,GoldG | [config](grounding_dino_swin-t_pretrain_obj365_goldg.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602-4ea751ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602.log.json) | +| MM-GDINO-T | Swin-T | Zero-shot | 50.5(+2.1) | O365,GoldG,GRIT | [config](grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818-169cc352.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818.log.json) | +| MM-GDINO-T | Swin-T | Zero-shot | 50.6(+2.2) | O365,GoldG,V3Det | [config](grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741-e316e297.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741.log.json) | +| MM-GDINO-T | Swin-T | Zero-shot | 50.4(+2.0) | O365,GoldG,GRIT,V3Det | [config](grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047.log.json) | + +## Zero-Shot LVIS Results + +| Model | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | Val1.0 APr | Val1.0 APc | Val1.0 APf | Val1.0 AP | Pre-Train Data | +| :--------: | :---------: | :---------: | :---------: | :---------: | :--------: | :--------: | :--------: | :---------: | :-------------------: | +| GDINO-T | 18.8 | 24.2 | 34.7 | 28.8 | 10.1 | 15.3 | 29.9 | 20.1 | O365,GoldG,Cap4M | +| MM-GDINO-T | 28.1 | 30.2 | 42.0 | 35.7(+6.9) | 17.1 | 22.4 | 36.5 | 27.0(+6.9) | O365,GoldG | +| MM-GDINO-T | 26.6 | 32.4 | 41.8 | 36.5(+7.7) | 17.3 | 22.6 | 36.4 | 27.1(+7.0) | O365,GoldG,GRIT | +| MM-GDINO-T | 33.0 | 36.0 | 45.9 | 40.5(+11.7) | 21.5 | 25.5 | 40.2 | 30.6(+10.5) | O365,GoldG,V3Det | +| MM-GDINO-T | 34.2 | 37.4 | 46.2 | 41.4(+12.6) | 23.6 | 27.6 | 40.5 | 31.9(+11.8) | O365,GoldG,GRIT,V3Det | + +- The MM-GDINO-T config file is [mini-lvis](lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py) and [lvis 1.0](lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py) + +## Zero-Shot ODinW (Object Detection in the Wild) Results + +### Results and models of ODinW13 + +| Method | GDINO-T
(O365,GoldG,Cap4M) | MM-GDINO-T
(O365,GoldG) | MM-GDINO-T
(O365,GoldG,GRIT) | MM-GDINO-T
(O365,GoldG,V3Det) | MM-GDINO-T
(O365,GoldG,GRIT,V3Det) | +| --------------------- | -------------------------------- | ----------------------------- | ---------------------------------- | ----------------------------------- | ---------------------------------------- | +| AerialMaritimeDrone | 0.173 | 0.133 | 0.155 | 0.177 | 0.151 | +| Aquarium | 0.195 | 0.252 | 0.261 | 0.266 | 0.283 | +| CottontailRabbits | 0.799 | 0.771 | 0.810 | 0.778 | 0.786 | +| EgoHands | 0.608 | 0.499 | 0.537 | 0.506 | 0.519 | +| NorthAmericaMushrooms | 0.507 | 0.331 | 0.462 | 0.669 | 0.767 | +| Packages | 0.687 | 0.707 | 0.687 | 0.710 | 0.706 | +| PascalVOC | 0.563 | 0.565 | 0.580 | 0.556 | 0.566 | +| pistols | 0.726 | 0.585 | 0.709 | 0.671 | 0.729 | +| pothole | 0.215 | 0.136 | 0.285 | 0.199 | 0.243 | +| Raccoon | 0.549 | 0.469 | 0.511 | 0.553 | 0.535 | +| ShellfishOpenImages | 0.393 | 0.321 | 0.437 | 0.519 | 0.488 | +| thermalDogsAndPeople | 0.657 | 0.556 | 0.603 | 0.493 | 0.542 | +| VehiclesOpenImages | 0.613 | 0.566 | 0.603 | 0.614 | 0.615 | +| Average | **0.514** | **0.453** | **0.511** | **0.516** | **0.533** | + +- The MM-GDINO-T config file is [odinw13](odinw/grounding_dino_swin-t_pretrain_odinw13.py) + +### Results and models of ODinW35 + +| Method | GDINO-T
(O365,GoldG,Cap4M) | MM-GDINO-T
(O365,GoldG) | MM-GDINO-T
(O365,GoldG,GRIT) | MM-GDINO-T
(O365,GoldG,V3Det) | MM-GDINO-T
(O365,GoldG,GRIT,V3Det) | +| --------------------------- | -------------------------------- | ----------------------------- | ---------------------------------- | ----------------------------------- | ---------------------------------------- | +| AerialMaritimeDrone_large | 0.173 | 0.133 | 0.155 | 0.177 | 0.151 | +| AerialMaritimeDrone_tiled | 0.206 | 0.170 | 0.225 | 0.184 | 0.206 | +| AmericanSignLanguageLetters | 0.002 | 0.016 | 0.020 | 0.011 | 0.007 | +| Aquarium | 0.195 | 0.252 | 0.261 | 0.266 | 0.283 | +| BCCD | 0.161 | 0.069 | 0.118 | 0.083 | 0.077 | +| boggleBoards | 0.000 | 0.002 | 0.001 | 0.001 | 0.002 | +| brackishUnderwater | 0.021 | 0.033 | 0.021 | 0.025 | 0.025 | +| ChessPieces | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | +| CottontailRabbits | 0.806 | 0.771 | 0.810 | 0.778 | 0.786 | +| dice | 0.004 | 0.002 | 0.005 | 0.001 | 0.001 | +| DroneControl | 0.042 | 0.047 | 0.097 | 0.088 | 0.074 | +| EgoHands_generic | 0.608 | 0.527 | 0.537 | 0.506 | 0.519 | +| EgoHands_specific | 0.002 | 0.001 | 0.005 | 0.007 | 0.003 | +| HardHatWorkers | 0.046 | 0.048 | 0.070 | 0.070 | 0.108 | +| MaskWearing | 0.004 | 0.009 | 0.004 | 0.011 | 0.009 | +| MountainDewCommercial | 0.430 | 0.453 | 0.465 | 0.194 | 0.430 | +| NorthAmericaMushrooms | 0.471 | 0.331 | 0.462 | 0.669 | 0.767 | +| openPoetryVision | 0.000 | 0.001 | 0.000 | 0.000 | 0.000 | +| OxfordPets_by_breed | 0.003 | 0.002 | 0.004 | 0.006 | 0.004 | +| OxfordPets_by_species | 0.011 | 0.019 | 0.016 | 0.020 | 0.015 | +| PKLot | 0.001 | 0.004 | 0.002 | 0.008 | 0.007 | +| Packages | 0.695 | 0.707 | 0.687 | 0.710 | 0.706 | +| PascalVOC | 0.563 | 0.565 | 0.580 | 0.566 | 0.566 | +| pistols | 0.726 | 0.585 | 0.709 | 0.671 | 0.729 | +| plantdoc | 0.005 | 0.005 | 0.007 | 0.008 | 0.011 | +| pothole | 0.215 | 0.136 | 0.219 | 0.077 | 0.168 | +| Raccoons | 0.549 | 0.469 | 0.511 | 0.553 | 0.535 | +| selfdrivingCar | 0.089 | 0.091 | 0.076 | 0.094 | 0.083 | +| ShellfishOpenImages | 0.393 | 0.321 | 0.437 | 0.519 | 0.488 | +| ThermalCheetah | 0.087 | 0.063 | 0.081 | 0.030 | 0.045 | +| thermalDogsAndPeople | 0.657 | 0.556 | 0.603 | 0.493 | 0.543 | +| UnoCards | 0.006 | 0.012 | 0.010 | 0.009 | 0.005 | +| VehiclesOpenImages | 0.613 | 0.566 | 0.603 | 0.614 | 0.615 | +| WildfireSmoke | 0.134 | 0.106 | 0.154 | 0.042 | 0.127 | +| websiteScreenshots | 0.012 | 0.02 | 0.016 | 0.016 | 0.016 | +| Average | **0.227** | **0.202** | **0.228** | **0.214** | **0.284** | + +- The MM-GDINO-T config file is [odinw35](odinw/grounding_dino_swin-t_pretrain_odinw35.py) + +## Zero-Shot Referring Expression Comprehension Results + +| Method | GDINO-T
(O365,GoldG,Cap4M) | MM-GDINO-T
(O365,GoldG) | MM-GDINO-T
(O365,GoldG,GRIT) | MM-GDINO-T
(O365,GoldG,V3Det) | MM-GDINO-T
(O365,GoldG,GRIT,V3Det) | +| ---------------------- | -------------------------------- | ----------------------------- | ---------------------------------- | ----------------------------------- | ---------------------------------------- | +| RefCOCO val @1,5,10 | 50.8/89.5/94.9 | 53.1/89.9/94.7 | 53.4/90.3/95.5 | 52.1/89.8/95.0 | 53.1/89.7/95.1 | +| RefCOCO testA @1,5,10 | 57.4/91.3/95.6 | 59.7/91.5/95.9 | 58.8/91.70/96.2 | 58.4/86.8/95.6 | 59.1/91.0/95.5 | +| RefCOCO testB @1,5,10 | 45.0/86.5/92.9 | 46.4/86.9/92.2 | 46.8/87.7/93.3 | 45.4/86.2/92.6 | 46.8/87.8/93.6 | +| RefCOCO+ val @1,5,10 | 51.6/86.4/92.6 | 53.1/87.0/92.8 | 53.5/88.0/93.7 | 52.5/86.8/93.2 | 52.7/87.7/93.5 | +| RefCOCO+ testA @1,5,10 | 57.3/86.7/92.7 | 58.9/87.3/92.9 | 59.0/88.1/93.7 | 58.1/86.7/93.5 | 58.7/87.2/93.1 | +| RefCOCO+ testB @1,5,10 | 46.4/84.1/90.7 | 47.9/84.3/91.0 | 47.9/85.5/92.7 | 46.9/83.7/91.5 | 48.4/85.8/92.1 | +| RefCOCOg val @1,5,10 | 60.4/92.1/96.2 | 61.2/92.6/96.1 | 62.7/93.3/97.0 | 61.7/92.9/96.6 | 62.9/93.3/97.2 | +| RefCOCOg test @1,5,10 | 59.7/92.1/96.3 | 61.1/93.3/96.7 | 62.6/94.9/97.1 | 61.0/93.1/96.8 | 62.9/93.9/97.4 | + +| Method | thresh_score | GDINO-T
(O365,GoldG,Cap4M) | MM-GDINO-T
(O365,GoldG) | MM-GDINO-T
(O365,GoldG,GRIT) | MM-GDINO-T
(O365,GoldG,V3Det) | MM-GDINO-T
(O365,GoldG,GRIT,V3Det) | +| --------------------------------------- | ------------ | -------------------------------- | ----------------------------- | ---------------------------------- | ----------------------------------- | ---------------------------------------- | +| gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc | 0.5 | 39.3/70.4 | | | | 39.4/67.5 | +| gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc | 0.6 | 40.5/83.8 | | | | 40.6/83.1 | +| gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc | 0.7 | 41.3/91.8 | 39.8/84.7 | 40.7/89.7 | 40.3/88.8 | 41.0/91.3 | +| gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc | 0.8 | 41.5/96.8 | | | | 41.1/96.4 | +| gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc | 0.5 | 31.9/70.4 | | | | 33.1/69.5 | +| gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc | 0.6 | 29.3/82.9 | | | | 29.2/84.3 | +| gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc | 0.7 | 27.2/90.2 | 26.3/89.0 | 26.0/91.9 | 25.4/91.8 | 26.1/93.0 | +| gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc | 0.8 | 25.1/96.3 | | | | 23.8/97.2 | +| gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc | 0.5 | 30.9/72.5 | | | | 33.0/69.6 | +| gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc | 0.6 | 30.0/86.1 | | | | 31.6/96.7 | +| gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc | 0.7 | 29.7/93.5 | 31.3/84.8 | 30.6/90.2 | 30.7/89.9 | 30.4/92.3 | +| gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc | 0.8 | 29.1/97.4 | | | | 29.5/84.2 | + +- The MM-GDINO-T config file is [here](refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py) + +## Zero-Shot Description Detection Dataset(DOD) + +```shell +pip install ddd-dataset +``` + +| Method | mode | GDINO-T
(O365,GoldG,Cap4M) | MM-GDINO-T
(O365,GoldG) | MM-GDINO-T
(O365,GoldG,GRIT) | MM-GDINO-T
(O365,GoldG,V3Det) | MM-GDINO-T
(O365,GoldG,GRIT,V3Det) | +| -------------------------------- | -------- | -------------------------------- | ----------------------------- | ---------------------------------- | ----------------------------------- | ---------------------------------------- | +| FULL/short/middle/long/very long | concat | 17.2/18.0/18.7/14.8/16.3 | 15.6/17.3/16.7/14.3/13.1 | 17.0/17.7/18.0/15.7/15.7 | 16.2/17.4/16.8/14.9/15.4 | 17.5/23.4/18.3/14.7/13.8 | +| FULL/short/middle/long/very long | parallel | 22.3/28.2/24.8/19.1/13.9 | 21.7/24.7/24.0/20.2/13.7 | 22.5/25.6/25.1/20.5/14.9 | 22.3/25.6/24.5/20.6/14.7 | 22.9/28.1/25.4/20.4/14.4 | +| PRES/short/middle/long/very long | concat | 17.8/18.3/19.2/15.2/17.3 | 16.4/18.4/17.3/14.5/14.2 | 17.9/19.0/18.3/16.5/17.5 | 16.6/18.8/17.1/15.1/15.0 | 18.0/23.7/18.6/15.4/13.3 | +| PRES/short/middle/long/very long | parallel | 21.0/27.0/22.8/17.5/12.5 | 21.3/25.5/22.8/19.2/12.9 | 21.5/25.2/23.0/19.0/15.0 | 21.6/25.7/23.0/19.5/14.8 | 21.9/27.4/23.2/19.1/14.2 | +| ABS/short/middle/long/very long | concat | 15.4/17.1/16.4/13.6/14.9 | 13.4/13.4/14.5/13.5/11.9 | 14.5/13.1/16.7/13.6/13.3 | 14.8/12.5/15.6/14.3/15.8 | 15.9/22.2/17.1/12.5/14.4 | +| ABS/short/middle/long/very long | parallel | 26.0/32.0/33.0/23.6/15.5 | 22.8/22.2/28.7/22.9/14.7 | 25.6/26.8/33.9/24.5/14.7 | 24.1/24.9/30.7/23.8/14.7 | 26.0/30.3/34.1/23.9/14.6 | + +Note: + +1. Considering that the evaluation time for Inter-scenario is very long and the performance is low, it is temporarily not supported. The mentioned metrics are for Intra-scenario. +2. `concat` is the default inference mode for Grounding DINO, where it concatenates multiple sub-sentences with "." to form a single sentence for inference. On the other hand, "parallel" performs inference on each sub-sentence in a for-loop. +3. The MM-GDINO-T config file is [concat_dod](dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py) and [parallel_dod](dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py) + +## Pretrain Flickr30k Results + +| Model | Pre-Train Data | Val R@1 | Val R@5 | Val R@10 | Test R@1 | Test R@5 | Test R@10 | +| :--------: | :-------------------: | ------- | ------- | -------- | -------- | -------- | --------- | +| GLIP-T | O365,GoldG | 84.9 | 94.9 | 96.3 | 85.6 | 95.4 | 96.7 | +| GLIP-T | O365,GoldG,CC3M,SBU | 85.3 | 95.5 | 96.9 | 86.0 | 95.9 | 97.2 | +| GDINO-T | O365,GoldG,Cap4M | 87.8 | 96.6 | 98.0 | 88.1 | 96.9 | 98.2 | +| MM-GDINO-T | O365,GoldG | 85.5 | 95.6 | 97.2 | 86.2 | 95.7 | 97.4 | +| MM-GDINO-T | O365,GoldG,GRIT | 86.7 | 95.8 | 97.6 | 87.0 | 96.2 | 97.7 | +| MM-GDINO-T | O365,GoldG,V3Det | 85.9 | 95.7 | 97.4 | 86.3 | 95.7 | 97.4 | +| MM-GDINO-T | O365,GoldG,GRIT,V3Det | 86.7 | 96.0 | 97.6 | 87.2 | 96.2 | 97.7 | + +Note: + +1. `@1,5,10` refers to precision at the top 1, 5, and 10 positions in a predicted ranked list. +2. The MM-GDINO-T config file is [here](flickr30k/grounding_dino_swin-t-pretrain_flickr30k.py) + +## Validating the generalization of a pre-trained model through fine-tuning + +### RTTS + +| Architecture | Backbone | Lr schd | box AP | +| :-----------------: | :------: | ------- | -------- | +| Faster R-CNN | R-50 | 1x | 48.1 | +| Cascade R-CNN | R-50 | 1x | 50.8 | +| ATSS | R-50 | 1x | 48.2 | +| TOOD | R-50 | 1X | 50.8 | +| MM-GDINO(zero-shot) | Swin-T | | 49.8 | +| MM-GDINO | Swin-T | 1x | **69.1** | + +- The reference metrics come from https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/rtts_dataset +- The MM-GDINO-T config file is [here](rtts/grounding_dino_swin-t_finetune_8xb4_1x_rtts.py) + +### RUOD + +| Architecture | Backbone | Lr schd | box AP | +| :-----------------: | :------: | ------- | -------- | +| Faster R-CNN | R-50 | 1x | 52.4 | +| Cascade R-CNN | R-50 | 1x | 55.3 | +| ATSS | R-50 | 1x | 55.7 | +| TOOD | R-50 | 1X | 57.4 | +| MM-GDINO(zero-shot) | Swin-T | | 29.8 | +| MM-GDINO | Swin-T | 1x | **65.5** | + +- The reference metrics come from https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/ruod_dataset +- The MM-GDINO-T config file is [here](ruod/grounding_dino_swin-t_finetune_8xb4_1x_ruod.py) + +### Brain Tumor + +| Architecture | Backbone | Lr schd | box AP | +| :-----------: | :------: | ------- | ------ | +| Faster R-CNN | R-50 | 50e | 43.5 | +| Cascade R-CNN | R-50 | 50e | 46.2 | +| DINO | R-50 | 50e | 46.4 | +| Cascade-DINO | R-50 | 50e | 48.6 | +| MM-GDINO | Swin-T | 50e | 47.5 | + +- The reference metrics come from https://arxiv.org/abs/2307.11035 +- The MM-GDINO-T config file is [here](brain_tumor/grounding_dino_swin-t_finetune_8xb4_50e_brain_tumor.py) + +### Cityscapes + +| Architecture | Backbone | Lr schd | box AP | +| :-----------------: | :------: | ------- | -------- | +| Faster R-CNN | R-50 | 50e | 30.1 | +| Cascade R-CNN | R-50 | 50e | 31.8 | +| DINO | R-50 | 50e | 34.5 | +| Cascade-DINO | R-50 | 50e | 34.8 | +| MM-GDINO(zero-shot) | Swin-T | | 34.2 | +| MM-GDINO | Swin-T | 50e | **51.5** | + +- The reference metrics come from https://arxiv.org/abs/2307.11035 +- The MM-GDINO-T config file is [here](cityscapes/grounding_dino_swin-t_finetune_8xb4_50e_cityscapes.py) + +### People in Painting + +| Architecture | Backbone | Lr schd | box AP | +| :-----------------: | :------: | ------- | -------- | +| Faster R-CNN | R-50 | 50e | 17.0 | +| Cascade R-CNN | R-50 | 50e | 18.0 | +| DINO | R-50 | 50e | 12.0 | +| Cascade-DINO | R-50 | 50e | 13.4 | +| MM-GDINO(zero-shot) | Swin-T | | 23.1 | +| MM-GDINO | Swin-T | 50e | **38.9** | + +- The reference metrics come from https://arxiv.org/abs/2307.11035 +- The MM-GDINO-T config file is [here](people_in_painting/grounding_dino_swin-t_finetune_8xb4_50e_people_in_painting.py) + +### COCO + +**(1) Closed-set performance** + +| Architecture | Backbone | Lr schd | box AP | +| :-----------------: | :------: | ------- | ------ | +| Faster R-CNN | R-50 | 1x | 37.4 | +| Cascade R-CNN | R-50 | 1x | 40.3 | +| ATSS | R-50 | 1x | 39.4 | +| TOOD | R-50 | 1X | 42.4 | +| DINO | R-50 | 1X | 50.1 | +| GLIP(zero-shot) | Swin-T | | 46.6 | +| GDINO(zero-shot) | Swin-T | | 48.5 | +| MM-GDINO(zero-shot) | Swin-T | | 50.4 | +| GLIP | Swin-T | 1x | 55.4 | +| GDINO | Swin-T | 1x | 58.1 | +| MM-GDINO | Swin-T | 1x | 58.2 | + +- The MM-GDINO-T config file is [here](coco/grounding_dino_swin-t_finetune_16xb4_1x_coco.py) + +**(2) Open-set continuing pretraining performance** + +| Architecture | Backbone | Lr schd | box AP | +| :-----------------: | :------: | :-----: | :----: | +| GLIP(zero-shot) | Swin-T | | 46.7 | +| GDINO(zero-shot) | Swin-T | | 48.5 | +| MM-GDINO(zero-shot) | Swin-T | | 50.4 | +| MM-GDINO | Swin-T | 1x | 54.7 | + +- The MM-GDINO-T config file is [here](coco/grounding_dino_swin-t_finetune_16xb4_1x_sft_coco.py) +- Due to the small size of the COCO dataset, continuing pretraining solely on COCO can easily lead to overfitting. The results shown above are from the third epoch. I do not recommend you train using this approach. + +**(3) Open vocabulary performance** + +| Architecture | Backbone | Lr schd | box AP | Base box AP | Novel box AP | box AP@50 | Base box AP@50 | Novel box AP@50 | +| :-----------------: | :------: | :-----: | :----: | :---------: | :----------: | :-------: | :------------: | :-------------: | +| MM-GDINO(zero-shot) | Swin-T | | 51.1 | 48.4 | 58.9 | 66.7 | 64.0 | 74.2 | +| MM-GDINO | Swin-T | 1x | 57.2 | 56.1 | 60.4 | 73.6 | 73.0 | 75.3 | + +- The MM-GDINO-T config file is [here](coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py) + +### LVIS 1.0 + +**(1) Open-set continuing pretraining performance** + +| Architecture | Backbone | Lr schd | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | Val1.0 APr | Val1.0 APc | Val1.0 APf | Val1.0 AP | +| :-----------------: | :------: | :-----: | :---------: | :---------: | :---------: | :--------: | :--------: | :--------: | :--------: | :-------: | +| GLIP(zero-shot) | Swin-T | | 18.1 | 21.2 | 33.1 | 26.7 | 10.8 | 14.7 | 29.0 | 19.6 | +| GDINO(zero-shot) | Swin-T | | 18.8 | 24.2 | 34.7 | 28.8 | 10.1 | 15.3 | 29.9 | 20.1 | +| MM-GDINO(zero-shot) | Swin-T | | 34.2 | 37.4 | 46.2 | 41.4 | 23.6 | 27.6 | 40.5 | 31.9 | +| MM-GDINO | Swin-T | 1x | 50.7 | 58.8 | 60.1 | 58.7 | 45.2 | 50.2 | 56.1 | 51.7 | + +- The MM-GDINO-T config file is [here](lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py) + +**(2) Open vocabulary performance** + +| Architecture | Backbone | Lr schd | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | +| :-----------------: | :------: | :-----: | :---------: | :---------: | :---------: | :--------: | +| MM-GDINO(zero-shot) | Swin-T | | 34.2 | 37.4 | 46.2 | 41.4 | +| MM-GDINO | Swin-T | 1x | 43.2 | 57.4 | 59.3 | 57.1 | + +- The MM-GDINO-T config file is [here](lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py) + +### RefEXP + +#### RefCOCO + +| Architecture | Backbone | Lr schd | val @1 | val @5 | val @10 | testA @1 | testA @5 | testA @10 | testB @1 | testB @5 | testB @10 | +| :-----------------: | :------: | :-----: | :----: | :----: | :-----: | :------: | :------: | :-------: | :------: | :------: | :-------: | +| GDINO(zero-shot) | Swin-T | | 50.8 | 89.5 | 94.9 | 57.5 | 91.3 | 95.6 | 45.0 | 86.5 | 92.9 | +| MM-GDINO(zero-shot) | Swin-T | | 53.1 | 89.7 | 95.1 | 59.1 | 91.0 | 95.5 | 46.8 | 87.8 | 93.6 | +| GDINO | Swin-T | UNK | 89.2 | | | 91.9 | | | 86.0 | | | +| MM-GDINO | Swin-T | 5e | 89.5 | 98.6 | 99.4 | 91.4 | 99.2 | 99.8 | 86.6 | 97.9 | 99.1 | + +- The MM-GDINO-T config file is [here](refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco.py) + +#### RefCOCO+ + +| Architecture | Backbone | Lr schd | val @1 | val @5 | val @10 | testA @1 | testA @5 | testA @10 | testB @1 | testB @5 | testB @10 | +| :-----------------: | :------: | :-----: | :----: | :----: | :-----: | :------: | :------: | :-------: | :------: | :------: | :-------: | +| GDINO(zero-shot) | Swin-T | | 51.6 | 86.4 | 92.6 | 57.3 | 86.7 | 92.7 | 46.4 | 84.1 | 90.7 | +| MM-GDINO(zero-shot) | Swin-T | | 52.7 | 87.7 | 93.5 | 58.7 | 87.2 | 93.1 | 48.4 | 85.8 | 92.1 | +| GDINO | Swin-T | UNK | 81.1 | | | 87.4 | | | 74.7 | | | +| MM-GDINO | Swin-T | 5e | 82.1 | 97.8 | 99.2 | 87.5 | 99.2 | 99.7 | 74.0 | 96.3 | 96.4 | + +- The MM-GDINO-T config file is [here](refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco_plus.py) + +#### RefCOCOg + +| Architecture | Backbone | Lr schd | val @1 | val @5 | val @10 | test @1 | test @5 | test @10 | +| :-----------------: | :------: | :-----: | :----: | :----: | :-----: | :-----: | :-----: | :------: | +| GDINO(zero-shot) | Swin-T | | 60.4 | 92.1 | 96.2 | 59.7 | 92.1 | 96.3 | +| MM-GDINO(zero-shot) | Swin-T | | 62.9 | 93.3 | 97.2 | 62.9 | 93.9 | 97.4 | +| GDINO | Swin-T | UNK | 84.2 | | | 84.9 | | | +| MM-GDINO | Swin-T | 5e | 85.5 | 98.4 | 99.4 | 85.8 | 98.6 | 99.4 | + +- The MM-GDINO-T config file is [here](refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcocog.py) + +#### gRefCOCO + +| Architecture | Backbone | Lr schd | val Pr@(F1=1, IoU≥0.5) | val N-acc | testA Pr@(F1=1, IoU≥0.5) | testA N-acc | testB Pr@(F1=1, IoU≥0.5) | testB N-acc | +| :-----------------: | :------: | :-----: | :--------------------: | :-------: | :----------------------: | :---------: | :----------------------: | :---------: | +| GDINO(zero-shot) | Swin-T | | 41.3 | 91.8 | 27.2 | 90.2 | 29.7 | 93.5 | +| MM-GDINO(zero-shot) | Swin-T | | 41.0 | 91.3 | 26.1 | 93.0 | 30.4 | 92.3 | +| MM-GDINO | Swin-T | 5e | 45.1 | 64.7 | 42.5 | 65.5 | 40.3 | 63.2 | + +- The MM-GDINO-T config file is [here](refcoco/grounding_dino_swin-t_finetune_8xb4_5e_grefcoco.py) diff --git a/configs/mm_grounding_dino/brain_tumor/grounding_dino_swin-t_finetune_8xb4_50e_brain_tumor.py b/configs/mm_grounding_dino/brain_tumor/grounding_dino_swin-t_finetune_8xb4_50e_brain_tumor.py new file mode 100644 index 00000000000..1172da5b641 --- /dev/null +++ b/configs/mm_grounding_dino/brain_tumor/grounding_dino_swin-t_finetune_8xb4_50e_brain_tumor.py @@ -0,0 +1,112 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +# https://universe.roboflow.com/roboflow-100/brain-tumor-m2pbp/dataset/2 +data_root = 'data/brain_tumor_v2/' +class_name = ('label0', 'label1', 'label2') +label_name = '_annotations.coco.json' + +palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142)] + +metainfo = dict(classes=class_name, palette=palette) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + sampler=dict(_delete_=True, type='DefaultSampler', shuffle=True), + batch_sampler=dict(type='AspectRatioBatchSampler'), + dataset=dict( + _delete_=True, + type='RepeatDataset', + times=10, + dataset=dict( + type='CocoDataset', + data_root=data_root, + metainfo=metainfo, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline, + return_classes=True, + data_prefix=dict(img='train/'), + ann_file='train/' + label_name))) + +val_dataloader = dict( + dataset=dict( + metainfo=metainfo, + data_root=data_root, + return_classes=True, + ann_file='valid/' + label_name, + data_prefix=dict(img='valid/'))) +test_dataloader = val_dataloader + +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'valid/' + label_name, + metric='bbox', + format_only=False) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1) + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[4], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/cityscapes/grounding_dino_swin-t_finetune_8xb4_50e_cityscapes.py b/configs/mm_grounding_dino/cityscapes/grounding_dino_swin-t_finetune_8xb4_50e_cityscapes.py new file mode 100644 index 00000000000..c4283413c4b --- /dev/null +++ b/configs/mm_grounding_dino/cityscapes/grounding_dino_swin-t_finetune_8xb4_50e_cityscapes.py @@ -0,0 +1,110 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/cityscapes/' +class_name = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', + 'bicycle') +palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100), + (0, 80, 100), (0, 0, 230), (119, 11, 32)] + +metainfo = dict(classes=class_name, palette=palette) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + sampler=dict(_delete_=True, type='DefaultSampler', shuffle=True), + batch_sampler=dict(type='AspectRatioBatchSampler'), + dataset=dict( + _delete_=True, + type='RepeatDataset', + times=10, + dataset=dict( + type='CocoDataset', + data_root=data_root, + metainfo=metainfo, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline, + return_classes=True, + data_prefix=dict(img='leftImg8bit/train/'), + ann_file='annotations/instancesonly_filtered_gtFine_train.json'))) + +val_dataloader = dict( + dataset=dict( + metainfo=metainfo, + data_root=data_root, + return_classes=True, + ann_file='annotations/instancesonly_filtered_gtFine_val.json', + data_prefix=dict(img='leftImg8bit/val/'))) +test_dataloader = val_dataloader + +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json', + metric='bbox', + format_only=False) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1) + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[4], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco.py b/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco.py new file mode 100644 index 00000000000..792297accd3 --- /dev/null +++ b/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco.py @@ -0,0 +1,85 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='CocoDataset', + data_root=data_root, + ann_file='annotations/instances_train2017.json', + data_prefix=dict(img='train2017/'), + return_classes=True, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline)) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + 'language_model': dict(lr_mult=0.1), + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[8, 11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py b/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py new file mode 100644 index 00000000000..e68afbb4328 --- /dev/null +++ b/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py @@ -0,0 +1,157 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' +base_classes = ('person', 'bicycle', 'car', 'motorcycle', 'train', 'truck', + 'boat', 'bench', 'bird', 'horse', 'sheep', 'bear', 'zebra', + 'giraffe', 'backpack', 'handbag', 'suitcase', 'frisbee', + 'skis', 'kite', 'surfboard', 'bottle', 'fork', 'spoon', 'bowl', + 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', + 'pizza', 'donut', 'chair', 'bed', 'toilet', 'tv', 'laptop', + 'mouse', 'remote', 'microwave', 'oven', 'toaster', + 'refrigerator', 'book', 'clock', 'vase', 'toothbrush') # 48 +novel_classes = ('airplane', 'bus', 'cat', 'dog', 'cow', 'elephant', + 'umbrella', 'tie', 'snowboard', 'skateboard', 'cup', 'knife', + 'cake', 'couch', 'keyboard', 'sink', 'scissors') # 17 +all_classes = ( + 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', + 'truck', 'boat', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', + 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'kite', 'skateboard', + 'surfboard', 'bottle', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', + 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'pizza', 'donut', + 'cake', 'chair', 'couch', 'bed', 'toilet', 'tv', 'laptop', 'mouse', + 'remote', 'keyboard', 'microwave', 'oven', 'toaster', 'sink', + 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'toothbrush') # 65 + +train_metainfo = dict(classes=base_classes) +test_metainfo = dict( + classes=all_classes, + base_classes=base_classes, + novel_classes=novel_classes) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='CocoDataset', + metainfo=train_metainfo, + data_root=data_root, + ann_file='annotations/instances_train2017_seen_2.json', + data_prefix=dict(img='train2017/'), + return_classes=True, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline)) + +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type='CocoDataset', + metainfo=test_metainfo, + data_root=data_root, + ann_file='annotations/instances_val2017_all_2.json', + data_prefix=dict(img='val2017/'), + test_mode=True, + pipeline=test_pipeline, + return_classes=True, + )) +test_dataloader = val_dataloader + +val_evaluator = dict( + type='OVCocoMetric', + ann_file=data_root + 'annotations/instances_val2017_all_2.json', + metric='bbox', + format_only=False) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.00005, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[8, 11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +default_hooks = dict( + checkpoint=dict( + max_keep_ckpts=1, save_best='coco/novel_ap50', rule='greater')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_sft_coco.py b/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_sft_coco.py new file mode 100644 index 00000000000..5505df58b8b --- /dev/null +++ b/configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_sft_coco.py @@ -0,0 +1,93 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=20, # ======= important ===== + label_map_file='data/coco/annotations/coco2017_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ODVGDataset', + need_text=False, + data_root=data_root, + ann_file='annotations/instances_train2017_od.json', + label_map_file='annotations/coco2017_label_map.json', + data_prefix=dict(img='train2017/'), + return_classes=True, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline)) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.00005, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + 'language_model': dict(lr_mult=0.0), + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[8, 11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/dataset_prepare.md b/configs/mm_grounding_dino/dataset_prepare.md new file mode 100644 index 00000000000..af60a8bf4bf --- /dev/null +++ b/configs/mm_grounding_dino/dataset_prepare.md @@ -0,0 +1,1193 @@ +# Data Prepare and Process + +## MM-GDINO-T Pre-train Dataset + +For the MM-GDINO-T model, we provide a total of 5 different data combination pre-training configurations. The data is trained in a progressive accumulation manner, so users can prepare it according to their actual needs. + +### 1 Objects365v1 + +The corresponding training config is [grounding_dino_swin-t_pretrain_obj365](./grounding_dino_swin-t_pretrain_obj365.py) + +Objects365v1 can be downloaded from [opendatalab](https://opendatalab.com/OpenDataLab/Objects365_v1). It offers two methods of download: CLI and SDK. + +After downloading and unzipping, place the dataset or create a symbolic link to the `data/objects365v1` directory. The directory structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── objects365v1 +│ │ ├── objects365_train.json +│ │ ├── objects365_val.json +│ │ ├── train +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── test +``` + +Then, use [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) to convert it into the ODVG format required for training. + +```shell +python tools/dataset_converters/coco2odvg.py data/objects365v1/objects365_train.json -d o365v1 +``` + +After the program runs successfully, it will create two new files, `o365v1_train_od.json` and `o365v1_label_map.json`, in the `data/objects365v1` directory. The complete structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── objects365v1 +│ │ ├── objects365_train.json +│ │ ├── objects365_val.json +│ │ ├── o365v1_train_od.json +│ │ ├── o365v1_label_map.json +│ │ ├── train +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── test +``` + +### 2 COCO 2017 + +The above configuration will evaluate the performance on the COCO 2017 dataset during the training process. Therefore, it is necessary to prepare the COCO 2017 dataset. You can download it from the [COCO](https://cocodataset.org/) official website or from [opendatalab](https://opendatalab.com/OpenDataLab/COCO_2017). + +After downloading and unzipping, place the dataset or create a symbolic link to the `data/coco` directory. The directory structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 3 GoldG + +After downloading the dataset, you can start training with the [grounding_dino_swin-t_pretrain_obj365_goldg](./grounding_dino_swin-t_pretrain_obj365_goldg.py) configuration. + +The GoldG dataset includes the `GQA` and `Flickr30k` datasets, which are part of the MixedGrounding dataset mentioned in the GLIP paper, excluding the COCO dataset. The download links are [mdetr_annotations](https://huggingface.co/GLIPModel/GLIP/tree/main/mdetr_annotations), and the specific files currently needed are `mdetr_annotations/final_mixed_train_no_coco.json` and `mdetr_annotations/final_flickr_separateGT_train.json`. + +Then download the [GQA images](https://nlp.stanford.edu/data/gqa/images.zip). After downloading and unzipping, place the dataset or create a symbolic link to them in the `data/gqa` directory, with the following directory structure: + +```text +mmdetection +├── configs +├── data +│ ├── gqa +| | ├── final_mixed_train_no_coco.json +│ │ ├── images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +Then download the [Flickr30k images](http://shannon.cs.illinois.edu/DenotationGraph/). You need to apply for access to this dataset and then download it using the provided link. After downloading and unzipping, place the dataset or create a symbolic link to them in the `data/flickr30k_entities` directory, with the following directory structure: + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +For the GQA dataset, you need to use [goldg2odvg.py](../../tools/dataset_converters/goldg2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/goldg2odvg.py data/gqa/final_mixed_train_no_coco.json +``` + +After the program has run, a new file `final_mixed_train_no_coco_vg.json` will be created in the `data/gqa` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── gqa +| | ├── final_mixed_train_no_coco.json +| | ├── final_mixed_train_no_coco_vg.json +│ │ ├── images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +For the Flickr30k dataset, you need to use [goldg2odvg.py](../../tools/dataset_converters/goldg2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/goldg2odvg.py data/flickr30k_entities/final_flickr_separateGT_train.json +``` + +After the program has run, a new file `final_flickr_separateGT_train_vg.json` will be created in the `data/flickr30k_entities` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── final_flickr_separateGT_train_vg.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 4 GRIT-20M + +The corresponding training configuration is [grounding_dino_swin-t_pretrain_obj365_goldg_grit9m](./grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py). + +The GRIT dataset can be downloaded using the img2dataset package from [GRIT](https://huggingface.co/datasets/zzliang/GRIT#download-image). By default, the dataset size is 1.1T, and downloading and processing it may require at least 2T of disk space, depending on your available storage capacity. After downloading, the dataset is in its original format, which includes: + +```text +mmdetection +├── configs +├── data +│ ├── grit_raw +│ │ ├── 00000_stats.json +│ │ ├── 00000.parquet +│ │ ├── 00000.tar +│ │ ├── 00001_stats.json +│ │ ├── 00001.parquet +│ │ ├── 00001.tar +│ │ ├── ... +``` + +After downloading, further format processing is required: + +```shell +python tools/dataset_converters/grit_processing.py data/grit_raw data/grit_processed +``` + +The processed format is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── grit_processed +│ │ ├── annotations +│ │ │ ├── 00000.json +│ │ │ ├── 00001.json +│ │ │ ├── ... +│ │ ├── images +│ │ │ ├── 00000 +│ │ │ │ ├── 000000000.jpg +│ │ │ │ ├── 000000003.jpg +│ │ │ │ ├── 000000004.jpg +│ │ │ │ ├── ... +│ │ │ ├── 00001 +│ │ │ ├── ... +``` + +As for the GRIT dataset, you need to use [grit2odvg.py](../../tools/dataset_converters/grit2odvg.py) to convert it to the format of ODVG: + +```shell +python tools/dataset_converters/grit2odvg.py data/grit_processed/ +``` + +After the program has run, a new file `grit20m_vg.json` will be created in the `data/grit_processed` directory, which has about 9M data, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── grit_processed +| | ├── grit20m_vg.json +│ │ ├── annotations +│ │ │ ├── 00000.json +│ │ │ ├── 00001.json +│ │ │ ├── ... +│ │ ├── images +│ │ │ ├── 00000 +│ │ │ │ ├── 000000000.jpg +│ │ │ │ ├── 000000003.jpg +│ │ │ │ ├── 000000004.jpg +│ │ │ │ ├── ... +│ │ │ ├── 00001 +│ │ │ ├── ... +``` + +### 5 V3Det + +The corresponding training configurations are: + +- [grounding_dino_swin-t_pretrain_obj365_goldg_v3det](./grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py) +- [grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det](./grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py) + +The V3Det dataset can be downloaded from [opendatalab](https://opendatalab.com/V3Det/V3Det). After downloading and unzipping, place the dataset or create a symbolic link to it in the `data/v3det` directory, with the following directory structure: + +```text +mmdetection +├── configs +├── data +│ ├── v3det +│ │ ├── annotations +│ │ | ├── v3det_2023_v1_train.json +│ │ ├── images +│ │ │ ├── a00000066 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +Then use [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/coco2odvg.py data/v3det/annotations/v3det_2023_v1_train.json -d v3det +``` + +After the program has run, two new files `v3det_2023_v1_train_od.json` and `v3det_2023_v1_label_map.json` will be created in the `data/v3det/annotations` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── v3det +│ │ ├── annotations +│ │ | ├── v3det_2023_v1_train.json +│ │ | ├── v3det_2023_v1_train_od.json +│ │ | ├── v3det_2023_v1_label_map.json +│ │ ├── images +│ │ │ ├── a00000066 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 6 Data Splitting and Visualization + +Considering that users need to prepare many datasets, which is inconvenient for confirming images and annotations before training, we provide a data splitting and visualization tool. This tool can split the dataset into a tiny version and then use a visualization script to check the correctness of the images and labels. + +1. Splitting the Dataset + +The script is located [here](../../tools/misc/split_odvg.py). Taking `Object365 v1` as an example, the command to split the dataset is as follows: + +```shell +python tools/misc/split_odvg.py data/object365_v1/ o365v1_train_od.json train your_output_dir --label-map-file o365v1_label_map.json -n 200 +``` + +After running the above script, it will create a folder structure in the `your_output_dir` directory identical to `data/object365_v1/`, but it will only save 200 training images and their corresponding json files for convenient user review. + +2. Visualizing the Original Dataset + +The script is located [here](../../tools/analysis_tools/browse_grounding_raw.py). Taking `Object365 v1` as an example, the command to visualize the dataset is as follows: + +```shell +python tools/analysis_tools/browse_grounding_raw.py data/object365_v1/ o365v1_train_od.json train --label-map-file o365v1_label_map.json -o your_output_dir --not-show +``` + +After running the above script, it will generate images in the `your_output_dir` directory that include both the pictures and their labels, making it convenient for users to review. + +3. Visualizing the Output Dataset + +The script is located [here](../../tools/analysis_tools/browse_grounding_dataset.py). Users can use this script to view the results of the dataset output, including the results of data augmentation. Taking `Object365 v1` as an example, the command to visualize the dataset is as follows: + +```shell +python tools/analysis_tools/browse_grounding_dataset.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py -o your_output_dir --not-show +``` + +After running the above script, it will generate images in the `your_output_dir` directory that include both the pictures and their labels, making it convenient for users to review. + +## MM-GDINO-L Pre-training Data Preparation and Processing + +### 1 Object365 v2 + +Objects365_v2 can be downloaded from [opendatalab](https://opendatalab.com/OpenDataLab/Objects365). It offers two download methods: CLI and SDK. + +After downloading and unzipping, place the dataset or create a symbolic link to it in the `data/objects365v2` directory, with the following directory structure: + +```text +mmdetection +├── configs +├── data +│ ├── objects365v2 +│ │ ├── annotations +│ │ │ ├── zhiyuan_objv2_train.json +│ │ ├── train +│ │ │ ├── patch0 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +Since some category names in Objects365v2 are incorrect, it is necessary to correct them first. + +```shell +python tools/dataset_converters/fix_o365_names.py +``` + +A new annotation file `zhiyuan_objv2_train_fixname.json` will be generated in the `data/objects365v2/annotations` directory. + +Then use [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/coco2odvg.py data/objects365v2/annotations/zhiyuan_objv2_train_fixname.json -d o365v2 +``` + +After the program has run, two new files `zhiyuan_objv2_train_fixname_od.json` and `o365v2_label_map.json` will be created in the `data/objects365v2` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── objects365v2 +│ │ ├── annotations +│ │ │ ├── zhiyuan_objv2_train.json +│ │ │ ├── zhiyuan_objv2_train_fixname.json +│ │ │ ├── zhiyuan_objv2_train_fixname_od.json +│ │ │ ├── o365v2_label_map.json +│ │ ├── train +│ │ │ ├── patch0 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 2 OpenImages v6 + +OpenImages v6 can be downloaded from the [official website](https://storage.googleapis.com/openimages/web/download_v6.html). Due to the large size of the dataset, it may take some time to download. After completion, the file structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── OpenImages +│ │ ├── annotations +| │ │ ├── oidv6-train-annotations-bbox.csv +| │ │ ├── class-descriptions-boxable.csv +│ │ ├── OpenImages +│ │ │ ├── train +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +Then use [openimages2odvg.py](../../tools/dataset_converters/openimages2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/openimages2odvg.py data/OpenImages/annotations +``` + +After the program has run, two new files `oidv6-train-annotation_od.json` and `openimages_label_map.json` will be created in the `data/OpenImages/annotations` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── OpenImages +│ │ ├── annotations +| │ │ ├── oidv6-train-annotations-bbox.csv +| │ │ ├── class-descriptions-boxable.csv +| │ │ ├── oidv6-train-annotations_od.json +| │ │ ├── openimages_label_map.json +│ │ ├── OpenImages +│ │ │ ├── train +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 3 V3Det + +Referring to the data preparation section of the previously mentioned MM-GDINO-T pre-training data preparation and processing, the complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── v3det +│ │ ├── annotations +│ │ | ├── v3det_2023_v1_train.json +│ │ | ├── v3det_2023_v1_train_od.json +│ │ | ├── v3det_2023_v1_label_map.json +│ │ ├── images +│ │ │ ├── a00000066 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 4 LVIS 1.0 + +Please refer to the `2 LVIS 1.0` section of the later `Fine-tuning Dataset Preparation`. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── lvis_v1_train_od.json +│ │ │ ├── lvis_v1_label_map.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 5 COCO2017 OD + +You can refer to the earlier section `MM-GDINO-T Pre-training Data Preparation and Processing` for data preparation. For convenience in subsequent processing, please create a symbolic link or move the downloaded [mdetr_annotations](https://huggingface.co/GLIPModel/GLIP/tree/main/mdetr_annotations) folder to the `data/coco` path. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +Due to some overlap between COCO2017 train and RefCOCO/RefCOCO+/RefCOCOg/gRefCOCO val, if not removed in advance, there will be data leakage when evaluating RefExp. + +```shell +python tools/dataset_converters/remove_cocotrain2017_from_refcoco.py data/coco/mdetr_annotations data/coco/annotations/instances_train2017.json +``` + +A new file `instances_train2017_norefval.json` will be created in the `data/coco/annotations` directory. Finally, use [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/coco2odvg.py data/coco/annotations/instances_train2017_norefval.json -d coco +``` + +Two new files `instances_train2017_norefval_od.json` and `coco_label_map.json` will be created in the `data/coco/annotations` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2017_norefval_od.json +│ │ │ ├── coco_label_map.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +Note: There are 15,000 images that overlap between the COCO2017 train and LVIS 1.0 val datasets. Therefore, if the COCO2017 train dataset is used in training, the evaluation results of LVIS 1.0 val will have a data leakage issue. However, LVIS 1.0 minival does not have this problem. + +### 6 GoldG + +Please refer to the section on `MM-GDINO-T Pre-training Data Preparation and Processing`. + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── final_flickr_separateGT_train_vg.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ ├── gqa +| | ├── final_mixed_train_no_coco.json +| | ├── final_mixed_train_no_coco_vg.json +│ │ ├── images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 7 COCO2014 VG + +MDetr provides a Phrase Grounding version of the COCO2014 train annotations. The original annotation file is named `final_mixed_train.json`, and similar to the previous structure, the file structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_mixed_train.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +We can extract the COCO portion of the data from `final_mixed_train.json`. + +```shell +python tools/dataset_converters/extract_coco_from_mixed.py data/coco/mdetr_annotations/final_mixed_train.json +``` + +A new file named `final_mixed_train_only_coco.json` will be created in the `data/coco/mdetr_annotations` directory. Finally, use [goldg2odvg.py](../../tools/dataset_converters/goldg2odvg.py) to convert it into the ODVG format required for training: + +```shell +python tools/dataset_converters/goldg2odvg.py data/coco/mdetr_annotations/final_mixed_train_only_coco.json +``` + +A new file named `final_mixed_train_only_coco_vg.json` will be created in the `data/coco/mdetr_annotations` directory, with the complete structure as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_mixed_train.json +│ │ │ ├── final_mixed_train_only_coco.json +│ │ │ ├── final_mixed_train_only_coco_vg.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +Note: COCO2014 train and COCO2017 val do not have duplicate images, so there is no need to worry about data leakage issues in COCO evaluation. + +### 8 Referring Expression Comprehension + +There are a total of 4 datasets included. For data preparation, please refer to the `Fine-tuning Dataset Preparation` section. + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcoco_train_vg.json +│ │ │ ├── finetune_refcoco+_train_vg.json +│ │ │ ├── finetune_refcocog_train_vg.json +│ │ │ ├── finetune_grefcoco_train_vg.json +``` + +### 9 GRIT-20M + +Please refer to the `MM-GDINO-T Pre-training Data Preparation and Processing` section. + +## Preparation of Evaluation Dataset + +### 1 COCO 2017 + +The data preparation process is consistent with the previous descriptions, and the final structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 2 LVIS 1.0 + +The LVIS 1.0 val dataset includes both mini and full versions. The significance of the mini version is: + +1. The full LVIS val evaluation dataset is quite large, and conducting an evaluation with it can take a significant amount of time. +2. In the full LVIS val dataset, there are 15,000 images from the COCO2017 train dataset. If a user has used the COCO2017 data for training, there can be a data leakage issue when evaluating on the full LVIS val dataset + +The LVIS 1.0 dataset contains images that are exactly the same as the COCO2017 dataset, with the addition of new annotations. You can download the minival annotation file from [here](https://huggingface.co/GLIPModel/GLIP/blob/main/lvis_v1_minival_inserted_image_name.json), and the val 1.0 annotation file from [here](https://huggingface.co/GLIPModel/GLIP/blob/main/lvis_od_val.json). The final structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 3 ODinW + +ODinW, which stands for Object Detection in the Wild, is a dataset used to evaluate the generalization capability of grounding pre-trained models in different real-world scenarios. It consists of two subsets, ODinW13 and ODinW35, representing datasets composed of 13 and 35 different datasets, respectively. You can download it from [here](https://huggingface.co/GLIPModel/GLIP/tree/main/odinw_35), and then unzip each file. The final structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── odinw +│ │ ├── AerialMaritimeDrone +│ │ | |── large +│ │ | | ├── test +│ │ | | ├── train +│ │ | | ├── valid +│ │ | |── tiled +│ │ ├── AmericanSignLanguageLetters +│ │ ├── Aquarium +│ │ ├── BCCD +│ │ ├── ... +``` + +When evaluating ODinW35, custom prompts are required. Therefore, it's necessary to preprocess the annotated JSON files in advance. You can use the [override_category.py](./odinw/override_category.py) script for this purpose. After processing, it will generate new annotation files without overwriting the original ones. + +```shell +python configs/mm_grounding_dino/odinw/override_category.py data/odinw/ +``` + +### 4 DOD + +DOD stands for Described Object Detection, and it is introduced in the paper titled [Described Object Detection: Liberating Object Detection with Flexible Expressions](https://arxiv.org/abs/2307.12813). You can download the dataset from [here](https://github.com/shikras/d-cube?tab=readme-ov-file). The final structure of the dataset is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── d3 +│ │ ├── d3_images +│ │ ├── d3_json +│ │ ├── d3_pkl +``` + +### 5 Flickr30k Entities + +In the previous GoldG data preparation section, we downloaded the necessary files for training with Flickr30k. For evaluation, you will need 2 JSON files, which you can download from [here](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_val.json) and [here](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_test.json). The final structure of the dataset is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── final_flickr_separateGT_val.json +│ │ ├── final_flickr_separateGT_test.json +│ │ ├── final_flickr_separateGT_train_vg.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 6 Referring Expression Comprehension + +Referential Expression Comprehension includes 4 datasets: RefCOCO, RefCOCO+, RefCOCOg, and gRefCOCO. The images used in these 4 datasets are from COCO2014 train, similar to COCO2017. You can download the images from the official COCO website or opendatalab. The annotations can be directly downloaded from [here](https://huggingface.co/GLIPModel/GLIP/tree/main/mdetr_annotations). The mdetr_annotations folder contains a large number of annotations, so you can choose to download only the JSON files you need. The final structure of the dataset is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcocog_test.json +``` + +Please note that gRefCOCO is introduced in [GREC: Generalized Referring Expression Comprehension](https://arxiv.org/abs/2308.16182) and is not available in the `mdetr_annotations` folder. You will need to handle it separately. Here are the specific steps: + +1. Download [gRefCOCO](https://github.com/henghuiding/gRefCOCO?tab=readme-ov-file) and unzip it into the `data/coco/` folder. + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ ├── grefs +│ │ │ ├── grefs(unc).json +│ │ │ ├── instances.json +``` + +2. Convert to COCO format + +You can use the official [conversion script](https://github.com/henghuiding/gRefCOCO/blob/b4b1e55b4d3a41df26d6b7d843ea011d581127d4/mdetr/scripts/fine-tuning/grefexp_coco_format.py) provided by gRefCOCO. Please note that you need to uncomment line 161 and comment out line 160 in the script to obtain the full JSON file. + +```shell +# you need to clone the official repo +git clone https://github.com/henghuiding/gRefCOCO.git +cd gRefCOCO/mdetr +python scripts/fine-tuning/grefexp_coco_format.py --data_path ../../data/coco/grefs --out_path ../../data/coco/mdetr_annotations/ --coco_path ../../data/coco +``` + +Four JSON files will be generated in the `data/coco/mdetr_annotations/` folder. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_grefcoco_train.json +│ │ │ ├── finetune_grefcoco_val.json +│ │ │ ├── finetune_grefcoco_testA.json +│ │ │ ├── finetune_grefcoco_testB.json +``` + +## Fine-Tuning Dataset Preparation + +### 1 COCO 2017 + +COCO is the most commonly used dataset in the field of object detection, and we aim to explore its fine-tuning modes more comprehensively. From current developments, there are a total of three fine-tuning modes: + +1. Closed-set fine-tuning, where the description on the text side cannot be modified after fine-tuning, transforms into a closed-set algorithm. This approach maximizes performance on COCO but loses generality. +2. Open-set continued pretraining fine-tuning involves using pretraining methods consistent with the COCO dataset. There are two approaches to this: the first is to reduce the learning rate and fix certain modules, fine-tuning only on the COCO dataset; the second is to mix COCO data with some of the pre-trained data. The goal of both approaches is to improve performance on the COCO dataset as much as possible without compromising generalization. +3. Open-vocabulary fine-tuning involves adopting a common practice in the OVD (Open-Vocabulary Detection) domain. It divides COCO categories into base classes and novel classes. During training, fine-tuning is performed only on the base classes, while evaluation is conducted on both base and novel classes. This approach allows for the assessment of COCO OVD capabilities, with the goal of improving COCO dataset performance without compromising generalization as much as possible. + +\*\*(1) Closed-set Fine-tuning \*\* + +This section does not require data preparation; you can directly use the data you have prepared previously. + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +**(2) Open-set Continued Pretraining Fine-tuning** +To use this approach, you need to convert the COCO training data into ODVG format. You can use the following command for conversion: + +```shell +python tools/dataset_converters/coco2odvg.py data/coco/annotations/instances_train2017.json -d coco +``` + +This will generate new files, `instances_train2017_od.json` and `coco2017_label_map.json`, in the `data/coco/annotations/` directory. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_train2017_od.json +│ │ │ ├── coco2017_label_map.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +Once you have obtained the data, you can choose whether to perform individual pretraining or mixed pretraining. + +**(3) Open-vocabulary Fine-tuning** +For this approach, you need to convert the COCO training data into OVD (Open-Vocabulary Detection) format. You can use the following command for conversion: + +```shell +python tools/dataset_converters/coco2ovd.py data/coco/ +``` + +This will generate new files, `instances_val2017_all_2.json` and `instances_val2017_seen_2.json`, in the `data/coco/annotations/` directory. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_train2017_od.json +│ │ │ ├── instances_val2017_all_2.json +│ │ │ ├── instances_val2017_seen_2.json +│ │ │ ├── coco2017_label_map.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +You can then proceed to train and test directly using the [configuration](coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py). + +### 2 LVIS 1.0 + +LVIS is a dataset that includes 1,203 classes, making it a valuable dataset for fine-tuning. Due to its large number of classes, it's not feasible to perform closed-set fine-tuning. Therefore, we can only use open-set continued pretraining fine-tuning and open-vocabulary fine-tuning on LVIS. + +You need to prepare the LVIS training JSON files first, which you can download from [here](https://www.lvisdataset.org/dataset). We only need `lvis_v1_train.json` and `lvis_v1_val.json`. After downloading them, place them in the `data/coco/annotations/` directory, and then run the following command: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +(1) Open-set continued pretraining fine-tuning + +Convert to ODVG format using the following command: + +```shell +python tools/dataset_converters/lvis2odvg.py data/coco/annotations/lvis_v1_train.json +``` + +It will generate new files, `lvis_v1_train_od.json` and `lvis_v1_label_map.json`, in the `data/coco/annotations/` directory, and the complete dataset structure will look like this: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── lvis_v1_train_od.json +│ │ │ ├── lvis_v1_label_map.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +You can directly use the provided [configuration](lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py) for training and testing, or you can modify the configuration to mix it with some of the pretraining datasets as needed. + +**(2) Open Vocabulary Fine-tuning** + +Convert to OVD format using the following command: + +```shell +python tools/dataset_converters/lvis2ovd.py data/coco/ +``` + +New `lvis_v1_train_od_norare.json` and `lvis_v1_label_map_norare.json` will be generated under `data/coco/annotations/`, and the complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── lvis_v1_train_od.json +│ │ │ ├── lvis_v1_label_map.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ │ ├── lvis_v1_train_od_norare.json +│ │ │ ├── lvis_v1_label_map_norare.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +然Then you can directly use the [configuration](lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py) for training and testing. + +### 3 RTTS + +RTTS is a foggy weather dataset, which contains 4,322 foggy images, including five classes: bicycle, bus, car, motorbike, and person. It can be downloaded from [here](https://drive.google.com/file/d/15Ei1cHGVqR1mXFep43BO7nkHq1IEGh1e/view), and then extracted to the `data/RTTS/` folder. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── RTTS +│ │ ├── annotations_json +│ │ ├── annotations_xml +│ │ ├── ImageSets +│ │ ├── JPEGImages +``` + +### 4 RUOD + +RUOD is an underwater object detection dataset. You can download it from [here](https://drive.google.com/file/d/1hxtbdgfVveUm_DJk5QXkNLokSCTa_E5o/view), and then extract it to the `data/RUOD/` folder. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── RUOD +│ │ ├── Environment_pic +│ │ ├── Environmet_ANN +│ │ ├── RUOD_ANN +│ │ ├── RUOD_pic +``` + +### 5 Brain Tumor + +Brain Tumor is a 2D detection dataset in the medical field. You can download it from [here](https://universe.roboflow.com/roboflow-100/brain-tumor-m2pbp/dataset/2), please make sure to choose the `COCO JSON` format. Then extract it to the `data/brain_tumor_v2/` folder. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── brain_tumor_v2 +│ │ ├── test +│ │ ├── train +│ │ ├── valid +``` + +### 6 Cityscapes + +Cityscapes is an urban street scene dataset. You can download it from [here](https://www.cityscapes-dataset.com/) or from opendatalab, and then extract it to the `data/cityscapes/` folder. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── cityscapes +│ │ ├── annotations +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +``` + +After downloading, you can use the [cityscapes.py](../../tools/dataset_converters/cityscapes.py) script to generate the required JSON format. + +```shell +python tools/dataset_converters/cityscapes.py data/cityscapes/ +``` + +Three new JSON files will be generated in the annotations directory. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── cityscapes +│ │ ├── annotations +│ │ │ ├── instancesonly_filtered_gtFine_train.json +│ │ │ ├── instancesonly_filtered_gtFine_val.json +│ │ │ ├── instancesonly_filtered_gtFine_test.json +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +``` + +### 7 People in Painting + +People in Painting is an oil painting dataset that you can download from [here](https://universe.roboflow.com/roboflow-100/people-in-paintings/dataset/2). Please make sure to choose the `COCO JSON` format. After downloading, unzip the dataset to the `data/people_in_painting_v2/` folder. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── people_in_painting_v2 +│ │ ├── test +│ │ ├── train +│ │ ├── valid +``` + +### 8 Referring Expression Comprehension + +Fine-tuning for Referential Expression Comprehension is similar to what was described earlier and includes four datasets. The dataset preparation for evaluation has already been organized. The complete dataset structure is as follows: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcocog_test.json +``` + +Then we need to convert it to the required ODVG format. Please use the [refcoco2odvg.py](../../tools/dataset_converters/refcoco2odvg.py) script to perform the conversion. + +```shell +python tools/dataset_converters/refcoco2odvg.py data/coco/mdetr_annotations +``` + +The converted dataset structure will include 4 new JSON files in the `data/coco/mdetr_annotations` directory. Here is the structure of the converted dataset: + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcoco_train_vg.json +│ │ │ ├── finetune_refcoco+_train_vg.json +│ │ │ ├── finetune_refcocog_train_vg.json +│ │ │ ├── finetune_grefcoco_train_vg.json +``` diff --git a/configs/mm_grounding_dino/dataset_prepare_zh-CN.md b/configs/mm_grounding_dino/dataset_prepare_zh-CN.md new file mode 100644 index 00000000000..10520b02fe5 --- /dev/null +++ b/configs/mm_grounding_dino/dataset_prepare_zh-CN.md @@ -0,0 +1,1194 @@ +# 数据准备和处理 + +## MM-GDINO-T 预训练数据准备和处理 + +MM-GDINO-T 模型中我们一共提供了 5 种不同数据组合的预训练配置，数据采用逐步累加的方式进行训练，因此用户可以根据自己的实际需求准备数据。 + +### 1 Objects365 v1 + +对应的训练配置为 [grounding_dino_swin-t_pretrain_obj365](./grounding_dino_swin-t_pretrain_obj365.py) + +Objects365_v1 可以从 [opendatalab](https://opendatalab.com/OpenDataLab/Objects365_v1) 下载，其提供了 CLI 和 SDK 两者下载方式。 + +下载并解压后，将其放置或者软链接到 `data/objects365v1` 目录下，目录结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── objects365v1 +│ │ ├── objects365_train.json +│ │ ├── objects365_val.json +│ │ ├── train +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── test +``` + +然后使用 [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/coco2odvg.py data/objects365v1/objects365_train.json -d o365v1 +``` + +程序运行完成后会在 `data/objects365v1` 目录下创建 `o365v1_train_od.json` 和 `o365v1_label_map.json` 两个新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── objects365v1 +│ │ ├── objects365_train.json +│ │ ├── objects365_val.json +│ │ ├── o365v1_train_od.json +│ │ ├── o365v1_label_map.json +│ │ ├── train +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── test +``` + +### 2 COCO 2017 + +上述配置在训练过程中会评估 COCO 2017 数据集的性能，因此需要准备 COCO 2017 数据集。你可以从 [COCO](https://cocodataset.org/) 官网下载或者从 [opendatalab](https://opendatalab.com/OpenDataLab/COCO_2017) 下载 + +下载并解压后，将其放置或者软链接到 `data/coco` 目录下，目录结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 3 GoldG + +下载该数据集后就可以训练 [grounding_dino_swin-t_pretrain_obj365_goldg](./grounding_dino_swin-t_pretrain_obj365_goldg.py) 配置了。 + +GoldG 数据集包括 `GQA` 和 `Flickr30k` 两个数据集，来自 GLIP 论文中提到的 MixedGrounding 数据集，其排除了 COCO 数据集。下载链接为 [mdetr_annotations](https://huggingface.co/GLIPModel/GLIP/tree/main/mdetr_annotations)，我们目前需要的是 `mdetr_annotations/final_mixed_train_no_coco.json` 和 `mdetr_annotations/final_flickr_separateGT_train.json` 文件。 + +然后下载 [GQA images](https://nlp.stanford.edu/data/gqa/images.zip) 图片。下载并解压后，将其放置或者软链接到 `data/gqa` 目录下，目录结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── gqa +| | ├── final_mixed_train_no_coco.json +│ │ ├── images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +然后下载 [Flickr30k images](http://shannon.cs.illinois.edu/DenotationGraph/) 图片。这个数据下载需要先申请，再获得下载链接后才可以下载。下载并解压后，将其放置或者软链接到 `data/flickr30k_entities` 目录下，目录结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +对于 GQA 数据集，你需要使用 [goldg2odvg.py](../../tools/dataset_converters/goldg2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/goldg2odvg.py data/gqa/final_mixed_train_no_coco.json +``` + +程序运行完成后会在 `data/gqa` 目录下创建 `final_mixed_train_no_coco_vg.json` 新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── gqa +| | ├── final_mixed_train_no_coco.json +| | ├── final_mixed_train_no_coco_vg.json +│ │ ├── images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +对于 Flickr30k 数据集，你需要使用 [goldg2odvg.py](../../tools/dataset_converters/goldg2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/goldg2odvg.py data/flickr30k_entities/final_flickr_separateGT_train.json +``` + +程序运行完成后会在 `data/flickr30k_entities` 目录下创建 `final_flickr_separateGT_train_vg.json` 新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── final_flickr_separateGT_train_vg.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 4 GRIT-20M + +对应的训练配置为 [grounding_dino_swin-t_pretrain_obj365_goldg_grit9m](./grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py) + +GRIT数据集可以从 [GRIT](https://huggingface.co/datasets/zzliang/GRIT#download-image) 中使用 img2dataset 包下载，默认指令下载后数据集大小为 1.1T，下载和处理预估需要至少 2T 硬盘空间，可根据硬盘容量酌情下载。下载后原始格式为： + +```text +mmdetection +├── configs +├── data +│ ├── grit_raw +│ │ ├── 00000_stats.json +│ │ ├── 00000.parquet +│ │ ├── 00000.tar +│ │ ├── 00001_stats.json +│ │ ├── 00001.parquet +│ │ ├── 00001.tar +│ │ ├── ... +``` + +下载后需要对格式进行进一步处理: + +```shell +python tools/dataset_converters/grit_processing.py data/grit_raw data/grit_processed +``` + +处理后的格式为： + +```text +mmdetection +├── configs +├── data +│ ├── grit_processed +│ │ ├── annotations +│ │ │ ├── 00000.json +│ │ │ ├── 00001.json +│ │ │ ├── ... +│ │ ├── images +│ │ │ ├── 00000 +│ │ │ │ ├── 000000000.jpg +│ │ │ │ ├── 000000003.jpg +│ │ │ │ ├── 000000004.jpg +│ │ │ │ ├── ... +│ │ │ ├── 00001 +│ │ │ ├── ... +``` + +对于 GRIT 数据集，你需要使用 [grit2odvg.py](../../tools/dataset_converters/grit2odvg.py) 转化成需要的 ODVG 格式： + +```shell +python tools/dataset_converters/grit2odvg.py data/grit_processed/ +``` + +程序运行完成后会在 `data/grit_processed` 目录下创建 `grit20m_vg.json` 新文件，大概包含 9M 条数据，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── grit_processed +| | ├── grit20m_vg.json +│ │ ├── annotations +│ │ │ ├── 00000.json +│ │ │ ├── 00001.json +│ │ │ ├── ... +│ │ ├── images +│ │ │ ├── 00000 +│ │ │ │ ├── 000000000.jpg +│ │ │ │ ├── 000000003.jpg +│ │ │ │ ├── 000000004.jpg +│ │ │ │ ├── ... +│ │ │ ├── 00001 +│ │ │ ├── ... +``` + +### 5 V3Det + +对应的训练配置为 + +- [grounding_dino_swin-t_pretrain_obj365_goldg_v3det](./grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py) +- [grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det](./grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py) + +V3Det 数据集下载可以从 [opendatalab](https://opendatalab.com/V3Det/V3Det) 下载，下载并解压后，将其放置或者软链接到 `data/v3det` 目录下，目录结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── v3det +│ │ ├── annotations +│ │ | ├── v3det_2023_v1_train.json +│ │ ├── images +│ │ │ ├── a00000066 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +然后使用 [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/coco2odvg.py data/v3det/annotations/v3det_2023_v1_train.json -d v3det +``` + +程序运行完成后会在 `data/v3det/annotations` 目录下创建目录下创建 `v3det_2023_v1_train_od.json` 和 `v3det_2023_v1_label_map.json` 两个新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── v3det +│ │ ├── annotations +│ │ | ├── v3det_2023_v1_train.json +│ │ | ├── v3det_2023_v1_train_od.json +│ │ | ├── v3det_2023_v1_label_map.json +│ │ ├── images +│ │ │ ├── a00000066 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 6 数据切分和可视化 + +考虑到用户需要准备的数据集过多，不方便对图片和标注进行训练前确认，因此我们提供了一个数据切分和可视化的工具，可以将数据集切分为 tiny 版本，然后使用可视化脚本查看图片和标签正确性。 + +1. 切分数据集 + +脚本位于 [这里](../../tools/misc/split_odvg.py), 以 `Object365 v1` 为例，切分数据集的命令如下： + +```shell +python tools/misc/split_odvg.py data/object365_v1/ o365v1_train_od.json train your_output_dir --label-map-file o365v1_label_map.json -n 200 +``` + +上述脚本运行后会在 `your_output_dir` 目录下创建和 `data/object365_v1/` 一样的文件夹结构，但是只会保存 200 张训练图片和对应的 json，方便用户查看。 + +2. 可视化原始数据集 + +脚本位于 [这里](../../tools/analysis_tools/browse_grounding_raw.py), 以 `Object365 v1` 为例，可视化数据集的命令如下： + +```shell +python tools/analysis_tools/browse_grounding_raw.py data/object365_v1/ o365v1_train_od.json train --label-map-file o365v1_label_map.json -o your_output_dir --not-show +``` + +上述脚本运行后会在 `your_output_dir` 目录下生成同时包括图片和标签的图片，方便用户查看。 + +3. 可视化 dataset 输出的数据集 + +脚本位于 [这里](../../tools/analysis_tools/browse_grounding_dataset.py), 用户可以通过该脚本查看 dataset 输出的结果即包括了数据增强的结果。 以 `Object365 v1` 为例，可视化数据集的命令如下： + +```shell +python tools/analysis_tools/browse_grounding_dataset.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py -o your_output_dir --not-show +``` + +上述脚本运行后会在 `your_output_dir` 目录下生成同时包括图片和标签的图片，方便用户查看。 + +## MM-GDINO-L 预训练数据准备和处理 + +### 1 Object365 v2 + +Objects365_v2 可以从 [opendatalab](https://opendatalab.com/OpenDataLab/Objects365) 下载，其提供了 CLI 和 SDK 两者下载方式。 + +下载并解压后，将其放置或者软链接到 `data/objects365v2` 目录下，目录结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── objects365v2 +│ │ ├── annotations +│ │ │ ├── zhiyuan_objv2_train.json +│ │ ├── train +│ │ │ ├── patch0 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +由于 objects365v2 类别中有部分类名是错误的，因此需要先进行修正。 + +```shell +python tools/dataset_converters/fix_o365_names.py +``` + +会在 `data/objects365v2/annotations` 下生成新的标注文件 `zhiyuan_objv2_train_fixname.json`。 + +然后使用 [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/coco2odvg.py data/objects365v2/annotations/zhiyuan_objv2_train_fixname.json -d o365v2 +``` + +程序运行完成后会在 `data/objects365v2` 目录下创建 `zhiyuan_objv2_train_fixname_od.json` 和 `o365v2_label_map.json` 两个新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── objects365v2 +│ │ ├── annotations +│ │ │ ├── zhiyuan_objv2_train.json +│ │ │ ├── zhiyuan_objv2_train_fixname.json +│ │ │ ├── zhiyuan_objv2_train_fixname_od.json +│ │ │ ├── o365v2_label_map.json +│ │ ├── train +│ │ │ ├── patch0 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 2 OpenImages v6 + +OpenImages v6 可以从 [官网](https://storage.googleapis.com/openimages/web/download_v6.html) 下载，由于数据集比较大，需要花费一定的时间，下载完成后文件结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── OpenImages +│ │ ├── annotations +| │ │ ├── oidv6-train-annotations-bbox.csv +| │ │ ├── class-descriptions-boxable.csv +│ │ ├── OpenImages +│ │ │ ├── train +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +然后使用 [openimages2odvg.py](../../tools/dataset_converters/openimages2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/openimages2odvg.py data/OpenImages/annotations +``` + +程序运行完成后会在 `data/OpenImages/annotations` 目录下创建 `oidv6-train-annotation_od.json` 和 `openimages_label_map.json` 两个新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── OpenImages +│ │ ├── annotations +| │ │ ├── oidv6-train-annotations-bbox.csv +| │ │ ├── class-descriptions-boxable.csv +| │ │ ├── oidv6-train-annotations_od.json +| │ │ ├── openimages_label_map.json +│ │ ├── OpenImages +│ │ │ ├── train +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 3 V3Det + +参见前面的 MM-GDINO-T 预训练数据准备和处理 数据准备部分，完整数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── v3det +│ │ ├── annotations +│ │ | ├── v3det_2023_v1_train.json +│ │ | ├── v3det_2023_v1_train_od.json +│ │ | ├── v3det_2023_v1_label_map.json +│ │ ├── images +│ │ │ ├── a00000066 +│ │ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 4 LVIS 1.0 + +参见后面的 `微调数据集准备` 的 `2 LVIS 1.0` 部分。完整数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── lvis_v1_train_od.json +│ │ │ ├── lvis_v1_label_map.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 5 COCO2017 OD + +数据准备可以参考前面的 `MM-GDINO-T 预训练数据准备和处理` 部分。为了方便后续处理，请将下载的 [mdetr_annotations](https://huggingface.co/GLIPModel/GLIP/tree/main/mdetr_annotations) 文件夹软链接或者移动到 `data/coco` 路径下 +完整数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +由于 COCO2017 train 和 RefCOCO/RefCOCO+/RefCOCOg/gRefCOCO val 中存在部分重叠，如果不提前移除，在评测 RefExp 时候会存在数据泄露。 + +```shell +python tools/dataset_converters/remove_cocotrain2017_from_refcoco.py data/coco/mdetr_annotations data/coco/annotations/instances_train2017.json +``` + +会在 `data/coco/annotations` 目录下创建 `instances_train2017_norefval.json` 新文件。最后使用 [coco2odvg.py](../../tools/dataset_converters/coco2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/coco2odvg.py data/coco/annotations/instances_train2017_norefval.json -d coco +``` + +会在 `data/coco/annotations` 目录下创建 `instances_train2017_norefval_od.json` 和 `coco_label_map.json` 两个新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2017_norefval_od.json +│ │ │ ├── coco_label_map.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +注意： COCO2017 train 和 LVIS 1.0 val 数据集有 15000 张图片重复，因此一旦在训练中使用了 COCO2017 train，那么 LVIS 1.0 val 的评测结果就存在数据泄露问题，LVIS 1.0 minival 没有这个问题。 + +### 6 GoldG + +参见 MM-GDINO-T 预训练数据准备和处理 部分 + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── final_flickr_separateGT_train_vg.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ ├── gqa +| | ├── final_mixed_train_no_coco.json +| | ├── final_mixed_train_no_coco_vg.json +│ │ ├── images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 7 COCO2014 VG + +MDetr 中提供了 COCO2014 train 的 Phrase Grounding 版本标注， 最原始标注文件为 `final_mixed_train.json`，和之前类似，文件结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_mixed_train.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +我们可以从 `final_mixed_train.json` 中提取出 COCO 部分数据 + +```shell +python tools/dataset_converters/extract_coco_from_mixed.py data/coco/mdetr_annotations/final_mixed_train.json +``` + +会在 `data/coco/mdetr_annotations` 目录下创建 `final_mixed_train_only_coco.json` 新文件，最后使用 [goldg2odvg.py](../../tools/dataset_converters/goldg2odvg.py) 转换为训练所需的 ODVG 格式： + +```shell +python tools/dataset_converters/goldg2odvg.py data/coco/mdetr_annotations/final_mixed_train_only_coco.json +``` + +会在 `data/coco/mdetr_annotations` 目录下创建 `final_mixed_train_only_coco_vg.json` 新文件，完整结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── mdetr_annotations +│ │ │ ├── final_mixed_train.json +│ │ │ ├── final_mixed_train_only_coco.json +│ │ │ ├── final_mixed_train_only_coco_vg.json +│ │ │ ├── ... +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +注意： COCO2014 train 和 COCO2017 val 没有重复图片，因此不用担心 COCO 评测的数据泄露问题。 + +### 8 Referring Expression Comprehension + +其一共包括 4 个数据集。数据准备部分请参见 微调数据集准备 部分。 + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcoco_train_vg.json +│ │ │ ├── finetune_refcoco+_train_vg.json +│ │ │ ├── finetune_refcocog_train_vg.json +│ │ │ ├── finetune_grefcoco_train_vg.json +``` + +### 9 GRIT-20M + +参见 MM-GDINO-T 预训练数据准备和处理 部分 + +## 评测数据集准备 + +### 1 COCO 2017 + +数据准备流程和前面描述一致，最终结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 2 LVIS 1.0 + +LVIS 1.0 val 数据集包括 mini 和全量两个版本，mini 版本存在的意义是： + +1. LVIS val 全量评测数据集比较大，评测一次需要比较久的时间 +2. LVIS val 全量数据集中包括了 15000 张 COCO2017 train, 如果用户使用了 COCO2017 数据进行训练，那么将存在数据泄露问题 + +LVIS 1.0 图片和 COCO2017 数据集图片完全一样，只是提供了新的标注而已，minival 标注文件可以从 [这里](https://huggingface.co/GLIPModel/GLIP/blob/main/lvis_v1_minival_inserted_image_name.json)下载， val 1.0 标注文件可以从 [这里](https://huggingface.co/GLIPModel/GLIP/blob/main/lvis_od_val.json) 下载。 最终结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +### 3 ODinW + +ODinw 全称为 Object Detection in the Wild，是用于验证 grounding 预训练模型在不同实际场景中的泛化能力的数据集，其包括两个子集，分别是 ODinW13 和 ODinW35，代表是由 13 和 35 个数据集组成的。你可以从 [这里](https://huggingface.co/GLIPModel/GLIP/tree/main/odinw_35)下载，然后对每个文件进行解压，最终结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── odinw +│ │ ├── AerialMaritimeDrone +│ │ | |── large +│ │ | | ├── test +│ │ | | ├── train +│ │ | | ├── valid +│ │ | |── tiled +│ │ ├── AmericanSignLanguageLetters +│ │ ├── Aquarium +│ │ ├── BCCD +│ │ ├── ... +``` + +在评测 ODinW3535 时候由于需要自定义 prompt，因此需要提前对标注的 json 文件进行处理，你可以使用 [override_category.py](./odinw/override_category.py) 脚本进行处理，处理后会生成新的标注文件，不会覆盖原先的标注文件。 + +```shell +python configs/mm_grounding_dino/odinw/override_category.py data/odinw/ +``` + +### 4 DOD + +DOD 来自 [Described Object Detection: Liberating Object Detection with Flexible Expressions](https://arxiv.org/abs/2307.12813)。其数据集可以从 [这里](https://github.com/shikras/d-cube?tab=readme-ov-file#download)下载，最终的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── d3 +│ │ ├── d3_images +│ │ ├── d3_json +│ │ ├── d3_pkl +``` + +### 5 Flickr30k Entities + +在前面 GoldG 数据准备章节中我们已经下载了 Flickr30k 训练所需文件，评估所需的文件是 2 个 json 文件，你可以从 [这里](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_val.json) 和 [这里](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_test.json)下载，最终的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── flickr30k_entities +│ │ ├── final_flickr_separateGT_train.json +│ │ ├── final_flickr_separateGT_val.json +│ │ ├── final_flickr_separateGT_test.json +│ │ ├── final_flickr_separateGT_train_vg.json +│ │ ├── flickr30k_images +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +``` + +### 6 Referring Expression Comprehension + +指代性表达式理解包括 4 个数据集： RefCOCO, RefCOCO+, RefCOCOg, gRefCOCO。这 4 个数据集所采用的图片都来自于 COCO2014 train，和 COCO2017 类似，你可以从 COCO 官方或者 opendatalab 中下载，而标注可以直接从 [这里](https://huggingface.co/GLIPModel/GLIP/tree/main/mdetr_annotations) 下载，mdetr_annotations 文件夹里面包括了其他大量的标注，你如果觉得数量过多，可以只下载所需要的几个 json 文件即可。最终的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcocog_test.json +``` + +注意 gRefCOCO 是在 [GREC: Generalized Referring Expression Comprehension](https://arxiv.org/abs/2308.16182) 被提出，并不在 `mdetr_annotations` 文件夹中，需要自行处理。具体步骤为： + +1. 下载 [gRefCOCO](https://github.com/henghuiding/gRefCOCO?tab=readme-ov-file#grefcoco-dataset-download)，并解压到 data/coco/ 文件夹中 + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ ├── grefs +│ │ │ ├── grefs(unc).json +│ │ │ ├── instances.json +``` + +2. 转换为 coco 格式 + +你可以使用 gRefCOCO 官方提供的[转换脚本](https://github.com/henghuiding/gRefCOCO/blob/b4b1e55b4d3a41df26d6b7d843ea011d581127d4/mdetr/scripts/fine-tuning/grefexp_coco_format.py)。注意需要将被注释的 161 行打开，并注释 160 行才可以得到全量的 json 文件。 + +```shell +# 需要克隆官方 repo +git clone https://github.com/henghuiding/gRefCOCO.git +cd gRefCOCO/mdetr +python scripts/fine-tuning/grefexp_coco_format.py --data_path ../../data/coco/grefs --out_path ../../data/coco/mdetr_annotations/ --coco_path ../../data/coco +``` + +会在 `data/coco/mdetr_annotations/` 文件夹中生成 4 个 json 文件，完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_grefcoco_train.json +│ │ │ ├── finetune_grefcoco_val.json +│ │ │ ├── finetune_grefcoco_testA.json +│ │ │ ├── finetune_grefcoco_testB.json +``` + +## 微调数据集准备 + +### 1 COCO 2017 + +COCO 是检测领域最常用的数据集，我们希望能够更充分探索其微调模式。从目前发展来看，一共有 3 种微调方式： + +1. 闭集微调，即微调后文本端将无法修改描述，转变为闭集算法，在 COCO 上性能能够最大化，但是失去了通用性。 +2. 开集继续预训练微调，即对 COCO 数据集采用和预训练一致的预训练手段。此时有两种做法，第一种是降低学习率并固定某些模块，仅仅在 COCO 数据上预训练，第二种是将 COCO 数据和部分预训练数据混合一起训练，两种方式的目的都是在尽可能不降低泛化性时提高 COCO 数据集性能 +3. 开放词汇微调，即采用 OVD 领域常用做法，将 COCO 类别分成 base 类和 novel 类，训练时候仅仅在 base 类上进行，评测在 base 和 novel 类上进行。这种方式可以验证 COCO OVD 能力，目的也是在尽可能不降低泛化性时提高 COCO 数据集性能 + +**(1) 闭集微调** + +这个部分无需准备数据，直接用之前的数据即可。 + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +**(2) 开集继续预训练微调** +这种方式需要将 COCO 训练数据转换为 ODVG 格式，你可以使用如下命令转换： + +```shell +python tools/dataset_converters/coco2odvg.py data/coco/annotations/instances_train2017.json -d coco +``` + +会在 `data/coco/annotations/` 下生成新的 `instances_train2017_od.json` 和 `coco2017_label_map.json`，完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_train2017_od.json +│ │ │ ├── coco2017_label_map.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +在得到数据后，你可以自行选择单独预习还是混合预训练方式。 + +**(3) 开放词汇微调** +这种方式需要将 COCO 训练数据转换为 OVD 格式，你可以使用如下命令转换： + +```shell +python tools/dataset_converters/coco2ovd.py data/coco/ +``` + +会在 `data/coco/annotations/` 下生成新的 `instances_val2017_all_2.json` 和 `instances_val2017_seen_2.json`，完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_train2017_od.json +│ │ │ ├── instances_val2017_all_2.json +│ │ │ ├── instances_val2017_seen_2.json +│ │ │ ├── coco2017_label_map.json +│ │ │ ├── instances_val2017.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +然后可以直接使用 [配置](coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py) 进行训练和测试。 + +### 2 LVIS 1.0 + +LVIS 是一个包括 1203 类的数据集，同时也是一个长尾联邦数据集，对其进行微调很有意义。 由于其类别过多，我们无法对其进行闭集微调，因此只能采用开集继续预训练微调和开放词汇微调。 + +你需要先准备好 LVIS 训练 JSON 文件，你可以从 [这里](https://www.lvisdataset.org/dataset) 下载，我们只需要 `lvis_v1_train.json` 和 `lvis_v1_val.json`，然后将其放到 `data/coco/annotations/` 下，然后运行如下命令： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +(1) 开集继续预训练微调 + +使用如下命令转换为 ODVG 格式： + +```shell +python tools/dataset_converters/lvis2odvg.py data/coco/annotations/lvis_v1_train.json +``` + +会在 `data/coco/annotations/` 下生成新的 `lvis_v1_train_od.json` 和 `lvis_v1_label_map.json`，完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── lvis_v1_train_od.json +│ │ │ ├── lvis_v1_label_map.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +然后可以直接使用 [配置](lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py) 进行训练测试，或者你修改配置将其和部分预训练数据集混合使用。 + +**(2) 开放词汇微调** + +使用如下命令转换为 OVD 格式： + +```shell +python tools/dataset_converters/lvis2ovd.py data/coco/ +``` + +会在 `data/coco/annotations/` 下生成新的 `lvis_v1_train_od_norare.json` 和 `lvis_v1_label_map_norare.json`，完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── lvis_v1_train.json +│ │ │ ├── lvis_v1_val.json +│ │ │ ├── lvis_v1_train_od.json +│ │ │ ├── lvis_v1_label_map.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── lvis_v1_minival_inserted_image_name.json +│ │ │ ├── lvis_od_val.json +│ │ │ ├── lvis_v1_train_od_norare.json +│ │ │ ├── lvis_v1_label_map_norare.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +``` + +然后可以直接使用 [配置](lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py) 进行训练测试 + +### 3 RTTS + +RTTS 是一个浓雾天气数据集，该数据集包含 4,322 张雾天图像，包含五个类：自行车 (bicycle)、公共汽车 (bus)、汽车 (car)、摩托车 (motorbike) 和人 (person)。可以从 [这里](https://drive.google.com/file/d/15Ei1cHGVqR1mXFep43BO7nkHq1IEGh1e/view)下载, 然后解压到 `data/RTTS/` 文件夹中。完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── RTTS +│ │ ├── annotations_json +│ │ ├── annotations_xml +│ │ ├── ImageSets +│ │ ├── JPEGImages +``` + +### 4 RUOD + +RUOD 是一个水下目标检测数据集，你可以从 [这里](https://drive.google.com/file/d/1hxtbdgfVveUm_DJk5QXkNLokSCTa_E5o/view)下载, 然后解压到 `data/RUOD/` 文件夹中。完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── RUOD +│ │ ├── Environment_pic +│ │ ├── Environmet_ANN +│ │ ├── RUOD_ANN +│ │ ├── RUOD_pic +``` + +### 5 Brain Tumor + +Brain Tumor 是一个医学领域的 2d 检测数据集，你可以从 [这里](https://universe.roboflow.com/roboflow-100/brain-tumor-m2pbp/dataset/2)下载, 请注意选择 `COCO JSON` 格式。然后解压到 `data/brain_tumor_v2/` 文件夹中。完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── brain_tumor_v2 +│ │ ├── test +│ │ ├── train +│ │ ├── valid +``` + +### 6 Cityscapes + +Cityscapes 是一个城市街景数据集，你可以从 [这里](https://www.cityscapes-dataset.com/) 或者 opendatalab 中下载, 然后解压到 `data/cityscapes/` 文件夹中。完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── cityscapes +│ │ ├── annotations +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +``` + +在下载后，然后使用 [cityscapes.py](../../tools/dataset_converters/cityscapes.py) 脚本生成我们所需要的 json 格式 + +```shell +python tools/dataset_converters/cityscapes.py data/cityscapes/ +``` + +会在 annotations 中生成 3 个新的 json 文件。完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── cityscapes +│ │ ├── annotations +│ │ │ ├── instancesonly_filtered_gtFine_train.json +│ │ │ ├── instancesonly_filtered_gtFine_val.json +│ │ │ ├── instancesonly_filtered_gtFine_test.json +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +``` + +### 7 People in Painting + +People in Painting 是一个油画数据集，你可以从 [这里](https://universe.roboflow.com/roboflow-100/people-in-paintings/dataset/2), 请注意选择 `COCO JSON` 格式。然后解压到 `data/people_in_painting_v2/` 文件夹中。完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── people_in_painting_v2 +│ │ ├── test +│ │ ├── train +│ │ ├── valid +``` + +### 8 Referring Expression Comprehension + +指代性表达式理解的微调和前面一样，也是包括 4 个数据集，在评测数据准备阶段已经全部整理好了，完整的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcocog_test.json +``` + +然后我们需要将其转换为所需的 ODVG 格式，请使用 [refcoco2odvg.py](../../tools/dataset_converters/refcoco2odvg.py) 脚本转换， + +```shell +python tools/dataset_converters/refcoco2odvg.py data/coco/mdetr_annotations +``` + +会在 `data/coco/mdetr_annotations` 中生成新的 4 个 json 文件。 转换后的数据集结构如下： + +```text +mmdetection +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── instances_train2017.json +│ │ │ ├── instances_val2017.json +│ │ │ ├── instances_train2014.json +│ │ ├── train2017 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── val2017 +│ │ │ ├── xxxx.jpg +│ │ │ ├── ... +│ │ ├── train2014 +│ │ │ ├── xxx.jpg +│ │ │ ├── ... +│ │ ├── mdetr_annotations +│ │ │ ├── final_refexp_val.json +│ │ │ ├── finetune_refcoco_testA.json +│ │ │ ├── finetune_refcoco_testB.json +│ │ │ ├── finetune_refcoco+_testA.json +│ │ │ ├── finetune_refcoco+_testB.json +│ │ │ ├── finetune_refcocog_test.json +│ │ │ ├── finetune_refcoco_train_vg.json +│ │ │ ├── finetune_refcoco+_train_vg.json +│ │ │ ├── finetune_refcocog_train_vg.json +│ │ │ ├── finetune_grefcoco_train_vg.json +``` diff --git a/configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py b/configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py new file mode 100644 index 00000000000..e59a0a52518 --- /dev/null +++ b/configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py @@ -0,0 +1,78 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/d3/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', 'sent_ids')) +] + +# -------------------------------------------------# +val_dataset_full = dict( + type='DODDataset', + data_root=data_root, + ann_file='d3_json/d3_full_annotations.json', + data_prefix=dict(img='d3_images/', anno='d3_pkl'), + pipeline=test_pipeline, + test_mode=True, + backend_args=None, + return_classes=True) + +val_evaluator_full = dict( + type='DODCocoMetric', + ann_file=data_root + 'd3_json/d3_full_annotations.json') + +# -------------------------------------------------# +val_dataset_pres = dict( + type='DODDataset', + data_root=data_root, + ann_file='d3_json/d3_pres_annotations.json', + data_prefix=dict(img='d3_images/', anno='d3_pkl'), + pipeline=test_pipeline, + test_mode=True, + backend_args=None, + return_classes=True) +val_evaluator_pres = dict( + type='DODCocoMetric', + ann_file=data_root + 'd3_json/d3_pres_annotations.json') + +# -------------------------------------------------# +val_dataset_abs = dict( + type='DODDataset', + data_root=data_root, + ann_file='d3_json/d3_abs_annotations.json', + data_prefix=dict(img='d3_images/', anno='d3_pkl'), + pipeline=test_pipeline, + test_mode=True, + backend_args=None, + return_classes=True) +val_evaluator_abs = dict( + type='DODCocoMetric', + ann_file=data_root + 'd3_json/d3_abs_annotations.json') + +# -------------------------------------------------# +datasets = [val_dataset_full, val_dataset_pres, val_dataset_abs] +dataset_prefixes = ['FULL', 'PRES', 'ABS'] +metrics = [val_evaluator_full, val_evaluator_pres, val_evaluator_abs] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py b/configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py new file mode 100644 index 00000000000..3d680091162 --- /dev/null +++ b/configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py @@ -0,0 +1,3 @@ +_base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py' + +model = dict(test_cfg=dict(chunked_size=1)) diff --git a/configs/mm_grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_flickr30k.py b/configs/mm_grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_flickr30k.py new file mode 100644 index 00000000000..e9eb783da97 --- /dev/null +++ b/configs/mm_grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_flickr30k.py @@ -0,0 +1,57 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +dataset_type = 'Flickr30kDataset' +data_root = 'data/flickr30k_entities/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive', 'phrase_ids', 'phrases')) +] + +dataset_Flickr30k_val = dict( + type=dataset_type, + data_root=data_root, + ann_file='final_flickr_separateGT_val.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, +) + +dataset_Flickr30k_test = dict( + type=dataset_type, + data_root=data_root, + ann_file='final_flickr_separateGT_test.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, +) + +val_evaluator_Flickr30k = dict(type='Flickr30kMetric') + +test_evaluator_Flickr30k = dict(type='Flickr30kMetric') + +# ----------Config---------- # +dataset_prefixes = ['Flickr30kVal', 'Flickr30kTest'] +datasets = [dataset_Flickr30k_val, dataset_Flickr30k_test] +metrics = [val_evaluator_Flickr30k, test_evaluator_Flickr30k] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/grounding_dino_swin-l_pretrain_all.py b/configs/mm_grounding_dino/grounding_dino_swin-l_pretrain_all.py new file mode 100644 index 00000000000..46241e2e03b --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-l_pretrain_all.py @@ -0,0 +1,542 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa +num_levels = 5 +model = dict( + num_feature_levels=num_levels, + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=384, + embed_dims=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=12, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(0, 1, 2, 3), + # Please only add indices that would be used + # in FPN, otherwise some parameter will not be used + with_cp=True, + convert_weights=True, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + neck=dict(in_channels=[192, 384, 768, 1536], num_outs=num_levels), + encoder=dict(layer_cfg=dict(self_attn_cfg=dict(num_levels=num_levels))), + decoder=dict(layer_cfg=dict(cross_attn_cfg=dict(num_levels=num_levels)))) + +# --------------------------- object365v2 od dataset--------------------------- +# objv2_backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# './data/objects365v2/': 'yudong:s3://wangyudong/obj365_v2/', +# 'data/objects365v2/': 'yudong:s3://wangyudong/obj365_v2/' +# })) +objv2_backend_args = None + +objv2_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=objv2_backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/objects365v2/annotations/o365v2_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +o365v2_dataset = dict( + type='ODVGDataset', + data_root='data/objects365v2/', + ann_file='annotations/zhiyuan_objv2_train_od.json', + label_map_file='annotations/o365v2_label_map.json', + data_prefix=dict(img='train/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=objv2_train_pipeline, + return_classes=True, + need_text=False, + backend_args=None, +) + +# --------------------------- openimagev6 od dataset--------------------------- +# oi_backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# './data/': 's3://openmmlab/datasets/detection/', +# 'data/': 's3://openmmlab/datasets/detection/' +# })) +oi_backend_args = None + +oi_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=oi_backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/OpenImages/annotations/openimages_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +oiv6_dataset = dict( + type='ODVGDataset', + data_root='data/OpenImages/', + ann_file='annotations/oidv6-train-annotations_od.json', + label_map_file='annotations/openimages_label_map.json', + data_prefix=dict(img='OpenImages/train/'), + filter_cfg=dict(filter_empty_gt=False), + need_text=False, + pipeline=oi_train_pipeline, + return_classes=True, + backend_args=None) + +# --------------------------- v3det od dataset--------------------------- +v3d_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/V3Det/annotations/v3det_2023_v1_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] +v3det_dataset = dict( + type='RepeatDataset', + times=2, + dataset=dict( + type='ODVGDataset', + data_root='data/V3Det/', + ann_file='annotations/v3det_2023_v1_train_od.json', + label_map_file='annotations/v3det_2023_v1_label_map.json', + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + need_text=False, + pipeline=v3d_train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- lvis od dataset--------------------------- +lvis_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/coco/annotations/lvis_v1_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] +lvis_dataset = dict( + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='annotations/lvis_v1_train_od.json', + label_map_file='annotations/lvis_v1_label_map.json', + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + need_text=False, # change this + pipeline=lvis_train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- coco2017 od dataset--------------------------- +coco2017_train_dataset = dict( + type='RepeatDataset', + times=2, + dataset=dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='annotations/instance_train2017_norefval_od.json', + label_map_file='annotations/coco2017_label_map.json', + data_prefix=dict(img='train2017'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- flickr30k vg dataset--------------------------- +flickr30k_dataset = dict( + type='RepeatDataset', + times=2, + dataset=dict( + type='ODVGDataset', + data_root='data/flickr30k_entities/', + ann_file='final_flickr_separateGT_train_vg.json', + label_map_file=None, + data_prefix=dict(img='flickr30k_images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- gqa vg dataset--------------------------- +gqa_dataset = dict( + type='ODVGDataset', + data_root='data/gqa/', + ann_file='final_mixed_train_no_coco_vg.json', + label_map_file=None, + data_prefix=dict(img='images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +# --------------------------- coco2014 vg dataset--------------------------- +coco2014_vg_dataset = dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='mdetr_annotations/final_mixed_train_only_coco_vg.json', + label_map_file=None, + data_prefix=dict(img='train2014/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +# --------------------------- refcoco vg dataset--------------------------- +refcoco_dataset = dict( + type='RepeatDataset', + times=2, + dataset=dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='mdetr_annotations/finetune_refcoco_train_vg.json', + label_map_file=None, + data_prefix=dict(img='train2014'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- refcoco+ vg dataset--------------------------- +refcoco_plus_dataset = dict( + type='RepeatDataset', + times=2, + dataset=dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='mdetr_annotations/finetune_refcoco+_train_vg.json', + label_map_file=None, + data_prefix=dict(img='train2014'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- refcocog vg dataset--------------------------- +refcocog_dataset = dict( + type='RepeatDataset', + times=3, + dataset=dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='mdetr_annotations/finetune_refcocog_train_vg.json', + label_map_file=None, + data_prefix=dict(img='train2014'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- grefcoco vg dataset--------------------------- +grefcoco_dataset = dict( + type='RepeatDataset', + times=2, + dataset=dict( + type='ODVGDataset', + data_root='data/coco/', + ann_file='mdetr_annotations/finetune_grefcoco_train_vg.json', + label_map_file=None, + data_prefix=dict(img='train2014'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None)) + +# --------------------------- grit vg dataset--------------------------- +# grit_backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# './data/grit/': 'yichen:s3://chenyicheng/grit/', +# 'data/grit/': 'yichen:s3://chenyicheng/grit/' +# })) +grit_backend_args = None + +grit_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=grit_backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +grit_dataset = dict( + type='ODVGDataset', + data_root='data/grit/', + ann_file='grit20m_vg.json', + label_map_file=None, + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + pipeline=grit_train_pipeline, + return_classes=True, + backend_args=None) + +# --------------------------- dataloader--------------------------- +train_dataloader = dict( + batch_size=4, + num_workers=4, + sampler=dict( + _delete_=True, + type='CustomSampleSizeSampler', + ratio_mode=True, + # OD ~ 1.74+1.67*0.5+0.18*2+0.12*2+0.1=3.2 + # vg ~ 0.15*2+0.62*1+0.49*1+0.12*2+0.12*2+0.08*3+0.19*2+9*0.09=3.3 + dataset_size=[-1, 0.5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0.09]), + dataset=dict(datasets=[ + o365v2_dataset, # 1.74M + oiv6_dataset, # 1.67M + v3det_dataset, # 0.18M + coco2017_train_dataset, # 0.12M + lvis_dataset, # 0.1M + flickr30k_dataset, # 0.15M + gqa_dataset, # 0.62M + coco2014_vg_dataset, # 0.49M + refcoco_dataset, # 0.12M + refcoco_plus_dataset, # 0.12M + refcocog_dataset, # 0.08M + grefcoco_dataset, # 0.19M + grit_dataset # 9M + ])) + +# bs=256 +optim_wrapper = dict(optimizer=dict(lr=0.0008)) + +# one epoch = (3.2+3.3)M/256 = 25390 iter +# 24e=609360 iter +# 16e=406240 iter +# 20e=507800 iter +max_iter = 609360 +train_cfg = dict( + _delete_=True, + type='IterBasedTrainLoop', + max_iters=max_iter, + val_interval=13000) + +param_scheduler = [ + dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000), + dict( + type='MultiStepLR', + begin=0, + end=max_iter, + by_epoch=False, + milestones=[406240, 507800], + gamma=0.1) +] + +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=13000, max_keep_ckpts=30)) +log_processor = dict(by_epoch=False) diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py b/configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py new file mode 100644 index 00000000000..bf3b35894eb --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py @@ -0,0 +1,102 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/cat/' +class_name = ('cat', ) +num_classes = len(class_name) +metainfo = dict(classes=class_name, palette=[(220, 20, 60)]) + +model = dict(bbox_head=dict(num_classes=num_classes)) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='CocoDataset', + data_root=data_root, + metainfo=metainfo, + return_classes=True, + pipeline=train_pipeline, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + ann_file='annotations/trainval.json', + data_prefix=dict(img='images/'))) + +val_dataloader = dict( + dataset=dict( + metainfo=metainfo, + data_root=data_root, + ann_file='annotations/test.json', + data_prefix=dict(img='images/'))) + +test_dataloader = val_dataloader + +val_evaluator = dict(ann_file=data_root + 'annotations/test.json') +test_evaluator = val_evaluator + +max_epoch = 20 + +default_hooks = dict( + checkpoint=dict(interval=1, max_keep_ckpts=1, save_best='auto'), + logger=dict(type='LoggerHook', interval=5)) +train_cfg = dict(max_epochs=max_epoch, val_interval=1) + +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epoch, + by_epoch=True, + milestones=[15], + gamma=0.1) +] + +optim_wrapper = dict( + optimizer=dict(lr=0.0001), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.0), + 'language_model': dict(lr_mult=0.0) + })) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py new file mode 100644 index 00000000000..66060f45ea7 --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py @@ -0,0 +1,247 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa +lang_model_name = 'bert-base-uncased' + +model = dict( + type='GroundingDINO', + num_queries=900, + with_box_refine=True, + as_two_stage=True, + data_preprocessor=dict( + type='DetDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_mask=False, + ), + language_model=dict( + type='BertModel', + name=lang_model_name, + max_tokens=256, + pad_to_max=False, + use_sub_sentence_represent=True, + special_tokens_list=['[CLS]', '[SEP]', '.', '?'], + add_pooling_layer=False, + ), + backbone=dict( + type='SwinTransformer', + embed_dims=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(1, 2, 3), + with_cp=True, + convert_weights=True, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + neck=dict( + type='ChannelMapper', + in_channels=[192, 384, 768], + kernel_size=1, + out_channels=256, + act_cfg=None, + bias=True, + norm_cfg=dict(type='GN', num_groups=32), + num_outs=4), + encoder=dict( + num_layers=6, + num_cp=6, + # visual layer config + layer_cfg=dict( + self_attn_cfg=dict(embed_dims=256, num_levels=4, dropout=0.0), + ffn_cfg=dict( + embed_dims=256, feedforward_channels=2048, ffn_drop=0.0)), + # text layer config + text_layer_cfg=dict( + self_attn_cfg=dict(num_heads=4, embed_dims=256, dropout=0.0), + ffn_cfg=dict( + embed_dims=256, feedforward_channels=1024, ffn_drop=0.0)), + # fusion layer config + fusion_layer_cfg=dict( + v_dim=256, + l_dim=256, + embed_dim=1024, + num_heads=4, + init_values=1e-4), + ), + decoder=dict( + num_layers=6, + return_intermediate=True, + layer_cfg=dict( + # query self attention layer + self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0), + # cross attention layer query to text + cross_attn_text_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0), + # cross attention layer query to image + cross_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0), + ffn_cfg=dict( + embed_dims=256, feedforward_channels=2048, ffn_drop=0.0)), + post_norm_cfg=None), + positional_encoding=dict( + num_feats=128, normalize=True, offset=0.0, temperature=20), + bbox_head=dict( + type='GroundingDINOHead', + num_classes=256, + sync_cls_avg_factor=True, + contrastive_cfg=dict(max_text_len=256, log_scale='auto', bias=True), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), # 2.0 in DeformDETR + loss_bbox=dict(type='L1Loss', loss_weight=5.0)), + dn_cfg=dict( # TODO: Move to model.train_cfg ? + label_noise_scale=0.5, + box_noise_scale=1.0, # 0.4 for DN-DETR + group_cfg=dict(dynamic=True, num_groups=None, + num_dn_queries=100)), # TODO: half num_dn_queries + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + match_costs=[ + dict(type='BinaryFocalLossCost', weight=2.0), + dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + dict(type='IoUCost', iou_mode='giou', weight=2.0) + ])), + test_cfg=dict(max_per_img=300)) + +# dataset settings +train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=lang_model_name, + num_sample_negative=85, + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) +] + +dataset_type = 'ODVGDataset' +data_root = 'data/objects365v1/' + +coco_od_dataset = dict( + type=dataset_type, + data_root=data_root, + ann_file='o365v1_train_odvg.json', + label_map_file='o365v1_label_map.json', + data_prefix=dict(img='train/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=train_pipeline, + return_classes=True, + backend_args=None) + +train_dataloader = dict( + _delete_=True, + batch_size=4, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + batch_sampler=dict(type='AspectRatioBatchSampler'), + dataset=dict(type='ConcatDataset', datasets=[coco_od_dataset])) + +val_dataloader = dict( + dataset=dict(pipeline=test_pipeline, return_classes=True)) +test_dataloader = val_dataloader + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0004, + weight_decay=0.0001), # bs=16 0.0001 + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + 'language_model': dict(lr_mult=0.1), + })) + +# learning policy +max_epochs = 30 +param_scheduler = [ + dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000), + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[19, 26], + gamma=0.1) +] + +train_cfg = dict( + type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (16 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) + +default_hooks = dict(visualization=dict(type='GroundingVisualizationHook')) diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg.py new file mode 100644 index 00000000000..b7f388bdd4e --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg.py @@ -0,0 +1,38 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +o365v1_od_dataset = dict( + type='ODVGDataset', + data_root='data/objects365v1/', + ann_file='o365v1_train_odvg.json', + label_map_file='o365v1_label_map.json', + data_prefix=dict(img='train/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None, +) + +flickr30k_dataset = dict( + type='ODVGDataset', + data_root='data/flickr30k_entities/', + ann_file='final_flickr_separateGT_train_vg.json', + label_map_file=None, + data_prefix=dict(img='flickr30k_images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +gqa_dataset = dict( + type='ODVGDataset', + data_root='data/gqa/', + ann_file='final_mixed_train_no_coco_vg.json', + label_map_file=None, + data_prefix=dict(img='images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +train_dataloader = dict( + dataset=dict(datasets=[o365v1_od_dataset, flickr30k_dataset, gqa_dataset])) diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py new file mode 100644 index 00000000000..8e9f5ca4aab --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py @@ -0,0 +1,55 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +o365v1_od_dataset = dict( + type='ODVGDataset', + data_root='data/objects365v1/', + ann_file='o365v1_train_odvg.json', + label_map_file='o365v1_label_map.json', + data_prefix=dict(img='train/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None, +) + +flickr30k_dataset = dict( + type='ODVGDataset', + data_root='data/flickr30k_entities/', + ann_file='final_flickr_separateGT_train_vg.json', + label_map_file=None, + data_prefix=dict(img='flickr30k_images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +gqa_dataset = dict( + type='ODVGDataset', + data_root='data/gqa/', + ann_file='final_mixed_train_no_coco_vg.json', + label_map_file=None, + data_prefix=dict(img='images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +grit_dataset = dict( + type='ODVGDataset', + data_root='grit_processed/', + ann_file='grit20m_vg.json', + label_map_file=None, + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +train_dataloader = dict( + sampler=dict( + _delete_=True, + type='CustomSampleSizeSampler', + dataset_size=[-1, -1, -1, 500000]), + dataset=dict(datasets=[ + o365v1_od_dataset, flickr30k_dataset, gqa_dataset, grit_dataset + ])) diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py new file mode 100644 index 00000000000..56e500c8693 --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py @@ -0,0 +1,117 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +o365v1_od_dataset = dict( + type='ODVGDataset', + data_root='data/objects365v1/', + ann_file='o365v1_train_odvg.json', + label_map_file='o365v1_label_map.json', + data_prefix=dict(img='train/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None, +) + +flickr30k_dataset = dict( + type='ODVGDataset', + data_root='data/flickr30k_entities/', + ann_file='final_flickr_separateGT_train_vg.json', + label_map_file=None, + data_prefix=dict(img='flickr30k_images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +gqa_dataset = dict( + type='ODVGDataset', + data_root='data/gqa/', + ann_file='final_mixed_train_no_coco_vg.json', + label_map_file=None, + data_prefix=dict(img='images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +v3d_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/V3Det/annotations/v3det_2023_v1_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] +v3det_dataset = dict( + type='ODVGDataset', + data_root='data/V3Det/', + ann_file='annotations/v3det_2023_v1_train_od.json', + label_map_file='annotations/v3det_2023_v1_label_map.json', + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + need_text=False, # change this + pipeline=v3d_train_pipeline, + return_classes=True, + backend_args=None) + +grit_dataset = dict( + type='ODVGDataset', + data_root='grit_processed/', + ann_file='grit20m_vg.json', + label_map_file=None, + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +train_dataloader = dict( + sampler=dict( + _delete_=True, + type='CustomSampleSizeSampler', + dataset_size=[-1, -1, -1, -1, 500000]), + dataset=dict(datasets=[ + o365v1_od_dataset, flickr30k_dataset, gqa_dataset, v3det_dataset, + grit_dataset + ])) diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py new file mode 100644 index 00000000000..c89014fbbe4 --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py @@ -0,0 +1,101 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +o365v1_od_dataset = dict( + type='ODVGDataset', + data_root='data/objects365v1/', + ann_file='o365v1_train_odvg.json', + label_map_file='o365v1_label_map.json', + data_prefix=dict(img='train/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None, +) + +flickr30k_dataset = dict( + type='ODVGDataset', + data_root='data/flickr30k_entities/', + ann_file='final_flickr_separateGT_train_vg.json', + label_map_file=None, + data_prefix=dict(img='flickr30k_images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +gqa_dataset = dict( + type='ODVGDataset', + data_root='data/gqa/', + ann_file='final_mixed_train_no_coco_vg.json', + label_map_file=None, + data_prefix=dict(img='images/'), + filter_cfg=dict(filter_empty_gt=False), + pipeline=_base_.train_pipeline, + return_classes=True, + backend_args=None) + +v3d_train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/V3Det/annotations/v3det_2023_v1_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] +v3det_dataset = dict( + type='ODVGDataset', + data_root='data/V3Det/', + ann_file='annotations/v3det_2023_v1_train_od.json', + label_map_file='annotations/v3det_2023_v1_label_map.json', + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False), + need_text=False, # change this + pipeline=v3d_train_pipeline, + return_classes=True, + backend_args=None) + +train_dataloader = dict( + dataset=dict(datasets=[ + o365v1_od_dataset, flickr30k_dataset, gqa_dataset, v3det_dataset + ])) diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py new file mode 100644 index 00000000000..6dc8dcd8df4 --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py @@ -0,0 +1,43 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadTextAnnotations'), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) +] + +data_root = 'data/cat/' + +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=False, + dataset=dict( + type='ODVGDataset', + data_root=data_root, + label_map_file='cat_label_map.json', + ann_file='cat_train_od.json', + data_prefix=dict(img='images/'), + pipeline=test_pipeline, + return_classes=True)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + outfile_path=data_root + 'cat_train_od_v1.json', + img_prefix=data_root + 'images/', + score_thr=0.7, + nms_thr=0.5, + type='DumpODVGResults') +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py new file mode 100644 index 00000000000..78bf1c344bf --- /dev/null +++ b/configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py @@ -0,0 +1,42 @@ +_base_ = 'grounding_dino_swin-t_pretrain_obj365.py' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadTextAnnotations'), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) +] + +data_root = 'data/flickr30k_entities/' + +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=False, + dataset=dict( + type='ODVGDataset', + data_root=data_root, + ann_file='flickr_simple_train_vg.json', + data_prefix=dict(img='flickr30k_images/'), + pipeline=test_pipeline, + return_classes=True)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + outfile_path=data_root + 'flickr_simple_train_vg_v1.json', + img_prefix=data_root + 'flickr30k_images/', + score_thr=0.4, + nms_thr=0.5, + type='DumpODVGResults') +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py new file mode 100644 index 00000000000..3ba12c90675 --- /dev/null +++ b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py @@ -0,0 +1,120 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/coco/annotations/lvis_v1_label_map.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type='ODVGDataset', + data_root=data_root, + need_text=False, + label_map_file='annotations/lvis_v1_label_map.json', + ann_file='annotations/lvis_v1_train_od.json', + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False, min_size=32), + return_classes=True, + pipeline=train_pipeline))) + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type='LVISV1Dataset', + ann_file='annotations/lvis_v1_minival_inserted_image_name.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + + 'annotations/lvis_v1_minival_inserted_image_name.json') +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=3) + +default_hooks = dict( + checkpoint=dict( + max_keep_ckpts=1, save_best='lvis_fixed_ap/AP', rule='greater')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py new file mode 100644 index 00000000000..28d0141d3e2 --- /dev/null +++ b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py @@ -0,0 +1,120 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + # change this + label_map_file='data/coco/annotations/lvis_v1_label_map_norare.json', + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type='ODVGDataset', + data_root=data_root, + need_text=False, + label_map_file='annotations/lvis_v1_label_map_norare.json', + ann_file='annotations/lvis_v1_train_od_norare.json', + data_prefix=dict(img=''), + filter_cfg=dict(filter_empty_gt=False, min_size=32), + return_classes=True, + pipeline=train_pipeline))) + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type='LVISV1Dataset', + ann_file='annotations/lvis_v1_minival_inserted_image_name.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + + 'annotations/lvis_v1_minival_inserted_image_name.json') +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.00005, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[8, 11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=3) + +default_hooks = dict( + checkpoint=dict( + max_keep_ckpts=3, save_best='lvis_fixed_ap/AP', rule='greater')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py new file mode 100644 index 00000000000..fb4ed438e0b --- /dev/null +++ b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py @@ -0,0 +1,24 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +dataset_type = 'LVISV1Dataset' +data_root = 'data/coco/' + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type=dataset_type, + ann_file='annotations/lvis_od_val.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +# numpy < 1.24.0 +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + 'annotations/lvis_od_val.json') +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py new file mode 100644 index 00000000000..406a39a4264 --- /dev/null +++ b/configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py @@ -0,0 +1,25 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +model = dict(test_cfg=dict( + max_per_img=300, + chunked_size=40, +)) + +dataset_type = 'LVISV1Dataset' +data_root = 'data/coco/' + +val_dataloader = dict( + dataset=dict( + data_root=data_root, + type=dataset_type, + ann_file='annotations/lvis_v1_minival_inserted_image_name.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +# numpy < 1.24.0 +val_evaluator = dict( + _delete_=True, + type='LVISFixedAPMetric', + ann_file=data_root + + 'annotations/lvis_v1_minival_inserted_image_name.json') +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/metafile.yml b/configs/mm_grounding_dino/metafile.yml new file mode 100644 index 00000000000..3071686e7ac --- /dev/null +++ b/configs/mm_grounding_dino/metafile.yml @@ -0,0 +1,54 @@ +Collections: + - Name: MM Grounding DINO + Metadata: + Training Data: Objects365, GoldG, GRIT and V3Det + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 3090 GPUs + Architecture: + - Swin Transformer + - BERT + README: configs/mm_grounding_dino/README.md + Code: + URL: + Version: v3.0.0 + +Models: + - Name: grounding_dino_swin-t_pretrain_obj365_goldg + In Collection: MM Grounding DINO + Config: configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg.py + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.4 + Weights: https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602-4ea751ce.pth + - Name: grounding_dino_swin-t_pretrain_obj365_goldg_grit9m + In Collection: MM Grounding DINO + Config: configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.5 + Weights: https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818-169cc352.pth + - Name: grounding_dino_swin-t_pretrain_obj365_goldg_v3det + In Collection: MM Grounding DINO + Config: configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.6 + Weights: https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741-e316e297.pth + - Name: grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det + In Collection: MM Grounding DINO + Config: configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.4 + Weights: https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth diff --git a/configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py b/configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py new file mode 100644 index 00000000000..d87ca7ca1ea --- /dev/null +++ b/configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py @@ -0,0 +1,338 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' # noqa + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + test_mode=True, + pipeline=base_test_pipeline, + return_classes=True) +val_evaluator_AerialMaritimeDrone = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' + +caption_prompt = None +# caption_prompt = { +# 'penguin': { +# 'suffix': ', which is black and white' +# }, +# 'puffin': { +# 'suffix': ' with orange beaks' +# }, +# 'stingray': { +# 'suffix': ' which is flat and round' +# }, +# } +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 CottontailRabbits---------------------# +class_name = ('Cottontail-Rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' + +# caption_prompt = None +caption_prompt = {'Cottontail-Rabbit': {'name': 'rabbit'}} + +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 EgoHands---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' + +# caption_prompt = None +caption_prompt = {'hand': {'suffix': ' of a person'}} + +dataset_EgoHands = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 NorthAmericaMushrooms---------------------# +class_name = ('CoW', 'chanterelle') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + +# caption_prompt = None +caption_prompt = { + 'CoW': { + 'name': 'flat mushroom' + }, + 'chanterelle': { + 'name': 'yellow mushroom' + } +} + +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' + +# caption_prompt = None +caption_prompt = { + 'package': { + 'prefix': 'there is a ', + 'suffix': ' on the porch' + } +} + +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------7 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------9 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' + +# caption_prompt = None +caption_prompt = { + 'pothole': { + 'prefix': 'there are some ', + 'name': 'holes', + 'suffix': ' on the road' + } +} + +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------10 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------11 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# +dataset_prefixes = [ + 'AerialMaritimeDrone', 'Aquarium', 'CottontailRabbits', 'EgoHands', + 'NorthAmericaMushrooms', 'Packages', 'PascalVOC', 'pistols', 'pothole', + 'Raccoon', 'ShellfishOpenImages', 'thermalDogsAndPeople', + 'VehiclesOpenImages' +] +datasets = [ + dataset_AerialMaritimeDrone, dataset_Aquarium, dataset_CottontailRabbits, + dataset_EgoHands, dataset_NorthAmericaMushrooms, dataset_Packages, + dataset_PascalVOC, dataset_pistols, dataset_pothole, dataset_Raccoon, + dataset_ShellfishOpenImages, dataset_thermalDogsAndPeople, + dataset_VehiclesOpenImages +] +metrics = [ + val_evaluator_AerialMaritimeDrone, val_evaluator_Aquarium, + val_evaluator_CottontailRabbits, val_evaluator_EgoHands, + val_evaluator_NorthAmericaMushrooms, val_evaluator_Packages, + val_evaluator_PascalVOC, val_evaluator_pistols, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_ShellfishOpenImages, + val_evaluator_thermalDogsAndPeople, val_evaluator_VehiclesOpenImages +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py b/configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py new file mode 100644 index 00000000000..a6b8566aed4 --- /dev/null +++ b/configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py @@ -0,0 +1,794 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' # noqa + +dataset_type = 'CocoDataset' +data_root = 'data/odinw/' + +base_test_pipeline = _base_.test_pipeline +base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape', + 'img_shape', 'scale_factor', 'text', + 'custom_entities', 'caption_prompt') + +# ---------------------1 AerialMaritimeDrone_large---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/large/' +dataset_AerialMaritimeDrone_large = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_large = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------2 AerialMaritimeDrone_tiled---------------------# +class_name = ('boat', 'car', 'dock', 'jetski', 'lift') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AerialMaritimeDrone/tiled/' +dataset_AerialMaritimeDrone_tiled = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AerialMaritimeDrone_tiled = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------3 AmericanSignLanguageLetters---------------------# +class_name = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', + 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/' # noqa +dataset_AmericanSignLanguageLetters = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_AmericanSignLanguageLetters = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------4 Aquarium---------------------# +class_name = ('fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', + 'stingray') +metainfo = dict(classes=class_name) +_data_root = data_root + 'Aquarium/Aquarium Combined.v2-raw-1024.coco/' +dataset_Aquarium = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Aquarium = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------5 BCCD---------------------# +class_name = ('Platelets', 'RBC', 'WBC') +metainfo = dict(classes=class_name) +_data_root = data_root + 'BCCD/BCCD.v3-raw.coco/' +dataset_BCCD = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_BCCD = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------6 boggleBoards---------------------# +class_name = ('Q', 'a', 'an', 'b', 'c', 'd', 'e', 'er', 'f', 'g', 'h', 'he', + 'i', 'in', 'j', 'k', 'l', 'm', 'n', 'o', 'o ', 'p', 'q', 'qu', + 'r', 's', 't', 't\\', 'th', 'u', 'v', 'w', 'wild', 'x', 'y', 'z') +metainfo = dict(classes=class_name) +_data_root = data_root + 'boggleBoards/416x416AutoOrient/export/' +dataset_boggleBoards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_boggleBoards = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------7 brackishUnderwater---------------------# +class_name = ('crab', 'fish', 'jellyfish', 'shrimp', 'small_fish', 'starfish') +metainfo = dict(classes=class_name) +_data_root = data_root + 'brackishUnderwater/960x540/' +dataset_brackishUnderwater = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_brackishUnderwater = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------8 ChessPieces---------------------# +class_name = (' ', 'black bishop', 'black king', 'black knight', 'black pawn', + 'black queen', 'black rook', 'white bishop', 'white king', + 'white knight', 'white pawn', 'white queen', 'white rook') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' +dataset_ChessPieces = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ChessPieces = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------9 CottontailRabbits---------------------# +class_name = ('rabbit', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'CottontailRabbits/' +dataset_CottontailRabbits = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_CottontailRabbits = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------10 dice---------------------# +class_name = ('1', '2', '3', '4', '5', '6') +metainfo = dict(classes=class_name) +_data_root = data_root + 'dice/mediumColor/export/' +dataset_dice = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_dice = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------11 DroneControl---------------------# +class_name = ('follow', 'follow_hand', 'land', 'land_hand', 'null', 'object', + 'takeoff', 'takeoff-hand') +metainfo = dict(classes=class_name) +_data_root = data_root + 'DroneControl/Drone Control.v3-raw.coco/' +dataset_DroneControl = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_DroneControl = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------12 EgoHands_generic---------------------# +class_name = ('hand', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/generic/' +caption_prompt = {'hand': {'suffix': ' of a person'}} +dataset_EgoHands_generic = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_generic = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------13 EgoHands_specific---------------------# +class_name = ('myleft', 'myright', 'yourleft', 'yourright') +metainfo = dict(classes=class_name) +_data_root = data_root + 'EgoHands/specific/' +dataset_EgoHands_specific = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_EgoHands_specific = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------14 HardHatWorkers---------------------# +class_name = ('head', 'helmet', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'HardHatWorkers/raw/' +dataset_HardHatWorkers = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_HardHatWorkers = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------15 MaskWearing---------------------# +class_name = ('mask', 'no-mask') +metainfo = dict(classes=class_name) +_data_root = data_root + 'MaskWearing/raw/' +dataset_MaskWearing = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MaskWearing = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------16 MountainDewCommercial---------------------# +class_name = ('bottle', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'MountainDewCommercial/' +dataset_MountainDewCommercial = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_MountainDewCommercial = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------17 NorthAmericaMushrooms---------------------# +class_name = ('flat mushroom', 'yellow mushroom') +metainfo = dict(classes=class_name) +_data_root = data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa +dataset_NorthAmericaMushrooms = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/new_annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_NorthAmericaMushrooms = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/new_annotations_without_background.json', + metric='bbox') + +# ---------------------18 openPoetryVision---------------------# +class_name = ('American Typewriter', 'Andale Mono', 'Apple Chancery', 'Arial', + 'Avenir', 'Baskerville', 'Big Caslon', 'Bradley Hand', + 'Brush Script MT', 'Chalkboard', 'Comic Sans MS', 'Copperplate', + 'Courier', 'Didot', 'Futura', 'Geneva', 'Georgia', 'Gill Sans', + 'Helvetica', 'Herculanum', 'Impact', 'Kefa', 'Lucida Grande', + 'Luminari', 'Marker Felt', 'Menlo', 'Monaco', 'Noteworthy', + 'Optima', 'PT Sans', 'PT Serif', 'Palatino', 'Papyrus', + 'Phosphate', 'Rockwell', 'SF Pro', 'SignPainter', 'Skia', + 'Snell Roundhand', 'Tahoma', 'Times New Roman', 'Trebuchet MS', + 'Verdana') +metainfo = dict(classes=class_name) +_data_root = data_root + 'openPoetryVision/512x512/' +dataset_openPoetryVision = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_openPoetryVision = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------19 OxfordPets_by_breed---------------------# +class_name = ('cat-Abyssinian', 'cat-Bengal', 'cat-Birman', 'cat-Bombay', + 'cat-British_Shorthair', 'cat-Egyptian_Mau', 'cat-Maine_Coon', + 'cat-Persian', 'cat-Ragdoll', 'cat-Russian_Blue', 'cat-Siamese', + 'cat-Sphynx', 'dog-american_bulldog', + 'dog-american_pit_bull_terrier', 'dog-basset_hound', + 'dog-beagle', 'dog-boxer', 'dog-chihuahua', + 'dog-english_cocker_spaniel', 'dog-english_setter', + 'dog-german_shorthaired', 'dog-great_pyrenees', 'dog-havanese', + 'dog-japanese_chin', 'dog-keeshond', 'dog-leonberger', + 'dog-miniature_pinscher', 'dog-newfoundland', 'dog-pomeranian', + 'dog-pug', 'dog-saint_bernard', 'dog-samoyed', + 'dog-scottish_terrier', 'dog-shiba_inu', + 'dog-staffordshire_bull_terrier', 'dog-wheaten_terrier', + 'dog-yorkshire_terrier') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-breed/' # noqa +dataset_OxfordPets_by_breed = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_breed = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------20 OxfordPets_by_species---------------------# +class_name = ('cat', 'dog') +metainfo = dict(classes=class_name) +_data_root = data_root + 'OxfordPets/by-species/' # noqa +dataset_OxfordPets_by_species = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_OxfordPets_by_species = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------21 PKLot---------------------# +class_name = ('space-empty', 'space-occupied') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PKLot/640/' # noqa +dataset_PKLot = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PKLot = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------22 Packages---------------------# +class_name = ('package', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Packages/Raw/' +caption_prompt = { + 'package': { + 'prefix': 'there is a ', + 'suffix': ' on the porch' + } +} +dataset_Packages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=base_test_pipeline, + caption_prompt=caption_prompt, + test_mode=True, + return_classes=True) +val_evaluator_Packages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------23 PascalVOC---------------------# +class_name = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') +metainfo = dict(classes=class_name) +_data_root = data_root + 'PascalVOC/' +dataset_PascalVOC = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_PascalVOC = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------24 pistols---------------------# +class_name = ('pistol', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pistols/export/' +dataset_pistols = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pistols = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------25 plantdoc---------------------# +class_name = ('Apple Scab Leaf', 'Apple leaf', 'Apple rust leaf', + 'Bell_pepper leaf', 'Bell_pepper leaf spot', 'Blueberry leaf', + 'Cherry leaf', 'Corn Gray leaf spot', 'Corn leaf blight', + 'Corn rust leaf', 'Peach leaf', 'Potato leaf', + 'Potato leaf early blight', 'Potato leaf late blight', + 'Raspberry leaf', 'Soyabean leaf', 'Soybean leaf', + 'Squash Powdery mildew leaf', 'Strawberry leaf', + 'Tomato Early blight leaf', 'Tomato Septoria leaf spot', + 'Tomato leaf', 'Tomato leaf bacterial spot', + 'Tomato leaf late blight', 'Tomato leaf mosaic virus', + 'Tomato leaf yellow virus', 'Tomato mold leaf', + 'Tomato two spotted spider mites leaf', 'grape leaf', + 'grape leaf black rot') +metainfo = dict(classes=class_name) +_data_root = data_root + 'plantdoc/416x416/' +dataset_plantdoc = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_plantdoc = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------26 pothole---------------------# +class_name = ('pothole', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'pothole/' +caption_prompt = { + 'pothole': { + 'name': 'holes', + 'prefix': 'there are some ', + 'suffix': ' on the road' + } +} +dataset_pothole = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + caption_prompt=caption_prompt, + pipeline=base_test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_pothole = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------27 Raccoon---------------------# +class_name = ('raccoon', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'Raccoon/Raccoon.v2-raw.coco/' +dataset_Raccoon = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_Raccoon = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------28 selfdrivingCar---------------------# +class_name = ('biker', 'car', 'pedestrian', 'trafficLight', + 'trafficLight-Green', 'trafficLight-GreenLeft', + 'trafficLight-Red', 'trafficLight-RedLeft', + 'trafficLight-Yellow', 'trafficLight-YellowLeft', 'truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'selfdrivingCar/fixedLarge/export/' +dataset_selfdrivingCar = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='val_annotations_without_background.json', + data_prefix=dict(img=''), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_selfdrivingCar = dict( + type='CocoMetric', + ann_file=_data_root + 'val_annotations_without_background.json', + metric='bbox') + +# ---------------------29 ShellfishOpenImages---------------------# +class_name = ('Crab', 'Lobster', 'Shrimp') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ShellfishOpenImages/raw/' +dataset_ShellfishOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ShellfishOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------30 ThermalCheetah---------------------# +class_name = ('cheetah', 'human') +metainfo = dict(classes=class_name) +_data_root = data_root + 'ThermalCheetah/' +dataset_ThermalCheetah = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_ThermalCheetah = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------31 thermalDogsAndPeople---------------------# +class_name = ('dog', 'person') +metainfo = dict(classes=class_name) +_data_root = data_root + 'thermalDogsAndPeople/' +dataset_thermalDogsAndPeople = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_thermalDogsAndPeople = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------32 UnoCards---------------------# +class_name = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', + '12', '13', '14') +metainfo = dict(classes=class_name) +_data_root = data_root + 'UnoCards/raw/' +dataset_UnoCards = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_UnoCards = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------33 VehiclesOpenImages---------------------# +class_name = ('Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck') +metainfo = dict(classes=class_name) +_data_root = data_root + 'VehiclesOpenImages/416x416/' +dataset_VehiclesOpenImages = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_VehiclesOpenImages = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------34 WildfireSmoke---------------------# +class_name = ('smoke', ) +metainfo = dict(classes=class_name) +_data_root = data_root + 'WildfireSmoke/' +dataset_WildfireSmoke = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_WildfireSmoke = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# ---------------------35 websiteScreenshots---------------------# +class_name = ('button', 'field', 'heading', 'iframe', 'image', 'label', 'link', + 'text') +metainfo = dict(classes=class_name) +_data_root = data_root + 'websiteScreenshots/' +dataset_websiteScreenshots = dict( + type=dataset_type, + metainfo=metainfo, + data_root=_data_root, + ann_file='valid/annotations_without_background.json', + data_prefix=dict(img='valid/'), + pipeline=_base_.test_pipeline, + test_mode=True, + return_classes=True) +val_evaluator_websiteScreenshots = dict( + type='CocoMetric', + ann_file=_data_root + 'valid/annotations_without_background.json', + metric='bbox') + +# --------------------- Config---------------------# + +dataset_prefixes = [ + 'AerialMaritimeDrone_large', + 'AerialMaritimeDrone_tiled', + 'AmericanSignLanguageLetters', + 'Aquarium', + 'BCCD', + 'boggleBoards', + 'brackishUnderwater', + 'ChessPieces', + 'CottontailRabbits', + 'dice', + 'DroneControl', + 'EgoHands_generic', + 'EgoHands_specific', + 'HardHatWorkers', + 'MaskWearing', + 'MountainDewCommercial', + 'NorthAmericaMushrooms', + 'openPoetryVision', + 'OxfordPets_by_breed', + 'OxfordPets_by_species', + 'PKLot', + 'Packages', + 'PascalVOC', + 'pistols', + 'plantdoc', + 'pothole', + 'Raccoons', + 'selfdrivingCar', + 'ShellfishOpenImages', + 'ThermalCheetah', + 'thermalDogsAndPeople', + 'UnoCards', + 'VehiclesOpenImages', + 'WildfireSmoke', + 'websiteScreenshots', +] + +datasets = [ + dataset_AerialMaritimeDrone_large, dataset_AerialMaritimeDrone_tiled, + dataset_AmericanSignLanguageLetters, dataset_Aquarium, dataset_BCCD, + dataset_boggleBoards, dataset_brackishUnderwater, dataset_ChessPieces, + dataset_CottontailRabbits, dataset_dice, dataset_DroneControl, + dataset_EgoHands_generic, dataset_EgoHands_specific, + dataset_HardHatWorkers, dataset_MaskWearing, dataset_MountainDewCommercial, + dataset_NorthAmericaMushrooms, dataset_openPoetryVision, + dataset_OxfordPets_by_breed, dataset_OxfordPets_by_species, dataset_PKLot, + dataset_Packages, dataset_PascalVOC, dataset_pistols, dataset_plantdoc, + dataset_pothole, dataset_Raccoon, dataset_selfdrivingCar, + dataset_ShellfishOpenImages, dataset_ThermalCheetah, + dataset_thermalDogsAndPeople, dataset_UnoCards, dataset_VehiclesOpenImages, + dataset_WildfireSmoke, dataset_websiteScreenshots +] + +metrics = [ + val_evaluator_AerialMaritimeDrone_large, + val_evaluator_AerialMaritimeDrone_tiled, + val_evaluator_AmericanSignLanguageLetters, val_evaluator_Aquarium, + val_evaluator_BCCD, val_evaluator_boggleBoards, + val_evaluator_brackishUnderwater, val_evaluator_ChessPieces, + val_evaluator_CottontailRabbits, val_evaluator_dice, + val_evaluator_DroneControl, val_evaluator_EgoHands_generic, + val_evaluator_EgoHands_specific, val_evaluator_HardHatWorkers, + val_evaluator_MaskWearing, val_evaluator_MountainDewCommercial, + val_evaluator_NorthAmericaMushrooms, val_evaluator_openPoetryVision, + val_evaluator_OxfordPets_by_breed, val_evaluator_OxfordPets_by_species, + val_evaluator_PKLot, val_evaluator_Packages, val_evaluator_PascalVOC, + val_evaluator_pistols, val_evaluator_plantdoc, val_evaluator_pothole, + val_evaluator_Raccoon, val_evaluator_selfdrivingCar, + val_evaluator_ShellfishOpenImages, val_evaluator_ThermalCheetah, + val_evaluator_thermalDogsAndPeople, val_evaluator_UnoCards, + val_evaluator_VehiclesOpenImages, val_evaluator_WildfireSmoke, + val_evaluator_websiteScreenshots +] + +# -------------------------------------------------# +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/odinw/override_category.py b/configs/mm_grounding_dino/odinw/override_category.py new file mode 100644 index 00000000000..9ff05fc6e5e --- /dev/null +++ b/configs/mm_grounding_dino/odinw/override_category.py @@ -0,0 +1,109 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse + +import mmengine + + +def parse_args(): + parser = argparse.ArgumentParser(description='Override Category') + parser.add_argument('data_root') + return parser.parse_args() + + +def main(): + args = parse_args() + + ChessPieces = [{ + 'id': 1, + 'name': ' ', + 'supercategory': 'pieces' + }, { + 'id': 2, + 'name': 'black bishop', + 'supercategory': 'pieces' + }, { + 'id': 3, + 'name': 'black king', + 'supercategory': 'pieces' + }, { + 'id': 4, + 'name': 'black knight', + 'supercategory': 'pieces' + }, { + 'id': 5, + 'name': 'black pawn', + 'supercategory': 'pieces' + }, { + 'id': 6, + 'name': 'black queen', + 'supercategory': 'pieces' + }, { + 'id': 7, + 'name': 'black rook', + 'supercategory': 'pieces' + }, { + 'id': 8, + 'name': 'white bishop', + 'supercategory': 'pieces' + }, { + 'id': 9, + 'name': 'white king', + 'supercategory': 'pieces' + }, { + 'id': 10, + 'name': 'white knight', + 'supercategory': 'pieces' + }, { + 'id': 11, + 'name': 'white pawn', + 'supercategory': 'pieces' + }, { + 'id': 12, + 'name': 'white queen', + 'supercategory': 'pieces' + }, { + 'id': 13, + 'name': 'white rook', + 'supercategory': 'pieces' + }] + + _data_root = args.data_root + 'ChessPieces/Chess Pieces.v23-raw.coco/' + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = ChessPieces + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + CottontailRabbits = [{ + 'id': 1, + 'name': 'rabbit', + 'supercategory': 'Cottontail-Rabbit' + }] + + _data_root = args.data_root + 'CottontailRabbits/' + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = CottontailRabbits + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + NorthAmericaMushrooms = [{ + 'id': 1, + 'name': 'flat mushroom', + 'supercategory': 'mushroom' + }, { + 'id': 2, + 'name': 'yellow mushroom', + 'supercategory': 'mushroom' + }] + + _data_root = args.data_root + 'NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/' # noqa + json_data = mmengine.load(_data_root + + 'valid/annotations_without_background.json') + json_data['categories'] = NorthAmericaMushrooms + mmengine.dump(json_data, + _data_root + 'valid/new_annotations_without_background.json') + + +if __name__ == '__main__': + main() diff --git a/configs/mm_grounding_dino/people_in_painting/grounding_dino_swin-t_finetune_8xb4_50e_people_in_painting.py b/configs/mm_grounding_dino/people_in_painting/grounding_dino_swin-t_finetune_8xb4_50e_people_in_painting.py new file mode 100644 index 00000000000..449d8682f89 --- /dev/null +++ b/configs/mm_grounding_dino/people_in_painting/grounding_dino_swin-t_finetune_8xb4_50e_people_in_painting.py @@ -0,0 +1,109 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +# https://universe.roboflow.com/roboflow-100/people-in-paintings/dataset/2 +data_root = 'data/people_in_painting_v2/' +class_name = ('Human', ) +palette = [(220, 20, 60)] + +metainfo = dict(classes=class_name, palette=palette) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + sampler=dict(_delete_=True, type='DefaultSampler', shuffle=True), + batch_sampler=dict(type='AspectRatioBatchSampler'), + dataset=dict( + _delete_=True, + type='RepeatDataset', + times=10, + dataset=dict( + type='CocoDataset', + data_root=data_root, + metainfo=metainfo, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline, + return_classes=True, + data_prefix=dict(img='train/'), + ann_file='train/_annotations.coco.json'))) + +val_dataloader = dict( + dataset=dict( + metainfo=metainfo, + data_root=data_root, + return_classes=True, + ann_file='valid/_annotations.coco.json', + data_prefix=dict(img='valid/'))) +test_dataloader = val_dataloader + +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'valid/_annotations.coco.json', + metric='bbox', + format_only=False) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1) + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[4], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_grefcoco.py b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_grefcoco.py new file mode 100644 index 00000000000..983ffe5c6f3 --- /dev/null +++ b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_grefcoco.py @@ -0,0 +1,170 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + # change this + dict(type='RandomFlip', prob=0.0), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ODVGDataset', + data_root=data_root, + ann_file='mdetr_annotations/finetune_grefcoco_train_vg.json', + data_prefix=dict(img='train2014/'), + filter_cfg=dict(filter_empty_gt=False, min_size=32), + return_classes=True, + pipeline=train_pipeline)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_val.json' +val_dataset_all_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) +val_evaluator_all_val = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_testA.json' +val_dataset_refcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testA = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_testB.json' +val_dataset_refcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testB = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +datasets = [ + val_dataset_all_val, val_dataset_refcoco_testA, val_dataset_refcoco_testB +] +dataset_prefixes = ['grefcoco_val', 'grefcoco_testA', 'grefcoco_testB'] +metrics = [ + val_evaluator_all_val, val_evaluator_refcoco_testA, + val_evaluator_refcoco_testB +] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[3], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco.py b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco.py new file mode 100644 index 00000000000..d91af473a23 --- /dev/null +++ b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco.py @@ -0,0 +1,167 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + # change this + dict(type='RandomFlip', prob=0.0), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ODVGDataset', + data_root=data_root, + ann_file='mdetr_annotations/finetune_refcoco_train_vg.json', + data_prefix=dict(img='train2014/'), + filter_cfg=dict(filter_empty_gt=False, min_size=32), + return_classes=True, + pipeline=train_pipeline)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_val.json' +val_dataset_all_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) +val_evaluator_all_val = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_testA.json' +val_dataset_refcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testA = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_testB.json' +val_dataset_refcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testB = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +datasets = [ + val_dataset_all_val, val_dataset_refcoco_testA, val_dataset_refcoco_testB +] +dataset_prefixes = ['refcoco_val', 'refcoco_testA', 'refcoco_testB'] +metrics = [ + val_evaluator_all_val, val_evaluator_refcoco_testA, + val_evaluator_refcoco_testB +] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[3], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco_plus.py b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco_plus.py new file mode 100644 index 00000000000..871adc8efb4 --- /dev/null +++ b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco_plus.py @@ -0,0 +1,167 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + # change this + dict(type='RandomFlip', prob=0.0), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ODVGDataset', + data_root=data_root, + ann_file='mdetr_annotations/finetune_refcoco+_train_vg.json', + data_prefix=dict(img='train2014/'), + filter_cfg=dict(filter_empty_gt=False, min_size=32), + return_classes=True, + pipeline=train_pipeline)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_val.json' +val_dataset_all_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) +val_evaluator_all_val = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_testA.json' +val_dataset_refcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testA = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_testB.json' +val_dataset_refcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testB = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +datasets = [ + val_dataset_all_val, val_dataset_refcoco_testA, val_dataset_refcoco_testB +] +dataset_prefixes = ['refcoco+_val', 'refcoco+_testA', 'refcoco+_testB'] +metrics = [ + val_evaluator_all_val, val_evaluator_refcoco_testA, + val_evaluator_refcoco_testB +] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[3], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcocog.py b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcocog.py new file mode 100644 index 00000000000..a351d6f9d12 --- /dev/null +++ b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcocog.py @@ -0,0 +1,145 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/coco/' + +train_pipeline = [ + dict(type='LoadImageFromFile', backend_args=_base_.backend_args), + dict(type='LoadAnnotations', with_bbox=True), + # change this + dict(type='RandomFlip', prob=0.0), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict( + type='RandomSamplingNegPos', + tokenizer_name=_base_.lang_model_name, + num_sample_negative=85, + max_tokens=256), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities', 'tokens_positive', 'dataset_mode')) +] + +train_dataloader = dict( + dataset=dict( + _delete_=True, + type='ODVGDataset', + data_root=data_root, + ann_file='mdetr_annotations/finetune_refcocog_train_vg.json', + data_prefix=dict(img='train2014/'), + filter_cfg=dict(filter_empty_gt=False, min_size=32), + return_classes=True, + pipeline=train_pipeline)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcocog_val.json' +val_dataset_all_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) +val_evaluator_all_val = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcocog_test.json' +val_dataset_refcoco_test = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=_base_.test_pipeline, + backend_args=None) + +val_evaluator_refcoco_test = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +datasets = [val_dataset_all_val, val_dataset_refcoco_test] +dataset_prefixes = ['refcocog_val', 'refcocog_test'] +metrics = [val_evaluator_all_val, val_evaluator_refcoco_test] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1), + # 'language_model': dict(lr_mult=0), + })) + +# learning policy +max_epochs = 5 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[3], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) + +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py new file mode 100644 index 00000000000..437d71c6b35 --- /dev/null +++ b/configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py @@ -0,0 +1,228 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +# 30 is an empirical value, just set it to the maximum value +# without affecting the evaluation result +model = dict(test_cfg=dict(max_per_img=30)) + +data_root = 'data/coco/' + +test_pipeline = [ + dict( + type='LoadImageFromFile', backend_args=None, + imdecode_backend='pillow'), + dict( + type='FixScaleResize', + scale=(800, 1333), + keep_ratio=True, + backend='pillow'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'text', 'custom_entities', + 'tokens_positive')) +] + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/final_refexp_val.json' +val_dataset_all_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) +val_evaluator_all_val = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_testA.json' +val_dataset_refcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testA = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco_testB.json' +val_dataset_refcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_testB = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_testA.json' +val_dataset_refcoco_plus_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_plus_testA = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcoco+_testB.json' +val_dataset_refcoco_plus_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcoco_plus_testB = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_refcocog_test.json' +val_dataset_refcocog_test = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_refcocog_test = dict( + type='RefExpMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + topk=(1, 5, 10)) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_val.json' +val_dataset_grefcoco_val = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_grefcoco_val = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_testA.json' +val_dataset_grefcoco_testA = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_grefcoco_testA = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +ann_file = 'mdetr_annotations/finetune_grefcoco_testB.json' +val_dataset_grefcoco_testB = dict( + type='MDETRStyleRefCocoDataset', + data_root=data_root, + ann_file=ann_file, + data_prefix=dict(img='train2014/'), + test_mode=True, + return_classes=True, + pipeline=test_pipeline, + backend_args=None) + +val_evaluator_grefcoco_testB = dict( + type='gRefCOCOMetric', + ann_file=data_root + ann_file, + metric='bbox', + iou_thrs=0.5, + thresh_score=0.7, + thresh_f1=1.0) + +# -------------------------------------------------# +datasets = [ + val_dataset_all_val, val_dataset_refcoco_testA, val_dataset_refcoco_testB, + val_dataset_refcoco_plus_testA, val_dataset_refcoco_plus_testB, + val_dataset_refcocog_test, val_dataset_grefcoco_val, + val_dataset_grefcoco_testA, val_dataset_grefcoco_testB +] +dataset_prefixes = [ + 'val', 'refcoco_testA', 'refcoco_testB', 'refcoco+_testA', + 'refcoco+_testB', 'refcocog_test', 'grefcoco_val', 'grefcoco_testA', + 'grefcoco_testB' +] +metrics = [ + val_evaluator_all_val, val_evaluator_refcoco_testA, + val_evaluator_refcoco_testB, val_evaluator_refcoco_plus_testA, + val_evaluator_refcoco_plus_testB, val_evaluator_refcocog_test, + val_evaluator_grefcoco_val, val_evaluator_grefcoco_testA, + val_evaluator_grefcoco_testB +] + +val_dataloader = dict( + dataset=dict(_delete_=True, type='ConcatDataset', datasets=datasets)) +test_dataloader = val_dataloader + +val_evaluator = dict( + _delete_=True, + type='MultiDatasetsEvaluator', + metrics=metrics, + dataset_prefixes=dataset_prefixes) +test_evaluator = val_evaluator diff --git a/configs/mm_grounding_dino/rtts/grounding_dino_swin-t_finetune_8xb4_1x_rtts.py b/configs/mm_grounding_dino/rtts/grounding_dino_swin-t_finetune_8xb4_1x_rtts.py new file mode 100644 index 00000000000..95c2be058e2 --- /dev/null +++ b/configs/mm_grounding_dino/rtts/grounding_dino_swin-t_finetune_8xb4_1x_rtts.py @@ -0,0 +1,106 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/RTTS/' +class_name = ('bicycle', 'bus', 'car', 'motorbike', 'person') +palette = [(255, 97, 0), (0, 201, 87), (176, 23, 31), (138, 43, 226), + (30, 144, 255)] + +metainfo = dict(classes=class_name, palette=palette) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + sampler=dict(_delete_=True, type='DefaultSampler', shuffle=True), + batch_sampler=dict(type='AspectRatioBatchSampler'), + dataset=dict( + _delete_=True, + type='CocoDataset', + data_root=data_root, + metainfo=metainfo, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline, + return_classes=True, + ann_file='annotations_json/rtts_train.json', + data_prefix=dict(img=''))) + +val_dataloader = dict( + dataset=dict( + metainfo=metainfo, + data_root=data_root, + return_classes=True, + ann_file='annotations_json/rtts_val.json', + data_prefix=dict(img=''))) +test_dataloader = val_dataloader + +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations_json/rtts_val.json', + metric='bbox', + format_only=False) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1) + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/ruod/grounding_dino_swin-t_finetune_8xb4_1x_ruod.py b/configs/mm_grounding_dino/ruod/grounding_dino_swin-t_finetune_8xb4_1x_ruod.py new file mode 100644 index 00000000000..f57682b29d9 --- /dev/null +++ b/configs/mm_grounding_dino/ruod/grounding_dino_swin-t_finetune_8xb4_1x_ruod.py @@ -0,0 +1,108 @@ +_base_ = '../grounding_dino_swin-t_pretrain_obj365.py' + +data_root = 'data/RUOD/' +class_name = ('holothurian', 'echinus', 'scallop', 'starfish', 'fish', + 'corals', 'diver', 'cuttlefish', 'turtle', 'jellyfish') +palette = [(235, 211, 70), (106, 90, 205), (160, 32, 240), (176, 23, 31), + (142, 0, 0), (230, 0, 0), (106, 0, 228), (60, 100, 0), (80, 100, 0), + (70, 0, 0)] + +metainfo = dict(classes=class_name, palette=palette) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', prob=0.5), + dict( + type='RandomChoice', + transforms=[ + [ + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ], + [ + dict( + type='RandomChoiceResize', + # The radio of all image in train dataset < 7 + # follow the original implement + scales=[(400, 4200), (500, 4200), (600, 4200)], + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='RandomChoiceResize', + scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + keep_ratio=True) + ] + ]), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction', 'text', + 'custom_entities')) +] + +train_dataloader = dict( + sampler=dict(_delete_=True, type='DefaultSampler', shuffle=True), + batch_sampler=dict(type='AspectRatioBatchSampler'), + dataset=dict( + _delete_=True, + type='CocoDataset', + data_root=data_root, + metainfo=metainfo, + filter_cfg=dict(filter_empty_gt=False, min_size=32), + pipeline=train_pipeline, + return_classes=True, + ann_file='RUOD_ANN/instances_train.json', + data_prefix=dict(img='RUOD_pic/train/'))) + +val_dataloader = dict( + dataset=dict( + metainfo=metainfo, + data_root=data_root, + return_classes=True, + ann_file='RUOD_ANN/instances_test.json', + data_prefix=dict(img='RUOD_pic/test/'))) +test_dataloader = val_dataloader + +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'RUOD_ANN/instances_test.json', + metric='bbox', + format_only=False) +test_evaluator = val_evaluator + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), + clip_grad=dict(max_norm=0.1, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'backbone': dict(lr_mult=0.1) + })) + +# learning policy +max_epochs = 12 +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=max_epochs, + by_epoch=True, + milestones=[11], + gamma=0.1) +] +train_cfg = dict(max_epochs=max_epochs, val_interval=1) +default_hooks = dict(checkpoint=dict(max_keep_ckpts=1, save_best='auto')) + +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa diff --git a/configs/mm_grounding_dino/usage.md b/configs/mm_grounding_dino/usage.md new file mode 100644 index 00000000000..123c6638cbe --- /dev/null +++ b/configs/mm_grounding_dino/usage.md @@ -0,0 +1,491 @@ +# Usage + +## Install + +After installing MMDet according to the instructions in the [get_started](../../docs/zh_cn/get_started.md) section, you need to install additional dependency packages: + +```shell +cd $MMDETROOT + +pip install -r requirements/multimodal.txt +pip install emoji ddd-dataset +pip install git+https://github.com/lvis-dataset/lvis-api.git" +``` + +Please note that since the LVIS third-party library does not currently support numpy 1.24, ensure that your numpy version meets the requirements. It is recommended to install numpy version 1.23. + +## Instructions + +### Download BERT Weight + +MM Grounding DINO uses BERT as its language model and requires access to https://huggingface.co/. If you encounter connection errors due to network access issues, you can download the necessary files on a computer with network access and save them locally. Finally, modify the `lang_model_name` field in the configuration file to the local path. For specific instructions, please refer to the following code: + +```python +from transformers import BertConfig, BertModel +from transformers import AutoTokenizer + +config = BertConfig.from_pretrained("bert-base-uncased") +model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, config=config) +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + +config.save_pretrained("your path/bert-base-uncased") +model.save_pretrained("your path/bert-base-uncased") +tokenizer.save_pretrained("your path/bert-base-uncased") +``` + +### Download NLTK Weight + +When MM Grounding DINO performs Phrase Grounding inference, it may extract noun phrases. Although it downloads specific models at runtime, considering that some users' running environments cannot connect to the internet, it is possible to download them in advance to the `~/nltk_data` path. + +```python +import nltk +nltk.download('punkt', download_dir='~/nltk_data') +nltk.download('averaged_perceptron_tagger', download_dir='~/nltk_data') +``` + +### Download MM Grounding DINO-T Weight + +For convenience in demonstration, you can download the MM Grounding DINO-T model weights in advance to the current path. + +```shell +wget load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa +``` + +## Inference + +Before inference, for a better experience of the inference effects on different images, it is recommended that you first download [these images](https://github.com/microsoft/X-Decoder/tree/main/inference_demo/images) to the current path. + +MM Grounding DINO supports four types of inference methods: Closed-Set Object Detection, Open Vocabulary Object Detection, Phrase Grounding, and Referential Expression Comprehension. The details are explained below. + +**(1) Closed-Set Object Detection** + +Since MM Grounding DINO is a pretrained model, it can theoretically be applied to any closed-set detection dataset. Currently, we support commonly used datasets such as coco/voc/cityscapes/objects365v1/lvis, etc. Below, we will use coco as an example. + +```shell +python demo/image_demo.py images/animals.png \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts '$: coco' +``` + +The predictions for `outputs/vis/animals.png` will be generated in the current directory, as shown in the following image. + +
+ +
+ +Since ostrich is not one of the 80 classes in COCO, it will not be detected. + +It's important to note that Objects365v1 and LVIS have a large number of categories. If you try to input all category names directly into the network, it may exceed 256 tokens, leading to poor model predictions. In such cases, you can use the `--chunked-size` parameter to perform chunked predictions. However, please be aware that chunked predictions may take longer to complete due to the large number of categories. + +```shell +python demo/image_demo.py images/animals.png \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts '$: lvis' --chunked-size 70 \ + --palette random +``` + +
+ +
+ +Different `--chunked-size` values can lead to different prediction results. You can experiment with different chunked sizes to find the one that works best for your specific task and dataset. + +**(2) Open Vocabulary Object Detection** + +Open vocabulary object detection refers to the ability to input arbitrary class names during inference. + +```shell +python demo/image_demo.py images/animals.png \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'zebra. giraffe' -c +``` + +
+ +
+ +**(3) Phrase Grounding** + +Phrase Grounding refers to the process where a user inputs a natural language description, and the model automatically detects the corresponding bounding boxes for the mentioned noun phrases. It can be used in two ways: + +1. Automatically extracting noun phrases using the NLTK library and then performing detection. + +```shell +python demo/image_demo.py images/apples.jpg \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'There are many apples here.' +``` + +
+ +
+ +The program will automatically split `many apples` as a noun phrase and then detect the corresponding objects. Different input descriptions can have a significant impact on the prediction results. + +2. Users can manually specify which parts of the sentence are noun phrases to avoid errors in NLTK extraction. + +```shell +python demo/image_demo.py images/fruit.jpg \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'The picture contains watermelon, flower, and a white bottle.' \ + --tokens-positive "[[[21,31]], [[45,59]]]" --pred-score-thr 0.12 +``` + +The noun phrase corresponding to positions 21-31 is `watermelon`, and the noun phrase corresponding to positions 45-59 is `a white bottle`. + +
+ +
+ +**(4) Referential Expression Comprehension** + +Referential expression understanding refers to the model automatically comprehending the referential expressions involved in a user's language description without the need for noun phrase extraction. + +```shell +python demo/image_demo.py images/apples.jpg \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'red apple.' \ + --tokens-positive -1 +``` + +
+ +
+ +## Evaluation + +Our provided evaluation scripts are unified, and you only need to prepare the data in advance and then run the relevant configuration. + +(1) Zero-Shot COCO2017 val + +```shell +# single GPU +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth + +# 8 GPUs +./tools/dist_test.sh configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth 8 +``` + +(2) Zero-Shot ODinW13 + +```shell +# single GPU +python tools/test.py configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth + +# 8 GPUs +./tools/dist_test.sh configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth 8 +``` + +## Visualization of Evaluation Results + +For the convenience of visualizing and analyzing model prediction results, we provide support for visualizing evaluation dataset prediction results. Taking referential expression understanding as an example, the usage is as follows: + +```shell +python tools/test.py configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth --work-dir refcoco_result --show-dir save_path +``` + +During the inference process, it will save the visualization results to the `refcoco_result/{current_timestamp}/save_path` directory. For other evaluation dataset visualizations, you only need to replace the configuration file. + +Here are some visualization results for various datasets. The left image represents the Ground Truth (GT). The right image represents the Predicted Result. + +1. COCO2017 val Results： + +
+ +
+ +2. Flickr30k Entities Results： + +
+ +
+ +3. DOD Results： + +
+ +
+ +4. RefCOCO val Results： + +
+ +
+ +5. RefCOCO testA Results： + +
+ +
+ +6. gRefCOCO val Results： + +
+ +
+ +## Training + +If you want to reproduce our results, you can train the model by using the following command after preparing the dataset: + +```shell +# Training on a single machine with 8 GPUs for obj365v1 dataset +./tools/dist_train.sh configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py 8 +# Training on a single machine with 8 GPUs for datasets like obj365v1, goldg, grit, v3det, and other datasets is similar. +./tools/dist_train.sh configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py 8 +``` + +For multi-machine training, please refer to [train.md](../../docs/zh_cn/user_guides/train.md). The MM-Grounding-DINO T model is designed to work with 32 GPUs (specifically, 3090Ti GPUs). If your total batch size is not 32x4=128, you will need to manually adjust the learning rate accordingly. + +### Pretraining Custom Format Explanation + +In order to standardize the pretraining formats for different datasets, we refer to the format design proposed by [Open-GroundingDino](https://github.com/longzw1997/Open-GroundingDino). Specifically, it is divided into two formats. + +**(1) Object Detection Format (OD)** + +```text +{"filename": "obj365_train_000000734304.jpg", + "height": 512, + "width": 769, + "detection": { + "instances": [ + {"bbox": [109.4768676992, 346.0190429696, 135.1918335098, 365.3641967616], "label": 2, "category": "chair"}, + {"bbox": [58.612365705900004, 323.2281494016, 242.6005859067, 451.4166870016], "label": 8, "category": "car"} + ] + } +} +``` + +The numerical values corresponding to labels in the label dictionary should match the respective label_map. Each item in the instances list corresponds to a bounding box (in the format x1y1x2y2). + +**(2) Phrase Grounding Format (VG)** + +```text +{"filename": "2405116.jpg", + "height": 375, + "width": 500, + "grounding": + {"caption": "Two surfers walking down the shore. sand on the beach.", + "regions": [ + {"bbox": [206, 156, 282, 248], "phrase": "Two surfers", "tokens_positive": [[0, 3], [4, 11]]}, + {"bbox": [303, 338, 443, 343], "phrase": "sand", "tokens_positive": [[36, 40]]}, + {"bbox": [[327, 223, 421, 282], [300, 200, 400, 210]], "phrase": "beach", "tokens_positive": [[48, 53]]} + ] + } +``` + +The `tokens_positive` field indicates the character positions of the current phrase within the caption. + +## Example of Fine-tuning Custom Dataset + +In order to facilitate downstream fine-tuning on custom datasets, we have provided a fine-tuning example using the simple "cat" dataset as an illustration. + +### 1 Data Preparation + +```shell +cd mmdetection +wget https://download.openmmlab.com/mmyolo/data/cat_dataset.zip +unzip cat_dataset.zip -d data/cat/ +``` + +The "cat" dataset is a single-category dataset consisting of 144 images, already converted to the COCO format. + +
+cat dataset +
+ +### 2 Configuration Preparation + +Due to the simplicity and small size of the "cat" dataset, we trained it for 20 epochs using 8 GPUs, with corresponding learning rate scaling. We did not train the language model, only the visual model. + +Detailed configuration information can be found in [grounding_dino_swin-t_finetune_8xb4_20e_cat](grounding_dino_swin-t_finetune_8xb4_20e_cat.py). + +### 3 Visualization and Evaluation of Zero-Shot Results + +Due to MM Grounding DINO being an open-set detection model, you can perform detection and evaluation even if it was not trained on the cat dataset. + +Visualization of a single image: + +```shell +cd mmdetection +python demo/image_demo.py data/cat/images/IMG_20211205_120756.jpg configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth --texts cat. +``` + +Evaluation results of Zero-shot on test dataset： + +```shell +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth +``` + +```text + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.881 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 1.000 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.929 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.881 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.913 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.913 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.913 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.913 +``` + +### 4 Fine-tuning + +```shell +./tools/dist_train.sh configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py 8 --work-dir cat_work_dir +``` + +The model will save the best-performing checkpoint. It achieved its best performance at the 16th epoch, with the following results: + +```text + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.901 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 1.000 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.930 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.901 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.967 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.967 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.967 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.967 +``` + +We can observe that after fine-tuning, the training performance on the cat dataset improved from 88.1 to 90.1. However, due to the small dataset size, the evaluation metrics show some fluctuations. + +## Iterative Generation and Optimization Pipeline of Model Self-training Pseduo Label + +To facilitate users in creating their own datasets from scratch or those who want to leverage the model's inference capabilities for iterative pseudo-label generation and optimization, continuously modifying pseudo-labels to improve model performance, we have provided relevant pipelines. + +Since we have defined two data formats, we will provide separate explanations for demonstration purposes. + +### 1 Object Detection Format + +Here, we continue to use the aforementioned cat dataset as an example. Let's assume that we currently have a series of images and predefined categories but no annotations. + +1. Generate initial `odvg` format file + +```python +import os +import cv2 +import json +import jsonlines + +data_root = 'data/cat' +images_path = os.path.join(data_root, 'images') +out_path = os.path.join(data_root, 'cat_train_od.json') +metas = [] +for files in os.listdir(images_path): + img = cv2.imread(os.path.join(images_path, files)) + height, width, _ = img.shape + metas.append({"filename": files, "height": height, "width": width}) + +with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(metas) + +# 生成 label_map.json，由于只有一个类别，所以只需要写一个 cat 即可 +label_map_path = os.path.join(data_root, 'cat_label_map.json') +with open(label_map_path, 'w') as f: + json.dump({'0': 'cat'}, f) +``` + +Two files, `cat_train_od.json` and `cat_label_map.json`, will be generated in the `data/cat` directory. + +2. Inference with pre-trained model and save the results + +We provide a readily usable [configuration](grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py). If you are using a different dataset, you can refer to this configuration for modifications. + +```shell +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth +``` + +A new file `cat_train_od_v1.json` will be generated in the `data/cat` directory. You can manually open it to confirm or use the provided [script](../../tools/analysis_tools/browse_grounding_raw.py) to visualize the results. + +```shell +python tools/analysis_tools/browse_grounding_raw.py data/cat/ cat_train_od_v1.json images --label-map-file cat_label_map.json -o your_output_dir --not-show +``` + +The visualization results will be generated in the `your_output_dir` directory. + +3. Continue training to boost performance + +After obtaining pseudo-labels, you can mix them with some pre-training data for further pre-training to improve the model's performance on the current dataset. Then, you can repeat step 2 to obtain more accurate pseudo-labels, and continue this iterative process. + +### 2 Phrase Grounding Format + +1. Generate initial `odvg` format file + +The bootstrapping process of Phrase Grounding requires providing captions corresponding to each image and pre-segmented phrase information initially. Taking flickr30k entities images as an example, the generated typical file should look like this: + +```text +[ +{"filename": "3028766968.jpg", + "height": 375, + "width": 500, + "grounding": + {"caption": "Man with a black shirt on sit behind a desk sorting threw a giant stack of people work with a smirk on his face .", + "regions": [ + {"bbox": [0, 0, 1, 1], "phrase": "a giant stack of people", "tokens_positive": [[58, 81]]}, + {"bbox": [0, 0, 1, 1], "phrase": "a black shirt", "tokens_positive": [[9, 22]]}, + {"bbox": [0, 0, 1, 1], "phrase": "a desk", "tokens_positive": [[37, 43]]}, + {"bbox": [0, 0, 1, 1], "phrase": "his face", "tokens_positive": [[103, 111]]}, + {"bbox": [0, 0, 1, 1], "phrase": "Man", "tokens_positive": [[0, 3]]}]}} +{"filename": "6944134083.jpg", + "height": 319, + "width": 500, + "grounding": + {"caption": "Two men are competing in a horse race .", + "regions": [ + {"bbox": [0, 0, 1, 1], "phrase": "Two men", "tokens_positive": [[0, 7]]}]}} +] +``` + +Bbox needs to be set to `[0, 0, 1, 1]` for initialization to make sure the programme could run, but this value would not be utilized. + +```text +{"filename": "3028766968.jpg", "height": 375, "width": 500, "grounding": {"caption": "Man with a black shirt on sit behind a desk sorting threw a giant stack of people work with a smirk on his face .", "regions": [{"bbox": [0, 0, 1, 1], "phrase": "a giant stack of people", "tokens_positive": [[58, 81]]}, {"bbox": [0, 0, 1, 1], "phrase": "a black shirt", "tokens_positive": [[9, 22]]}, {"bbox": [0, 0, 1, 1], "phrase": "a desk", "tokens_positive": [[37, 43]]}, {"bbox": [0, 0, 1, 1], "phrase": "his face", "tokens_positive": [[103, 111]]}, {"bbox": [0, 0, 1, 1], "phrase": "Man", "tokens_positive": [[0, 3]]}]}} +{"filename": "6944134083.jpg", "height": 319, "width": 500, "grounding": {"caption": "Two men are competing in a horse race .", "regions": [{"bbox": [0, 0, 1, 1], "phrase": "Two men", "tokens_positive": [[0, 7]]}]}} +``` + +You can directly copy the text above, and assume that the text content is pasted into a file named `flickr_simple_train_vg.json`, which is placed in the pre-prepared `data/flickr30k_entities` dataset directory, as detailed in the data preparation document. + +2. Inference with pre-trained model and save the results + +We provide a directly usable [configuration](https://chat.openai.com/c/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py). If you are using a different dataset, you can refer to this configuration for modifications. + +```shell +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth +``` + +The translation of your text from Chinese to English is: "A new file `flickr_simple_train_vg_v1.json` will be generated in the `data/flickr30k_entities` directory. You can manually open it to confirm or use the [script](../../tools/analysis_tools/browse_grounding_raw.py) to visualize the effects + +```shell +python tools/analysis_tools/browse_grounding_raw.py data/flickr30k_entities/ flickr_simple_train_vg_v1.json flickr30k_images -o your_output_dir --not-show +``` + +The visualization results will be generated in the `your_output_dir` directory, as shown in the following image: + +
+ +
+ +3. Continue training to boost performance + +After obtaining the pseudo-labels, you can mix some pre-training data to continue pre-training jointly, which enhances the model's performance on the current dataset. Then, rerun step 2 to obtain more accurate pseudo-labels, and repeat this cycle iteratively. diff --git a/configs/mm_grounding_dino/usage_zh-CN.md b/configs/mm_grounding_dino/usage_zh-CN.md new file mode 100644 index 00000000000..5f625ea6ca8 --- /dev/null +++ b/configs/mm_grounding_dino/usage_zh-CN.md @@ -0,0 +1,491 @@ +# 用法说明 + +## 安装 + +在按照 [get_started](../../docs/zh_cn/get_started.md) 一节的说明安装好 MMDet 之后，需要安装额外的依赖包： + +```shell +cd $MMDETROOT + +pip install -r requirements/multimodal.txt +pip install emoji ddd-dataset +pip install git+https://github.com/lvis-dataset/lvis-api.git" +``` + +请注意由于 LVIS 第三方库暂时不支持 numpy 1.24，因此请确保您的 numpy 版本符合要求。建议安装 numpy 1.23 版本。 + +## 说明 + +### BERT 权重下载 + +MM Grounding DINO 采用了 BERT 作为语言模型，需要访问 https://huggingface.co/, 如果您因为网络访问问题遇到连接错误，可以在有网络访问权限的电脑上下载所需文件并保存在本地。最后，修改配置文件中的 `lang_model_name` 字段为本地路径即可。具体请参考以下代码： + +```python +from transformers import BertConfig, BertModel +from transformers import AutoTokenizer + +config = BertConfig.from_pretrained("bert-base-uncased") +model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, config=config) +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + +config.save_pretrained("your path/bert-base-uncased") +model.save_pretrained("your path/bert-base-uncased") +tokenizer.save_pretrained("your path/bert-base-uncased") +``` + +### NLTK 权重下载 + +MM Grounding DINO 在进行 Phrase Grounding 推理时候可能会进行名词短语提取，虽然会在运行时候下载特定的模型，但是考虑到有些用户运行环境无法联网，因此可以提前下载到 `~/nltk_data` 路径下 + +```python +import nltk +nltk.download('punkt', download_dir='~/nltk_data') +nltk.download('averaged_perceptron_tagger', download_dir='~/nltk_data') +``` + +### MM Grounding DINO-T 模型权重下载 + +为了方便演示，您可以提前下载 MM Grounding DINO-T 模型权重到当前路径下 + +```shell +wget load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth' # noqa +``` + +## 推理 + +在推理前，为了更好的体验不同图片的推理效果，建议您先下载 [这些图片](https://github.com/microsoft/X-Decoder/tree/main/inference_demo/images) 到当前路径下 + +MM Grounding DINO 支持了闭集目标检测，开放词汇目标检测，Phrase Grounding 和指代性表达式理解 4 种推理方式，下面详细说明。 + +**(1) 闭集目标检测** + +由于 MM Grounding DINO 是预训练模型，理论上可以应用于任何闭集检测数据集，目前我们支持了常用的 coco/voc/cityscapes/objects365v1/lvis 等，下面以 coco 为例 + +```shell +python demo/image_demo.py images/animals.png \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts '$: coco' +``` + +会在当前路径下生成 `outputs/vis/animals.png` 的预测结果，如下图所示 + +
+ +
+ +由于鸵鸟并不在 COCO 80 类中, 因此不会检测出来。 + +需要注意，由于 objects365v1 和 lvis 类别很多，如果直接将类别名全部输入到网络中，会超过 256 个 token 导致模型预测效果极差，此时我们需要通过 `--chunked-size` 参数进行截断预测, 同时预测时间会比较长。 + +```shell +python demo/image_demo.py images/animals.png \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts '$: lvis' --chunked-size 70 \ + --palette random +``` + +
+ +
+ +不同的 `--chunked-size` 会导致不同的预测效果，您可以自行尝试。 + +**(2) 开放词汇目标检测** + +开放词汇目标检测是指在推理时候，可以输入任意的类别名 + +```shell +python demo/image_demo.py images/animals.png \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'zebra. giraffe' -c +``` + +
+ +
+ +**(3) Phrase Grounding** + +Phrase Grounding 是指的用户输入一句语言描述，模型自动对其涉及到的名词短语想对应的 bbox 进行检测，有两种用法 + +1. 通过 NLTK 库自动提取名词短语，然后进行检测 + +```shell +python demo/image_demo.py images/apples.jpg \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'There are many apples here.' +``` + +
+ +
+ +程序内部会自动切分出 `many apples` 作为名词短语，然后检测出对应物体。不同的输入描述对预测结果影响很大。 + +2. 用户自己指定句子中哪些为名词短语，避免 NLTK 提取错误的情况 + +```shell +python demo/image_demo.py images/fruit.jpg \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'The picture contains watermelon, flower, and a white bottle.' \ + --tokens-positive "[[[21,31]], [[45,59]]]" --pred-score-thr 0.12 +``` + +21,31 对应的名词短语为 `watermelon`，45,59 对应的名词短语为 `a white bottle`。 + +
+ +
+ +**(4) 指代性表达式理解** + +指代性表达式理解是指的用户输入一句语言描述，模型自动对其涉及到的指代性表达式进行理解, 不需要进行名词短语提取。 + +```shell +python demo/image_demo.py images/apples.jpg \ + configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth \ + --texts 'red apple.' \ + --tokens-positive -1 +``` + +
+ +
+ +## 评测 + +我们所提供的评测脚本都是统一的，你只需要提前准备好数据，然后运行相关配置就可以了 + +(1) Zero-Shot COCO2017 val + +```shell +# 单卡 +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth + +# 8 卡 +./tools/dist_test.sh configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth 8 +``` + +(2) Zero-Shot ODinW13 + +```shell +# 单卡 +python tools/test.py configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth + +# 8 卡 +./tools/dist_test.sh configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth 8 +``` + +## 评测数据集结果可视化 + +为了方便大家对模型预测结果进行可视化和分析，我们支持了评测数据集预测结果可视化，以指代性表达式理解为例用法如下： + +```shell +python tools/test.py configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth --work-dir refcoco_result --show-dir save_path +``` + +模型在推理过程中会将可视化结果保存到 `refcoco_result/{当前时间戳}/save_path` 路径下。其余评测数据集可视化只需要替换配置文件即可。 + +下面展示一些数据集的可视化结果： 左图为 GT，右图为预测结果 + +1. COCO2017 val 结果： + +
+ +
+ +2. Flickr30k Entities 结果： + +
+ +
+ +3. DOD 结果： + +
+ +
+ +4. RefCOCO val 结果： + +
+ +
+ +5. RefCOCO testA 结果： + +
+ +
+ +6. gRefCOCO val 结果： + +
+ +
+ +## 模型训练 + +如果想复现我们的结果，你可以在准备好数据集后，直接通过如下命令进行训练 + +```shell +# 单机 8 卡训练仅包括 obj365v1 数据集 +./tools/dist_train.sh configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.py 8 +# 单机 8 卡训练包括 obj365v1/goldg/grit/v3det 数据集，其余数据集类似 +./tools/dist_train.sh configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py 8 +``` + +多机训练的用法请参考 [train.md](../../docs/zh_cn/user_guides/train.md)。MM-Grounding-DINO T 模型默认采用的是 32 张 3090Ti，如果你的总 bs 数不是 32x4=128，那么你需要手动的线性调整学习率。 + +### 预训练自定义格式说明 + +为了统一不同数据集的预训练格式，我们参考 [Open-GroundingDino](https://github.com/longzw1997/Open-GroundingDino) 所设计的格式。具体来说分成 2 种格式 + +**(1) 目标检测数据格式 OD** + +```text +{"filename": "obj365_train_000000734304.jpg", + "height": 512, + "width": 769, + "detection": { + "instances": [ + {"bbox": [109.4768676992, 346.0190429696, 135.1918335098, 365.3641967616], "label": 2, "category": "chair"}, + {"bbox": [58.612365705900004, 323.2281494016, 242.6005859067, 451.4166870016], "label": 8, "category": "car"} + ] + } +} +``` + +label字典中所对应的数值需要和相应的 label_map 一致。 instances 列表中的每一项都对应一个 bbox (x1y1x2y2 格式)。 + +**(2) phrase grounding 数据格式 VG** + +```text +{"filename": "2405116.jpg", + "height": 375, + "width": 500, + "grounding": + {"caption": "Two surfers walking down the shore. sand on the beach.", + "regions": [ + {"bbox": [206, 156, 282, 248], "phrase": "Two surfers", "tokens_positive": [[0, 3], [4, 11]]}, + {"bbox": [303, 338, 443, 343], "phrase": "sand", "tokens_positive": [[36, 40]]}, + {"bbox": [[327, 223, 421, 282], [300, 200, 400, 210]], "phrase": "beach", "tokens_positive": [[48, 53]]} + ] + } +``` + +tokens_positive 表示当前 phrase 在 caption 中的字符位置。 + +## 自定义数据集微调训练案例 + +为了方便用户针对自定义数据集进行下游微调，我们特意提供了以简单的 cat 数据集为例的微调训练案例。 + +### 1 数据准备 + +```shell +cd mmdetection +wget https://download.openmmlab.com/mmyolo/data/cat_dataset.zip +unzip cat_dataset.zip -d data/cat/ +``` + +cat 数据集是一个单类别数据集，包含 144 张图片，已经转换为 coco 格式。 + +
+cat dataset +
+ +### 2 配置准备 + +由于 cat 数据集的简单性和数量较少，我们使用 8 卡训练 20 个 epoch，相应的缩放学习率，不训练语言模型，只训练视觉模型。 + +详细的配置信息可以在 [grounding_dino_swin-t_finetune_8xb4_20e_cat](grounding_dino_swin-t_finetune_8xb4_20e_cat.py) 中找到。 + +### 3 可视化和 Zero-Shot 评估 + +由于 MM Grounding DINO 是一个开放的检测模型，所以即使没有在 cat 数据集上训练，也可以进行检测和评估。 + +单张图片的可视化结果如下： + +```shell +cd mmdetection +python demo/image_demo.py data/cat/images/IMG_20211205_120756.jpg configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py --weights grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth --texts cat. +``` + +测试集上的 Zero-Shot 评估结果如下： + +```shell +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth +``` + +```text + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.881 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 1.000 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.929 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.881 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.913 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.913 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.913 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.913 +``` + +### 4 模型训练 + +```shell +./tools/dist_train.sh configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py 8 --work-dir cat_work_dir +``` + +模型将会保存性能最佳的模型。在第 16 epoch 时候达到最佳，性能如下所示： + +```text + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.901 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 1.000 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.930 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.901 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.967 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.967 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.967 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.967 +``` + +我们可以发现，经过微调训练后，cat 数据集的训练性能从 88.1 提升到了 90.1。同时由于数据集比较小，评估指标波动比较大。 + +## 模型自训练伪标签迭代生成和优化 pipeline + +为了方便用户从头构建自己的数据集或者希望利用模型推理能力进行自举式伪标签迭代生成和优化，不断修改伪标签来提升模型性能，我们特意提供了相关的 pipeline。 + +由于我们定义了两种数据格式，为了演示我们也将分别进行说明。 + +### 1 目标检测格式 + +此处我们依然采用上述的 cat 数据集为例，假设我们目前只有一系列图片和预定义的类别，并不存在标注。 + +1. 生成初始 odvg 格式文件 + +```python +import os +import cv2 +import json +import jsonlines + +data_root = 'data/cat' +images_path = os.path.join(data_root, 'images') +out_path = os.path.join(data_root, 'cat_train_od.json') +metas = [] +for files in os.listdir(images_path): + img = cv2.imread(os.path.join(images_path, files)) + height, width, _ = img.shape + metas.append({"filename": files, "height": height, "width": width}) + +with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(metas) + +# 生成 label_map.json，由于只有一个类别，所以只需要写一个 cat 即可 +label_map_path = os.path.join(data_root, 'cat_label_map.json') +with open(label_map_path, 'w') as f: + json.dump({'0': 'cat'}, f) +``` + +会在 `data/cat` 目录下生成 `cat_train_od.json` 和 `cat_label_map.json` 两个文件。 + +2. 使用预训练模型进行推理，并保存结果 + +我们提供了直接可用的 [配置](grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py), 如果你是其他数据集可以参考这个配置进行修改。 + +```shell +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth +``` + +会在 `data/cat` 目录下新生成 `cat_train_od_v1.json` 文件，你可以手动打开确认或者使用 [脚本](../../tools/analysis_tools/browse_grounding_raw.py) 可视化效果 + +```shell +python tools/analysis_tools/browse_grounding_raw.py data/cat/ cat_train_od_v1.json images --label-map-file cat_label_map.json -o your_output_dir --not-show +``` + +会在 your_output_dir 目录下生成可视化结果 + +3. 继续训练提高性能 + +在得到伪标签后，你可以混合一些预训练数据联合进行继续预训练，提升模型在当前数据集上的性能，然后重新运行 2 步骤，得到更准确的伪标签，如此循环迭代即可。 + +### 2 Phrase Grounding 格式 + +1. 生成初始 odvg 格式文件 + +Phrase Grounding 的自举流程要求初始时候提供每张图片对应的 caption 和提前切割好的 phrase 信息。以 flickr30k entities 图片为例，生成的典型的文件应该如下所示： + +```text +[ +{"filename": "3028766968.jpg", + "height": 375, + "width": 500, + "grounding": + {"caption": "Man with a black shirt on sit behind a desk sorting threw a giant stack of people work with a smirk on his face .", + "regions": [ + {"bbox": [0, 0, 1, 1], "phrase": "a giant stack of people", "tokens_positive": [[58, 81]]}, + {"bbox": [0, 0, 1, 1], "phrase": "a black shirt", "tokens_positive": [[9, 22]]}, + {"bbox": [0, 0, 1, 1], "phrase": "a desk", "tokens_positive": [[37, 43]]}, + {"bbox": [0, 0, 1, 1], "phrase": "his face", "tokens_positive": [[103, 111]]}, + {"bbox": [0, 0, 1, 1], "phrase": "Man", "tokens_positive": [[0, 3]]}]}} +{"filename": "6944134083.jpg", + "height": 319, + "width": 500, + "grounding": + {"caption": "Two men are competing in a horse race .", + "regions": [ + {"bbox": [0, 0, 1, 1], "phrase": "Two men", "tokens_positive": [[0, 7]]}]}} +] +``` + +初始时候 bbox 必须要设置为 `[0, 0, 1, 1]`，因为这能确保程序正常运行，但是 bbox 的值并不会被使用。 + +```text +{"filename": "3028766968.jpg", "height": 375, "width": 500, "grounding": {"caption": "Man with a black shirt on sit behind a desk sorting threw a giant stack of people work with a smirk on his face .", "regions": [{"bbox": [0, 0, 1, 1], "phrase": "a giant stack of people", "tokens_positive": [[58, 81]]}, {"bbox": [0, 0, 1, 1], "phrase": "a black shirt", "tokens_positive": [[9, 22]]}, {"bbox": [0, 0, 1, 1], "phrase": "a desk", "tokens_positive": [[37, 43]]}, {"bbox": [0, 0, 1, 1], "phrase": "his face", "tokens_positive": [[103, 111]]}, {"bbox": [0, 0, 1, 1], "phrase": "Man", "tokens_positive": [[0, 3]]}]}} +{"filename": "6944134083.jpg", "height": 319, "width": 500, "grounding": {"caption": "Two men are competing in a horse race .", "regions": [{"bbox": [0, 0, 1, 1], "phrase": "Two men", "tokens_positive": [[0, 7]]}]}} +``` + +你可直接复制上面的文本，并假设将文本内容粘贴到命名为 `flickr_simple_train_vg.json` 文件中，并放置于提前准备好的 `data/flickr30k_entities` 数据集目录下，具体见数据准备文档。 + +2. 使用预训练模型进行推理，并保存结果 + +我们提供了直接可用的 [配置](grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py), 如果你是其他数据集可以参考这个配置进行修改。 + +```shell +python tools/test.py configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py \ + grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth +``` + +会在 `data/flickr30k_entities` 目录下新生成 `flickr_simple_train_vg_v1.json` 文件，你可以手动打开确认或者使用 [脚本](../../tools/analysis_tools/browse_grounding_raw.py) 可视化效果 + +```shell +python tools/analysis_tools/browse_grounding_raw.py data/flickr30k_entities/ flickr_simple_train_vg_v1.json flickr30k_images -o your_output_dir --not-show +``` + +会在 `your_output_dir` 目录下生成可视化结果，如下图所示： + +
+ +
+ +3. 继续训练提高性能 + +在得到伪标签后，你可以混合一些预训练数据联合进行继续预训练，提升模型在当前数据集上的性能，然后重新运行 2 步骤，得到更准确的伪标签，如此循环迭代即可。 diff --git a/configs/rtmdet/README.md b/configs/rtmdet/README.md index 4574dd613c1..1677184af76 100644 --- a/configs/rtmdet/README.md +++ b/configs/rtmdet/README.md @@ -20,14 +20,17 @@ In this paper, we aim to design an efficient real-time object detector that exce ### Object Detection -| Model | size | box AP | Params(M) | FLOPS(G) | TRT-FP16-Latency(ms)
RTX3090 | TRT-FP16-Latency(ms)
T4 | Config | Download | -| :---------: | :--: | :----: | :-------: | :------: | :-----------------------------: | :------------------------: | :----------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| RTMDet-tiny | 640 | 41.1 | 4.8 | 8.1 | 0.98 | 2.34 | [config](./rtmdet_tiny_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414.log.json) | -| RTMDet-s | 640 | 44.6 | 8.89 | 14.8 | 1.22 | 2.96 | [config](./rtmdet_s_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602-387a891e.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602.log.json) | -| RTMDet-m | 640 | 49.4 | 24.71 | 39.27 | 1.62 | 6.41 | [config](./rtmdet_m_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220.log.json) | -| RTMDet-l | 640 | 51.5 | 52.3 | 80.23 | 2.44 | 10.32 | [config](./rtmdet_l_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030-5a0be7c4.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030.log.json) | -| RTMDet-x | 640 | 52.8 | 94.86 | 141.67 | 3.10 | 18.80 | [config](./rtmdet_x_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555-cc79b9ae.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555.log.json) | -| RTMDet-x-P6 | 1280 | 54.9 | | | | | [config](./rtmdet_x_p6_4xb8-300e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-p6/rtmdet_x_p6_4xb8-300e_coco-bf32be58.pth) | +| Model | size | box AP | Params(M) | FLOPS(G) | TRT-FP16-Latency(ms)
RTX3090 | TRT-FP16-Latency(ms)
T4 | Config | Download | +| :-----------------: | :--: | :----: | :-------: | :------: | :-----------------------------: | :------------------------: | :------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| RTMDet-tiny | 640 | 41.1 | 4.8 | 8.1 | 0.98 | 2.34 | [config](./rtmdet_tiny_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414.log.json) | +| RTMDet-s | 640 | 44.6 | 8.89 | 14.8 | 1.22 | 2.96 | [config](./rtmdet_s_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602-387a891e.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602.log.json) | +| RTMDet-m | 640 | 49.4 | 24.71 | 39.27 | 1.62 | 6.41 | [config](./rtmdet_m_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220.log.json) | +| RTMDet-l | 640 | 51.5 | 52.3 | 80.23 | 2.44 | 10.32 | [config](./rtmdet_l_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030-5a0be7c4.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030.log.json) | +| RTMDet-x | 640 | 52.8 | 94.86 | 141.67 | 3.10 | 18.80 | [config](./rtmdet_x_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555-cc79b9ae.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555.log.json) | +| RTMDet-x-P6 | 1280 | 54.9 | | | | | [config](./rtmdet_x_p6_4xb8-300e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-p6/rtmdet_x_p6_4xb8-300e_coco-bf32be58.pth) | +| RTMDet-l-ConvNeXt-B | 640 | 53.1 | | | | | [config](./rtmdet_l_convnext_b_4xb32-100e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_convnext_b_4xb32-100e_coco-d4731b3d.pth) | +| RTMDet-l-Swin-B | 640 | 52.4 | | | | | [config](./rtmdet_l_swin_b_4xb32-100e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_4xb32-100e_coco-0828ce5d.pth) | +| RTMDet-l-Swin-B-P6 | 1280 | 56.4 | | | | | [config](./rtmdet_l_swin_b_p6_4xb16-100e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_p6_4xb16-100e_coco-a1486b6f.pth) | **Note**: diff --git a/configs/rtmdet/metafile.yml b/configs/rtmdet/metafile.yml index 7dc72e130be..a62abcb2faa 100644 --- a/configs/rtmdet/metafile.yml +++ b/configs/rtmdet/metafile.yml @@ -104,6 +104,48 @@ Models: box AP: 54.9 Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-p6/rtmdet_x_p6_4xb8-300e_coco-bf32be58.pth + - Name: rtmdet_l_convnext_b_4xb32-100e_coco + Alias: + - rtmdet-l_convnext_b + In Collection: RTMDet + Config: configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py + Metadata: + Epochs: 100 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 53.1 + Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_convnext_b_4xb32-100e_coco-d4731b3d.pth + + - Name: rtmdet_l_swin_b_4xb32-100e_coco + Alias: + - rtmdet-l_swin_b + In Collection: RTMDet + Config: configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py + Metadata: + Epochs: 100 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 52.4 + Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_4xb32-100e_coco-0828ce5d.pth + + - Name: rtmdet_l_swin_b_p6_4xb16-100e_coco + Alias: + - rtmdet-l_swin_b_p6 + In Collection: RTMDet + Config: configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py + Metadata: + Epochs: 100 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 56.4 + Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_p6_4xb16-100e_coco-a1486b6f.pth + - Name: rtmdet-ins_tiny_8xb32-300e_coco Alias: - rtmdet-ins-t diff --git a/configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py b/configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py new file mode 100644 index 00000000000..85af292bcab --- /dev/null +++ b/configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py @@ -0,0 +1,81 @@ +_base_ = './rtmdet_l_8xb32-300e_coco.py' + +custom_imports = dict( + imports=['mmpretrain.models'], allow_failed_imports=False) + +norm_cfg = dict(type='GN', num_groups=32) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_in1k-384px_20221219-4570f792.pth' # noqa +model = dict( + type='RTMDet', + data_preprocessor=dict( + _delete_=True, + type='DetDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + batch_augments=None), + backbone=dict( + _delete_=True, + type='mmpretrain.ConvNeXt', + arch='base', + out_indices=[1, 2, 3], + drop_path_rate=0.7, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + with_cp=True, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + neck=dict(in_channels=[256, 512, 1024], norm_cfg=norm_cfg), + bbox_head=dict(norm_cfg=norm_cfg)) + +max_epochs = 100 +stage2_num_epochs = 10 +interval = 10 +base_lr = 0.001 + +train_cfg = dict( + max_epochs=max_epochs, + val_interval=interval, + dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)]) + +optim_wrapper = dict( + constructor='LearningRateDecayOptimizerConstructor', + paramwise_cfg={ + 'decay_rate': 0.8, + 'decay_type': 'layer_wise', + 'num_layers': 12 + }, + optimizer=dict(lr=base_lr)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 50 to 100 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline={{_base_.train_pipeline_stage2}}) +] diff --git a/configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py b/configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py new file mode 100644 index 00000000000..84b0e0fa7d1 --- /dev/null +++ b/configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py @@ -0,0 +1,78 @@ +_base_ = './rtmdet_l_8xb32-300e_coco.py' + +norm_cfg = dict(type='GN', num_groups=32) +checkpoint = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa +model = dict( + type='RTMDet', + data_preprocessor=dict( + _delete_=True, + type='DetDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + batch_augments=None), + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=384, + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(1, 2, 3), + with_cp=True, + convert_weights=True, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + neck=dict(in_channels=[256, 512, 1024], norm_cfg=norm_cfg), + bbox_head=dict(norm_cfg=norm_cfg)) + +max_epochs = 100 +stage2_num_epochs = 10 +interval = 10 +base_lr = 0.001 + +train_cfg = dict( + max_epochs=max_epochs, + val_interval=interval, + dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)]) + +optim_wrapper = dict(optimizer=dict(lr=base_lr)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 50 to 100 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline={{_base_.train_pipeline_stage2}}) +] diff --git a/configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py b/configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py new file mode 100644 index 00000000000..37d4215c3f0 --- /dev/null +++ b/configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py @@ -0,0 +1,114 @@ +_base_ = './rtmdet_l_swin_b_4xb32-100e_coco.py' + +model = dict( + backbone=dict( + depths=[2, 2, 18, 2, 1], + num_heads=[4, 8, 16, 32, 64], + strides=(4, 2, 2, 2, 2), + out_indices=(1, 2, 3, 4)), + neck=dict(in_channels=[256, 512, 1024, 2048]), + bbox_head=dict( + anchor_generator=dict( + type='MlvlPointGenerator', offset=0, strides=[8, 16, 32, 64]))) + +train_pipeline = [ + dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='CachedMosaic', img_scale=(1280, 1280), pad_val=114.0), + dict( + type='RandomResize', + scale=(2560, 2560), + ratio_range=(0.1, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(1280, 1280)), + dict(type='YOLOXHSVRandomAug'), + dict(type='RandomFlip', prob=0.5), + dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))), + dict( + type='CachedMixUp', + img_scale=(1280, 1280), + ratio_range=(1.0, 1.0), + max_cached_images=20, + pad_val=(114, 114, 114)), + dict(type='PackDetInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='RandomResize', + scale=(1280, 1280), + ratio_range=(0.1, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(1280, 1280)), + dict(type='YOLOXHSVRandomAug'), + dict(type='RandomFlip', prob=0.5), + dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))), + dict(type='PackDetInputs') +] + +test_pipeline = [ + dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), + dict(type='Resize', scale=(1280, 1280), keep_ratio=True), + dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor')) +] + +train_dataloader = dict( + batch_size=16, num_workers=20, dataset=dict(pipeline=train_pipeline)) +val_dataloader = dict(num_workers=20, dataset=dict(pipeline=test_pipeline)) +test_dataloader = val_dataloader + +max_epochs = 100 +stage2_num_epochs = 10 + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +img_scales = [(1280, 1280), (640, 640), (1920, 1920)] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale=s, keep_ratio=True) + for s in img_scales + ], + [ + # ``RandomFlip`` must be placed before ``Pad``, otherwise + # bounding box coordinates after flipping cannot be + # recovered correctly. + dict(type='RandomFlip', prob=1.), + dict(type='RandomFlip', prob=0.) + ], + [ + dict( + type='Pad', + size=(1920, 1920), + pad_val=dict(img=(114, 114, 114))), + ], + [dict(type='LoadAnnotations', with_bbox=True)], + [ + dict( + type='PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor', 'flip', 'flip_direction')) + ] + ]) +] diff --git a/configs/solo/solo_r50_fpn_3x_coco.py b/configs/solo/solo_r50_fpn_3x_coco.py index 98a9505538c..0d5abbd2f4d 100644 --- a/configs/solo/solo_r50_fpn_3x_coco.py +++ b/configs/solo/solo_r50_fpn_3x_coco.py @@ -15,7 +15,7 @@ # training schedule for 3x max_epochs = 36 -train_cfg = dict(by_epoch=True, max_epochs=max_epochs) +train_cfg = dict(max_epochs=max_epochs) # learning rate param_scheduler = [ diff --git a/demo/MMDet_InstanceSeg_Tutorial.ipynb b/demo/MMDet_InstanceSeg_Tutorial.ipynb index 1cd020e5750..4b63ba340b2 100644 --- a/demo/MMDet_InstanceSeg_Tutorial.ipynb +++ b/demo/MMDet_InstanceSeg_Tutorial.ipynb @@ -411,7 +411,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -751,7 +751,7 @@ " annotations = []\n", " images = []\n", " obj_count = 0\n", - " for idx, v in enumerate(mmengine.track_iter_progress(data_infos.values())):\n", + " for idx, v in enumerate(mmengine.track_iter_progress(list(data_infos.values()))):\n", " filename = v['filename']\n", " img_path = osp.join(image_prefix, filename)\n", " height, width = mmcv.imread(img_path).shape[:2]\n", @@ -2088,7 +2088,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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JJMzpmiII7IypOCsjBOiUR951mfd886OMpXClrilfeYbTCnQdq7OHSM6cvXDWIgnFjbsWN9qV4gBaiyISp9A0DgSMudjd+86Bsz1qfGdwsI3bj3bfdmv7e9esU6JwibU+w391ZiA+/hgf/ZWPTuFE8Tju2bNn+fB3fZjj28c8+9yzfPWrX/U9b1IH13E4cycT22HXvxtPfNP7+NYPfpAbN27wW7/+6/zAD/5Rbty8yUf/xT/fA5XCH/ye7yGXwuapT/N//c6LdMkWbTm9hcwPkJToL72beHjJnkuTb1QlLQ9ZPfEh+rOX6M8+Mtlx0UA5Pnk9ftx78vu/G2/cYPvCC4CyHQZOP/Mv0TEze/Qd9N9ykTraz+owMl6/xnjr1h2fc0+suv9dCmE2o3/oIQe9O1AEEPqe5Tc+weL3fRcX3v1nqJq5/fLzPP+rv8BXf+mfUDdbO9xhAiTBzxfUIlwmGxG0QvF1fHl7kXedPkKsykILfSmc30RubysvJuHaYs5tZ/5TiogWMhELW295//sf4+Vrt/nq1WsNSbmT4VIBJzhE/QyqOs2H2aMmR3HmV01yFcSeo9kGi25QHQS6EZtYrdrIW0GCGzU/Y+yk3dkzkxNBdeSn1YEK5si386fNfXGzOwHzooQkDGpERzuS0ErX7WyXBBzgud2t0GQ3AETIRY15w+4rF2c2dYLXZD/LaBI0NWtbwIgSds6BoKQEeRhRB2vq0rAGXCsyeVyRti/t3wEnkxuJIib1cGjj63TnrJTpFLL/ac82iDHMNbd/t9cYU2+vE2KyZ1kbaMUifoJMUpGGZCXae1eryNmzB1y6eMCl80cc9AtSL06iBUL1YLdWYkz0XUeM0RloAcwpmXUw6AgISe4/1ea+AaUddg4+ghu/9mCDoB4KVgSRyjjadAY13WXRXViubeyYIiLRgWlwA9/Cj2F6aAAqgZg6ojZAw3T4NfRoi72xREJj4KYwdDtgnFlU1Ul7Waod6g3U1mpsHGJgKDY9jxpqbzo6YzRd+xDjBFAsFOobRHXScKofVKUYKxFECDG61tDDFCrGpDlj2kK6E0gW8RCB/aZiB/+QqxkM9vSaagd71eqsjM9n9bBx9M/w0It5xDJZjMZIm8fkzkE0AwrY310sYqFkM9K1uCYFA70qdiTHuGP9goNlEWO6jeJ2g+/hMTN6oxlbEVt7VZBqujkR0BqNPYumuZTkmthqBsJCDtWARlVKxDWBglqsyMMAzXCIb/pKlZ3TlFyPZUMdjBmrIPZGu9cglBZGc2DTjG4DHY0Bi0ANHopWM94axNZqMSNoutxqTLsbfqXtj0DzcxuQk6poqA58mxarMa8NlO7C2n62mD5abW5VhBgjpWZcvObrYe/o193zVmnfb6BdxdkIE/jYVHh4vEZleWmBLizseXh2jgQYixK7jm6+ZKUwX86IYvNuYUM/ENsjwA/kas5baEBbol2LOENRK1ohxMSkXw0OJKrSiVJzoWi2J61mK3ZGVkES0n+AcPYChN8hdD0f+OAHuXD5Mp/85CeptfLt3/7t3Lhxgy984QvMVyuGWgldx3d994d57LHH+MLnP8erL7/E937/H2az2fJrH/8Yf/gHfggR4Utf+hIvPv8si8WS5776VRD48Pd9H08++SQf/OC382u/9gm+8MXP86Hv+E7K7gnw0MMP8eHv+z6ef/Y5zjzxBN/45/+cAaMQ0PEU6RagoBny9Rv29/1HWAqnTz3F7S9+jvXTP4PmDCjjtWvUzYYmFPCHb/vDqJrpmStAzjv5AZMHw+bpr0w2sGmoHcFNzs+OhWp3dSe0nP51esJw7drrXtH+fuOTnyTMZiwee4yjD36Ih378x1k9/E5e/cynuPr0k+bMyC6UrMW0z0VtPwVtCjzbEDGmSTNc1SJJUkwrf5aBoxrYlMqzKfJ8H6m1M5uOy1RqZduv+X3f9gRXf/UzHG/XLqkCxLR30qQkDmZ2zt/etbBzOnBQWZ0QicF0rnZW2HWbpl2m/WnvcomQehRBGuHTT3pzaVfhjlmumYB6eNU/aQJFHp51hzFPVw0MuzuofnYAlKyTvKctk6pAcClRe2GAnI0rsO9QShaGAXe4/fNwsDoWY1Hd3k3wR5pcyK4/ipIGmX5fse+tfg8G6nQCtoUmC7O7j+6QawP5otN3qnLH97aIVwuOTWc4tt6STaURZSJORjS8YNdRtm1CmSJZ0dlwi9qL64MN/C6PIpcunuXy5Qv239kDOpe/bXMxbadLNlMQUoqELpGiyfrE5yIgEIWuDyTdSeLuZ9w/QxnacqvTmUR1MKVMB6ItOPMWJhbMF3iQ4EAtTAeT1iZmtUPdbjrclYQjpC4Rqn+uNCDXKOodgi65OPqOfmgHVPZYG188wcFaEGO82v/l6lrHYDow00LaAonuwdZi11n9IShqG7R5DzjDFOLEojZPqTGTU/hXhBSjaw0dtKslFSEW9lcKlYxm2321moeL2tHSSfL3ARonUF6KA3wTuKGqZGfZRCd4wSQ4bqwPTEC7AZLmSLTnbmS0C8uTrYEQohksf0jNy1aZ/Ldpc9u9G3to4WCbixijZx+Yt6mhUukApVDsQIiCBiFYhgzqSTkqycTYYtS9nx+gauColClhQrD5iUE91Onss90g0bWW7ZkVD+dENw37Wl4EkognNukE0MVjTNVDWQ1MTslDWKgB2YnZNDSvtIE4P8T3DnZ1B6K00wtzYydJg6PExro3Z1A9uaa55+2pNGNsb7U9nST6aREp0mLczZVmel4thOJQ1teKzWNjvcPEvjdmXjm+veb2yQmpD4zbweY5gHQRicGxud2HehJOcOclBpnCyb0kdwQrAdujQc3Fys7g2HIL5FIwNlYoubiTaM+kuoOJBCoQpbrUAAgJiSs4ewmWbSPA9evX+fjHP84P/MAP8LGPfYzNZsN3f/d38+yzz7rpqhyulrz/fe/jp//B3+d/9G//WY6Pb/OJf/VxPvAtH+B93/wBzp0/zz/5R/+QH/43foznv/oMpycn0/y+8vLLPPzQQ+YM1sqN69f3mEnTP33/D/wgT3/labphJH/pizz7n//nCJCOztBdvMzm2WfQPFJu36as1/tYj7YMtUlwgjm3ISViSqTVCkJAoq3Tpnefko721sJuUQB7Ttc9X+D3MLFd1Vj6WgqalZozdTSNeCnZ9rjvBxV279+7j8nRCZFzH/4e+m99H5/52Z/k+Y/8HDefe9ocPMymBDzBUoIxaZ6gKFVJIlAtDDhWcw5VhBJMjhN8fVAKMcBSC++tkaMaeKlWhnRAoScqbmMLR+fnPPGeR/jKCy9zazuw2WZixUPBTGt0X/nZ3L92su3C0XfOa24h60lW4e90fG6PQ9whMwLHuKCIaqQSCRInMGeu+Ai1GMjGdPpOS5iMSo3FbPaDO84SP7H3nlNzYgyIyRShKS7fokInMiVwJpW9ZeQkApBVGdXt4/Q9dt2ZRhLtyVu02SY/k5UpaXF/GGzxPIn9OfN5NxyvtBnW/fWnO5lMC71PIHR3+9NcKdC1Z4czqQ0E684eq95rGzm4Vwg+jwjMO+H8UeKRy2d47B0XeOjyBR65cJbDxYyqhdsnG0todKzU98ZKppRIIdJ1HaUUJ4Gi226/d9W7p+tNx30DyuYciS8KoyAcrbeDRkyzZwyBvU9i8DCYvbc4Y9PmSmQXw7eDdffAgouB2vEbUzQwa6hwSqBpTGM7hFt4C5rQNoDeqVWcBMcCQQM0PYoIEtUOaPswA5gYOC17E9y8m4Aiatnrunf9qDowlikZZAqxumFu7GaK0XV9gSAWXk7RwGGz3NLmozam0w7pIWc6bUlTBiBKaSEAsDRxB7Eirpvz6xTZCwfW6dpwEIKDfUXNWRBjd6KD3yjRAI7z9uoJUCFET4JqzrJt8+p6QAkGllOKxkCJoDWhoRBSR/C1VWqdsrrV15yohVjbwjTtoSW5BHFRsa+RtsaMWbaZrFos5BwCHS5boCVhVGfmCkGqs5RAEEqx+Z+84Vr2knF2612r0vkBV2sxfZO6lggD0tJC+ShJsZCFNsBth51VMCieqWjbo0oDbpDCFLidhOOKWgg97LRQpXpClDor4msuGr07rQOt6vKNNGklJ6dP/QLQKVEON/yT/EEzpZpWmuLGUYIxCRWyZls3p4VXn36Vdy3Pspj33HjhGjooNRjIzrVQVdluR0ppoTEDHo2pCb621WNglmzk4foQ7fCtdk+lHXhtK6nrpErT0SZ3AtQSfSWiWkCFIoEwOyAsz9sz8GxhgOVyydmzZxnHke/93u/l6aefniIKWgtaMrVkUkqcu3CBYdiy3W44d/4Cs8WCzUsvcvvWLY6PbyMijONI1/cApJh4z+OP889+/uf5oT/6R+n7nrPnzrM6OGB1cMh8PmN5cMjDDz3MIiUiwkuf/CTb519ABLYvvIDy+b094pA/GGCU1BG6ZP+lOEULJgdlAovN2sn0d3d9pp/vfuM/27NZ+6Md0e1X0nRgfgxF+uld2v6nQi2VmjNlO1KGkTyMlDG79lR3VyDC/OGHufBDP8TLX/okL/z6R7n1/DMGhVzqISGivn+MyXOb7TryiunKdrDE0EypQnbWu4qz3WrXphQuZzgzjLw83OAmBVkdUAgUIlEK3/ieizz/6muUk4K6pW/OfANjsjez7dt3YVPZAYxpjmQvYWY3w+2zWsRANSKSHNC5zIOI8XqJSiKE3p2+StCMyEhkAN0iZFTz5LgbcDL7shPCWC2LfTClbquCH+K16lQhoKLTWSrK7vn4ZIhAi5Crr9/JWd8DasXJjqAe3dlNg5M9babcHu8t5xbs0L05DA2w+U0I+/O7B/j8v33NK+yA7t27Zh9U1r0/CTtpwt3f1cC5PSWXFrQL9Gc8mwuXznY8eumQRy6f56FLR1w4f8Bi2RMj5LFQSmW92bIdR/qzVjEjpUSKBizBiJyqljytwXIjgmMCe839jftnKGNyurTi+xh15lH2yq1YNngyQakb5WZDLGzq4bzqIESDlxwC9UO0zaaI2OHUwKM0GW4DbR6QcR53n1Vr4VBa2DgG0yyZW7N73M0T0BY+LlMoXVWpuU7WrVAaEts7+P3zxIB2KwdR7wq1T1nVDgjbFVQHtaIm+m2eVOODW3YcGIDx48oPSJ0Wg6TGDloouGrTahm7FT0EX6UZUweOfj2wM0RT6NBLMOVJ47ljg0NoZs6hdmjlY+y9IsZYZs/Urg5Iwb43BoipZzabTeVzqJUao3nEEgy4VCGP1WBTMRDGBH4DUUy3ZCVOTI4QnZnL07Nw3Ur0Kx6DsYVSbHlKdE1VQKKx8DEFQnVQgpLFWFFtYKrdY1urGGs7OSotjB3caZHAUIuJ41UnxrGtfTBZgAbT69l0lT3kpEiwRA/VgkftzVCJkASKVnJp69X1og3gONivVamaCSKMpZLE9rGtCWP4cs0GdqtplLbZyqI05lf93ltJJJNZeAgM2Wk9FQQrG9ZCX2POhCBcf+WEl5evctD3lONqTl1VNFfyOFIUhrFQiqA17nSKKIVdUpyoOVHGHhmzPEpEnQHJ2ox5pWimHdt4pqyT9+54unOgxs5oSMT5itAvDEzrzrDeuH6dL3zhCzz88MP84i/+ImfOHPGed7+Lj/7yR3j1pRf5jBbGYeTq1atsN2sef/xxfu5nf4b16Zrv/K7v4itffoovfv5znNw+ZtgO/PZv/AYHBwekruPalSvknPnFn/s5vum97+XjH/sop6enPPrOx7h54waPPPooly9f5tbVa/yTv/N3OFwuWa4O+KZx6/vYow0xEbqO0PfEvkNSIqQw2awJ4Pmfu0PwrlNyet2dIHL387vg41Su416fs/fqyed7/esEfx7RIl2h70nLfnqflkoZK3kzMK635O1ALYX1cy/w1f/7X+fSD/9xvv8//Ct87md+iuc/8S84uX7VWXxfRXvRBWi673aKgRLcEbTbKRLInvSZBbM71ewUCqpCEuXyqJx55YRXz1WOj1ZInaGixB7OHK148eqt6Szcw9aTPm8CZjibz51a6jYtIHtAy526RsRMT8gcU1ELbyPOSBJRkv1bOoomRBZU/6ZONgTdUtWy15lc4d2zaedVagxh3QG9iUH0c6wBqZ2eu52fTPO9B9/NfkY7Y0J0KZ3u3vP6a7kbCN75ez89d2euv6YBu2n+976fBnb9uewA507u1djQfdBY3eG9e0yFBLAw+FTuyBnOPZyIyk6X2mx8s5/F2cw+KqtF5Mxh5B2Xj3j04fM8dPE8588u6XvXlEtEJVEznNw+QYPNZZBA59V5okceVI1gqI5BkAZrXu8Yvtl4G0k5DigkGTPpLFGrS2mlg6DrOvMCpRIrzmaafjDFhEr28KgvJLnTVIUQPFEkTOWI/AlPRt8OgOZ1MP0bn3hBJnDZNCtW7mAXnm+vRdWMS3XmNAakqrEk1ZjHXdYsUC1LVLWFtP37J/bEUw/EQWQxNqzxKLvApdB0C+qCZHOGdzUqqxaC2mFaa3EmLpCSoCVO2WxNMxU8WUnVElCKKina90WPV1dV14bIDrgGC/lEn6P2RNTRtmXVlwms0xhgwZ5TuxeauF2dCVBP6LEwqnjeUlUlpUA/m5FSshlxsJo6SwCqDayIkjrXpqr7ks6IBcm+GRqjKLsswuaXVNdBagWKs50ti8+eg2Bh2RQDSES0krWStYHgVtZIXQytuFR1ElSren1Sz7rWqY5loNRisg91K2aP0TxPz/huxWlQEK0WYq/Vdcktk7vsyi6hZGDK6nbgXNr9ld1cR7H6lGPFn3tj0ZTiN9AyFYsYMCa2UDlsthbKb7pSmfaRZQuWobHu5gQ0aYjuPQtRKx2ELR9unQ7cWg+kGFE/9NpeKGNhLIXNekPOQskRq5PntgNBa/DkPYPmrdxLRgiknRMTAC3+e1+3ikc12iPxyMke612DEBfnkJkxUaLOxbh9kap86UtfpJYt5eQG12++yI1nPg8KSeDK09cmA/vqU9d59anPTf/+1L/8ZwDMgFee+hyHAi988XcA2ALnHTdde/ZJfuPZJwFYKXzq47/C0aVHWcXA9SefJN8+pquF7WbD9vp1lpcukY7OEmczB5BxD+ntTjm5w+Lufnr3614/5K6/6+7Pe77trs98I5z6Ng8tAkgIpBRIy465HthaHgvj6Zbjz3yG25/7PJd+9Ef40P/4f8n7/+T/hCf/2U/z5X/+Tzi9fgPFDlyiO/nV1m6T3rTkmBoD1a+tiJD9lC3iiYMSSMLkgCtCj5Cq8vCNDVdUGWcddAs0Fi6eWzJ7NrD1msINSLVwfXKyQoI5V01/V2BCKc1ZbqPqTj5ly3PHxolDpCSJ3ACZ4CAzIiRqTVSZU2VJcQOtWog6WHhfd8A0ik6OHUDvEbFGDmnRiUlEbK0HNYmKgaLdqtjXPDaHLgEdYlGm6HVuGwfrZzjopPsWdmBb3LSqYHKDdpU+X40lbQDwjuUk7HTwuluSzR+Kfh8VqFJ3K192Jl3bjWnz1wKNaNiXAjT9c/Dnn9rr9y9oD0wKUGRH9ihKH4XlPHC06njHhQMevXyWRy5f5NzhAYtZZ1VuPNkqiVVKSaGDVic0pTtYx0kiFiNxSqMVu2nqLhp4H+NtAEoz3ubN7ZBrq/ZjdSd12hwtcaXpFNvTiiqUmk2LgWspq2kDxAW7QaKHUNsCtu1aSp3C4FYep7j3oNPiFH/QrSRICBix4KKP5ndMJX+qOMBtoV8LN+8+N3hoRKckB/Pud4fPBCo9fNgOfWN+1IG4PaQdMPL1NyX3OPzYO4yZII9le+uEJYIDCZ+zEEgh7mU/A3VXjicmM6ATMPZ7LxPgMEMiPnlTwXP366LgTJn7meIyBq2oBoeeMtUaNE/Ut4Czj1P2WqPQ085hiOLZ1uKi59rCrNk83Wqi+F1thmZAvayRU66W0GShFAOXFo7KXr+U0rL9bbtWt3KNFc+eSNQKi2dP5mjXkT2cL2DshFZqwAqjC5QaqQSKjoTKFI6tVCtV48DKjKPSRaGMamEPCVPBbsOx4uE0F5YjVM02t7WtZWM7GyNQ1EJzjRG0tekhp+phdz9YLLggjBgbsO9mhwC9mOcqvhbsK1vhchzUR8YCqlbOCTFGoVbXw3ky1BSu85IfIGxKYUgKh0LsEvNVopy6o1eUnLOx25iG2HybzpPb4mSw7RAxm0ENBIlWNosWmsTKS/nxY6bIAo42bwVLAsi2zvJIIRGXB8h8gdSRWgcrdSUJVWERIv+P//gvs/kW5bmf++t0n7/BsgYkezKSs/gpJbzeByUXy8qt/px2ZsAArBPL6k5rxQ6SxqSmows8+u/+n1h/8SWu/uzPUA8WyKOPEGYz4mJOnFmJkFlKu5Nwsk87dufe7ONb/27/8+580z3A5N0g9l6aSuH173vD67rrEtwGTwhA7Bmn2JEWPYvzh9Sxcvujv8Lt3/ptDj/0Ib71x/4sF77pA/za//Ovcfrqq+Ys4iy166glxN1nBy9p63ZbJSApTtEOhamMXHUHF20Z2JFeC+eON9xItyiXO1TgaD7ncDljPDmF/cRH7PolGnJoElVVN7vgwNbBYkvI8Yog9jEe2WpARmRykBQv90Z7Xatw4vPr82jJOJWqwUmEXf3limkYG9AScZLGUV0t7EreqIGwxA5s4wzqVHZHML02k9khYc5YSiY7M72j5WxHj0y1CjYTKL17aJgccJnWiv2q+hsFX7ZuiyYb6fO7q3TBRHY0u1WdeDLQqhNAVH/dFDVyMBnFojyCUHwftter6KQOaXMwJQftXQvTdyh9CMw65XAVuXThgIcunuXi+bMcHS2ZzeZ0XbS1lOwZSrAI5nzWERIsVzP6WbeLNDnmsvO/3atHgKQgexjsfsbbKGyepofYJsSQL6Yl81B2DBH1+kuCepFgvWPxdslLyARpW8RupC00afo9r9bvqe5N5K8tYxqmn5VqGb5W1sWASUrJQnm1lajFa0AyAUoDvNG8q1r88NLJY6mttEdoANqRvS+YOyjh6a91561I8CLT1aehaSdtZY/FutoECSSsRE/L7rZyAfb9QS1UpWJaFImTeSHFwCwG+mTC+m0xBlWqg+yw6zoRUpzOgDCF5cVLbhoITDFOekERC7Fa5NWBuwu2baFnMzjedUG1mkZGQcVYa0T8+ShaLMwcqrF+RZVcR7RUch7ZbjeM2XWTJVstz2KJVsGnthIYa6G4HrAMI1ozVqihulNiALdUZdiOljFdKwQPx+yFaKpaZmt0sGFyBStb0YCchc53xeNNuOweqANV+71MGs2WkKZijoRJUu2Z9RG2uRkqKx9U/JCrHrZWHCCK7JwJ2ZWpUAeMJiGp5OphCwe+U3J2UPcH/HNcChB8TdfCxJiKq6IiyZjNoISQGUfXKLozhgaitK5Y7SSZ/DasomcrKu8+vHgN1yhkqYyrSj1T6FeJ1fme9XZra8mZ2VorubosobZzKJkMQUBc92dr2BMLWgeYKISEMeu48zmdQLrDMw4YaqmUOlI0IP2KODtwxy4hjA7pR5BbQOX0k/+AW7/5W7z71k26lKkUSLbu+wQhRg8jBcsgT1AG16kqjK3ihMffius9petYve87WH7j74cQp6jM6gN/iNMvvcL6Ix/h4sERabkgzGYGQtjdV0Np00+bHXqdkbr7kNA7f3w34JO7Xyt3vv6OX++tiTvet/cP5a7f3f+QdgLf9X51IC0CoQ/M+yVaK5tP/gavPP8sD//ET/BD/9v/hE//w7/Ns5/8dcp2NIkEtt/NZjjD3sCY23fxahjt361enxCoHgFQPI1FBYmJFQq31rwWryNnzlKSMl/1pM3auhW1syOosa7RKzp4gmXw7/OAgd+522B2ctfpbJ0myBNwaBpVu7bGijaHSjUTCSQqpYwW1pdCL1s6smVyu06rYp1dAkxa81rrVG6NScZlILFJcqZKJHWvjF27WgeVd4S7A8Sk03lb2ZXysa+yu4p+lqrsHKFOTPbjvS2MyMCyxa1yxQ6cNZzRDKmEKRhFcn27Recc/PnnWyKwfUCr9GINrPz8UNNyeoEzS/oVe13v99Hq/hroZyIARDCCwqOhLUmobSe7F+Vo2XP5/JKHLhxx4cIRZ86sWCws2heSRQJTTAQJJmuKsFwkunnHfNY7E+/VDpyQsvOq7Co3TKwDe3bzrcfb0FBGo+QnT8bbJ4qAGqMYghC7zgBEsQNWvWwN4kzmFC41wxrEQsctnLkfSm3ldSQ0RtESeLz+i+vJzOMqxdiM4kWki1ZCrVaSZ2IDHEQ6c7pjK2XyuCTESVOYUucH2c6ziiFOQo2pXWFjZLCElVJhHAcHo6DBtF15KJRqLd9IlrE6jJmSKxFBNBJDPx0ADVChVqImhMAwjgyD/TyCh7QrMINkz0CqsTy1mO5T8+ieuHjbqTAJwu1AbiDbNlfTWxoILqhWcq47liMIUoN501rceZC9PwExw1qxxIZRC5rtP4mR2EXWm+2UjV9zIW83jMOWYShWaDpn8uD58Y01rpUahGx5RpRayFqgFpJWyN6RJRqgVAIlWwjZitU3k9Tu1wBl8wC1Wku1qXYbYQKY1F04JziTW/Lkbtv6dcAg6u3GmuZWbG1WrGRD9gi4yQp2tRPtugx2Tu3qYFfrTPY0tNXWrSX7u+EWvIixTMa7JfPs65BaabEdu77Tlam6syjmpdra25UfChIm4NbkD+3asK1hXyAWPrPv0amygqqxuqOOaLQKDvOjGbw6+PU401zMEbRyFpUpgaIxHQjZa7k16iI3FO032KIGKQSTjZDN63Zj3rqClFLImtnkyOHBLmSEjP45J9x46hM89zN/ndXpLfovPsvDD59Qykitha5CDZaM1eYzhIhW26tl3BWINy3r7mBrNZZEAuf+0J/i4o//BU6f/Ap4spjExPozL3Dz4x9lfumC11O5NyDcqRvvAoh3ve51/7wb3L3u4++FHO8Gs/cYb/Dj6XdvCCrvA2062H7jz3M2aRbh1lWu/cx/z9k//IN835//3/PClz7LZ37+H/HyZ36DkK00VXbPSjyLv8lIALdlvqbEuoR10Ut27V1DwdoxikIXhIXC2ZtrrodECUI370ghEaWQ3Vb4NiIEAyNdH42YaX4YTrYQJtkWeLUDXB/tEStVCARStHJAdn2RXE0zXaoxqEzrG5BCEivCE2IlRfV2w3PGnBjylhp600tqoeTRJVbKvJ8xDAPjaMmDqYsMQ+b0ZMtYXAtaXSbSzIMbyIhMNkNQUoS+d81saC0zCzHCok+cX6aJHW6nd4ppAputCkTrIma2A6BOEUs3G5ZLMGUB2cTlumtAYIC21W10O13N2ShqZ+swFjZj4XhrLYZXs0TnSaaiDqqDQCjM+khtdTd9kZbqa6CadEExG1nU2gyXbC2ctxnLbg9w5jBy6fyKS+fOcOn8WY4OD1gtV3Rd74k2lgfQxeiVRipdVMKyI81mdMmazhihJXsMt11bCN7X07fgVMXmPsf916FMyTrlaMEydq1TTkC8Y04kdZEuRfOsamQYiov9s4dDW3jXGbJgjNyeZWVKEKjqZT18orH7DBIJriUsakxGBDSZixF8okLA9VXehq66nF8sA7rUAmJZxiJCH71d417RbAleZoHW87QVkFaqjuSSGcZMKy2jYgBxsx0Yh5GSMylaj81ZSpQKm82WJIGuS4w5sylWIiMSqTlQcva6lMp6OxigROmTZTFvB2eLUJIoYwjELlC7TFc7AlZSaJuVIY+UPHhY2LLQ+66zBBoHRBX1Uk7Whs4SqjJVhJKzOQ+1kLPpfhpFPoFyB52ituF2nHNgrKY7KmLZ/2XM1qLSslRInQm+Qai5Uga73zwaK1lLtlBhLR7uxTPcI2PZCbVHimn7at3VVnRvtCUgWXJQ8wxtEzd2q9C8QHVmQKaooUp2fZAvQD8/ChZar9Xgaa075nDKoCdMDJSBUzMmpe54glzM6y1NJSM75rSFxVpSFoizmEwetahOsZ+2t8xG70nOW0a53bxnmLv3699p1+3hHv/82AqzC64thtZ1Q4BKNi2Wh0iqFpdguNpHC5AJCQ/7Vy9RYsWF18eZsu5JXWAmc4Icgxrja8/dysWYs6bTfAQPD9phIwb+dLT6n6HtUQOOEmx9tnZj6s6taZXt0JYCmjO3rl/h7IWHTMyOS0xKoGxe5cu/9F+y/sW/z+JpIZTHLUQ2bIlq0ZMiwViT4IeGu/VjHqEBB3f6m0K/+lZAIKgQD89y/o/9e7zwN/4Wx5/7LKGfkQ4PSYsFBLGEmvZUzVyyx7m8yZDfo9e81fu/hrGfrfB1DnmDv9uo5OtXufpPf5rFNzzOw3/gwzz0v/6L/O7Hf5HP/qO/y8Yz7amBMauXSWI6TJtuuFXAkNhNTpo0BsCdoQ4LkRc3FstaGW+dMp8v6dOM2M3IdU2HotV6TKckdJ0wT5HlomfWe/ULAN21uA0e1TPH3zXvsqsV2RIAU+wcUNqaK1iTiOIeqvjCS7EHLMrRBaGPAcIckR6VyjBmtsN8d26rshkGQgqE5B16hpE+RFIyvfzp6ZZXrtzi6u2tM5mu/LBLtLXsi1f8zxig74XUiVsur/0cCqvlnEtnllxcLZm79i94pQrprOxRnzonVrIDJGuhaxFB3zfSrK6DQrcJtaiRNLgN9+oZLfKk2aptoEzSpe2gnG4Gbg0bQjolq3K07DharVikGUlc7ypAUJtXy0o2Z9kjMKebcUpWDm7fMpVtHtluNhxvBmSrdEWZzSKXzsy5eH7FxYtnOXv2iMODBX2X6GKg7wxw4zkM4gLh1Ac6TaTZnNjNiJL8jPLKNE5s7PIgmJzy6oD3fsd9A8rVzHuvSqTkCiqkmMwrSZZ+buUnxGoqVvPuVYVak2dYNr2XeSYtqaXpNRpK17bwmm5iYk7aoeKHNpZ9WooxUOq19aqa9xY9TBGTAZFSAsWzglOwJIEYEl2XLKSfIiF6Bq6HB1vfcWMK7YEPw8Dp5pT1sKFk63ihxXq65tGyOzUX8A4yfZcoKZOzsnXmMsYI1YrHthqHfRdJWwvZj7my3ZqODLHkGiSQSyZbEi6CGkCMykncIMk8o1KUscB2HNA6WFcGMcMVQ2fhcgdVpZrGr22ilJJ76DuAr7lSRz/UqVMtLwtjVy/fUSfNpkkXonVJkECu1UvHWA2YXAoamobSjFTOFvZuC7hkY0Zht4kb8MEfRalMoSmRXTLTPjnTjsldeJFda62wd9C7xTGHVkjONivs9FZtn4lMyS8iMBZAw5ToM0ULxEMZbkumULRa8ksrsFxd3GPF292rRxDZF9b59anpBhMmXm/7wrGiAT4HNrR7buDDWUxRmaQreBgbdw4soc30sV2y7+1S4HQcnYSd3kkKMwuRVDfYEZKzi0HUSkxFQIzbqQFEi7WvzMrplTXD+UI/FxbMWcxmRJSoroXUQpSMhurg1zZ+6zdcvOeuevb1VG1CXLupSgijMTd1l+0fPPFIRDmcdWgNvP/dT1C21bpCqFrfaR04vfZpnvzJ/wPyyU9wVEF0Zb/fDpC9LmoFCUr1CI7VlDV71srFarU5t2xYYytbRmiMHfPHv5kLP/bn2Tx3g82zzzG//BBhNpv24Rsyi3bnr//9Ha95E6C4j7zeFE++HdR39we9xXvby6eXvY3vapv8Pq9F88jpk19i/cxXWH3rt/Mtf/AHmS+O+I2f+puc3LxODVYhIxA8i9vWVlV3qlxA2BzE3SW7nlB1AmvqDmwS4bAGLpXC815yrTjTv1h0pn3tIn0XWfaJWZ+Yz6KXL9p9i+UpeMKqeJcr2REgIVj1jBCi63ibYxy9T7RFz4hCCMbaR4mejBoheqIN9tlFldPNmlrniAo5Z6pEbt0+9eQ9ZRYjq9kRsy55AwZlfTpytOq5cHvDULI5iEUZcmE7Zrab0cqcqXjky71bS8aYEkgVpe8SRwcLHjp/hscuHrKaz6bzWbAqMH3fYfUTFZFq+AQ8h8PAckiOH7BzyGrcCjVbaZ1hHL2xAeYc+1mUs6KjS7C8SsCYC5vtyMmiJ60DEo0YWq3mnD865Mx8TgpMyYmtA5Mtj+p6/MI4ZpbdzIkaS54C2JaRk80px1LJFKvLrMLhKnF0uOTg6IDFas5qOWc+60i9hbrDXmKZtPUQI11v5bi6fo7JIWzJhpAmvajNi58BuiMrTHbxr4GhTDEQYjJEm1pxbu+37aUdUpfsgqKFQ1P0ReNidDsPjDcM0QS4FtpuN9Q4fm2OjO9V15XZ/qLWpgkslpQQKjHaw4ohUKYQqScINE0eAiGS+o6gputr1HZBkZItI7e4PiS0ctfWMD6PVnj35PSUWye3GcfRi+7aa4ZcqA4wqQawQkx0vRJDJudMqZBrRnOr5Vct2SNETjcA1cJvuTIMeVeKxxFDA9UhuFGJxQBdY399I7WM3+ybuWqlT3NSMABpIM7C1FAsozzgYYu2LBxQFtBSHYQUJmhZWztIB/J7xbJ9ydIKurfUklKczZTgFHsr+F58Mdszy4UJKOGevAHKtjH9NKYxgk1Jo1NYuKoRRZMOBXVDVSdQOF2wxY0saYldqMbmQKbQsOms2obzzdYupR00xQ8B//x9ATi+SWuxYrzGZtm8WSFuF2IH75NbWokhD/v65VpYmyk0n4L550m0lYt0zZGHR1WosqAyI8mWyJqgnkFaqjtf5lgELcxSYNlHRhXWXcd2m70YuJI0oJ2D7tSTFKjCRsUj3V5AnCZz8cLqmNcuYgWjZb1BTwe61LNKM47mM7oYJy1Sksqsq8RgmQo7mQpIqKTOkpsm9C5NC9UcUd87ahKcRoYFtfBaCJXlEuazFX/63/wxukUHMaIhQN1w6+lf5cn/+i+xfPZpetdYhtp73qkiFG8ZJ1b7U11j2v63WqjbsyG8cLNdRAimv+vf+U1c+vF/n8U3/EFOPv15bvzLn6c/d3baf3cDojswl94LS72V8b8LfN4PMH3D77gXkrvX+98S9d11DW/13W/0Grnrzzd4iYKOIyef+k3ytSs8/qN/gqDwqz/5Nzm9fd1r8OFOYvPpmg0w6YURggpB3Qm0vRsInrzntsOBz6zCmZw5FIVQXRKhzHvhaNUx6yOLPrGaRfo+0fVxsl0W/q0WdQvNgQ6es9A6ZZlTY/IUi7pF4hQpKc0xFdMwSzBSJUg03V0woJE83C8hMQyZruvtNkUQmZOrlecaSiX1kaPljGUf6TorkC0Iw+nA4aLn0nYw+58thDvkynbYsllnjtdbcs5shsyt9cCQW0k8mCo0aEUlsph3HB0uuHDuDIfL+bTCUrLay11neQctC7xki0hYRLsl56bJyaueJKcVstu1XJIlb1aLFpRsJdjKWCnJzmRVk9TIxpbRoJXZmFj1M+rWIjohCn0fWM5md5yFdl8mL6tVCcWiOkGylWSLViuUquhQ2AZzqlNKLJKpQeeLxHLZsVrOWC6Mmey7jpisVGN0NqNpQwWvRNPP7TwIwRxlaUGtlmS6kwC2jnGmwS93nedvPe4bUHrPlWnhxRg9C4ipcLnqzvAj5tHVoiY2xmgaaZtBKiH5hq1e/EPqNPHRS+BMNajckDQvwUTUdl2lZD+7lZIzgxYHkcaubbdbcjYtRMssThItzKruNdQtlhFcPCTudLmXZcilsh0rwzZzerpmvd0wDqOlMHgYpHgsX4tpNlKMSKyETfa+0aZhya6LtCzp4tq0Ft5w/UTVST9pTJJMtlrafUgAjB1sfVgVLDsZY0XQRCFYmHi0EH5gSS4G9EW3ELYgFZGWszbStRJE7KhwY5KNFW4NcYxEE1pGe2PwgssVDEM51HPgGwQLp4szweLgVE0PGkJTSLt2MNAkeUTP8rcsOVuZOw7HdtH09wZ8/T/DHDqRfqWBVAek4OWVmha0JSyF3a4yDW77/PZznZiklljWgpParqLtiSb2d7CZtSkArB5lcRBcFYYMyQFhCMa0p2hetbgHDBBj8lBYE4YLXYykNAOZE0KklDmvrt/PqT5Exw3OdL9JxxU2Y2bYjoAy64xliSocJjjqE7lW6ixQtlBiY2mVWRBm3Rzpjwh01PE2q7LxnOzRNDq1OrDDEmRkmjUylVg2hFLppOfsInJ8uKJPkZRgVirzUJiFav18PWRfq2vGVImRab21Tl5tiAN/8We2c1KVLgbmvXU9OloteM83fDPv/KaHoRZ02IKuefm3f5pXfvL/zOrV14jZkvWktiSFnZGt6mq0nf9gkopSdomC2F7LxdrP9QkIHavv/BEu/6m/wPoLX+Xl//pvM16/xh0JfdPd3AukvQX4ux+geP/Ew318yT6Y+7o/+D6+9w2Oudf9Sl73t+Z4qsLm2We4/nP/lPf86I9TFT763/4ttjevewh5dyeKO1++H6XZmjvsjTmAdkC6NMSTVkJQFhUe6gJlHChkZqtAPwvM54HDRWLeBfoOUoIYdYrIiDP8Ibqz0iRdAayhhgGwGJ19p9InT5hrthuTnYh7ealzzWawawuuQW4MaCkml2r1exfzSAyJYVDKqmMzml5jNY8s5x2zeW+hZbXQ+WwWOY8XL1dLEByGwrAZON2MnG4HxqFw4+YJ8cYx107WjGUXTmkVXVS8xFyfSH3yrOVducIYIVp82ZMKLZFQi3qbYU+6bU6mNuLDyA3r/tNq++p0TuM2W7yRRlDXXY5Wvm8oI+vt2rv8dYiYLGwzbNj0kRQDXUwk8TiUWBWRljEtXo4vJWesVcllsEhK2aJq84+qAeegrOYzDhYz5n1i1nX0XUeKieistz0/dzrUEFuMiahN+9te1aLASsZrC+ud3mnrLQ9MkoH7GfcNKNfbDV0olGiT1Xo8ixt6Dcl1J8GNoT2UUkdQL82iwcoveBUmC7228i2ezWuwzyq3e3Z3qS2snV2rVmmFsEstrlNqVHJFq3WPGfNI2W7RktmMI7UaEI4xTeHGXJSSR2rZMtZqn5WNdbRWV7bIS1XGsbLNmaEUxpxBrVuJ1biMziCZlREdLHXfOGNUI6UYsGuhV6ElvUCjMSxxwGnoahvGPAfPPvfieSGaZsTaK1b3ThqYF2OmPJQqWPZxoQNdMuQzjLJEFTpuErnqfVOVVla3OBiYcOxkOO3zW4KMCK4vFHcKXItXKkG87aZ64pMzWC2U3fSKrXZo0/NpUVo5TE9un7xP6y1rwN2y5awPenHQ0MpKtBA5xYCX58bgS22Xvan4LzxppNYp1JSCHQo7I+C4sGkN1bLuooPOVoutVAxIiDtA4uEPwYT8qkjnhlO8l6sfFCqm6czVEncoSgywmnecXc2YdcFBaTU22TW6IXj0QIQuBPpuRt+dI8QzVJ1xenqW5Y3v4EQToQycXYz08lvcPl1zenIKw8AsOOAKwrk+sAzGJJakbLrKWIVBhaiBPiZC/zDHiz9Cz5yD+CnS+EliuUUN3lM9+qSDH4i4jtK0tbVkxmGLSGTWd5xbrby2nRq3XQuzJIx7yUl0zbI4gBdnIqOiNUwAvyVT4bq4oAUJ6vNsHSG6rqPvI9/1Pb8fpDLmSo4bXvn1v8v1v/+3Wd68hpTRkpp8Hbe+x7APIMUZanPiNFg1iankhwqiSkrC7OJDzN/9AQ5+3x9n/u7v5trP/iLrJ3/X7EwL1TQn5c2G3uP3baO+7r16xx9v8IFv/n1vOb4G0PtmH/OWtMibgMo3/Z05RPtuwebZp7n+8z/D4z/6Jzm5eoXf/Id/121gcxTNbc3FEz/U2sVqrSTPJa5+MLcSVV7/we3Urn/z+XlHL4GxWARh135W6VJzEveel0elrK6y7Y3miOPSMWMjW3g10AVPKlMvvI47+YTmVVs1EXE9Mo2BtHMCInkcgYhqJYXIrOtNFqaF+bwndta+6vBgxWJuiTG12jzEeSDMuqkWNBjQLqVSth231htWQ8+wGUkow1i5cbqGljXvEbskwlHqOTtfsOw7y5YOOmUpt+dXqiXiTu6qnzVGUNi8FU/iLaVQx+o6bfuzll3DDosutoiZeoUYi3wEDNyOgyXgFi0MuWEbS8wdxsxmGOlCJPQGxhtWac+hdaALyRKAS86MuVid3qrmvIqB/ah2/qRo0rzZbGZg0mtJxhTuWumefLpXv7iVHIxT1QiX9dBKCBbTi6pO7xFfD61KzP2O+waUx9euE7Diz31nRU7b/wXEC1jXSbvRJnjqolGhYoVicxHUwWUNPpF+g1qrpaSSps8fS7auJ8XCu1ZWQaEKOQ/UYgkwpTigzJVtLpa4kQvjMExUs6iXNhKmRWZhXQeRfg3Vwap6K43isY/qYM/AiYnxEWNdy5Sx5Q9RsE1iyBm0J0jn9+ZllSbPwinnBoTUwhHm8VaESGsPFlq9NFo4fF9H10TbrkOR5ExcD3VOqUvW9TLH4RFUOrrwCodVmeWrxASWEdeCduLlgowOHms1obgWWlnI4l56mAyaTGJoA1/mYLQEqakUgShT1qKDDEWbjMbn3NhHq83JZNyDfycEaumouoI6ImGN6Gib1s1LNIrKsvPwQtieSGGfsGOGherJHnb3IWBhoWCHkBU/92SaKi5+t9JYQiCream5FGqxA6V3gN0nC92kEEgoogWkeLkIA8a5KENRRoShGHtZinXgOHtmzoXDBUeLzgqwayWJgcmYAl3spvBVnxJ9N6fvLxDiOYpEjk/P0PeH3Fgnso6cXZ2jr2foYqZXA69dtPlKoiQp9NHW80wqcxfgp2oVGXpWXIk/zIuP/llEZjx89VHec+0Zktz0CLtOrKKZOdwBa4yw1fjcnJ5SxpFZv+BgtqTzkiQl56mOZ8SMmvMG9nz8c6dsdQzAmgNwQK3nTX8ka6gnFknQkdYiFIU8Vg4Oz/Du976LSqFPldPPfIQb//1/w+rk2CImUihY2SxzrpzhbtEIL5Elot4i0qUSQcDbRtZSCXHG2e/9Cc79sf8VdStsn3uRV/5fP8V47So03DLpJe9n7L/27ve82ef8XrCHb3Wd8gZ////V2P/Ou8ClgzT3SadXb776DCef/E2+6bu/n6c++hFuvPo8sKvBVyto8nC2O44GFMU6aO0ZbtWmnd5FYBoE6qTSx8CtEWo2G2gGMUwJXS0S1Fh3s2dMDr54pYrmpE4/8+ihgFcg8Raj7IgA8OhRrdY2Vj3BpVTv2pUYi9md1tp31s+8HWskdUI/S0gxJzolaPIWFaCohdRlF03ab7UcOjjUxBgrG60M855ZF5nFyJAzKtlIJynMZjMePnfIpYMZR4tI38lOIyi7rnjBDguCJ+y4gnoKMYtWZ/4qZRxdn++yveB1kr0jnsnFGvJopcaCS1vs/A/TPAtjzoylTMC9qhruwCJcVdTb6noVFLGoVa6GT4acDd+UQuo8SThAVIvgSh2tgk1K9IuebtbRz3u6PplzI1b719oRu3bUE1nF9bQQdhFkB5Pm6zTdvDGS0uYytCRLTyZtcsP7GPcNKF9++RqdCpFKnwJ9AIlQpNXw8sMb18T5Tq2+UFWsZVXRQPaWdnXPJhVPeSxegws/lEoxIFlVPWmjhXO9rmGtVuctF+tugyKlGvNYd7UcW+hYHVRoLS6aN6Y1Nv/Gf9YKZKsnRjTNXvXqrRGjJ4pnjlfNRu2r3UNopT1UXRJgyT0h7Aq9iyeF0JisqWySs6PiTeZkV7algcnglFvr7908LHv2YZfEgBJa7T7t2NYlGz1HmT0OcsBYz3CSbzCXE8+g3k5z74/BgKL6PdDAooLXYtNo8xPU9CfVjai9TwnBg87qi8YTLoIbhRjDHoDzErNawLsEtbCReDkdFXUx+ZJb22+nlm+ksCbKx1jqS/StmHUtEJthMMAfJFjdRXasknVJCVNW5ZgrMUT6GAxQRisBkgT6vjNtKsZIa21hEiUTPBmqkkcz7PMUWM1mrGY9s66ja8Io163iiVFjEU5HRTRZdmYV6pCRoHRR6foVB8s5F45MY5XU9YhBCK4h6lOiTx1divTdkpiWxNQzqjCbrdH6VWY3DxG2HCyvkIcZqjPqJrFJW89MVOv+AXvsotJ5GF4DlJooued08QTrmqiS2Ry+l+21GR04Y41zN9r8S3ckWhanPdPN6Qk6FrousZr19CnZvqwGzBvDj6/n2t7fgKrbnlJx0HmeV/X7WX7bT7C++hrlqz/NmfgZRE+msFDDGCLC+UuXOTxc2Vp+5Xe5/rP/HYvjU8+6rBQyIXi91qZNxdZPKyXWwKBEM8oFQbO6Uw1VIpf+1P+G5Xt/mBf+9t+jXr8+HYb+UewubPfHbtwF4O74/b3eo/f+u9vn17Ob9wsy98HZmwHFxsz6K+XNXnv323R3nfccd/9ceX2m+JszlNw97f6z0899hssf+g4uP/5err32gqvFd99rURSTXQQHi8WTO1CxQvTtg3X3LmseYZUhQnPyYQJ/QYXOmcY7dZIGnKwlqp0XwUGCATizwU3zlrDoQQNxKBTNSIymIayWfS7BwufG7ltb3hJM7x41GWMngsZs0Y5+QRBnYBuhgtJ5i2WxePpkw2FXUUO1Tol86uSNqJJQ5inRpWCtAmNAiC7dCvS98MiFFe+4eMTFsysODxZTDehGnEz/aVPW+5rzuS/FGE/UwtvjdjAQqS1fwxsnKO30Z9of6hGndma4Rj8FIcVKSt4POxS65ZJSdsCwlMpYipFvoSMgFK/dlmtlPWZOtls242jabgpdCsz7GUkiJY/k01NAGUshpMhsPmOxmNP3PV2XLEzf8IPbPjximbMlIlKDS9N2ukr1e7bIjd2rBzWNbJFgpa9cP5lCnMiX+xn3DShfvVboQ0cXZzahUlHJhrpDADE2xpyUYsh8KrVgHUsqeBmX4BM5pVjYQhL3CKsS1DV47JIjdt6flwBxjVvxLFtjF4uHmJgWzj5oY1rkdepRbRlc2ZOAGptqrE3xUkTV689YpEFAE0LLUnYtRrAvtcRWD506G2eljgpRCyIDQdLkuZVaGR2g2UV74UzZGQ2qWtcPN7hhCqe5B+1hY9MuQovPWo3F6LX8nArv38+FD/wIF57oufo7n+L4i0+h8hJStwTpTPcpbfPbnFh42ZN0HIQnEQ7Sgq5PrMspNUcDb+odSdxY9Z3Qd4nozkaXEp1gbff8GQVRYmfF6GMwPYynslPFn3tRxprZaiXWQAwXub3+dko5YD0WZvH9pHITdDQ2HAuFGitm66cWJZedWL2KeYteIwRRZTlPRBG6aFmTXbA6dX2KxBTNaLS6kr5WBq+NEWIyLy/BIiUOFzMWfWKWEr3XCYOK9dOeURTWW+XW1tditjUgYrrDUisVk3OklFjMOpaLjs4NQBNChWQF6VPq6GNk1vduJTI1F2I8YTF7mXMHimqmi7cpIXK67gzQB/VEGPF9U1yIbgdTwvGUF8zr0imPlN/luH6IoetYrr/MrFq/5Cq7Ml/tdG2ShbaHAnj5oIEyDnS9MJvNSfM5pRSvG+rGkHZw7Ei8ILJDk9p0QImbwzfz7v/5X+Thn/gAIVc+/Z+cYfuJ/5RVOCbZCdeoRkopdKsVWQMH2xvc/pf/iDPrE4pfX83aCHVLWiq7MDe+B/yvrpcDrEuklYISJfbC/PAihx/6EZ79a38TtltC3+/A5BuOezCQ92/X3+BzvtbP2B/7H/DWoO11/77jLW8FSr9GdvN+33rXa8rJCeOV13jH40/wpd/4ZQMRtlS8YLadJcHD0kWZNHIN07Ye3PvZ1yJYof1gyYVjKbSi23YZLfFul6G9a6Ho1VDYhcPtdTvdm9VPNk29VtCm7xFzrKczwU4IQL3cVp2SUiOChp7TDON4llzOE7vKweImIaxRrVTEK7wwAbj9Mn0GWOsExgSs8kJVLwIuu0oLMSKdkPpE10UHopZX0PeBhy4e8Y7L57h4YcWZM0u6LtG68IVg9WgbS9hAdctSbiXtUCwxrhZyHqcyZFrVM+Sbl+zPX12j3QAllp1l9aSt4UM701qt4BDM5rZ+cZs8stmOzDprqNBJMQZbrInJ6Xbg9nrD8XbDZhwoBWbLROjnDOrVULIy5sJYMhKgnyX6mesm+96Sa6gTUSa6u+YyZl93Xn839jvntfk+VJfD2Y3bUjWwUqk7AgzTjL+dft73DSjHuiDXOZEZnSiipygDQbwOpBQGqRayCtEfnDT3nRg70Oq17JQYKlMh5do6j0BQQ/IxJA89O5isxbw0p2Gr2sbUUgldsPB6sY1Wsi3glBQl77RU4h1LPKMYwWnd1unE7tW+0oom5yqmZQxNZyKkOKPXI6Ks6LsZXdgi8SZST4l4u0WENGWe22EfJBnwdW9RYmDUylBhGGGcQO5+aMMecxDT30X7gWsYjbFpc9yuP4rdhDGnMFZjeoJsWXWKPvzNvO/feZx6Fj70J474hf/gnxNvCbOqdHE04OpJIgBjyXZK4sLuIMxjxzsuXuCP/IE/wEPveowrJ6/ypU990rwusJqEqiy6nsP5nIPF3Bk6Ky9RMYZ4HDPDOLLN2YyRYMBN1eqcxsBIIStsx2yC7rEgFIIkzm8KJc9RzWjpoB4yDqecjlNyLbOopKheSLYyVgeo1fy1osmK8IppVrpgGcwxVLrk9VbFwjshKolArTBmC32P9shMHF1huehZzjrmXWDed8w7YZaih4CscHyQhMRIDYFbp4WNFGr2enLRl2sVtI4gkaqRXAOExGzWMUu7osrGbJh+K/WJFJOzupnKKbBF6pqoG/qQqZLp+zm1BJbzxGlKlqHt3mrFQLYxt37uVluHxi5AjCOPlY9wdPOE49BzcPwJQryOqnjHocmngSZnwP4d3LGIAMOIMjJbJZaHc+Jywe3tsZm3Wg0EeqkgVSU30Fvb3gAwOYOGOZv+cQ4/+B6ef8nQ3zs//D6+/OtzJFrB+6laQFBUIl13iIxrxic/xuHV54FiYbUy0kqT0FgALDsccAmFOZeTzkg8KaOaDrbGyPzoYc79if+Q8coJoWbou68NJr0hEHTAKG/2mv3PuNeLZP8FbzL2Xydv8pb2VO4BhO8JLO++TvkaseTXDkIbm5avvcY7vvMPcPBz/4Dx5tVpjYmTDeKJIool8LlLsTuw4Q72ue1NdYlPlshYixMj0sgwLNrT2uvuHBXUiQRjMoyFr5aEaaDHQFXTUDZ1aFFcbuYNCCqIWCQjRSM47H3mICYJDFU4PX2Mz5bvY/74Bxivr3li/Qne1f0WGjZuTyyJpVX5AYscquxAsLVsdT18rZM+T9TsnpXxc+1/jE7Y+JkTlPNnljxy8YiHzh9w4fyK+SK1I86BagM5OhFFIQRCseegnshLxUPdZQJY9haZcjEao9mAZ0v4mypTKJ5hb0xxrpXNsN3LoXBWue+RLGTvfHfrZMNmLIxFmfc9tWaGcWC9Hbh1csrxds0wZmZ9T1+EWjM5C2MV1scbS1qqla7v6PrEfJY8m72B6jBF+NTvpWTrEWTaWej6DqpSpek3A60dbkPkTZZhGY/ijR52rycoId03THwbhc1ZMXJEyStDvumY2N0khMGzxAIh2AHQQqLBeHpP2lEDm8U0gLVUK9XjrEHLUq2qEzDUwMRSBFoGa0WLlcPJai0TS/Fw9xgsm1ut96e6riIQyN5CUcWNgjIxoFUV9UWmlifjNRplqhEo1QuEpo5ZWrIIZ+njeebdAYso9PGUudwmyi3GekxVywA3tq2ad4qDVTKoeWmjwlCVcSZss2WBl2rXGoPpLpIfwSl5qAXTsla1dnvNqFU/+0LTQ0igEthUUEZntUauh1t0l4XXXoOXwoKHvvFhFl/MzOtAFypdzBBMDCrsNCXZRc4xwLIXPvTEO/m+H/hOzr7rMcKs5/3vfYgvf+YzjNXKK5ScmYXIar5ksVwyn81NMO5lNkrODNuB9XbDerNmm7eIVFIX6CSasQkBorAtlZPNlpP1mtPthjEXKEo4+1tIfSfCiA7PMxwHNus5cVMYPcTSp0AXlbG6Q1PNWYjBOgZR7d6ahCAGiChdsr+rmBzDMsNN0G71Dw2A5VrpCCy7yOrARdOd0Hd4fblECoKWQsTKMlkHhYjERD+vlDCyySNlU8liYCpUJbpzUmpknaFIpEtzFovO6jzCrkxI8vIZIbrXXtEyUsoGqSNRRkQKMVqGoMbKcpaYxTQxisWS7RlKIY0jTUtjB6jY/gtWQD7Jqzw8/jNUhVt6nUEzWQxw7g5hN1zsDLWTJ8wiRCwhToPQz5csDw8Zy0CH0AvMPDHKtJ2W8JJ1El7smL4qCCPz+hW2zzzD+e98L+dWlSf/3q+ykNt2TdXY4UnCEQKHh0vC1S/TP/tZklirVsoAmgHvZVvxEkTi4NgmK3o4qThQEz9gCkJ45Bs5+0N/jsXj3836mRe5/rP/5C0s7NdDHd4Pmnyz1+qb/G4PoKm+/mdv+V1v8tH3BIB3AVW56+9v8TX3Hnd9z71e7z87/vSnuPwt38blb3o/t37jVyeNuwfIAJ2cCFx+ZQw6O9ZavFxL2wCCl7EJXN1sORm8kLC2aFu96zsagMT3sx/60sKT9rnRE2BN++jcYyMYxLWRTqmH0Jp8tARB8XsTCDBUYczwXP9ufuIv/Djn3nOGfDzyU/9xZnnrKVbzW8RkpIfHUyeCxemY6TGFEEx7X+vUsc0tghMrxsJFDVMtzUAERo4OFjx68RwPnzvgwhkrjdMeTsvPmFzJOyQjloxixcjtWZRiWEGLUvNUlAgrD1e9c41FyGoDonUHJttrp5bPBMbsUVnB5Dm08m+JPgib9YbtZsOYMptxS9ZKN4zUmtmOazbrDafrDQpWdzpCHQe260IdE+vtwDCa3Ad/Tn3Xk0Jn1yNYGBsll8yoyjbnqeRijLZe5vO521rx5GI8mrybN3PSA6qmsTRZWMuDccdImJyU+xn3DShVIhrmCIdEr1WYOuvq0iXTecVUCEHpxRaUhEAK0RNUmudirKFl/9rFWrFhT9efNBGgobIviEWysRrqWdclU7MyjFu248h2GwlDRoIVYTXS2TN3fVH00cCsaRIhVxNXW9IP1GLf34C8rU13x5yF6kJg1sPBvGPZz5mHaMXRZ4lZmJHywhjcOtoJTSZQqbpjEZU2F3YgjUWpvWks0F07uyCtFZJYWLJtWLEF7k6sM0teGqllt4XAqJCq6XZSFTq5zbtu/jSnv3SeR77rg9x6GsrNLatZpdfKMlZ6MQMRtGWBmRxBRQkaKNnAwGIWOffQw5y7/A5if8Die5egJ6xvn7AthTJal5C+n7FYLAxQxjR58rUUNts1s9PEbB04XQu5bAEDlX2XSClBEMKY0VjJdSTX5Ie4kGRNSl/wpItMv0wklDED2RKJrBhxabaToOp9Xy2HvxW7rrTOOVZaBmebo2t3WvcmqQY2UxfpRThYJCtO3Sf6JCz6ji4FUick98YDQh/npNRZgeTY6sglVjVAHBjrhu31NadjpQamtpSokAlsa2CTA8SO2WyBJN87tAOnZcMb822byMLClYJIoYsm4l7NFWXJOAzELoE7b6LG2pfGlqvtM1F1Z0IQEima51XLhkFHynZDVEvCiW7sTePkAvc9myReW8mc7cJ6c4IK9PMlq8NznG62dFyjr8ocM9gJM6RVLRGstYFsLHTQQK4bzvFJvvJf/R+R934/af0KfOUXOJRXLISdClIcoSjUmlmGSnzucxxtt6YuCt6VSdpRUUFlr1zVdBO0+Ls4ehYBDYnFN36I8//Of8rt33mK5/7630Jv37T96zXx3tjIvtEvZAIgb3toe/+bfoG/dv8htZd/PUD37uu4x7+/NkLxDb7gbXzY/vXsvS3fuEm+do3Uzya7Ck2q5WSJi3i1ClPnVU+ybHUh2yFsnWwwxywkXrp1i1y8L7WaTVXv5a1YdrP9rSX7tcfQQuBKS0hpeyhgy4Ng4LKoerJa0xla6mEQJziChTWDGsue1Bj+XJfURx/m4jcccixw+SixenTGzU9VStkSAwzjGqHSpQSu73QsvecwumxsKgPk4WG/1gnZehQtYhrSw0XiHReOuHw05+LhnOWiN2KJ1hHI7qWRQcEJgcAeGPSHVbK1OaZ61EBlb3lbeLzpB5uESxAodcoBafKWVme61EyT1wiQusg8wHYshGgkyMFiAZvBImHbgeNSrGxbLQz51MrlBeuc1/WWeJzzyHhqet2xOrlVlEiylpFqEbFSqhVUj6ClsgmRWCviGKbrE/P5jH62QKJl6Ie9JV5Rr7PcZBCeyOUVAapHQo25tLOnvM39f9+AchZX9KGjj4FVF+nnC9LsiBgSZw56Yq/MZoF5Siy6GcvU089mLPoF3/DEN7E6fx4VZRi23Lx1lVdffoFhs/ZNFwkhGtjwzSGoFewMwVhGP9RT1zOfLehmC5BALsrJyRWefuaLPPXkl7h9/ZjtdkatW0qubLfKMIwe6o7T4gvAABznzOkwUjaD1Vf0EIED/ske1yqUavRy0ZExXGNMG3R2ntifcVYq2iLo5x6u3BK1QB6odbREpJpRtbIqBKOX+5SYezvzqqbGsBKUlYAl8kgQcHZsB0qhaVlMCmKZ4Li2tAYhkqB4m6oYiQw8Fp9k8/P/Bdf++TuIEnhPfZLQF6IGZiLM3AAkCdScLaFKvegtVuer1pGrLz/HS899mctPfANh1nEwv8Q3fMu38uUvfYaUgSGz2Z66/2q9kltFgJZ7GFIg9JE4WjmnUoZJr2n1BTFDmYSQhRDFQ9BKlkIhYw2/DCjFlOi7zLy3UHRVQTDNUmoVi1XJLqUQn0szdt4i0qfQykz5XDt7IAJdSiy63uqMdT2xt05RfReZ99FC9qGxCHYfISWk6wiYB9nFHpFATN4WNGVuF7g+ZIbbA7lYrbVaPX9eAputcLpNjFUIfWI29/3ijIVgZZNKrcS9QnpFPGzWWXvAmCD2PSkk5rM5s1nnyTJKdgc2V9PxBPHswaoUqcSUKLUQxZJxSgXNmVAacLXSHrTIQvR5w50ogVbzs3nLm5u3YRg5ODjk8MxF1kMmvPgcEWWGMg9emsUPb6ssYA9jNIUMotWTZU65rJ9g+4UvgBREr0NYo9Xqa5phbd555GzIzK+/YvhQC7VAN0vkcXTpitW7zNXq2k0kUtUJcCh2WPUPPc75P/0fkS5/C1d+9ue4/ku/QDpzhnhwwG7Hvs3RQN0bYaV7gj7Z/6X/Vfd+dhdd+HuEGd/euAcTea9xP9d2B2h+k++6j88M8wXh8JCT6y/Rys60z1B3lKYpn8CUV8RoeiQEvPB49TbBBLg1FL587dQLALhdaFKiokiyDi3JQkl3aNcm1mjK7G6A0uxJ2HvmIZpjHBzImv2oxM6u0QqT+DWC6TpH6/u83cArN0ceWfXcfO2YG194kke625aMWgvbrbU0DdN7dedctetyh7TtEyNBrLZ0A8smETE5WB8jR4tEWi546MyKS2dXLA/mHvX0pE0vBqzq8jhnDrXNv4OnWgqlmP4xe3KfyXDEk2DVohni+7ZWdy5xBCmuwbTZrF7xRaLdb5MYQCNZcIAW6NOco2Wg1hNurk9AK3kzWnKxFiqZIMK861jOZ8Rosr1NVjaleOKP7ICfa26HIbNeb6zV8jhQNjO23ru7C4EumkysS4lIJMXk9tyTb/w+m961sdPNGTdHwPIsWllCdIctJu3bfYz7BpR/5o/+IVaHSx66dI4LFw5IC+s089orV/jgh34/s7MLQgddt6DrOrrUWdp76unmi+mB2CKzcGgZB3bsm3ioTqYNL3sT4D/x14ZpMTnuZtgc8+XP/Aq//vP/gOe/+BR5uyIUo6ivHx8zbDLDWChUiljLp5vjyHajMGyN+chMYe/p26SFFszLWQ+FQU+oMRDTmhRvkeIhIS2JtSOlXe2mpB2BaH3GS0JLIdQApVhrRpjaYJkeMiBey6uVkPBIh4W/aYeaevi14ukPWG5pBKfjjS0RtGbr16rqm7Dj1ubdXM0foGfD5fg5SLc8s923SxRC9aPXbIAL0Q3UNkbs2mtX+MS/+CXe+f5v4fy75oQAF97xfl599TluX7lB7SNBZ4zDwGbY+pyq1SwVr5cl1n1FvOOEBLEexxVyzl7/y1GJ7kBmyyy0KlPm/YoXuS0xQGy9nPdYhZapr8VDQ/4IEDRF6ljREKi1kPG6XaJTTcko0IeO5XzO0WrFcrmgn3X0fecJMaansYoB4sxfMBCZEirBit17uBusaK0gHHUj57dbrp1sOV6P1BqstmTy644dowZORmWTA4VI382mdTJ1mSpWO7LT4MbQ9koKwiz1ZJTYd3Qzy1Ls1h3d3GrI2r36fqyKlgEJPZ3MyWFDoWWlBEQMNEf1doKuK+vcGSC0vEkPB3qpk1rxZzId0QzbNVkLqzOHrM4eclRO6WZWd64LjRHSySakFvoLSidWwDwGJdO2aaHIsWtkR1s6YoWpxb0CdydYlQ2zvLW+t2qf3RLRphC3uk3dJ/BaQp00eyusvuUPUcolXvyrf5Xtyy9C3xNXy8mWfG1D7/jjdT9/q3HXdb/5Z779j//axhvNxlvM0hvh5umXga99+GqNkTIMDOvB14gzjWo2iT1ggwND8IYIeyeS1cOAGpQoRhC8cDzy2sl6stFeV86iDA4Uo0tpovOcSEvKaZnTzTVjStaZrt3PhiYhgzqBstbmdldjwZPcQqCOo4PiDd9087P88v/tnyLLOfL8V/jW9Dlm6TbBy185sbgDiQgET1JxkFa9ugpqZ4W183VnziMnDRSnZIXRH3v4LLGPXDq75OzRghgtHB78fWhLxNMdE1qqV4Ewe1DUq5yoJRVq3SVRldr04NKOAauH66Fx/HG4igDKnTpYexQ7J2jMhdPtFhUhBesUFIN4sfFAoEK0qKHWYsXXMS3obBbpeiFFJVVhHHGA7o1e2tqqhTqsKWqNFbSOaJkRxkxYFuh70qwndj3z+YLZfEZIiVK8gUiMtDJUXhPQ1me1/JUmZ/NDBKSx5L42RGgF0u933Deg/JE//YO8492PsDq7Is7mTt2oe0IJWpmcBhAnh0m8Fp0DRTGKJMZkn9McZqa37pzWVg5Adhu7vXD3L9t6/eIs3/wHfpRv/MD38OynPsLvfvQXuPH8i2hRTk57rl875va2sEW4PQzcPB3IgxXwtjqgrcSQtq1sugIBEdMeiIcvaqmcrgu1ensmVWBLJwuCektEsRIKEgMpJmqN1Jwp40gskdjJ1CmnVOv7XdVaQcZqzF2rEWVJLuzE3YIlJfjGzLm1XrSi6C0kYr+3GlVJrJf3Jl/m2Uf+Ld79H/wIZ84VXv6bP8XR7/w9+nhCiVZ03TSBuwQ4EbHwt+z+LUAdCi88+RSf/Ni/4Hv/xEWWh0fE2Vkee/wDPHn71xmzEhXGUsnjyHYc7FEntU4QVAIZIRNiJSWTHJTawhCVnLOFUs2f9GvYJUGoWNgmoqYtiaC9UKO6xtCuIXr4tar3422G14X1UWGWrNVgEWMWioeqJcAsJo5WS44OD1gs5hwdLej6jllvSTIhQqWVQrK6lCl17vw4oCQ4i9E64oiXfxJWIXDu7IIzN7dcuzky5oGggagdECwUFoSTTeF4bUlcGjpS57pArWhpoqBdpmnrG2+dM2x9pb4jdh1d3xG7RDfrCCnC4PUWHRDPwpxFf0BV68u+HU/t4IgRJVpdOoEaK9uaqCGbodaWcMeklzRyx/dEC4s5U7kdR0bJdGcWzA5mHOYjUp/8QLID2nTDvvNlV/LJPtpMnoXuhFwLEU8kq9mLnjsmDS1ZwCQn9eqLxIPR2p+lYCGv4ro2pyCbpy7CVJewgU3TrUHoZ8ze/W0cf+lLbF960XTmyxWtUOa+mduNe6K9+3/JG771bvZx/4V3f+D/IPTk7824Y1K/dsjubob9WQppuWJ+9hLKl6dXtLByKeZ0It5Mob3PtcNEXGNp2veoEIncyMqnXr3RSDArywOgTa+GObu1Ej3zayL+xCRb4o48zvYFl5D55bDrJlWJ0xmge/+FHdPpJA7Vy9FJQsicnz3HI+t/TDhV8mxLmW1JYwY61ptCzrbHYgzGiPmxXxFvsqE7NlzadTKBy+Dl81SVLsJ8ljh3dsVi2RE74ejMktBFokvlJv3onnQN3a1i8XBdyySvVdBi7RQ1GINbq2W1M5V4smtsyUJggC4Xq99o+NKfQTSSxzKmEzlWVK3BynYsVIHDRU8QYSwjwziirr/WOgLWFjoFC2VLtC5FXafMu8B2O7p8DZcmGMGUtXo940xFSDPXd1chSSR2iX7eM18sWK4WLOZzZrPoGf+NQPeamaGthd250LLipyL5jhV2W8ulPMhk/+5n3DegfOTdj7G6cIY470BM1yAKIdlDEqdrG5OwW1Nt1/uWnZxjmYDbHWJv3W3vCTHfcSXOlPmngLq+C1Q6ZqsLPPE9f4Z3ffB7eeHT/4Jnfu0j3H7tFRaLjqu3MldPR65sB24MW24OAyfbkfU2sx10R/OGRm375g8GBGKMBBJQyGXgeJ3Z5sJmyIzbSl4pZ5aJ1SzRR6BW02SGREiJruthNiOq1YkMqlCtW8g4DN5LvImBzaNqhUsb0G4FWZvn1pJ31D3d5v2aftnCqUmsE0uSymY8y+J7v431ew54bQPv+5/9KM9/+he4zDX30CJSx2lBTe27XOBtHQnco9PA7VvH/M7HP8o3ffO38s5v+31I33Hm3Lu58MiLvPrCC6Q0o5sp22rgcGS0LGHBrlULgUoSZZaEmgWtxq5prnShmwquSjNWLVHGDWhRy3YvjjKqeMHXIEhWYmj9uT3pBq9D6uFcC+8LGmyzZg1oyZaMEYSzh0sunT/L2cNDDlZLZvOO2dx6qAYjIY1hTxGrUGpF+6OY0bW2aFZgFtdY3dG3XoVZEA7mc84erlguBk7WI6Eao4lCHzskCNsCN04rtzeFo5zp5gtiiJQy2h5wr91alwVil0hjoXQWHkk1kLqeLpk+te+Sd80wLVIK5sCoCl1c0MU5OSspzkh1YGzbNUSqJC+Xo8ScoZrDY8XaGwdYPAkuTwdkc0zabi515HQ8ZlsGhpoJLsSvxZ9NYkKmAXtOinJH6QuMQc4oRZRRrWFC6yyi7cua3RAhAfM6GrD2pggShTxmt1ieLFh2UZ9mySz0ZkxU6Wac+4n/Hbr6Zm792t/A6RTCfHanEb03qvw9Hm8GJr+Gj7mf690DEK8f+wD2zT7s93JiZG+u3+JzZXeeiAgH3/4hXnnhq7z0xc97QijN97T6s/YXL7Dd+D57b0WRWrBauw5kENZB+MyVY146XgPqbJ5OlRkamGikRVuvFU+giZ5804ikiblzckFBgrqIyJI9jdTwFoVBJgkO/n51p2wcs3cEysTUM48DsKESiZoBJcYEUuliYjbryarGyCXTZpq+dFdiptVuNObVE4Km8jythI2d2X0vsOqYLSMpRbpZJMVuShyahoe6G1OoTiqptyduybsmc7Q60BXdsak+N+Ckke7vDNMxWpk0j1BKIUqYbLf6d1jPbG+eoWK9sWNkO46UsbAdt5xsT8hla8nBQaY63GCdi4Jg1UNE/Wcegta9hKJaPfPdpIB9isxmPQerJauDBYsDy0tYzOf0/YzoNUFjah2VgnfSSbTueW2N2x1j9UkbWG+OMkYATm0X9/73fsZ9A8qhBOh6wFu/TfVEcDai3vnV00bd4du6W9M7r5/dg96NfVnz7j3+bXt/+lJpYIsW1g3MDh/l8e/5t3jofd/Bk//q/80zn/kc156/yY2Tq1wZCtc3I1eO16zXmXG0Te7Om5c1MF1LjInURbo+OoIPxgiqQIU8KLe2I+NGGYuSa4fWDukjqz4x6xKhM22flQqKCB0Ra5EVyZQ8p9bCOGzJxTr75HE0D8p0xbaBqmdvN++yaUdavU8PQe44G3FhdwNuI4fpKq88/3kO50+wHYRhdY7an6WrlmXXHqWxeR4+N7/JnpTYhrUHYjq0Ky+8wm997CNceNe7WJ67hNJz6dHHuf7ay9S1ILFDwkDV0UFl9XCNomWEugOVXUzk4rq9Zsm1ZbcH91rt+oIY02dZe0B0A+0gLfrySMHWqzHeBnZibEXg2zqzVosSA0mhxEBU5aFzR7zzkQtcPH+O5XLObJbsGqJYhydRS8iKnW1qSTQdTIuqR/9LCKYTxQFlrQXr5W5sxnLRc7jsOHM44+bxhuJGxuqdGvNZJXFjk7lxWjg7Fpb+ZCyjf2op4GUuvFpAisRilSQ1WjJRipYwF6LJTMTZQAvtyqThUlVrH+aSgSDGGA5FGCRSi7GS1M53rdVSi7VaEplY8ksTgYNMyQ2trmRAufbqq9y6cY356ozpmXNptM2ukLRbAq+GsgclfY+IJQcGshV49uS7qRZga9vjiapdhERFQqKOWzzn3xNwDP1G1PoyF72TFZVdFGPx+O9HLn2Y5/7z/4xy64bZNJ/b+zPFbwL6VO7x+zdjGO9+/T3e3+bhzb73beG7N3vxva716wWP+58jd/38Pj97ApvtP6W7dJHuW7+Vj/1nf5nh+LaHgW3kopSEVWZwRg6lRcDtf9Q02J0KTddRNPClG8d86uVrFNf7Rk/qsRJm1crsTddlQGUCgO1+ggFBMODUDqvGursZ3A0HZFbE3IGEWg1FRFAxbV7Raq+RyqIPpFSn4t9FCp0KowxECeSaHXAVQujMfu+BSVBPtDHCwM3uxIq1ML/tw0DVTIqBODfwbU1dPHnHCZMp5L332C1k69EC/+5aqkcXg9e+dN2aazgbMdKOLvF5VFVybk1Jdn28TUJln2fRirC3lQIxWJR1WwubYaSM2aJw2w3b4RRL8bTja8guV5BqNZGDS6hCsLJNyFRJBfVuf5h0oOsCs75nOZ9zsFqxXC3oFx19byAztgRobRGvMJ0zU49u/9zmCEu4s9xVK1rfNOMNbIt/3lvXzN2N+waUP/fzV/i3/91zSFSCRhALNDbwJ6+rHbbzMNrltGLcbQO25/M6E+CW+0392Rb2mr7N3zEVvzPGcnXxCd73fT/Gi1df5sZzN7g+wK3NyI3T7VSmR/GG8x5GbSHuIjahqeuY9b2BOrUSASkGSs7UPFK1cnuTWZc126xsByUfLimqnOuUw75DUkctlbzx7igKxIgkQVJkFntmy7l5JyUzDAM1F/KY2W4H8pCpodjBtu95y948VyWIdZchRKqLj2uxfqRQWMTnefjX/w4v/eWXWXzw/TzzxWd4rLxMjKY3KXuy47A7tY1x8u8xEKLu0UU2JwNPfvLTPPGBT/LeP/iH6GYL5osLXHr4MZ5/9jkCHambMeSRWkYrMhsDIhbyx1nK1iUiuI7RSm1U6/s86VsdGFZjxa2Jju5AjJimD4IDOSvvUqQiaoYL2Ym7WwjTSiH19LOeNlvnDla88/IFLr/jIqszB3RdgjrSeqV27gUShNTNSWlGwNi+QnHAWCcwKSFY4fMQKNnqppbsLFwQFrOOMwcLzh1uuXJ9zXZ0KYOYXspE9JHT7cit05HT00o+qMwXnR8MASmujxEL46uH0PraUcWaTxK88Lx9pDlNIZFltH+rIhoYiieqqbAZBwO2GumIlJo42UYWNZEyFmaKJ5aQo4rGgshIc0fMGZGJwWj2omBNDG68fI0Xn36GeX9AHgqbzQbNXgfO0KAdkoo/9wTBGcJq1QiKQPUDhareCLe6s6gTSxHUnnseK8eb0Q/wRC1bc/gCVrZJ9g4jXKfrDK7ZCP85eQpZ3mHDzMt5Eyu2D4zuYbTf0I7Lnd91r98De+Gge3zmvd5/L/D5Jr+Wu35+bwbg/sbXjDX3T5C3DybV15agHH7Xh3nq1z/BlaeftpaK7RECTWem4NUGPEpU3SUN0fSU1SVHNUCEF9aFX3vpFqfZGTWXgFn0VVBakw+8VqVLMtSjGmKxlTCBAF+EuFMWXKuJXUtwRjJ4uBhsv4Czn1gYePSElQZmZl0ieq3eFCM1F2MVxTXmagRDqc1htZvIdef8C54w6ZfZWv1prVMZJW1yo+Ltdn1+zbyJ22bjfdWv3RJM7gzXFtcBqtsD69ld71ijQaQp8+yMqLvs7qItyrZXj7KlNomxr4q3bPSuVyoGVIdSrW3iMLAtmVIHas4M2611mAmOyBBLDNLWIAVmji9iUGvD25JMY0vyMwLBem0oMSl9H62Gs9cXFk81afaIKIQI6vW7jfCIfo4acVFrdsmDd9qb1oQ7KLLX910b4GZ6Fvc77r+w+WrO7W3lfEpkKnmozOKuzIF9593Gc9oC0wJh7yd3/m1vTPorf/89QynTy+5C0A4qqR4lE0rXkw87rpwec/30lJvDCZtS7fBJwTqisPOimpYyBEgpMut6eq/UX7VSayLnvMsKroWaC2NVrh4PbEZY5zWXz86RWSIMyhyr0l+GLXVjm1WjMHaWHKF9T9c57d939IuZGZlaqWOhDAPbzZbtdpiy1sHaS7X6ZMG9WzOU0MQUpj1U0EyVyqXuC1z48lNsn0oEUVIYzCg2eULT66CTVzxlEgfnSx2QKZZJff3qDX7jV36ZC+98F+ff8Rga4czFd3P16hXK7TWx64i5B82UWhjHka4DNCPq5RhIXtPUjE6tlSGPaLbN0LSVKAYAgrXkbKHujAm1SzHWsObRwIfr5TMu0C5NGG5sNlqZdz1HqyWzaH3e03zGmaMVZ84fsTq74ujMESLCdnNK2W4nEX1IPSFFYupMmxg6K99UILRyFVh4K0arOxkmpqEYgKkKBFKC1WLG2cMlh6s162FLrp6574bVAFnk9knh+DQzDAUWLRXdwkzZekuaU+AlplKXbH6M6jCAG8V0oKGz8l6y26sAYxkYy4hixfGtT0FHrHOyLsl6wGnOpNFYg04W0N9GO0toqBTT3tIOQ+++5LoradneVcknp1x59iUuHL3DkO6I15RroTycbQjE0FPUvHtrmmoazEJlFAd8DoxVd5ZH/GCpbjwKylOv3OD733PWsmuigFqP45LV2VMrDbPz6ZvRkZ0ObnNMd+EMcbWiHN90cFXRPEDs9i3Tm4w3AJVvOdqnvgFAnGjVN/rst4Hg7v6IN7vcezIFv1fj7czVPlJtC7xxVDa6c+c4/K4PM5y/wGf/+l+j5GrRtLojPYw9aoklrk9W7GAWK9WDQpd6EFtjL5xWPvr8a9zcTvXn7HzB3tfOGUXIRUE7C3+2cG9jjNr1S0tMaeQJHigz+yHRri/FXVOKKbG1JXU6gBjGAaW6Nl+saLbfZ3OEpw473pkuVyuj1c6K6oi7eo1ncamYlWGLXhDcIlETQwnWI8PD4W1eLTmkOYBeui31tEqfpYkLPCwteOINgFqegIE+JuDZnnsjJqozf1N3uVqncxS3WBYyNkmCGgczOZS1Wvea0+2W082GzbBmzCPjALkMoEZMgYFjS3Q1JroUc0hnapGhWRdYdIE8RoJUK5uIlwz08j0STIffWMfmtFmc1KUErsOXGD1CFpnKSgnTM2w1PCenfs9uNOA52bO2VjEHKI/3b5fuG1DKjU/xk3//eS69Y05NS/TVq/zhDx7w2Pvej6QZ0ErB7G/WO93Ot9PC5+28fv91rW6fSkQoaL3BC6/+Gr/56d/m2Zdf5sqNLVatyItLV3t4KcRpkxrBYQu0S8E0czOjkEve9RguAcoIoViWW+t8cDoMDGVgPQ6MZYlm5dw8WsbqOCLjaFnuImzX1oJwNpvRz4whS10i9B0ETHzbRWTZsZKVFXEvhWG9YdyMbDaj6WBy9baTzcu18kNTuSEBodIDKiOJ7EJve2YlBM+CtQkIGrwThAFr24Peo9yHtakyAzdsB55/6ik++4lf4bt+6EfpD1eEuODipUc5PfkyEhOxn5lQecwUzYSsJG+DGbyKZgrQxcoYDSzXMTtIjl57tFBzdRffjZBWqgRGsYzf4uLzkCKdJAvLIkTdz2oz5m4eevq+52h1wOF8Sc3VQuYRhpw5HTOH2QxS76UattstVTCtT+qJXSKmZKHl6G24GgvhHYMkBCQlQvCMxwhSM1LLpAmSICyXHWcPZpw/nHHzeKAMu5JIU2cE4Pi0cGudOTkdOFoFCxe5Y7fLRnRQFM2AxM5YazAPNqboOsqOlOLEOthbK0MekJRM6K7BO10ERJZIWKL1DCd6SEgdszEjXCOpouU2oROg+DmoLqHYWQOLKOt0iJVh4PjqLda31hAjw5itJ/wU6m5pWVaaS0ie2ASBOZWRTR3YUBlRL8ui02E2zYUbeWskJ3zu1ducDhdYhMIsmwyiqnq5rmCVGXxOa9GJ9DM2HUgdBz/873P9Ix9lfO3lnU1SpWzWpJnZxn1H981Zy/sZb4Hs7itE9Qag7GvBtG80JlD5FqB3/yX3PeSuP1//1fbbCVnd4z2V+TvfzcEf/2E+9/F/zhf+xl/j5ksvoNph7qfsSN4W1nUHRYNpt4tAtFxm+mAH6kjg+bXyK89f5/oW3wPRWezqjCLkLNTqbX2n0hV+1Q4sbT+bzCeFQCu0PbF50pLGdvKmKOaTNUIFbX6F2UeLfFmWWkuumcCJN8zYCZ2NOCiemOJKEAMfTiokT402INP0dzqx9vtgsjU8sbmskxnHE4zasxJnKiU0Ekfd+dcdgVTb3i4uebHog+UCBI+gNSDpDGDdtQ7edYTZ4RNpYNmbmhicsPKEQx452WxZD1tONycM2/UkKwqtTFRxCQHqUTchSmRELaISK30UItDHxLyrJMlTxZJGBPXJbHqfPNHGsUn1JOKWTGoR1GT/xTQxuNaZSLwk4y4JZ/Lm2VWvsPMoOlC/88yvGW7dXt/vprx/QHnx3Hkujp9j+8yWYSykWvjNX9ny0gu/xRPf+p2cvfw4MS6Mu1YThe6YwzuNl9xhZH7vh7q3Meav8JnP/Hf843/8T/mtX3uFa6/BdrAF33nmTUm26ZowtmWUlWKC/q7v6LpI3/fm/YVgtiarhTd9oyaJlNFCuForBeH2ycBrtXKgsDroicEyjlVsU4kaDU/ODLUwbNakzjvE9D3dbGY9mbtETJ4hlhLz2BMOlrRE1O1my3qzYTgZOD5ZM+ZsBWtFKGoC8iRqtTGB5FlgSQsZq88ZMU2HRcsjQa05fJSIaiEEO4SnrDi8aDwVgnmKt2/c4nd/47d59N3v4dH3fQDpI8vD8ywPX+b6jRuE2KFpRtBiK7VaNlygmtsazHCkYBsuKIy5UtThZikMJVPUk2m0hTWiFYdXryGJkMQKvcZcEQ97Jtfiha4j9omum9GlOQUIRIqahlKsTRLjOvPaazfMmMeOs+esb+usn5FLJibLlg4xeA3MGRI9O7nkqUBHM5DGrlmI3LohReNVnYILQZjNEoerjvNn5ly5tWUog9VADMFLRlUkRDY5c/3WhtuHPWeWHfODjiCtiIkzG05ypNgyFkEzLvYWutDT9yPJs7xVmcCX2Rwl1wxEovQEEqodqh3CeW7oE1w5+g6O3vko2yuvsXjtkzxWP0usz5DqCcXrfqq2I88PQdcXtYQkwbRNmpVxyFSs40VxpnW6LoRAR60RlQVZV1A6Amuy3qKI9VseKxRpYMDBpP8ZNOxCUgpP3zzlky+f8j2PxEmUb+s+mDNAM7p+jQ1huOMpMRLmZxivPt8M22TZyumatJxBt3JvHxALad3b/u3ZSb3Xz79WtKd3vVVf/9fpct4O+9c+QKa/3fFR0w/baxqo5vWv1LvfeB/j7tfbQtn7XfvOdn37wFYJKXLmj/wgn/hHP8kXf+7nLcxdBaGABqrssvqDYpU1WoY3tp4Vs5Hi37+l5ysn8Gsv3eDaEIAOrZ6QhqCW1mbsVQarZ1q8HuJuEmy9OID1sFlVAxK0pDS/tSZQCmBgzaMnTfs+7b4QPDu5Oel2nvW91cQVDLC0DIYm8aJF5hpDplZRobVqnTT9YhpNu3bvCR2glT+aNNTsEpGQpoesBAdn+8zspMHEpGAUf4p7TvMuU9vuNnvtyP1l0kBWa82Yp57XPo8YKDTQJlP7Zq2VrIHNNnP7dMNmu+Hk9BabzQlQTKfv7620xCGrtpIiU2OMLgQkQR8rUdQ6qKXAJsou2UpMW2+1OYXUBZKXW2xtH1VtzVRPukwpmVY7GAANwVCilcVzAS0e3dL2fD0xSXhdFLjhHyjUUTk+3vDyazfvbz/ydhhKXdIloVvesiKdo8XhX33mOa699AxnHzrH5Uef4LHHP0y3usxOV+mLo4nLxWhUGnPwr2MIaL3NFz7/T/n4L3+W9a2LrOYjN8J1CEYxWxZZpRaj5k2s2pgI07wFLFQYu2QZvCIQKn2wAyeHDJLQPEKsRAq1BLIW38p2gNWhsFkPzOdWgrslbKCmnfNLNs91LOg4ktfrKTyauo4075jNHWTOe+shGq2Y6Xw157AeoLmy3WbGsbDemM5ufTpQhlMoEJmhGhHWlCBELWiN5u0EC1MEhJgUKcV7Wgv9bE4FNqOBVZmE1eYVWl1HoASuvPgqn/vN32R1/hIHF86jCGfOXOTmzVuoKDHOoAwErAOSqCJSEM1E1zumEuiCkIOQJZGretigTB6teWdi4F2dLRX7Eyzk3VcIWtBoGqGDo0Q/nxO6joySizCMVp5BUTa10BFIpTJbdJSqXHntBtvtlr5L1qN7taDvF+iwBc/ItGy6nhA7q2NXC7WFmbKHu5tQOkQT5seIZqG0LeCMQAyB5XLGuTNzzt9cc+Mko9LK0DqT4EzG8Unh1u3MyWok9pFOWnimHULufap4sWOvKyqKJGc0xABu15thqtVKRBFNlhHbYeQlrEKIDNqz1bPcOP+d/JX/4kf4Y997madf2fCX/tLD3P6lLee4irBx4+rrZHIu3TtGXYDvQC8EgvQMm0INwrjJzigz7RWClSuq+YCT8XFeufCHmZ0/S33u85zh16i8TNWR4kC1ZXM2VXB1hgU1/0XE2p7+/Gdf5kOX3sHKVhXUQK1CJx2aqtW6LJbxWu848EHyyPojf4NLf/KvsH7qSwzPP72zQ0MlH5+QznRo6He3T2HK5rgjk+JeYPIuw3Y/YG86lO/1vjd4/5t+55u94H5tuIOl12nt72e8wX3fDUIbC+lgah9U7hwt+1PHLfXsQ2xVeP4Tv04dfZ817O3OSNh7JO282ukInVUsgRIDa13wxdcin7615rQsqMwAK2ht3dK8bBqjXa4/I5VWJLtO2bo4yIgemQjuUNpWaE4qvid3CTgAU9Fxv+Yw1c4MjIOVI1OUEJXZrPe6uK4PVT+hWia0s5C1Nohn3cKspbD/26tEEMKkv5s6sfgjmBiynT/mjUTanhSPGdgSiYKFtx0E6wQsZQKYbQHcjSPsehsjufsTtQ46hpGDO6wuyREjorQGa27n4H1TCsebNSfrgdPTNZv1Kceb2xQdTUol0efJWj6bqMjP9yp2JmA2NkbouughbDvr+s6lZRgWSP4sUgz03k1tzJlxHNGamOR8XhGgSalS08wG3wOhVcPAbLwAuxmeEnkNBNsOse44tu5qgc1m5Oq1m7z08pXX7703GPcPKOV5tJz64u2JwRC+ZbsFbr92i5uv/TavPPMM3/htf5ALj34zsVshahoibbWW+BrsydsdCrWe8MrzLxLKeZYHPWfPbbl1MrIZb6Kj0i19aw+FPI52yAbL8pquUStd8raKnrFZVUl4ZpUEYrQuKHkYqWKZXhSjj2OARReZBesGgHgx8omxYW/DMWW9ggHWko1BHLZbwjpwHKxT0Gw+YzbvmC9nLBZLrycYiF1HWlipkiMNXCzKuNmyOR04OV5x/dp3c7w9R6e/SS//CiUisYAOQG8AWMyARe2YReHi2QPOXDjHWCpXblzhxvXbjIN5ktbOL5jX6If/rdunPP2F3+Whd72Tx7/120lLYwEPDg65eeM6IUa066D0Fv53IxQRcqnGlAoGKFNgrAZmqGXaDAhefNVAUQ9I9IQXAn3smGk1oJ+31LESQqKgjFoZKpRSTFidrefqZtyy3QzMZc4j584SFx0pd6RN5Or1DQdXjjl74ZT5/IB+Fmzdi9WdjCERU0+KpnepQGsZNmn6PaQlIXhIXCZjX5wJqH6SzWYdZw7mnD+a8/KNzPGpzUvwLBr1xLPNtnD99obzh4HFIhBnO2bCSCmd1pZpcBz8uzRjO46cbDLHo1BrJNUjRt2QqXbYhWoFndUzEEURIqMsONaLfMsPPMGf+cGHmHfCxceX/Hv/i9/P/+VjH+fitocaG71greXEWe1gDKXnTXnYKjGfn2O1vEgIC3IeGDcjWsrk5AVJKIlSE7We4cp7fox/82/9OS48esBnfuEpPvoX/yrn9GPAMV0YyVm9LV5jJhqWsXsRy+aiqvKFq2t++aun/Og751w9PWHRH/HIOx+mcsprN606wdmZsNqesN6syCKTM0EIkLdI6tBhcwf2ESAfb5F0i7g6hDD3Xxa8VRZ4Qaz2elvcbwYsZR/x7H72OhbyHu+71wvuiRfvA4C+wTfsD3dtGh91/2983Xij65bdPxxkuXG44/vvuJeaGTdrTm4c885/40/z5Md/lRuvXgcirX9zS/hMAvupoW0vVzGGLkogC1RRXtwqn715yvOnSzIHVDlEpZiOmJFST1HdENhinbuKZ3K31oKt086u5WKKwbp7SZiAFA4ajb0Mky0E7gBWgiU3tuibiDC4UyQBOoGuS6S2jlHXcxo40sr0/qqmZ27efIrJAZElCbZGHrL78juyg/Wuxx9w59ZlZVp2dmpCydgzKLlMGka/SdOwtqfbQu/+fU2q1s5qRYyIqKb1LEWn5M4QGtj27YigxRq2lFr/v7T9ebBt23Xeh/3GnHM1uzndbV+H1wAPDQEQIAgQbERRojrKokQpkiW7YpUcKlFcrnIqif9QqlSJnabkUlJWIqdCRYrlkl2KrUSKJDeiKPZi34sU8NA9AK9/993+nLPPblYzm/wx5lz73AeAvJZLG3h1zz33nN2sNeeYY3zj+77BMEbW246LruNis2Oz3dD3HWMYle5yiVPro04aKp9B8zoFGlKOu+V8lfy6Ipo4VtZgpYiEcmKb1DYoxUQwFh+Dem2GQEpOb71oV1H574KIm1BHk4tm6wpfP2qSnO93AV5MLsBKfl6oBsMYeXh2wTt3H/Lq23sqz+/0eOyE8uzBLzJvtFVobf4AFhrjgKwmEjg/PeVf/MKPcHzj13j2xW/l6lMfpmoOYY91PPab+5d/6AYafWTb9fgw4iVgK0vd1thkqZ2KJ3amQzdTnmOZbWlCUL6gczpWTxPKDL1Lyh5futBdgNEKwWvrU0aPDwkngdYptO3yOVG0prkAzgtgb/dSFqRuBn0YMTrk3ieGsWPY9WyNzhKtZi3trGFxMKeetcrzqyy2tki0tE3D4tBxOH6K5uRPcDFWeP9p2t0a6V6h7y8YvMFHIUmlh76oIW9TGZ566glObl7Bk6juJPpuwI9Djt9xojboVBJVWN+794CX/8VnOD454eCJJ0k20TYzLsxKbRuqGiMBST2kblITanWbeTnOUiWhTurr1rgayVW6tWrJ4qxOZKrrwiFRFFBtJDzddstuc856tWbsohrLB51R3Y0Du92WzWaXWxkeSZbqwFE3FqKOsTIS6Ubh7umGm2fnHC+PadoFDYYQfG43ZJqCVWJdaTT5vEELSVotj6zyffKowsv3XVC+ozGGxbzh+HDB8XKkH3qU8J0TypRN+IHz7cDZpuLgYKSudQKR5GJnImebMk4yZiqHJiV9TKz8yOrC8crmO3k4fy/t6kvcNL+EkQ0pDcpEMGqsnqTCJwfREKjZBj1Qs2EqF+tegzdKodDzPWY0W/LIzpjtOHQUYhJDO7vKjZsfYja7ibAkhS1+1MNq8kmL2tkwVKzkKs/8ke/i7H1zXiPx7B98ntnf+jjpq79OTafX3agViCQz7a/CJ4MsrEBAIjvgH/yLO7xn8Qy/9NKOT37LMc9em/HaF0756d+6IAThY880fOrDR6zPT4gIPlmcCDhL/fE/xPpLrxLOTpnOxJzfSYTxvENSxCxGsDPyZHKKPlfv/P9QXuU3SBgp306/88/9To/LT/HY74kc5N71PP9DulMlmZz+zGjvpecs6u39NyJh6NidreguOk4+/R2kq1f50g/9NVxQpCZl/1oKDSOfsgJTAiACNgpjiphk6I3hC5uOz58OrP0Bg63xco1BlnrzwxqbLnBYklj1JkQTiMIhlmRVAJSyWCIp6udQ3nJRaafJoDx/ZjFZd5nRwmSwbi/qKXzrJIoGeh/0c6bErKqoXcWer1iKmEvVkOh1jFFUiZ4teJSrp3tS1dl6phVUsiR2l+/wNCtb9DUKxczHMNVPSgFSaklIQV0bwqVOZgJSmJZTKTYfef+5XH13Mjl6pdHEkIWs+UNqIpptkqIW9T7AdvBcbHZstx3nmw0Xuy3d2GkOYCDke1LM0YvziojGcGcMrmpJxmkJGdSBJKYAyWXPUQ0SrrIYURqCopxmUvWHWKa3KY8fDOIqrLE4Y3Hist+nnZZ/4VxeZpeYrFKAkkzqdjHTtdP9Y4zB+8R6PfDwwYrXb93jzfurx96aj51QvvX66ywOFsyWNYtZi6/mVFWN1EJVZeK/gFDTjT0Pbj/g4t5PcOWJz/DkCx/l6pMfwbVHlPFD/0ofAmJmuNZwen6LdWcmb0cB2rZl1jSTb5/3HvGF55YXnEkZRlZIWRdeyImBU1FFvhE2Waz3hOAQo7OojQ/MEOZOaJ2hEsnMhP1dLhwV3fS6CHXh6POanJyklLSNXhY/AlHUTH3c0W16zs8ucJVh1rS0i5b2YIlpZ1RNQ40lVAYOHQceZlzhZPZRzBgY+o6LTcdu6+miMCKQPH63xiXPwckhV568SXSGno7bdx6yWfd5QpIGcisOJEyGtr4L3HntLV794hd4oa6pFjMchlkzY+i2CA7jKippMWHEYZEIVS3oLPOaKA2BGkylCUqZrjTNd1e0z1o1jZ82B7p5h6FjdX5GSD3x/AIfPKMf2O12nG+2nO82jKOqpMeordejxYzr1w4ZQmAXE666Du0R1XCL9arj7r0LTo7PqWeOZjZj8BrsxTik+A6aPDn8UiI5kbZLdQqKkPlSMRYLpZQPE2hbx/Fhy7WjhvP1SO8h2TwXKik3JqaR7ZA43QRONgOz1tA0tpTbk5q8IJ9hHCHGyRh510U2a+Hl06e5++n/JeaZ97O99w53fvIv8UT3Y5mCYHIVa/EIg4skv+YwfYW3fvJn+Gt/+wrf9/ue4rU31vyN/+S3OBpv4dJAcnlkVwKysryYwKk/miA4Do6vc+Xmi8wOniK0S0x1QBoTSdrMnUVJn+RWVgjUqad75xbHg+7T5aojPrjLfAyI9QQT6U3C4gh+IJJycs8e4ZD9IRcS3Ok8f+vXbvED73+SNz93n398a+SJaw2ffG6BrR2EHZ/9zCnhfse1uMARlTYilt1v/gjLP/ZJdt/zBzj/Zz9KGsepYCSBxMR43uO8xy5GqGZgGi4jbHsUTx75/jeKbzmA/Dbff3fiePnv7/q3fxlk8zGSy+kz/Q4A5W//kK/9shwgEyIpfM0LZB4eKRD9oInkaktMjif+tT/K7FOf4Of+7/9X1u+8owicyZzwjGQXqUv5qCkGsJo4BCOMVNztRz53fs6bO8NATS8N3tyA2bdx5cVvpXI9D2+d0t3/WdLwFSQNGuvKKFlRZa2TbHGWVPgVRUU/xaUjkoUu2SJISdDKlyzvUjJ9ZD+BSpHOGKN2ZgZ11xBR1MrVbm/+XfiGmZen3r4KnkTIyFju3ElWhCcm3mTJ3ct+MqaIZ/a3pgiayr+Ril1P2gtziBNaCrJvV+d7acRO68BkGych8wIzqqwCHEX3go8KxGhvN3tVloWUHVEyb1aT2MQwelbbHeuu5/xiw2a7ZbPbEGLQaWwK3WY+aZyGX8Qcc63T1nbt1EXDuIoEeFFql3OSb2v+TMZgBZwVnUGeAQOdOiSkPMe8qqqJ+kC+DyZTNMo1SWWdlH832s0q1KNy/VKes15a/0JSUVSGQvqu5+zsgtt3znn79kM23eZxN+vjJ5SvvHGP5fIBy+WCg/mc5UHL0fKQMGto2hZXqUDBVYrYhTAS05z1ww1vbH6D2699luvPfIhrz3wzzewaXyvayV9NX369QPa4USkBNcvlEhsrNZodE2HwJB8xlY7Cs1aoosd2u0sKVAEiJumMTmuKqTTsB9RrUlc2WEJRqnH0xBSprKEeR66YxMnM0rqEc5nYbQxhQoompJlCyC3QveIy+irlOUEtenR9lF0oSuwetYXZr0fS2QbqC6gbmnnL4eGcpv01KnOCdx+gcg+J9UNcPWd5ULO4ck03tW3xxuLHjmFzRux2XHniJkc3nsQ74aBbsTh+h/X5hqHzWFNhbQ5i1CDq4VdZS20sm9OHbE8fclBdw1aGg1lLcAYjFUY8Lh7hZMAaRYeN0SkzIjWYGqTKz2nVtNzouD9bRCZCVkaAoKTuEvz6oWMcB04NDKFjtVmzXXdstlt6rz+nimdLiolZ7Xji+hXquqYPNZvx2+nSh3mwXdGkn6cKL/P2nTMWh0vmbUtVN1R1hcQyq9aooEfyfZaCPuZZuk6nFpTRizYA1hIpZPAcfXMCWtcV80Xg5HjGwWpkOPc4cdTOsKjVM27XJbbdyHrrOb8QZjO9NnXmNMklntPYe2L0edpRRUjQDYltF7hjnmf31HtZ9ZbZ4mmap78VXv1pTApqN6VTvEkuC2rsimZM3Fz9Cv/wf/eQv/9XDpF+5Ki7zZF8lSinwKhWkEZfPwqZJ6l7zFU186vXuXbj/TSLa1kQBT55vNfCL4YySlRIMeDjDsHQuNd58E//M36sf8ATH3iGX/m5X+favR9nIReMMhBRNf1IZBSjnMqM6KpKVaaDTNX2KrL48mrgv3n5Hf7Ct9zkKQKxf0iL5sFVBe2RIsvDm+CsWoxQNaSzd+h+9K9y/U/8n9h96XP0b776SKAvIcmvA8lvsQuP1AO4FqS+FLNgj1gWNOcbJZeyh0G/4UP2f6Tf9ge/8e9+3W+9Ozn9eo93ZZHvzm2/YZZ5OY3L1+AyAiGXf0amf0vv/t2kFm39xQXdakcYdQEcfuybOPxd38lP/eX/kLtf+QqStAsxTafJSlgRTUzK+FtjLRAYMdwLnq+er/jKdkfnEym1iFiStAzuSf7Av/69/Mf/++/isBX+0Y/f4S/9+2cM9+/QpB2OCqcyRFKa2L2PyLQkFz3ThC3RcQEmt4PLzxUnAsloJ7JXVE8tZxF8diYho2uuslMyWc6eFEsyl8+drAZWgCVqgpNUxOOMVXEq7DmTOfk1psQ+mRKtlPYJ32TZU9rTUtLWPcqofxQ7n30Sqt2WfN5MyOTeT7IscU0mFVFNObGMKUBUvmRIeo9B9RIpGnyM9KNnnRHJ1XbH6mJDP3SKGk5xo8wqTzm5yxQaUb5rsV6zeYCEVwWkItN5y2qMV2GuNYGq0lngAZVsSbE8dKYoArPXZu5mpph5qzmpFpPtny5vI7U0FHSKnKXQDEqOo+fDHsVWnUvfjZyfXXD37ilfffuUs40OWHncx2MnlPce3Ga1bqirNYu24ehgxuHhOSfHhxweHHKwXFA16qVogDEmPAMxOFJy9OueN770z7n/xhe5/tyHuPb0R6hn1yit8HJTUqmupXzsxyiFH3lkfkVyzGdHHCyP1JvvqmW17tiuzwg+K928qsGsDpGGpBAzIhPpVWFlg7MVqt7yIGphIxlZEBEkqlqrqhTJXFjhpLYsanQCQV5oWhFqyxz2kH5JTrXyuKwDvcTzQJXlRZlW/LZC0paGpL0qMY7CMIxst4nzi4HGXTBzr9I2c2xjMEuLtDXRKQIz8BR34gvIYsnVg3c4OX6NxgYOnnyG2fEhIcLyaMnxyQLZXaeqK2aLObNZS9XoTGjlEArOVdRNTXuw5PDKAe28wVYVyEwRMjGZ1K33ymSOHaCBmaxOk0K4VuGQ+qzJtCE0CAVC8nr/vM5vjirgJ4nOEd9serbbXj0byZN2QnoEFzpcHrBoW4Ik8HPudzd4MPZ0oeVa+yym/yynD7e89XbD4XJGO59x2Bwjrsqfx2aSviAhI8kmo5SmQkytAcTaTK8w+Kwa3QcmclKpVe+irTlZNlw5bLnYbJCUOJ7XPHkyY9Y4umHk3umKbd9xvk0cbisO2pSTb12/IkLyER+Kd6l+f4zCdohsh4Rbv0K9uk3bvIcnmnPk4RcwDLr3DMphFZ1vbogkCYhsWKbXOfZ3Gc/a/HorKtMh+DyxSHevzcWYyWIZU8954unnuPbMs6TqhK6DfndBXTkaKxgPLkWSUZpH0XEkAkl2GODJ9FnCT7zF5icsN9IWa87wacAmjzgQUURbomFIgSDFf1JjTMxIVFGNmowEffW856//6tv86RdbvufZijr0DEmYNY7ZwrH1cwYjhPkB1XXL/OA6u13F2dkDbmZVpVS1Uhqin8JXyYNiH4ljh52P2NkArgbbglS67tED5dKGuIQ4lsc3ioU50ZNLXz8CSn6jRPB3Sjbf/bOPk5z+dgnpN/jeI4mj8Mg1+Hq/mH/mcnM1RRU09hdrhnWfTalzzBBh8fzz3P/MZzh95Q1M3B9/McneipSoZvrs83kvcMePvLzueW2zYTfqwIWQ0XeLxUpF2x7y7/3bH+JDT6oryL/xR57ih/6zb+KVn/9lYrgDylAmpIARhzGaiEhOsg0yuVcgEIsFREmscwIwxZsEYsoUt4w0mXJ66FmjE7006XPW5FZ3UfoWQIBprZks7IiZuxiyCblYoa0dlVVvXIPVsb/GI9FSdFCqL1Qhz3TvSkJIHl+Zz82SqF5WYe9VSZoTFHvkWK5PPheKd6LWD9rZC7F0eti3ozW9VQV8QuMQieATo1cqwHZUb8nNdsdqu+Fis2UYRqUPoO1pn/UPmoBJVnDn9r01xOxN2ziXuZOQUsiA8j5hnjxCjU7CcVKQcZnOc5GciyTBWU0kQwz5OmpbP0jmrooO5jDTtdb9cblLRk7wtQbNJ2j2JjVJ3WDGMXB+uuXOvTNeufWQtx6s2IyBcdwn97/T47ETyqPlnG4Xuei3rNdrNps5p6s1p+dbjo9WHB8sODo8YbFocJVljCB0iEmYWqhcQyVCv9vy1hd/gwdvvsTVp97H1We+hXp+AzGVVmyZV8LEISwrSq/Ub8fBnH4+GZCRYVxR1YaGhoWBGzev028952cdvsuj70qVbySrYjVzT1EXSVW5jFJKtpCz08LWg0nRxpCnBpTxVoet5agxzBtoXMRmnqCPIc+BVQRGrBqGqTglL86UpkpHqw6boXz2HDhER3aVyQzld0XfU4qRZB04R4go9zFs2Y53OQOatmLWNiyXx7jFe/jc/BM884O/n2efbLj7i59n/MqOZ5oV7fKIdr4kpMjiYM7yaIaLFddu3uDKjevMDw+p6jqjc/vRT8ZatdSpLbZqcJUiMaXSm7hA6AZJAHkqS8pIbrGy0H/LAcSmPKlCLR2iHwnjqAlTVJ5MContMLDpe4Yx5Q1tcNYRo9eE3ghj0Gq8tpZ5rcnwGCK78QFnpy+xMR8juTl+9zbD+gLb1Ny584DjoznHyzmLxYLZYp7RSR4BSCSLQXS2usUYizXVhFCWqQVCqRStUihyFS5iaCrD4aLh2qHn4elI30dOli1P3ZhzuKiJKXLtyHL/wZpd3zF2Xu0w2mqa/x5I+BgYhjKb3BIjXPjARdezXW95Qu4S/9lfwh5+BLP6EjfWP02VkbvcmJlENJEEJmJDj2k8KZ7RiNPKnZ4ybtGYgG4ovRYme+lV1Zynnn8/z73/Q5hmwfmmx188YHNxizBsmbsWsAxhh48hy1byKLXszBBSj1qa76gzxJiK+IG4n6SUTX9tdisIKQt1kJwopH14kQwGJLjdB/7eyzt2oeZ7nmn57FvCxz48o2mFz7yaeHqA33x5wG4jv/9PPY09vsov/+c/xx++/TJP/vn/CePpOf58Tf/224SLC7Zf+Czh/EEWIOllCetA7DpsO2CajlQ1YGswzaXEcv8eIV7K4y4nW1xCMb82Gj7yKInm16CVlxbu7/h4d9L6mL8CX5sYvvs5vubf3/38cul75eoIpEAYesbNhv5iy9h5JEq+LvnnjeHq7/pODr/rO/il/9ffwCc9JA2JMCGRe45yEI3y0Rh8gt84v+BXt2dshoQXhUEMuRuBJYhhJBGNjs4rBWL0kdiHKT5re1n5dEWHULpfhWetnP19O7t85hAizurPXrZ72Y/FlUv5W3m9gPdBXTWMWoiZ3BBJ+QwraHDhcadLSCNJiN6DqDdh5Zwq340mmDEJNs21O4S/9NpmHwNhKt6Va5+IIUDQtrdOsLkk4slkNJPPXTVIB5ufz4hO3ymvVdrpMV5KStP+OiQyPzJ3AEn6GXvv2fQDu23HRbdl1/dsdjvW207RaWFCQEPI2cflrSKZLylaEDjnaJwm3GWYgxjDkCd+lVIsJXWZqazF+/J5kprbR0VuTe7sGGMgRnzwJHWRJpEmTmUxlzdip8RZcvFReJSC5NY302SiUkhYkylACYbtwNnZijdvPeTNOw/ofM/oVeD4uI/HTij/3A/+IL/0Mz/Ba6+9zWrtOd/0nG22nK3WnJ8e8HB+weHROUfHB8znM8Q62qaGGLAimDnKm3MVGCEMiTuvfIF7b36V45vPcuO5b6E9eAqh1huRcl6ILurS+nqchxbihuXiCt3wOaJou6Jtak5OjogJzlcb/Oiz4iv78edkJ2aul3Uq9zdO0RWy6avO99SWXsgLIRSz2DAyk8hBW7OsYF6Jyv+TjntyxkBUpW0RaOhmluwzqLOntQUsMAULlF9YUMlcXUZirggzR42SWGq7xJucvEUQH1RgijDsArHb0V0kzsJVLv71DzL7+BFfRXjmd3+Qz/zaz3AlXLBenbI8aElOW7tVYzFHjsPrJxw9+QQHx1epmlr9+Ao5G8n8mj2J3GTEMd8hNHTFjEqXdkWcQBW943EKEtpmgBAUvxnCyJBnp4a+x4894zjgfcSHxGa74/T0IecXW8YgKIE9Yo0hiNozmKjvr7aGedswpsAQAqvNA+r+x1mk10EiMb5KTDuoDJtN4q2373F0MGN5fMhssaRqGkrLWq2NAmVGakq5FWIcNlv0SNxbe0hGHQy6AEvAFGMxtXC4bDk+CBwfdJzGHfN5xdHRjJPjlpQis1aYN5aLjQqdSKj6HZ0uEWOiGzpGr3zRmJG/dS+criObPoIZeC78CuPtn8TKiDGBJJnWkVxOJlVUQ1L/UmMiwqACm+SUiiAph1FF8l2uoGMOuHVd8czz7+e9H/44zeEJF9st3vdcnL3Ow7uv88T1D0MciYyE2JGkBOLS7opadDDqNZMw7Q8jEZ+yilWK6CUHTEkEkZLfgmSVp+gxZ0pygO4b56AL8MNfHnjj3PHU/JBf/M2eD73H8MS1I6q3hY8+e4z9psRLv/YF3rq95ommIfyTv0o8OSYZaJ74Jtrnr9B88Pcyxj/JO3/jh+hf+eKUywmAT4R1wO8CthkxtUWaOieWFQlHqVRkX1pR1PP7pPJSS+rdaOaURPKu75WfT48mbr/N2fEotlnej3z9vFLe9ZdH8sCvkyhOX+6/Tpd/aUIsizrYE8eRYbtlXO8YO4/e/pJgPfo4+bZPceWPfT8/+x//X7j32ZeyATmMeVFEDBITwUWSgR2Jd8YAfc+LKfLV9Y6LzMcriV4EosljP4lI6gnr2/yV/+SnmS3/ENcPLH/z73yetz73C6T4AFI3fSrLfgKWiMFZq2JKIxnRL+3IlMUbqujdF+2aGFl7aQZzeV8pd0BCyNxHLazqymX+Yy4Txewt86bLvke5JCnokZnypAi1a7I9jUXfVqUDPRzZgi5O/MYyDlBJmRlJLEV2Fo2HMkkmdwhLPFSkLhKD7Hncmo2RLq13/SQ5wcrJ2P48uZRgUpwuNEL1Xc9Ft+N0u2W93rLdaUI5ZOeRkgAW8V6RaJXU2BTERwAjtHVNZQyVVXFoXWlxMkYtdscx5sK32MhlGyhrVGlPOfPAJG3xa0fLZO9TTfxC0JEMBc2cblfKGgJROkBBYZUJkQWg2cZxouwJUxs8jML6ouPh6YpbD9ac73q898TRf53C7hs/Hjuh/N4//j/n237vH+fzv/XT/NJP/xgvf+5lHm46hq3n/vYhq6rmwarl8OyCg+WcqmmYtw1Xj5akkJBkCHVA6oamaTC1IaUa73vuvvo57r/9Fa498SI3XvgW2sOnSFKrcWox5HpkCZWFn9713UuhTmY8+eTHifIjrM4HQhBSiDSNZT6v6YeBXQgEnwCrFz1NerkJ9pdcJVmLEvujklhNthhKIngfGXpPHCJzSVxdOq7UwmGj/EkRRc1srtpiUl5DimUKhzZ/dZ+UpbuvrqfKM7dRy7QErdz0QLUZNQiZ2xFJpOyHaCVhU8CkoPYEVr29hIrgIy7s6L7yNifxm7mVAru25vXbHV998xUV5iwMtrH40ZNCZBw1Aa/qmrptqdsanLaldXSWolNFbUxe6CXOm5wkkAqXMWbrBbVF8N5P3w8paJIWEt5rS8OHSD9Ett3IZtux224I3Y6x6+h9YusjfhjpNxt2244wJJzVQGqDUSPiFLEiBANtW1NVDj9GdQbYbHHeM+PXEdQJ3CeHGSymarn7cMX87XucXDnm8OCAejbDVhUxBlVlYhRxxGCyIb1YyeZqGjDTpVV7+WGyYKb4Q7ZtxcGi4fioIQTPYq6TfSpniUmYtRZz0tC0Ce9Hmsz1Kqr7YQxsdiPb9cjqomMUi9jIbhAudhAHQdIAJmBNjwkeY0RZk8YSwpgrcTVOdkbytYs4lLtlbEKtp/Jot8wJM3nWtRWLbSre894X+MBHP8H86Drrvsf7HafvvML9W68xDFtC0MLAR0UwioqTzIGcOsEp6n0RQxlVpkEyT7Aq7DAxmJio0BTX5W5EMb4X9NCsRTXWhqSTmhI4k/DW8JmHI2+sTvnItZbn1jXNLtCkhN+u8Lc6rrUNzz97hXppcR/43VQf+YOao6UEriG1N4m375N2F3kv53hVbn0CvPK8wi5iao9UHaZ2iLM6utFUIOqmQSHDFCjp3WtILq2rb5RE5m8UW6kiLnxXSH3X493/kB79890Jydd74Ufi+NdJer8mgXz05VLo8f0Ov+0Ydz2hD/lsyTHxkXd1OQEXZs++h+7efU5feUVFWkaRRj1YFc3vCZzHyNu955Vdz71+oKkcqVWj/EeKgVzk62ngMXhsOseNr/IbP/ZT/Klf/TyOwPbsNqb7MnW8g2UDMuT1WuJ3oTspgmTs3mc2Jt1bE61KiiCD6dwIIWjCkS61h80eDfQhTuhn6bblLZQdGbKoJMZ90mbK+hTGMeED+DCqCNdGojEYagwHkCzQEUOfrcYgJctUvZXzLZZkHExSi7gQE2RBTEE1AUUfc3sapOAkYBTd1GuwVydfRiaLgjmlpLzR3PEqQEUIkW4Y2ew6zjc7ztYXbDYbhmGYwBpSFipNzJOc6Jl90mczvdFZpXuVbkhVWaqmYVY3ECIuRnzsYIiZuqMdzIK+SjY2V8RSzfRDNnt32V0myeXiTYtLk5Q/GinJYmKajlc6f+yRzvLOU/7+5eskGMZxZL3teHC+4/75lmEMpOCVs/m19dk3fDx2QolUHBw/w6d/z7/Fx7/9+3n9S7/Or/zsD/Obv/pb3Lu7YttH1g/POT9fsVzOmC/mLOdz/OjxKTGEwHLR4tsW5IAKnc5hjJBsRbfteePL/4K3Xn+J60+9lydf+ATLKy9gbLtPFi+3wd8d0C5VwSU+zuZHzA8it9/eMfhA1wWGrifFMJFmYzIErxl9zKMLyw2xxuXWqxpOx6yui5ndn5Kq5/wYCaNgk+GorbhSw3FrWTQOJ9oOj9ZMVVTKi8I6B+gEAknZFgJNCLQ6yskk6OGZkZWiyjN5ARUfLv1mLgiLJUkYFbaPEYu20lNKOTEYCXjq6hbyK/8lP/p/3PHEd7+fX/7qA8bXvsxb4zvE8RTXjLTLGb3vOT+9QAbHsO0Zup5+3CEODI5o8mb0Q7Y/ACNqnh000umm9wGfAiF4YgyZ9J0YYsKPga4fGMaRMYx4PzIMA+MYGPpEiIbBC9tt5Gw1cH7e6c8OPYMfGD3shkD0Iy52LFvP0gnOamCa5qOGEUGNaMUZ1l2HJIv3A2GAZDw2anIYjWEMERkGnDEEMbzz1j3eOphxZbmgbVqWR0cTR8eKqs9x2cbH5CrTKLpQZgFP5OgpODKh8GpULLjKsZi3nCznxCGxbCpqmyD0yGYDuzUGtQFJxuVkVX3kRh9Zb3vuP9iwOt1xsfEE1xKtZzdCCIYQRyry2M3o9ygn4NmjAaXQIvN3nA2Y6MlACc7aXKTk5pZkkwqxzJqW9334m3jxYx9F2iXdONJv19x/+zXu3XqVcdcRxeBjYBx7fAQfAhL0oNVgmFXepeWVAiJR90VuMYrRsYr5lCQlLdIcOnbTku24EpPfqROhEp1gZUXJ67XRPTKmiMHwwMPPvLXlpTsd37084XdHmLWW2ZEDm3BNpHryI8z+8L/PcN6z++orxN2O4e1X2b70dxluv03cbZHLScnkK3QpxiZIXSJ2gWACUgmm6jGVgcpdSi4Nilxeaos+8jSyf42vH8wfiZ9pOoB++4fw2z1f+fLdH+rRf9ccQd71/X2htf/BXBAPHb7r8LuecTcqpzZySY9kpjNBrMHO5ti20UN/PqN96knsfMHyQx+kSxHjnM7pFuW79QZWRO75nte2O97ZelY+K3bL29HsTI9k0SsVS4aSEkk6RE3GID5E4ufh/FVkjFT+HEln1GmDiCemnihejzOj8dtmlEpVu7Ifbzgd5EbPm0gefasok35tJpQ55Sk3iJ41JaFCLgkHydw6w/Q8ZXrKZX6ioHZBXe8ZBvUlNAKbtcfaJRe8lzfNtzOzh9zsXuHm4W9i7L2siNA0e0Jzc6JXWvDaQTHTXO7C7Z2EQQlS5j/G/F501GXJLPN5TDkv90rv/fLJXb1LZuqj92x2I+vc1j5fb9jutoz9oKgkcQI3ivVl9lnJZu56nWM+R+u6zp6cOrFmWVe0TYNxFc5Vqt6PgZ0fc+u+wAiyB5FsoX9loosATrCuUtqYtYwxTEWBqAcUMXPLy1ouHM0itCnlmdLHslYgJ9bKvktTMRV9ZLfbcnax4c6DNevdBj8OBO+JAovZ46eJj/2TZqqODLP5VT74LX+I933ku/h9f/SL/ObP/VN++Rd+kdffuM160/Pw4QUX6y3DwYFuuAR917GZ1cybOeMVz2I5p56pEXTKzvRjCISLnte+8BnuvPplnnr+Azzxvk8yO34WY9tLcWl/yDwS5C6V/QnPEC+IaDu67wYu1juGoWe36zUJDIGULIEZKTY4GfBsAeWLOKPGrxatHES9oUlJvarG0TCM4IPDJjishGszw1FrWLQ6s9Og8zXH6EmZw1GnLJ4RvQWmKMEoRGl1rA9oC7HwREKB9PN5ICihVpOUkBPVgqDmgz2CECF53RToAPv9BAEhxA3PtL/J6qff4p2fbjEh8rQ9J4nnwZ2HfP63vsjyZAnieXj/lHl1wOnpmvb0Id4Zmn7AVro+QtCkgGwOK8ZhbIXO4oYYdfZ4P/aqvhuVMjAG6KJnu/OsVmsutmt2246xj+y6gcHDGCqiLBhH6HpPt4vEoEinD5GYD9AQLSYlZtZRu4HoDD4HN2vU4qhyulGDJPp+ULU1wjD2xDhm5EuyD1vCSSSMHcE4HJFd7PnqK29wMJ8xaxfU7YxmsQATidZpAE9ZsOMqjDMTed5k5WjiUjIp+9RgogtgcM4wn9ccLmbEALPWYR3Iric+PCWdnREN0LT6X1VBpWME/TDSbTvOzrfcfbDjolc+s7NCFKvCg4x8lHY8MRKTTnIg+YxgKcFeTMJmorGqSjNCKYaYxilw+ZQN6pPBVi0f+85v52Pf9Sk8c+4/eMDD1R0evPkmt197A7zamKSgIyjHoScBY99psSRq56HuCNrpSKJUCUVavB4uJT/LQHgG+zMtpFTmmVOJEvNtTjRrq56YRlAT91hMrIU+RnxMjElYDZFqNfBtNvHDn+uoLno++Z4Fz98Aeedl+Id/iXT4BO3Jc3C0ZP7MN7H86IcYH5zS37qNP33wtfTFHLeGt9+if/3Leqim/AH6RBgCQQKYEVP1SGV0LbkKrCMVP8Jser+vqvcF9r7mfvQb+7Qyn56PQnDveo+Xn6g8Lj/fN0oiS6L49dDJgqQmUgokPxKGgdAP+G4gDIE0ZhpMUXghl38TANM0HH/7pzn82Dfjbt5gGHoy14SzW2/T7Xa89iP/Hbd+9dfYXpwzGFgJ3I8jt3Ydb2wHzseRMeY576IJhE97M3OQqQ1K4XznrzUf99lfsqOWM5K9wCbBDWsCO2BAp3z7TNLYo1KVsdP5Si4ktRjPyVlJGiZEPuVOlfIKFbRW5AsSIajoNCR9DmcTldN/81GLqPIBTLrk4VhuUy4KxtEz+sjodXygSCIGy87XfPH6J/gT/4vvYzm3fOnH3s/dn97x9PEKYczXJ9sCxTjN7SaXP2lqs2sSHCUWYFTHW4pR30uUZhMk0ntNusoZ5rNQEJIK4Cgcyvz8hRqRv78bPOtuYL3tWG03nK+3bLZd/l2jdCoSgfRIwjXFipLci96z1jgdqWgds7rBVRWzpqKuDFXT4mrVDKSup3YVzmrhUzkmFFRyMlGEWcnrPii+17ag18iESicpnFFFdyVmxNIYkmEafWkmAC4SfdR2v/5kLpT2qvhxjKxWO+4+WHH7dIXP6GSIkcoI167MeNzH46eejzz0g1q35Kn3foqnnv8Y3/1H3uQzv/Lj/MJP/Bif/9KrXGx2eJ/oxsBFN3LRb6mtMHdzVucrrlw95PjKMYvFIreVLU0918pcDHVVszk9494Xf4PD67dZPvEC1fw4i1gEbAPYzAsoobEMRtsy9u/w6pd+kduvrdluhKFPjN3IdrtlHKEPkIIhxZbV7jvZ2k8xs1/hWH4Ma3YY8cwrw0FtsRUEoxB9CNANga4P+D7hY4VJQmstx03DsbMs6oHGRZzVKpvJv9JkWwowBXIm5akduTJzCbIJKrlaDdnyIZKyAkwylI1C5tbqnG6TsmpVBUV6ZOr/XMojlnIA0oklgvX6OyZ1LOU2NTXB6KSUKB4fInfeus3duzrpp+t7Dg4NQ3Wf8yQcPdzQLA+0RSfCGALb7ZowDKp6s02eE+0ISRj9SN8NDL2KTEZviKbCRyU+b3cdfdfhh4Gu27HrBrwPhFjh3YwktY4GTJHgB1LSFq1yJN10aMaEHrjGEijekFl5bQ0mabI2Js8w9vTjgBhhu9vqVIJkdX3ltkcK2eZiHHFVRHCsTju+9NKXWTYtzbzlqrO4tsWlihTbbKWRkGwEbHK7UtGHvafYZPGREycmFMEgJtE2hsNljTWGRZPnsvuBeP6Q4c5thhRgtsTMD0htAwdzfKUG72EYtHURtHggoWixQGNhedASpGOzs3Q7wcZy2JtciOTzhZj5iJJNmJkCXsoXKSYl04cpm4P3vPc5vvMP/UHqxYIH52vCsOLtlz/D3TfvEncBoqKbIQzEMDKOYxaVhYkLZc2e60PZF9mvz2TEXcGiS3idKci+LgmXr6k1CScFndQD1QgT3SQmVL2bhD4oDWOMhn4E6xPbJKQ5vPzQ88v3zvjRV1d86pkZn35uwQdXv8rCDriUsDGQ6iVy8hTN8pj26Y+Snq4VSQqKjJcMReoZ8gN/njf/2g8x3nrj0dwuTaGN6CPsIkEg2THvfdExmtYizqk7gtHqN+XPTKENXRaplFJcLv9N9tCfXPpHLv1dq6BHToMpX83HX7r8K5fWh47+KAbTyoGMoycMgTgE/Xyh3Md9S/Ndb+LSK+r3lh/5ME//uX+Lz/wXf5u3f+M32Ny/r0lJgr7b5glZgYsUuYXn7W7kLMLp2LEbPeOYeXbZoiVlFMfYRz9LSQTenXiHmEDGbAunY/AwDjERZEOKPVF6TZozEmwNLBo3Db4oPGtNtth3nBLZoiafcYZsm5YbmPk8KLctiqEfR0LUe2mzsLRY35V9qc+toMOESkqxMIPgoe/HaU9XtWPmGiKJ1XjCC7/3w/QnNRjLB777CX7pnx1QhnZIisp/loLo6pop/MbynDGvJS2gSms+o41k5mLYe2EikIxkcV6mgeVktNwo7TLmWySWcYx03cDFbsvZZsN227Pablh3w8TPTkkV3KQ0eUAnFCFWLYIoXcoK1oGxFZVxuMrStg2zpqGqHLOmoqkrqkpH8PoYqYPqRqwYkk3UTmis4AyI1XaijtcURIpqXdFa8r3Qlv1eHzHVHsh0XhSKT1kzRWycYpiaOAqm6I8YUbcY7z2bzYaHZ2veuXfOartjTCM+ehLQLiquHB/xuI9/yYQyLwTJQcQ2nFx/H9/z/c/z6e/9H/HyZ3+Rn/unP8wrL38V27QYqdh2O1bbc1ppEH9EXVnqqsGZlvmipWlqnX5ias38K4eraqqmJY4jmztfxTXaJk9mxuLqC0i1ZA/5e2I4Zb15mTdf/3Ve+fznef0Lb2K2juR39ENi9IEQ1TzWBq0YLsKT3K/+GMk+S6i+mTg+5Aa/xrzqePH6Ec+/7wapTjw4P+f+6Y6zVU8XPSYZqkpIsYZ4ADzDEE9IdkXbvI7YjXL0JE/cMZKtteKUZMS8SGJMpaOtQcnKVGX4oP5j2j5ULie58iybarIRIqk3XkqT8CQWgi+SOXBo+yBokLcGQtQgatOIw6v4xwhjGLA2EceI73pigoGEx3Medsj6IXa2wbZzXNsQUmDsA7tNp56fySCuwdiGkCyIJQRPCmHyQzS2QZyAdbml4Rj7QOo7NaPPfMokAkXQFT0uBERG1R9nS6GIgHH5ukESh2JqQrJq+J2SnrkScxD3OvGk8yN+0KqsnLtFEafL3WJMVPTSh9wmhPt3HvLy57/A4nBO3dYcu2u4qsrqe50BXWak521zCZUk200UMU22eBCViiRRc+KmqVgeeOqZwwHWDOA7RLY4uyUMHn+xJQ5nUB1Af0SazwhWrUsOW0e8ajlKFbvg2AyKvh7PKp65esjY19yNF9zd3CcGr0r6HGALKiM221Wk/QEUfARCRibLjPoyI1ZomoaPffJbYN6wTSPBeL768m/yyktfwCTHrFlkRE4TQ1IgxpEQIyF6PeCAKZDmM3XvEHAptUj7PCdI5uUasEmJ6bXJQdakiT9Z8idSIiQdMReCMEbofaL3+n3vk6LiEXqT1btJKRqvX0Tuvr7jZ291PDeHT9yo+NiTNc8ewIFfYXcr+hjgSz+HtkoNJngVH03TM4SDf+M/4uoP/CnOf/aniX0PQOx2+NMHum5DhIK8paSE7lEPWz2wPUiPCpW1aBKrfrvFRmpCuTKv+TJyuEfcLieN3wCBvJzPlYSXgg5lkWPIjgtRKS7RR6LPyt6Qob9YlMUlcTOP5Dvv/irlGAiCaRr1pzVqHh13O86/8mVWr72KFyE6S2+EcW5ZSeCWH/nKw3NurwPeWaLTYQQ+eH3fUnho+mpK9ZDJimWffOfFE1MpuTIYrEI2m1EiY1ulZFSeMHSofDLvfRJt4zictyybmtqp8jrGUDy28vU35aroHjBZ6EIWvqQc+2SPnvqgnMeEPpVzBlcpp1GVwPmzxux1WPYWyiPWnFMYvGcYR3wI1LXl5GhGgyMm4UgWnI+OeUosDLz11QcIp/l9FYRwL4YpyS9pr8Au03SKNZEBTIoUkVJZfSkpiJJyooh16vCQk/090rlP8gW14+uHgc12YLPrONtsudhu2G17tQzMP65cyzwycbrf+o+VY2ojO7FUVqeVVXVDWzfYytK2FfNZTeW03V05tZCz1uJTxA8p2zUJPqldYG0NzlmlYSEqyLIGZxPRRM1Ros7rnpxoyGc+5HVgKRaDZdPos6V8GbSFrxiG4bIRfCr5RIJx8KxWO+7cX3H39IIxBMbgCTHhKsOVq3OuHh7zuI//HrO831WVppRFM+T2lz5dO7/Bx77jT/CRb/0DvP6VX+Dnf+wfc3p/x9jNCMs5s6bh2tWrXL1+jaOjI9r5glnb0jQttow5FBDnENdQuUq596K2bvhIYs25/zL10SGmAj+e8/Del3n9y5/lzVdusX4w4ILjqr0K11Z0g2e1WTMMgyqyY1KEUAy1gJiaEDzrMODmH8GPv861ecV3fPOzvPix99IctqzWa96+dY9X33iH115/wHZliKZm8HPePP0E2+d/D9/9x9/P7S+/wcOX/h7P8QUSvSYihU9oK5JRsnDKHlPkBaLcsD3ETR5c74pdUeanIZcLkTQRsvXqy/ScMSlxV8j+XUZwWu7uVcBJTW+1ja7CIWcsPRlMSJExqOBHkuDRduhmELoxsl6NDGKRqqdqIeIJY68ztCNgKoxLVDW4SkdQEWMmNwuNcdSm0kk7Kiti7LXt5buB6D3Re5I4bFUhpNweCaQ0YETVxDrDNHuiCYRIHkVlcgAtXJNyXiZV10VHbRLj6DHJYSSp3U1UhbQYMxHCRSzgL7VvYETv5Vtv3GJ5sGQ2b6msY354pGr+kv3nvSOZL6S8lnyom0t8ynxglUpdfS0tYgyzeU2VktpPdR5HpGfEzALVYocZImFoGLsO8T1sFshsToXhyqxiOXOMUnG6c6RztVu6eWXBEzeX9DugP+b0tqEf0mTJAYrSkLInWglQqShJU15ruobEKD1Ai2PD/OiA2UHL6vyMZAyvfvlX+Nyvf4bohaoyxDhASvjRU8ATP/aKEuT1PnHKS6zJCG8ppPR8ytcXpuCLEUxCBVjGUmycJR8aZW5xuZ0hwRgMQ6Zf9KNSWrzPxs5kbnIm1kU0pzNRP+s2OL5wHnnpdsfBSzved+T4xNM1H71pePK44qD21GT7GNC1aXIHI8Lux3+I2bf/GeZ/4tM62xhBDp/Eb3Tud8LRvfkO/uyU7rXXSMHj790lrFeE3ZYyTUhrk0TSd4yXMV+PHMMvJ0Xl79O3yxfss/NUjteCnKQc/3PYmagzBRFB7aZKpjXdmXJwCMVM+XLyKOyBv5LPijFIVVGdnNDcuEF98ybNk09g6or66Sc1YTUGb4VX//mvcu/OG2yOHX3rGOYtQ2PZxsTZrued046zIAwBFcqlsOeg59dWuziwNk8SiZdG4uafifnDG8NEszB58eqkrhFkhJhnabuIGdNkPJ2AeSNcOayZNaq6VuQ/EI0mS2Iyf7KgZaIzn8vgBH2fGkuCaJs+Rb3vg4/TPbLOUjmHFTDW5TWiI/hSSrlTrnfAlmsukJIwjAEf9Ea2rmZWOmEYnmx6HvzSz/JT77yOSx77pbf40PJLCEETIPxkaRNSnACEy4KXmPbXljyZrswnL0bnJPAhMuYLr/dIP6+kwjHNoh1UpFKMzHf9yMW253y95WKzY7XZsu222Dz+0qAexSGG/Lsp+z+nffcvaSevspoQ1q6irhuauqWtW6q2om0cs7rCVOp76yqXY4zBhCwUFINJUBmlo4lNGZHWj+msyd2eLFoymW4VE6ir4NTtMpmP/0gpWM60tN9yxe7IGIuRPCoz5SubMiUhwG4zaLv74YpNP5KS6g8QYTF3XD8+5MrxAY/7eOyEcl9tTN+ZyNxToKcc2AZbH/C+D38PhwcVn/+VfwGdxUigrmva+YzZfEbdNDhXU9VVHlunLT7JzvTGqAIqiVX8IypplnHL5uHrvPHFO5xePODu3Ts8uHtKtwO8UBvHyUnFYmGg7jjdWO4/TGxTDs5GPbMEy6x5Bz/8XS7stxKqA27Yr/Bca3jxqRNe/PALPPPe99IsW4Zxx40nrvCe567x0fc9ZPVgxb2zHfcfnnCx+Gb+vf/b7+GTz7V047P80P/5Ad1n7tGkW9rqlAodraequmQMY9iLH8pAexUdaCCz5dqWZLH4TE1xPmVz1Zg9MjP3KxWLlTJtISDJ592oCzbGQMQwep+nCWQ/KzE6TUXTNF2gMVEZvfZ99tqMBryrCKYFmRGpCEEnOpBsXsyFMO0Q0xKpSNFnQnFP5ZwGHCxiciswqiouhRHiAKIGvsm2GKOzvA2emAZiHDMRu9Cm1UdOJFE5x+j3pPDS7km5PWDEwKgtw9FAZQ0hBXxISqwv1yKpAMWg4yVjPnC9N7jqCFzE1ont+oyXP/8V5osZ1lXcfDqxPDia2tZQzmc9gUxJGK1BvAHK4QZFxWuM5PGSmhg3TYONEQmBuIXU9cgwgAxQjXpwWY8dHP3gEd8h/YbaVKR2wfz4CQZpCETGEJBouHFtzvXrS7ptYrdqJkVp9qGakASmIBUm/hMp4KPHSeY2lrrmUq/24HDJ2ekpuxDphzW//KM/Q9p4bG1xYicxzdTqj3l/5j+NUWcG7XJJOV+RvE8EXedqjaGvmcr9RouAFEZFPwrwkxP6lJSbGRF8EOXoehiiJpPDCN7vE9aQSiKl99BKohYheS1gokSsGHwS7m8T97ee37ozclwLTxwbPnjd8uIVw/MnlpuN5aDOLQmCfs4Hb7D7J3+VkHEiSWDqJVLPCUkwx0/iTp5i9vSHOHrfh6CZw+JJ4mg4/dlfZPWzP5Fb6VN43sN6oBWVEaTYpO3Bo68BHPePcsQxJYD73ym/KJd+SaYk6Oslk+XHZPq3PRdREEytyWP7nmdon3ma+ombVDeuk+qKzZ3b9Ktz7nzxMwx+4OIfv81me8HOwS72rOKO3UkguDlBDEnUxiz2WgSnWBOxBFF+tKAIUpJcI+XETEx5N2lCdC5/ipQ3czGfNtPn1qtFisTk8z20IIEyszsZqB0cHzUsZzr1ClHxW8wX2JR2dk4eNLmWqRgW0ZisdjZGqRopElLh2ANROeJ1ZSdkegIkckwv4ajsJy04dN94n0nEScUvdW6ZpyJeM/f5RPo5hld/TbPqeoNJo46NjJ6u26mQMiaGFLJYSJMm50x2otD3Td63kvZKbf38Gh7KuMSSYHnJNoR607TIi4KElM25PRfbkfWu43y94+xiza7bMfqBKIEUve7nkCbj9ZQLBHuJKykCzsKsVp5kU9e0dYutKuq6Zta0VLWjrZUfaaoajHKZrVWeZyJop9XpZ1dwIOVYErBS44naMUuJlNToPSaPGIdPmQaQBVsmJ88xFHu2fVegLNAJ+JPMqc2T5WJMU6wk81S9H9ltes7ONzw8XxOCVxpZVMP2k5M5J8sDlvOGx308fkJ56atHxIOXkcuCsGREK+E4OLnJ8y+8gOkMUjsq6zCVWmKUEXrGlA2JJl5hZKJDR4CRNPakYSB1O8J2xbg9Z3f+gDu373L/wYaLne4OC+B0E9sKjuaWDzzVcrW9xjigVVRSwrI1htq2GHtKkl9FzAFNY5nV38n1a0uefuF5Dq8cU7cLfOqYzRqWiyXXjg9Yn19w5dZDmtcsr5uKZ65ZugCDE77j+z/Fr3/mZ3le7pBinxMbrXojyiuxVhMvH7xuenJFhLZEjJRZnaoMz1ZkGKO80Zh8bqHkwfNpD3bHoIE6ZPGHxZDiqC1AIQtxMqKRkSetWgMpGYy1pJAIydCHpJzO5InR6CKvhGQbbLUE26pCOI8aMUAwWv1UxpKcUx/CJEp4DlGN242a2YZoITmyD3e2a9APK4gmnsapuEdcMYRBREdjIglnHFE8xjUkMYxB7W209VQGWGqFrcFXJ0foLHCLsRGysjOmzMRNez+46aA0FRJbxvTt1PNvI5mHRPk5rGzYrHZ88TNf0irWqon3bHmAtTaLvyJQocwHTViVzGoxRluC+7Fp2XKIfQJsrMGkQMCT0oDvR+IYFb11BiMjQSKYQMQTQ4/3FcYvqKuKykCsHEdWqJtEZeHq8Yz5rCHFPgdBOxU4ZT1FlD+UMpkvFEPo7LuZVyESVBQSUN8ya8BVFQ/vncHZijtvv8L27jk2Wyqp6lGIMU83ipaQPH7oIUW8H/QEzmKzGON02Kd8mBdYK6GjzrSlq2vOklXeJiKFV5VvcIhqgz5EbacNniz6SvrnqKPbSs4UcmyykgOm5GS2JJnZPgxRYVjK8W+Iwv0O7t+OfO5OoLbCtbbnmWPhfTcqnl7Ccwc1V+rE3CVqohbTuTCkPwPO1Erk/lsQYDT/LYIhRIMsDqjf9wmuft9fZPPPf42wOp0S3kdjdv5bTqTT5W99vZ+Vcp0fTaf2SSSXoBCZnnv62cnC59LZQL5mAGKUuiRCfXxEff068/e9l8VHPwyzlvXd22xvvcPbP/8z9OcPObv1JttuS6gM46JiWFTsKugOEyORQctmtMshJG+IJmGDRUKtIzxjRZKahArMnFhIe7oGSaY1XT5K4eeWK6BrQLsgsSRD5d+Kuj0lndhlnE65yaIdE6EycHRQcdBWeXa3Jk3dEJi3dV7TOrPamGIBo4bkZSJKKgl8FJJoPE7kNT0qB69ylqrOcSGjrpOh96XugyJfkgU+exqJQdQdwcCQBONk77sYA4mBFMGlLseLkRAT3W5gvd5ysdvSdT19iAx+X/TXtaNpnKJ6laOqtKgySV81opVh8RzW9n0iYRQtBUwyOjQlRI0f6DjF0Ue60bPedZxdDGy2a53F3XWEMGZPYB1sEOLe37bUz1Zkut/GJupKB160TUNbt9R1w6ypsdbQzma4yuGcnZJtY232YrYTeGDEKsfZChh11GjE4rKgR4zJbhPqGZ2KDDdFFayaSOUsxrhsJ7QvaMpUoBgjBPXrdNVeOJ2x1ulMjaIdG5LybBPQ9ypUvvXwgtWm14QyeFJKtDPH0eEhx0eHLGctj/v478GhvJRSlrhw6aYkkenvUvRScU0cRpYHR9hFRXKWMs+3JKK6DUuFErJdgI7REx8IPhCGHew2pPUZrE5JZ6ekrmPYDOy6nn6XCKO+P+csyUO3HThe1iwXjmvH13j/iwuqaqnqVVGbGkkgpgIqxBxgzBxsg7W18tauHVDPWozT9qi0LQ5hViVqkxi2Z1w97Lj+zqv82I+8wh/8vudoa8urd3fYZEjJZBBGW8piJCu74oQAFf6HYgGaTmJtTrITYFTJ5x7liRjj8s3XGxJzAqaHX+ZLOj14CJ7yizEqlycXgnpgZsJzzHyS3g+M0TCKMERVfNmswsVUKvWxjrppGUyLSl68JrJJEBzFDdAlUYTWGQiaDOs2clhTY4zhoBWOFg4riX49Y3vecJFU1JOSto6tMxinRUDCEb1DkiZhjWtYLmeMyfGwG3i4i6RBsjJHP5uxmZlsDEmCEuDDgOTK1JiEiYCxU/vIpMIb1GTB4ojpCvXyW3HLG4i5Tt2e0m9fI21G7t99yBde+hJV48AlrtunaGYHSu7X/iCQPcgw2opIduIkFtNiRDDFHobMV0x7rhNjIA09ofcM0RK8YBudVysu4ZpRGRMyg7GlqSvEGJrlkihC3w/UlXB80NDOKvrBYDOPq6BaUQfBZvRc9zcJMHv+VUhpb3WCzdxTp+iiNQzec35+QYyeB+/cJ4wpzxk3hBj08E9aZAVJDL6jGrcZ5RlJhNziYqJ56JADdUtQc3+wrkhKJAffCovNtkeSS9NEirpPfMrJYxDGkBh9NswPQhgzpzmfu0b2qvHL6VHMiWxCCzrjDFEbllNekdADSq+dELxwa5u4GxJfWA/MaziZwaExXJ0bnpwLTy+Eqw0cVcKiqWjqJSZ6GHpCHLHJgwRIHi4eMHzxV5n9gYc8+e/8u2y/9DL+7Iz+jddIXrnK49lDVdCLqILfjxPcmHIs+Zrm0yOo4xSq3p2hkquynIwA1iHO4ZYH2MXyXT9qaJ95Cnd0RP3kE9jjY6SqYNaw26w5/cpX+MLf/a+4/fLneHjxgK2AzGe0J3Pap+ZwcJMhr8+QIqP39MOQ/QzjhCDbwm2hxnOFLhww+g0x3SalHitbktO2b1lUWqDuP6M2FfIQifL280c1qcS4nNexRyk1uQL8iM3jDW0WB1VV4vCo5mBZUzlHijDEROp3VO1cEUI0GUiyX1uKSuYEIabM19RXDCm35jN1KWVDc2sstasmgYdBpglrZU0W/p0YVf0WLp566CpSV1WWFM2e/01Wl5M7CGKVCjWO2la+2HHndMWDiw3dbqcwRYi58NcYs1jMmM9qDucti3lDU1mlYuW2fShgSEiMMeIL+CGaHIfc+o4pUlfKO+i9Z7PrWW86LrZbTtcdm0xxU6cOJYAoZ3O/L/W+yoRMimTOqTO0taWu1JWmbVrquqKyQl1r21uykMpUVpPRvO/Ve7fYeeVOYy4E8iCc7B2ZwOi9cRlYU1GW3scQ1V90DJHKkb2wS4yloE/K9dXWy2TNZLOXabE/LD3kJPuRzmGMdNuB0/MN905XdCHo9Q7aHT4+XHByuOD4oGHR/CuwDfraqKONUZInxZEUBlLYEfyO4It6b0cceirXKiE4Iwz5/8opyzOYUxiRcSB1G2ToiENPGnrodrj1GXL+kPTgIebhKX61oaqWVG6JGOWMqDddmd0J/RAQDPO64eTqIe3hMdXsGGszupeTNZ2h64AWkQVJasBROUszd5hKNIAXJ38TMQ6MC5DWxP4ez1ZbvvB3zvnNf3qV+ckJ8zdf44PpLT0US5RKgE+T+kqErO42U6uw+HBJ5n/F/P29can+mTJ8XRLElFFNSWXbJ7BgnaNOBuKoiyjzMkPSwfEhRXwKeBLjGOlDYPQwJsHbCi/CmBI2CiYaDHkOt1iSrRHXUFcNPnj1xcKSktpiaIBUAUTrIk2TeZvWkUKidjWLhePq1TkfeO4a105mJBnZrGY8vO+4OF3QdYGYHLZuOTw6pm1bmsoQ/I7oe1KIOGdpFwvquuH+queV+6ds31qzHvx03SUbyerGjjp3lqCTCEJG00QYyMKPwp8Mo/583irKqRmJacPQBWzjaGtLbGoYtqQxcvedO7z8uZrZrKWuW67ccFTNTA+JdKnVWJJIUza/bvqYwJqUaRmavA1jjzglbhufwA/EcUeI6jFqTMJVqqwMeY0Za3EOTLK08xnu8ABzOGeMnrpOVFWZzGGyYEvAXrJGycVhLFmHUTVXuCSoqEyjfNyS7Im264yJkAy77QiygRgJfUKMVS5rjOpZmrnBytJNBAJjVOFJjD6jj7L3USP/ObXoNJCqmCuPrjNO0Q2SojchYCL4KBn1iIwBxpindARN9EL2diOp8lsP7j2CIfl0j+XaFF4sOWksGJaUIpnp/ZftH0UNrU3QsZ9bEr2HuxJ55TxSXRhaSRw1hgMSf+wPf4Lv+ZN/Gru8wnh+we6Vl/nqj/x9wtk9GqsG7NWmZ/uf/m9on/sI9RPvZfHx5zG/68OKtMyPCD2kcdS1NUa6198khf1s3rBe073+2iXkap82p0t/fwSdBLCO+QvvxbTzfOsN7bPv0QEHiznD6owiqFDWM+wuVrzz+musXvoNbn3pS6z8yJ1bb3NrtdIxoD6wip7OJo5OWt6zmPPUkeP40GKdR3JLVxPIohTWQClRkx1tlTqG4Vm+3H07zXOf5PzuA+4//Emi+0U9gwwkUT9iKTzm8llLQW6UylDucxL2fN5yt0WFFqlcodIySih1yCScGFwtnCwqDo8aNa8OiTEkumHLkan3V1l4xD7IiEwJZSEvSz4SLjEQGfPACRHtzLS1dkmETAdhn0QVtNAYmVDPfdKcxTnFPSBe5ruWgnZfpEf07N31PWerNbfvnfPOvQfsBk9T1zS1opEx6MCIoetZjSN91zD2Az4uOFi0zCurU3miAj2xJF6lhZ+57KNPiGQHEmcJXgc4bHY7LjY7ztfqpbjebokhZrQ339aYHolnmRVGsT2zRgWvbW3z+25pmxltrYMk6kpzgjK9xrgCBOi5UmIS+ToXsZSITs/R2KSx3pr8HIZMwckJX0q5o6admfL75f6EEPK9CBmdDIToda3FCoy2w63o+T/lC+X95XWcIngfWK933D9fc7re0vmR0Wte1NSW48MlxwfLjE5eLqV/+8fjJ5RxRQwDMfRqg+B70ugJ46CLOWZPwKmeya3NVCt6KWCDDjcP0UMMiB9h6EjjAN2OtFvjNuewvoDNBjYXyHqLXGyQ1Qq2HWYYSMkyzFtknnBOkSf13LKKthkQaQCHSMTVDbPZArc4UKQs5pmjGLVSEYukKhOjA0KFMxbjNFCmVFrwATEeGBmHLeNuS79dEXzHjWrFU+dCtYPFuFM9Bp5YJhflU6ZwRHS8U8jcMK0ujEiGzEv1BKATeUQ0iGhSqyOzrKh6ORZScjZELzYdIW9ScqI9hECxsNJ2QmBMUe18gvrtpahk5SiKQlb5PTnAJnBWBTKxqvB1TcLQmohxAWsTwxAhOrXdsZbl3PLMU3OevHnEcuYy/zHQ2IrDg0OuX7/G1SsHNLXBh56LjeHKUaRbtRAtpppRNwtmiwOqWoU9fuzxY0eMkaaqqWcNYgzzszUXpuONO2tWPk5egtYatfLI5Z0iagkrDgkjPrfHrUmID6qOz0lPjFl4mQRMxKYz4vDDLMKndYbr8M/VMNtoa4oQeeeNd1Sks1zQtHMOTnS+/eUxkoUPNZ3SJUExGkQSiX7wdH1gHCKzecu8dsgYtaUWB6T2VHYkWa+8J0GTSTGk6BAq6tmM9uCQ+uiANHOkHj0ccrMoyl4BjFjIBv6lti0HSkgajWI+3PS31atVaPBhiXqdbhHU3217viaMmo2lKKQyajQETQ4jEz9VDZYDPgyKCEafnQ/kEX9VPXQ1AU2l/V7M4o0jYghi1e4nBMIYSUGdDHQSk0wWVSHkpCFqkWbyQVP4cSkH+dJijzGpNRfwQbvE1bkwtoqMIhbcSKhC5glmdFVykZfFaM7oJB4XQbwWEybpvxsjVMlRJ6G+XdG/dIo5aCA1pNUN3rj3JG+9NjKz0FrHrKqZve2YvfRFGvsStUl68BGxTUt9fBVr9RpV15/HHt+cIMcEzJ99kaP3ffhrEMm8KB/93oS2alxav73m9FZFkEjjb3Hvl3+Y7d232a437M7P6GJkENiFkS4knZvsg/6XoIuaaNpkOWLOMfCMCLUxXJE5x8Oc4/OaRSeZIpHfdzQEn/C547G/R0a5oqnh9u46n/yjz/HxjyX69QH/5V+/yXZ9k+gC2AA0JBnBzqeBFaVkURsywMKH7B5pDeXC5NcrFzFJ2k8FI+/vFLAq3eZgUXO0rKmNWrUMPnB60YONHB5Uk/WLkUIB24/YVdBAP7jJ1I+YghakKanTgldeoZCorPrMiqgyORInVloRnrg8CtiagtDqBzO5W0bplJSTPBU7mzKiWLmmKaqgrutGLtYDdx6s6IbA1cNDThYzmpmjcRr3xjGw60e6vmM39vS7yMZqt9LOZzhjcodLXy+VNnwu1CIw+KCdjRgnbrWale84X284W63Zdn3+nHnPSn6O3IUrBaCzSo8To4rxunI0lWPWOGZtTVMrINBUjqqqVLntNBlUYYy9JJIyIMrDjyHsW90iSvFJ5GutYsRpVrsxJK8dvZTAx6Bc/qQt8DIaNkWdHhe8JY5BC4gcC8UIptAHM8IqmWYxIZP5Tk5uAEmT+81mx4OzC/oxT8RJOhJzuahYzBVJ74fIdtzwuI/HTijP33opl0b7qmfahGJyS6gkkzlrCWSBRcwXpcOMI2YckKHHdGvYrWG3xa7Piasz5PQULtak7Q47RsSjCi9foN0KHxLiPS6MpFRNVYwYRVycqwhUij5QlHxqdmpENDNKChdrwqgcK5tqtaExgxJlpdnDNSRsFqdEPxL6nnE7MGxGuk3E+BFXWRpvMGaEFIiMyi1MZTFLrpT0OQt/x6Tc5rS5Ohb7iA9fyhzLYoBdDGMRM7XBYwx6cIfsUxcNfUkih8DY93og5kM8xIzsiCKXAT0wyR5gLgaMrYhIbvknqqiqX2csgyhqJcBR43jyaMHR8kANwH2eZV3VXDm+wnuee4bDwzl1rXwbIjjnaGcz5ssDqlmDdQY/drh2wLmBftFgMFTNnLo5oG4W2MoRU8APO8LQIihPz1YzMInBwuFZRVVnHqaxOlfVWJxLVALR+0KaQbyH4HGjxxiPSWq15FMRKZXVL0huLUgckeGrrE9fx/U1Eir8WGbrGgyJoR9489U3ODhaMpsvcFXNYnmAGJvVjvlZcxsnO9ZSSOnd4OnGxG5IXKx7LJrU1yK4ECCbr5tKCHnqRgwWjSmBgINYIVRU7ZxmcUA1nxNchQwJCYOuS4RgdDxnLnMUFUU9JtXLTt+sMRYveT8VJFAEn5bcGr8D//7vpduec3LrJ3jC/BbG9vihAxGS0ZYbxhKi17ZhSJQ51dZYEIeUVmJUDrSxBkkmJxLaRldbp5ygpsIl0n3hC6fRCWOMjGPQFloQRq/tbWV/SP7+XuwTJmHpPgiHnATmVBZSYktglwK/v73B7083yvKYkjQsMOdRNG+KlY/+VXRC6dc8NPhD+/Oe01//BcimxCkGPrRueJ9/4dITyvT1/i5qfIlAJ0WoE4AvA1/Zv1BKJPmFfCB+7fuYfuzyF1JeT4jRkoxDYmLwPcLIApgnQA6+9kkERQibS08q+68LWiZGsJ1g7ghyd8IO9+8lAVTTL777UoMhyCvM/9E7xP8alsC/s/UMeKiv7J/JomdBdem9fJ3H7dizK36H734vBcGbwIL8M0kLrsWs4XCm8SclYQwjq03PxWbk8DCP7Ct3TCRbLUW1QcstTGN0ilsR86VUJsokiDmZNIIzhqZyCpRlmlVpiWh7WxOPvU2X7hspCFZKe1qWpLxvVUQSCFkFrWZJMRYkNjAOns1mxzh6ZvMFx8eHPHHlgNnMYe0e/e+6gd1ux9lqxbofWK22ivCKsKhrXNIconQPyySg0et+7/qBbdczDMPES/d+ZNf3rFZruj6AUYS1uNAAGRTIxUK2Gypt/qaqqKuKWa1dJdVQ1NRVTVU3VDZz2rNXtsu0vTL5DFHk0KeIzzQe54TKJCpTMZout9UhZI2C5CR30nVnj2KVZ+aNIuQxthmtzSIiHz1ITUEkrFXvZSOKeBoy8mwy/11k0qikTNeIIdL3I6vVlrPVBu/zqFyEpq2oqoZh8Jydr+l3I7uu//ob4+s8Hh+h7PMc6Xdvq1wpCWQFoUdCII0dMo7qlzcO0G2JuzXSbbC7HdLtMOs1crGGTYcMPbLdIV0P3UiZAS2ZoxMLzJeUblopfoAzUNMQzAGbeJNtOuKoPiMOd9j0gWvJTNNUJPqpstT3rKRgKRMmJOTPZabsH8mcGdEQHYOn73qG3ci4i/jeZHX1SIpBmYOi6etlI9sMQDxiYq22FPkR1UpAc/SI2FwFBx0TFzIpufBmSgKoI0dzCyhodTwmYUiBPiXGOBK2HRLi9Lr6cpksnu9dmbhjjPI+JVdYwVUTf0zEgyiyZ23FiBDiyMHC8r6nW65dXTCf1aT8Oeu2ZXlwwuHxCc2syaMJB8icM1fPqZpZJugHMB2uE2rnMLMZxjiaekHVzKiaCrEGHxPgsCYjuq7CuopA0KkTVnAWrdKNeofV4mhcohaIBjVQttlvMjq8r3A+YENCxoJ6oYVMTgJz85XJLNT3jOceY1tiMUAHXL6hm4str7/8KsuDJc2swRlLM1/sfQNzIBFbqY9kiPS9pxsGuiGw6QMXfaLbeo5mDccHGXkMgTSMhNGTPPhoSKJBRUdlRUWWq5owNsyW1zAHh9AsiGEkoOi0sw1WahUuoMFuSs6S7hf1PxWKylTJ+mV+vEGk4sI/T/jX/gI3/8ffgR3hzt96nu7n/zJz+zqj77RoMjp+09kGnwTvu2zxFKhslVkdlrZuqF3FmAQbKp2SItr+AZuNcMLE4fRBpv0AmoA2ywNSVWP7gcGPjJluOAwRr2C9+j9SkteC/uSt8WgPMCfZmiCIM7xJz/9h+wUqzEQPEAdubpVTFhJhN06OC5kFqwePaP7oXKJpoK6EqtJ4JqVCN7rfGwc/8Ae+he/5gT+COXkeqNjeeZN/8Nf/U1760muEVIOZ4dMR6/4GMR0ysw84tG8h7AiMmrxbkx0jIj4kjJ2RxBJTYrvdMPY9eS7AFKMkv09TCo2UCPl6WVdh6hl102JYct4fMY47jL9N350zRhXI6FmbpvwzZaWulLbj/vjAR00CnIXF3PLEyZLrhwuODme4JiM8okBGyNSjmKIqYuO7MvJkCGbBm+GjfN//6s9y8/kThh7+8//op3jtZ/4+tr9FbCLGeSSO2ebssnuGxn29YprQnYWRB9niisTETZNcCGqsL12H4h9rqJylbSqsUxfKEBNnmx0XF7p3dVlYjQ2Zi1csdZRKsnf2mPwdU/YeDFq4i6gFmjXQVBYjTIiruqQUK5wsFkkpf+8SSGFQKx2ZSifEWFzS+dwmOgxOC8GynmPCR02k1N85KtJXW5p5xfJYOyqaHwjJRxazlk1bE2OiGy/Y7HoG6YgG0iKyqGpF2IzVCVCIglEJNtuBi4sNm+2WXbdTmhV6DYZh1A4NuT4LijqSclJOUpRelE/prKGxyumc1S3ztmVZ1zRtg6stde1UOGMdLncNjD6B3gsrU2I/jEFHBIfEoG1SZsZQZWTYGEUntd1d1OnZfzMr7yUnt866fP+n+gtrLUNW5WtBkYGLmNHPgkBK3nOyT8S1MJBLBZtuRu89282Ws/MN55vdJDAzxhDFsR0TD1c9/XimYxn7fwUJpcnj6CbPvCSKZ6RMEQ5ZHuk1eZR+C90O6XewOSedPyCdnSHrHTYTfdPokUHFNymMGTGKU7VX5poquivEUBSmyjF0YmiTobVL7o8v8rmb3813fM+zvPryFzl4+Sd5IpyTvAp2YvCk5HOJrIilJkmGPbwQEdEqQ6cc5OASM/SMx489fbfhYnPB6VkP0mCdtvD74PHjQHLKyiyIZGmRqDoQiJcc8XOYtRkpIakCGnS8o4+QkjBk/0wfVfUdQmIIXjdNbkeoIXQiWcconq3XoJsGj9WTdOLPaLVJniE7Ha/6X9QxTkmSolJ5M0gAkqVMpEkxYgjUleHosOH69UMOD5dgdFFXdU07O6BdLHF1oxy/EYh6+NuqxlROydZlrmsO4hPnJHN99msizyO3TmedVrW2OvUkwIkGA8xI4bFUTmgrh0UtKKIY5fkmwYQKcSPk0VjGWAWwk/plxhAIKBLmECRZnCm2E4mURvbtV4NPnkoghcCDOw949YuvMJvNaKqaw2wvUYJyjImhD6w2Hev1ju02sO0C696z6UZ2fcIky5XFAbWrqKxRv7OqhVATtpboK2J0WtBIojmyWKlgFOr6iKq9iqsOMabCj0IYR0KqMVREE7FSaTvGZOulVIQskvedBqKQlOWoNoxZXJSEnmNmH3iOt15WO6OnP/ki/S8eMdcBaXjfI7aZEhZt0ahoS0RRFGOdFg9NSz1riGGk9j02gU+eFA1jMIzBKx8rZupGTFMwFgFbtQxJkVmsKrnHoIrtCVEJuhFFCitNizLFXVI2GEgko219nSWc+WlOTdPfomfiYyUwzlC5WgvSMJLciB/y4Rv1+k3uDk5wFbQ20lihtlAcBQrS5Ywwq4TNiaf+wAn2+tNAzbDccXoAb9hReYJiuRc/ysEf+Td54bveyyv/5Fexv/y3OapfJqSeKGMeAQqRwJA5g9bVekibkUF2uu9yXqbojUyTSfLGw5OTzWhxRNoEFR2+OsPLjiHt2FY9u36kD5f8Q3N+Pgm4Lk02SYncKtaY1Fi4sVjSHkF9nOAAjMt0hBwbY9TCOIQwfZ30UND1EGugJ/iv8Df+Pz/Fez7+ae589Tavv/RzbNM79HGtrxkDEnr82O89EkmTuDRGBYaVoiJTDNecLJ/QUzySvE7yWYlORJlnRTMpMaTEZjNyvvKM475ucZVQNYbGGWprGCXhhzjRJIxRe62S5KbsJRnyWahoV6IyRik4MQvJcifRlEQ5t4+Loru8gWk6zYRs6yeoiOwMiDngvP8wtjtmWX+VytwhiHa6FPHKe8MqtShJBJuy16Pbi32yRZIZLAlLSLAbdtmNYiSNgXE2Yz5vteuUz4AQEl0/crHdcX5xwepixTBkYZnk/Uo2ac9Au80ahBTVDsgY0Y5HnnpT50S/bVuaumU5nzOvq2zxY6kqxQtLB0nE5GZK5nNLTqZ9YPSBfhgJEcYYceqGnsGqjBCKclItdkrqyhIS9H2JSZmeYKmtCgc1LOfrmu9fyrmQSC5ysyOKumComLe0/PVrfT293Uob8YOn6wYeXqzpxkFje1JE2pqKYUycXQycr/pHQa/HeDx2Qhn6AcmVpxp1CmYcMNmYV3xH2q1ht4PNGtltYLdT5HG3Jjy8D2cXmFGgnSNVpVy/gpplmHtfyaoyTNuE2s6VZNTLUAwSEzZmboI4XrXv40/+zz7B+59s+NZvv8bf/Tsb1vd/hiEoakgKED2SOZ0lgdy3o2HScuZDiiwiEkBHhg1sd+c8OLvPrXtnPFh7Op9U1EJi13e4EBjyIRS9IpQ2m4qGAlVmyo4mtVE/Q9I5pcW6AiQrGNUPL5T2dUqTb2XM3FFQNSExTYFostMJgyZoUd9HY9Sh3xgztT4m9Z8knWhBnDaqE2jqekp+bDRqVxGjjtiKOglDjFDVFXXjsM5hXEVVNzRtQ9WoTZTynaxybq3N3LMCVcQpoO29tBSLVSPz3HImC1kMatabfRqFQnlwNLbC2W7afM7oJlWObSCKmnJ7tF0RrSUZq9xQ0Wsq2bpBfX7UAkJ0YVBMmwu/JcSIiPL3IoaQIjYqR/Wdt95hsZyzWC5JrsLO54p6pcQwBjbrHafn55yddVysPZttYvDq9ynGcbSsaWcVTVOpuXuqCQeHuJOb9AP47Rlh12sgbQziPdFHnGmommPM7BDcgoFDHmyuszoPNOZt2vaMREMwRRSW21kZUVKfuz3Xl7IdTG7UyIghccgdVl/8LE9/93cxv2m5//d/jgPzMKM2EYmCkYDNvlAmaVVtchfAGlGTeWtoZw1u3jCXJaPUYCKbfsPDswv80NN7RUNiScmTiqlSjh0hBEy2ndJv2YlHW6ghis5k9Awpwv+paDViCKJTdsYEySSMU96jqbNZvjBNsDLiqJsa29QIgjc7koUoIz7AOKhnnHUJV2cBmNPWm7OSESW9rkYvEClpAt+P497ZQX8ElwRSpQVCvErzbd/PR//DP8i6Fb7997yHn/jTrzJ7+BbG7LS9mZMFH3TilDGFU6cCh5iLuJT0ipQpJzHkhKNcSz3BdG14jzed8mhNg0meutJBCUkERqX5ksNdyvE0lr9cQmRF9BCqnOHm1QU3riw5XM5om+zkF/K9mdqXcUoky0MyKqevo+r2uX3Ii6c/zvCTv8hyteFJe4833DljlYhWD/EpKcsCwinmpOJ/TLbc0aLeZNZVQSmVW6hFyd7uDepaR6XOGqdjRWOi7wLdkIhJCxUnGu/b2jFv1E5H9Z8x+8OUtmjMTgo5uczFrF5bvXe1yUrhfF/LWilJRTndLk+gIdvoiNHz1oqbuJpi9D6bUHOv/w7e4i/wrF9wY/wlnpz9HaI51TMZLT6cMzROEdax9+y6ka4PHLZWizZRJ5HNrme13rLedVxsN6x3PT6myUR9GBJ9gMXS0FYaH3adKshPVysenJ3RD8Nkam7QArUI9ksCXT6jMYLLGY41QuOEpq6p64rlrGU+n9E0s9wZKe3sPOnIlGsmOSbmZDIlej/mGeeBcQhZYQ9iBYdgkgokgclfu2yGlAWNYYyMlSbdSj3L53mMxABitYtgjQqWnHVAwlx6LkPx483fkpJn71HL6SzNqGcMkaHzrFZbTi+2jCEnk6JPoM9nkYL8p32h9DiPx04o224kBa9k75jAj8iww/QdKYCMW1ifw25Qw+WhVzVh8NDvMN2I9KP67WXzViEhURFKQiHa6uFerk4SIRptzVLEArmVVItQO8FKIKaeG61wRuSVquL57/l2bv/XX+Gj6RaMMVvs5IkccpnIHXI1ocnkHjqWvEozdExg9Fsutqfcu/eAN9865fx8xIYKUy25ce2Idx68xe7BfUavCJUVozZIKBE0SE4mk/I6S1B3JaHMQTuZYiKkrz2EyBBihvl1URg0sVQrAMmpTJymzljrcK6mNkbvWzbXrcRSV1qxpTy3uajJU0ELygpERzlK49Rc3iXoovLeM+IZ/ID3tXLayLvcZmucPPJtUsMhim4mnyvjwgnK9UTeHCbfh5zy6+dMEaLJFahachhrc5KpD2PUB8yQ/fxSxCR1V6ysJpFGhCDaqhEjYCzW1VgXcC4RYsA6DQg+BsKoYzYhJ5qFeJ/Q0yZFjOQ2vzaXsupYr2Pf9bz16hssDg7YBXDLI0zdsBtGfBIuLnacnq45P98xDAmixboKcbnl4ipGD5vBU7fa3vcHB5gbT2GTxdkaHj4kdmtNeHvBOYdJC1x1hDc1hJo7px/gC9W38s685unV63yYn2EpHVF0HOAlRg/T1Ah7SQgm5dDVn3ECtQQq+zr+R/8Kt3/1Y9hq5PqD36Byd7Jvpc2o36AjHTPnVqdEqLJScU+9F8vljCeefpr3vO+beO+nfjdXnnqKh2+/yv/3b/9NfuUXfp4QgiYYRrApHxgZuQkpkoaOyAUEr8lgRvdDiJk7rOg2k2erFlEaD2Q6oGNCr4uAl0RtDakSUuV0veYOAaKmz+J0dq8IiKsYR89oVOHs0WlX1mm3JeWkTNtSSdH6vIAD+j6dsYRoGEaUp0gFZN+6Sp8nABINiydnnBnYDIllW1MfXCE91GyocFJLq9b7oCPgsiVUQSamvZavV84VJqRO+aoJaxPOJExImBDxYjB+qzPRXaKOeQWZ7EXrdepQmOJK7gtJQYwU2VrMK5575jpPXltyMHNUld6nGJWKITDV+pcTyTTFhdwSjmCSx4tD6HF0pPQQZzqC2ZJkJFaiLhX594pAroAICfUhlCz0Ms5M1KKi5FXxacxrgWwLpueKM3ByULNsdG68D57R6yz4ZCyusZCdMJazhsOm5mjW0LaVvo9B18SohwhGKjXFzjE5BDXvVhqWCmzUJiY7E1ibx/oVdK0kFLmNH2JuB5fPvbfuKoi0ChATu2S5NXwHr3VLmkVDU3+C4/EnaaszJo4P0DjLcjajrdZstzvWm4HNoWf0nqpRH9DNdpstatbcfbjiwWrLplMhqA893mv3aojQx8RiPsOIYbPdcbbacHq+YjOoW4E1+yFkKbeNTdnLkAVHQjGUd5Xu01lVMW8a5u2Mw+WcunU4U1NVNdbJJFKysueVlssXUcN37z1jinTDSD+o6DbGhHN2QhvJxTSm+DurJVDjamZVtt0zjpD0vEhol7DKVmigyaXL8dE6pyp0s++WFKhFSiu91Hu5zZ1MdqQoCXbK+yUkxmHkYtNxvusZYyJmhxFri+2ZxWZHGAzs/QR+58fjJ5TbjjDuiMMO4z2x2yHdBWx3yqvyI2y3uiJyO1RSUAPSwas6dQzImGfOKjxBsdvItC0QHWBeWg+gWXocRq1AY0Y4TEEWIkPa8KL7Mj/8D36Jp77zvbTXlrz2pTf56NCr11xSQ+2UibxCRRlFpAE1L8R9+QnG7tWkqi5iHNZsL845e3BO9zDw4tMf5yOf+jTX3/MCJ089wZ37r/H3/tZf5tZLbxGjkLxHTCSKnSYaaBqdb1ZOQ4qVQZmSAHr4mRxAnbGZHJ2V1pmAC9oOsrk9XFmrKGVVQ9PiJQspmlrr55Cw2dpFcvXqI2qsnczEkYkokkQyOBFMpebi1gpdGHIbKOBDD8njvc0jHD1gJ07WNJdaSlIpSDKQ53pPuxVy9Rb39iWiiFHKCyPGkLkrRZXM/k8SJiq6q+iroomFIiFWFY2VEdRMSCs9FXCpfU7T1BATMfZAlTmmmgSl3MLTmduaeBujyZKJe8sRSRAlz83N19KHxOpiw1e//BqrDuTwmNQs6aOQkmHXjezWA9FHVaM7HSNpjMNg8GPk7GKgbrSoaVzeDwcLbLyin9c5wqomDVtCP2B8hZsd4OpDBhp2fcsrmyf5YpqzW1uon+M5f4UbcovKkMn6FXpIpn1AzYe/tmQNwZDtStRjVU/yHSfNl7m6fZ0RMPVAAdnV3ioXawnKye+co8ojwbBQN3NuPvE0H/nYh3nmfR/gQ7/n+zl4+gXAcO2F9/KnxPPS53+Lh+90maqhRZjV7DALotQ9IuzWRO8JtskODk7XDihSUICosm6yh2vZ/2VFBq/itmg0FmFVOW4nFEiRoj55RCqcSYhVrmGiIhlFirUFqC1vmxOXYucRYyLZjJSm/bUNEayJeYxe9rQTh9gWVzeKgkdwco+7P/X3OP7WJ7nx8ec5/cUvEl//GRIbYgInjohXmxUfctJMno0MMWhrVMn++X5l9FzN7Zn8cA2SRZYosu9B4gCpJ+S52M5V2npLEe8So0aaaRRjkn0nRu8bnBzP+eYPP8sHXrjJQesYh57tbsN226vPaH4fMYm+uGhyF0IxS8tFUCYnR+PyTGhV+CrKqpQfY022Pyum0FktreE+nzUZncwFfkqZV6yvoqdVtn7Lf5k4oZWD42XFwdxRW+WXDz5y3nlCUnSxqmpMiFS1Yda2tFWtXoxOC3JntXBXixh185CYufdRVd0FwbaZuqRcb43tJeYqfzInmplipcs4jzZMZR1C8bvVJpBOSYvBQBhx8g51pWb7jZyROCXkM4Ck+7CuHG1bs5y3XPQdu+2OzXZHd9hQ1xW7TTeN97v94ILVZkvMVm4pKre5HyJWRpJ0JLH0g4IZ69WW8/MN601PiIKzl7tqun9FUYgp6bdGcBmudJWhqioWTcvhbMaibWibinnbUjVasBf00RiTOygJRCGdmFQm04/acRhGnwV/Iz5EBTCsJrPaDtd7ZTOoImKwFSzmc0Rq6lqoM41DfUV1H2jHzVCjc9RjRiwlD5swosWzs4qiilWRkNLVdDKUm8x60QJissrL3SW0s9L3I+ttx7rrc8Gm/ypG84h5XdNWBiuRKrf+H/fx+JNytjus32E258hmjawvYHdB2nS5OiyRKiEZniUEYvCIj6QxKjo5wU4xE4FVyFMqPQVEJFeIgsnjEsUYxAppyBvMamvBGWEePVfs61Sv/BO+8rmaoWm57lfUsw3DUBRSNTGNxBQUxUslGckHhJRFlKZEpxwvWmWMBN8x7AbmXOEH/uQP8qHv/cPUB8e5OhQOnvsgf+6g5r/4y/9rzl47I45qh2IwmY8jucmdJ9iIkOIeGbJG22lioK6sijCSIgSjswzBZnGO5OumB2X2JtH2UVUxir6Os4ZoDDHMSCOIKB9t+rjGU0vCR00Y9kY7OsvakAh+wGXoPSKYKhFz5R2sJ8YRPwrjMEwHUjFVnUYPyn44fcKpCbsIklW+yoEpFYU+TG7PlEBIkbNMiaQeJoUKIVquakDMB8XUTRey7QMatDHa5h41YbRiscbiKkeTEoi2sZ01pGAJRjmXZTW4rK4XMnoag463yoR8IpnonP3sxsjdO/fZjWAOrhGaQ2iW4CpC0jaJE22nUlBWUWpBP0ZO1x1DGLjYbFm2sGxgZgTT1JiTI8RZZNZkmsmWNERMdYBpl/TJcXYReOftM9YSaZzh+GhNnQaSVXTVGEU0bOV0j8W8J3L5oxc9I77iJsQpp8yaREmiCjIFpiACttHDV7wa0yPMrVOD49mS5dExJzdv8OGPf5KnX3gv7fERy5PrLJ94JiPvCaqWK8/eZHZQYW5brPU4U+fWTF4bUZHLIajJscQATeHBVYj0U1Vf9vfEqthHuInXpNxi2Y+UJOjkCnJbNx+AmgSq1UcrcypXQbCEMBJSRUqDjvs0GR3PizEqaXNCkEQKZ1EK84cUBe9zJ6AgR8aCU4GEEElpzcn6l3nzP3ibN5oTmt1djswbJHaICYraJo3BscjYgSJ0TAmGUVXwvsQR9kWlAIS034el2xaUQhSTDp5wqLgoogdSipFkIDoNsFqHaUGrRaHSCNq24r0vPs03ffBZnn/PVVpnOT895+1bnRYDSYu5EhZSttNKyTNp2VMuEPPnSnnvK01G72uQCOSEMmTP06g2Kcbk+FDJFPFt5vuGqHvYWTXUpnRS8n1Mea2r60HkYFFxuKhx1hCisBsDp+sNXYDaNTjTEqShnhuuLWuuHi1ZLprJDzZEdb8gJkJw+BAz9ziLLcfMicfkM0JNuIHJiqaqnCLi2aJICvKSyp7en2sF5dy7ZSeIgpF8QonnqeofU5sVT3Kd4/Q5nHtbY3V2A0mSNG62NYeLOettz2Yc6Hc9u27EpB2bdc/t+yvunq7oAxwcHRFCwu060rZTipBRt5YQEmenF9haufXb9Y6L1S57UKbcAJOJAqH5c8pcw9LqTyrQrCrqtmYxmzFvKw7mC+ZVRdU46qpSax5jyI06nbOU62QyIOGDZ4gqvhl8ZBj9VIzokJYiVCLvE5imIyWonWW+qHBWVfhVBSnsIPVIRlsNGgtiiEjSszJKVOFtUE1FEkEyT1Vb79qF0PII1S1kEqkp9/xSjFOLokjwKiperbc6ehkok/ycFW5cOeCp4yscLBpqZzQvmPyyfufH44tyzu6QQo/ZrEnnZ7DeqOVPP5KsRaxT6NY64pgPX++VExJBomb/+ADOQzAYqzY3JiciScylDZAvUEjEscxGzYiV1Ra4jVG5KM4icUszdjxvAtUomQvYsts5ht4x9COz6NEyG8i8FJmSSN1wpeIt6KB6jHli6PDjQG1aPvqxb+N93/vH8zSIsgJ1ST3z/t/NH/3Bf5v/5q/9TYZTAxhc0nZokLhvDSdNmoq6W01NNWgb0QAhmf+SIlDVjMHmxZwh9aT80piN88ohWzlLHyN4bUOBITQLZOiwySEyEKLOxdapNQVKN5OnWjFbIkXEe8RpADHWKL4adTxejJ4UNMGKmfAkslfF7cNXqR4tE1RTHqkETf1smXyQk1FN2CjzXwtCVPghIns1slV0Rc1nHTHqLF3QpDYZVNRiBBsjVVURY8qt5UgIQf+MgvUj1liCRHxC+XjRY6c1w3SoSX6Pkf2BkzJKIJk3Fr1n9eABpou4g4RdGGS2JBYVva2yr5nBWaeKeKsra7Mb2XWRi3Xg6oElLQ2mragrg5nNCNZgmxrbH2D7kdgF2sUxHBzQJ8vq7CHt9he4GjsWV494qvsSB1feBGmm5JwUscbpvtD5nEhOZvbJY8w0AqcICWAk4JJT4RKRRI0LVxA3YKXDiCeKxSaPsQ5XNTz3wot875/5s3zw274LYwy71V3GfoM1NVQCjBiaXGjs+M3f+nFWD89VwBItZVIuMBVYg48MUc3KdeJdmjwzrVH0PU6JsP4ZU56rWwrI3OozWT1XV5AaQSptm/sxXaJmCCbpkACfQp6uY/E0rOLz+LSgCl+kNnezD50msSEmxMn+ADKFapLfRsr0HoHgR4jqESdGEyqpK6wNBAIhCbNwzlw2mGHEWwt4ogxgZFJAx5TUnuUyTzIf3t7rtZokGSnnF5djv0Yh9fP0Kk7CqIVUMNmGJpbZ0/u9nYx+sGpKnm3uxgSsMxweVTz19BE3njjm4OSIWWVZX6zxQ0KCJkSqadRiS9X5ub9NLupyYVk6TYVfWxLNSX1dWsAIOnxBk1rrBDF5T097VuORgrIpx6e8bqakUpOpQKKyiYOF5WipwrmUEn0InF30rDYhA4AeUo91jvm85XDZcrJoODxodHgGQlX2mbU0TkGHYkE/jiFTlDRZdtbkFijYLGysnKUyedwienbFPCyjxMt3c0/3o2UVidb6LGRRiOdYbnOt/v9xbbZAjM/t/+ySYKx2payhaRzztmZRVWyGns1u4HTVMTSezXrk/sWW3ZhomhmztlFLrygMg2cYdbJXHyOj7+i9FqBCZOxHQlCQRTtxeg9IqlaXUhBIEbeiNkBtTds2zOYN87Zl3lbM6lZja2Wwxk0t7kJL1OfNLireMwyBfvQMIagHZmLyhAYu0eayt+elTqcOANEzad7UzOuGxtXEONLtRlIcJ8pGGcOZJuV8YMzzXo3RszZrCvPqln1RkMEYyUiUsvRMacRM8a50HYIPdLuB7bZT9Nvk7m0aWcwcT1yb8dwTx1w/PsI5S/QJXwjRj/F47IQy3npNP8A4IP0I/YiMCYkKqccYEJer0wzTq8+aJo0pj/ATna+kkSnu2z2FuJryzUgJUvCQ3eGNcRps64o8/BOTIk5E52wY8MMAEvEGxAkjF2y2C7Z94tgHTUzCqJYxZUNRInl+pP0fkm8YRFLoIAVqt6A9uo6pKph+1+S3JCQz46Pf9ed59XO/xW/8o18gDcVRP6EqeaYJBCICNoLEPDc6L1K0HSXJ6MbKSEYSoRI1TVOVY9DWelBuUgH5fIoYE1XAZKBOS9b+kPV4AiFwtXobsfcxWGzeIMbJFCghT9NB29CMI6nyGFNRWUs3jAQZiGJ12k4I0yGrCYnNLZcsrCqFgkguFJgOdT00PMVi6RKUpO/r0o0RKYhlDpB5zZR7KGIxTtsYyei6Crl4SKIthZjAIVBnxDgk/KjJvLGCSRbx6k9qrAYegwZYPRlCFjKgJ4voGtGZ1GRcNycLSdWpJifpKYzE3Tkpi4m0rihIpaqdFWlQtCFmo0JLonLCctYwd1EPLZkWMFSVfvZ2jo0GlwzV4pDQHhB2kdPhnIvuFjfcV3kqzLgpDU4qjOh1UjZf0gAjuof3h42iZCIGSRHBI9GSMISMdAloa9fNON1+ijvPfB+uqTh885/xVPNLiN0AlqZyfPMnPsWf+Yv/AVdf/KiuD+BwfJqzO2/Qn5+x3a65++bnWFy5AjLw8z/1D/m7/4//J3E9KmprzCQeCHnsXow689cnDS2FOaJBOO+LVCw6ZJ/1i3ZFQtBENMYsshBBLNhawGV/y5JE5jrJZd5aAKRyNLZGrGMdPs1w/c+T3HXsw/+OdPHXqaqHOi8+6fsJMRGtTEMGFGXVVVMmpRhEE8p9hYKIzUi2/mxlvKJVdGAS9bSPyOsSpab4gA+a/Og91MNF5yRfwmhLXc2+bit/FI5d8ioaiFkJb12ra8WoF3HwfmrJIprkNLVTsZ6dEYKnGxUxbhrHYl4xn7c0dYMkT9+PauIcAqRGEznJM7pRSoxJBswARgt0zP6Anf6QzAVEbXhiXssm89M1tlZgIiKRwo0uLWIp9yBlrnm5qPnMSPl6iEnMZpaDpaNymnqPMbBa91xcZBeOBMPgMU1DY2Ys6pbZrFIz7SzcMeW5c/vVOost3NfMAS5Js4ia11sRKutwzuEqi8tCywJuZCRH18jUMSoRvmDRZW1funzGEsJAwKtSujEEM2BG7U4VFZt2+5RrXFeWpq4wldNOwbrDy4pFZfF95GLTE1CUWgspk2dHx0wnGrUTN0I3jpeoFvqmLCmjfAp+kJSvWgoBK1BZna29aBsW85bFYkbT1LRVTds4BbuyHVNxD8lXaTp7vA8Mg6cfx0ksNGYxbIHolTZXzvA0KdnNpTwixkiyeQ/UFUZ0HKYfE+yybVBSoaqU6y/KBS9uLiIGAohLUywu+X9ZJ+raYKeawGQUf3rCHO9UXBkZ/Ug/jnT9qHs6JogjRgLzRcXR1QU3njzi6vIINeGPjN3A4z4e34fyzkOkdiQLDGoIrVCrZPg79+JDxGTOlFbzYb/5nCGOPRIEGSVH5ioH8IxMmsyxIz+nyYiV1YuPcYgTbEqYutbB9UaRT73ITjeVaAN313v8YBj7gTB6qly1T2OkMgchQwBTJVdk/6qkisQ4QPTYquZi+4Dd+VvMrj2HSMUlPA9IuPqY3/dv/kVufeF/ylufuYfHUCWhQFhlu6c8TWFyxM/wh/LFtX0bsymVbgIVQxggVY6YHD57D4YsYoqGrBBjQk0CV/kKP8Dxv/u9HLaBN//ff4/nz/5bRE6JzitPJO0nKKgq3KgqPUqe+qDvyVlts0jmpBZEwBirdg/W7fkjZdNmLs8kxbuUw2uFpQchxQ9LbB5Tpz+j12uPoIjZTytAVMGriluLFauKOHHquRhTTir1fTi0nQAFFU2Yfpzea0KQPLvde0swCWOVtuBjUQbkNmQOJLH4QYkHUxGMFlMFXdh/jkQae/zmHCs1SSxia0xVq0hDJKOrmXguQmMNV5Y1xwc185mhsgFrY0ayVWwWQ2TwASuWxXxJNZvBfAlU0G0ZuoFNt8JLYtnOOY5HXHEnVEVIQkavoo6KiwrxMc0OLsiGlHQZeq3yVOhiwSbDtr/J2y/8IB/43/4hxBhu/1ef4N4vPOQZ+1mMSyxODvl9f/bPcu3FD+t9E23xSP3/p+3Pg33Lrvs+7LP23uec33CHN7+eB6AJAg2AFECCAEhKlEhKohQqZcuWaLlsy2UrlbIdW+VKuey4MricwYlTSqriIXHCyKLjQSXJlKzBNDUQkAgBBEAAJGaggUbP/V6/4U6/6Zw9rPyx9jm/26Blt/7Qrwro7vvevff3O+fsvdf6ru+w4Mpj7+ZUv8Frv/0VvvGFV3nx1S/y8ne+w0vffIN4ViixkPE2zq74ZA0JIWXIajxjVYcWD+oozkZYblxv9QGSuvUUrRGkzsbl4p0J5wCqIEkwXmvTOkqimrBXV4X6/Ioz6k2WJdvwMTZyG00tXP3dhO0v49wpYywsGGczq5KKxSeOEryRtydQ5wbGPxWseBMfzNYKpXNGH0rscOrwdQ+ijmBNrW3FlJZxPG8F8JgvLMg0iRm7IKXuL/VMGg/0CgoZxWMXYW5m9M63lQNmPqiZxvwhcRwdLrh16ybHx0s0R87Od6z6HoInp2Q+eglijORtJMYdm/WGYSgU9QiBwiFluE0/zAjNOa2/A1g8p2qeGqCpIlJ9m6LcMXr1VVRYlazVUf7SuHcUJ5WKOhVVyFI5rBPTv/6d+oh4aDvP0bJh0QQoQq+J8/XA+YW5n3hnvFpfhKZcZZd/gFU54LHwAPFbG2nWYItxBB/q+ZOKs6SnskfF9MezAAABAABJREFUwBC44LwVkt54l6MIZx8torVgGgsRqePzMn2acVystWgevzOnjKZEjvacduJxUY2mIVZISp0NWxqM5W2nGt3XD8nSkWLmLHjykNj1O1xwxNLQ73bsYmS725BznFBkreNVDwY81VUrjE2cFfAOw5RcvR/eCV0b6NqO5XzGwbzhaD6nm81omkDTWHKN0cCs5Am1KBbnKnce+jgQB7MB6mMi5boH1P3PCmG7dhMntSLClq5kf2aPo63DPfxpH8Bj52NOFTgqYy9pkzsvnjTYs5uy4gJ48XuhsHN7T9GpEr3UHtQHxagn9gvMR9aQ5ZQzq+2GbUy2J2TbI0Ijk8H7LNhuYi4oxWya3uHrHReUsrqAtp2SZcSce22hVqGHyZsFXCW9FDFUsootDEHLSFLUO2jmICb2sBPUWdFosyErMkqtrp1Dmhb1YUoAkFoY4IRBCqUKFlyxy5uA1W7LdgcxNpScJ/K2MKqMa2Fi7aZtUCPyMyJLxSyDKAllRklbXvnar7F45AmObzzN0fXnENeyL308x9ef5w/8C/8Kv/S//XeIZxklmCBGLac4BOPNece0ARo0XQ9MtfGY5lzfZzL+RFAQNaQCanJJfdAL1bvR1eQPB1m42D7CY3/y9yJ/7Flycjx75R/j/v/hCzziLqwZqIpLGVEYoabSKDhFJZt7DjZGDM78znC103ctvulwocX7Fudb4/VVLuD+pRO6ONWHVQxQcrbDuVr6GCBgCrixyB9tMJwPxrGs0L73gkqaOrZSMqIJ8oCWCBqm7nHEZIK3eXLyhdB6QvbE6MxWhdoZY0X16EVqKFGN+RsXr47iBbW4d82MLOtUDdSlHnKGpoDGDcP6AR6HlxZCi/OemRfmnadxEEQ5Opxx7XjBlaOWxcybCbYrlBKJQ1+5VhYpuBkSKUV2peNKu6TxDYOawf2QIsNuw5AyF7NC3DbkcjgGsFgBkAxljXmYEFUT14woDaZ+pVAk4aS1wkhcPcQCG10y++DT3L2TGc7g8Z94F6e/8QTiv4F3A7NFx/z4qDYUVsQa7mSHu/fC2b0T3vrOA77xuZd449U7kCx6VNVX+gC1c05WIGVvHL0iODW0UbrG7mDJhoA4Z+NagyqNpFLhb3E2zpYwimIMQXTO0Mu2aWhCYw1ebZS8VB9A3RcXEEF3SLqD14zS0+S3QFfEUvbCknF7y6DO7l3Vlpm1Usk4CXVPGMV73hrpusag0LhK5QAoCSGYYq8oHkcRJWlV0SiUbLGZUhy+2HjOizUGtuZ1siDT8X+yLyrVtkMQGHJGdj1+3hB0UYshR8lbcvakUpgfLXjyqUd45onbHF6Zs17tkNfusTnJSKw26eKJVQG8CR5SYrPekZIipWXQa7y1fp7T2Uc4fvQ2D195hafcZzlefI3CgMmzcu3rquPFfmvZF0pi4gUVozxpNhQ1iTDGDVYyQC2c7TkY1e5jI6LKvuH2wrwTjo8a5q1F/8SSWG0T5+eJnKzpcULN877KZvYLHD/9L1J8w1vDr/MIf564e4B2StZEwfj9UhFR0TpiZZ+a4pzxJJ0IvnG4EHDB7Tv0Wv2PgrM9kbxgUcRhsqLSYs25oXRVJFYyKWq1wqnNog/V09AcWmzqUveyinDGlNnEaPGau4FVjOwGV3UCClpwxVT7/TAigcYJzUpVMWfLEZep9avnmNZz0vydw8jzFlszs6ZhOTdE8mA242g5o2sbmjZUNwWhOMVT6xRnfpmK4LKSUqRPFioxDJkYLXvccIhaeNdiXSuFQJxMwMnocwmuTkuK+UU7rWEvSiSh0WgyKp6kFtRkDaZNDjSXifbnnPke2++sWgsZWyM754MYGGXZ4H4qtGUcAdpGZ8V6MXQ4RWWIhb4W/+Nq9yLMQmDmgol7tBg6mhVNIzTyP/5656Kc3RbJabIm0GyFj+UOw5gOAGIxiSOT2qAypFjOJclGVL5ziAtGtHbexuWVx2FolVkrmDFUg4YGQrDRSsk28tV6WKjapu39dED1KZJjJKWBzQpybKs5M4YEuTChZlNBOXZzo5S/Iqw5D5Q0kAaDlH1jiuC7L3ydV7/xFZ549od45D0/Sje/Zp2GgBB48n0f4d0/9hzf+Jvfpi8ZD7ROEO/Nx877WthKRXInBlDt2kbfMK2jbBtxZbdXphpEbihwwE9j66b6XxXnmfvCadmx9C2vnWWeedeTvDy/hgz7omzs2Efekq+LCTAEIBfLDHXBBD71nfkaRdW0DSHY/8x7q/pD1g1tyqYV6qh6OomnE0xrI1Aqj0Xq2Gws/A2ltc9lP38/tjBAwor1lHpIZoaXYyRnd0mUUH+mVvuaxtHmxja43uILvctkV0nX3nwzLfrM0MmxGDLs3E1FZlFTd0fXEl2i9Z5EoanV+ugsAIU4rEkSmDUNeQVdA49du8LyYGaejF3L0eGCw8M5s7mjDbbRxrgj9rVj1Er09oGUBk5XmbPdiq02bLQhqePkvGez600ZOyin5zsuNpGYMkihZBsx7nY7hmjm6GV85hzTczjap2i1rRA1Dl+Pq56DGZULNt/4Krd+5F3IDU//lS9wqN80NTCO9fk53/rqp3j8/T+Ma67YYYNQikN0zWp9j/X5mpwdKdZCsSr1ay2IKqRSLHEnC5oDJDcd9n4qgYI5QpSCph6hmJckdu9zRV9LMRQgqZn4F1WkBd94Gzt6XyMgmfYardODnFM1FFaStohfc93/KpzdJckxsv11Ot6gxOoVoYxYJM0Im9YmyI28akcdw5p/oY3afP1Mo5eugre16MUR424aX3lfr0Ad8dsSGlG78Xcxkfkn0RWMgPvllVn3y/3Xi1ojpaXBl5ZSruDiERvt0N1DvDwAP3D1yhWeeOJRnnj8NrOZ56xZc//eGTlm0hApqsSirPvExWrLwtnUox+iqc5V2PS36Z/9/fzf/48/zpM3Oz771ef59//1HYfxPtoOGFI5vtPKfaRSCYriaka8YMiWemEASnGUFOsZlXGuYnZ1zCqIoVPFvm/8uVRAU5zStdRGr0FEGGJhvYtcrAzZsr7ZULzFPNAevIvy+J+AxTPGcd/9PnbDJ+jndzjbCcuZM4u3VFFXxhhUsYJLTODRhMb2reDxwbjqFlRhtB0uTeC07t2jd2HRYpZJ9aJd5lSOyKUp6O2sLnkPNtjDyXSd6nzPrkkykVOfMhebnSFapbBLxRTXVWxravlE1FqMCkSbXuO1nsswiWQMgLAHz8l+gClo9Zg0D9jDxYLjgwXLZce8benalqYJU2PhZPR4leqQ4sgk+mSxkUMayNvCNhdiSlD2xv6TcI7quuIqP7K6TLiRtyjm6CHYz8g5V/GaEnOij4mytbCGkhIFs6hzdWmPNkclW1uLgDpzrvCV5qa1+RvPQleFOJcFsNOZLfV6+tG31PYtzUoaMlHLXnhH3Tcq13nkhqImyi3lHwGHUrc7tN/WO1R3w6qQEj/yH80/UhqLq7PWZ5xLRaTvrdoNrVkdFKBxhno6654hV9X3YH9HGiv+QmtVzki6j+Zf6YLFDgqFmLWaHxfSLhJTj4s9m+0C1cYOR9fi3NJEEBNaZlwwxQoGJwGHWdtoiRS9IMcdZVCEBfgD6wz9hrIbeO07v8n5/Rd55Nn3c+Wx9xNmx8CG7foexzev4bvAsM6GaBUj4ToMnRCtXo/O4YpOC9xNhdhY2ElVVRZztQxQ5WhM6QklQgg2WQ/ObJuA48XLvPHLv0JZwnPvOeJ7n/gat+MFTQO2EXvSqKQEK8nr0yaIocQ5ExGiz6TRdL6Oi0Iw70TvGzM1934q/ibSDuPTK/XguryZWaWghUv80vr7L6HIpoKuvlvOTYfxOLLLtTDabdZo6i1WrViiBrUYoRbnjHwkZ2PmELwJiYb94hnTegoj125vG2Sbnb2vMSNd1FN8U1W4gRQikgt+HFtSKopimECOK4aVUURmV5fcOAzcuH3EwcES56BpGmbzFtc4hEROA0bmtJFrqYdlGxyz2QLd7Dg5T5z3G876GRnH+fmOi9Vgo4ucWW/h4UXkYtNzLZo4oe93rNbrt6n+RpvEkcvENK4tqIwG+56Ct2JMwYV7XHn5F3npz3yRcHTMrZNPc92/wlCy8VBPIn/ll36J+2ev87v/8D/BjdvP4f2czeYuL37j07z+299juAhIjbKUt1UzxvnLKhUFU7I6pHikOCRbOkZxgtBVxKzAAK60Vig6E175elCoqAlMpHIo63rzjUBwuNBy2TJjPCRU1TLhMfRBQo1KJdD4O9wIf4PcR3Z5S8y5WoZVJbUbn7laxNQm1xB4V0eKiYKrvpBW/oFaYawJxJNUaH2g8Q0+OHa7LVp9UccDKWuplBmZGnun9ns0V8cNnUwieLvp+B7cqkMd6lZQUakGX25xmt8Ltz6O+AXpzS+xiF/ioPUcHV9leXzA8uiALjjOzrZsYuRitSIOA+KEEGG9tjzhDpCc2W77CZHepiv89M8/yQ88NkMFfuKDV7j9zHPw8t/H5YFUC/vR1XdEZlSt4DAAwVMI5uGblTJEUm/ZxVkjOBMnOVGkmKp2tOipeIJde60jYwezRrhyFDiY2UEfS2QzJM7OMkNSJj6mg8W84faNQw6uLTm53rBKwtBlDkjMGyUXYbUZEPWmbK7XPKVEjNmeH1GapmHeNiYqcYbmO+fqSHZfNJrA8xJKu++h8ZecM8qlBBTV+mxXHvEwJGIymkSoKD5uHPPK9KxabKOQsNhfzUpOaTIYNzxQiVnNLq4WyENWfDCdQCnm1zmq2cfzodRGyLsRvawiQIUQRquiGcvFnOPFnOViRjdrzRdWxv25NlfOo04prrCZZforWwaB+DDC1rHdZcrO1pyryLCbVNL1PdUi2NfYRedlCg6Y6Fuq1eqqNuLOkNRdH7lYb+j7odLbehZdoZPAmFrHKPRU44WnnHAyTvh0ujaMynywZ7LqTcZ3Wur6nWpyjJqh4hmIkxtPygYcBLExu68bYVZzrSjFao1xsvxOX+985B13xj0pmdHuxT6fIsFXJrxDfIPmOqIZeVgpoX2PxmhcR7fviscbYe17QoceUm/jbAk25nYBaRomv6CcTZKfrdDRlCg5st0NaDZSa8nKzHkef+w5nv/Qj/DYD9xGypu4MAPXIn4+CUUMIaQWNRmVMBVDWRKalBQLQ36cWD6M5AbyWwxDREg4NyNH4f5L3+bkznc5uHqNZt7x4K1vo7Hj6JEbbL93lyFHMo7glDAComrFm9fxQRiVmCPiWw+eOgbWMibgmFjI4pcKSUv1tspIF+x6loLThHCX95Vf5a1f/DQ7p1zLK46bUxMtiKUeoSPx2NCUUQE3PqgpmZo/C2RxVQSk5osZvHFV2gZXUWQZuR5j8cUe1RwL4fGny7Rw6yKhipdGC50xbWBCJ/fFpH1PIRcb+fa7HSnmWhRXVX2xbnAszq0ANI6ZIY51MYSA82naMM1WwpBZV7KZ44/rAUCNO2S+jNdYpw8iMmfuXsA19806SBQZye0VAhpxaIjkYU1cPyTvjnCl52jZcuXaoaFijLzOVM25tV6veu+tD6IJDUuF4wgX/cDZReKs36DSsVkXtjtPzgGVxJALJ6uei1Vmt02EYibKsR9w1BEgl/D6YpsdYsh1KkrOO5J6ijND9+KEkj1FMnP/XRbxe8weNrS6ImomWilFcA3Dmxf86n/6y/z6X/trXL19hdC1nJ2csrk/8BMf+nmuXL1hhzsRsMSanA0BSUXI1b9TFFwWcjIOmjhvXVboUNdZE1QEiYNlNbtgaI13lJImtFyx8bZXpmfaDuAqPKjPjZYy+Zoamm48PEUgWoHWzEwwYHywwdZUNnW0QeJMI706mzCupJ3X9fsMYbW1aeIte04jkhNOOlRmjIgcQZi3BzhxbLcXEzKVc7U3QaZqcBzXm6dopWrUT1F0vwYd9b2Mn5XaU1DfnwqNP2Lj381zP/fP8G/9736Km/PAf/Afv4f/7j8ZuNLtQAvbfsvpekUXGu6dXfDwfMW631ZhkKePkfVmx/npGj8MNM6QPlTwWvBuy7devkvKP4ALsN4mNhenHPmCOfaNtCVr1oqWacplFKJAUseQHam0DL1S0gBFiGREe4QdKnniTRY1RGc0ohjV3SOaOwuOq8cdh3MbDaZc6IfCxWogZqZG2TlluXTcvn7A4zcPuXbtnNXsr/BW+3M0OuMx/0mOwpv0SehTYqOZeXC0wYSOoxDEiSN4ZdYGo0lV+7w9+mzopRvPi1FpfAlW1qlZuHTsipgTQrHrRhV4plzYDtGMu3NhPnNTMTOicdOPqf6qMPWb5lQShMZ5WhF2u1z5whZhqYMZabc4SMXOQi9T7WbK/DqYrNMnJ7ZGA462ccznLctFjUxczExB3Tb4tno06hidOMYWZnpfuPf0ljc+JDx8fMZ56jl8JXH9UxsW3wx4bewMdiPntgoRR2QUj2DiWTOSt2J7vJal7hl7rrRNMwtKTspm3XOxuUBLoW2VrulAbdTctMHsnryjA0o0D1ekUtjGZ5GxmRunmftmVCqaq7A/c8XX88OOXEvQ2a/xMapTqm+zqjUEcYgM6uoOJftn5x283nn04tAbN60oqgMiludcxEbN6mrOcCm1IGPPIUgJTdm4jWKFgRoj1IpTVVN9p4SUmo1cBOkau5tNsCvgzdZEmwZiMPQzZkqODLsdqe+Z+wXXrt3mB9/zAd7/Yx/j9g+8n/bwEGRA1y+we/g14z74g0tFSV18KKLGjWJMU6g8pJTnrIcf56z8LvookD4H/bdZBs+8cSzaDgkOSYXNW/dIJbHe9Fw5eoQn392T+sKbr92zIqfSn1RBfbXcoZKxx6hJLtkzVLhdnHGjjIdZhyK1+AkqJDF+hnWEVmw3Ra2A13tcC2/i1LOTgVjEilo32igxLQa7JDb6GS18xv8W73HSQvE4jE8ZvN+Pub3HBX+peByhxnEXEiZWVt2FpkU4/e4K49eC0rvG1NaXC8r6Mp6UHfZpsK6+1BEMfhRU7ZHQvbK8qj8rMjquWB136cuH/HSkjK2gTPwyG4ktuJN/hvuP/Zvk2QFP62d4IvxH9KvXiOszSkqTktqOJXuN/Ny0PeX8wRucvHWV248ec3B1wWJxVPlddTxdT3W7F1LHT4IWjwuOg4OAAn1pyQpnu45dXLIeZhV0LdPBe76JnJztWK8jbc5sd4Olb9QEppFLV+rny6pm6juqTYWKjnq0CIWAejvQEgXJA8rGFPSBqZDuiUgvNNETN5nzVx4aH1qE+WxO8FLVohAqVJCyMlSUOZdgKGUpNC4gxeOKJ2NFlrgDSjig4JG4g2GNTwmRaIdv3VxVzHPVCinjJ5URrS7gXGNtT20gpCI6qI25nT1MU8WVTeuNKwmhQT1khDzUDOZghZhvTK0f3EQZrz6I5v1ne0KwZ9o5Q2DVhDlg6VJNs6DINVKMaBgmxdF8tkBLZttvJ6SklJH0PwoKbBoy2nmV6XixnzGKcewZ3//DrtlkM4yTQBNa1kc3+VN/+iPcfHrGXIQ/+Sc/zN/6879KiS27YcvZyaqiX5E375zw8HRlqkGMG7eNhYfna+be49KM1puNjDUMA0fhJb7+Vz/Jvz40fOiHjvncp9+Au79Jc/Sm8TU14XU8/PUSOtlAEVJ2pNLRx47VdsYmBTZlh2ONZ1uL4zTt/xMQbyV8vUbjdVDmjePaUcPRvKX1lmrS95nzs8iut+GBUxOPzJeeGzcW3L624JFrS25c9TSzv8Xz8gW8BvzhA3abgX5lBe4uKquh58C11tA4K8xaB23rTagXRrNspiLG1cKwdjm1oDCRSBkpQ1ixY9GbOjVIo1WbltEfOpNjZruLnK97siqNb82WrrUNUeq+c7nhkBrI4MSZUbgz8/TGeQ7mnvVmoI81NSmLscK00DmhqelLOAvZsJ+tNL6uDbVisvEWmzifdRwsO44O5izmHV3b0TaNIXmVaGpIrNQ9rBBd5u4PbPnyzy45e9djDN0Rb6Rz+mtv8OjZmo+9qoTMpQZPKHVSNl5vqfuAOCqvVKZ9LVUT+hFB3Mtz7bqLCmmIbLdbXHA06s0jFzeFSDjnakMPrbemQuVSBKTqtH8jdS+vSO50biPWrKnZD2WN014exJNyPe/E/Ks9btoTRT05VhQ/K9lZ7TFyQt/p652PvFVrPKL9d9GEc6Ea1WKfzKnxIdUW6ogkkbMhBVrMIR+QktEckWKJIyTjBmlW1NeUGvFmiVILSWsbnQXNhxZPokuFOYFHj29z7dFHeO8Hf5JnP/RjHNy4YahmPcpFA7p4glk5R7cXSDiYEMo9tA1GHDNfJnIPfWS33bC5SJycP84bw5z7AxR9jOX5GY8eF/yN2ywOjdQK9qAEFRo3p5WWoJ5SHLsUuf/GCZLtoVcwnkXlVUYFXxe9YMRl1Fc/NyWIXYcilwQIUkUVlesmIeBDFSBpJuWIyw5LGzflsebK0RKrDlLZK87s3o7KpcrXEKFBLTw+tLjcIS6AWMHXektacc5SB3Qi20ARQzHNK2vf69sqUqM12B1itBca74vzVqAyKsWdQ0YLkVLM/88kvqQ+E4dIysnUcWreXWZOPBaVUq1mEjlm85GrxcqIULj6eV1V33vGQ7+OQciT36e9a0ePY7X4UVb+JruUWV75MDfjo3TzE0rqSamnUWXMSa5SNntWnBUqJw8e8uILL7I4bGlbM8Kfz5dMfakoY+KQxSJa4eG9Vp5fy9FRRySzGYSL/ip3d0/z8voQ3T3kePg6V92LiJyTs/JwteHe2ZaDwbFZWzatpxYNThmyFUGpjqVqPWVFl9j4CY2gmU4cvmlIFcVQcQzDYBVzwZ4JUfOirQex2WAZ8du81tQUq53g+8Yao+qBllImZ60loJnQmyec8YbhkMFdpTQ3iH5B1BltOcXnMwJKowknPdll1NlascjJuj3UKLpShWzjZEWcbahjx19SNmVpLRzsmTG1uesgxcE2Ya1tU2tdowt2CDlvyVPe2XZmj5CN9YyXqBWNCYg0VuxO4kRB/Jx1eR+vLd5NSplrF5/hsXAHJxERx3y+JKVCn/rJ6mjkIxraAd6rjf9qIWC9lEzq/fE1jv9tz6jFpwjmBuFpZo5Z3nD61kM8V4kqrPuMpoxqZLXdcfetntVqS9/veOvBCRerNVmpvC87oLfbzGnYIaoctIESq28iiuoDnvS/yet//TVe+m86rs53PHbwKjnfBx3quqwF/qTPNFFVLg19cazTAQ/So7ySn4VHn6SsN2xPXmDe/zZeE147HPb7XTWbHlE4xVCyosq8E64ctRwtZgSxfWQzZE5WA9s+T89CCLBcOq5eablxvODKwYzjw455Z6IT4Z41DgXaoHSduQfErGwGoQsFX9PGGudpg+4zpp0gZHs26/q3H7ZHDp1zI0g5oZjIGMRQm/eq/jVwwxZ2AZvEDTBEWG0H1MFCG+M+1rE3FdkWLMY3auWt5+rFqkrrHBETPqaYGNVcJduEo2ssOKBtdIpRtIbH1lvjoZkU6oJvPItZw3K+YLlcsJy3LLuWWdcZuimuxjnb3oVmnGvQYs3o/SsrvvQjLV9/5jHef/g8j/kFWh7wvZTYPnJCXOw4WCm+UJ+7Krwy+M4oBiPX2Y9r1U0wvvku7M/Okl2dtAniLELR4S02loxmy+m2hrbCDF7IVOszBR39oeseIUK1s7LrqNnqMapwT+pe0e92bDY71n1Pn6LZDyk0IaBZOD/f0KeRz58qjlJ9S1XNUigONvoXU6Ofrda/ox78B73euW1QJdooo+ego5pQ2CHvHOJaRCzbFieMAfLjqDJTTORQdzqb7kgt/Gqn6UdPJYGmM1uhIGY5NNoiBA/zhpA8h1ee4sd/4mc5/sCHmN+4hm/ntWufdmwmOyB/DOEI1TUqBSfNvqAUqZfDG7kjCxp3pPMt8cGG7Vv3OXvjK5zvbvFK74icc+v8hGYllpLhO8rRkm7e4ZpAcIHQGi/G+2CIQ46U4eucv7WmH8AFpQs2ZpgiFql8EzF1naUjqH2eYgvR695OyIj7Ndu3GsyDMw/KYptPJlVBhXXaFmNWKvWAmrer1du21K5oRAsziI28pXpkFefJMurNXOUghmqh4CZ/wZFvo+PGZi3bNKqeZs31fU08MucpRSbisYhYxzVkCgMUIzpvhx05DnTOGUo9bBjimiGtSdkKTPVWHBethfco6Cp5Ev+U0UdVQKQa5mLCLVN916jQurBdtSrRuuA9iWb4Fi5e0IYlXb6LDifkEitCyoQclHp9TUGttZcxTt7d1980iyKFd6HozZvMl8vaPEzwCSJmzl68NW0umIDEtYEZyuFaWd2/zbeWH6bcfJx0vmb7nY7D/IAubIHCajNwcrZh2Cib8zXDtq/iAyuQYq7F0pTrXD97HX/4im57lOCibay+M9GDF6RtkBgrHlsuNQJGaSjFIVKm64gEJHRI6HDBTISH6gGXs9pmrFV9i43PHA2ZhtRcJfqnWHfPsGqfIfkZi82bHLoXkODwuTCUUyTvDJ2UgLoKybkyIXCja7WqRdVRTGlpY287LA2dtL9qZhM1vjHZmD6pq56Hrhrn51pMmmOklGyig1qcj6i9q9dHnLdzokDUCCRT/QaIuuThR/4wP/6vvIcronzy3/7z3P/Sn+Pq8i6zYIhDN5sxrAa7rmUayts2XNe1d65OP8p0YO3XYS0kKwJVMNZDrk0hDvzMI92OQ/dd/j//zp/j3v3/KTevX+WXf+nTzM9/Gz8/J28zD1Lk7GxDHHrOzs9JQ7QzxNl+4Lww7Aa2jTkcSLZrqZqqr6kCd7ga7uAbYR4sOU2JFN3ZkzAWO3ZB0eIp6hiSskue1XbBa/o8v/Bv/VH+1B95jgenW/7dP/N5PvuX1nTDVwgS8QSK9PVck0vrzA7xNghXD1uOl52lj5VMHwsPT3esNplczDVApLBcNlw9nnHtsOPqwYzlzO858iPNotgzRih0bs7WRyQr/VqJjcO3VijOWqF1Np0bBa9obUULTDicjkJKK9RLdQ5w3iaBFltbHUa0csArgi3wNhQ2xkg/DGz7ZBnSBMTvkbTJUo/qW4ubvBt3u4FdH0HMdF01Gb3EJGegShegDRCCZaaHivaVbM4WiJ1xhkp6Gu9ZzGccLGccLQ7oOuOVN02oe7Vj9Oe8PJmNMTGUxDr33D1e850rC/r2CO87PAvmrCxOcpOZJaGRMHa2tW6w9eKDaQRGxwvq75Tq0yzOaE9ZYUiZISUkSE1rs/eSa/Ql1fe46MjpH89kW/B2jcRstMbxu9pzVbBGOFUqxAiE1N0DVWWz2XL68JQ7b51y5+EZF7sVQ46IeA4WCxyO3S5xf7Whj8kmYG4vFs3J0e8Ku+0OCZGswmaTeXi+4Z2+3nlB6bwpsTxTVzOO38R7pJ0ZvzH4mpxjHmOaki3MUPmQqkzReSWZitMr0jZWBEEdS0pND/E2IhdL4RFxIB3qE84teOznf4HwIz/GyK63pVg3hZF3WE9KkRZpjknpRdz6FG0WpnJWjIPla0FcBIkJ3UTK+Sn6xkN44Q4Hb/5lHnH30eYWO/ciQ3/B3QdbcGYZsd0esDw+YLE8YLlY0DQts8USFzxZTDEV+y3f7r/DxYMtrpg/mcHiNspz3lXek6Fl4k1skqKOPgN1ZGZE4Tgy6kVQHyiCHYAlQapFUy1qJp+1cfuo0WLoqFLeq7zN6qESi33lVdZUo+Iw+6ZsG1rXNTTN3oR9vIdvf9nvnfy00Mvn2H7M7h1kQ4lM0W1mtJtt5Hy7YztEclbWuy2r9Rllu+PGwYKuUdbrDbttT0nZslZTNq9B1f3oAKbCacxyL5ViMKJIPnuCN6sV56Ip+kRQ72qqgBU2WcGTcW7LI/I3aO+fkOdPcWv1Jdrum6SSKEMPUnlZzp5NBxM6y7hRaSH1PXdfvVupD8pTqly9dYtZN7P1pCagqlEwxu10DhcafNPgQ8uhClePYJhfJXePUlIHy5Z86ynivWC+mgJ9Lpxf7Bh8YXuyZrvLXJgwnlxsPFZpuFAHOhUDoAmu+rrV7jibSa5z5gOKb0A8uMYOzpJrhGodt4mjaKif0/igikPCjNAdIKGQcFZQJq3xe3aQWTftUdcwNA3OLRn0gNPmafrbP8E/9c9/gA+994C/8etv8Mn/6tNcOxlQPaeRcwgztItv4xYVDEERMq7kSnOx4s9XOxlf102BUeY8jRpLtme5DEKKBRe2+FYIocO3DbHGUWb1dr1UcVltTdUGq2DWW1ShURG1FJ5YOb6W18JOD3nsJ2/x1WXLWQPP/Ynfw2//+l+l86/THjbMGkOqd7uWzbCrV0roVaeC0Nb/fkowFujUx7E6NALsUdy6cLTuPdJkxPdIcxd95VP8uT/9eURaFvmMq81roBfskinsI8JuF9mtbZzmQj34g6P0kQZhaDyDd3R1nphzHUPnYspZcXTBxoTR1WcOa+pccHb44ijZMWTYpshuKOyiZz0Erj33NP/2P/eD3DgI+Cc7Vv/qh/mn/86vofe+S9FTUgkgu8obHzmTRoNonHD1IHA0b2jEhE59zNUtIRGzVGsgMzi/dmXO0bJj3llqDmAxtcXVvc94juIg0NLNlHn2rLcQy8BqK7QBmkZoXUMI1UpmLOQqkj0Kj8Zddp8Pb3uYufrVxnj6s1HEie3tdZ8vjM+GVMsba3qa4GlaR9uaz2WpKFYF7IHR/iez7XsudlvO1hs2MVe3jFouFakoGUgwIZx3YuJUMcqRDzJ5SzqBtg3M2pbD2YzDgyXz2Yxl19F1nVnnVVR/f9bohKj3aWDYFdYpskqFu33P+bon9iu+2L3FC27D+fYei9ff4v3fKhzvZvjgSMnW9ng+IlI9gcfrOGIi+zqjYM1WykofM0POED2jG4TCpJw2TqdN9caUHJXKo3SOJniGYl6Zlh6UK/ex+krnXAU7uY6v92LTkjJpFzm7f8FXv/JdXr5/wjb1qJpFV9s1NI2lGa53hVguaRcEUKEflIv1wMNuzUyUTV/Y7JSz9T8C2yDXtAiZMhh53w6Aioh5KyQJwQQ6lZw6btlotq/TWgEpDrR6Vqah/owGF3xFK4yyjvd7c9DQQjdHuwZpZ7iYcVcfo3n+A2jwe7Br4rfte69xPKmANFeI23M4vY/v5tND6ervcH6B93Nrjtcr5Cwhbw2EO1uOH5xyeN3z4Y//IQ5/+Kd58+RpPvep/5YHr7zBen3GtatHXLtxlaOrVxgOjlgeHjKbLwhNw5Xj61boZsh95ttffYH1xcCQQIJB5kXBVTvsUAtd5xzBeUo213pfCsXbgnSitCEwqHVNEUdMhkYSB3ypCvi64EaU0hbh/l/G50qZKi3G5TAmR8SidH2Pb9V+t/c04ph3LYeHS7ouWGPhRhuDimiw3/TsN1Q1Wx0XjHwghWqkqgzRUEQk0ZYC4jlbb7nz4IxX75xysTZ+6ur8nDD0vPeJK9y8ObNis5rCeicEF+oG5eq43pBPQwutu7Zkj4rdVksguyz7zzHyhayoqEW5jCMku1Kde4vHwl9FBsHlSL+2EWGdZ0JF6IPxQ0Zgyhqn+ju8CKmPvP7ymxVU91CEK7duMJ/PITQ2XvU10QVTvjsfCL4l+MBy5lgsM7eXO2S9JWlHU5Rme4e5rqwwrCkv2+1AlsJm3bPZJXZZScnSfcbCW6pM1Iv5v4VAHTONfwZFCl4Lmgc8gSRKcY01gq612qXakKAN0ALBGjkKIpGiLU27QPwMHyLiAjGbKn7kAI7hBs6JTUFkRtIFu3jEWbjFv/An38O//6efpPGOP/ozV/hnzwc+85fuoJt7LJtzCFs0jOrqcULi6kh5NBO3571hjHMbn1vqOKoaF1e0spR6oDDOWBTXmONCCDOCLFnn22z5CJ4dy/7v4du7aLabbhObwJCoDZddb6cFl7NRgyq64UMk3T/Ft7dIGeLJKfNgKL0XM3Bum5ZNE3BAH2AIZs0iUeqBO3q7mpn7UK/teGRk9qO3gpLFeh+PhSp0rdC2heAVKVuye5FjSSxCwHfJ+KShIThH0h2bbc9qPRD7GiVYRm5cFZU1BbNCKnisYRVnYhpRU+TnktnFwRTB4lCnNM66MU25rkNDf3aDcTPjoMQ0kGIP2le6ib18NzKazX3AU0h1vzILPhsBhkY4XAauHNloVQUrJi96zi/M07jxMJ8HjheBo+OOo4PFZJq97jO5DMxnLWUWDGip+4h3Vsx37Yz+9F28cP4Irj/lqfJthvkZXSt7VL/u4VopSqP/rau2bBWfnRohqSNM1Io11UoX0dHhojBm+Y0+qqo2xo81xM6HQNd5Gl+V44wCrlGIY8r3lGG3i5xfbHlwesGqT/SDIt5SVEMwpbNthfb1sZApmB+rqzFxvp57bdswm3ccLWcczecsF3PapqELjY2hncW8eufr4NRG5UMslddqSTAXQ2QTC+33Cte+8pD7izvsbuyI4rj2xgPe/+unPPvNjrl0aBMpWciUcRhV6T0yKbFH+yjzovTWfBW7DjkV+mjWSa7JE8JvdkcmhBsndt65aZonzjiiUlH7QkVGnSPmRIoJFwIpFbQdrQzrwemMUlO3h3p2W1Gng5L6UpFMZbeNjPJ7pUHVwD1Ea61hhvT3zzbs+i0L79kNhe1OOd/0vNPXOy8o53OopscaRzFNLYYm/oLYU1ftVYraglURKzabBh2i7SoiaPUCsxzrA9Rs4evNdHu5oXdIN4PlIRwt0W6OxIS/9QTSLqz4rFd0TN+wjto6xFqAA4LKkjIk9OXXzCDbOePUScG7FmmWyNFNmM2h73Fxh+8Ts9zgSSwfe5Qn/+Dvgeu3ueV+iOd++Md44fOf5NXvvsDrL32D9UsnHJ4cceXaDY6vXOXw+Ijl4oh5t2RxcJVHHy+UYWC7WfHSt9+g3yQrFJ3DS8Ficg3C99N42DiEuWTjmCrQKh7Lf575QBZnpOaSzdIkxmnpj8VkvZNTsbe3gLA4QtHK06sIhgMjHKsthhIHXCp0jWc+M1P6K/OWa1eO6GbdlPYCZkFhF994g9b7j7wYps7KEhYi223ParVmu9qw3W7YbHeINFy/cot2FrhYCW++1fP1bz5kNzggUXJioZndtmfYOGLNNffO18Inmc1DaKrNk0fERgUW/WV2FzkX4+lV+x/nPE1rheKQHBLdNL4Ys+RLtamZNm8KInmygfEIWQtBIVVeYakjyCC1t5WRT6b7IlUsQvTOK3dsA1VD8+XWTdqus83JewsGqNu8Hz0/faBxwtEi8wNXv8d731K+sXscPb/Ptc0nkfbE1geGbqsKKRVyLEi2cXaoNkFqGhGaRioiZ32g82r3tNrPFB39HY1LpZoI0jDkYohgFYr13lJVYIZGD8MSXKCkLeg5YebxYY73LT50lUdkjanlkgYIDV4aRFpUWkr2ZFXOc0Kl46MfvEYb7OA/njl++Idu8Xf+orBUs9Qp9TOIiGntdH+/rIhTGiqtBCZkMhcTJWk1CUasuCyRaYQ4rjTJ9oezUBEF5lx0/xjb8MN0Ugjec5D+MsFHvDQUceRSDc6dIdUORUrGpVhpQwFxDQv/kM1//Xd49fyQMFtw+td/hRvyMsEVGw8G4951XUOZOYbjzLb1sBPKK8nQ1PE5LOZDl9SQyPEa5HrQJ6B+FEI9YFsHbaM0TkF6y7HIicYLwc/wPtK2nitHLa7x5Nzx0qsnxL6va4fpucvJ9rgUI6jHOxt/tr42v5ot1nXktdW9KGH8182YMkZFior93JiTIZc4nEZm8hZnX/t1/vf/yXP8S3/iR0kIv/iffpZy+goz3VU+oNpQVkb+nB3qh8uWg0VAySQVYoazi56LdaKoefwtFp4bV2dcO54x7wJBPDFm1sPA+U45mHccLWYcLHNV7EpNR3NoCdwZ3sPLH/2neOIn3sPp90756i/+Ch+Nf5NDv0ZdYsyFHsWC4oBqbVTIZgPl7Sx2YvxJTXn6GqITqiW1KLIENd0j/moNtLlfCMOgDH3kylFrBfxYxLi6/6mdI6iQY2a3G7hYbTlb9ax2Rk9pcXhviGmVFlgUs9i5YoI0c74IdejSNQ2LWcts3rBYzjlczFh0rSm4azrMnke+t0DKSRmGxHbIDAl2vaXdbOJAUmV5t+UDv5548PprxGtvcDAUnnnVc/NszlI8NJ44gJdErmjiVLCJTObhpYoSR9GROQDINIqOpbBLmabYXoNYHWP8VXuOL8cGj4VqllpUY82tCWyMU65OGMWPNtoeC3RnqXRtTabznjDvuHbzKu/7gWeZdUveuP+Ak82GYYhkzUj1AxVn/PSh1PNPM+rgfH1On3pWbaBxnn4opAh9/EeQlEPXIrly45xlbNsmH8wqqD4ompVRFTkiUdLY2NpED0IZErhAzgPEiCtqCu5Ree2qo3/dvVXqExgcuA5pFyhbEwZpnpAakMr/rSOd6f/Hf7NF1M2uUuLrtKkzXmLOSFbIa3ADEjvkqrfNXa0LDN5B4+muXEWOr9p4H2V5/CS/62f/BB/4qcjp3Rf5zpd+jRe/8gXefPkVHt6/z9Xr17h2/QbHR1dpuhkleA5uXOdd73k3w6rn9VfukZLd1E6CIYqqaM5kymQboHiCa8yHT5TOC2E2o3SNIQpxR9laKpFEi7MaxTYipaLK9lBD5Z5URCWrjSudjqkllXNY9mb1qKCpFmziWbQL1C1ZLA44OLiJD3NrmHKxkZ1UJKQI+HFMUgsDsQMjpsh6s+Hh6SknJ2ecPjzn9GTF2WrD2WpDKy3PP3fM0dUl26Fjk+ZcbDIpRbz0eM0kyQw6kEpnfYo3UZIPjuCjcWvqQhQfSCUTi40QcykWPVXU0LACY4pEqQiWr2N3VzvKUe1rvY5OXKYiZUq4MEqXCSx0fObxZPGGulBRFQQngUwyRKfYE9qKkOPAyy+/RhJHdnZPjq/foOtmGLqX6701BNtyx4383c081w7O+MjxZzl6uOEinqPNGeSEU0dwHd4HikKDjVvEmdpSVSs6Oq6fvR2FJV1aIW1nzDi2xUbGFc2SMuBKrteowbxkOxwtSZbs5Cb3/fvZXXkaubjHzYvfYlbuIO0hTXeIbwqDQnHOUpcQfDNDxfwpU1FKHQNLSQRdk9M9fuXvv8rP/d4rHHXw1pnyqU+/gsjW9hpfjaBlv2mr1caoKo33dMDMYXSR+vyXohR101hrEjyKoM7sgkbvt6xKKOCGqgQNStRA4TrZ7Ri0QWc3afsGkYwWR8pidkIoqtHQUzxIpBCtoBatUxvhqfQFmr/y6+QsPOJWOFYWf+jNtxTs4HDLQJwXUqMwF/Kpozy0tR7GkURFNgaUMXdn3C/dpW3ThghC04Jv1MSXmI2WlEzrG1rf40W5evWQxx+7yawLrLeR07MND842lHFLVjU4dFTcM6JkinMZJwXxDV3lkqZcaDzEaKhPwdDhscCQus68My/FrJFABu9o8JRwTtN/m0/+h7/E3/qlv85FiVy8dodZepFWT1G3o+iAlz1a5xwcLM1nsm3MG3SIidO1GZcP0VDpeSfMlp7DgxmHB3OCE/pd5ny74/7pGhpD94ZYJk6cF6k8ZCFpy8vz57j5x36I70rgXc8f8uDzH+L8lV/naDinW7aYotdoS1ovohZQbwUkYs3BmASWa0MsWJpTLlaYg+59frVy2rlUpKqjJDEP52L7kncGGHkvmOuJNc5jEVWist5uuRh2XMTIepcZhrpvNAXvTFxjNBdFgkWdOrXC1YXR5N+zbGcczBcs5oHlsmE+M9HNyMlXIEuZCskxAnM3JLZ9IkZlNxg4E1MkFwOWvIIkx7XXPdcegveFZehYNi3zWWOivlr8RZ8Zh3Ti6qRzek5r2ln1N61V9iRajKkwJMsAn+Wm0gtsv2mC+U0G5/GV7zver6IVUMjGrW+8UHKFKQxZMxSS6tYggguBELxFbobGUnN8Q1g2NM4siI6vHfD02U0uLjZs+96cQqh6hKycnm158/4JDy7Wdi8rqJL6ngvxgEUYK1IpKO/s9c59KH0wVMQr9D06SJ2TVJB9LOjGYtLVmxDsTUkwE1hywXnz5Wt8IA8b8/KLPUEPwYX9vI360Bc1FXjMEAd0WEEfK+8RRjWY1k1yRMJGaH1aPHaLCfMj3PFtnMzMJmjYoH1vqRpNC/MjtJlBHuwzjZwp54nr1QTh1yuDEAhN4PoTz3Pt8ffwQ7/7Di9+8zN8/fOf4s6rr7F+eMLm5m2Ort5AGk8aEk2Ycfuxx1hdRE5OHpJKYVClFfClKrmd2WsIAvmA893vZhN+kFtXzpjd+iz4nt2wNg/BXNW32Q6hjDCMC3l8OCs5ehzT4gyVHBXnxhmp6G62r+f6Ye1SuslqpxBQDmkX15gtF0gzZ8iJslmberwm2TiEJjS07Qw6X4nHme1uy8Vqxb2H93jzzbd47Y373HljzYP7a843PX2MXD+Y8cyjkdlCiCmwi9D3kTwMtCEDCWkyjQuE0NkoqQlIE5iyuaXU4qsw5GQxhDmTh2hqULXeyDgqpRYQFb11rsaO1dHyqDIXY5pN43AdVcv1ia0drhXVdURezAEhq6lugzPT+ayK01AnUBajaXnShdj3vPrSq/a7nONJhSvXrtPOZmbzoNWWZmy8AJVCE5TDpXL1YMPx7AFls6J3EYRq4O6skmLktBoK1Xgh2RKaisbpKR+LzKraniBmbPwkVJRODNayka2DMuBDBw5KmEFecjd8iJ//N/5x/smff47XHm75P/+v/zsevPAJ5svH6A6PaFKAdo7rlrVylWn0PXF8FUqOoI6ZO2Gx+23+yi/Oefmlh/zoB6/xmS884Guf+BRX+68xb86N81kRSSOHjh9DaZ3QYoWW5dobilOKEKMVlolKYRhBeFHEWfOEeZxXxNomCzokJBU6f5+D3SeR8HG8CIfpt/GUal1U/Q614CgguaqtK+8Zrfy5DlywuN9mYObukdQsmpxkvEZSyQw54aTYZEgsQlZqhNzglO7SGC7WhjMBUfe8yQ5DfYru1bxGITK/QCvK9ybvnlypAZYt/djtKzz79C0Ws5az1Y7vvPJWbVtrQy9SbbpGQYJdyymBydmEww5kpWsdTbYGeuT1WlFvaK6I4r1Y6ogCLpt1lWZmXaERZd1cMN9+hdNz4aXziKSCcIbImiKGqruKwqsoB8vA8bIzg3uUoS+s1onVOrOL1SnDaixms0DXNnTBVNfrfsO90zWn5wOLw1D/or7tbESMD5pJ4DKzAGRh2EHqN8RdYb3JzFqPa7JZI40XSyqamPN0BrnJzHy/F0xizaLTuqnaD/bpL7b/G71E2GVYR2EoyqztaF1jAQoZkitTE6aqpJi52Aw8vOi5c7bl/sWGQQs+GMrcNUrXVJBJgSDEbGujYjPMg9B4x3JuaTfL+bwWkoFZ15oX7/gh6/TGygFDBHdJ2ewS6+1g3MUqNHN1Y1bvKoRU97yotAS64O2cq3SjusXYOad7JN1p5atW1B5lKhSniZ8KMZpIa4iG+IU6ilZVXKgTJof5h7qKzta9RzVVUMBZc+89Q7VRMp5r5co6oxYhhly6ILjGQjl8dUKxr88Js4aDKwfc3g0Mu4EUk6WgVdh020dOz885frnlG997nZPdxko5deRSiCnTNo6klpwW4z8CDuUEvWhAWhs5aqy501Rodyy+gq/ryK6e8SFNLWYYeKzNqkIMyLC1gqVExHVVBZirsmr0m+wpq3MYNrCylEuuXFhxhyBTtIZj3K726OSo3KqGLWGOLJaItmjaItl+38h1s10e0IKmZCOGVCw28vQh5WKFn88ZEc9pYwScNCyuPMEHPvpP8J4f/oPceenLfPsLv8abL77A/bfeYHnlKm3pOF8/zirf5MY10Agn63NiHKqHWD3cs0VlFQ1c9O/lu8s/zfnx47z57nMeXwee1M8Qc0+iR2LBpWQmt97Rq3FMUkmTMMXXDXNclEZAcRMlY1y0qvvN31/CLUYDeMt3NqFJ09h4uaBsdhuGfsd6G82qxJkx66LruHblmMMjpUGJKXN6vubNuw948dU3+dZ37vLqGxdsV5nYJzQ7vATCQYcXRdUwlF1/xrDbEncDJRTaoEhwBNfSdDMEoevW+LaxhRwCmVgXSUFiIuZiMVipGC8ll+n+qdq1c4zdoCdWz8vR0kKoVI+KPmoVMo3iwCBjBFpdGWrAuqLG46xZuGZTUjtP54xWUDf4sR8UhbhLvPbSG4yZwxTh+No1ullH8KE+g1QEw7pQJ9A2nqPDjuVSOD0DV6wgboLHi8NJRR6SWrZxyUgZOZ5akUmYJlzC1FXLpSUltbAcDbTLpJAGl0vlHiXEbZEUyLnh6EPv5V/+hR/gkbnjg9ePeONf+zj/2f/qFWaHN2i6jm6eabulebLmqlzKxlkro8G4epzvyJJpZMsVfZPT9W/x5b/2XX7rb9ygzRfcKG8y9/cJbovKFtFIThlHmlAOcTIVdVlzVeXbeDMV884TAQ3W4AWDxSp3yg7IoEAyKkEQ6LLQKrgCIjseSb9GWv0WxQlB7pF1qKiQNVyqY4KNNcU51+dCOoo7QN3CikoJ+EboGo8rA1EjTu2ZybkQYyIEE2S0Q+BglyklEftCWFn04vicB+dxRSd+pKsNuTWOY/lX004Ulo3QtSZSCE7o+0IZMnZeGrdvsZzx5NOP8sRTjzDrWroHZ8wax2iWX3Qcr8ql/dIQ8LFuqA5H++MmJcsWdtV6qkZSKmBJKMVQ1sFi6oyPmei847gTDucwDGe8XM4Jw4zOR5wmYhlQEo5URSyWSLM8aDg86GgFCoUhKttt5nyVDP1DLhVogjfYntEP1yhCha5xHHYNM+/wUvOio6cLgZyTPVeqPLb6LV75C3+XD37oB3n96/c5+O4nmHX32G12nIWCP/A03kbk1DFr3U3qHl259mNTqfVPlcnKp4yR8PV8FnGVWxnwXlA6hqjmhbnt2ewc1w6DWe1JDRPoE/1g9mIxF7a7gfNN5MHJinsn52y3O4KH5dz2wDaYCKfkWqA5m4Y4pzRemHWew7mNuI+WB8xmnT0zXUsIowipPitKFRmZU8eQMn1ObGJhtenpqzXRPMCiNbP7mAshm5erUnmQWqcrWtBi0yZ13pwM2FssGertas9sdBSyjlB9NZGpoRZq/pkmIDQO53geTJGI3uFDjQF1RpWQCpQ5rSr1caOtZ7E4V3t+Ey9Z3KbDNxWoqWCHt42kNgkmlnTB03YN7lAsQhMlJ7PYExy7PnF0vqSlYRcL+Y03udhFlEJKvQnPUqxULKZM+3fyescFZUk9zo8dl7d4WepFr6tfvaknjdgils8tNX7PB2ib2kWAhhZpGkJJ5NMTtN9RaPCxICHUkbKvnAmHDkfI9nFy3iL6XTRsSQmat16BJ561sdqlw6/ui/W1Ly5t6tpSQouTuY0T4rZ6XTKatWE0TEVjIveJvOuR7JCLC/LpQ/zNm/b5R0S0Ij5axxmIo10c8dTzP8GT7/lRHr7xPb71xV/jha98iXsPnuT1J/4oDx6fc+PVv8/19GfJpedcIyU6hjpubd0oE/Ccx9ucLW/RS8uLp0vcIx/j+t3PotsNujrFbXcTDI8o3pt5MzjbWBi748qHHAU69X0beuVq52WKYq1FpaEBMnWoJWX63YbiAp07YtE4vLPc9NOzE157/YSLi0xUyK6ha2Y890zi2acC7SKxjZE37j/ghRfu8bVvvMW9hwO7fgY5VjTDxhWNDzgSlAukJNL2AWnYMgyZFCPStaTG7JWaWYcopgJ0hljE2BNJZG3IlXuak9bkFbtXIt6QCMEI2ZqncUnOtgFYlV6bjbJvnhj9xkd0slphjZsJorXdyBxLy++Jj9Go8SdNhGD7FOIR9UxqxbrFjUW9DBC+IRy9eY+Dq5HuYEHXzSon1G65yv5Q8QrX4kC32XHtdM35qifF6rHpI971NafaISmRdoVhc5Wh7+rvHA91vaSk/L41Na572KMI1BzlcR3onu9ka6Kj5BXDy38X/Xdf4aE4sig/dH/LP/fgBa7+8kOa1nM1DvzBl3s+Nlyvm7pO/1RGlNAoFepDTc+B4t6oiv0OnOCbOv403A3KAdSYt5H76mddFWJkxjKnqNTc4XFt7+13/BhEUN+LqDMrEEqdZNRHpa4pqduk9WKKyhEALg34WHA12o/aTMC+Sbn14jGr/9dvMlx504rr/oKnvvaQP765Xa2vjG9JEmZpXv0KhaFvSTkRLwrblMjZhi3OOWa7GSEGKMqqCSRXKsCqtYizB6pM78r+11VDdhfBJ2fNWO29vYIvwuGw5LFvOw7fXOGd4+Z6wx89u85Pzmf1uajOIPX3iUAnwqL3zE4bZut99KBjL+yT2tlIRfq0NsRuWi82Gk+p2BhelNY7e8/eVOAf7h0XA6xdIc/Ga2z2RAr8Rj7h6+2ag8Wsfo/9vM02slpbDGGpTeL4tDuAMo6PLVt62c24cUXZ9pGuseztlKDfASWSW6PWpKQ4SVwNL7P4xn/J+psHPDVEmqtvcNEnckls1mbgf7TsaGSPPk6nmo7rsV4X9pQVtCK6qVQbISuqprUpAZUWXMOQ56x65WwlnG8iPgjeJRPViVjedT9wsdlxsR3YxsKmHzi/2HK2WrHZrsi50AV74D1WTOZUXSJEEbVM765xzGaeg3nHwWLGYjZnOZ9Xp5CAD+BktJ2zRgmElJTtkIwfmRKbOLDpM5s4IAqLNuAbR+ttHTnvyN7TR2t2vXMUzZV+VYtMMUSulJHKYpOby5nYfRzjSzFaUGNpRmP2uT13iT5FYo71a1pPbabzWMQ+V8mJkvL0XDtMw+GDbRIx7z09S50ejuHm414/JkShUqcII7e7AgGMfGwrLkUVFxxkax1dY0BEehSeSwnnCq+8dcLJasNOQ7W5M16xJTPxjl//ED6UluErYr6TBI/qMHWSgKkwgzdot3HgGiS0qA9IO0NnnWHd3QKZH1iBOaxxFxvYbZDNBaw2aKoHeY0/VNdAfpKyfZS83uHLOTL7Ho3eof/EL8NHfzfhqfdahnblNsqeBYRe+ndQJCzMHF0C0JmC3Pd1YTpQh6hHc83sjRmNimTQzZb4xpu0z71nOjVsOVunsB+F258ZTDfn6pPP87HHn+P5j7/Bp7+84Y3hMV4+92w/8rMcnH6Tm+UhQmK1zZRoi0bsreClcNB+m27zDfKtD7DYFdKdFfnBCWE4R3Yb880c53i1IvAVCXDO1fs0IivT0WkopA+gNvcVqWbgE/rENC4f1bY2JgxoE5l1G7q2p20aiutYLhZcv6LMm8L9sx2v3Flx7/Scs7NC13R0y45Njnz1pbf4+rcecnYipBgQNUheqjGv5QZUrmCOlLimxA0lRUoyDmYvmTQ3U9vQNmj1/NM8EOOWmBLZmWm5cXEC3pvprZHd6mhWs3EYpVCKx9VNRij4kGzU521Uo04rqmTrfBwVjlxJHauI8SCsnMvHSsuPD1d5Q6ONzMrbj+wx7tP2hTTx/Cbsewuc7zh/9S6riqx9/wHz/S+LxlSuTMXYhDWzf5vKYXC00lKKn37a5Z86NWnKpZ+x3+DY/2M6sKbioRaV9r0CuiO+9lW++V9+j245I8eBtN7xwVZpX9zaKCpHnl4V+ry49FP3lAIujfjH9wSA662RZQsobdty5fpjHDxyE9c2yLxj9oPPVscJkK6lefrReuqNBeT3f/qxYFbe/qp7xe+4Um//e2MM2766tIoz3z8lPTib/n587S7xzj2GBycMux3bi3Nkm4hffZPSntmPyAMHDza8a5jt1yJ2oIQ+wGDlWikjX91QabyQXCbGjMtj8ARE7yhupAT9zk83FU6u6hPAxqaXPv74dxrnOJAFyxOhWQ2oKqGPvHuY82jw0xTkUv1jBbqAT0KjDj8IcmkDHRFy+4yX39Hbr/LIAbNrUr0NiyBVS1AwP9VotTcl6KWfC7dcRwO8PIs0HtBMzInNJnOxSuS8fypG7NZjCGMqGRVHaBpapxwfzGiCsOkzfYpsY+Z0HemL0gZPF0xEl7PSBcfxoecKr3PLQ1k0bH0kS2SzNcT5bJWMKrHo8E5xFaWUCuaM60vRyU5IRcy9om5OoyDSfEQtFtlpA9KQy21Wu1u8dtpw9+Q+u/VLLGYrnBvw3lNKZDckzs93vPngjJPNjl0q9MPAbrtjiP3bHgovSnDVb7HItEk0ARZd4HA542DeMps1HCwO6JqWdhKWCEhGycabzEaPGGJms4tc7AYudpF+SGzjwBAzxTvjaWIUDXRvRm9TJjtLDFUu9bkKePG1MTGf55jHPd+aQl+d4mO0nPZSsmWIa2PFnBjgsIvZCt0h0sdyKY3LHniPo/ENiCXKIUZVGC2fnPM1WCHWs7Y+3EXr4V/3GIu8qthGRZpLTUNy45NZ3QDGpsJZ9g9a+fyuJsg5mM07jq8qTm/SBbh6vODh+Yaz9ZZNH82RxAe6JrBctr9zg/gHvN45h1Ir3C5WMJrayFu0ogepGLe0Ddo0aNtY0djZeJnFEpktoO1MsR3MTkTXZ8hiA32Ci4cQ7iMXq6qfN9EPfobGzkzhdz2luYL3M0gF9/I3edCfsX3iC8yefo6rj3+A+eFt8F3dxC5tPToWeDMrvjRb8Yg9IEr17qgIUkmZMkTKZsBFQ7c0Jlbf+x6Ln/xJK6wvCYAmruH463TsLMRI977l+NYz/NxPF35PcnztjcyvfLPj8PHnuJUeIacepxtWmEXQLma6xkEDx0ev857df8Crb32AxkeekC8ylztkTQTXULyZQedkHd04+rDzqxpxFx2hJy6Pt9JIupXKD9G9atWND3hdrSJC03hT3s1mXDlc0i4amrat3dABi25OPxTmDy4YFE7Wa966v+Pu/RXtamCXI6+8esHZBWTnKToYPcIpWnzttIvlimYzzR2GyND35BxxamMKzZXAXFEwkYxIoeSBPhoR2WxiDN1onIemNQJy5dx4bGFqRW9LUVzdXJAMfeXPYuP7Itm8WFWmtJYR2Rkj70TH9kKr55ilJVxo5v9yY0c6mPPbv/WVPfeSAK7h9u1bvOvZZ+i6hi//1hc5OXlYn6Px9Nt3qq4qRac6ZVqownve84M8++yzfO1rX+Xu3bf46Ec/CiL8xmd+g912CyI899xzPPfcc7zyyivIg9d5dpYZ+jNzGhgN1xhxUrUYy1JH28WaFV+fpZHoTTUojtlGxkag9zjXULInquDSjHW6zb3dVXb9nDYLh2z50Ptu8D/7V/4Ui8M5p6cP+XP/z3+P73zj26Bu4jdpTZOi8r+Mj2rHu9MAmggEjg6u8mM/+XF+/h//Izzy6CNw5y3ivft4YPfKq3Cyoe1muN6x/bVvkE8fIKkHzTa+dZdRV0uEMmgg8rZSRmaom9UDXg3dyFvMEk2nxsL2TcEmNQ3SdITHHkF8MOGEONoffRfd4oP4R26hixkn2wvWm7vces/TdDdu2Xo+f5nP/tn/N3/2v/yMxaGpefaFtuHK1Ws2VgO2m55UMsfLq1y9ssQLnJ5ueOnOQ0ozYz6bU4bMm6+/yaYfiPX5Usw2iLqjBScIhdnc0c2gaeooNQkpWlHVeuMwPnH7Br//D/wYH/qRD3Hrxm3S0PPKt77Df/Zf/Qqf/61XTMOJcSBdbQ5CEI4OhOsHLbeOZxzPW4IfxTb7Ql4q0i9FRtzezlyhCvsSq1XPEAeO58rtI8fVWYP3sMumtr57JtxdwYOVstlZYVEyHDrP/3X5PG3jaYI98SlXZHKTTTQ1FcC14BivVRFi9W1ECsuloZveCTn3bPue9W7Lrt8iKwg+MGsaE7uI4+pyTjdr6BpFCAhK1ymHeFBP34MmZbONtG3gIARUUxUPOZzWCWHZC2HNb3LPU3WXGkknDofH1zCSGI54c/VBXjz6Iebvvs2bv/EinHyZefxNSn6AZmG3yRAjD0633LlnBaWKORhoSZX6U/eIqfO0eyTO7JGC9yxnLUcHc46XCw4WrUWsdjPCmC7nqA2Kr/dGzYanH1hvB862A+fbyHrX0ycb4wbz0qDgzaWj9aRaPhdMf5DVqBAKJqytBaNqIWazADMVvNFz/KT0d6Ro58RuiAwp0zaeVNexFyVFZddns1yL5kjQuGqqWLun4K0A9d5VgCLQtcHOLK+IU8TL6LpIyOCzUWGkrpW9x+/YmNfmtyKfpUwW1bVRGnOvrEItlT44ORjUvXQ+m+GuK74Vjm8est0MbHfRKGGqNK2nbQJt+85xx3deULZz6/59wwQLFBtRlKBoO4P5AuYLZHkEBwdw5QpydBXmc2hnZmzuKjKoQK9oOUaa+0i3M+5k6S2qUTpo52g16ZZ8B52fGefFrXGdoB50s2H3yiu8fudN7r/wVY5vf5Gn3v2DPPrMB1lcfQr8DKoybBpcuQI5wqiirHdCU0ZcRlKCFNHYU/oBXQ0wVGQqF4aXX0a3a+TgeI8xjejD5Wt26T/HsYiIWdYcNsJHnvG87+aCi3f9KK9/5XXk7w3kF1+pJsSOWAqDM1sDPBwvv8ZR/rJ5cKVIlMg8WARWDkamLlW5rPVAc96Z8h6g+l+lXCo94JKQBEiXoPVcxAruWpCKWDcXxKHOOvLlYsa1q4d0sxl4h9bjqGkbfCMsdwNHRzNCs0WLEHtHRNmlQh6aetiaNQYOtFiiREmQinX4/TDQRmEYenKqB7WW0YKLnIWc9hF5pdgIYo/8ubognRX1oswaz6Bj0o0bKzQQU8HiMiWVar+x9w2TymXJOZtApxRwFkc6jpmoRbwqJiTBFKhUcjfzjvd/5Ee5+dSTfOpTn+L69eu87/n3872XXuX09JzmyiGzeUduAx/82I/x6COP8LnPfY6cCx/7+Me499ZbfP3rX+djH/s4s9mML3zhNxmGgZwzZ2enzLoZH/mp383f+pu/yu//Qz/Hl774RdzBgu1myw999CN84pOfJDjPx3/m9/GFL3yBH/09P8ln/pu/yEYjUUxg4WuBYXnA46oxNGuf+gNOq4eaiiWWqpJFiMCgNZEJj5RYD7lCzltSSXT5hNm2sY23ScRmgcwDbtHBMCO1Qu+rolsw8UtVWGoxpXbB0LiQBZGB46M5P/UzP8cf/xP/NI93B2w+8SVO/n9/E+68gi8XeF9wojjfkpuW7LCmoQw1VGHcqusHnA5ImRZzxYJsDwsLaDP4xjArTTDsIG4stlVHAublfcGa2vz696zAVEHx7L74W6hvkMUcf/0aR08/yY3nnkHOF5TFDHfQ4g5uoW2gz5EBKyYthUvZidJ445eeDxu6rmNxpaM97mw0Hhtk2dBni4/LAXZO2WomUqMYxfRFRrSBKIZ0FF9IYhGqJQtxsMe8caAeus5x9MgVjh+7weFj12mPjimbNXLQsiWzKrkKqexejgi/z0qLJ3roXWYIitYM4fEe2NTBFpQ1LgWzbYFUCpucOI2R835HkMyNudLOG1ybKaJsU+IiwUVWLgpcqLJTqgE4zDoxD36xpiDmwmabWK0yKe3FdmNTV1Qnz86YjQ8ec6wJJoVQhSQWJmFteUyJzc58AL0378RuFvBemW8dXbNAse8N3vKtl7MGUSUWE0+t1hsav6RrLcnNjXAxhpSp2wtFci51GxrXsa9cT2fBA42Qdc6uPMaDZ97L/+lf/RFuzT3fufc4//K/MUO+cQ/xW3bDhu0ukuOWh2cbLjYDKWXj+LtJ22ZTr5HzWBFc54wesmgbFrMZBwczjhczlrOO+ay1AsvJJQ9GQ9dsRC/shsR6V8fsu8jFtmfTR6KpVPGVA+5EatElbFPBJ6UNrgrLMOP1nKvABxqMtmcOAqmK7wxR9MHXM9rej2qp3pyFXYyG/O6MSyu5UDIMQ2E7GDff7IXsORrFO9RdReqzXJKi6s1/0puQFqmoainVf9kAg8ZZL+sdFfCQuhWN2fV2n121NxrrnDGww3abPXWKWvyLWMqh4Sue46uHHBwvSDGTYp5oFN57Qk1he6evd156zmamqnbezMlTxjX2TEsbKEdHyLXb9r+jK3B8jBwcw7yz0barv6pkNEYkgurMUL7Go9yFbgtdB9dvogsrRl0zs9H60OM3W/T8HPewh9QiBbzPSIpsVmtevrNh+M4rvPjCV3jkkc/y9HPP89QPfpjDG8/gm6VdcB3Q1T10dV5tiLwh9q6mvNSkDI0R3fXoxQ49S2huEAKaA/mtM/LZCndwxDgI+f5i8h/0Gkei4+JbHjgO3/Mcj7z7f8Fj73mOX/3F/xv5hfsgLbvOLD0GhKYkcikEjficcHnAUyB35FbACU21FhkYM6yNgyJiXSIYUbsKyMzEuFhxVMYNs5RpVD5+slwMyi/TWMWKpfmsYT63By6rsttuOH14Rsy2wZxcbLjYbBniwLJpqJN3Q4NrV2/K2WpJ4Q35R6wQHHJmiAMpif19rbGf1Vjd7CQ8KZnqsEiaFp73ntA0iERykSrGMFSlYKIn4y8641FV8qMU499I1sk6ApjG5qWMRr/R1N610CjZiBU1Ch7LOR85dVWBWq/pZr3mey++yE/91E/xla98hZQif+jn/gB/6b/+y6AWR3jt+jXe99738clPfoLf//v/ADlnPvOZT/Oxj3+cvu958skn+eQnfo2PfvRjfPazv0GqBu05Z1JM/OAPvpenn36az332N7h963aNlyzTPX7w4AHPP/88u+3WRC7NXrVuvZEpC0UrMqt24S5P4c27zgIMRhpFsYcD74U0mGpaKHaP6vcgG0LTA6N5P5S8IpVI0Uwq2bKovUOl8vlEUepnBIuTIzDTzJWu54c+8MP88X/pX+cDjz5F/zf+LusvvoCenrNwEemqqa+M0u4BxOg60+zTOdQFOxA1UT1WplU73r1xm7aCs6+5pTZFMWPKnX3fROgepwLjgzTOIev3hNb2VfH20/s15bVT+ldfZPj7fw85OqJ517voPvAB2vf9IO957g/wnoMv8trpXbKPFMlmIu1sbBy958pTT3HtxlU615NiIUfoczRrEvGgMh08ooY2N1LTdGpv5e1sRX1t9jAem2ap7h6Kc3ZQzWeBG9cPOb56yHyxoO1mDENv90yrAbxU1GpE8rEUGlG1NcfoiliL9um2jCil8VqlFpMopKHYWHptqMrBEo7ngcOZYzEr7HrMnkZtrx0tvxDbQ2adsFx4ozJRGFJhvU2sV5GSYRQPXaYEqFhT4zAFdT9kUio2fq23vVQO3OhvW0om9pkhWexe3xeuXi9sjhK7vtD3FvUbXa7Kcvvci1nHtrcYvhSV07MNy0VH1zaM/asJ8cyUfEzDGSkNY2ytjIvame0N6vFlzpvxOs//6FPcWpiw6F23Gn70ozf5yrcOmfeZlAdS3NLvTjldrxhyzyhGFMxOSTCELSVDqoO3hJ3QOA5mLQddy3Kx4GA5p2sDXXA0jSXp1U1kWlspFbZDYVuRyPXWCsl1HxmieZA6sRG3iQv3e1UuSlTYVjCiDVY4jpOGwtjXOVK2ZDuzilOC98xaK2a0IuMj6loq/anU0XtKFrwhlec8DJkhZURMDOkp0y5hjijViQMDJpBCTAmRsPf2zXXiU/fDnOxnBO9xwU1BFCZGc1MNYWoDe4+BumCROpWtwEYZnRXKdP7sybelJhQFWlpKa82yigUIuNGy8O08vv/B1zsuKLXtkNYU2BS15ItUoAju8Ai5/SjukSfhxi04voIsDJWckt9V0TTUkOCCqrcNtS2Q5iitoYauQ65fR249BkdXoWnt4Us9bNa48/tw53XKxRLZnkE/4JqGBmjShoerHWfrgTdePeW73/oOj3z+Uzz+rqd44rkPcP3Wo7TxAr73VcJpRJcNhAZqTvW44ASsoOwj+WJHWWU0B1QCOQZk50jnW5rHRtGKHaj/g9d9PIPe/iWCghJwPvD4+/8wP/FP3uHv/NIvIveEsoukIZlBch3R5hRp2Ru79kTcoHShMVJ+7diGZKMY20z3kYHV29SQ4vEB1f2oBKwIqE+eoUwidWzH5FelCqHxNG2w8UnMPDw957vfe4P7J1va2YxdUu49iPS7wlErNFWsFYuvlVegUHBSpmIBZFqsuRiPqORAyamOwZy9p7rwixaGFKv/5UhA9wRfzA6pSP1drhZLxifJMgosCllGBMQ2cvNStc0/FwsY3AuZLMG+OFfHX3sBwzhcGnN7g7Mx1phQML5yyaScCU3gp37vT/Htb79gmyxWNEs9QFWVlNKUV5tSrhuE486dO9x96y1CCJycnNRNAoZh4K//9b/G448/ziuvvMK3vvktzi8ueP/zH+D07JTj4yNKLty8eZO/+Bf+An/sj/0xK3yrR56vnLoRj3Mj/bjuQY4KuhUrtks9rEc0T6ugZSjGWRtj3rSiW/iEcwrFU8TXQ9vRtYHgGhDzxyzOEcUKD0N8TS7qc+HAe24fNDwza/nAY7f44Z/9Od7zh/5p9AtfYfOf/xnyyYnRm7vR+WEUnjlwnSGLOr05cC2EztZy2sJwAUTs05b6z8urFvt66e1/Csj526ttvfT3ZX997D200CwsolITpI01S4jtc3mwa4ag2zP6t16j/9ynad79Xp77E7/Av/mv/Xt887/5+7x497O8tPsmD3SAkkmh4dqtJ7nyzHO4xrNe3eH8lVdZv3XO/fNTNrtIWMzAyYSmqOxjNUf1qtrE1byFaxOYKmFYTUhtectiSFTXBQ4OZywWM0KlvoTg7NmRUg82OwDH/Ok64SQ4IVTXAXBIGVdSRXpqxrFWfvJI38l1FLnpB/rdjsMGbh8Ih01mGQKNZKIrtAFmraF+UtEg500gcrDwzIIYSK3Kejuw3Sq5RsrK6OxwaeIQy7jWTbw1Fhgp60R/Ge3ItMjkLVvEsdlGYqxPUIbdoJyve7omMGts72q7YJxtlK6d0TaefogIRjM4Xw3M58py3tKMSFS1exkbQK3+waMa2HlvRucYIu5oGETYDYVNSqiYsGqXCy+9tiLHnlVJBNkxxAv67Zp+GM2t1SIlxYpwC+BQQmugxaJrmbWBeddyPG9ZzDqaWUfbetq2IVRUcUTUtO6HMWa2u8Rqm1jtEmerHZs+shkGW73FxDKICU7axgonUfO2lFqQRl/wPhi1op4n3huNylVFt0WcKjGaQLgNRu0QGYWsI2LtJnsfK9ptwmWUaxuHx5rN7bwJWEIwVfcoOhu3Dqn7wChaHNXgzhUkOJrGmdG7swZeqy9l8FK9oO1njJPOkTtp/1nXBpc5yKMPgP3CceQ9Ud/qPp5Vq+ocs0mqa2xESS/veO/k9c5H3sFDu4S5R/wMcGagnRMsj+DKbcq1W8jV63BwaFxJ7+2hS9EiFlOyEbPUjbzZ7QmlfYZc0MUV9MajcOM2HF6BrloElYTs1miwiDeWC/S0QYfE8bt/mA/evMHi9W/w9a9+jrfuvMWuT5ycrLg4veC1V+/wnS/+Ns/cOuZadDyujqvL61CMqG8j9bxvRTVDTJRtj57tkJ2gSVA3I/EYD08/wNF6yWy8Nu/k+lnfbaa21khcwjvqkMfPefdH/jFe+O1Ps/rMC7QFyJldGsi7AU1qWbphdKuzggrn2JTMrEAjjqYJOKfE7BCiFf71ocu1MrCG1dJ5EkymvuNIbyruKifYSM+FWCLqhNZ75lXxBpBK4myz4duv3OfFly4IvsU3gVI6VNqaAWyjUdtPaic+ChsEVKUWsDZuQEFTsfFzUVxV/VOTAXDGoIkxEuMAUiiRysK3hV6yoskIzapMmdiumC8hVF893fOQihajG6RUzc8zaYyOUbNxKNlyk0sxMZfCZJ0qRn6haKKZ/DhtcT58eMK1Xc/zzz/PJz/xSZ566imeePJJvvLlL3N+dsJrr71K2zS89tprXLt2jY997OP87b/9tyi58PEf/3Fef/11vvvd7zAMA8Mw8N3vfpdHHnmEGAdee/U1BOHpp5/msccf51f+27+B9573vfd9bLcbPvsbv8H73vc8L774Il/84hf5fT/903z+859n3fcMoeAqQd3VwnpEg+1VxqU6bVIUDA2pBUMqSlJlKEJvQwy75pcbqayUnICMDx6RBt865kdHhKbDu9qglELSmkxREktRbs9bPnD7Fh973zO89wffxY1nnmbx/I+TucLqL/0yu8/+hk1PLtd/dkPqKnWob5Hu0JrZ739pNZQUZyPrvLXqaf8X2H+S7+sOR+Tx+1b92JjZf5pAjzC3r6eNjcdLZDK3HYtSqXDh+HtSIn7ry6z+i55b/8w/z2P/81/gp75wg4cvXfDm+UNeHRzfHVrS4Q020Zs5tS5548EpJ3fust5FcIErCxNNqFRDCxmbs4rcGVhqe0Kpl2I8gArkqGiGVCAO0DVGq2nnFsrQ9z1B7NlMMZldV70ETrDQBmoR67GcA6cTuq1vM642myAYS0wqBQpi5Zjvhp5Zozx6CE8cwtUltE1EBFpXOJoJKUEIpWIbQtvAct7QVk9NgJKUfltIqT7P7J9/KwDseXd1bGjiZaVkJWZlyELMNrHQMvKNseJXrVj0jtrI2ki173vOq+eod9b4zmaBrgsEcRzMTLiDFnIGnCP1A7uYyFk5njXVONz2yrH4dVIpOq6maFXXDov6M9uli/WKbneHT/71L4N4Pvj4If/tZ+7xzb/zmzxVXiHlHbt0zq6/IMahrmMbM3sRoFrg1KKnrdzAeduwnHV0XcvhfE7bBkLT4JxOKbQZjP+JEnNiGDKbbeRs03OxTZytx/G27g3x6xnp67Nh/rl+Qu7sfogBJ0kpQaFSlLRI5cwriVwnOdYodk2z3x5E7d5REUPnzb7O2X6ViuGDtnZqUWyLp17bKugZz3OM3uEqbaKM8IOr57ZQObdK2yhdC773OGeBKpqt2KTY1IqieATvQrU1qsEkYh66l+pXrLkve4urKsi9xAC2PHmtRXr1G5UJOB6Rzkuem+/g9c4RytAgB0vkyhHMjpDQQtzBbgNhhhweGn+y6yzXW0BzhDxQ4gCp0r2dQBuACLqClAwV0A3qHXJ4Ba5eR5ZVBd7Ucblm0Gh524fHMFtQpMFdfZTjj/40cnDMzR/5KX7Xz/4x7rz0DV74yud49dtf5uTeA/pd5KRXrm1g7mDo5uTi8MGjZT7dMKmHP6mH3UDe7BjWO/Kg5NhAs+SufpTFP/uzHH7w6r6I+Icp4WXsVi59YbzGCs3sFr/r9/9RXn/hP6TfPDTkLheGasSVShWYBI8LHooVTijkJDZJQ0zurw7fWMJAqt87Phw6vnlXC9rawdjDVqOlsI5mEoDY2sH5QOM9866haRwi5oFoBGZlu7MRddMKwWdca2ks3tWkBO9spGmVHKPtwQji2P+MKG0Fnm0srvJuZCR8qXWLOWVKjtXbr6Ko2UYLUao3WEVbnbcuLjhPlNFAWeo1sZF+jpEcM2lI6JBsXK62OUg15hvHvVo3mHHJibNN3YvW4kjsuSq21O688Tpf+8svTbf9/r23+OIXfrM+CcqDe29Nz8Onfv3v7f39gV/+i39h+vfThw8R4Auf++xUtzhvG/5v/9YX+cqXvzQV7b/2t/+mvT9VvvbVr1CK8oXPf57Pf+5zoMozx3NDVafiryK2rsaxjd3tWOtA5bxW4+TaOaeKyORSxe1l/FQFJuV3PRCcoN6EPU07o+vm5BLN9icPUJQDaXn25oIfuLrgQ09c4/nf+4e49YHfRXf9Ov74UUoKbH79M2x+5c+S3nx9fHTfvtjAqhY/s33KtfXZGUCaS8XjxopHHef6Zf8sTsXjqPHVffP5fWv4bV/SS0WnBAgHJkYsufIsd7WIvfQ73vZjdf/1eojH73yDi//8/8vRP/MnCT//UR79zss88q1P8ENnF5xteu65Ha/3j/GKwIsXDzl/44SLi4EhKs1MaMXRiWcn2ZAdGYVh1uw5ZweqqzwyMGRZVCgJYtLa3AkuWwNYCmy2W05OTjk+PaHvEtvNmvV6Sx/zFBRA9dgVhKYxEYKrSJei0/M6IkVKfe6mK1zbcoWSzWuwcYVrC+HJ68KNw8LhXGibGvnXCL6H9SA0wcz7Z0FoWss9B62+kUo/KMNQ+cKVnz26OOh0a96uiJd6j3Ol5gwpUJxjKIWYzUomabYmzSnLuaNtbB/uY+ZivSHFhtWmB2frtwmetnU03nO0mHO0mNF5RxMcOSezpCmO84stXuFg3tg1KUzJcs4LUi38xiJHyWQ1M5ttP5BiJuT7PHH/C/zd//g7/OowY3f/Abc3L4C8zhAfEtMpKUdSddWAQucBKTW4LtAE87adtx3zWY1ObBvaLtC1bQ1S8KBGzcg2U652O8aTX28iq83A+abnYjPYmB+bduyXnwmNnHfT0nJSaVsKI/5NsalO0Xq+J9sUnPPknC3NbPz+uuH5On4fG4Vx3G3nkvFOBQtjMfW2mzif44Kt9eik3h5LylI/gKpWT8hCzlVx7yuPUs2yz1k4Xz1XrDw0fqfFiOAsRKJo3A+4x/F8UXNsGPcNLfXPx3O9ft5L+zAV0ZwsiMZvGKWYdfsZqVLv5PXOOZQSoJ2hh1eR49to1xjquL6AIVElcmZAHGPdnDMymVEB3iNNC8UBPRp3trGXHaQt2rQmdDk4hG5mSJyzVlpSQvNgnJFuhnZLYIY88gxyeFR9I1vmB4/w7Adu8/T7fpLNxVvcffnrfOern+fud74Km4TGYDc09sjQQwgVoTSTU3FilkjrAb3YUE4Fhs7GyLmjv/E47/u9VyvadrkKegeX8HfMxP/7vi/w2HM/y4d/5gt86r/4y/hGaDvHTpx5xiUllmyTpMrnk7ppxSKkPtF4aF0w7M/7sT6DbOT4NOqQLp1gzlXl/lg41MU0gt42RgHvPb7xzOeB+aKtXojWAVMcXj3zxrONUr3WAFV8MFPo4hxahtp57tWxqsUSdsighZITMQtDilCxYO+cWVLUcc5olRGjeWCKQClm8dBHSznKpVRxTyTl1oQ5GKJqn9UKypwyMUZyLMQ4MPSRNPTGBSs2+laoHbojJqX1Dcnt2B95trFPRY0Y6pJiZjtEGid8+LrnYfUrKyHgmhZxjY1MvLPuVrUaUg9QIl4V1TGucX+iOWcGxeaV6VkeHnDrsRs8+fTjPPL4oxwcHTKbzfDiSVk5u9jy1v0Nr9294PRix8X5BbvTE1ifAGtGvz+KVpFNmZauakWsKpI8AvpxLCC1RhPWAmOkBzhXr40zlS41vxepAFwXCE3Hdr0j5Z6ic1we+LlnH+N9P/IM73rXoxzdukn7rh/GPfth1M3IDx6y/vu/xfY3PsPwta9B7L9vOV1aV+MmKYqWHZLX6GDVrjSH0B7A7gGk1R4dvPzS7/vvCW28XOhd/rb9s1BxrFpMzq0xzj3ENZSqbHnbjx9H7P99L3tvgpC+8y1O/x9/hsX/5I8w+/C/iP+BP0h48a9x/bWvcGWdedfqmJOHN/ju6Ys8cXPJC8cd97XhFGvmnRjtg6ngcJPFSq6F88h/LNkKyoRWIQN7tDFBn0xUtLkYuPPaXQ4ODmnaB2wvNpyfnjEMeRrzUQ2vZ42jaUD8fpxsCuXRA5DK1x2vtUzNrrMHH63FTTcTbi0z1xdmYj5rFO8U8aDeDulmY4bzbRAOZi0mXhD6FNEok9BsVI+LVDRRR+ur+pmdCWDG2ysIFCFns3lb95FZEwyxEispQlX4usbhsBSS1caKtO020u/qqNxZdKZzQgiZ0HhOVgPXDxLXDjuOFg2tM145Yvv96WqD6pzlrME72aNqPuBcY9eSPeghWAzkbpfYDpE+PqSLZzybC6uh53Q4YcNAHC7IZUcu0RrzSpcKwT64c85MzDvz2VzMOkMl24auDcxmwQolX1EuLfV8MY5iTIldjUpcbQfjrW4GNrvezMG1UmnqfVfKBBaYAFJIMUPQabrmxFH7VhI27o454sXjJO8R75EUL1LBEVdFhzJxJaEKeqoVVS7jhGxsIqzgmqIQ4dKf1fepxkXci2NM8a7qzBdWrQTWyuuW6hE4KvWzlurk5yz5ralfk7ovs6eluXE7GgtHgb0QWeu1t/1FtRbQVQS0b7plKj7HnzN5wP5DvP4hfCjFLC+6A1hcgYWpzcQJrFaGKvQb6BsbExRTMtlhUqVKTQdNa46nYFGKww7dbm3BLq4gxzdgMYe2QUMFcTVD7pHd2grVpkXaBbgGrQt4GhvXQsP7loMrj3Nw5VGe/cDvYX32OqevfIvzr32J9Mq3q4P/yH4bxwZias9dD+seLhKyK2RNoC3eJ/zpG/R3d7jDpQktpFDqQHNvnbB/fT+ngekWXkYm3452OD/nPR/5SV74u5/g3ndPiS7QNWIE8JqIQdnf+OADTg2SVxH6lEiu0AWLzgoBG+njQBpEkqF2xTZFe++lFix2/XJdeJZ9vMcIgjhmbcu89Sy7hsY3iOtoguP60XU+8Fzk5tE5dx6cc/fhjr46rcxaX9E7R6ljq5okZQcXChWRJNcEgmgqO6XgnKf1geCE7CwC0nweM3EYyDHhPZQU6wbmkJLIqmz6xGazZbsUfOBS2oqNPow0n4gx2jWOPcMQiX00A2nU0OCMRYXNZqQsrDaRftigkpk4M1ItK9Q8TFUh5jz5Bi6dEuvmhiREoOkaxHt8MC9K6mYqB8XQrJIoyXhyJce6j9iox1umFxICmjPrk1NWBx3laMbhtQNuXD9kuVwSXMt623Pn6hnLxZLvvnpincVqbT6x2TbRiSOJTJwwgWmjzaUyCioKOaTRmgOyjt0y9VA1ALBoNdqt/MyMEHzLYn5As1xweHTI0dVjlsvrXFne4tEnb/Lhf/lR2m6HHFxF/QH5fMfuc1+l/60vMXztK6S7b7KXnNt7G9Hct4mqUdBoPGnbyPaob4lWSOae/cj6H1BAvu2/phP6+/7OuP4vF6aVtyneRuhpXU/FS7uBeNS1SDhAU297HdH+3u94N/Y78oP7rP7zX2L7yV9j8ft+hvb9fwr/fkHINKs1t/sFN4c/zg8PP8XJyRmvvPoS97Xh22/c5cu//TViylM6h60vawy0YOK4+u9m8l59/coeoSuquCKkUkAdmh1337iPePO17ddbLs53DP0wNT5OjM942Bl1PWtVsKIT9xtxtaSuLMq6zifAV61pdEQOm0KDcNjasxazFTCzlqqAN7tZKxBNRBi1Iedi5ti7jG48eW4/N6kJTCqrxoIHshUto+OBEyuuK4hPzhBjYtf3bHd+Grs65+hCzcPGVVGqIkQaV+hjJg2Qc6qNPKxLb2PIAKFJXPhE6hND3xKPFhwfLCp4nvDeW+LYxZqYZhwtOpbzuTlSeI+TYFQF54waVWkCq1VkO2TWg9L3RlnbbhOr8zM2/YahmPNG0VzR24hzdkdCFcPMmsCscRzOPV1nPMl5Z36STfDmauGlilFsDeRcSKr0Q2EYCus+cd4PbIbIts/kWEhKlbTINB0Y+ZYesNGsPS+piBmniwliwPbMUgtDzdnG+67Ue1m5HPWZM29VPwEeot5U2HWaNWR7r32fiEUBb2eOUtuO2nCpEThyRYizQkYpYx3iRhTQpjK+ovSTvZMajStVVNJ5h1drPBRr3lHzWc0jbUsCopVvPArNGP+Mir7aNfE1IKaUMvGYzaXCaFijVsHAGPun+Y+WiQI2OeG8g9c/RPRivQs+IMGjIUDWWvRldMhIv4E0R7RDXEBHqaAIo6ekKSmtmJPYU3YbZOjRpkMOr6LLA1N6B6uijQgeYbtGtxsEhzQLdH5gbyvvbOebuDYG64+HjClOHQfX3s3h1XdTPvATbL/5CfTv/SoGa5qEvqjB4RIVjQPlYkc528HWDMZLHtByxiOb3+Cr/7bw4I9+gPf9oWdpr84qB8jeq3zfDPztxWL9GjChF1w6E6dPkEA8Vx99hPM31ohGgni8L/RDqXyHMpFsnW/wjcOXQiqFLGZnoao0wbpJ7218GaRQvEwF2Rj/Z3GKdRM3GGDiTvmRaORssw0CrRNmTcC5gHMz5t2CW1dh/p6OJx455eXX7hFeOuOluzs8Qje68xdDFU0roYbGVYsN8zazxZFzpngbXwtWOPlgKF6ZmjP7u7thYOh7mkbIKdrooRQaMUVcHxPrbaa7iIRGpm5UtZByIkfj18QhsxsSOQ1WpCp7A+la/N68coWjgyW7WEDO2fae0id7VNUOnSz1M47jlf1khKpLqpYRgiOjcYOT+fRIqBMsJcrhmwbVjNeZxdCpiZNyyph9iCmwk5iZMKuBV194leHsnGGzocTI7Sce4+jwCgfLOdezsOnh7sM19+6WaZMsujdsR/ZFoWXZUg8946ymIlOXnbIVG6ke9qNtiK2++s9KdRij/9p2Rjc/5NojN/nHf+Gf52M/9lGutQsOFkvK3VcgR+J3XmF3/z6UQnrlZdKdO5SLlQn7Lq2it4H8Mn317V+cCryxG6+Fnyaj3PyOBXr5p3z/v48XyD7r5Xv7O/9+FeCIM75kqXvV9/99EcR30C2R+RVIA/TnMJxeKnS///eAlkJ6+WUufuk/RY6P8Vev4W/dxt+6dekteK49+y6uf/h5aBp+78EBX/zsJ/lr//Uv85Zb0Es0ykzBCkypnCnHhESPhbJgB3wdUFcUpx7wSblY74iv3aNk2K57Yt8z9GkiaHsHjTOTa++roKKOjHP1flXLxmOMggSjuIiYQt3emjJvoasWV97BNkISZZYBJ8xaICs5e4IYkmZ8O+hTZjMkNrtCyFOeCYpx0YLHUE777WYdU6xxelvrX5ullLOFKCSIJEZK07jGvRNCY4VlTm6ig2SpS96pGQRUasF2sBG58xlhR0rmc7gdlMN5y8G8o/F1kYqwixm2ESUw7zoa8ZiUqYXkSEnpc2K9Saw3mU2/YdtHnDpK2XJ6tmW17il5IFPpXwqOTPAm0sJZ5n3XOZazwHIx42AWmHUds66ha6oVTm0kcz2JUzFe9pCqiGqX2Uaz2tkO0QCDpGjJtdF7u7DEealTmz0ks59u1eLfjwKujBCMp1sbOyuWTPHtxJpavJt44uM9NnDFBF9DSuyixWNuh54+FjJWj+RKOQuY+FLHpJ0aZ5lyzf2W6f/szAz2+5MWU3urUCJsdwmnZm+k9bP4YnndpY6zx/c5jbYL9fzfXwOzEvK13Kqfqj5fWsW3I41kEujUDk2EKiwa4SWm2qL+B+/09c4LSh9sG8kDGjcwLKwYkGD2HiXV3sKD69DQGv9R6hMghk6Np7OkjPY9xAiuQWZLZL5Em6Ym7oSq+MtI36MXp8hmDe2hiYNmc/uzbYRsD43pA/YHxx6xHB8cRcIRy/f9NEN/j/KlF+zmViBA1KBt3W4p6xV5tbL3p9kg5pJo3Xd5172HxP/q73Jv+HFu/OEfJ9y4gfgZjmCFZSXc7u2tLx+0l2ai9e9NhaUmtGwYLl7i4YvfpmHO4fKQ890JQxK8h7Y11NZGUJMrHsEZMuBKJifzAcwqpo4T4z2GRiBnmlzfZ84UzJcyVMWFyjjS0Up6Lmb2Wkw846XyNZVKtvY2VvcdbSccHTdI47nYRa6vBt44SfgiNHNTPoPZLAwxgbNi0dUxq8NGqCUlwAQ1ZtRud9A5WzClVC1esUUY+0Lso1EW7Am0sbQvSE07GgbYboaJmG+jDEMOdTAkNJVcbT6s8I2pTCMIxTFvA48+cpMrV45Z94kSPA/PH9ZCyq6Zd/We63RXLdPZrjiN97bha6mWQmaY4kumxJ6UesR5fKiFSE1cSKoV6QlmwRI8WpL58lV0WSiQCsOQuPPatlIBMs4J3VMdB8dXuHLlkN1QuHr0gO/5YrZRBVPW1/1jdLxRqLGEFRlSJaaac62G3lgXK3sKArondDsoVSkbi1kAOXEsZw0/8K6n+Rf/l/8b3huWDH/1b5Ne+x4naaCcn4NmtAyXCqjLaJ6NjzXvkDIWl2+vK9/++r6CcNoc9dL36O/8Vd/X5r39ey+NivRt31TXzgivVFSk9LWY/AdszCXCcGY/Nyyo6o/9xf8f+VhaFD05pZycEl98cfpzeytiHsK+haZl9pGP8NGf/RmeeeT9PPsffZVf+9qneWH9CYaytf2zrm0tdQQmak39uI/pXgBgfy5EhDv3zpjPAvlkxW47EIdIigliNCsdrZdDqsI1CUOuxdSY3CXUBtmKuL2gwJqZQAFn/OTGwQyHqw3PRW+WoMxg3ih0QkIoOeBdMIuiksmlZ91nVuti5vuXLqQTG41bGklF1YvWI8ycIabCul4LESEOxsGMqdA6N/ELnXgkCFkLjXMEEZrWlMA5mS1ZVhM+epTizVvT14CAkpSz80jOiV1v66ENjmtHcxaNn9KBLF9cWG3hIiacLCnuEPQQzZ6cejb5AcMOdpuBftihKaFlYLVds9uZeKpoQdVysVWgbWpWvW9wXpk3Dc3MczBvOew65vOWEAJNCNWYvFTktgqJUibmzHpX2OwK2yGy3g3sslabIwN8pDZ4WgtAVbsXxkWvquOiqO4t3grUbG5PyWo2V1Qk3LnJm1HEno9MjTKsYALBVOnj7ymaGFJhMySGmInJOJ5DEbuPiFHNxn1OppmoKaVFpthp53w9O+vJ5aorQvVYjoM1YFkhpUIj3n5nTmS15CXU9m3EuO1BXLW8q5OCuv4Ao5xdcupwuIr0j2E0Ix/UCmqtvOHLsaYTNUnVIojrFlPymNj2zl7vvKBM0dTaMcLQo8FD0yBtB4tDGCK0cysimwZpGps5aC1SpoVrG7NWkYkgiGvQpkVHb6paYJicbwebE7g4McuhZQuzDm1bpHSWsJOjjdIvoyMi048Syn5sK4KGA8Iz7yd/91WkdCZGyLmOvTPab0mrc+LFqppkKxVMx1Hw5S2azX1OP33B6fbbuGcfpXnkGkfXH+Xw+ruZL27Ypa0+djJ+7LFzGLstAUiQt+T+hP8/bX8abVuW3fWBv7nW2nufc27z+ojIjIxspVSLpJQSG4QECJALYwxICBlctgGbUe6qylWF/aWaUVUfqvGwXWMYhnG5bJdHYQozbGEherBly4AL0VuWEKSa7CIzmhevue1p9l5rzfow59rn3PtepIIPnBg33r3n7LObtdde67/m/P//czz/CmfvfJmLd97j+dtfhbFnsVqxvbqy9IRHVaxzxPnhz3VEQqSjZ9ElShAQ475UraaWq4EUrGpJ0Gq8PIcNwUPqQaKJZaon8aXBIPUqMfbg1WrnEGJHDZEpRzbTwPlVDxPstk/Z5opGiL2yQrh354jVaqAT2OTAg6NIHwpbyWxrYZyc11aLV66JFBFyHdDagRbzx6yFtgQ0y6BCKclsg2qz9HGuppiYqJNg4Ln9ODbIOSPVajGXUih4mqfqLAianA9YNNANSxarU/rlETIo96eRvjN7FBMyGHBshGl18VAIpswj2wSKWESoqgFf404Zl4eq6DRRx9H4qTHNaSCJptCUzhS1RcXTlqbQjsbjcHN3ePzOY6aSqUEIsefDMbA8OeX0tOfBvSXDIJQ6EsT8SM3Ax9dX4sC7OoG+KlP2qFW1VF9TRJqFRRso1AtSWVpqchK4dffIR48H/vFPf4h/4t/4N7j37gVXP/bHqFdnkLfQhDAvvGT+V51vdJvb8+KQpwejwfsPiC//RF/8V1/y3ktP1sFkXJoQKG89pDK37OEwNUceoMJ4YZZFbbY4AJOHgdAXgqIv3V/Dumpc9WJFATZ/42+Tn5zx6g/8Fv6nv+d7+VU/8gY/8bm3+esXP8PfvcycVSVpwArg6V6hf7BP9fFLsL5wfr0ll8cs+o71OLK+HklRqVMlFyV5laXOvLW9woywyxCSskAMeGtAgwHaveuCUt3aqCpIbcba4tJr236czGoniaXTzWorsZsS2xKZaiHXym4sXF8XsvdlDbK/Lr828eiiOj+4zJOyR5vE+OBQZ06xUVsyWaPfAxf30OYA421GMYFQSeLveqYo2RaTOb8jVdluYczCeqPkXDk9KW7plhkWPTF0Bi4qFkHzKOr5tuNLu2/nfPlp+hBZXr7Lg/Fvs919ge1uJLtYdre9ZjeNe86emm1ZwLJSfTJuZtcF+k44WvR0Q+RoObDoO4beuN9W9KEa4NMwUwG2U2G9y1xcF652I9e7LVNWB3tOpxLjkNbgtBqPcKIGlFSbNXed08uqDlwMsRng9JaObpPU4jQmLHQ6m0KmWHUeMZcP1UCtkd2UvaSk+VOakCowuPJ7nOo85pgXauNc6lz7Owr0Kc6AtgleWkhEtRqf1kKE1FyQGm28rBDFKiHl6gJSm1BIXWcVpLBFDJTZ0m6fTm8lIR3jzDS14ur/MANICY0zHXys8HCb0+ha5BLPzqncGG2+5usfIOXtfixiExtdMuCoViVCugzJKuEYWVlnQOUM6zn1MctA8YFWzM6A6JNPcYJdmWB9hZ4/gatL6I5tpZ06W+6mHsIGpuy6jRcvvBFNmw2H+DHD0avUkyPkqkc7kMZrmqBOSt1WpqtMya6MVhCxwk4tCpEeVy5/duLx4y/zfLGgxMryTs9nf+Vv4+Pf8mu9Duf+PHBhBZKhrKnbJ4xnX+L68VfZvPceF0/PubpeUyYlhI7V8X3CQwNa19uvMF5msiq1FudfeKm/YinWmIrzkoRFHwmFOcWci5F8g9sdRDcTr2J8HBC3jAnzwGppAuNUqAohJGox64EoHSEsEBaMY8/ZJvHFd5T3nizotLK+uuC95yNHIfD1r9/h4x8+5f6DU0IfeHR/wYdOVlxfrzm/vuL58zXPzzY8ubjm+fmWzVU2nUXtmKYVWo9Rrm1QQcmY6rtqRcWsJ5SIiEUj+jSgoRpFI5t1RhLz4KxayZNXSCg2gOZi5ScrxQFTNTNhdWCZi6e2lNEnyYq6Kby2ZxgQS/soBAKSDlPmvurTxkkRUkr2Ew1oSrXUj+GXgtQMOiLFo9ISIdoAlCtI9Wo+TIi0SIo9B6YWLDx99yk/m38OLR2lwmtvfIiiQpCJRTcPOQgFmVf69ijZwCqz8GbSgwgWBiJFZrE+jUeaMTA5WhiBrgQ+PCS+/+s/wm/65V/Hp37rD1PllLMf/fctIinCbCWG7HHaSxCU1AmmC+ZQ0Usf+z2MlIPfb0cnb4DNG6vw4AdtpEKdT+PwcHrw2/5BD6bm7k6gOzJueRktfZ2vXnayM++pUTiAF7Q5evv3F1Dl13jVaiKgktG8ZfrFzPWf/4uc/OBv5Vt/X88nf/LD/E9+8Rl/6Rcv+HOfH/n5beBZXVvGBI/usI+IOLWWoMpmp5yfjZRl5bmu2e4q01hZ9DZxNjZSF2HoLAIoquQMu1HpGwKeJ7PWsi0wgKXeYqLUSiT4ZArirgLu8M3QCcdLWC2F5NT7ECOqhVxHxnFiOxrfd266g/ueoiDSeCc2fKiIG3gLUa1CTPQJ20zXPQI/FcapsOuKgR72S47g4qcopogehs4W5eKgCaELQt8l1rsRlTqneb0Xor5onbuIQhcTaWEWQzkL45TY6hFbvp5Xv/d7+L/93s+wTMof+zNv8yf/wJrXeIs8BXbjxLS9ohQrzqC1UrEMYwgWBe1TJEX7dzH0rBaBrk8sFj1Dn+i6ZFxN9yO1fmZzyDgVdlPmarvlYp05X09sx4kJG5OjVvqYTORUbUxvdII5Iu3PaJW2MAdCdMN4RVJwigYmMPFASPRFS3QDVQWCxJm+BOpZm2AliwlMo6W4N6P7DtdKjNGySRj31EzQbaFBMBeVWtT3a2A8hmTVe1KYM0927lZdznhRViO7S4kUorWhQkrJnRX2osZKQUKguOEQodlC2UBsgQqvClRbMQIHhzTBaXtuPVMZxKhUtGo+DVjaogkPKjQ3GHP/+IcAKGvOdpNSZ6KYbkC7HghI6tBptLQZoHlCgpG1aak4cFAXHJyJS/B1HwCoCjVDLUhWmLbo1TlcPqeOE2HZz1V3DIBGWwXutnB86sgcWn3R/csYOYhz96iQTonLpXlMCs7dESg7puJCjk0xk9pqwo3OV7NSMqEU9LIyvg3rTeT5SnimBYnK7tmf5uGjT3Hy6keZnaE1AxNlPGN38WV2736JzTvvsH52zvrqks3VBsnQxZ7F6oTV/ft0R0sowsWzx2T5cd7+3BcoW7HO61OlcZCqVYqJhaE3MnjXmWVNUvFKC6DFIJmoraY0GLjOPqHNd0qNyA3B1Z71gORr9UpT6OjjkhAGQKglc3W24cu/eMUQrklsWKjwDZ94hW/5pg/z+munrE5PiH2k5BUP7x4x7Ua245bL6zXriw1nZ2ecX1xz+fyKq+cTV1t4eGopJLMXFJJWOsVNbgMBZTUk+mSWG4uho+t6QsienpioUpEYSSGQq4P6VgpQjaBfHJyb5QSWcsuWehtHq+pzeb3j7cfPKWJRk+fPLyh5P9H6PERthrO1CYeYrYUmIAdBNBjPKVhtBRHwm2LlxxSjYKibI0swXlOKHgUQSjFD/qKVsWaSRHL2ijcNLpXK+r332PwPP8XVeMWnN19P7Fc8eXrBNI4I0SZrN1y2FA/uBiBkhV11r7dqA370CfIQ0cyDO/4MBlgKfGjo+L6P3+Of+OwbfPKbX2fxqW8mv/FZzv+dP0i9PGcO37cGPIw8zr/qwXZzZ725nR5sy/73ec8vHRQP92e8JvC27k7QOiLT5XxOt8Vzh9Byv0vLcuDZA1v8ArljjlIebu57mkHlyy7p/V4tknPrim5foq9/Qas5W9Qzxr/702xevc/q1/4q7n7/r+LO8XNe7y/57PBZ/vKTT/Onn/wMn7/+G6yxSkdFjCfbzqv9jBkuLjPbdaaKcSqjBxRi9CEdiGlPz6kI02gRQCsrp2xz5mo06nzLhmixEzemTIugW3pxN3m1NmySHpJQBLIK6wmGmJg0sFNlYi9gNNMQr+RUDNCKWOUsMz+3RaFF2fZ9LYGLTOpMg1UVF3digDIX4liQ3r1qsUhjjJ6KlEIXjTqjxcSr1aksQzIAE4NVM0tRkGIgzx7RQBetFrfNq8kKMHQwdB2LIbKtPcvplLePPso/98Of5pP3AyKR3/3bPsyf/JHX2fz9FWPeMtUtmkeUiurkfqSm4o4CQ2c1nIe+Y9EnFkPHohfS0DN0yc4jBhe24AsBMxbfTZXNNHF5veNibfSCzS4zlYIGndO4h/10r6Q2nmGLO1Wtpmto4UbvQVVk7ouCOVEEaepm9iUdq5LVhCVTLky6X/x3Xcc4wa5mi0rmwi4b8zOlROoiKRitoHEyu87SyDFGH+8c4Kml6C0IYf1ZfCwwy732fAenchQvvmFc4lahtT2vKUanXRWjiKgxIK0ARTSQF12MPEdNHcdgdLCGrRovte3coo/M320APojM6W2tlcOSHf8gSu8PDCiDh3lDSCawCQkJCUtTGDxuggo0e6PKAfFTIARUkgHGltJWUx9LQ5UevVStphq/ukA3W6RbwmoFw2DlwMCioSLobjdPoDeH1/ke+btt4gDCgrLoQXd2M0OHRek27HTiatpxtlmjY2AqBREllEJysnSgsC1bznXLk6vCVwflKbZKuXj7Gu3/EL/ut/42Hn7oI4jAuL1kc/4W68fvsHvvPcazc6bNlmm3M2vOfsnq7gknd+5x9OB1Fh/+BOnBa8iwROvEq5/9Zfy5P/Rv8vRz79JNPRKFUZUwZkoxHmTFkmq5FmKNdF1kSFY1YOqqiU8wk1yDi8lSSxnz7FIHDGKE4Eqk1GzX7mXphuVA1wViUhPJpEC/yDyKI9/80crrRwFlIMjHWPUd9x8sufvgmKM7xwyrldUFrcpitUInK+91urlid3LFo3sd1+sjNlfHXF+NXF8WhtTTLRKbMZCnxNd/9IS8syo9MQaGJLxysuLh3RPqIFzmzLDs6GJiDNZHigpTsahIxSaSkivTVBlLZcp7JXYurmTOmIVQLh6RrNQy8aW3HnO+uUSorK/WXF3vTJggXhpLxVyxFEsJe4DruthkuinKdbUBImkmTxA1ItW4VlZG0hX39qxbNFqMsRNDh8qCKU+QIzVbKiVXRZwOYYmhfapZJ+XyrWc8X+9472zLyeldrifl+dkVKmHm8jDZoiK48tYGNW+vNjAFW70mfwbFOUiiXsldhV7gnkS+5417/MD3fJKv/6Z79PeAR29QP/lbufrjf4H8pS/un9VZyOYN935Qarb8OHio9eYTvn/ib48AvM8I0aIcA/R3MDgUoFsiZYeVjJmgbjBDksM93IJ97TTqBnZmiaZltCpcL9n89tcOP9eXXc77ff9rIU95yVfyBJdP2PyFP0P3xocJn/gM3ad+hjvj23wmvsWnXv0Iv+bys/yZtx7zF9/6Il/aVra3KoHNkXCM31vzftEEsJ0g5P11KZXYBSRb1HI9mgXKbhQ2u8zzsGFXI71b+lTBgFOt8zrDq0C6kGxyY/FKBFadgNgdGrawSEqulefrDddb5WKn7CYDBpapsCIRKTRgYpOvBJwTzOyj2pToraxoW4NY5RQbM/OusttOgFCzAccpT5Yydl61lcC17FCKaQaUZikTjOvc9aToFc0WAlIg+TxX92IoyxYmWsU0RFimQOwX3O/FRDuYX+JuCqzPt8Sra2IyQGNVZopXZzHhapegT52ns3v6PrAczLB86BMkp03F4JFJS6NWHz+s5vXIZpfZTIXtWNnuMjlbDzdqmdMJ1AIcuvdssjbCorZg5yUe/WxzEyKuVgZNexPyeRjwjIIJcECzLbhzrXMd8EBzxzCK1GgGpJSqpG4fXbaKZlZP3UIvzH7KZidlApt5e6zPtRK4VmOt2fqop+ax9rd4iEXERfeZLI8+BJHZdxmxcsA+0CIxeJ3tzkFm84y1niwtOtscEmROkNGaqf3fsI9xVNu2IQRftOCLjn2m9Zd6ffCUdwqgiuYdMm1hF6GMqCuLqFbf0hQhO7QE4wwecoFCwLgyFc3Z8nbZLFeoxaxtSobdxtJ9l0/Ri6dQQI7uIMMJ6sCPWgjZbA30+hzhI9wA4z5M2yM4xwFoE4iGSO4q+uwxXRxopc/qdmN2LxFkCYXs9ilCpzAyGUctFPM4rMJuV7lYr3laRgKB68Wav/OX/hvO3/4Fvuk7vpm7jx6SpEezkjfX5O2astkhRem6JcvlkuVqxfHJHY7u3Gf52kcIj15HVw/tJqM8+vrv5Vf/c2/yV//If0HeFJRkq50yMY2ZXPC63QWr5mIroOCdLxcrVzbVlsI3gntVMwwyPsbessZsYzz163y/JIGj03uEOx3dqhCDkkJlMQjL5cjQXfGhRyPoCXBM1yX6Rc+wWtINg/lfCYQUkCoWySuFBT0hLEmpMgyBcTlwfLxhe2eL1kKQidUUWC1OuH9sIEpC8pVmYOgiy+XATmHYjPSLQBcNVO1qZTtWzq69bGGobLbZgaJxZrRif9fCWDwlliu1VKap2KIJZTOuudxNPLvakkKlTFu2W1N3NsuVgFUiqcUmr+oDzbrawHsxCufgwqFKiqNxejwqYgpQU3uWYtzGSjLAyopN+S6m449Rd+9wvP0f6NnQbDRCsWiyL0AtouN2J6iyfu+a9fQmR8eXpG5B1YQtjZRdFcYJNz+21FdzpNBDdCPiKRFMIFGhek1nIXAUOn7V132MH/jOR3zrN95h8Qg4vY++/j3Uo1/B5Y/916x//McNBElEQ2d4Mvb2eOY1XtAdDv5vz/ZBRPAWqNw7dO7hk7AHW3s19sE+2t8iUCc0j8jyviuz23glVnhhUijX74PeDsOLTcVUYfuMZjFyQw5+EI3g9q/vByRvv1722S3g+T74s82W6PWayz/8h+H3/l7kG343MRwTy1/k3uVf5F7/Gp/87EO+760j/su//RV+4otnvDcVtvMhWkrSFy0eYjW7cIttTNX4hmb1YoAoR5gm9dresB0r9VrY5omrzUiUvZFZEKPlzCX+HNxlsAioxx8QWHXK+Q76a2djhYlW+nAqledXcL4WdjtXyVYnNviCTz11XnKdq0QZh9LCXy3N34DN3PU8+7AblavdyERhDInF0GOcyWYtA+plI2MQK4eI0WOMf2dgUz1y1AW3dWGcC0FUtdR6yZYxq+pjdLJ2jloRXXN/fJv/5D//O/zQb/427i97/uAf/QUuP/fTnIRLqu4QsYBPFKWLljaNnUUml33PsqW1o9APHV2XnA7gvrJq0cQS2gLcyxDmYrzJbWWzLYy5zkEmFRv/YoNN6vzcajZyEBxcYYpvsSWxRSzVeYuKVKNShCjgdnJdMB9hddClVZmwY4/FggabbLY7OLArOxtjVHHuYCEEo3ZRK+NoVZAazDPgHfYpZ3wBkAJky27VDJNUxmmaBTYilRQ8vY1z5t2tJMVgHEtxuoEY77YUswzqYvRqR1a/PKhV1uk6K2PZpeSeo3F2FMADD81KCdTpGcw0uX02RD144BmxKLMCvEXp0X3pxw/y+uClF2NExx1cnJnbzniMxoTxH5tXIRY1TMnq1IrQvMTA1FkS4j76eHkG6/V+8BMDikSQcQvn78H5M0QWTuLKyHZnrSEK0xa5vETLGv3E1yNpgUqhpdrbimc/swgqXgqsrtk+e4v8xZ9nkBUdthosE4zriXHa0Z9UE0dOuIWOoNJBqKTYU0OlRCGPO3QCLYEitnJLApdPz/i5n/pZHr36Knfv3CF2vXkwlgylkCQgXWQXjFs0onRUYsksasHWT8n4RcD9D3+C4fVXkCdmyt1R6Dpr32k7sR2tOkxx8+GiFsLXlOyBzoXqRPZQW3q7zia8XdeRuo7FwgFg11s5vGT/LpYrlqtT1mHF1djRLwohTaQeUlK6rjMwZHEuYky2muqHuTTVAeHwYES2FKFSkFCJsXj93Y7saadh6BkG4eRo8MhdAMw0WESNKzVlYgr0MVpE0T0cr7YTtYxMU0Elk3Nh8koNOdvAkYsRoXN1gD26HUTzKxVBs7KbRtY7L5VHcXFKm3xcndhAzDypB3YVNMK6wrUDtbZy7frM8dIMghf9wrxDu0ipsJ0yZ9c7rq8K5+M38al/9X/Lw1/xTVx/4Tl/99/5v7J4+qNEtvuJzvctwU2gy/wo2HltC5u8ZrkILAZ3bgimOJdo1UeyZfgdSHrMU21/wSM8I5Y6PDk+ol8NvPH6x3njwWv8pu//Xv6Rz3ycXt9FF8cmtjv+ZsYvnHH1n/y/Gf/e37PyiBKgO0WGu9iqvMDuzH6n6Se9HQ/+b7+KfZ+yf67nbfxi9cY3Xv7HrcWnxOSUm9HDBwndPkHKhr0n5MvQoO77dOOLH26jbWHdviA3/rlxXjPwlJsA+vDybl/KCxHbl1zy7ZcD5rrZcflH/wvKP/YbWP6j/zzxG38QWZ8hZ7/I3S7x3brgG777c3zfT7/Nf/ML7/Df/9wX+Mq771rVEZzzNXOvZI7Y2aRm/ytaqV4RcypyowWnbBzdzdaeHVsMueuE5DlppVjULskhNveAgbRUuaWrmxJcROm7wDTB9VYZJ1ustVrGFZw5aDNFbuVCRWf1r0UifWFYW0TSrqXF0yugRagbYSyFRWeLuj5aRG9/25QuRvoQGTUbh9ARbRNI9l2iZC9NmTpKtUjVVDK7aWKcjI+4GTtrf5mIXSJoh4hS6zVp9wW6nyz8H//8X+fseWDz1ld4VX8WDW+jckkkW7DBU8N9lxgGq3zW/CRTsnKKFlFl5kq3yTrXgubAVEzQk4vV486j0YYsY2YNJY7EpXkyIs7b9Epcfj+0dXtRnAAL4oLQamWL60FlGzf5NP6l8w/HqXjFGhMEmzG7qaoVm5unSSnSSsl6hiWaIbzgRRvQ2eGjcWWb0KVWV2K4t2gKxtMtWqkZ49mri7ncmaSB45ly4cBUqWYlVWyOiTEw0PRmQkxWSMQCQ+IFMBJ96uhidKcVn3NkDwjNasj4l9aPhUbMFr+Z4vQCASQUb/+baXR0j+0+yOuDl15UhXGLXJ6heU0dFkjqXCpvTNg5RiDBUuLRlLYieI5EnbenkHfIxQV6fW0jxOYa3Vwhq5VF2TZX6OUz2GyQ/g7ar9BYYTuY86sokkfqxXP02XPqvbuET3yLVfOROvM52zjehhDRSq3nXD7+W3z+c5+Dx5ekek4niX65YPnqh0ifeIU7eWT91pusv/IOerGDKZP9uqooU4B1yVyWzLYYL06reuUYgZTICOtt4dnzC8acGfrBuI3OOxE1f7B+mtiViatxy2J7xWpac1cnjmUkLe/YA1ze5p3HP88kiWkBUiOLVDk+jiCVMlV0MzFdjZRdoUxNwez5AREKEXV+hjQFPkqMkePjFfcePuTuvQfcu/+Io7v3GJYrUj8QU4dIInQRVLjcZp4+X7McIEYjQocYPAUSPa0UCbGDA8XbjVfA6m3XQi2ZkidKrpTsinzfPDarKTE1X3U/y6asFO9+IISKD4BGklanXmxGs8pY7zLBpJBQTcWaPe2Si4Hv4uBda5iFAga8cZCi5MnOW/1CGpCr1e19PGVjSsDqzDm7IBusbMFTVVitOu7dWXDv9IjjxWJWT/Z9TyBSVbm4Gvnqky3nV9/G0We+lXcuFJH7fOzXfDdf+aN/hhr9esSYtVZ4w9N7DghFIMYEJJSOKZslVIqVVSfcfXBMyEvGPHG1y1xsR643Zq1SCrZw9Im8FyX2Pb/xB36AH/4d/zSny2NeOTphqEpKVmZtLG8wfe5NdLNh/IU/zu6nfxrdWgEDK4N4bFVqnKhuHGMDiDd7yksGs9BDfxcdzxDdHWzSikNjoPB9LHcOJ/g9QiuQL2E6dws0oDtBNDPX+H7ZuOrAZh5s5pfs/51LlcjN78zA8fDELPrxAnqcPztskw8+0L9wuYKx+EOgXl1y/Sf/FNu/+TeJd++SPvQK8e59QIiPHnHn238Z3//twvd1Pe9eXfMjf+K/5I/+Z/8ZT8/PZnDXKuwcvgx/6DxRFe+TLSpX1fleDjaDWPTRHn+d0/6OR+ia5FxajEB90rNntKJNeznzw0SKJR59J3EOMriwxiNnWZVNqfNcIWApWQdRyv7c5uZrzao2xE47ZZqUsDIDd1v0NOGEVzWrAGa2zSGfTYUk0UaGYGphrYGqzldWt7QpmV0ubLYjXTLR4W7KpK4QVBl3he31e+Srr3DnvYo+nTjimtitGeMlvUwkzEfYSrUqiyGxXHQshsTQBbohzhG0IOKpZ7/OVg63mm3d6GUZixqlqFR1ioyPjq5UNs8lZrmSAq2kn/khG1VI1UsGRsHSrba8bNzKoBgXc47EiTEjFGopNHOdzjneuTSbHS92Iftocanq3MvmxGELabM5q1TnLWo7jp+zqkcqm4jY95GrelbLAHVxsKxqnzdrPsQigIJlBVMMdMmOb9UglRQjpVaCA2ocW6Tk9dSjEJJjDeFGFLE9E/Mopy2oxzwGidcXDW0B2MpJhsar3I93/1AApVSFaUQ3F0hOcJVmkLY/UWuwPV8S41a2EcEHV6GaWfl6CxubEHS4RFZPkGFANVs5tc3G5oTO/z4fjL/ZlkxaYX1Fev4e2+fvsP7G/xHe+AThzjGxs9JToOCm2VUzUmFz+SZv/r2/xeOLhB6dktw+5vWPfpJH3/lZulc/QkgwPPkC25/56zz5mZ9j8945uykDkSKRSYQ1wlOFZ2JpzNFTFzsVtiUylEhX1GuTbui64jfIqr/gptKoOdqH4DU9kyDpL9MfL1kcL0lJ2Fye8eydZ1yfBXasEQ28uuo4RQg9pKEnFNiRuRgnprFQsxF7dV5dxTZaU7RYelYrDx/c4/jBA05eecTDD7/B/QevsTq5T79YEKL5giJ7+kC9WjOqkNSiW60eMMGEI9Xtcua+AH4v9sapuF671kwtE1oqWs3apPqPcbK0zQYgHm5T+77c6HOWgugCDMn+DTFAjHR9z9DDyTIgVPIuU3MhV6/Y1K5M25kVf+5N0S5iSrwYxTilwSw+Cm1SC/YceI64WTkUxeunVx7tjogXkQ/dv89xrASErutYDpGjVc/R8ZFVoFj0DH3Hchg4GhYoynZXWJ5c8tYXtkxvXVDXJ7zx0R0/+6XPQZ5QDUQpZPUB2NtFqxHYxXJNpBDBbTeKZja7zMkC7h4F7i1XLFKk5on1lDnbjjy93HF2ueN6ndnt9kK37mjFP/Mv/cv87n/8tzH9mf+a/tkzxusN22Kl85iu0GlrPrM6+f0+AFW+akczjGuYLi0zwW0w6UMLrR+0lUUP/ZHZjW2fWb+LPcTB9p/X7h/bjnu4o/2EJv4szuAubw7OQWF8vv/Mvz//dcjnhANwwH4Ve2ODgyuTw19uv/+S7efXy4BkezaYn+33B763DlmL2bJJQEch/+KaCZDJKwcpVsii7+2rxyfc+67P8q/+jn+ab//EJ/m//Nv/Jl9+/B75ABM3EU6YD6bzZOz+H7NfcANvDfbPkX3H4I2y2Jq2YE1blRkIFAeTbZu2H2kg05vk9tKiLRSHdt7A5NdRlRkQHSqta5u/9OZ525Xam7HiArcmoNjfgODbdV1nZtla58naRkbzRiQ6pzIFxmIZpJrM/maXC9vJaoFnp+P0qSBayEzssnK1XXN2fsbzs5GaR2oqVAl0sqMLxq9MURliJA1WKnHZJxZ9ci5lmj1ygwtRq49jpZTZK7JUmLxspHHTrQRl8SpkLVKmtZI6cxLR6mN7bMpsnUEZyswpNf/Gxh1lzsDgM4jZs3mll2wp+NYLzaf4MKLo/pFq424MFjYNzmcNwcBh7AzYMwVqVqoYgSN5tHNsvsXFzjeis+ej+vNSncMp6jxNZM7OmfJaZyEtapzMkoR+UIZOnFfKvHKxYIWaqU204EyKyaOdsh9vxPrQodAGhVDbsBTmxV/1AAOEuSIO7KsQ7YVpLVDyDwFQ6jjZgbWgk62+BG5aALV+4U+xilr6O7TUt38ualY/44RsvYxgt4N1gi7MdijUghAhZXR7gSSTvBPD7AUlu4Jen8PTCy7Pznj8s3+fp0vYqNdzcGNQJRsfxv266q6ylTtwL9MVpdMEH/0Y/cc/RffgNVSE4e4Rg6zZXFzw5mbDs/Nrcq0+aAY2NfNsu+VyzKwr1JAQiWwmeHK9ZVMKy3FDf90zDIPZPIToWKt4PxC0VJBArhOIVaIQ73Bz+sjWM+ymiSwuEho7jhYrVp2VqSylcLHe8fR6x3q9BQKllnkFjGZPXVq6uwuVV++e8MbHPsr9h484uXefu3cfcnx6l+H4lNT3RnVooFDNKqdfDqTrNTqalZRhSbF0EjjArG6SXhHaSsqVtGrp6FKyRSeL1d+2Qdn7kt+7OQJRq/c5B+O0FfDe7ysG9ZW10HeRLkKJkdWw4uHpEQ/uDvQpshtHssszpWL9MwSvg9tq0PqqMlplp+Qm49a31Ye8wOR9X73ea5mKRS89AjoVZTdlXn0G6TrwkVcfcdGZ11uSwNBHuj4yLAa6wYyChy5xcnTE8dHS1Ii1sonCN1x/kb/57/9bnHzLZ/jbf/rvcvWTP8JKL8HLw9XgsS33XWuASX1Qi90A3v9yHoFKHzruLHoenXSsVj1EM2h+tBt5tJk4v9zw9GzNs/MNF5cTuQZ++Pf8Xv7F3/zbOf/3/iPiV75MHZawPLVKWOMZOl0h6cSKEIznNH6yjyRQ1hZBLGsoOyxNfGu8eQE0+YQhEdLC/u1WBiQbEKujHW+6tGMerLJtNzcPMh/j9nZw83z9d/F9vByzfS0g2MDnfj96+7Pbl3r7AO0a54X5yzbW/e+H17S/BG9GRzkzmVAMjMcOSQMQYbwGqi1YHJzrdsv2v/rvmD73mO/9PT/E//l/9b/m3/63/+/8wvMrNhhVJItFp0z52s5B93ZrDVQe3gsxGN+sY9qpRvByfHNCwyY9x+tVFReCzzi5wZG5uV/oV/Py5DacN5CELcisaIc3U2tZVct8CTdcA6ofP+7XHTMiDtKiR849xviEeDTMxk8Xu7C/tQ1ADDVRSyVpIBGZJlNNr5Ow6DKLKoaKa4AEoU7oODFdb9mOVyhCqkrsEkfRQGoUZVhEhpgYlia+WXSRoesIUUgxzAB9ytVb1JXSPi8Z597AVClW9tfMuW1hi1iZ2rYGRLxOtQM9o155pE/xIhP4GN9mvP0dkpkqZTxxKyno23umrUVSzdPX+1U1f2VpZQUl2N++O/HnyHix+7lmrg6nTsUQ4/tbFNKLTEzVBVvW2UKIJAfRrqUxgSuGW1RM/BrdKspVOXRJkCGy6JOftFggyJ09qu57rbHGbDxXSxjO9AzRhsv244J4G1sVIde1aEvlG99bsXm2ZrP6EM/wVrVrLtNLqom9z+uDi3I2G/PJK8nrdO5PDi+PNy+WSwvzAjGbGiuI8bIaab1kdJrMYqgq5AQ5osnSdjLlGfVr2LiVhzW4dBE370OmSr2+hnEiTMK4hbNlxwXVTUcheJUOU/fIjLytLntgF2FQYaOK9Avi4hhCYEgjR48eEF69w/bdFWeXOyatZIEqwq4IV8AkwULYCuZjqGxHS7XsVFnmwDQJQ6+EUGz12fiCuABDi6sIC7Vm8Pq5BtDce02bGtna+1JgzJWFKjVbpZdJ7dib0UCFLVaUWjNRDXEsQuTB6REf/ejrfMs3fAP3XnlIEWG5OqFfHUE/GJ0hJdRD4OK+jVoNlKpWHxT84W/8p5ctZnweNPNbe8hryZRppE4TJe9Lr1Ft5SnNJxNfoQUxQwQHllQDksHBpAq++kyEFOmSKZG7vuN4EXnjtVMePTxmtRyotTCNW69wEMieWkkSjJcSjKtCaNV5wuy1ZhVljLpRS3E7nUrOIxWrSVum7Cv5QinGw7xXtoSYeeXRPY4GcfsT3OJCjBAdbNKJ4s9WjHSLBX0Q7hxveHjnbR58+U/ypT/xR1jkHXd06ylZYURd/ekiiNIiba767tlfUxTqbqILyjImolQzVg4mmFqsOu6w5P6oXJ/uuLiz4fnlNc8v13z4Y9/Kv/w7/1l2/8F/QvfOm8gQjdhWri2L0NLFmq2EoO4HtxvIpk7GV5xf++jkHky2dxJWIWfhP8v9Z85jYrqG7TNXYx8Aq3lPemPPfiD/9/B4cvilG8DhxdTP4bZ66295ny3l1t/tNLyNZn6x0tiGTa3/wn7nQ7ZJt6WXD86z1SmVaL+HaG2mCjo2ZAbsfLseju5YoYhptKhtyX58U7KUL3+VzR//cX7lP/Mb+Td+++f5I3/8b/Hfn73Juj6np523n7M/o9WjWk3mYLDBf3fQuQduDqoOLrFZWRn9ZI8UA7fvdBszfUJnTyMG80htWdgGdtv/i/8ENYP0g0CPHUf85PyALfhY/IDKPn1ea6te5Tuq4tw+8co41i4aW+rdy+65/Uu7hyHuAWmSRNkp6/VEL9DHCCuLDFYKA0JIlk4dusQywWbMnrY1rmXfBfooDK7eTl2g7xJ9F4nN1sfvRa6Vmk34mbV4ZbZ5xPf51dq7KGT3B60Y9099DAx+p1u0V2YPCmvgltJuYynimbpmxh0M4BotIsxADLGx2CJsro6ak+o+//tdCsGyh0053lLEbaEQNZAnDw5IW2dZ4MrsgKz84lQKzdeoqKIxEkJ0+ydf7TifM0gkuNCo2f2UWjFbPmtfwagQGmxuERFCTEjJPm8WtCazuKt1ztx5HNTvhFcGmldWTrmq3n/9+a9mRG2Vq5yWUL1NUNwXdLK5XS2AVabC+mLNB3198Ajl9bkBwL5D+oTGjvZktkFXPRzcSLjNRwoNc5SSWs3UtRSrvjNlKLqXupeCqrGjpRpfQzyCxOgG6KO7l2Lh9LDeIuOGKB0xdADk4HUpgiLNmkD2ptLViXHRw8i5FM7Xl1xenVGPjghdxy5fsZm2bCrkmJBFT5gyVK+V6n5cxsuQmbuT8Ql9qohU0AnFbmjfCUUmU415ZClotEiXDyxom7yat1W2lEs1WwLjVgjrXeHZxc6ik2qinFwKm93OSL26n+cEG8iCKB96eMq3fes38rGv+zoevPo6aWHcOVOyMYuAZnNTu7m+EjNlWow9qY9uZ9Aeav85mPRsQFazTqltBVgoZaJmq5td80QtGS15zm7bdwMh2OrY3hcTe2Fdqj1OhDYomwqxj0KXAiEVQrW6s48ePOD1j77C8cmKPG0ZR4+MAbWJJopzP53QVWnKPus/bQDQapYh1asr5TJamgcrN1Zy9uo71dNEmZOrSAwX3L1zxLC0qPRUmgBFrWKH+ESTOkLqkJhIfQdSiUmoFETXLPNzVlVZiZX0yn6fLJpjk9pel2Y8s9QFkpvg5jJRypbjLtJLotbE1XZikw1YLhcdd06OOD5OrE5W3Dk94kPbY2qGf+L3/W8Y/ubf4erNLyJRQYqBuHIAGAWLPu5Hj9Yb9m/dSAsfbnW4bbBShOnIVOAhzc/9zZeARIcyB2Bm33kPQGN7U/c494X9te/cfu8m2Jz7qf//EBLf/Jbs35X9uy8eUm58rrfePzjkHu3wPpciAboBI9VXpFuim3PbawvdNdTVQOXoVX2OH0DvXPWtQr1mzkFPV8CO8af+e4bv/CSf/b7fRffzP8CDL32Bv/DVP8iz8efppc7gzBbJbuOC8XuNj/diDLpFlarugWIDbeHgGksDjf754a3Vl/y0rlaUmU/Zqlkh+8tvqfaZgtbu02E0kheWHS6u863FAJMdzyNxKbj5te0rqdCHyKSetq0N6MgcDQs+EIoD2ORq3+vNjjhEVkVMgFmTLbq1EIIpf7tkJXojjUZkApLUQTcEFl2i7wOpE1Nwh87GPLFI3W6ayBVGryymzcZPxDwYsetp1Vf29cz3whcb06wfqnWEg3azPtG4e81VBA6ibT52NW4tMleZNjqCL1JSsgxSiAZlWoq+3QsTj7k5955/MKd6HXtRavG0Oc72UGJn3NfiAs6qDnJnsKpzcMXgi1O6NOyzej7mqloGq1YTCE1ZmXKxCm/BSim3hQU+7+wXU/vIZKsUFjAAKd6BG7i36xZ/vHUOCNme4tyf2nZ7OWPLuuH82EyeCpdn1zx5dsYHfX3wCOVu7cVveigddGpyzxjnh8xwpDNLGgfHCCmIG5pTzSiUafTIJr4iKITqRYK1oLl4JRNFotjgFgJKmTuGeqfQ3Q6ZRvp+yyJPdJOlzqvzD2zgEUP77cGuatVislKTdfD3nj3jy1/4PA9Lpj/qGafnvPvum1ydnaO7iZR6qnR01YBxqYU+JkqslFk1bZN6wOxnQrXOE6cy37gYWlUGf8CqlVuyW94GSRtVc61eeUTnxXHVBiAS59eZGidKqVyNhev1SMkF0bgHc/PkbSvX1x7e47VX73N655TF0RFxGEi1ZxrX5DwSy0TJeR48ZnWeq/lTVfrOjJoltOvwB1na4NrUYoJNAd4lVCllouQdedpRJjvW7EfWQNBcFspFLn6MPZfG31d7MAz8CV3q6IeO5aJnuauIFo6PTzi9e4ej0xNWJ0ty7unHrXNNItRWOajOE/VcgkowxbhWo2L4wCBZoSZKzRb4ztkWQn6X2yq4FLOnSMn6bHTLiGpyP0DoYgJXg9pkZmlIiYnQ9UxlZJdhvdsxrq9ZVeUYZQksVSgR+i6QgHVRpuJ90AdM8cmi6ARVyHlCdaSPCzqxQe5yk9nmkSrCajGx2SoP7x1zsuo5PV7w6MEdvum7/0kerB5w9uN/lBmQHIK1rwWWEDuRg8ozL2w2I6IGBWwxRr6aSf1WhebIfi9bTNGRjOu3egSb9yBf7zHjrajiDLwOAeehpc98IgfwtqESubXJC3s+3B83FlYvbPey1/t9dAu8atv3QbvrzV8cQWVY3EXSwhaCBJjWFhnWyhwyOrymMsL2zK+5rdoCHPRt1Kr/rP/0X+DOv/Y/51u//4iTP3vMG/J7+dE3/yBfmN5htiJx8NeyLRW4ncU4BGgtEhX9GuXgZgVpcMRS3VkszdxhXoRFdXYKbWOl0kRAB+0nBi5jg6R+Am3ebc1q89nNhm9dZT7fBvQPQb2qp2FN5FOLW9oQ5w4VMP9MgjC1i25dzPKYPg76WIiSklGYxmlizBYVkxioPq9VzK8wdUalaYtQDYGQbPE/9B0xBq/QFRy4VIoGcoXdOLGbzLcx+1xtdc1dnRwMIAaxiNuYJ680Zg4jAF2ydLS6Ol69EASIZwr34LGyjzo3Dp/DBBdM6VytJRh/wOhEfrO0VmJKXqO7zgApVwPAIURKKQ5UDzj9DlxpWTCgVou6Rp/rSqkUEa/msx9TtUVGD8CrpfTLfiGj+3naFgqBKNHmmYqpztUWBRoCU7bnyyr1eDBMLKubc3FBbAs4udBo/tfwDS3w0vrg/J6B/7ZQUqeX1XmY84yjtmBIRaeJzeWax0+e8taTp3zQ1wevlDONBDXNKKWah2TygdwnSyPRBqR6utpZ0eLkVtwHCqmWsk7JJnT1FPdoVXJ0ykjOVtNZAImoOrhRNdud1nlKxibljq5kFnnHQjtLc8+EXweXYtyBmq0LqZj3lGCik6fnz/nZv/9TPHz+JsuTxHb9hLffeoeL99bkYg9oF+0mlzwRsim34kzW8bD+DIraEKqUku13Kc676GjGqDkboIwi9KENcOLNXC2FAnuQ44utqsrVulBkQjH19VTE7Q98PpBIMw006wVMUZYiMfXELiHBfEFLmRjHkSpXSErmTxZ8ItqjXyQGQrSwOezjMiEEX822qEBbiUJTXNdaqXlEc0bLZOW/tMxAI8x7tOO1trQB2z5tqzdbpTGP6CEk+r5jtRw4PTnmarMjlsLRakW/WrJYLhkWS0I2tZyWCiF6it1oB+Ieaw24qirF66Ha+XtKIBTzPsviVAIrxSUxmlpVi6XoNcCsxhOry9oHt+VRJEXjtErwvgg1T871sQFxt66cb685v7pgd7VlCdwRGERYBoU+sDodOI6J86ycr3esd5aO9ynTBse8Q0OmluJKQi99VizF8exqwzpnIsr44JSkykqP+PjHHvGtv/xXsvqGX83zP/gfUq/Ob4ZkbqCz9nOIGgQzDj+2WtV1y8tet78FFYqnsFVsrEjH9vd0he6eQZ1soSERCf3+vG6gxgNweOs3CNDfN/5lvmAvtdg/0++Hj98fOL904xtY86Wvl4DUBqFe0jgHx9dbbzmwVAfk6ur3YWWdYdrC5AvzuTzuwUls1/vnKkTvQLcuN0J59wtc/9iPcPzbf4hPs+HOn/9mHqb/BX/qrT/CT2//Po9zmauetPNv8eW2q3rwe5g3tTYsPh60L3ftNweP7RV84Vob8PSftnRp+3cvfppmUDyLdth0h93nsMkV3Gx9Hp4NNNDA676PWdczIaj2wS1c7INZMBEgavAyfwbwYtpXqTIQwpz2NaBoCDpr9uhksfSlp0JbdkKCuW/0ycpd9h0MHfRJSQmG3mg90RFGRtnuJnajmb9vp53NFQKLFJEO95Ru52/RMcvSmAfjNJmoLiUz/rYxzeavUoplFh3Y1Kpu8+P8QIlmXYYZgrdAhKmwDTRKMpxR3I1jJky0c8JBIljEUMK+LKLUuQgK7BcXrbZ2VVws5GnlEAhlDzqrt7GdlfMNuQlOreqaLSJKKXPqHmxejDGaAXsBDVbpbBwz2x4GAnk0VXjBfFKnUsA9pANh5k3GZqy+X71QtRJcmyCtY2oTmjYBTlty2nlXLy6jaku+onXWAWip7DY7nj55xuMnT5j+YYhyJJgFjUxWXrBZqpiyNRJcCQygsVmMWEenVKe+RRfWVF8VtBCxh7O1IJuCTjsDolk9zcUMzprxq60Mo0/WNnwsBFYKQ61IVgtquEngfgXslWl9MIjO6SwINWeePH2H52dvG5j16GouHdIpvUyECnWqhBYxcZJvDFZ+UEXpvQOFaICghZNVlWnKFp1yw9CqZlBbtd7wNavVVkT7GqfQQtwhVLfTCUyq5HWmImzGghal8zR0cF6PraiMQ1eBTSlsdztK2VHLjigR0WLAWJVxuyXIJVGSA1yZ7RMs6ls8mlfnh2oOjhwM9OKzZ1OqmdreQGTOO0oebV+VOTrYplBLJ7QptcU53FgX72oiNOa8iJJSYjEsOFoeszqaGHoDvaEb6PqBvl+YHU8QJpQcMkIgxM74NW4Eou5WXD1qGpqIaDJFeyljmxOoFPMTkxaZFqcKtDvpaklP7XRdhy56e5awMl+5FJIo3dBTc2ay9aNFL6aJq82a6+stF08v6baV0yCsBI6CgUpNgcVi4Hi1YqGwOpl4fHbF5dXOy9eZ0IYw0ujdx8OC5jRRNTCO5sl5vZ3YbbcsY+SNReQf/c5v4pv/sd9CfOXbOf///pdMP/e5+X7vp+rDlTrMAPAQ6OjkYplmWn5zkJrxadt8/kv3mwYru8p4ie6eWl1v31o0Q929bCcvfx2CI1HollCufGFzACZvbfySM7+x5cte6l/8WtvcxqctOv8193oDod4+M1/Ab6/QcI2EZCKpmo2/OiOjX+LESzm4iIOXR713f+OvIkE5+sEf4rUIv/bHH/HR5XfyZ997kz/5zgXPirCDWTzT6gLvl9o3lyHALLvb33pPxfnkqMoNYD56xkJv7e9wPG0fzEDz1k0TPLXK3g6o6l5ZfnjZ+PfloHs2EDunILNCn5mmgKbkINnHw9KiZ0IvRp8SzIUhthP0k4rBiw2IGbZbCjYwVmHMRnGydHNlWS2K2KWevkssu8Smh66LLDrztk0puF+/A6isbHNhOxbW/qPFygwPfTQLvibA9UVxG5tbJa0p2wmvFr3RpsRLw0429qeUUKwWetZgJRE9EBQlINHmioKSJJGrLYZpWSofg6nMwh0wIaThikCWRrUDkehAVA1zqEUnYzSxlXqHaGLOUD1KOQMy64NUu5aKgtrCIJdiHPeKiY4FCPv5vTiVqQFL9aBAkEBKnQF4LUxjZZeEdSdMtVB2lXGyTCQhmE+0Qi6V4BZCUcRsD4tlzBBmyyOfHQ8eUJfxSsMOzTz+wDPcgbR6sE0EUuy53lzz/PyKs8trigRO7yz5oK8PnvIOkZgGZh8HnJvoWSxtSu4WYQu++q1AUUInaBJ2ETaauFZl55yRpLCQwKJGkvP8ZLsh5AkhoalHliuEHukEQkCSudqLK6YClVQrvSqDVobYc1VbgzZrF1P1tkiQYIHSUrMbidqNqNVSKKlfGLG2VmLeMW63yKRMeWskagnkEJGQLSUQxSuFmTVSjL1HrFp5JPUsk8wrsSDuKeXnV0OT/ltUqa0yVJkfEhpAwfkZ1sQeRCxWI9TLddXq9hSuZ1GB621mmksKByR0pGTClk5hu85M48i4W7sPZZpXeIbrLf1bavHKLtVjCfvVvg24SlOUNUCptRhnMo+UMs40BOaJRmcuU5sFmi+WrT51bh/xWaDx5iQo/dCzWK5YrDaEweyI+qGjX3SkPpH6hHaBKYFuN9TRinGH2LkZromfLBppAqhaJupkJcTMg82FRNOOPBUf2Hxlmotdo0c+YrISa/3CaosvFkvKEJHq4NmJ08tFx9BFthsTvhW1QeN6veHycsPl+TnXZxecAPcCnERYiFEqRAIlBLTv6YbIMStSn3gcz7m63M7ZTS3OkxFLkfddR4jJ3AOypXVEIkcRvulkwe/6wd/C133/D5OfKs//n3+Y8ad/2u7VIfqRmexAu2M2qjc04EhEyx6ccEtw8pLXTXjUOoTCdIlOlwdg0j6Xl+xp5lMeLlJedpTxmf0+g8nbL533dnDlLzneIRC+hfcOzsi++7Jv+yeHOPxwby9Vo+utf2+djRakBtCM7i4sUtRS3i5imNuW2+3+kt3efl+U7V/7SQCO/6kf5uFnPsbxTwys/orQh4G/8GTk53eWmq5aXwDfLQI0U37ZA77bUUbLs8ieJ9w+kybs2b+H7HmX7cKqMlfoacKGJtBpAPTmQkh8DLsJeg/va1PzzutbNRFeLso4ZqTvLNrUY4GPmi097/NBkoAGq5IjfnzxtLCJ9Bx8YvW+hcB6O3G06r3QnFELcrUsSgomd4oi9ElY9JGYIn0MbgguFjXEAOl2LOymwvV2nKvKJAl0XXIBUVNOyzyXxpSYdu496anloe+MduOovFRlcmGplOrm28mDB4UUrHDDXiFvGcXs6ezii3IJERGjA9l3fayXxjk10LkrhS5GS4EHvABDmxfCbFaviGVUbRpl9l8sHqET62k6dxDvS8HuAZiS3XuH328TvlkWSOfKQQ2wqgtzqppwNk+m3dimiGwLIWfyFrZTs7oyz8zWVxuQr6XOYHi/srLfRZpgyR6eRh2QRr9oX6ktLe4+sLXdW+t/OReurjacXV2zRTm6c4cHD+69zyDw4uuDA8rlEklL6yANIASQFJEYEFeQtidYfbIPc0fJbHPgiUSeS+I5wrkCVHqUO9Jxr0t0R8ICWFQlbiuL3YZ+tzUrA8SjFJ5GREA8fVjMIb+vxbhlITKkCJiRd2kTfIx0Eqzqgqvqikbz9Sq2UmnWAl03IF0yo+NRmcYRCdUI0iKzQrcbzOw8BDVir7bBxlIAXUq+BgUawTaYuiuG4GAXk/Y7r0Q9L9M6lFVgaSWn7EGQ0KoUYQTskNDoBroOPlPqkGLluqwrVjbbkapCSgOpG0j9wjiCpaASKWUHeFmvkolaCa2250EUsZRCLfYAtEjw7QlOPVVgVkGWbtVi3pO1ZDQX7/AH/A/26avGyRVtMUo7thnmNh2lRaxD6OiHyGI5sFolQh+Jk9Af9cjQkVNiEwLbDBdF2OSK5JFF2XEcO7rQoWR2u4ntNlPrhIgp0sfdjt0me+rD3xtNxVxL9Tb31EZK9iCLkFJnKQ7ZoSi7Uthm9So8GanKousYhoXV/B0sqjput2yu16x3O959/IyzZ2cMux33onJf4TjsIyp5yuh2Qk6hXw1osEkkhcCTeM52Mxrlwie8GMNsop7ceD5opAs9p2Hie7710/y+3/+/58FHv5X1X/kbXP+5P0c5O7s1IBw86zeA2+G/3Pp9/90GqG7Dt5filxa+rDuzGpv5fO0bewB3Yy8vO/Tt13yah2Dy8EsvA5g337uR9fd2ud0CL57ri/t9P3B945zmxVYbGw6PffD9GfX4YCMWGTLDd90jsXkHBuVeega3b+nNSzFQ+dd/kvzmmyx/w69n+E2/m09/6pP8zh/5D3njy8/4I5+/5HNbJYsc+Ms2XvtNIHvLYIra5nX16Nw8DhyAxwOc3d5r6eh2hW0ObgdrXPX2hQYoq+zBatwvjw7S8fa/ubd4xK5hfdc0stu1iT4z9tHSr2K8fRQrGxzatdq43nX7IgnzOCt23Ulg2Q0MqWMzjtRSHUy2SJYpqpNAiO5b6GNA40o2PmEuwnaqbHaZbS7spsyYy4xP+s6BJDKnWoM0Hr0JWIqWOXjSuTH30CejFYhVi6m1Egl0cz+z6xCUUSsEF+6KGJ81BqMdEUwHIBbRtuCKelEULEUuzTDe6GxmxQPu07QH+gdzqC0QbE4JIcz+khqgVJmdRew/ny/1wI4IA+wj3lAtA+f/V5r4zI5XfRFlj6Cl+qsa8NxO0E8CO6FXowyMZZrBn7KvAJRrwQqGOH+/CXKalsSfW61h3x+dG2/DgPWj6soz415ati3EtgDpmPLE5cUFZ2eXXFyPTClycnLMcmhurb/06wMDyrA6RkNvU0E170iJtrqSEC2CZa1gqwB1GmswH76z0PFEIo9rx+MA56qsRagiRGAVAndD5DhEjsOC5XDM0W7H/e01p9fXsLkilEyMR0Bnx2ykGB+KQxD6WjiKwjJGjrqOjPliJY+UCUrCaqUOyYYlK9UdiCpkrcZZ0EDqOjSab1dIEYlCKEJMga5GFrUjILPVQhD2g0EIRhaOZkUTXXShGIcy+gOeYqTrOvOLVCuRmLN56DUD7doU4GFfV7NNS4qpzBurJDa0Cv5Eme2BRGb7hs1uy3a3Mw6kBEKyurMSJzqUoQjTmFGUkjPzCHtjlqxMJVsZqylT+4PzEp8omv+gAM7RqGUkT1umaSRnU/gfcmAaqNzPh74vJx8jZiYbZuuIcDDoQeosItkNye5BJ9QuclYrk1p5sLPdxPl2w9nFOWm35iMBPtT1nA5HTHnk/PKS6yurIy5iq8pxt+P6amfZ0STk7JZBxTwlT45XLBYL+mEAhO125yW5jCszTqb8fn52wVgHxlzYbjckAq/cv0uKiWExIEG4XG+4Wl/x9PnE+fWGJ88vePL2E46rci8opyocRRsgirsjkBUdM7EK/XKgXyxZLZecLJa89+Q5l5eXbKcJhbkKRuo6kghdinTdxEOBX/ctX8c/+fv/T/TXS57/u/8+0+d/0RY3BwDyaxMBX/KS/XN6A3i9L4C69dkcNWqQ43ArufWdmwuag5285DvK3u4nHGy3B8ZzyujgqDfgZkMYbR55ydXst38ZjHz/12Gq9WUYdw5YelRi/vVwu3kfljUx8Vu9tWXbyGOCt0/ya4HJubmU/JWvcPWH/1N2f/vvcPw7foiP/eDv5+SP/Xt041v8yLtb/vbVyFbbudy8qJftvorzI1XnS2z3o11B4MWm0ffZH7oX/LTvqXDga3mzydxN5WAhq3tfRT8HrbpXjKvxNI2zDCVb7fJcLCsQHRwSjMNetLod2t7OBvYAJnrfyp5QlyB0weJkIubusJ0msg5M2aNqDgJjCib+TJEUkwn/1DJZY65cbEbW28Jmmqgu5ojBsmmID60OIms1CzZziLDjZBeqBFH6zsBkn6xeda1KDcrucD5SGyv3Wm1rbPOTtNSyjK3wBwdBlor6MqOdSzu3xnG3ZnL+vVbMb7Gh9QPVuL9iNIpam0NDMSP5JJVdtYxVPKg80wI/SQKS7exzdq+BRmOo1kfMiaM9R3rjwM02aELRqdCNXi60Fna7yRZbanXMxzEzCjY/h0D2yGItBj5rLZSaXfjjXEgHvdUzs9Ci/fu+od53jNPqGVURtAibyx1Pnl7wpbef8vTikuWdI1ZHmfeePeeDvj54hHKxNNNbUSsFUPacN1XjO5p6KPjgViAo23HieYy8HQJPEd4S5VJchCCBmAQNMKnwnirnqWfRd6yOFtyZMrvxlHx0xfHVOd3mmnR5RhqOCEWtqo6XMDLQIXSp0mllkYKpzQREbRVzyP+rDiS7rgOFLmAreMvfEyXSpQQpEqow1nFOAcQUSdVLGHrdJ+mFIXUWtdM28ARSirNZbANAtmI0ZbEZclvEsxG2I8FLAda5A6CtTNTBijkYhy8Gi9J2SRD1SK4D0ew+XSE2QBuYcuXy4prRKy2E2KEEVw129EMAdpb2LdlAZYxOL2iDsJmsd2Jm3ns/MOYHvT1pipiCrKm7846cTZlupHLFDNCD8yaZIwrtgbHBx8U4LlixCj7RQHYwMUyi0HWJPnYMXcc0FbYF3tuOXFxf8uRyzduXl1xuNjy/uGLYjXTHS+4cH9NVYb1d8/TpU86fn1FyYegGLyuWOXt+ySJ1nJwcsRkzWWG32zIMHccniZg6iIlxnNiMEykqY9lytVlTr66ptXB1ecVORq43W9brDcfDitcePKDvF3R9z3a34+zygnceP+HibMPZeseTZ8+5fnrBJ7JyKsJJTHRUj/hgfbwAu4m4HUlDx7Bacro45iQlFlp4u2YePzNqh0avTe+Rh5TgtV75/m//EN/7z/4+JDzg+f/rD1CePcMITpU9ncH9IOtoCwZJ9pneNr+V/Y3kJpC7DSaYP9O5r+uNbV4GPJVDYPjiFjdR0CH+uflLODjPQyS1J77vF2+3dv8P8rqNqQ98TW+d0Lyt3nhrfz2HMPD2Vd5oiTaruNxW205fAPW6314dar3UUPbgdXgS7fcAWivjT/2PXF5dc/ov/s94+Bt+A7/uv/oT9Pr9VFnzt67+ClO99Ia4yXs83Nf+lhgvualbb1dUl1s/82UfXNIhUGwwYSZftDELB7B6sN0BHpgtVWBmC6hzc6ruS5wGNYsfPBrVon5NoChiv9u43hb+BpxjwItvAEQXzQR7ZoMQk4E2Eayud+kYc2YcJ8oQfRzVvbdtsCxVi5pZalzZTZXNtrDeThQfy9tYK2Lgbq4e47ZpMaS9+ThQvXBC1wlDF82GKIpXmql00byApRQTKyIm8CrF9RMWfQsOKlU9aFNMnFqDzucTo6WbBWfSiQF5FFeNm41eFwIxmno9hrg3Ukfn0cts4GxOTSJO4TFw3yXT3ufmOOJBMxErlJFEiElIk9H1plycwm8dziKybQGkiNT5OSq5UKv5UxaMY7vZFbpkHpOTmn+pBDED+2AdrYFIU5E7bUPV+4nzSAuWHZVCYc8NbWKhFnSprirbs/uE6OLa68stZ08u+cpXHvNzX3qbSYR7CJfrHdfX13zQ1z8QoPSnwBvJ+GkUs/gxky8LwVNGALYqPAuRt2LkndDxTISrTtBoytoYo/HlQjCeZbQVyTXKVgLroWfSyHS04vR4yfH6mpPn5/TX13TThOQloVtQAwZkBSQXFgJHoiwEiotijMtZzNxTYcqZWgzYSkiWui+W2hUsBN/1yUpH5uIPWSR5yagu9ha/J6Oxt3RxLmhJDl7xB9sUxcBsv5N81RHD3pzbAJ+ptWzFGhDJ5Oo8jOrF7OeltQNM8NC1ULS6fQLGwbOD0qryEIMJY1R5dnbOuHHrHOzaQuiIVhCbqDqruGsZibUjhuAE8UCIkaqVcTS1cy1KTIezbAOVYpxC954s00QdMzqastsGXadQ0Epk2fdbTewGoi0e27Iawbi0oTPFZOyQaLzWoetYdj1D7Jn6wv2TBY9OV8Q+Ua+t3N/JeMly3JAqxNJbhYeaGbdbdusdZ88u0aLcvds8Qo3DIl1A1agGkbZC7+j7gRgiuVSutjsur9ZMZWK327LZjfTnO+P6jBNXlxO73UjNmRwmlsOSfuiZaubs6pL33jvnzTff493zKy4u1lxernlApSuBQUztGogeGTHxWZaKlpG4HZm6SLdccNT3LKuQjo4oF5dcKYy7yi7D5mTHlEG7yiud8hu+/i6f/fWfJbzxK3n27/7HezDZH5u5dR0xVL+C/hidrm2Vi1qpw1sgZQ+TbkIeG2/1YKuXxZduvua0ld7e9vbrAIDqi+/v/38IR156yBu71/c7HHYNL/3oxdDmC/vd7+Ml27XT/BoHb5uIT84v7Eaa6hWY7dxetr8DUHtjP/oCYCQEmnsHIUHO3DiwB2emz3+ezU/8BKtf84/z2nuBXz3888grif4Lf4i/9u5/yFTH+b5mv1+NN9lAZgiu5vZ2jOBVz16u4j7Ex+3XZm7e5v32nnDYfv5zADwPmyAJnsXaN5+opbD3QLaVVjw4lv+kIAR39BDPlEWRGVDZWC57WpHYXKFR5uyQoEiA1CVyFcZNJpy08q9NiWxZLhDXF7Qsl9MMPLBiFW0MnM8RXmEGWlESKUb6ZIDDRjqdgx6uH7Y0d0z0MVgdcm2CWQe23sJdsGMXB7zN0zIc+ihiz0GKxuMDG1sRi1BK8IIPeGr7RiTdal937kzSJ5ufTG297yBNzIu0SjYG1ESBTpikMhWzaGrp4laqMQYsUKFK0kzJWJnSOYorhJCgFF8omN4g14k6Fnabic20QyV6jfjKLisX14Uh2Zi4y4Xo42MKkUHdYzhY/G5XCmM226jsHqFGHYsEotkYhTDbGZnhObMLwLwEVwOeodjcvL7e8uzJGV95+yk/9+UnvP3kCmLk6dmabZ4o0yHF6Gu/PjigXF9D6my8KdkGkvYzTZArmneU0SfO0PFkseBtWfFWEZ4WmLpI9RB88LCyPYCC9K4MF6v9XYCNCu8IXA0r7g6J02Hgbj9wd7vl6OqSYbtGslJTIITeJvkBlrmwpDIEYReMqDtbqolalZ4QTF01TqSkRBzISFNSGw9NxW145IBTIgHpPJTuBuUhg6ZInhoPyVdr4j5aztcwCwET4tgg5Gk1igl7xInYGEiacqWJa8T9wNQHWVFXfvkDE9SrywjWaQPm5ekDRVshEwIXl1dcX19RdqOtYlMghIRGS7cPEs2vsIyUPBLzYBZR7vgfk3Xc3W4ycF6VSEJplXSaeKZVktlRpi3TuHUfxOLzfvVBBp9Umrkr1Dr5LOPlpJQ5FYJE6z/O3w0x2T1226SYEssucjwMfOKVO7xy/4Ru2fH6QiirnnE84XKzo5bEMg70waw0pFSmzY7p7inTVDhZHTHVTJ96Vn3PamWp7VzNjmKzWbBaLlkskrVtrUjJjJsN2+2WcRyJIbKIHSFkFv3ARiYW/cD1NHHvzgnL4yWTKhfrNe8+u+DzX3mHL731Hk/P15TJxEZdDO63F9yZwPpezsUI9rVSc0F2IxoDZbEh9AtiJ5wMkVe6jmuxUpFbYHe9Yzre8uEo/OA3fYRv+OUfIX3XD3H5kz/F9As/74NwdbBoymwNHdIfQ+x9Ebi9pdyGPfRn/vswusbBVn4jbZtDVNAUm/5Z23auU3vwvZuvW2DopcfzvxpwailvvbWVAwjZnxhtmD44oxcOf+M9ufU5t68bZw+8BDDefus2Rr6xP7nx1/5DvQnEb7fNS0D3jRPwSXdO84IByn4wBJMW6OYKmcabX+8MdG7+25+g/5ZvRn75r+PO+RW/9mOf5IRv5Pj6hL9+9ZSn6tFHxeNPHrGU5iixb1LhJkD3oXoGjw2/NzCKCzNu3NIZsDae2QE3Ul8kPTSwZeCROXuyH9/3S5QW9VGPejUrI/vOnn9oYMJPqqoVx2hBYZ9bgmeq5u+5mjh2Nu6GaO0zlULOtgjvgplpVzEUK8gswLC2rHOqXKLMAFJpjiGWfk0h0neJFPDytabOTiFQgIiBtD5F+mSL994DJk0UEmNE1AuSVKgOcLUJNA/ajSDuUsz+M2lt5tcdhBQCnUQX7RggraYoIQZTkdtj7FZQnrpp3HZbvHgmM+wr9TQvY1xgmquSnYYVFEvQONUshT14jFmJVb29rT9Y+WCcqwwUZRoz02bH9XrN5fWa3WSpbaM6BLZV2I2FKU/UanoOb0yYbI4X509OrVa6mgDUqHF2fhIsKLV35TdqYvU23S/iA0gk5EoeJy4vr3j6/Jx33nnO5770Ll94fM56UpIYxM8Ke9+DX/r1gQHl+O5XTfUc3TB7zGgpBiYnE1mMGzdglsTlUeKrKfJ2UJ4pbCqkonRqN11cDS4GoS2dqfPawwYYgQnhUqGkgd1px25YMe1GdssFd67OOL7aIbuKSkYWS6J0LFRZVDtWEHtIcjP3bBFLbedgKeJyEBpuaQLr1G4BFDxNHU21ayrtiITO/ChjZCpKCF5OSW3VFnuzU5q5iKJEk7IbSHSuaQjBOJookvYp8lrFjGVJfg42qE1FLdWRyzwYRR84xuwDlo+0zdcsznnyym4cuby4IE8G2sw+R1GSC4bs4ZkkU9Tqbhe3MAghEmIipcRuW9wQth2Q/YChTjDWjNaJWkY3+i40/zuzoHCPL297Gwqqr5hbWc+WUldX5/mEEHwgDjILnQim5ItJee3eKR+5/4B7d45Jy56aj+HoDmPesisT01QRTTZ5lImjbmDVd5yeLClZSXEAjEy/G0dCiqTUeTWBiXE1zNxWVai7EcmZTpQ09ITlwPHREY/qjhCecbIaqGng+noDiwX3754gUtmNGy6vLvnqu+/wpa++w3tPr2xiUCsDtxRYBRObdRItfUUGMWVkzoU8VtiMSIiU6y3TYkPqI4nAXTo+LIEShfdQ2FY+oT3/zD/ySb7+uz9G/GXfz/btwNWP/pilSdqTWDNIgriYwSSoRS2nC6i3BTLv9zpUYr8MDO73ccijPdz2xW/c2k9Y2Lnmq1vn8zWOd+hUPVeqacjkQPjywlm8DEx+jc9ftu3tTR2A3ghuzs8S8zl9LWhpO/OwzAGQFAfqLwuavoC62nsvA7HFqQ2LE0iDA71zj1Qe7CoIut1w9SN/nDv/yr9C/I6f58Hzv8mvOvkfWaQjjv7eGf/108IlPubpXnQnDuZVnRcnhzue5+8ZIO6v8eBzvfl5w8Rtv61LNMwvgsfhbgJKkAM/zH1U1ACtzIC7eFsFNZAhFZLqQdSzTeh1LonX0q+lgWE/sFnw6ewn3yzHQlAWfaSLUINQfH6ordBGKZalioFO8Cpufl7ShJ3VNAEeKQyeHo/B5rAmsOk7U4b3gdmA3ZicNr6mEOm8XKNlpuxA8cCL2DUg5q/o6XJpF4mltBtBq5RCs91p9yMEIYVEFAeGuBOxBq+AY8eYnNcpUqysrASnLljJ3CjqmgWbN+cxK+DgzKK226xuxaRzhxLBhZgu4PH7bLzKOt9/9fOt7h9JcJ/NqZDHkavra67Wa8Y8IV2iZgPDKqbOH6fqRuNeftPBt9Ep/ELV27HafW7UMwligZWmrwhWl9tKDE9zX0MC4p7Ku41l4d59esabbz/ji195jy+/e8b15GKoaJHgWOWlo837vT4woLx85z1WKTF0nUcQvHEr1FwY88T1mDmn4+my4z0R3s2Z5yGSuwKakRKIo9UMVkcDTQXcbHTUSaJVbCXX+aO8lsokgc1qYOoHdn1iGgZqd87x9Zqw9RrXEohaWVWzD4pixq0Bsw7QEO0hSh1a8hyyVvaAVrwntRBynSZfYVrUMdH4kHbzo4g/CJWohQjcWR3x+oc/zP1XHtAtejabDc+ePOHs7Bl5Gmk0DesgezBrLzN6TSnMq++2Gp/JxDtF1ax+1essBhE023K3rcbMKsL3qrDoAqvVMa8+eMSdu3fohs6BqpOgqUY/CAEYUJSct+SyI9XeybzmmZa6jl3YMftfHZCQbcy2h6DW4v6Txf+uczq8/biuyVTsrvyz647zPVHnmASReSACH9h9KxCPCAeOVj2PHp5wcrJitTqmW4CWQBkSfV5wNBWzeSiVohmtHX2M9H1iuVr6+Qa02EIgex11BSuZVWzVWmshe/UBLZkuBh7evUPqEv1yQUqJk8tzgpxxcrIii/Feh/4Od++cIrWSN1uuzy948u67rK+MsyLB0kzLqJwE4ahWevGkTxBCsYWX1Go2HsH8SCUEQgqkxcCSJVEDi27g3qJDp8xqEl4/PuFf+fXfxtd9/2eQb/5+tm9lLv7wf0q9uuYA4UAcoDs23uR++jYe9exHdDArz6PPy8DcIXjb7+om9Gofvg9was+I8sLnGjpk8QC9ViCb0TnFKue8dF8Hh5p3ehinaijl/cHhS4KQN3f/NT5/cbd2Mocg9sb1vgxUvnS0v4GAX9KS7X4dPIN6cL3zCVRe+ioZ1MQL9D3IHdhtzDC9VPtxRJa//CWuf/RHOf6nfgi2X+T489d8l7xGiBvqzzznJ55mLlUoojduxSy2wMHWjAj328yiGd0DwNZEHIDJ5v4SxLP1ctDDdA+cZysg3X+v8R1p+xdmS7Mmijo4tQMLSTvpFKydgwRELZ1cncEpatkHrUrokwUafOxGzKO2YkUvtEUF+8TQCVejMpZMqZXNuGOchFIGBz5lzsi1qB2YyEdDMK7hvHYyJ5KkgSSwSJai7qLQxUB067++sx2W0jjs0biNUfxYDptLu3f+nwqoV5ARPWiflnbmwFx+P44IBoK7aJnC2bJOcZuegz6sVmdFcvWgio2bXcTs4CSAVoqblZeiXjDExvKpGKAbp8puytZatc72QgEhT8W4sVjBEVET6WRAJTCViUKdaRylGs+U6ubvZV+Rp0wTfbJ5d8yTg1g779ScW3KmOcIYMm2UFXVg7XOeCytEBEJTgNv91uK0Mtc3CIZTrq8uefrskrfefsYvvPmYN99+zuX1ll227dvioz1N9WsNcrdeHxhQbjcF6oh0btgtNqnlXMkK16VyUQPvLZa8tTjhybDgeRQ23jkGDMlXDVDFHiR/mEPwmtjqMhSpJPeemhsNs9NZAzUFtnHF2A+UfoWuLjm9ujZhQkzma1mVRa1meYPzXYLsTaZtmQFYY5fqBNeWFj4YSKRxHV2lZTwTaJZEAfOMskqUwumw4OMfeZ1PfOpTPPjwh+mPFoQQmMYdl1fPOX/+mMfvvM2zp8+4vLpit1MjPbdgT7C/Y4xotVR4afzCA4+yGK1zK17CUoutNlUN+Irxf7ohsugH7t055dWHD3jl1UfcuXuf+w8fsTg+Jnadra7UVzFSrUY3rXylRaBLHgkSEZQoiaHrGZO77d6aOVt5xOrek80kHudsto5rDdzuQ4tKzswevxcHPDWRedwxg3r1lW1BC6aO22V0gjurFXdOl3SDWPS1yuzvCKZA1KjW3hrQ0iGYlULX9+Q8kXOm5kqpGSlWMqvUgkpBKCSpTG6ZVEohxcji9MTMhRcD0hnJu1/uELEowHKRWK1WDH1nPF2BnDNX5xdMO6v8EmyRSwzmN3kMLEOgl0D0CT9IpBNIMUNWxu0ItVpd8K6j24z0Q2+DbDCvuzsh8C2vv8Zv/53fx+u//bdT738D67/2U1z+6I9SLy45aHTmabJmv2/Z0tziqZUCTdSyBy0vAsmbXMk9ipOXbNv+ev/vtd8O3w9ARGSA0CPHrzkKybB7ftgr/Z99HzqYde2604nxRcv21rEPr+2FPb5whi8FkzcG5pcAwhsg+eAcD8+zoRg9/N1HytRbJMM57O97soKPfQIh2aXv1i9eX3sOb4c1BYvyjFe2TepBO2vvOt66LvOprE+fcvTDP0T6tn+Bk/t/me/c/Qn06oqhZv78c7hSj2D7pbToYIPYXg56bgKffqyE/K1mjdKWBsJc/s4/CyLzGP/iPCkzkA3AzA496N9xf5j5Hreo1R5UtjKw9l4KyS1ubB4zcWOdo3C2z70yN3oW7Ub0NEaCGF1s6BY8W1+x2Y6U45WV/PPULojNpclcRg4wmkM8CwZZZNnT8NXGPIvmubhFLGVukS8QqXRdR42tVrdl1OyQDTgKxVOl+9T2fuy2v91GB4vuGtAPZo2Tm5+xLWZC0xp4llHUIsBNZKs0RXOcH2HUzr/3ErfReahKcA/hypQLYzEPz6kUxlrZTqYDcI8a57taW7XI5DjlPe1GKjHYtlldyKR70If4vjylHqO5yPQhEDqrjkY1v81dqeRciV4cxqBJpOpkWVDvJyF4RaVgfNGQoke5HYQ6ADSwbhzcoOJWhgaKry6vePz0KV986xmf+8I7vPPk2uwSVch1RCVCSDR/zhDMM/mDvj4woOxTIE5eQzOreUZNyk6FLJW1JM76BY+XS86XCzargTIYSdVCwBEJEOK+hFFFWUrPgwePOL13zPXmnOdPn1Bycu+kvYGp+sApEpmAS63URaL2x8RFR1oOrLYjISQD7WpgMlYIwVaFFhm1sH0zbG0vEaFIQVr5PVFqzbPhuG81r0CskIkbuUfjvQx9T62VO6fHPHx4n0cf+hB3Xv0I/fExIfaoFh7UHeP2ko9fPufi2RPeffcrvPvO25w9v2SzyW4fpB41NA/DKpVa7Ilp9ctrEFO4uwrN7AICKSpBhVU/cOfoiPv37nN6esTpySknp6ecnp6wWK5IixXDYkW/WNk1RkFznVeMWp2bSCRqh+aJOk3U5gMqJi7qQ3TFfDYLAm0DFh6yt1qitVY3JD4ID4ClrrFJo4mWmvfkLOpxo1kbuBv3ckKnLcQFlYDWSFblejfx/GJktx05XfUsh4AEK3ZvvpmjE5mt9FSLljaup6X2JxcRlfnf7On6yTkwtRhnUUtFqhIqSOjolonlwko8Sgzkmgl5mk12U0wcLweGrid1afYM3W6vmaYMmBUXAZIIEpW7IXK/JI4RlmLVJTJQQyTWSsInurEyaqHohC4qxwh9l+hCYRcFCQPf8Su+md/8u/5JTr73t7D5wmOu//B/wPT5z6PZeZAi+5uD4C7/oBM6XiFlxz4nuwda85BzI1/bXpE5diMdYBGuQ8B4e26/uYebgFMP9y8d9HcgLSF0PnN2BoJ355AvD/bx4q+GXhaodPbXcB+drpC6OzjuYRzq4Oty+zxvnXL79xAZvN8XDq9pBpY3QaU1bXvvFpJSIHZWznZzUAln3vUt5KrA4gjS0n2FK1JsQXLTlP4lJ6zAbu2/CtItQKJnfF6yLcr4cz9H/n/8uyy//zew+DW/ntVv/kY+wx8ghL/D+hcn/rtnletqPanorebSPQWqOAgx6x8bwwvMZucGk2UGdu3VaFShpdFvA9GDezDHyxy8ttRtA7ONpta0SYe7Cg78rUqaC3KCUJ2m0qg6cx1oj6aBZWdadszKDloBj4p5NoakLLrIouuI0Z6DXSnkaS9PKlpskSyWiWvgJoiNKXZrLb+jUs0f0VOr1bmXZm9kFj1ZqgVP1KBCSnvBSSuRiHMCG14kuPk8lvFSXygUD32oO38EV8PbqYmDr+oCTRzIekaqZeZqmZ871b3VUaMPLFKki8kU2Z7NqiKUbDZ3YymMubIbi5U2ngoZy2JWoI+BLgb62BYAVls7RV9KlMZrZG92rlBEUCKlTuYfqXX2hO6SsOgTq6FnUyYDjrVSHYwTIjhQt+fWM3ietbR2jp6h8x9xEZfG+R5YRSPv/UFIkggSKUUYp5Hz8wseP37GF958zN/74hOeXKwtZQ6efTN6R6f7MbZpIT7o6wMDyuWqJ+2yRXWySf8zQlbhWgJP+xXvpiXnqyV6f0VYmJBlqJ0HMsSqxoSAxIRGoQ+Rjzx8jU9/5rvo7xyz3lzw1S//NF/5/NvkAhLa6e0J0JhmjSKRcypThNJ11MXAK+OW41qhFnpJDEHoETaCRRltDUyrk3o4olTByMoa0FJnlbQW4xyYBYF3FLdMaOAuBktXVAIhFFbHRyxOTxmOTxlWJ6TVHaRb4YiQ/mhidXrF8b0zHrz2Oh/71Bnnzw1MP3vyHs+eP2e3nSjZztvcI4T1tlA0EryTBw1mTB1h2fcshiUnJ0cGHO/d5/j0DgwrUuzouo7VYmC1WBC7SEiJlHqzuhFFGp+zRRa10oUEkgjao8lKJeYsSOysLaIB26JqSm81fqZ5RjabJgNfM2DzVaJZTNkxZ2NgvydVjXdjhe+ricDUdl08zZO9qkEhUUhMRRgLrMfC1drqrN97eOrip0oe12gtlDIaz6UycwUPAWUDkNM07f/NkwPNkWY8DNgAGOz+9zHSDQv6YcGiH9yQNlOnStlNlGIq6aFL9IsFyQGloODio2bUa1QF47EMotwNHXdjx0m/5N7xEmphd7lmWq/pUHoRuqCEKuSpstaRXDKnquRa6JOyWC75xu/7Hn7zv/A76T76XVz82f+G9Y//t+ja+YZOLdgDSgcukmxkH6+QFn26EepqEoeDl60m9n+kFcQFRgiLMJ7jxfjmrxzqvm++9sdo29+ARpqRsrb9dXeYmXA+wLf05OG+Dg6Km+ciwwM7T5mTSZYqP6g7fisQdgPz3YBebWwRDGG4sfiNbyt4Xcz9l1RvnmFrw8OI8WGedUYy9rlOm5sY9DYWPDzRkp0faxOaLO9YnfXdZp9e+1ov34+gMG4gGfd67/jN/pn286zra67/xI+x+5t/i6Mf/iGWv/Ff59vX/wfq9ItMtfCTzzNrdbqT7iOJAQOSM0CYedbM5WJbXCt4uxXxnnCwvmkgp203A0U/17l5xCuPsf+Zm+3wttz6MVjnyV933rCfYHSsYFmFOtlYYHoE4z411wr1NKxlJ0zMYpkxA21DHxk6mQsSVLVxbzspm6mwyDpnQkwHoNYeJdN1PREDuripdfXo4lTt/CpGUcJBZ9WKqGkM0gEKN0BnrRk8alfVBDO5llkMYn3EI6N+H+0Z80WMg+iAWfJEbVXfxB8JsyyyR8mq/SSwbGa1KGb0jGFM0ftI8O8K02QG8LupMubMdsxWxKRahNLOyeYS45EGKzThdLNSq4lxCG7JZ33T6BatNKR1lloLrUxl8OubtDjmM0FtULM5DNFAeYyRXmwdGBRqmRCCe3baq3l1tqiy6oGfJBDUU+DzuleQkHyBEKjjlsuzC95+9wk//8V3+bkvvMuzqx0aI0JEMf5zW3i2iGitjfr2wV8f3Ng8mmzeVgfCNk9sVLkOifPlincXS57FQHx0xKNPvcJGhPcu1pxfT0jFw7IRYkJjpEYDS3fu32c4OWZblF1dcPLwwwzPzpieTXvPRZ9kW11M9Y5UUDYIT0SIoSd1A6GMpHFLzjqvUmLnKYQ2NIh1jFZ43kZhr1nqN8vEIe2WuoAHP74y27RVTx+oGEE3kegXFi0ZRxjHguZMShWJCRiQuCR2PYtuoF+ecnT3NR6+NjFuL7m+POPs2WMHl88p2UQsm+3Ik+dr1rtsK48QWHYdd09PuHf/Pvfv3ePo5NgqriyOqMOSXey5qpFdEUJSwiohXY8JcHwlrgYmrfyhTRChTYDiIiSUmAdKvqLmrW3vUVHVynazRkugao9IRWv2NLftt+Qdu+2W3W7NtNsxjhM1m1BHazG+CkaHKNVAfS4NfBZwFXnNhalUpgxTrhQiY40UjYwVxhrI1Qbjj716n+OjgeUiIVqYdpNHSrMpLasNVk1BvweVLTJZKTl7WcUyD54iTaFvRGgb5DqGxYJhsST2HVLVODWjtUMZd3P0ZrlYUFdLM81XRbTYwC/7dEmoShdscDwJiY8+fMTXfezDPHp4h0WX2F1cc/bFtxnefIdxvSVmE+54Io3romy2maNd5tEIx8cDn/7sN/KP/eBvJH3kOzj/Yz/G5if+0kHf33OCb77UQE++xlJVcd/x2xQ745tDsHe4LzFQ2q3sz3xtlafmR+sQQh2iJL3xzs139y9xjqTmDRIGG5/Gc3tmUw9lAWVz6xh+pi1IVCtSJlMnE1x4tAQKjCMtZjXv4SW4dH9Csr+sECxqGOIBUG/tWqEelEFs7Xf7Ag/xZvtD/ACH4BK3aWmIaW6wg/jZIeIVtdT45rmfZ49OO7dSubX9HLF8ofH32zVRzsGxX1xkYJTWN7/C5X/0H3PnX/tfcvRrfg+/7Ozf4of1jGkn/NUNTIpH01zG1UCyN06zr2m86oiBh3lBIuYLiY/60dtHlTki0wIU4RARNjBIC1vsu6ccNqPYJN4u/ga294BFM81RacCLma0TQnQRaBNLeHEMn99q3VdyKW4XVzFrHEmwHDr6GJh2md20YyodV9dbVouB0fl5dryW9QkOXj2dH+xEzKfZIl1WAtgr0CSzuuuSw0rvD3Ze0QFuMxvHgDC4P2dzNQnEYIhdqS4s9Tmj7TNaRK55UYqTW4XKVMseMHk2rDrwbmInDZEowSrfRVOvB4WihVxAs5KnakUksvEjp6lQqrh6ufH0bSUknlpvCnapkMWijDOXcY7c2fdrLfvqePPixtpSwfyX1eaUhiisP5jYd4iY/2XFIs4esOpSZ/Pf/CAFWwPWlkGs3n/DfhUkDnxDI4lEik6sr694/O4ZP//59/h7n3+b801GJRqw1WKLpKpQfQEkRu1rVYwOlqi/5OsDA8rokTBFGLWyVbiQxLPVMe8dn/IkKt39JR//ttfp763YFkF7ZVsLl1dWbi8lSwknNbPwIoGzzYZXpxGNHQSlMDLla0t7aEcrRdj4Ia1+d21cFxEmhfeiINozhcQCIYfCFAOa3LfKO6fVu4rz6gfx0kZiN038B3e8Fx9x9v9FH2+LPYClUsWc+UULESF1PdtJeXa5oy5HFrLjqAS6RU+Mgw3gBEK3IKSBZmA65JHVnTV3H3yID71+zvbqgjJtePb8XZ6+9y6r5cDF1QjS03eRe8cnvP7hN7jz6CHD6sgGp9RBGthKz5aBza5ymSv3GTmuMFV7YAWL5IYOS7FiPpQi8UZKMUhEgpLSQE0bU43VrQ1EdYKSeb7eUkpheZEIns5sMEDrRJ5Gs9DZXrO9vmba7ShTpuTJUi3ZBqeqiVqD1cLNFomsOSP+b9VK0UBx4Jg1UGOPpAVVIsSeLgROjiL3To85Oe4ZFp1xUCsegTyMgolHn9XHgr1tRXuppxKamlyIiHNYUuxJqafvB7quR5LX4J52Vhu2jNS8JWIAUSRwtFpSVitCStSSoWTKZFZLtVTybodooROhQ/jkax/iOz7zy/jEp95gdecY3W24fvsxebNlfHbGdrNlEYRBK6sgXBWQqmw3O86vrikPT/jYwzv86l/+CboPf5qrv/J32Pylv2ITcDh8/PeD5fw3Le5y0CCH6dsZ6mlbpM/TuPce6I7sBwycSkTiYCrxg+M0BfiL3pQ3z6jdt/mvg1U1FNg9R3Zn9sFhtPVwQen7mC1KYm88wLz27wR0/R6i40vPpWGul5+lHy6IgcnY0vC3NlDPP4ZowK6W+VoOd2PH83shN3dxCCqlnZAc0A8OJrmXNua4832pWUE14NPO4vAXOWxv9t3j8O/Dk+Yl21nTmnnA+SXXf/xHOfkXfjen3/69fFf5caYCu7e2/P1tpjTDyWoTZWkdTG0RX11EGTw93M7z8EpFZE5JNyygsg/AVqD3XhGAJHqrt1vEzrUVoHuhjxwsSFrTSPtdLNVbaZx3POrjESrROZ3bnDfMjcQia0H3dnIpRcaarfINQkiJIUUWqeN8vWG7HRmXhSnTIgRzqeHgFV6qRwpTtIh9Sspi6NExG5DEioCkqKyGjmFIpBQYOus7TeNeimVOqqu4G8jfi3Da9XuKXdXhows9MApAs9dsz5p4JDWKlQct2fys49ze1X1/g9HVUBLMtIAknsVDnJOYKQq7XCxCmYuldKuJd6oa0G6FRJq62qKGNpa17Nh+MWl80ZorKgcCXo+GlprJtdKlaPoDsfubFJttA877tEiiKnPBE6sxXtnl7NoSazuZAS+oepDDVfB75phTB24tiIRArYXd5ZbHj5/x+S895nNfeJez69FgveA+2G38Pqh1Po9XdvCXDR/v9/rggDJFKBa12ZTCeYbHIfFu7HgcAusEn7i/5OGDFelkweX1xPWgSJ1Yb3aUkshF2U2JRepIMUEX+Lkv/CLnuytO7i2hn3j8zls8++oVQXtC2hGcC2IPX2dgUvyhVFttaYAtMBI5V1hlm5B3CmOeKFLJHn20OcZuiil2PSWL8TPFFXXiksAQm5Wq+sM6obXJ+i0lqmpO+1oy3bAkhsikwjvnE29PF8T3Mo/uTTy8M3CyGun7aDVNBUuzR7MfYlgSh2Pi4pTh6B5Hp1eU3SX9sgPdIeUZZ8+vyD5RDsOSkzt3ODq9T1oe2UMSzCtyKoGxJL5SRn5mveUNnfhM7XiQldAVcC5ijImAlbGMZnLpgzXzRCQxEruOLvfkaUeZJnuQqlkdXFxvefvdZ5Y2KiNaJ4TmuQm1jOy214zXF0zra8bdzgjYvsrPBSoR1eQcWLfgqYWgSqyuVhNBgxioxH6fq1wlI63HUHnl7hF3jgZWy56hH6hajIekMCvSQ6WWOl/jnL5pVRuC6Ra0MvNozdi+p0sdXTfQ9x2pWxB8pT/liTplas1M04487qAWL21oHq79ckldDSbS2ppasdZA9WcrTyMJm/lWMfGx11/jQ1/3Bqef+Dj94pi6Pkenke1XepKv3AcCK1F2odJVK2N6dNRz3Cmv9CO/8utPOfnoq0wXies//edMtd6tLDpWGqiY/8fBG9xMZx+CzkNg6TBS23Mi+03qDp0CkhYwnqNla8DlFm58ccySl3zyErDZAJNWGC9ckHJAjp/3cegfebBftXNk+wStxiOVxT3mUf+F4+PXehskHn4ORhhPN8DkjbOfB+wI9MDoCzE56JOH2zoakkP4vd/ZoYjD2n1GXy85/Vv3UMEQiR62zMtfh+Dwl9qGl2zX7BwqTJ/7HOPPfo7Ft//z3P/Kku+efh6OnvHnzp/yOLiGr+LRPp/4xFS6tWLCUMw2S32fLdXafHeLOtfRF5IGDqtxDRVWJdJnYRWE+xJYj5XN1JpP93j64FJael0d8BxibgNVSgpCF4XkAQn1qikxBELxtLY7hFgU1o7VNdsdoEUuuy4yTpYCjTGwGAJHy96yVSFZicdsPsW1ViYyitvqFEU662at/vXQC4sRarXzs3ONpE5YDpGUhD4lus7S2dNULJ0vVh2mS3tfwpati9G4nkmtiks7f/zaRGVOHSuKhuD3twlgCgQTkkzBngvzXZYZtLWMYheig0Jb7Ftmye79biqM2YQuu6kwudJVEK9qA2DZxODCE2uXaGGkYqnxUkxg23u7tfKHpRbjkYrRAUq1lLpij7wG8QBT9OpJrYYSVkClGM2lUTpiqPQpMmmlC7AtXpSkNLBrbTULfuzs/XkwDMLc1gcDQIXdesvZs3Pe+uozfu5Lb/P8+triliF42jyYPjbvfG5somVxRxt/6v4BEOUHBpQigUkzm1x5XJW3JfBWFzhLwkWEiUy3DAzLQNebNxThmquLc87OR3ajrYL63uuKCpYyDMKXv/pl+mSd1UoFdqQg9J2F/U+XJ3zowx/l3qNX6Jc9BLM4kJDMFzEZuTegJPHjJDPmvlchY3ZEMXWklDwloUiIpL4zEJWLpbW9EY2HkqilErpE6DqLpFWdRRq7zSWb8+dsrtYWQg7C0WLJ8Z1T1ll4NvV89bnylWdrhm7HG/cGPvHKCR+5n3i129H3hbjoYbFC+qVNQEEg9oQBJFpqe5V3PHpwTacdYz7l6doeuNOHH+Xuq68TF0skJZJY+aZKpSBsS+GrY+aLa1h2PZdFOQ3GCwlq11vngcuiKVILxtKsViEILLUQEzIsKXlkLJMP0Jaq1TJxfbHm4nKLaqFMGytDJYEQFCHDuKFur6i7DTVPBAIxWU32qtH4l546qWIziaitIo3RY8R2JJK9mpC0UlaqpFpZdRMffnjER167w+nJin4YLF1RQSWZdZS6IlyLp2TcoLYJh2qzLGpcGDGBTEx0XU/X9/ZvWhC6hMRILdnU4DUzTRt2u2umcWd9UgJEiJ31u64fqH0HWikJy6lotTR8HsmbDW4GxXHf8dprdzl95QHD3TsM/ZJN3lj6qCpxqvQSKVFYoqzV7YVOFwz3jvn0o57f9tmP8dq3fwb98K/l6o/8Kerz5zbg5M0BYLrNkzkAiS8JTu1fe5DjgwR7QOSArIz+/Qpl51Gwg5DOC1Jo3YPEQ9j0wsF9spojHeyjni/BnS98cANbFigbvwyF7dP97y8DtnL43vsgK3nxo5vA/ODDEECThc7ck/WF0/dD7c+Lg/Y7vCaHm4f3RG9f8OF+D0H3+3x2+4RuX/7Ldn2wzY3TFGZQqbmw/lN/hu5f//103/Hrubf5Rf7RbsHYr/iZmDjvFqCCFoOUpZbZ8kfbIqJWipgNTNQAtUXk9kGAZg+jtcXLbFzJNTNMgf69yHFIvLLseHY1kgtsiwHQg946PylV9xPnoeFNFKEX6BMse2GZAilGhj6Zv2MXjasueFUZ56qpQrDFrIBXJHNlt4JQ3ffQqmP1Q0cXE12M9J0FM3Z5Yr2brERgPOAESqKy58V10QzQh64wlUgM0EcDkV0fSV0gJAHncFv61upwV1+cZHccsfR44jAyBnZbSvHSkx5YwNXfIQZf70SLyHnkFYmEatHBGCzdGmIwex2xxUOMFQ2gGhBfbWhbMBRL3W7GzC43/v2NJ87uv+5LUer8vABqEcwixq1UwUzkkwc4vP8VsAV/wOcf5+JWz28GU0mrBkSizVOYzZ54YKJUy27WWjhaBI4GYRcD06hsJ0/Hy57babSwJmbCxV3mqS3BFisNs5h6HvOZPr/grXee8AtfeMy7ZxvyHMnV/SJ1snm+aAEJs71WaBYKYqLWD/r64MbmpbLOcF4D7wh8SeBxjOTlgJ4suX/cc//hPfplx6QbxumazfUFF2dXnJ9v2W7MTyuQCKk91mKgLqaZQ2JRIuOpDanjzvKYb/mub+E7vvO7efXrvpHlg1eQZMXn5lFbDi741hzTkLe2UW0mwxxOBu87Ax28L7feUaSO6G5j3KMQoOsAZTde8+y9d8lvXfLmY+GqRJ5slbfPd/zCVzOfOVG+4/SKV18Vlq/eMSAVjdMgVaycpRYkRGK3YHX8kKEbuHfvdV77+BHPt0tIHffvnrA8XaJM1OkKnAsYQmSQyHEV3lgueDwJPVBlArKDpUBIPTV0qCSQjkBHSL2l7stEDT7XBR8lJSJ1INQN02hAYUgTQyp0SVHN5tlVqgtZDFAGKpIzOo2QC1qUGtRLb0U7lxCpBBAvXZkLQYpzVCARnXZmE0RRu/+RwiA77i93fPLhijc+umL1yorV8bGBdQoiSqedk/1tYDLiudsOHXAobbVr3pTaWdWgGCMhRhPS9AMxdoTYOeieqDWTpx153JDHHSUXxInbiFXxCdELJqYOQqLWYhUeglKnQhlH8nqL7EZ6NYB7p4/cO14xrAb6LmEK94k6jjBOhFLoEQiRqQZWVHKEuoi8saz8wHd+kk9+z6+GT/1mzv/zP8/m//dXDx4SL7w2i0UOngW59Vy8gKluR7hu/X2YDldMdFRbBOxwZ+EWWr2NUG4/l/riu+2QLwGf+yf28Nm9dU0vvMTb5nBne4C938wWBfAyAOiA2H1xD89LDs/h8CLCXCDwxjXcfL3PmHWIrQ+/Kj7eHYLGF5vz/V8vwdIzYD08hV8CVN64tagtnGMAyZTHj1n/2I9x/IO/lW73dTz46Z/gH0FJpePLi2PW2LAdxHwE7Rk10aQ2+or4JOu86KJtojf/4+ppzSgW/ctVsVKAhX5U0rPAso+8sloyauZsU2fva5Xb8jF7tfeyrTfpFFYJjgdhOShHRx1dZ4GNVR9YRAODMQZ2Yvu1wibWt2oDaNHSn014FLuOWKBoplDou95Kyw6JlKy4BQRWubDebFgNycawqZiwkQJqkacYLNsSSnZLnb1mLAYHE7CPXnk0UYIg3vaG46qLjaw2dwxetUYt6lkLnpyeNd4ulrExPAhMagGbWcSEq6ajAbla99Tj6NZBNpx6EY2AcSTV0sC1KlPxKjfedmbn6efGHls0GlN7LLSlol2BX3Ol6y3BHp2OUKotWhCrnidiALUic1S8Fs+oOaYRceFV19F33T7iB0x1Yjkkq4OezH9yK9BtjCoxVfOmbHSXECPVle7JF5ZV1VPf6sO20RNyzmyur3n63nO+8MUnfOHJc6O6eUrbrKr2hVCK+1XaHOelO6txfUUE+SAiPX99YEC5IXCeOh4LvF0z75XKdddRtNJTzeS0Syj7Au1Dd8xrDzokbXl+ds6758+5urpi2llYvBTzkLJa1kLseisBKBCDMqREuCd0ywXd0ZJ+uTArHen9rPYDrDTukLj3Urt1FnS8yQuYQaccjBRyI9gylw58yczjt8V4R4uIDitw/ya0suiPuCuBu1slPN465z4yqjJOIJeFuICgiZkMYctuVDOSRxg3zrNVRBJxuEfqHtLJgoElcbkk9R0SI+hk6d9xTYpWU3RJ4Bjl1aq8kYN5bUlgDMKktk2Wnlw7oGdRO6QGJEISEylEyf6k7ykGEjNxKIxsKDpShjWXw8jdI+HyfF/ZR5w4bCkrEyZpLpRpslRR6BAiqhE9iBjUaumV7OAz14yoka9j8FU7Sh9h2Qmni8qD08prd7e89qFjTu+fMBw/Iqw6ckiU2tKBZhYPttKzQcbtGdqM15T84mBHjTAuEi3tH3tCsjRmrQqlkPPENO6YdhvG3cYqIqk57RhByqK/KfUWJTD5NmBKVBFhHCem7Y683hLGzOAr0EeLgdWwJIbOJ8iMlokwVUIxe6sUlFqFZYgspDKJcKTwm375p/mGH/xd6Ee+m4v//M+z+Ym/7OSx28BxRmQH7xuwN5fiuodUMz/vAEGoHn754NUsMBqwfBkYOkAj77umO/zcANL7ZmBuAaAXl4Iv2e/X3MntbzaEJNAdW78az91S5nBbv95a0BA5jEbeTB/dYoweArDbp/NBzl7ghcZp53aj/Vvb33rp4X14/wX2C+d3e9fv9/mMa9UM0VOPbq7Z/bW/RvfxjyLf/S8R7r/Oo7/xJ/mWx2skjHypT4wEggYiitTogjo1b150nqilANiCNFfj3RlwtDaQ2sCDBV+KBhOeBKFPkeWiY9lHulBno6us5kd5qHUKfl3N3ihg0cmhg34Fx4vA6XJAovn79iESBANxYqlUdW9uK+RgY1QIQkrR0pgSZu/BVpKxPbJdCKwWBkauxwlUqMe9c0lN2azFJSCumDeKhglZJUZUbZzSBijFxE32yFpEuGq1+QRTV7dKNnulfKVvghw87VxtyRGwdHl0EmDVghx0BrMBsn5pJZjt+KVWUoqUbCr3VslH1KKeYslISqlkVcZcXDgJWsWjiXmmtwXa8aF5K4LOQK0JjgQDmbV65Z+9LnWGDU1tb/fc/Jor7kuMfUepczTSDNntO1psXrOa8MkWDa5liNGPtWtG5s0P+0AgXPXAdrZxnW3eVMcp6nN0Hkcuzi746tvP+cWvPuZ8HNs0brQxbSIq7x/+H1oo1egM0XypXL/yvqvvF14fGFCeDUc8SfB2GXmvBNZa0UXH0emS1fGKk+MliyH5wRNBeoL0nJ4u6BZ3+NDDR7yRd+zWO7bXG8Y8sZ0yu93I9WbH5fWWzXZiys69C4Gc4enFhjfffsLXX2+NvxDc/OGQEzk3LwcNK+Zl6ZPRjclFDmDiQWMdNlt7Ww5SSipuXzRvGPbgo0UY3JC9Wx3TrwSRQqtlSimsRLh3pBw/WtE/OkbunsDyCDoHyWU0RfDOI47dwoQ2oUeHJaE7YhEGpOs9deRn3q/QOlmaWRI9wkKUuyq8UgJvTZVNDlxUASI9gVJNvJO1435I3K/KUfPL0IzIZIBAvAJAzEi4JIVLkOdUfU7WynHKnPSwTB3byTp1dT9NEKZq5uDTqOTJBs5YlFDLPKhUrYx+78o00tWJpJWE0oXK3U55dBo5PQks+kKgEGTHSXfN6fHInTuVk+OBxSoRu4fkdAcJd5HaGaFZ2/rb7mdLbWub/J18LLJfSJi/p4AP6q1TlFIoecc0bdlt1+TdlnHcGcj0xcte7Sh0cSClzgal6D6Tnt5pvMlxu2V7dY16tYelwL0+cTz0JE8H1WnHdHVNud4QdhOhVjoxsrgSOYpCkMyv+XW/gm/7l/935HHF9f/nR9n+tb/h0RyZ+/EeSMrcJjaKwsz/cx4POIyYgcaMDG6CUg4+as8FMBOt0Jvf18Mv3QJyoangMzZMqSGGG6rww1cDTRxcz/u/2lhx66T9/3rw7v76ZlmPBCQdISg6XfqnhwDav1yLRWbDgIdU9u3hG+0hZePz3jrv98PrHLbYzet9AdsJEDs0Z+RGkvbGVu97nPd9BS8+MYfzeLHpX7ZPrbZYSQPSD+huy+Uf+y+YvvQljn/oB+k//Ble+e/+KJufecLVrvBsuUEkUoJVXAnBXA0Qtf7vhkCiFh2rTpMZs3nzbsn+bDZPW+z3rGhxTp9HnVIMpLAvwwjzOgYwSyPRfSq8TfIhQt8Ld5Ydp0PH0bIjI5Rsi9kQTeDZhVZaT7ygh0eDtBXPMD6jFo8MehQxuho8pkTXZ4Yu0cWOPgpd73ZjZV+bejuOLijaO5TMa0g1eljA/Y611fk2AOasejqxhXHBFfIKqiZiDURSChS1CmJBgmsLbPyTiBeLsIipswhRWvS/yXiqR0fN77KmQCqVOvsr235rsG5Ti86+maXxa5W5ulEMllEq5f9P258H+5Zd933YZ+29zzm/4U5v6NfdD92YAQIkSAIcTBKkSHGQ7MiUZFtW4lhyLCu25bIlV2K7UpVUypWkUqmkXK44KWeQrT9sS5YjayIl2RRF0oI4YBAlkSAAAiTQQM/d7/V777577284w9575Y+1z/n97n23gZardLpuv3t/vzPus4fvWuu7vitbDgR7yKCQXLNKyZ+w+WmExDlnJFiDm0PJRmgIAY1KVcTfDQyPMj5aeJSYLJTzxDSC3DTalWg2w6abnGi5tGVPzjUxmxc0F2KuZvOs5hFwZ0vUUi3qKTlNa1WRlDB4OkTa1YpHD8944dW3eOtiXYyTZMoEauPFShVbYRRXErdSHIo8V6DKhfIwrn3vcHvHgPLl+TGnOfGQSMwdLkWyE3KpKiGxZIG7NNXtdBIIlWPmhKHPLEU4OJohh8cgVg+6rmuyCNu+5eHpKa+88QYPHj5m2w10UXhw/pif/fm/yec+/2k++u0f4gd+5PfyXd//SY5u3CGEBqRBNZhGlEDxc5cuu/t/mRrKv9aRRK5+v7fn3hqzmx/V5IEongZN0JsH0s2Kl7IMXC+BSgKVOkJWBiIeZUnkaOGojufIwQE6P0SqhjHz3MLzGwOTsbUkmzAv91NGiC/agONCp1r4l8VdXbwSkhwSFKkDeRg4zQ6fHeticUYNrHOgRXhPzByba6xIEpYkAQUTVc2gLbhHoPfw+S3gAU044qARTuZwY5Z5FBMDLSF3pGSWYZt6hn5L7AwsQ2ammZkfCHiCJILrqavMoo40ROo0MHMD8woWc+V4qZycKIvDhDgDYUPf4bWlmQ80R1DXDp+3eD1C8xxkjvoZSlUmnF3GmhRhYMPkY8hDdgN0JCazZxVqNi3OoWfoW/q2ZWg7hqFjKhfpzIMas/WF4D1VXeErs/ycKzydnEgxse06LjZr1ust7dbkbZyDA+c4njfM6grJkb7d0m4vSOdnxNNz9GzNDJOwcmI8mpMKvu8nfg8/9O//n2l/86usf+7nSY/PGIGfTuBoB47t9zHsvcsWNI3C/XB4AY86ytzsHTvudwlw7oPOArbGZBwTAbU2LiLHo+E3efP8DAkLdNgivjLponS1msseermKH69wB/fH8OW/L4//3Zyxd24XoD4purglOhHmtmdzx+YBomWJE3dQTbOVqMSBvwHa2Rjav4MRbOfMtGpferbL93cZeu/d4zfB2YDxo+tDdPt4BwCnY2TX9vsc12+1CUi9QPvWZJf2r/lNgPAkWUSe6h4TI+2vfpr48sss/+i/yPxH/wzv3X6K/ktf5Evuq/SzDBpxlad2iYOZsqwTs5CpvGXHOpgIbTFm2u2Mxxt4a+u46EykO8aMqCNnQUsynHVHNc+gJqoSah2DF256Jt01t+4/ppVYnNXCQR1YNp5Z5RiSo7P6qAbcBJO2KWPPO0tIGQ0XO49nkFySQWVKSh2VCJ0TmsrTNJaPEDEvZ8xKnzJd0ZYZYrIyic5f0tr0YmuYKU8o3im1txKyFDqBE4fVV8+FJ5iLd7LUvE6RUAlN8Rw6hFTmUV8q62QMbOYs5i1WT8qm/+iK3mVZpgAt2c4gsainkEtFHttyFoYUJw9/HHmaE5e/AL5s1zTXovXzNL5DCggtKvU7D5+WKbGImWejZEXNNK5CBerKwwDjqBtVrc3RYDxDhRL616IeQpGpy3RdpIs9MSnOJbwvsoYj/WycX8vyo4UuMKQIYqB8tMPGMVb8iuV92Xex61ifr3nt9Ue89MZD8wkUmT975lzAcmVh/D7SD5GYk1XriYnsBgjRqg6GUKrmvbPtHQPKVw7v0PrIVrdIPOeo6GOFuibURpzt+oHNZk1MA9v1QBwsC62qapjNqLuBbhiKBpejCRWLWU3V1Ig/4uk7T/HBD3yAs9WKl155kZdeeY2LVcc2Zl54+T4vvnKf/+FTn+bZuzd4/4ee5Zmnb3LjxtN8x8d/Lx/4th+knp0w1fy0d1/C2HuL59SJv/k2Vedhz7OjCc1WzYSi5SXeQoNqpAPGjuqAOmQO6pajpqHbmJW3kMSh91RVhtojIUzHTTfnPVrNLETbOKQS0JHQqHsL+niIA4zbkQtrfUBpE3QRYrZwT5czDwblTI0P2IoBkqUoBwILlKCTPWeTpUgBuw4okkf5EZLfQNyGql5w4/gATWv88Ig7Zz3twYZ+29H30MVM2yfLeF4MuDwwaxxNE5mFSNCeio7a9yyaxGI+UKWMS5FZPeBDpJ4r8zk0h+CX9nKHLtNtMrFVXMBCM6pI9yYSfpNaDuhnFdndJcuhtY0ooxqJadfZwB17hpGaRx0vG7BjpZ8UIzkOpKQhKm8AAQAASURBVNgZoOx6Yt8XAdoyqchoBRsnFDwhVISq3gnrq/mIujKQ123P4/MN7aZn224IlU2eh1XF4WyGVyW1pSrJZo1cbJCuR1ctmi2XUyVRieO57/gAP/S/+T/Rfuofsfk7v2i1XH29x1/kkiGy8y6O8Z3yXZG1mr6f9ssQW+NEjufwjV1jBG+qkEvFFVeyQUfBfEnl93LtQjp/EgwG8+pVC6Saw7C6fE3YPc8lJCUTNoL98Pzb7f92n+x/A+SIDudIcxPqQ0psy7bm0DLl47o8055/UykApwcZwC+LoHi7d40yrkSxFUv2QOXe7V87Z+nb3vz0UfHoamyR+gBZPmWgMra78/qiExo7Eym//kx7N1I+ywbAZH4M2zHDfnwsD1eB6/6r0GTxZAp6K5N2fOlVzv/sf8bhH//jzH7fT/I+PePi65/nzRzRxtPUwslcOZz3zJrEvIYqbHCux0mResmQB892WzMPDqdzNHlW0fqbZBMDT0mQoYzzpAx9Jsad2QVj5Z1dsvz+d5P9owZm6gCzKjCvK+oQLHxaEkh9AVvOWUaxVdkzsOhKnxm/GyROsZTKe/qCaIcSVakqT9NUNE3FdhhIWWn7SMw1bddb9bJRuaT0p+LgxGHSPZVTKmcJMJVzO+WFAjgnj2yRWrP610qvaiobJavciyveOSZ9S8uNACmQR5UCsIz/mIq30IZLkWYqPEZLUoqMkjkjJcm4fkXJwDkUyzMACqfTE7wnODPkYvGw5VzyEsq57Pw2To1fmSZqGUKhT2iZqwtAJEz8TtLAmPxi41sQdUZv0Yz6RNYBzZl+iAyD4aB26I1b6t3kBbe63YWnKIq6MbsaqhCmcsvjfCmy49yO43rEOwJoTKzWa+4/POPVew/Z9rvxaOuO5YdbGehEypm+H+iGnrbvIKmVEfbmJXehYtbU+LzL6v9W2zsGlPefvsu86TjykYY1ul6R+rasG4F6XpskQxRSTHTbLdtNS7u2CjIxZZJCnyJ9EtRVyDxz7BsOFg1hNjNvVtdRNxW3btzi2z70Qb7+4td58aU36btkRGxVHj/q+MI/fInP5xcgZ47+8i/wnd/9cf7Av/i/4AMf+z6qeoGIv8Z5sYOW39yELnsLjLOhqiI5QkpWNL6AOCOX24iaJGlK0x5Wnvcttsxv9TyolUcX8FT21DHg49zIrsYWJztv3JVUvDe1oFQWEvIBqBCpUDdpQEz3aGPOMWhmM4BKxUbhNAmPorAdIgGh+C9BAkmFuYPbjXLHwXNOmXud9LxwlbWOWLYa4lHnEbcg+wYJJ7jwHN7dwjUNITyg4mW6xZvEbkUaBvreQgFxGEh9h8sJL56mWeAqIbgtpDNcbgk+M5spdZ3Jg4HDxhcOTa2EBvxCkaa8M6fkqEVpxRmAiUDsyf2XCW5GcEsyc7QS1B2AjnLFkzFIVvcElXCqmqORlCK5/KQ4kIeeHE07MheyshSyzTgpq4weCU9V1YRQ4X0ok6tN0NuhZxt73jo75/R0zXbTmuiu2uIzE2FR19D15K1J++TVirhak5MltI11in1WlndmfO+//x+gLz9k84t/1yzZagk6llSEKeQ/rYjewNvI/x371b73chwnY+yGYmCM4dmRm1d4oYwGV6jAVWWojUCymNh5rNIynj4zuVHCzCrWhKbcg93zFDYrY9JCjTvfziUai46fXvZD7iDlk7I716GyfWAqqUO7M8TPpkgAYAC7e4R59PcOHqeX0QBMLYQDcIcl9BzBzcDP7R0M50wCiTI9xJV7GQ0dLj0bV57lOpkP0Wz3v7iDLG9BWwBgNYNqDjg0dcilprgOZuvlX/vWjl/cQNtzyMmSJl1t13jCG7p/KgUs63mU/wLQiw0Xf+G/5vBf+xMcfvKH+eDpp3Dd68iJcHwrceukYr7M+Koj+ISTAS+J0VOlCVKbGbYDixDIQ08X5zgcG/X0WckixIzpA6miOdH1HTFaYkcxs0rbPQkkx3+zlqCRM6BWB88shBLOzkjKllAixrOsvC9ZtK4k94ErIpeCVasJbnRmZJMvK8ZwOQlBAovZjEW9YSWO7dAzKCQ1SSXvbC6Kw7BrdM1laCtelMoJVREb96pTdaIYE8EJ0Qsulkoy2TiLw5Dph8SQFF85oo6FJAoFQaXwVXUCyGMEKGclJsummTy+Yz83pHRpxI4JkuMYpHAKrca1ljaRKTHFV546hOLVTbTRJIPGwDqUHAkd/fq7tX8Ch7Ir7WmObgtDj8k3YBx4A33FMHCjJ9lqaufi5c0plipxyhAj274jay65ACNwhjYOjNWdh2jzoGDe66oYANntSjC6Uk1t59AsFK2sxL5lfb7lzTcf8fpbjxjLXBogH72wkIZITMowDPR9WxRZbBrK4xyTIipK7Ht8M7tmHrh+e8eAsrt5yFOzObcbR+17crRs026zpt9uqInUgZLinkrjBY4O5zhfkTSx7XoenbU8fOshb52eo1l46viEdz/3Lp5+112Oj26zaGaEKpPxnNxYcPddT/ND/1SibweGtqUfOnztaWYNKrBpe2LXsVmv+Zt/6c/yvn/wi3z4u76Tp971PPPFCbPZUzTzGzhnCUOITHP9t9quEuhhR+K1TQxweeASmASkZ+ZXvOek5elZ5OIoc/8hHJwH5q3HrWu0W8B8Bt5IyZIjdBtk2KC+hSqgYQ4ys7dNBRKmag+7e7SsvLVWvLntGbxjo8p5hke9EhPMVFhgtbe9KPNaearxPFUrh0E5EEV8IfiKGPwcidtaklpkBu4WMnse/B0cN0ACLnbUvuMkCb17ldw9KuBLSQlyIYkDuDCjapxJPmmHxhaXehxCVRc+SxR8Lsu+Bwm6F+W3sL9Upfe6QrZPNhkoEHJLHD6PczWBGsWjVcC5ml2RNp3ar1AoQUpN2xKOtjKNkRyTWfw5TgLrYwYfYJ7CvJPSkNInvDfvpFWpsP6hYhIXbew43Z5z7/Q+j8/WtBcrhpxJAjWOmQRqVxHbnti24BUdBssWrCtSE2x9QQizGZ/4Y/8cB+//OA//4//csFq9xKR6Cm9vApMy9Vt8bR5GGQM4122jxVJCmmG2A4Q5FrBYgKbGYiDVjBQOGzZmkBTTzLyNsbsMKlHbr1qisbf2cg7iBtVsYe90Xb7t9bc8bhPFYf+dX7Or7O7u6inKvQWkOrD2yyW87UIB0vspG1fabcyyTkaLQBor85jHiIYz7+ZwwU6E5sodjIbtlUlrxKuXH0ovt+m0s1ibx9YMjflNe18j97u/QIbu8jOP/1w3WY73pAJDB/M5sjjZeabTYJ6ey0h+D2iP5yg80hJyHa+p6zWbv/lzHP+ZP82tD/0I+Xf/CsvnBo4/dkB16ylcPS/GSQe6Al2h2hXKd43GBBdr5vOerJY0UV/AeQ5sVHDZ02tG1bi6MQ30fUfbQT8YwPDsvJXuymOMP54R5ykhOELwBCuGbdzAEpkwcFUqsVQVaeLcSckiLsZoAdijXJkToaoCQ29RBpPqcQQvLOc11YUjS4WrhIglflSVZ4vx73IuIt651Mh2xdPl7d+crDyrldGFQBEHHyKqfvIuDslC6jnvvHNWjjgwMRULUFZ0AsreWZ+2qi8jL1FKWcjR8JYSETTvofkHDQjthOGFEAygepwlSxUj1VRhrLqPJRZZMlHSwqUcX5aWNUKFMSFTRiBbgKD40WNail4kYUiW9DOKzU+FBork0yhYrsiUjDpJWiWjAMY0FGUREB8QLFkn90rKJsPXD0I7JHJZK6Tck2EWm0uMLlkq2oyAu6xhbdty/vCC+/fO2bSRNNIBRtBJEXmPkSEl4jCQNeGccYel1BBPKhBtLQ4oteyrXnzz7R0DSvU16iOuCtRNhcaBWDVoXSF1YJYGZvPKPEwKVaiwnDJnAtChYT6fUVXBqIcxcf+tM+7de4hkYbW6oGle59nn7/LeD36Qo5OncMERNRGjMgw9bXduL2bI5JTwoWJIvYmLJyurl9LA5z/3Kzx8/Ab92WPu3vwAP/XT/ypPf+f3W1WaS9PBP85mi7GWSXCsKjGZCrgyKArw1ISj5WixYR5aasmkdWCWPEtfPAyDQOdAB1QCMnTo9gzcpnhqThB/YBqX4kGrPVC8IzqDWRYXKfDatmPtEhFHj6PTjMdRk2lEaJzn2AlP146TOSxroRYhCKjP2PgvU6WZKrbwEEA96hdQ30Z1g+gMxSNuAB8JjSALiK7HD4NJ60Ul1xS1JsG5gRCSidLmTOrVqKFaHFoZGyAlZCLOauLidHIKqwBeUKeIF5JmCz0kNWdxBHFbiL9J5ZaI1CT1UJ2QJVDyGU3jbZoQDJXGUpEg96VcVo6TwrI5vsQo60XD0sLiyRaDEpJyYuTsqqqsvqzfUfxzsfa7mHlwvuLeo1O2q3PiZm2TmHhchqXUzCXgoyJJ6S42SDcQYkZXG0iWyag1fOe/8JM880f+Tc5/7tdIj9dIcwiSduHLMV1xDG2DNbafs8//mIyUyyN/MixwY9WXMZRdJpo0MHrp8aGMhf2TyqXT4SrTvkjdHoYavXMeqQ9sx+HMgM7kl2MCIjqdfASJe36HvV33t8ug8e1Q0hMIjQmASzZvZOohbi25qjmxSjtpe+VZlZEsb8BzXu5dzRryan/nFobHO1B+9XbG+5ZLH73DrRgQYsBXXMVUHck5oCqPmdF+bXPat3BKTjfgvHkmfW3VgMDeqxsB9LA3R145vswFl8h9rrgo09gukF59lf63v0z9kR/n5hs/TzO/T/Oud8PNj4M/KbzTLeQz0HMDlrkoBaYWOTxlVr3M0/05KXZUQCM9j9aeNAhroI3RQqRRWUfY9DDEYvzatDNJBCm7PjStIOMjSCnR54s2sndUWelktKecaS4GK13oCtXfwKZjDEjiHU5NfzKbuKFVvPGWmCE46sozqwLLWU3lDVyJFwZM3q/tI22M5qFULfRcsYSWYNnafUoMmlF19GZFl8IY9kgpuzK3KTEW72U2WRoRLWJv1gC58KDtp0jZaOn/5XUm2NVmH8WzTSgOj5s4giMnEnbcTRM316k8pBNPztGquKlOZTRD8NSlNPO8CqTRy+ZGu66A9nIfMEYqxgReSha3UQRyNk1KHw3Ej5zFXMbxEBN9SiZmbhlCeAmFX8lkA04GSZHm0Wz0gSQJ1Yh485YPcZSPUmJMBn7FBNmjJrIkkLEIi+x4nAhD37M63/Dg9IJXTs/oCh96DIk7gSEnYorEFEnRin1I5a0+ujhQU03wWUlEKu9Z1p7FbFTV+dbbOwaU7RDpJbH1W0QqGm+p8Uk7EhVSQdXMqGdKFqjjQLVIaK7xoTHRrdTh65rF4ZKn+huoBro+Uy0OmS9vMl8sqJoZfTeQU6JZLqm8WIZwGiDYwBMUjcW1LyaCmgsBWNWq49zpnkfWW27Wx9x49t3m4fjH2p5cjqZVsnR0wRYBdeMCun9MjfcHSBNMkDUOLJvMXCuqowEWEeKArDt0ewS9opsLtN0gd27D8W0knACGxqxMlEOzo4/gglDvxWRin+i3yqqN9HWg8Y7KZY4KHzC5UXjXcVjBSaUsq0xTVZNUwY76rQa6sgc6RNrJm2Bx5cokOXIHCSRf4PMZSEv23ioCMFhruLFka5kmREE2ZRhE44mq4RDnteRU6ZTHId4sdXWgnskRLE5N2UZGMjT4bJmbuTiNfDpD+3+AsMTTEHHgjhDvgEjOrkQkLSPPZHksiy6lbBzEcfIRh1MluyKnkAvQ2usvY91TcVYD3QjNYZIX8UCfBi46OOu2PLp4zKO3VvRna+J6UzL9HAv1LMVT1YFQB1K0hSUNmdQNEDMaM7iaD//Bf5r3/6k/Tful19j+2q8X8JBh2BQPWUHhznPJaxiaApJ04kvZU+ilZzK+rpSQ+DgOxv3LySYNlMrOqeM0fXU07fl4xjB7TmXHYN7S8T7zgMZud47p0NGTJXvn3p135D3brY2L/y7Eve+p/GaQ8nLmtJpXtX3EftKKZIH2MYSwt295066EfV1T/i1Vc6arigHUPBRAfrl5dvvsP/r4xz5i3rvuJSys1hfC3IzTAip3iVbj+W3hl2qBxgFJkeve3KU/y3xhIDjZwBz7wAjaCx/5Kt9718i660fjOc3lzpj8oqln+0t/h/rf/dM07/kkevrX0eo2bvETqL8N6hDtIK8gv4HoK8AGyIj2yKyH5ibNxec5vH/BOig6KMM2krZC3MJ6YyCmzfC4g/XGgpu+NMHI3B37kDPT0+yiEsoWVca8BRFM3UEclVPqEAxMOk8IwTQVvcflhHN2XBIMHBVjNIkgOeFQvA8klwlVxheA4UNF7QdmdaAKnnbIDH2k3XS0Vc2WCKMWJxTZGSGqEhIoji4mNn0kq2eU1PMC6h1ZM5UKg5rRmrOaSoYEkF0VMe88ZAM8Wa2oiEV75NLLTmq8yTHgLRNfskgGOhtvSXUKNauaZqiUDGQH1E2whQAD2eq9Fcko3G/NGfVmyFc+Fe+jJcVGtXNJSiUVoXhU2ZWmhF2sISUrDhKdw+UESQsVQUnRkpCGoaz/hZqQx0RDCi/SK1UVyFXFopnjdFWcJBaKdwjiKkJdEZKgOTKkWLyxHpKFzINzRRnA9D+9d/giZYezNu/bltX5mjffeszpprO+Wvqkn7ikJXxfxl7wBialvCfZA5XLpmYxazicVywX/wRC3vc2F1QX53RnljTR1DVV5VEixMx8IZMHx/vKsp6coC4QnTPxci/U4lnkwInOcM0NhiQcLJYslg3LxSHN8gT1DettS4fiaxtcMQ/03ZbYbQmi1N7EceuqZr5YEOoG8TVZPcOQiH1LrYHnPvBdNHfehcg7ftS9bWeXXrVRM8WqU8zyv7SECioVrj4wjhJrsvQ4TTTB4ecZtxhQ/5i8egArkLc64uuPkKM7+Nt3QWZkGbUaM05Nb4whEQeTB5DKUTmM17lqOd6sebdTkkssKkcVzOrfJuUMZR2VARNbdt7kK/yYoZZBinVm2Y7lUdKAsoZ8XioJXSDxFNIFxAHSBh3uo/FVJD0ya9s3iOsQlyZlI2Gc3SKaVkbgVtPhMg3SPbzijRNilr3ugLyTAjS1TNzCWNZLsUlztHJdsv6HPiTzGYLMCm65S+ImijcLvFRPMAs5TyK1ZqXKtB7aFYy7OFIhMkw8RuNvBVtonCNUlQENcWjhXwZV7m033Bt6Xj5teePRlocXPbLuoE9osToPvOPQZ5qZw808Og9WutEl5MLhqwpd3uWZn/xRvuPf/Tdpv/A7nP3Fv47GsiqMa7Q4A2ehhKBRC9eOIem9HnsVSk77jmDy6uaKwTF6KXVskSdh2u684zgqYfKcdnxE55nqXmNAWOpD6B4XsDKeSHf7TCe/AoCKNbLjQO89o45v6/o7vLz/lfPq1dBPcaXltOujJtZqSS6usUV4AuH71yrgKyygPjYO5RRakiebUXbJPk+08HVeRTAwl3tM1XikPDhr5zDfA6QK9QHiKrQ9g2FbjIy3ObFinsS8NS/4+O6ag132ezWDWYZ2dU27XT3ZnptPMLdgQW3x1Vfov/hFmo/8YeRzv0F87U2qu3Pw34a4GmSAtIb0LJrvIPktlBVwAbIC8cgzb1F9+Su4pKxXwmvnyulGebSF84vMkGHbK6fbzJAtWuNQhvKYMt1nwbw69vQxw3gM94qBFmeh76TZ5tjgCpB0+BAQ6fB+3NcA1XgN55wlamIhZQt510QySC4hSsEHoakD81nDkIV2c84w2Hxk4EmmUoVOfAERJUSblJShz6CajNUoDhx0ZKpSZ94pRQjcHl9F8ZVMEkNg53NFwD/n0ljZ7iOV0rxjoYgdd9Lec86KC1LayZNyNIUYbDYqsNO46qPhqwZLwSJDKgZURz67RUesPGJdGeDVlNEBaztKfXctEkvs2Z6IeTtLl0zZpO2SWoWjUQVBNZBTKvJAxudM45rEjvvpvSNUAa0rZk3Foq5IEs3mzonFbMbB4QH1bI5GWK22PDo7o81jJnYx+CnJOKV5DYQLUqhUOSa6dcfp4xVvPHhIUjOKxLnSglLC3INpZiYDwJYQVioZKSXErlTBM2s8J4cznrp1yNHB/JuM38vbO0ZZ0Tm2m55N31NlZbtp8diLzilzkBs0N8Y/zxZGUCBmR99HchpQMnEo6em+YrnwqHM0zYxqNicHz3boiBeP8Z1HzyObzTmbiwvImZmvOJk3LOtAPZ8hdUByTZaBvq9JrsKFOaI1dWiYVQc0N54mu1AGwdWF5JtvI6+D0klG0vwoMYMIOQ4gJmexWwXUvA/q0eKdSMnRRKhcj4RkVVgCiG7g/Az92im82OE+ehNah3RaYrIJpwliD4Pi2kA9mMc3hGzh5qGl7ltuaUc9E2KVaCqzQJIo5ykTgDYK6wRtHtXAdPcjGGDSZLw7jSXc20I8Q+Tc7ocVmu5DeoQOKyt3lx6i6RzVbRmLAaSG0BrhfPSsjDNTjkgGyfY+QlBCJeRhDzM4sYpKZeLxQlmsrd3HKgrOjxZymQASkISx3rpV/Xkd7T6DpjmuqUiVAzkGwh4nBjuvs3JZDgrnyPgweRK6GAdgmgRlCzPG/u+wcozeMeREn1rW7Zq8PePZnPny2SkvhZ7Xz1Y8fLBiuFhTdx2SEpqgUeW4rlhWC5p6Tr08YlgsLKyyqch6hNx5mo/8az/N7Q+8n+0v/DLrX/wU2gHNkmnhc4XbFGa24I8SNrKTl7JV4jrYUMCT6rTvJS4cioo3t3IsK78WYHVFt0wvnZMCJAd7YRJKiFwhRbRbIdUCRNF+VV7JnpdrH1ROIGTsV9Og3bviiKzV2mLsf0/cnVz5+5r22Nvz0l6qu+tPducA/Rm7zPkS9vYLoxnAHsgSqE/Mozmcgfb2mVxJHtgHeE/8Pp7pCjDWbLxJunJfGVStKtfBXZDKAGd/YX3EBWR+aL936ytA/m2aRbH3HpMBAN+UWGFbKocdQDeGoq87gexW9FHU0ZeLZiBmNn/jbxD+nX8b99F/le7L/1/Cx34HOf4EuBlQY6ELm3dUjhE6ND9A/W+DX+BmJ8RkdbofnMHX3oTHg7LuHEMrRcbLwrwOTJdR4BKvWHbGoxPBpKnt/TuwyEr5LwSLLkSNuIEiZu6KCWHzRwC0JK14JwzZDFgXPFqoxSM49ZXgokmQWaUvIVSOeVNx0FR0MXOQapJYWVmkom4a+k1r2oWSyTLWrLFnyupL0Qcr1+ucMMQ4AV2NmRAcPtvYMYA6RgULWMX4l4jgcwkpeVeKlihRoY+m++nG6SYnsvfknAml8IOUL4t5ZlGnAkidtyo5XnxhRlhSTNJE8EIfzaMac0SpyEQQtXfgbCxlPENUKymsCS8jh1MmPvwYeRjBVc5KkoxLEJMUypkl1Ri+sTXTjAFL0ImaCSKmOFKu4Z1DamHWeBZNYDP0xGyg7ejmkhs3bnB0eIIMmUdvnbLZbNi23RRuT6M3N3uCC/ixtPQsUFWBIJ44DGwuWu6/dcbD9dqyw51M/THGSEx9STBN1CHsZtCSkGbTaKL2Vif+xnHD008d8fRTJ9w8Pn6bCeDJ7R0Dylv1jGeObvLUAmbBG/DIkX7o6Lcdi5kx0zSbVt/Qd6TeXOU2dzpSGuiHzJCUVLQGQlVRVcHc7ann/PEZm+2GbujYtltSN6B9R4Ny52jJ8dM3aG4cUqtSS8UiCHWo6NOaYYCLxw8YWk9oGp658zwSvBVWH3lf73jbW2RUkRTRaGn4UjdoseimsOsldnyCuCV3AynN0GFBbDNN31HPWgOb9EYsSRvYruFsixsqdN3B6SNkfmBJOqJo30PfwVaRPtAwt8k6KOgWjVvUmzV82MxINfhggz85Z/M9wsNeeDhkThXuZOFgSiRyxXmhFkrNCUmdiaxrRtMF6Mvg7oHvzTORWiSv0PwIzT2aB5QBVTMcxFnxLeeK3IMWv9BoARag7kp0cFKpccUScwXkasnzGFtXdlIYtl6PfFIpa7t5oaIqIcvkCdX4Euiv4aVG1RMrj7oljOGJMUQHGKlz5PAYqDGyhU15WUtJMc0kbNDnlMiq9GqWaxoim9jxuG95q22pzlbcTJlPP9ryxbTh/P4py/OeZZeZtS0uWhhklG9aLGfUyxnUHl/NaQehZ079ru/gQz/2SWZv3ef0//Fnia+8hBKMN4kaLzHFHcetTITmbaTw3cZF/Eo/n/p7LskVY5hbLx2jSEHzJbyeSzZpTowJOJe2PUNi8nr6iklWaOyCWthqsbO+P2aHXzM2d16j8bfi5dQChsebFQBvnsBSs/sSAN3dJMW/UC4R7Cf3O/B3xRrd4ds9o0lg8sDuAXCyoHlAXOFxxzXkNZNEkq9AbhRQ2U0X2+E5feJNjdnHuzbm0mu89GjmYrLfYyyAL8CwRTdn9nqllIB1RcrMRPuub6qrDQHI0JfkI9DtmYXkQg1VY55M1cvdbrIexWKocU/easyCUUgPT1n/5b/C4Z/8E7j6TzC89FtUH/0xaI4xms7YjxxWZLZoheYAzEhxQdx64jbweKVctJ5OTR9RM+AFrzArINGJJagkxaTGsGQVBFKZJaa2FwrgEUYpGud2CSc+BLzzE4XZB1dsYntA7x05OatUU5J2EpngA2ksjqAWSfJiFb7ECaHy1HViOas473oOmjnbOKBJLBFShGGUslHHFAJXcyZkQJwvYtqZlGIBsVh2dAi7qjAF6LtJZN2iZimbZzImxcUEVUCydZshZuJgPMMYS/lc71Fv2fC+CJcLJpuUy/lMXqhkiRfAGdXEysf8AcEcCc4J++N/PFbE+JSIhXHTnnqCSQkpYyY6o9FZDM+csyVEeVc8n1pC25gggHcmbi/Wns7tyqt6tdrovuQTBm80B60qZrOS0a8erxBqz2weODo+4MbRCbkbaNedqYFoa2tYSsQiiSReS76bm36k5Dl02y1nFxfce3BGNxTJp+IJ1lT4mLkkC1HWyDJZjS041os/mFfcOJ7zzFOHPPPsCbdv3eTk8OTJOeBttncMKI/EcXPecDwXGq9IFoYopNgTHNSVm16tkwTaEbuWrlszRCVltY46hnFb06PMVcD1Nb1zpJRp2y3nFxesNmtim6BLBI3Uc4fXHr8UwoGjSuAHD92ASkccOlYXa956eMa9tzqrBfPBgee/4/fg5nto/B03TdlUjc/Vb8jd1jJ765IZa3FXgxo6lqjLaOpIrd1/3y+I24HNSjjqIn5hnRVfLDOvaBALQ84qXGzh/msoibxa4jSjQ2fUxcGh2RZiCQIky4glIQcLODgsVVmYBNSCOBZe6XxiXgnaOy4UtngSzrgkFDA1yj2kjPadAQVfwgBphcaXLQQ9ZbYOkAckbcl5sB86jGfjCh9Gy2RbRq2UMErhDxmglKKpaHaqiBSPJKV+775TpgCbcqy5FKSo0ugEKl0GkpAlF+rggMQXEF0Qsh04BEHdDJExY9o8uqjHyjAyTVpmkGZizsQUGWKk7Tva2DP0A9sUWedEB2zwrJPyoG95Y73hpfMVzz0453uy8sXTLa9KRtaZenvOsN1aXW7NeBWq2rNEWCxm+GbGIAv6dsnqtIZ4wLf/9CfhM5/l7FO/jK4eWcJWs7RFOXVmbfpRWqqEuUdvY6knvnPijaBFx5anmOfl2H2+XTnVJW+dlNKCI/DLoBHVnZfSwiglvK0lA1hGPuF43gLAXGDypI33N/GW2buu7r6fUJzaPTcnJo+T+91nVQnF9mdT/9HRwNl7on3fJn4Osxto+xAZVlO3u7pdBnljPy/eNvYgqmqp+lMSX9KFJeQIRnAWBzKG/MfzjCe/5vd9/D9e/RKY1Cd/n/YTdFgj1Rwd2mluRLOJnmt/af9L29thcTDLb2hBgsmf5WwgcdLZ3es9es0Jxoz4cSt5Oij0X/kdLv78f83hH/+X0eEj6MWvIeJRFkg6Q/V1JL2FUgwW7dB4C1kfoy8eoK8nbjzu+c5aeebpOW6mbOOWzfmG4wc1d3XO96djeu/pgVXKtNrTxoEuZ6LLRGXSsc3FgzmOnVySbJzDsrtDwGvG9bEASqHyFvq2SlpjWNoKIbhiKHtnfV5FrE51VhpnBq0v8mgCuOAIIbCczQgXG5II/ZCg7QkCnUt0cWASQBoBrzPw6wtQHY1vS5QZ+1MBnKoTb3csDDGZ3bLjHqoYgBSnuCKq3g8GKGO0ijAmUm5h3DFRRHO25Eps/h7lcHIGTSVJSLCEIoyL6fZ6UVa1GknFm5lyImjAiRBTtHAwJbN7GlJaPJzFIzmFvW0tp3iKx16ZFSQL0fSRcM48vYbCzN9rmqK23gXvqCpnIuvO4b2QvTO+onN4scxTW54TQrK1UgQp3l2heFBLdCZmizDibZ8xscv7QIyJtt9yvr7g0fmaiCl/KAaGKVV1UllIvXPFriz9bDy3KE0dODqa8/SdI+7eucmd2zc4unHM4eLomkF//faOAeUM5eai4faBMJ87c/3GyHpb0a7PuXko1POaRFvmB0dwSk0yoJESEhVxVptZslIhuDyQO/NWDmmg32zJ/WBVXpJVlzmaBY6XDQcHC5q6KXyCbJmAsUP6TL/dsl2dc3H6iDffaCHWHPvX+djZQxbHN8y0GjM9xlFzSaZj5BhZ99r5BfLUCaW28PWYjH9pwaNkkMWBuF2Tty1972i7hvOLBenhwI3OW5JJrYifWyykzsgywskAF0ATkL4nX1zgciy8xdHss5iweo9Eb7pxQ8Jy/I9hPps8paJiHEIxaz0EZVE5fKVso9CLnyxVm0iKXFAh+Vr1iGwyEFKDzhENiK5QKgttZgEJZEZPhuKcosGyaGSULRy5Nc46O5QsblfyOIKSe5tUhijE7BCZQRT6oUUkM3eOKlVIdEBFjJlt7BjSwJBNe847IURFvZK9hWhKuVLEK0KLpC/jJKDOQtUangEf9gBwRMlGIi9WqObMkCND6mmHnm3X0nYdj/sNm37gok88SsrjpJzjuBcTD7rIw4sNjy7WrLcdP7KKRp5WzPURW6TtyUNPSsogykyFI6k59HMWN+5SHb6Px90dto9vsD1d8rF/5iOEF15g9d/9Ai5v7N36ktiCcUAlXM7c3oWin/Qc6t7/7dfRO+Qvn4O9ZJdp7BQ+4sjTHMXLs6XZK8HAlKYi94OFt8dqUugEYmzFKrpQmkErJjH2kgk8UROuJqaMwxUpnlKQxZ0CKqNJ5FQz88iNVWvKgjClDl2KLpTnTi0MJfNZRjB0GU1ZfWQK70ovI859PDehzlzGcm+gbcLDpe0mr+bem9kDWPp2YPJtsOfu8/KM47kFkwiKrf1c3Xd8z99imx5r//p9a57J/S/L+9dOSNu3ixRlmMqjXn7G8ff4ma/QvfCfcvxv/Cvo5jnaX/+vyGtBcouyxkL7vvQfgW4Gp0uGlzr00bMsJfHt2VvpzDQQc0vUlnnV8Z3NnOeObzO4ml4WtFHp8xkpbtimyEZb+jgQYyZqJjIy/Oz2qqAcbgNHDxbMV5WV/8tK39dUweODY/lWoAqO59qmaDJSsms9Qw5UIbB4PVk2dknQq5tEcMrQC0OygokHr1pEYOiU93Q1H1wfM8TEarvAec+sNn1ETfPS9EK1qWjqgC/ZQ21X0Q6WSZxK//XFs2pVayzbePQijmHuwjhirCg21kaX4v0TgRiLckamSAvt9Tvv+N2DjnsHJrM2FMCTga5P9NEKpEyZ4KWGdYrJ+lUB392QLHEy2xgw/4GSNeJyRewNYObyvSXgUEpsuqL9CZpTobUxJRSpFieIWK6EQwt105FS6ZClSENwQhinS3FFFkrw1WgcKOKt0EXwntwNRmN2YjzMYaDtt+Qh0w/D9A5QJjkkxvt3ruQ/WLLnSLkbNi2PHp2z3nZ4xlRh89dbzfpsGt5j8nAxhoynaRG3pvYcHc65eeOA208dcfupI45PDlkul8xnoybwt97eOYdy2NKvOxKZvh2M/yDg+4GFh6aZkSTR9wN9DDiZcXAAdSXMe/PmjK7nlBwxUFTyhT4lUizadH2HDAO56yEmZlXgcBY4Xs44WNSE2upnZzUupvMyuXRTMr2sOGRiStw/e8jjB28we+oZZHG89zSF9TZxs4SJZq1XvSK2oGhokFAzCX0/4a9QJA/0w5ah62CIRVZAiOuKqoVqrriFoLWgvjYwUGVo1sjcwUKQujJLZCL7W0/faVKZtUocjFcZI9pv7b4OT2zR1HqyoJBgwrPOqjmE4FgDvTiiZhO0RW3ygCKf4CB78jBmOHuE2jiycg4+WXUACeBqA9vZMhVVHCqW/el0b7ETCyWJ20luSLWLwCpKjMJ2M2eV7jC4I1oV02DUxHJRMWNJFWeIeoYhs9qcs948IG1WhDQw9wPznJk5qF0mCgQ3mgo28amsyfm3UV1CbtDsSHqDvDX5jT4NDHEgpoGYB/ssW0mzbdex3vac91sed2seDT2nXeZ0gIdRORuUdVYuYjLuUN/hh8RRVA46C53d6DJDjFSbnlubhI/gtMKjLMRz19/k9t33M7v7MfrwHHl1SH8fbr7vhKdvC6f/2c/g2nOorM9p6i1Eue+ZHDvvGOoWKbyCPQ7xVVDG3t/yZN/e/3UvMFxebCjAMO2u6bD+OuoSyj5I3QNPIpfvGZn4fKaSrFy+4mVYJ1c/iVuolsj8pt2LmAdVY1u8EKOE0+gN2wHBHWgW0FgEy/ebYWdo7p5j/HgPjF4ySvcAq9hza1pdltTR8bvrm31cYC69sjHaold23muXt/1aMZC3Pd2VYXziiHL/Vx/58qM/8ZnEuNM+vXKq+Mix2SjtKMF1zbbXatff+6M3ePB/+3Pc+DN/nPNfW3P+md+4/kRqxvHs7tMsv/sj1D/6IfzTN9lfGpvx3MPAU/Ml7/Xjcjh6qEY0K6TTM+KbDyBn+hdfIz4+I7cd/eNzNucr+r5FIviV4DY7CbmUQ6kgY4LlzkFKnqxWB73kGKKUEos+7wE1oww5KVVmMMkZ761aixbeZ4wzw+15gUbFDSUsLFIq5oDrZLoPgJyqklW9M1JGoChcngKmd1FezChj9WSP2SWOwW487XeDZfTEpLw6XxeeqnEgh2zSOTHZWh6xRJ2czeMbY6RPkVBUwGNW+sHaxZUKclo8sIbtxgjiziJx4lBXwuJa7lc8abzDAjLH5BooIuwlQWaUQhpjOd4b4aJyuzZzkgkSGKlRzjnUuSJ9ZB7sXPLdzYE/0LVbUyKLcZJNSkVppE8RBXwyOTvNuTh7HCkquo2szlsenq5pcyzJVyYolHNmiINpTuZs4HR6f+aUEyxSOJ/V3DhacPvGIbdPjjg8OGI2P6RpFoTqnwCgbLOy6Vo2ucVVAzhzvwZXEZolWWvWbSL25oXI6pjPF5yczBGBnIw42/cdse/p21RS82HbDWw3GzY5Mbge1Y4YOyR7glQ0VaCpfUlOtUbIKZkUghMqJ6SUyAgxF/c48Gj1mFe+/rucPPMu5nUD1RwR85rkFItnxyRVRHYdRS4tCGocFDcCyTE8NfbTjKpV0Bm6FavzLWnbUWtPTq1pYmVlOVOaW6AHGZoKfI1KAFpAUC8wq6AuyRSWjcJOQxBG2Q/VjA4mN+KGHl2vbALtWjQOu7USJuvSS6byVsmhz8oGbxwhi20YN1EExZHUkzUwpEAdFR9qPA0iS5RH9sxubLva2s9ZFrp3DnERdT2jSK9FXGTKqaEMLKnsR5ON5VaFt/pD7g3v4mGe81qMbNeJmYOTYcYBB1SLGueMiLxaLdmeL9FNTzNsOHQbjhYDB61yOFPqecRXRRDXWcakCcPXRFbk6jX6uoGf/22qv/N1xmxvX4j2FTo5dnKx5HMu4ZXCRUpZp2xvHWejaQ7bvYh5VG73mf/0V+6ZOmv0+OFdkJ/Fqs8JTmERZzSvOPybX8CFr3CYhWdw3Plf/Uv0P/NbpN/4HRJKeHaGO66ZZHB0BI1M/XYX6q4ZV4A92HNp2dy5ma5xs02f7ybncYhMITXnS2fSXXibck/7XMnRKynshULLFZQSHi/1wkWsj41h4jEGKhOKu3K/FsKRKbRe/Ee5HD9W85mSc8Yw/Qie9n4f71V25758vX2QPQLTEUByeRsPzQppi+SOkXc5voNdycb9d1G+mu7nyvd6aafd3V1ayGXveXW3PwLDXqnEazd54l7eZq+3w7VX7ht+rn/E17//g/zmZz7L8MT1he/7PT/C17/yFd79wQ/y+c98du90eye+EML/8UvI4QEvn79kc/neVtc1H/++72U2m/PGqy9y8rUznuuf57W/+QL3X3tjOldV13z4uz/Gvd/6bf6jW/8Ut+tDk8fJpQgGPRDBO8KH3kU1b2CxYP6TP2FT360j8smC1154kd/6mb/OwwcvEG4K7lAJdcW6j7x1dk7dBOq64tbxnOUs8OjxlscbS7xY1DOLsmhmPpvz1M0jW0uGji4OzA8WHC7mbNcbtkNk0/XcPrnBvKnZrracr1peu39G3yfOVi3blAmV43DmmDvHxdmGqJnlwYzDw0Xh3jlOz7c8WnV0ycKhIkLtA3UQmuCoghmJ3vRw6JOawLcqVeVoqsC89qiKVXgZJ/ZgICwn87R2gxnl4jzzJPxrr902Ee8SUXIYd7KPGABXcwq54Is0m/HYc1aSZgZSAc/Yuypv0xUQ5QuPWp2ad67cs3Uvq+QzkrwULcLkkHejpUQzpVQ4Mn4nMNGpbBcxWSenBVOU5J4yr6Rk9BZxecfzL7a3Fw+SJ/M+DpHYJfq+t8xxtaSaOKRJEL4q06YFggxYpmFg2LacX2x5dL4hljXITdzJgZgiXd+Ri7SRFsqTK8/inLKY19w4nHH75gG3bhxycrRkvlhQN3NCbVXe3un2jvc8z8qrqw0XwxkHbDhqPAfLOWG+oGJG8Kak33fGFYp9Zrk4YrY4wHSVAs6pJesMPXlIaDSZgK6PrC7OObtQagZyGthsMylZBYI6CHXlJiKuJYokIw1LRoIylPBnVAOpSZW273n9xRf56MdX5rkLjU2rOZJiZ2V1y0IgRbHfC1hQu3hcBMQFlGrPKhsXOHPXx7il32xYnW04XSVC33GjtgznRpXQRBa3FLmpyEEFVVng+x42LVx06DoiMrfPc7JkgGJBjUBFnbEjJA4GHOOAbltk20H20G5hGCapCMVY1GaFCEGE2jmyh7UILcafqQCnNiFk5+mdp3cNkZ4hDzQ5MGOGcAgsgAEZxYjFYeLmHnQo2o09wjDhbpm8rGXEliipVK5I2CmalK6rOdMbvNrP+LXhBr+8Uob1mlnw3OCAxh3ghzkaAu0wEC9mhIuGdLHiKC152vc83UeeaeBgDssO5pUjVB5xEEQgmLXZZ8/gzuiq17j7jTOqdssv65pHj05NKL/0+9HiPj46Ztu2HB4c8ODhw1348R1uc4Sb6nmYjFowtFY5paoqDk5OmB8fsXnwkMWdIyRm1g9fp912/PQn/yd8+Ps/wvzDz/Hob/wc7rAin7Zom+GkeP1ytKSTfXB1JdQtMppL43PtTaB7E+701Jfw5Qhan0RKgmnjTV5Kjbv9psSb8fgyI07Zz+M3ZeGYKvCU5JzRq6kZIxGbZb9zl4xPUYCpn5ko+lWtRVchs2Pj96V+71yyu7cx5Dp5CkfrZwSJurcvV34f2xsm8bf9Tff2GS7YZXjv63XuzrXzSF4FjuyByWu+2/v7EjidsOQ+QCzg+ZL76cpzvR2Y/FZdX3fd5uquvWSO3vdu3qeJ0wcPePmFF/jId30Xoar47d/4DWbP3qH9xgvUT98mHsz5yMe/m/X5BV/90pd437d9Gye3bvE7n/8tTp5+ihvvfTfHz9/ls7/2aW4/dZt7b75JSonf84Of5Naz72Imnv5szUc+9jF+81c/yyd+zw/x4p//S3TtFhA+/sM/wEe/7xN841c/R9zeI88M6Gs0vb59KkT/9TemRxfvrekWDf59z/L8v/L7eOrf+Df4xt/+azzuXqc/cCQnnPYdsaoZsDrjqfb0ATaSeThEUswsvXnRJIDUStc4PJmuVTrvkAYWi0DKnsEnuqx0jVItHNs+0ddCW3u2OdLP4HwzoElJybNOEIM5ciREfEgWnkVpfeZCBjZky2YG5l6YeUf0EJyNY+cMiGxypsUiNRXCUT2nK+MxSvGCepMHrIMnJ6sEM4SK841pg7bZEhszQh+t3F/ldt7OXPSdM1ZZx6slR9lIcORsfMNRr5JyjMkbGUfRIfg96bRx/qZ4jG0Ym4z8CGB1r6NORTLAOKEUgFbGf8qmAGDa0CV7X+w+ErmE+W3+mnSJvVUvcs60NV3lqauaWTOnCg2iJkze9SX/QEZlAG84p3BEBUqI3oDwMES22y2n5+dcbNsCWkdvrVXp6fvBMtmd0Q9cyf62PAelCo6DZc2NG3NObi45OFrSLA+o5zOqOliS0L7N/i22dwwot044T0K/jTxenzPXzPHRMctjR91v8JuM+kjbbmCARVNzcGCAz/uKOpTa0N5U9n2gZHJGhiFxOPMcLwO3DhccH6w4nF/w6NGKpRfqYK5kB1bKKMZCGHZoTJa0GDNd39MNiXYYGIZsxTtgKn81LqFOTBg0k0kxk7JjUMuybUSZO6FyCRisYLvXkgRTXMXZhoWmSGw3nJ8/5v5bax6eJk5XnmMfWd4ZWMwSwXUsDreEgwhHCjOTSpKhR1cX8PAx8vo5POpAjiAnlAGpBqTyVsc7BcuCJJsuZIxIHEy7sE1IbxmZuY1TffBx4DA9M1QOGi+QYQW06oiSsESNMrC9w8/BuYpQOXzu8PSQlojcROUxVmOi33PgOJuAc0QYcGoSIgbGmaqqSeHlICC1Q6oaVMkltNzlGdt8wH1f8xttxde2oB14rXHtghwOUL/AJfDJk/qB3Br/0m0HajKzkLlROw4WkZOZ56iujL8ShLoSXKjMwhXHQOAiZf759UAzF37zuz/EZ3/lV7l//z5t1zFrGrz3bLZb/ukf/ihf/drX+OFPfoy/8Bf/a5rawgDbtiUEz6yZsdluTRIkmMd7u91Ooa+FOj54eJPv/8N/gAz88q/8MtvNlt/3Uz/OKcKvf+pXiRKZnbX86O/7CfCef/ibv8nv/5//BLd+/yc5/6/+MlQ94ZkFw6qf3qv9M0oBwQT8xlCmG0W3R27OZWSwc0zKdPilbQIb188qE4FhApXpCugp9zN5LPcki4BJIPsSkBRMU8sXz2csLJV9zyUGiEYvaLUsWe3hyq2We3AVNMF4lkNbvKAlk3wCV4XLKCOfT6cn3PdQXk3jKSqoO1A5eh6vNqkqU0b8nrv48tmu4LsC7gUuv7r9fUbe9JXDrtnx8vGTp/fq1eXJfff2uu6bq/f39j0GZvMZD958g4//0A9y9ugRqsrzH3j/lIVq5xN+4Md/L6+9+BLv+7YPUzU17/ngB/nKb36eT/6+n6Sqa1589RU+9rGP8fUXvsZTt29z7403cc4xmy9Y4FnOl/D8c6QYOb59k7vvfTfzgwVdu+XW009xcHzEmy+/umdT7BlZEyoGK8qwB/5LCVA9i8TPv8DFi29y8O/8YT7y03+E0y/9Cut0Tp41bFPmXZsLLh4/4GzzmEwi9ZHUZdbr3hz7fUA1cnAwM2eBM3kb5xyT0hWmnShq8jVJtXicXOHnjdnkA227JdQNMQGk4l0r/VQsfCzZEnycmEy7G7PQSxPYcLXM6tGBE3Oij4k+RjKONkYEz1QOUI2FUzshOMji0KTkPhYabZ7C/ENMtH2kqXwBgJb5LTiGAjBH+y1rRtQRi2ybJT85nNMdd18sIdgXAfC6CjjtDWA5j8RS+MTeWpkaS6KOWAUhoNAM7GH8mDwk4+fWwccKNarZMskLiJRsa6A404XUrOQcQfJe+L20cc7UzYK6sYRQW9YjMfbmndwziJ0rFZJGYfOSMBRjZh07NquOR49WbHqjNpn2qPHpVS3kPc1DamLn1loG6Gd1xcHBjJs3jzg5OuDo6IBmvqCpZ1ShxrlgfOR3uL1jQLkIDT4swDcMMmOIkc15zyJeMNtGqnlDlFJlQB1dP5DUAOZsVluNbufJOSE5ERSCB1yxKJJn3swM7LmaeTXjXTeOyDlSOdNtUkxsNQ0JMjhLPSNKou8iaYh0fWIbjWhb5UTdzHD1DHywkLXYYzuvVHlAstLmTOyV7ZAJ0cK0wWck9FYiLgiECL4yb4waaJOuxZ2fU735iPrFFfle5P5wQHu74r0njqaGxkGYR4QerXrwCYYOXW3hrVN44SH5d8/gQuD2AL5FTo7gWNGmuNF7D4PDDR7tM3QeuhptA7QN2vUM6kjrOX6oEBWqPW/ESKiufOYkBE4UhuRoix8WBfUWWvdIySADnTnINS4mtD0jD2ucnAEb0ADao1hVgziA0wGnPSNHdSxfNXklKZzM4JF6Dn6BxhXQk6LQDQsu9IBzN+d+CiZWm62WKglELcQiCD3BOINFTiLjidmzSXDaOSTNkTagIVhNdBdwVUmKkoJ0FVSFT/bwdFJmszm//yd/CnGOv/azP8P3feJ7uHv3Lr/1xS8UQrSF9J9/7nl+/0/9FAr8nV/4BX7wB36A2WzG/fv32W63fPCDHyD4wF//2Z/hI9/2bfz6P/yHsOm4++7nuf/wIS98/QV+7Ed/jGEYODw6YvXwlO7RGXEYWD51m2Uz5+HDBzzzzNMcPH3A+n/4NNvP/LpxZsf0fXG7sO5IRB23MWt6DHUzAvp9tuG+B2YHJq6bOkYu2WUV1/HYPfrHmKAjCoz8TXb3O4pr792n5r0QNzBlp0+JQUaiV2QPVMLIbd6JiHtLMNGI4naeyin8Pm5iVYKsLuikuWr7RSYPpYxRiH1B8T2P5Z7/g+mTETi7Iqt4WRlyBIdTW+87DC9DlksgcdfcVz2Gu/NdO+U/gSP3gfw1+ws8yRO9csq9r6ZWeJvd92ml+9swRM4ePabvOt71nvfw/AfeT9e2LA4PL+1XNw1np6ds12sWyyVd2/L44UOquqbvOl772gs885734Lzjq1/9KqqZZj7nG1/5Xbb1G3z4u76D+6+9wa//3V/hmXc/x/3X36BvO2aLBe//9o9w8+k7vO/bPsS93/wivKhFU/XJ5xv/PwLNMSnT2C0Kj1es/9O/zvJP/3M89WN/iKPtG2SXGGLH09sNFy+9yPmrr7A5e8jjdsWmrzhKDhWH10CX09RFtUiSJU2WaxCNF6dCiaA5C1EXKaFRTSF4x6JpqARSinSdIzQmn2N6jmE0/cryZV6sseijlscPzlnGdhhT7IvLoFwfNUCiKTMUTUZxUPkAamouFaYdGSnetKwkCnVsz8ZM2aqaebFxMj6Pc1h5QKwyjmZzUOTsqYIlABpdyyhamk05eExcCZg4fXDO7FR1VuW8jBkp17NiFsXBwT6IM36kQ3HOG52u3PMY7Rs7/iRhBwRvYfLKu+lHZDRCbW5UXNHyHA0Wu27MiSGmqYxkLAlHOZr3lWy0NC0ySHFQYtpyfrHm9Gxr0dqimpbVlEhiHCOvBkh9qeDkndHPquA4PJhxfHzIyckxJ8dHLA+WzAqYrLxFHnN++/ng6vaOAeXdOUjryHHB0HUMecs2R9KQiN1Alc3lbVGUgd5B3w+sLjYEB8Epoua9qtRCr01dUc/txitfoUXzaz6ri/r8wl54CT/7Yh3knEhpIBW+ZEZo24HtpqPbRmLKlsQiUFWNJb84v+drME+Jrx3eJ/wQcTowdAMXa+h75ancchASvokGrOoSvvMZYoJ2gHWLf7zi4ME5y9XAna1n3ife6uY8boW6Ssy8h5BQfwH5HB0GZLuBBxv0xTP0yxfoGz3SLMgk3IGiiw5ZeKid8TN9A6lCo6BdQLpA3jgrA7weWA/wVlTqYclNZizEl+zt4v4ugDI4x9wLBwm2OHow7mSp8DAt4HiC80BAc40MCc0nSLoAvYHqfQQTK07DOblLxFYIrkdch3FHbE2eEj7H2UychWf9kUnOsCIn6AdPl28wuBlvasPjVKO62TvYmdOoABMhF3FtZxVpvPUEi/AJmj2SpOiFWnZcUjeB0qmayuTRzcQY+Xuf+hTvf9/7+dAHPsjBwRLvHd/1nd/JvTfvUToPH/3IR/jM5z4HwPd97/dyeHjIf/nn/zz/+p/8k7z88kt89nN/n6OjQ+7efRf/8Dd+g7btmAOvvvgS3/NdH+VHfvhHqLzn8OCAr331azx/5xnuvvt5Xn7h63jveeuNN1mtVtx45jb93/t1Hv/W7+DHUKm5Gad3ZVqQe9zJ0eM3grtpuwxmLm1l0Zdr5409CFoWlH3ZnnFBm8DQFPoeM5pjqdTjdufb90rqCGwLQHa+WOj7otK+SA1qAZUlj1HVQOBwgaolro0GI6GUPJw8pnLZvSZFVE+8gcsUIbsdCB5B615W+GVP4A6ET5nWYwOqef0v4bj9X691Aj7pRTRsu7fz1f2vwZe7v6475jqDYO+7Sy7Gqye/fIhc/e7ttivPqgr3X3uNT3zyB3l4/y1e/Orvcuddd1FVHrz5Ju1mTbvZcO/113jz1Vf5+A/+IJvVis9/9nN8z4/8MN/zIz/MP/iVX+Xpd92lW615cO8eQ9fxie/9Hj7zq7/GYrlkLoH3f/TDrM8v+J3f/ALv/ciHeP6D7+cf/N1fIefMt338u/hHv/Jpwmd/nY9/8gd47evfAPmYuefKvY4eu32/7e7Zde//BVucbVj/J3+V5qd/kObHvhv37E24MwMHN97/3aTzFduXvsHq4Zs8/bu/w/tOTnnj9B4bHKduRhKH5AuG3sDeejOw2nbMYs3hwYEltIpF6mK0fAHzSKnlF2RwohwfHnDRDcSUcC7QBE/X95MguQDqjCtuPkEDVVmNF94jqHdYtoCBwbHcX1OBc7XxKtWq30iR2hHvLE/CZ/oYGXUaQfAhFNmaXZv6kqSSUyJS1DRS8WQmIBWdUIHKF8+gc+QU8d4XV4hOmpKFbmmAyXsWVUXfZyKxaBc7RLOpjIgYAFN2STxqo10LNWt0wgCl4tp+x7c2GT2+OSdGLWM/Jl9h5wguIAohFD1R56i8GFVuiHSuRaOaJzGniR7mvYPBym9aMURbA3O2SOowDLCNnJ2vebBdm/92jNhgD5R0QCQXb2nGBUG84J2F1eczz+FRw80bS46PDpgvFzRNQ1VX+Lo2764rGOQdbu8YUH742LMZHOscWG8rzoeBPiU2baTtM4tGqSqPrzziTby8y5n1Fpwm4mAaeWkYkKR4jcwrRxVMKN0LzBcz6qpiPm9oQk0IFXhnGCSM/EklDoMl94zWmwqxzwy9MkTQVLK9RFguDy1LqXC5Ji+LWLY4YtpgS1f4k7njLCZeX2cW7QW3cst8lvG1IsE4f1LCznQ9bBIursF3HDRznu3nnPeBlx/PcChLH4vu5Aq6t0wYfLWGN1vyyxt4lEBrpJ4ZaK0dEjIsPDJvyKlB2iVsa3IXiSvhYj3jtINtrthUSrvMcFzzzDNHuMMlUklxZO14c06E2gkzB0deyOKLxePJfszQHgGIDTqRUtpJGwjHUF2gaY3oIZpPMa23lth3xLYnS8usTlb8xBcrrAxOLRaZ+oBrllA1FiYgkJLS54ptWNJScz/P2ejYNd3eglZcnRip2aRmSrjXxRJhLRnxOaKpgGSHvQMKgBAKT7XwY8qEUoXA933P93Lr1k0++/f/Pnfv3uWVV17l1q1bjL49zcq9+/f5+Hd9FyLCl77yZe7cucPv/dEfZbPZMERLPEtxgXPChz/4IX77K1+BrqfvO776ta/ynve+lzffeJOnbt/muaefYVE3dG3Lhz/27bTbLUc3Tug14YfE6m//Ku7WAlxiquk+biJT9nZpIANYms0IGb+5BF72VvbxNNe6mPa+3/9rBLJ7i+oU2h4tbuensCCTpFRp5FGTMhegJ8KkTXklSWf/Tkx0uxpdG7t3qYDm3TOoIBSpIinXebtw/uQ5LefKRcg87ZJmdh67fMV5ePVkI3gbPx/vUS7tOvl59xZXnb558rn3Lrj75JuCuL37mK4hl9+fjv/q7rMJUF4BoZeR1JMg9rqvLi2+V47JmV/9ub996Y5/6a/99See4osPHwLwi3/1r02fffYXfnH6/f4rr4AIv/NbX6Bbr/ncL/9dvASWiwWr19/il7/x30/7vvBbX+KF3/rS9PcXPm3G4BATv/5Lf48bzsHBNQ/l9vpNvvLgMhZT2GvdTUf7l/8e3c/+Gtw4wB0vCR96DjmYIzcOOXjvMxy9933c/fhPopXj9PWXeelzX+YbX6w5XSc69w1ce48oFzw+H7joWm6Giq4fJqNIkEINsHurqsBiXrHpM5qsdjjbSMy58BRlAmOTNqxIEQ0vaW4FjMWcrba4mvcwBDeNae8yi6ai7weCD8SUiSNFu9zLEJNV9FKmajRjIqP1L6bgkEkSmdJIjDavmcxN+d0FEmJzRtk3F09gSql44kbJIiHmVITNrapZFUz30kiQDkmjigm2RoglxzgpnMcCNn2R6hGYNCq9Cgk7ZsqQxpJtUkolefYyOHVYwzpMi7IKnrryuC1oMV6HvrPGz4rmgeCFypdbZkxRkCKhZNWKskI/KF3bk9c9D8/WbIdoFIkC0LMmun6YNJMp5/ICwXm8GHdyOW84WMw4Ws5ZLmfMZg11VVNVtYmplyje0A9PjM+3294xoHz37RNO+zWP2jN8paRGcD6QowG6lBM+WYwzQalzOXoq7IXFQUnR0aWBNAxlYYl4VSpvsjbz4LmxnHNyuGB5OGOxrJnPG3w1K5I0Yg4IAZwnDQMxKW2f2HTRwqNii6QTmM0axBdQMnm1R35UYqywIZUw83DHwaLqOF/UxO0hr55GDt8652ZnmduuyuAj4gdUevNs9JbaTxNYdJ5IxYubGcuQuauBHMU0JZvWjlkP6ANwZ0vQiM4CLAIyw7yS88oywZsG0gKNDZGKi1TzMHju3azZzjxSOZqg3FjAycmco+OG+dLjA0WmoHB9ygIjqgTnmXvHJmcQszANdJTkjdJCOnpnxBZzcXPUH4Ieoekm5DeQvMIxQF6RY0uKHSFl3EzMOeTHhaaMMPFIVaNVhbjaaqymzNALMR8SwxGP5YjXhiXJp3J9y9gV54q30e9AgC/hXleB69mLS9j7zaOHLFgbZG9eKD+Gc/IE0BT4+V/4BRYhMAwDr73+Oo8fPyaEwHq9YRh6ttstf/sX/g7377/F48ePUZSXX36Fb3z9GzzzzDN85nOfpaoquq7jzXv3UIXDw0NiSqYPljJnp2d8/vHn0SFy7ytf486t27SbLQ/u3Tcvzb37+IMF88WC+6++wh+59QHEjVyh2ioITW9J9pJAdAfUZMxw3mGLvSO+pVNpOu6JD21FGEVxZfzsiYNGD3ABjbm3tMKsl0PQ4ooxMIbx9y+272GzE4t4A5Vo0XLc9xFdebg8mCMzJ+sfVzxOu1ve+8J5kLq0oe68ayVhZycjM/7sA9XrQN94jnER2r/u5XaTvWN39WR2D3SdqtDus6vvQJn4pZeeWPYO3DcC3hbKvu3H18Dpy5fRK/dcLvFTRzf5xHPN1eaYjLr9fbXg+OkkTiZ+2Zg0M//hHya89xbp7/+/CctvJ/zwv876//pX0U13+WYuXaicXEFFibriQMKTDzKuGYVLN3HTgR33dK/6jxbjrI3wxmPSm6ekr762exYnMKtxR0v8B57l5J/7EW7+83+Qd390zdd+/j4Puvdz/ug+a15F1p9G9U2MY25gJ0aTrU4pGwhyjqxWZCFnM4iDD7TdQMwRL+Ydq4ONVc+YtVx6Wwn52rAuFKCcyVLC36U0o8nNOCof8LXgXEAx0BqHZDZkcibfhyOHjIiasb5nzJnzh2K7mFdOgT5aaDw4x6CZPPL8komMB1+RsvXlTC6gqWB8VbQ4moYcydIYAPMlGXMYmMoa6Y4T6Z1nSKO2rIIbPXwGRL34yYHlRs+uXDYGzU/hzbFdQK+OyT/sSl26EJgtZhwdzTjbXJikdBfpqhaGaKBRldopYeSkju0eB5xoydDOVtZXHdu2ZbvecrbalNC/VcjJOZYKO6kkOdlxrshSOTHtzDp45rOGw+Wco4Mly2ZGHSyjW7Da5VmVlJSuHcfSt97eMaB8+umbhOGcRrY8c/uE3nnW2y0Xj884P7/gYtNBjoTQYCX3zCvllMkKSLWSYsYPA5142n6g7XuGvi9Fy00KZxnOOVo23DhYcHRQcfPGIcfHRywXC6o6ICXRJ1BC5UNH5SNeXHGdW8aVCcUbaLma4yrlhdtmC7OTGrf0HDWeWdvRbgOrw4aLgyXr1zYs3jjjeOiZa4t3PUgLujLwlRdICmxqx1kT2HpPTIJPHtkIslKIHlxjSaarCH0G8Wij8JRHnj+AOwe4G8cwL0kGYUa/dTxWx/nhjOFOw7MnM5pG8CiVZJrGU89nhFkoBGtBJq7ZblFxgFeoVJm5zIEPlmXnXZlu9sAJpZOOIWHx4Jbgj0BvQ14CD3EhEaqWWJ2TU2boDCfUztZmwx5iEkl+Ac3SND2zSSal2BMHR9abRH/MeXWD03SC+AvUVRhnTqd3tFsrix03ppI7o0NQPI722OWNp2w/3pKpbIfMWPZCBPCO+3NP7jprpOef5mUKt27uYT6HozlbVXj+Gb6hvZ3nuWc4RTldncLJgX22nBXA61k5j3vvczy1yXz4wYbH9SG3nr1F/vp9ul7hjRWHAoeLW8hF4ukPfRshe4ZvvMkH3vUMi6okY4mz/pCVS1zEXMosjt4/VRMQv3Ybwc5u1bzOH6h7v497yPSdPnmUCJYhbXIWMoaoI8CeN3LyiJVw/Chmvncju6vtQKLsLd4W/rbyeqT9c+6BJKR4GUtofCqpZCe8lOE+tUf52QfIrrZny2OFm5LAs3N1XG26y3/se+hGj+eVmej6g58Ep5dP/TbX1avn2I/IYCUQ8TBs9555/9i953feBOH7dpfgdWW7DuQ+8Sj7nyk8XdU8d3A5aenSre+DSnMEjZTsaZxPUQtNNCIcfP8niRc/izzzPvyt51lxBNXePev+iS9fUFE2GcJUSmXPkMnloo7p80ve3v22mK6wGymogaLx05wVVi1p3ZIfPWb43ZepPv5hbv5Pf4KP/fQdXvm7F9x/2XOuJzhe4rTaEPuOSj2aBtreElgdisY8OVCrKtD1CXGlIos41l3ECzTBRLWHmIgpG2CQcXq0+XKUxfE+mFi4KsE729/ZM6gqWfKUkJKS6UAjWvQibazFaPfhXbEfs2Vsj609tiFia3NKVtpQsehfVgtv57FMoIiVPBTBOY9mIReVBOPkC2RLrMnFQeFQmmDZ5sE5RGzMqhsXD6M4eWdhdVsDimg7u+o5YPxDLXxqUbnUdiO+ENQwhmRLLRCZ5izxQqgqZosFy4Mli+aCdduSk2lt5yYTpMg0NY7N1oMYIIzJwtuJaeqADH3Xk/vI2cWWx6utaVeW7O2clT4ODEW/UotDCIG6NgpE44VZ7VjMA8vlnNm8JlSWHKRZySmSAKeeoYtsVptrevz12zsGlIcnT5PbDQfzObPFDerDE8Q7+vaCi/NTzk5XnJ9dlM7pabuOLvZoNL2lnCJD39N2LZvNltU6sNp0+NrRbZShy7Ra3LnbgdN2w+tnGw5mgduPNtw4WnHrZMHx0ZLlvKaqPSEYH86Fmnmj3Dp09KkmDhdcrAdi3xOHwYj/l5bC0XbYzVrmdfGW5exg5gLB9zR1YLGccXFbad8zsHrzlNmDLcfrlgPp8X6DI5KSY5uWPPIHnM8rmlpYesEX97pGhQvLSicFdKtIb9IurhF41xzuHsDJAl0skGYBLNAU6FE2TuDQc+uWWTqhNsFmIeFChasaZPLEKMbo9eVJjRcSVFmkgdvZceQ8t7xj5i20Pc3gYhMGUMSXzYuZfYVUB8AG8i2QE1ReBA/VrEFyTZd7Uiv0W7P2agdUZQz7BqoTJBybBqfGEi5PJHUkf8Tgj1jLIduwQKuI1C3aW3UThRIDKN4vtwconQcfyEEhjhN4LmvGGEDYty4dKqEUZimgY9Ygf+CnJucFyfqtThluUqxYtcx7LYAmZ7OCh4iUiVedh1kD8wX1bM5HFrf4Yy+2/OHPfJHj/+d/SPfC67z4//lbpJNS2UUMW1WHM977b/009/7Lv8X8k89y66NPs/0bf9O8bdWiVJrZAS0DVf0O903euBFw7kPDfTB5GRQ+AbCm/co1RpA+jZe9BJ0xdCrFuncFBSgWpp9ErveSbiav5BhSu7pM793P1dVbyjt1NTuu5hM7WJtpBhlF18eRv/fUE4jKV5633GeYscsyH5jqeo9e8HLcrqWvAy17qHL6U3fPIQF0mDDPJX7qE2ccEZzuTn/pF7iUtb2zJW3zjfFZY8+uOg9MbsBLYNrD7MhO0K6uBYkTpp6y7y7f8B48e/LYvd9H2+8qxt19yQRIyWqGYDEo42uvoW5Ofvbb0dMXqT4xwz11TH790fVXmy40Asd9JLvXduMF8/7noznwZPHPyw83SsZc/s7tvoYhw/mG4de+wOr1hxz8W3+ID/yhY5b/Q8/jL/0ut+MF5/MDHle1lcsdEu2q46Izb/9B1zMLMjF1syqSHXUIHC9mbLdb0zdMzop9pDR1Ha+m+jHO787L1A9GeZw+RsvwVQdiYVTvzHuXUyn2pankP3gqD75ydEMy+lpdoSXsHoKn6we8+GnecFLKTorSqUUKc7ZoJ1g2txYPWfK+zCtSyg1aFa3Cgrekn2gJrMEbv9N7b2B4fEeFLjTNfgq56CQ7V+p7U6rzMNYaL7FMZ6H4qXeI2AmceVBdBnHejikvOBUnlnrFV4GmnrGYL5k1Ddu+t0ShmNDKFG9mVanl7oOt6d6hQyzJNmKh80Lx6buePiln5xs2m26y7GJKDFnphljsd0sqEi8s5w3LecU8BIJk5rPAYjGjaYKpuXhXqvKMTI9iOAw9m/X2ag9/2+0dA8owO2FxfAt/cMT84Gmqg1tQVWjuuZ17curJabD09z4W4XGzZHJOxNjRd1s26wsuVuecPT7jwVunvPXWW5w+OOX8osJ3A64eaFtL6Nm2kVWXebxSjk47jh6dc7isuH10wM2jOQfLmtA0JtOSM40TjpuAniw5DS2xzQRfuoHmS9MCOnpbmDp4LgaMEJBgmWKuGqiTMm8q+pMj2meOWJ8N3Huw4uKsY7FpqbqWIfWcUvOKX7AJsKzh0GeCKrihLEQO1zu0y7g2odsWHSLiD8wjOVtiSt8e1do4hn2CzuRI5ssFh4eB+aIqKfIB0QQSDMTsmf2mDall0lAkgY89i65nnj25mhEaT3BN8eyNwGsfaI8g0xevUw35ANIhyE1bS1AIFWFxTE7RJIOygpaSVhIQP4PqGKlPLCGneBeIK3TIJObEcJNelpz7E7auhmqLhJKlLVK8bgUojUDRCudapZhQEZzgayX2Aynb8zhXEbxntqipZjWuCqwRolqiTuNs8ltUmXffOCSinMVE2w3oEHFJiepI0/X2PCSSrTKIS6gzCQ4QC+vPl4RZw7efPMs/f+N9/N5nZjz9Q9/Lg0/9Jvd+/h8hfdyV2SotXh8s8Ckj246nfvwTDH/75+x731jbOyu3OXk/Sr8md+VduV34+Ju5iS6hjCf3uxbIFGPj0qdXAdk+KhjBnu04Ac6d0Pn+k19/1bffpHirC6gcSWDT86kthGPyTo6X+XBXrzN6uaEcV6R99u9ZPCRfgGoq3qsxK3jvuuPmajs2d+yyZXW3izqoD+3c3cPpu51euz55q/vg52pT6f4v5V2N3Mjx2YZtMUDS7oDrUJxilbjas1Ku8ppt/5C6sWTDFL9517tyi/tdcfp6HzyCvTePGYr7AFoAceSzM/L5Bf7Wx4i//SnSa7/E7I//OJv/+89ihPqr97F/nusfbWcwjPdT+kKpCHZdF7p0LHvHvN2W1ELjEolf/Dpn//v/nOX/8qd59x/5GHd+WLj4RyvOXn+B83TOuTj8rGboa84v3oSZMPQDdTY3YCjKFzFG+qKhKx5Uc/ECQh8HUhHflslS05JIUqJaauLhCsVTlYnZDOScMmjEFR5fyqZP2dSBWROoS9GMebJzLmY1mmHddvQxk7y3HDwMwLkCnro8lOT6XQU1Y8bkCc+nqKVcoVokyo2g00TRjRPqLTs+ZbpocmBdETWfQr8l5D5qXdoQMQBq1XYcyFSqwRJKx6nMyfRqx+SmMbgXnDlupExLPpjjYxRNF+9Me7KuqEJlz+YtouMkMasDTXDkZNnllGo5wfki/G4GQ85KHBKb9ZY0KKsLS8ZxIlYNp0gq5pSLn8WjqixnDSeLOcvGU3sxh9msZjGfM1/MmDUzQgiEqiIEk9mzZByrYLj+JwEoXbPANwdUmqmPbiGLWyV707xwOmq6qYl9SyoTZOm4SkRTT4o9udvQdxvWqzMen97nzdde4bWXXuPR6WMuLjY8Pr9gteo4X23ZdpGLLrHeZt7qWuaPHfcebrh92HDjqGExqzhczGiaBnWBRfDE2hFrR3KepiovW8dFZxxMlzcb/+MEXBY7n3EEkMQsmOB5VQm+brhYeNqLnvZsS17NOFsNvLaBr0foAtzMysINeClyJLlwLdsWNiYblFcrcAvgEIknEA+RuIS0sJOkjB8Sszgw10wzU+oaA1fFEtOxKPYUApa9tV2KJ2JAs+KGnrA9RzTgQii4yOrDygjWgFG0VQqYxO/Nr34GMge5CXKIcr9k0VfUBzU5tJBbRAckVFAtkGaGVoeoPzaPTOogduiwKYP1JgNHrKsZpzqjVVvE1dUmAu+chetGKZn99+Q9NBXHTeDZ4HGaWA0999YtXYzMZ4H337jB+2/f5MbBIVGEe0PH+RCpvedW3fDew7e4lQP/8gc+yAbl9c2G082GHAc2MfHqtufNbceQQCsPyZVQXL+3EMrkKWXR0MwqfvSZ5/kPf+An+TA17pU3Of2zf5l7r7ekcQJ3O+I1Aovnb6Pblmf/2D+D/s6XGV591bxkY9IIgqS+gMyqZP93hRQu5vmb+MGXeveuk1/zuVy376UPxyl2TzhoPFcuEkX7CSyai0evZHZOC+seUphwzjcHkdd6T8fN+R2ovMqnnJ65ZJOzP7avguP930dPqkz/TEtMKMTgnMzDpEO5Q70M9hBUE7J8Gu3XSP94d97xetUSZjfQ/tyMQmR3DpW9ZtG9f66gsmsVh2Xvtz1gqex5Wsfw/Tdpe1Vo3ybUdfUwcTBboutzrgaEr76V6871zb6bjMnc7+F3hSIbpTHS/8ZvMP+xfwptjul/8c+x/JP/Bd37niH97qvf5N5H0HilDZ+gE8hekKPsm8Y17brzfovPZPePKjthgNMVq//krxB+/u8z+2d/kNs/8c9ya+EZ2jM2Fw8535xy/5WXuPmNr/Dag5eYZaHynjxkQhUY+sHoyji2MdHlTOPsIjmVyjJAxCqGiS+Z1VlL4o4BPUpii/ce0ZLYUjKicyrJMYUCUfmAczBvKirv8Ch15QghMAsOL86ykaGosZR2UwBnYd3CjTR5IVuDFUqlmGj3IBFfebxay0nRzxy5raIZcRat7HvjmfZDZNX2xDQm/FASfkrrK8UBUJxJWYssUgGvXqiDrbGkbJ5CKEARBEdw2YQpSl/wRcZwdFWVs5vhPSW+GfVByZYL4jBZH2wetbyTImc0dhRRolpV8WFQZBhot9FohmNEUSHFyJD6ErhzOIG6Ctw8WHBjMeegCWS1KofLwzkHh3MODiyzu5nNqZuGupqVBB9PbDtin9mu/wlwKF3l8VVjRdX9zCZXV7OrZrFbIGQc+ROwMdiCJnwRV5qlnoO05Xa34t0f+Ha25w85Pzul37RsNx1nZ2e89eg+99+8z5v3HnH/rUecX2xou8Qb5y1vrTtmD2DZVNw+mHOyrFksGuZNg71uxTuHxkgeSgUXr2Uhu2yVy97iomMmsWALVW6hbxEJ1L6hCp4gSuOUrvZsFg2nK+X+feHFdeRRdtSYTFKjPY4ejS3EHhmiVcZ5fIGuOlyX0JMjOHgehqdgvUQXNbotvI+hA4l46VksjkxtpyrC6CWAYnSPAgZLp909T/E4jlZnGpD1Y1CHHh4yEkJGMDmKcLMPJseBQGm31KB+jvgjJL4blYeoXoALyKzCB4f2G1K3xvsl1Ido7SHMEZnbIpxbNK1IcSBmB+4myS9Z+yMe9TUDCbyYdzJUSPDT7/gSqo4laQgH3jMLwnFVoam3KCUD2+Q5ms15/mjBu5czFrOaLUYu7+oB55Q7zZzD4FlUgeePjrnIAzMHXRPY9h332i2EQPSON7cdmnTCV+IKv2ZMEHIOqhl+1vA9h7f4333v7+E9X3iVl372V5GXX2Px4jeQ5z6IrxyhtkxL50tZLics332H+u5t3IP7XHz211FXWZUmTbtKRKNR5HzJ5M6l+kvprxjlZLeVkOy0UF6nJrk3FsZxW/7QyXuNTXSjF3YsazhWvhnPM+pK5ri7T5EdDy+nsiC4wjXav/A330Z5nn1lyEugck9rbvdYeUcFeJss8qmNxnlhLwT+ZEUkV977VZC8t58qQkT7C2RxBw1z2D60kosoWh0g89s2vvr1ZTA63v8+b3TvPr81ioERravsaSWSd0BS89sf/k4/2/86R2R+aKUch9ZcN1a02vjX3+x83+q9J4NBk/tqauqRVuFoP/c5Zj/yI4QP/n745T9HevnTVD/0EdJXX73m3t/G2Bp/nchqex8qkIsFNFJodbfr5TPuXJv7Ty4FkGqpIz0FGKajBGIifuEbrL70InLjkPBtz+PffYeDj7yb4/d8B8//6A/w7Z/c8IVf/hle+/o/JGUhiiA503Y9qM3kTYDGK5VzhCLrEwf70aqsgWqcc0GmpBMr+FH0KTP2U2x278Qigd70HVNShszueLGkQ8hUTnBFrNykfrQwIuyhc8xsu8FkivqRiwkSStWZXGSN3Ci+bthhTHQRt6PcKExZ1Zqstnc7ZLo+0cVEzFrQgJUzND7ubrQzeStH4XBLtHFiVXfEy5R0M430YiW5oifpvCsrbZ4cBYh5WWNKluM3Pnu5nmWWO7wPFip3ruQ/ZHxwuJTJfUmsKfc5pMymHejE0beRPpYqOpqIMTGkwSrjiBIk4CvP4VHNycmck/mc2gkpOpyHw6OGw+M5h8sF88WSejbHVzNcqCmXI6ZMt42cna6uDqK33d45oAw1vm7wKiYoTUAZPSJu94JHwKaUSVdHYM5YqN2k3zJeB/xsoFq2zE+2HA9bcr8lDR1Dv6Vtz9muzzl/fMa9+/d46cVv8PWvv8S9e485f7zlYpu42GTWm8jqqGG23jCrheBrnNQswpzYt6QieGwTU6nqMtrOex4L3VuMJs3KmNDNBcREnh8i8yV1CHivzKrMvDJ75OE6MVtm6hUsvbJwkSpHJPeQV+aR225gvUXWa2h7cm6QWzeQZ55Cj24i9dLCKoOzRTAlcFvwGVdFpHIGLC55Wfb+nQYG9k5KSSx1VnVkrFduBU3HrO6rM/ro0Rg9XYUIKUXPIDRIWqDuGAnvBnkViiSDJUscQlwjVQNubn+7xrK6UUS3kHt02JgDyy2JcpM+LFi5JfelAbdF8bss7hDMgAkVEqpinRats+KBGUTonaMhMHfOMs5RqvI4gyb6ktWvYlZ4Ja5MNib/MSAkAQ3OMuecJ/iKpkrMfCFoF0/Prgc5I3sLSKgJ84ZPHN7mj7/nY3yHX/KNv/r3yI/OaNQ0xsJMcIsir7XnlJLgaO7eor5xyOpv/fcwDEi9gNiV8Ubhqbm9934VIGULyRY5rNGjMvVr3d/9qifmm/w5hbG5PJ6tw+2OGIHkVBWn8CWn2yth8JyKh/3qBS+jy0vpQVeyNqb/jx4sDQV85N0qM+5zlSN55UF3XjXdeVyhhMqvtMjoHdsHkVefY7x2fw71MdIcQZih3blV+Jod21gZ1uZhhl2bXfci3jb75ep2xfOqI0/0CpC89tBxDintVzU2/1ypkw2UeaBiqnoUCm93cQypeNRzhM2aXUbNlW3si/v/7j3G/iux93rN5+XfvFrTfvrXWHzynyH8zi8Rv/hLVD/0H9L+t78Mmz3vil49yXU3dc3Xl4wUffIenthGjcoy3xawhHMwWI3r3ViSS8eNHkF9eM7w6S/Sfxrjzy9nuBsHLP+FH+Xj3/n7qe6/yb2HrzBIphHP8XzOoJYtXTWHhJA4O2tJYxJK1pIbZ+NOsgFDj0n8eGfzvbrRY4iBX29yQ5X3JgvohaSZ3mVil3DAECMpiSWROMF5oalqoiZSjiX0u+NHJs30QyaqFlBk5RNzkiI3Z95MyzVUs9WxtY2sppHpHeIS/SBjBoRxLskmEeQ9ddWQynwoI8+S0btZtC33h8vYPZyasHmpIOQmXri98FzWgaRCI1KEwhXFFzqAErHvxwhiFguFe+dKBaJEXTm8CyU+KEU8niLULgVLFdeRKjkNtJ3HuYpt19Mn0xtOUemHgT72RgGobK1v6pobh0ecHCxZVAGnSi+RZtlwfLLk6OiA+fKAppkRQmWVccTKO8eUSN3Adt3x6PHF23X0J7Z3Dih9ja9nOKUkB4yLsYOxIsRkde1MyX1bQotEjbmAiwwMAXE1vlriZ8PEUdIUOYwtud/Sb8945rkz3veB9/Gd332PN169z73X7nHv3j0eP7ogCCwWDSln1qmjv9jgUsvNmZoOUy5heIuv7gbvE4BsXN3tV1G1UEu7Mst0dsAYWvQSCCHj/MBBHLh5KNw5CrgceGoRePYwsqhqpG/IfYA3elw34DatlemOgt4A+c5j+NAxcnwIB0tklsvzb8lpCwy4ao53B6irscKV5iKT0Y20W/XKc5X2LiELHfmPoUFmc7QzaRl7H5cBpQFSI2JPbgHNRbInIKFB8xGSboG+C+QusC77B3AmK9P3AQfUlQOZWca2bu09DAmNXbEsG9Qv6UPNqcy5p4HsTEdUiwaZ4owz6iuTjSkuAh1swVdRzrueN2Pk2Amx7+iHRHSOqMob25ZB4DDDgPCg79gMkVoct+cdH+9bfOz53YszNgxctB2rtqXrB1qE06HnYujtehlIghS5DS2hbsHjm4bvWzzFH7v7Ef7QRz/G45/5NMP9xzTzgOusv1WzCql35QHHrueamubuU+Szc+Irr6Dirb/mWCrhqIH6agaTd3AM59q7sVluAInFI7cXAr8CJncOS30bQfNxOOwBp+nzsW+Mf6sZbGmvRrb3O8AB5f6MFqOaIJtxpNOA2+u/u4tfufYII3UHEAuVZfTGTfe8v2DrCLT37ndvHx35l1raVOPu+NH7O7bDyL3ORRtQd59fuU3zUm7fMsMrNIi/zc6jqzBsS7j7bZ71WwKXK5vs7ayJKfHm6v1xDawSgfkJ2m1BMzI/Qtdn1zsQx75UWZKPuJHn7MHPLemn2xYt0n+M+7/uBq8eex349IH2M5+j+fgn8D/6Z9Bf/I/xt3r8c7ct7H2dt/e6e3o7rKl7v3xLbF969LhfcWyap1YnZ8aU2LP/XvYGouyvnUnR8w35fMPqv/h5Tv4Pf4L3f/T3wt/+S8jQ4qpEKxXdvMYtAs45bh0tePWVhzxerUjZ1FZEra9PlX6gBKmEUUtRjbBHUgOLwRtnsaqEKlhVO8mOPiYb4s7W85iVrh9QMRHvlAMirgw/JaYexQxjLYaOpsLx1P3qNTa+nZRM6aLeMHra62onbecJKMIQ+wmExpypvKPxwebPqEAkOVNdGUpxCDeCffYicxMc2OVXWHuZoToJUoixHgbdC+NjdIOo1rrGC/Xl3nVnN4h5J8WPXFItCTGuyD5lnPcwgIjDScJ5T/AezZnYDyiZISWyF0tFSEofTT5KSgjee2W5qDlazFk0FZXzDF1L1VQcniw4OjrgYHnAfDajrmpzwjmPx9l8mJWh660Sz/k/CQ+lr3GhmbKeLPtXd8Bjkl3YD7/u5vDLwGX83bybWTxuBCR+bi8wZ3yd8LOeMD+iObrF0Y3b3Lp9l3fdPePi9C3OHz/g9NED0jCQEqy3PavtmkdvvsXF6RrxHldXpZbmyDMsls7+G96/rxGkiVnFmg1IyOzAkmbC3MKuOCBbieB5w8ki874bkQ/cnnHrsOZkAUufkb7B30xo3RPPTvEPI0ImB0U+cIh87AB5yqOHc2SxQNnaIBgS4hJSL8hySMxH5NzQqMNNXiprwx2w5PKzjDO/OjMCqhnUcxg2du9q4E+uHGqeM7c7xwjgJJi3MRyg4RjR24g8T9bXQLcIVrEGl4i91SCtlhTxcAEdIG3J8SE5bpEcEHeCumOSX/JYG1opdcuDGDHY3kLBKcVj6bCF2XcTczoqvDX0bFMixRYnwSZGImd5xetdT3V2QfRC6xxddnjnWXQdP77dQjvwqddeoyWzHZKV/sqZQZXtMJBKPVhNJcURKeEv61dSBz58cMyf+o7v4Q988CNc/Pef495/97kSadx5IlyQnfN3bGzA1RV+OSP99gvkvkeq2S4hIlmmO27sd2UbpXFcbVzLnHb6jGlMLAmXvCBPZFO/zer45BK8B+DM2ip/5z0wqdN9GsjenWXSEE2jlzIW7/KlxjBD8wmAtoesJsBYPJ3jAj6WbCTu9p2OLYaAxuvPNT7HeN7x+zGLYNr2AdpIL3gSrF3a4gbi1jiTwL5X2QBHQHS42thPDudvte2DyemZrtlnv1n2t2ztKQd7oFdPrzvUfukHiOcWPWiWpkIw7tl39v1121X74Js5DKfnuubv8dgiSZPWWy7+4n/D8b/9p6i+/48im3vIjcXeztdcR/WJue+bbv8YwPjSYypoVJS0GzY7q+Ntt7Fs37ijKuTTFRd/9m9y/O/9Ud7/E/8C+rf+ErPtiqYJbOuGdNTggiPN5/g+k/PA6WpNVsvUHpe+sSa0olaFp4A6hwE5q5xjIuG2nHuUbJGbzLS/D24HKmMkZmXW1AxDMrp8TiYJWMCs0UEALVnQOumQkAvXcaxQM9LlcFYNrxYh+EKTcUJV5gpHMECXEwloqpoQPN4pKSZ6FWKhc1tGdybLbk42n4ygSRHxRQQeozPBLtFf7d58gRIZo76MguuKgUD7zLCN8+V85XnGZzJnhFGequAmIXjEFaklm5+896XiXSZF89qmKcnIZJpSsjZmerfKclZx4/iAg1lDHQKxHxjIHMyt1OLR0SHzxZy6rgk+4J23/lY88KmPtJuWx48vWG3+CXAoFXBVbVm8aQDXGuhKVoTcrBEHEkySpQCSUQR5Z43tfqSEeSZOo4xLUDF9KGFZ5whVTajm1LNjlodrTm4+Rbc9Y3XxmBw78pDYblq6bsP5ex7QrSLN7IDbzz+DnzdlgRvZ1U+YuKUDFwtUgAK28HM4uIHMDsmzQ8TXBkhLKEE8zBfC3dBw92YuEj414sXaIB/BjSN46hbh+Ij087+A+9rrSPTwrhO4uUSPGtMubGpEo3HmcgVxBhyg6Zi+XzIMDbMc8BNXcv8ZrtvKPYoja0BchVYN6tqp7ae3e4kTNv5pI0nVtBAnIROpoJqjugB9GuQGqmYl4gr3JVCqE5jHU3JJZBkyOqzN6nYBwhznDwnumGOZ83ztSINw4WuoKvtxRdAeIwtbhXu18nq+AzEOXSRxRsaJARcRJQ9KFtgMEe+Nz5NKVRZV2GhmnZR1ynxj09KTickMGhP+LSBiVNIt38nExclI3fBcs+R/+70/yh+4+RyP/ptf5MGvfAGNlhXpgslRWYvKE2ASger2MRIceb1BRm6yloGcevbrctuJitcNZ9+NpQRh8vKTBiNCyS70rLtR+S23J9bPKdzrdr9PfMkCJn2hV4zNM3avESyOMjxF9WBneO5JE10Ko+vla2vhAu7f3Qhqp3D7VZCXLdIwuiAmNMKVfUdu4QgaZTKmdvvtA8qxPd4GaajskqouoafypLObUC3Q7SMYVsgYHn472/CbbfuP9D9mU9C+RWYnZrTlDPUSOiuxeglMjv8m4+9qysjRbPdF1aBug7yNfuXV5ocrf4+vyZeKS98MaCtI6oFAfPllVv+//5aDf+l/hj57C6qvWv/PrrzLqwbUlYZ9u9eoOxPgGkf0ta9nn84yPtO1kYBr22IPABdR7v2eG7/8Euf/yV/h+N/7o3z0T/2vufepX+Lm6Ws80sds1aF1QyeRp24cce/0MQ9Xa4YiDzSCScMuDueK1jAy0Uqc9wzR5r0hKz4LQ1bUuwI2QVxFkEzwgSqYZmXOSp9MFD3lopqCSe6kUkZxQlXj1CdCUdotXkgLwefRUFCToPO4koltGdneW81s8zWC4ojZvHURS3bxFdRNYJsiLgtOTNdxKsXtC6fV2RnGkDRSuKOiJdtZ0KoqwFAQl4vPI1IYAnjnoZT1tQz2iir4iXnkilNLxJ4jZhOGB6utHUQmkG/LcUnmKWtzLmtoxELhuUTIEkoXe2KRv3POUQfP8dEBB7MZy/mM4KCNg2GV5YzlcsFsPqdpGryv8L6y11HE0FEhx0i77jk9Pae7jvbyNts7BpSU7DBVJafOxMqHNbHfmEcIh68W+OYQCQsLuY1ua3wJbxtZ3TwFw87DIA6VGikubOtppW2nsDgQPN7VuGpONT9g1t9kfviYHFs0CWnoGYaOoVujg+BDw+HxXarF3Cxp2b2w3RQhe5ZDAWrTLg6qOXIgaLUwnccx4WG0HrU2zau5woxyjdGh7lDvcKExz+AnjvEntxh+6RfwX34Rd/AUzG+iB8e42RwLQ+/qMksEcoPEORIXdH1FylaTfCft8/bbblIrSRSuwvmGLK6EbJUrzf1NzjWGBQR8hegcdQcgTyHuNpruYd4hC0/7OiFpBDgZXI/mFZIikiq8zFD/FK76ALW/y1E44juk4UyEf9A5viCwCs6qBTlBam+JOlBm+JFf2UBdSNx9RIMvlQNMlNy8gVbgXiWjYiH1UU9QMRWApLBNqXxnE40tZqPVtktkkFyeSYEqcKuZ8YfvfJCfeOY9vPH/+hlWX3mZPGScE+p5INQOxoTZvUliv+HrOzdwVSC+/mYBf2rAeXSYTYkv43kKJ04qGKt8FDqGtXnZJ8eyKBVAzrhuXQcqr+sF+6viyOEcr78HJkv/QnYl3nbPO3UiJjUCHfl5Yv3eFbN/jw9suo97nsMJMI7nzTvO41iRq1SMgDFsVUbzE0Lk+8hg39Dd+073vysf6F5bXPru6ubQ6hCZ3zJQefWa4/t1C8Q3MKzR7hyGNTLWzt1/Hf9jQeUeQNsPw+6er/zu/Y7SU/aR2YFxXTdnTDqTb3ddFPoWHVqkmSOzOWyu16/8llvG5Lmqamc8XAFnl7YYGSNK3T/6DfCek//g3yd8+3uJn/kS12fD23m+2e1dAoyq+yvDdae6/pvyoRQwpZOHzl6IcuXAyfaQCWBNSWg7HEb80ouc/1/+Aos/+uO87w/+S+iyYjusOD97nbOHL/Hyl///tP1p0G1Nlt+F/VZm7uGcZ7zTO9fQVdVdPbklhGbJAqEBhEAChGzADAGOcDg8YDuwIxzhL44wX2yHP5iwA4gA2xhsY4UkBFJLdEtIIGghqedWdXdVd1fVW8M73fGZzrD3zszlDyv3Pvuc57n3vSWjHfHce87ZU+7cOfzzv9b6r5/mQi/MJ664Nu1JBhXyxkuxBI2smWLm7uK6pBi7t43gRoZRPGRzZ3JlsTSSW31MxAj9kHFYYIc1M8ufrQVc5hG8lv4+BsZIWcCN1ZDVUkIGX6LC1VE5S8UcnOJUoADYNI4lORNcRe1Nz7LuzdpU+QAkUrKASB+cMa+qmFyQEryJoVeVtwxDDpqmJqkJuLvC5FUi5LgFOrxXmtCgEkhq6SS9N+Y2eI8LHh8CTjzBeSo/6iAX/8oMvrbsPoixkK7MtRahn6ycYi58lo0HhpjNf7WkoB4dGapQ4fDGg2Qt0koGrpfLhlB5qlDhXSAUXKRqSSnM11bZbjasb9Zcr9YMr7LAHGyvz1AOa0g9uR9IScnbK2LcsLq+YHXzjGHV8/DeO9x74z3cyUOoj42hGxtcDtgg3KN5RequyNs1IIT2BGlOUd8iFL+rcVVXpgWKrxrOIb5CUo24xvSnouXe1TSySoP5aIWKqrmHa4/AhR0Qk2m9yW5U3V8mSrmfuhnrIkKWXDqnORnb5DlO6PtXsq5idSBuCacV8uXfTn1yj/Tm3yI1meBPEdeYP6NgEzJiPUks+MkRqJKzNMM6j+D99G0CgoC6ElDlBS1AQEZ/1gnjzIdGYaz6cb+O71QqcMfgThH3ObJ7n8wKJzVSOaq6NlUVcZA6Mx8MPRp70HMIn8G5L+HC91PV73DP3edLWtFo5DNHLd839PxSWvGtrGydxzU12/H1jJXsihnflqZQYYyJ25bCFvCSKDpixTF+mmHHAKYyUjuThNlFUguSMHA5zqfOBjAtGpInzZJ/6Owt/uQP/Sb4O+9z/avfJg028FaNp2r3Tc5The6qFxCatx6gfU9+cVXKXlIohros3z17J42C7GXhNr+2ODMBTakOiWamL8FZr7cdAC7NswVZAXKTsJw/uLbeusT0ddRKSmkHEh27SX9+jaJnaizoDDiiOzCnypTikTGn9ziFzfDfxPrcBpXThL1HKd117OzvrkG2AAGkhuaeBeP40Y9U2bOO5ALIxy1UiLtvbPP2eXnW27e4tTkP1THab5Axe5PsNa1Pv4zY/UFhewX1ElJEtxeFZbz7fe4NE7FH1zdIjjAMUBe5rzGg5mWFmGH+PaA4LhpDA3G7O3YGkKdtXOCVNVf3sz9P9wu/iLxxz5Q90rigmJ33WnOkHnzb//6qS+msbgQsKKfyiCp5sHcrwSNJydEWp1o0EBExik0oLiJ2kWlNmRXxQnr/Y67/T/9fOF7g37pP+KHP8uDHvsibP/wHOG3e4cVf/H9wFB0NFkFciSfgcMHjJOPF4TVZJLUUU2pJd5gzZIGhmKpjVHyiZMExjeaRf7ZXnsnq0DwwxEgXLbgjJUqqSHbvQJXR72dkDB0Gbu3eBjyzlvc25VG3qO8QzN1MxNEGjw6e3jlcNutFhSPgqERIDtrg6eNuQVRVnhAcwTuCr4hpJLaw7DpBWCwCy6aibWoLVnG+yOo0aHKk7Yr15or1xgJzKl9BqOkVXMrUviqMZImCL5YqH3xpwjImfjNAn6MlHykLP8GCfYydNN/G0RQ+Bv6kNBCH3iSFcsKVeypCnxKbrufyZk3lSrrFtqKqHE3dlOjyXRlHPV/Jig6ZbtNxfX3D5XV3e/56xfbagDJdf5t+m4CGjLLqXnBx8THPP3nC9Ysrcp/o31zRNg1HzRKpF4zaeZMfEwnNK4btU66ef4uLxx9BL7zz1vezePQeshxNczZpqGaEiJB2jgwjnejBMsW0+GKisdWf25ngXYBwjPq6rPgKS7M3GcxHJ2OxJt8vzWYaThF1Paotwi7K+i4iZgKTE5DbBQHhGvLiAfKeR37fMe7DD5AY0KRo7s0My2CDeBQTUh4c9Jl6gCY5E0Xl9UDBnuSJYAN8qA2EJAM9dy34D57k8Ko2GIgHv0Tq+6CfRfQLSPqG9QbvYVnhs7HLaAdpQJNDeAeaR2T3NkneBvcmVTjnyB0RstLmjgcL5Z008P1yzNeaLb/eCd9FeC5Kr7n4d5bnqSokN+b0HjwSrZfKlLlj56czithaMzJg6UoAEGJR3ebrVwBTSiipgGKmSW7k9prg+EMP7vMv//Bv4UeOTvj4P/sreCk8nhNCG3Zm6Hm72Jvxwbc1Rz/yBfJHH5GevrAf82DveZK7GYFM8QPU8ubcAYgbP01mbt0BN1VzFSjC4jr7d3fuCLLm18yzQ3QHJsd3cJBCUXVnwL6zTY3+zDnOJgsKoC+D28g4xm4HJA/B5LggYv/3fWD4MjB0AB45OOdW8fMd58y+jvXjl9Det4UO2fyVc7HGNKf2PjWjmxfQX5byH1xszP5zF+11sN5TCcjinllPNk+nchyOcModjzQv/9ChfWd9J1kgmHTb/WMOt/F1aobtys4DW9zlO8SQD/H5ITAcfwt+t0ipjbVBMReOlPfr+/ABBUiR/ud/geXv/SPQ1iZwfusZXj7yvarYf3dbuU/loA7FuCVGjmx6uN5MR0kQqE1bV3BoP6D9wCRHYyKIttj1Bk7yZkv8xocM3/yQzU/8bcKX3uXNf/Wf5sd++Pfz9JsfsAoLVkXeTJ2zJLsyAipjpWIJjjFxHoxIQVGBISecimWRU5MFNGulMERLZDEkYZsyw3idXP5iAYXqd11dHAmMVXPmkxi8K4sfLco+Yu5GmP+kltgMVSUOkaYSanFGZIeKoctIVpxmnGaqrKbiUXn64OlCYqjAecV5JQQLdBGkpJxMeGfak3VdcdwuOK5N43oZWrwTmmVLFRpizPSSaWJH23Wm3EAgZwPrGx0QQy3mdz+2gPJ8YwKVVNhbcUIVPL3bJV8ZI72996hWKI6cEl1fpIhUSXkwv9GSUGD0Se37xPXNGs2J7WbNyaLi/OwYV1eEysCxc2EyvxvoHX3XhWFYs9l2XF7fsO3vSA7wiu21AeXTb38dcUckX9PrhheXn/D4Ox9y8fiCZdtQNY6LF59w9fwhzfE5oTlBassWoaNcUO6I/RU3lx/x+INv8fF3PsINmYUuCO2SKrRIUxf/Nps8Nfc2qYyMjA/gakR8kVOxaEZx4yQgpSO43aBUlunzIWTU5R9NAGPKqcmUrMaI5LRGV8+QsEDOGgjmXD8ByHL+6wxOBmpqqO/hzh0MDbp5CtcZpxsICjoUMJnQQaG80CoLtUKKJZDmdV8cY/mAkW0NAcvTXSLfD82pd15dyyKu1C8OXI1yDPU7CF9GeY6wQb2lodJ+gH6DpApoQD4H9WfJ3CdxRHbHqBwhriW4ClHlgcCRCKdOeTPAD9Q9Xx3gp/uen+0yTzQVLKUG1qqAUhc2MSCdR31AJaHbLTKuTPPsPRXTq4KlExtZBFdogFxYNHEGnlULC1JAS04E5/nDb73L/+Z3/mG+r1/w+N//S/QfPjYttZgJtceFHch6afWKcO/3/xYqGXjx//wP0a4AijQUNw3PNFsOK3To0Hlk8F77m9MiZTHjanYpA8eF2Qgq3UFh7gBKY0T36Ac4MZPlGr56SXs5REMzkFYm0+n6c1BJglTyR0/AsgTA7PkwzsHjCIL3webu1rPOegfrqHvluwM0Kuwo6pdsOvsQV9BflkXUgFDabBqQ4zfKMWsk3eHsfli8l+2fwFyC7noXwHWIJO/4aV7U6f88i/bvV9Acv+JhD7aUd/JH400Oq+sQPM5/G91Q50xl1VjbEgF3ZO4R80w8hw81v7aD9PFH+M+/gfvsW+Rf+Zb1470K+d5G0N2JLxkbp4KUT7JriopaCuLkwDdIXU3tX8esKTEXrxRz79GmQn1Amsr0i4fBEmOMxMkouF5XFhWsinYDedszfOWbrP/8T/HFP/57efprX0G+8jN87AeUQFDFqTn6+KJTM0afm56+MWkppymntSssmy/BJQZILX92ipmkwjalEqvoJhlSESkpEB3iss1rYxfFFt3eW6BNKN49MSm5vGTnZCJowcZqj5BTwgdPXYij2nsGB8l780McCQTMTF2HiraGSCRlwTudIqcFZwLqTnDB4YOnCgGPEBAqHAuExjmCGvOZPFR1DT5Q4QpoDGQXuM6JlPIk4ZRzQRo6Lt4zzjkSuSiFWePNhSSYe+WllKCYxhWKmbtnuzWfSSnpJNFxqrLsdDkrXTegKZJijXcLTkWpmoq6Nhe9KVCZnYuSeI+5ewndtufF9ZZBx6QQr7e9NqAc+sQ2veB6e83jJx/z7fc/5ObxNWftkkdffJPQVmy2HZ88+YjF8T1OqgVhqVC3iDfWIccbus1zri8+5uLxB/TrFTnB4ycfsbz/gJN2SXDFbxEwvcIb+vUF/WZF7Suq9thMSSMD6oIFskjpZJM5sVSYs9ezGwqMqJf54LJDmeXYjGgi5w39cE139RG6zZz6Bjl14BoopmwtjWBnLt5tUz7Xct0s1pnFeWhatDlBr1fw4hpWW6Q2IVXNHuI4lysiGdGBWpTtYP5+7rXf8QiUwXw6TdNRY0KKWOzrXWpWSZqKn1cCacCfQfUeqm+i+Xlh1IruZxY01yifgeqLII+IugRtjBHUYJ0GG+ScqwlNoPE1J/WS+82WZnNNv4p8koRnfTTdsZJqUsHMhQJ1tnfeu0weGojJFiQoSJF/KC9LhbI6PhDXFimV6+z/oiM2+ZwiCJkfPrnH/+53/WO88Ssf88Ff/BsMTy+YZjsxJ2z7/GqwVZ2fcP8f/C1s/9P/lHx5WcyN3WyCHX0lgdwj6I7VGhk8X2PR3OxG6wlEFZYT2QXDpAJaXWBKg7T3lsfgg4IKxmc4BJOuYvQX3rWNvcebfh/7wG5GGRdutnC0co3fDwDkHSzk3sxUPu9cWNj7ff51Dj71ThB58Cwv2162L67tr2wj3hdA+xvID2xHmq38b9XXa2zj8TnC5vk+GHtJGWX+2x3Ac9pSsmCcl1znznIcbnesJ24dOw2ceb8MQwJd22++yGQNheUdrzv5Fpc/J7OFidD/6leJn3yX6rf/CN3XvlnalRyU4dUP9mmvRWf/Wt3vjta77uPcqBJefPcyNBXuuEU3XZEly9AnqBISAtQVOmZF2/bTM5q8j7Nr1dXUjCVlpBvofvJnqL/0Hr/1n/zv42Pm61//Clfq2OLIYmyj5rG/GvBDQXMmixC8LWS9M/DonFJ7Ey93Ymn5VB0xGqOZUsI5WNYVbe1pgqcJjlg5mtqzTRF/gOldMZ9X3hOC+VumFM11XAWnjoQWUzhTtjsnxkQGhKpYHgfvGcU0xhwTXhwOpfIGOhcVZFzJuW1jjXGgxrZmbF5wTvCCMZ7dQEiCHwQXKqSyuBDnPF4clRq20ZTYYPnNbaYVlIwbFxZOSiR3AejIlHrX9JQV52Viap2zdJKTkH5Whn5gvd7SD3HqOiMHMq5gUo6GYAarT+cgnSyoGk/bBNqmIYQw4Z+UEz55y+fuHJlE3/VcXV3z5MU1Qy6Sga+5vX5QTjVwffUJ73/nm3zrN55w9TRyvmh48/6Sd956g3B8xE3Xsbq55v33v8Zbsefeo/eoj+9Zg9fMdvucy2ff5cNv/QZXn7xgubiPLAMXN8/57nd/g7clcXqvpzq+j4SKnLZsN8+5efEhF08/QZLw5puf5ei+w7tq8m1UZy+vjCxMpslS2yIZI6A9h6OndcTd5CUlklTzlmF7wXa95npzyfDsBSE0LAXk+BH4GlW/56P4UleDMjK5MfE9iqiH7BFfm0xQHy0CdgoeCrZCFI+6DJKoXGY7lAjj13/Hs0JY4IS0xzZAj4DiU7Z5lhWKj6rGLeQBkdrSMbozxL0J2iGSrKW7lqSebn2KVm9T+c/gmnuohtGpYXJStw5eHMcRnA9UVcDVgYuQeTMnHg5b/HaD5gpfWYRbFuuA3jmOvSM4YeWVVRmcdWS45uaiMsAH70r6sNmLG30FtTDccvCOnecU4V/80o/x8GuP+fDf+4vmQO3M1UKLaHFKGTea5m+1i90ktPjSe3indL/4y3bcsLYdrtqZlcXNAljc7BqFGdK8i/SeGJwRzJbl62j+HkHlmOLLK+jsvDnqGP3YgL0AnAImRXYn7WGjAxZwDJKhgGEl70DkZMYux9zBQrKXAWd37ORfvcdKzoHgHShmZDbn72EPRL4EQpSfb5Gcn7btVcVg/oCuKvqTdxzDK/DlHDDuXVf3f3/JOHSrWl5x7KQh+Xe73fUAcvA56x4I2zs3Jui3sDhgwA9BeIkTI4w6p2pTQLdl/ZN/maM/+Mfo//x/iT5bcXvQfFlN77eOux7gzrNKfcoELmXqh6MlS2K0zGXeWyBU8GhbT2tZ7RO6LZqpzhW/8LQXFGUeAGq/lWQBIg6qYE8kgvYDN//Wf8zx//CP81v/5f85D/7GT/CrP/dXeao33ORkZlrxlvWppE51MGMl2fkAOhDN5Dgg4oy9xJJDjM/ngSYEKiecLhpOljXBCzFmquAIXqhdmAJvpAiLU/KQo4WBdA5RR8Y0I0Wzgc5gAMyHQCBSeU/tHW3JV115iGruS40XGidUYrI+TXAkcai3ubcKwQDkkOiGaCOJCE7B4yxjUc4MMjDolk0GrQNBHU5rJNj419RLmmVEhq6ImSsBocZRiWMH27JpT2LZ+1Az4ofirpRSRPyYEc0aQiLjq4BPDrKQUmLoo0keqS3jd5Y1pphD41lSeS2CVEKzqGmWNe2ipgq+CKuP4zqGR7KZvPuuZ7W+4dnzC27WG5KYl9Trbq8NKOtFTUxrLi4uePZsQ50bHp6f8N67b7E8PcIvF+SQefF8w4fvf8xHH36Xd9/7PO99/sssH9xHFa63T3jy5Bt8+M1vsxgalvdb6pNzrjcv+O5H3+DFiyd89rM/yBvvfo7QNPQpcr19yrNPPuDph5/gMjR+SV0vEe+R5tgqk6InLxnRnVfYjsDIjKbx3SPPJ5OMaIemHs0DOW3Jw4bYr8ibS1zscNqzvvqQ0FY0OSJH50h1DFNU9st5vklCtRQqqaI9hB4M5DnENYzR8FKAjI6+ZqLglFqUOiXTNXwNXnHOmloDc5bO7+hecewetftek+5UQLI5Xm+ukdQR6lMsE84x+PdAr1B6k77xie3Qcbk6wrXnLNuWpmltVZ1jYXfnAy+ISnFncIhalwxVh1YVm3iJX6+5H0ymxwfHhzHzsRO8CktsgkpVxWbRWtaEknfZNMWytRE8QeDIO2qnu9jn8pJsUM7F1GDlE+9xIlQu8PuWZ/yT736Zm5/8CTQm8/mimI1KR9Yy4Lqy8p9X4eyFsPziu3S/+BXiJ0+gWhjgyGVkyNn870Jr0kE5ziaWEfyUY7T4ek6Ac1xMjSJBMmMqR1HuolXpZv1i6hblekZbFEZtBk5nqycdm5nOL7Aro45AUgvAnzLqpJIWdN4XZ+DyFsCcWvMd+2fvsHwe5ch2xTq4z/xd7L+du4m+vxsmcbaJZnJ3g2tObPJ45bEHtzoEjNPC4aBcdzGVzM7VO479b3r7tGu+gkWd/665SNqkBN6j0RUXnYPzlF3bH4OZROh+4Rc5+Rf+eRb/oz/B5t/4s+iLDfsR33eJ+t9VOS+pqJnj/F7MTyUGOjJob/1Gs8BqQDfRzNrLChatIbchMgkllj/pEsgWU8cwy9Z84QZiGcq2QzGb+x37GTwMCV133Pybf472j/4uvv+f+Ec5e+dz/OJP/L95PFyQnOdF6FjnhGbTmKQs7k1Wx0CeiAW7OhmF1qWww4JXiqncmDWP0FaB2geaqsJ7YdkobT2wyEoYbNjwAlWRgkuaGZIB2aRjAK6xeCY+ZgE0dZEnCsGyodXeUXmoK/NJ7IpckABBhECmcYFBlboSkgtoCGRn8kBpMF/NPg4lXXui9YHGWQS5A9Iw0PVK7RzNckEONbmpCctlSTcZCL5GuhWb7Q0OpVUhp97yi6u1EcvPscs+BKOAoRCcERoZyLM5ChGcCk48SmSIiSGa76QftTiViQ9ylMw+qhPzGwIsFw33zpbcOz7iZHFEWzeE4Ix7cA7BVAAsUj7TbbZcX17x5Nk169iRcYS/F0E5rq7xVREv7TKnLbzx4IzTR+e0J/fRIPjUQRVY5Q3f/rWPeP/Xv8Xv/l09n/vBH0Lqms36gpvLay5frDk6PiZUZqqs6oYu9nz7l3+Ji0+e88NDR3t2xDZGVusLbp58jA4DiYoXl89pT5YcOWhSj69qJFjHNM3AYbYqDCXXckJd8SEro9leoG3u0f6SuL5E04Cmnpw6dFjjui2t9zQnRwiR/vIjQrfBnzyCs3dgeQ98/amQTGQUMzVdrm2XWPZCJZX5uZFBAnhnDtHOmtwcUArZUglOJsNP2w5H7iIfJNXkmncY/PjyTa0eUXJObDc35O01y0WmdvegahH/iKxvWnpF7pNDS6470kLpU43vFakHqtqy3cylj0ZZDnWW11ScMKTMVh032fGdXvnmdcdZavgn7p3zZmOl+nbO/FLf81QT3jli0rJ6rdjWrbFl2x4YbJIubORxVbH0xsz50TWhBG0Zs62os06KQJCapRd+rDrmf/2b/wGOv/oRH/3adwq7qcXVr2iZeUyuYTBmbfKlZH+hE06OOP7RL7D6v/17qDoLrJDO3vkYBXyosTiZtKd/mMzDo0lwvNMUADS2o/LZVexpVWoqwTojqBzZfrF9eden5gE4OrtPeaEGGG8xitm0a0e/SF4WZDNra3cCxZcBz9lvB/vnkbnlTc2Omd/2NmC4tesQ5LGP8+7ccbBThhs01DMQe8e2wyl3XuPw+y230Pnnuzr2Hfjozv5/x/O+1vbSSvkezlcb9og9bLdQElRMUeOHW+x35xa6T58/Z/jar1H/jjeR//EfY/1//vPo5YY5KteXIPddO9mvLL3jwSZJIbH1uRzXaNsgGfLNBjaDAa/JBdQC/iRj4/1mgG7YSZuKoDHDqjyTkyIVo9M7sbrJaOphKxi6KpnKYip9TdF1x+bP/BcMX/kmj/7VP8Hf94f+WX7mJ/99erY8Wp4wxEtu4mBpAjEZnqTFi7Es8J0zlx8Y834XRjSbvx5O8ZUFONaNN/9xL1RVoK4iy6amS5lmAqyCV/O9zAqWyDbjBRrvScU30DkhZqGuAlUwk20jUGWovaP2Qu0yeOhcNh9OlMbDwkPjsmV+IdOrs2xB3hJeyEgJi5SgmkIIB2M4K4FaAsGpvaO2geUCd7RElseQMy4pwQlaOaQWfM6ErNQp4JxZl1xpH6iQs5QymrlfnWIueEXwp/g/ZjXLm5Z2OMTEkCIxRQrRbfOOWFDTGFgqODOVi6XVXNSe8+WS8+OW05MFi6Yh+IALocgfmXUvZ0UyxNizvVlz+XzDk2dXZPykAPC622sDyspXVFWFLyZA7x3Hpy31cYuvjQ3KKInEut/y7PkNA4HLJ59w8+ZbhOUpfUpkUbb9lk23ZdNtCA66OFhYv5jp+jvf/RbN5RExb3n++CPq6Ln/8BFSN1zeXJI+iJxdX/Lg3iMWJ2dUR/dwI3hQNVNnyT2tjuJ+ZV3ZFP/NZ8JWYRlJW3LcIjlamEKoUWcizNnVpKadAlicCj4ldHMN1RWuaoqGXvWK2pO9gUhV2SJUGghSAlZIJn7tzHdPZoDSXENtYq+o8d/LGx5LUEjAafLn1n+v3Gx+tzrMGtkMK148/ogHJ8qDukTauyOcexfNHXAPF44IxwMhXbO5vEY2KzOj6DGhCRODqqrFJ1SKfJIjKWxz4ol2vD/0/MqlUvOQ33Lc8rtOFrS6JucN5+kat13xiVTE5QnbpuITjazrQIenFU+XryArznlUzLemddCIkmaMnnNiIFIozuKe2lkqs9p5zh38S1/8Eb78IvHRn/rPCvtAUdGxic57Z7IXKZOTsZYBKQzDVJsgwulv/2HLjfvs+S54bBQH124HuEpGnMnvb3aZHZU1AqTZbD6/ZanfHbh0TL6KJZpylxZQCk0xsollf8mAc2fjGuV+il+WkEt/HGV/hnKdEUTOweS8/Hego9kx9sjjUPsKYHnLp3LWB/eOv+NxXr27bCOovgPgCIUtCjB0u6ATMADfXe1xTbfu/SnbK03uCjvHLeaPbwvWURy+7LuTyHzF9W+Z/P8bZDn3LiVYObvOxvO+R17FlIxm/5kPvaZI/PBD3OOv0H75HeIf/vvp/vR/ddBf7ir8uFSS2bf9ffsF3vU5Lf6JUtl84HIm96lELO+0UXUAbjob82Nm7gFxqz6TlsDW3W2nw8Ym3hcXnxF0jwGECGQlfvXbXP9f/iwP/1f/LD/wpd+G++W/RXSZbbNgyJlVNH93ppAYGy8n3FXalAXZGHuZM+Ro5lWfBefV9BvrQF1bur/oHE3lqJwQRhVwAedLIEoy5jMmNW1I5/ClTyUZBc0ttiA4T5BM7QO1VxZeWQRBsoHAQc3/s3JK64XWFXEOhK4s+EeCGzVGLkYLnvEOgihN5TlqavPRTAo6QDBzeagq6rrBVZW9z6Y2AqqqAcMI2g9I9vgyHMaULdpdKVqbjqzZLBQ6uhaMCxJXuqZM+pvDkBnSQD/0BvTFfES1dN7RXcuJ5fi2p7XrNXVDHSrTvqwCLlgU+hQIlMdx1Mzo227D1dU1Hz95wfUmoirglPw9dPDXB5S1o60b2jZQBUVI+DqTNNL1a4YMq01ku+6I68Syqlk2DduuY72+IQBd3pCjEun44OOPyU1gcXxCNwxsb9Yct0ecnZ3TDwMXnzxmGC558cEHvHv/czT1knCyRLcrPvjo27z/G1/jC+99H+9+5rOcveksS6gzZidrYftK97OA1wTaW7qkrExZOrSs8tKA8w0u1DYZqEn5+Njj04DkbJPkqEmYTUBVhwEJqei4fdpmXdU7ITcVffAshhqRBUoPGrH86MyWoWpmH4lIHghEi5ibyxG91ibFvDybYO+cUEbQe3fxJWdSjKz7zK999ymPlrBYnrD0Z7j6BJEAriPrEvEtoe1ZDhXddiCljs32Bu8Dzi/wVWUDMGrBO+JK3gPY5sxFHPjWqueXXijIA37odMkPNQ1nR477w3M0P6G6vCCvnvMDi3Oa43NyfcRjhL/w9DEfZkfAs6oqVN206quDxwUhkxjj+p2YUO8gjhMfOKscbQgsXGVqSwK/7egR/+jDL/Ds3/pPiNcrLLdqZDQNh6IxpjrmZzW9sKGPuDHavLAMi8++ycl/64v03/wO+fKqiOYr6jwSGvOr1S07aZ2xW7+sc9vkIXvdX9j5GY6njtlYymw49hVXlUhdsc+qTHI3I5gcM+DMqC89BIV55ht5Sz9yFmhzeN6tBlcm9dEMtgcYXzbJv2Lg0zuA353H7Z7uFlbSMmQAWh1bu+2vd/vnnWn0h4793lVE1IJ2XsJgzvHf39UmoKEFHNKvZr97aE4sgjv3++fM7/+quUNu4af9wt6xFvj/axt1SAFJypg15lblzO/vYLeoEtKHH8KPfR/6s3+J+h/8V+j//N80H8U7t0PQdvshplvNnn0EKKqCJIu2xm/NzDkkxvzd4/lTa03F5/qgBNNiY16/elgandriuFOHjEg2smS6hE71Er/2bfq/+ct89vf8EboXz+i//hWiq5GQ+SSu6HwiiozkprmoAIJnJ6Gq+JLFJY+e8AlyUlwNVVCcGCj1UHz2rMJyKvOZKL6osjjnSWr3MH3LEgwpZjkasmWh8VoCWcSEzRufaV2iFhDv6RxEb0E8tSQWTjn2ENWEvWscLilRk0VZp0TXW5ra4KCpPUeLmjYYpxlCsLHLFwCM4tJAGDpCZYv/nBM5R8tUkzIRZcgmnWSPVwTjKX6KUobdEj2kpZ2aX6lZIL157pnAuELMyhAHYo6l7+kUeyZiVlnniwSQ2IjvsBvFlNmmxM02crXqWR5F2jbhs5Ciua6JJLyzxB+rVcfl5YaPn12wTTuGNb9yUNjfXhtQhvaU47M3eevNh1zcf85x5eh1zfXqAt9bYNrT51dcPO8YNspb9045u3dMszwxTb4w4LY9PgdOz0/5+re+w9Wm5/TsjLYJnBw3PHj0Bg8ePUSalqv1Nd/+1gu2q5501rHZrAjek8n0fc+Txx9RZ8fRYsHR+SN8uwC1FbimVMyVI5uRSiL6RI6RoTcBYFd83cgDlXh8c2xCuqEpvzdI6HEFUE4Tn3gcNSqt+b3dxdocbrMBwQOhdvSVI/fe8k6XlaRNumLsKlJApcf8zZLJI+05/sw/74+0O0mVseGOv82On3YfRPaOl54CUjJg/qWb9ZZnN8p3rhve/9YlbzzoeLdNtKExWQxJkE2Xi1BRt47jk4Hrm+dsth0hbPBVTajawsRa2VK5aZ8SV92WDzYbfuMic9Wf85mjc47ckjdDxcZtiOEFzbCh2q54o0+cthX3Fyf0dculc2jX8kvXiU/iwHedoFR03uNDReshiAXN5GwrQxFzpJbgOAuBd+qGZR1YEmgqz1LgT3zuR5C/8z6br39QVui23NWUp+fQ8psPvgBwJQ2paI6ZiLFbeO79A38fN7/wNY7fObX84L6xdzz6OQrmCjFmpHkJwN+983EKnM+u86CVeVspNNNo2g4tOG+R/yP1oMXsTimH87t2cqvdUQJ2koHS4h97KEQ+sZY7dDtrjwfXOzR5z3fdyYqNIHV+/GsOhHcctg8tjLmxahOQgNTnaFztXsF+V7Nnzwd03svQ4h0AbXyMu7Dbp5KCaUBO3kRdVdhQgfbMxrXt9d7wcYBZ7t4+Dd2OJ88vchfInN/0jmve+Urv+v/wnvPvZcyiTKrDN75J+/f9MPnDbxB+W7Agn+3w0jK87PHuBHWzZ5yYxyywiug2MgabzANq9kZuncu/7Sru8B63A8HGvnNwHKW7lVFA7+goq//wr3J8esSX/ug/R/jrf4Hmq38H1z8lu4HHeYuKWPpZzAzqnbGHKhaxHNTcprRopaZoWW+qHCApTiGnZHMwQh9TiR0yXUooFqKqBsEyy4zDkNhCfAeCzT3MNDNDMdiJgb8qcVJnKkkMmk3ZQyGKcF57TitlWRVJ1CxsUVzJBDOoibib6pzNrXVwVH4nJp6K3I9lknFAotKBVgdCNImsFLdsuzVxu6JLiZuU6FSN9ZTMMOUwL3P2pC/rSKlD1U+BTTJaxoryiFMlDQOxT5YXHSH4oslS6ssXOaGsask9vFIF84sERzdkrm42PH1+Q2hqq58hc3asNHWDOF9M5oHUZW5erHj27JKLqzVTIJkW9ZzX3F4/KKd5wMlZx9tvv8fqs09pPCR/xeOnA+qecb0aePpxx/NnN8iQePjwnM9+/vs4efAWR6dnHJ2fcaoDR8tjS4HUw3e/8R2G68Rb5+/x2c99hgdvvMPy5B7JQ3I9VeMZFJ5cXuOPrwjbwejfdcfR0SmKsFqvSUOPxoR6E3/NOjpb75azqmqrweLorZpMPi1HiFuquoX22NgZ1yBkJqFl8YzO3qZVWINbIK6leLe+doWDRTIvKlgvK+LKU6edKVE1I3lOBZjUq+SKLAmp5CAw+3BKePnLP4y+tX9zYYESotaZbeVbBjsto1mOpLRls+14cp351mXNh/kNPlld8sWPI2ePEuHIEUJd8lYbI+XU4euKdnlKNwxs1h3b7YambknNghCqacR0KvR54HLoeb7Z8K3LgQ/WDWfNmzxqTlg2NW1O9N2WnJTc3ZD6SJUzR+J5tDwm156H4pGF4+2u5xsp83NR+LgXnnlH7RuOA2geiOLIhdL3AnUFQRwheOrgOfWeU19RecdvvfceP5KPePwX/5L5KGEmGicWjQhaTAjFZ3I0RXhbfYvZKRiGxNkX3rU+9fCM+JWvoMlywqtmi/ofKQXvIRdx6dG0rWOkteVBt98KeBsXPC8DVOP7942pFAxrYx1DA2m7y708Rxwl7/kr21cufpazYJuJjZwByN3C5HBmftkCaSrEbJ/M/sp9ZKyD+fk6/fwyqDQySyO+vvugAIsHaBxMHxOPhIXJlg03h3h+V8xU/NhGc7ceHCZ3VEHZeejWd+vzYVkPwJvkiPZr5Oi+CaxrNjmq7bVlspmf9zqY+47XPoKsafSZP88hwH5Js3np7T+tTIfX3Lu+7r+TFG346q8ITcI9PCe9WHH7BdxdjPll906Yo7zCDI2HGBk/M1Pr3c962GQOPwEl/7aQU96R7HL4ws2Hblqs6eywMcWjE6gDGiM3/85f4Ohf/If5gX/8X+DdP/iC8//sx0n/9V9ls4kM2dL45XIdscTRxaNFpz6j2bLgCFIsNQO5qiyLXrJsYymrpV7UTMwRIaBqZtq2sjGxT+CyRV57V1yGYrQ4QFUUT+aITEPWASdKK8JpJZwEZeEzgxSmt8tk73izhUet0jQ9fRScq9gkpakMoA4xIziCH4ORsmXOCaGknLSo91AH2lDTKDS1o3WJVntCsreZtSfqgNNESgNd19HFZFI8vp6GqVHvswrBUjE6QbTIvElhk7VEvWOSRQsnbNCyOAcnShWE0FQsmpZl25pLQUysVhtW686MWhmLtxAlRbi5HiBd0PWRzbpnsxq4d7LlqG1YHi8R7wi+oV93XDx5wUdPX3Az9MYnUKLJX9e6w/cUlPOI5YnjjXeukPSCzepjrleRi6tLLq6+y3ff37K5CLTO8bm37/PorYc8euNtzt98l7A8pVqe4rzQnjzk+OwBjx7c58X3fxsGz/m9Nzl7+AbL8zfwi2NS7vBNYNtd8/ijj/j2tz9m2wvHZ0dUQThqA8cPTrl3/z6n986wsPwxWuvuYUqwhpzVEaqAZk+KkZQTmiOWJN50LbWkPzSGMBtbOMobiIBrCvC8reN316Z3lKf2wnZRMYRA3dl1zC+iLD9k5suitvrLC8Et6tkED9NyZTa63saNyjT5jmVRy6muuSfFaCb/XFJKUpn/KEIuHi2aB1abnqfXHb/xHL69PmJTB/Tth/zKyvH9157FaSZUUkBILFmGFO8CdbNkeRxJaSBtNmw2K6p2WYKqvK1GU2IVIzfbS66utmxfLDjThzTtMQ/aJYs2kIeBtM00oca5mro9olttWG2u8arUOFrneK9yNLJiGQZi6PGxYZtLB3FCcp5IMElGZ4sCIRBcZX6Vzhm4FPjBs0f80ftf5Nl/8FfYfPexST+4MQeupRXTlHHZmGcZZYhKUxRnuWMBnPec/fYfRryjiitWX/kVO2gSkHW7zyI7wngEk6ln8o1ypuE53WgCc+ybmqWAu7Fr5N5So/rWfnAeHaJJ2UzIYIZuKEBQ5aAtj20plSj0ErxzqBmpew8xQ2+zjDqqdzh/z+9X6kacaROGtmShKW4Bc9Q2AdhZGe/aXoK794+J6LBGjt+EmSSZ+UEszV84F13WueD83G9ydv29Ik3UF3socg7SXoqvPw13d2tY3DMzd6lzjdv9oepl5XqN7fDQmZW5LLDdTmtz/M2VSEA5uMZefRw8x+H2MmS29+rnoNIh7QIXMrL6AHl4Cr/2wWs9o9z6MoJI2d1SrH9b+rxcGKdXlH9v01cfNmHW4qY1/bh/zOjjqGM2r/nucriMooXOkTc91//XP8f6z/51Fv/Y7+a3/tF/jrd/82/jL//H/wE//eu/xGp0sxXIalqGTi2VoWmC2FySybvIbAoLmbL9qRLEfEa9EypnEfoiUHmhbSwOo0kQsyAhIJoI3jFkk6TNyePDKcJ9RAONU5pwwXGTOGkjp5VyWgm561kAS7GEEp+5F3n7fEslDatY0V45NoPwuIcOh1SOGAWfBYKbtCld8Jbnum6oig9o7TxtVlpVKomE1FF7A8PZw9pl827xjmNfw3ZgSNDUFpXuvAnCq1cbvvwoKG4vL2Uzy/epBBWV+BS7pimL+CAW5V4FTs+OePvNN3h0esZJYxTs1dWax88u+fjFDRc3GyIFDIoNSdut0j+54fp6y/MXKx7cP7Wo75MlR8uW2ge69cDjJxc8fnFDhJI5xxzw4vcgIfb6ubx9i2/OODl/m8p9gedPBrrhCeiaq4sbnn/S0UrL5z73Hj/4A5/njffe5fj8nMXxGeHoHOoTxHva9oRqccrxyT3eeuMLaFJ8VVMvTpHm2ES3tUe8561hxee/9AEvnj7l4w+/yf3NPT7z3tu89eht7j98QHtywvL4mNDUICWBuo50/G7i0tLzxfniYrNbXabsSeNkNLKNUmFdISGSzWdx6qceky11paOWXv09bea3EVpPWgZydDitEA2oDiV16259rFp0/+oarQxM7tiX0bllTgeUz7PJXUmgA5IGyB05bhi6S7Y3V3TXV8SLaxYdHHkQXdA3D1gtTnjmWlJbE7xyvRm42MDHa8e1NlRHLWfLzNM88GuXcHam1LUSnEkXqBQBcfH4ytG0S/rhiJvNhq7riENPFXMhgjNd2rLd3BCvLshPe85vltTHS/pmQdsGGu+I4jk/OmbBEpfPyPcecnP1guvVJRfPn7JoF4TW0w6J++LBZyI9R6K86QMfycClerpsYF5dRlxRDHOe6GEQ6DNsUI4d/OE3f4Duz/4NVj/zVURt4Bx1vMzVMJMG8CGXPLPTa7ZtxEIiLD/ziOat+7ihY/Uf/ZfmxK0JjZ0xXyMAm9Itzi6mkUl8LEcYtqY/6SumQJvR/cIpewExk8JBuf6wsVSA5driGtTZIkNGM7Vg94yUe8zlgtSc0PMYhX4Qub0HHmcz7MielFa5e8I7KLCxg4kr9x6fsTbzbe6QGGeAdaz08T6fDiZfNZmPKEniCl09tyw3k2apIO1JEaIfYLiG7Qssgn3/Hq8swx7Vp3vnzJvPrUu9il0UQSaf7l2FS7VE44Dk4TUKd3Cvuz7PCzkvk4gxuH2PDr2d0iywlKbx5efvhrz9673s3nOmcn5czmPkAvnFC/AtafEIv31K+PJniH/zq8xF0D91G9fr47ONC7oDdK7FMjatx76HKr5rM2+tPFEGB3ebimZyZXl3/1m9TEelDF0En8tQEhne/5jh3/xz1H/j7/D2P/eH+JP/0v+S9O/+H/hbX/slnokSRfCS8b7kxnYYSCr3T3nnkziKpFsObgt4yZos1byHxlvQjogF57R+5EUcSQUqM2t7gTWZTd+j1Dg5wbmKqgo0daCtr1hWA+eV5+Gi442jjpC29FtY91C3mTc/P7C4HxB1dFfG9q1ixYeDR1xNBPo+QTDArE4IlWdx1HJ6tGSxaC1jjnN4hZAi9WDam85l8xEVhUqomoqQI5U6llVF1TZsu4GqdTRVmATiZRxPRQsJMWa5EXIJEE5JJ9/VnC0SvAqBLvZUIXB8dMT9+2e8+/Z9PvvmQ86PloSs3NxsefLskneeXvHxkws+eHrJxXpb3OOl5FIXblLPzfYxl9fXnB0fcXZ6zPnJCcFD1yWePrnhZtvZokOLhSebpvLrbq8vbC4e8S1Vew7pDZabF5yebNjerFiFisW7x7z5xjt87vOf59E773B8/pD2+AypTArHHActmZH3Fa5qYPEAigSB+AacRVeLJmoR7mnHD/7gb+a0OeLZh89p6yVvvPE2Dx69xcm9R1SLJeIFV1cl52UqjsKOkU6GsjojmElxYpbUPA2SJzuHeEtxNOoy7nzRfPlTuy7FnKsJdASVBvI+zXd1PsY4VaoAuhBk40ArlNqAqrNoQUsP6IEa8Q2yDAXcjsrm88l6PupZAzA5iwQk+39MZTnckLsr+usn3Dz5mPUnl3QfPOF8oywqj8tHDMdf4Nk9x1eDoz8KnC6FgUBMBrp8A8d1RdJIPwx8fRX5vpuBk2PTDcN5TElsJ9EQqoa2OSItN2iKls9ULcp4SJFNt2XYrsg3PbJJ3PcGIq+8pxIhiKXNOls0LOUE9B55uME5x+WLx3znm9/g3vkZx2c1uV9RqeehF9q25W2f+bIb+I6v+K6Djx18UglbDTRifkGhmCmcC2zE/G5+9Owt3nzS8eHPfBXnLUJvjKBUNf/InLCBIcLQZRDBBdkxlbOZ5fhHPo/2Penb3yK9uGAEeJLLgmYEfFKxx3ghxopLgGKSAS1pCoeyrwAva/TW7sUVPdO+pK7Lpex5tmgRqJbF9N1Bf1VAqNi9RpO6hJ1skJpTtz183geTB36St304559mbO5Et5TfRr9lCSXFI3a/4cb+UsfkAjAunu7yybwLdNy17YG7WTkwKavdCnJ8rgTDGo0riBvk0P94vumsCMLLjzv8fYY17yTuDlg9dTVSn0B7whSxP1ZAe2LvuLuxdI16wDy8jP3b+3/2w8R6j/VR2lJWe3eLE8SXnN71Ephl3xkf5NPey/eyVlcKLcNU0XmzQddrVCF9/FXco98PexDt9s33cK1w8L7uaMtZS0pEO0GKZuP+FV+9HVbDvH+I6lTvenDGrjh7K7X9okLpxnEyq2oBL6KZ/ud/g4v3P+bsX/tn+Mf/2L/C5b/zv+dXn3/IhWRLswg4l6mrikqkyKslUkpF1c6AbBoiccjEWSCScxbsuKiFEC24pXbCIlhWFs2Z4CuqNhB8yQ+elE0/ptjNOKdUYkynxGOaHDgJL3hwdMMbD7f4eg095Ci484z/zBly9g5koX12wyMG3k0133EtLQuywmZI5BTN5c0JVV3Rnp5wuliyaBuTrssZzQntDSD3KdNiwUgi3kCzUTWIN23M2nmaJiEumX+mGPgWMQupai5BOFLybRQFELU2JOM7RxmSZb3xzjCJ8462aTg9aTm/d8T9s1MaHzjfRk7Ojnjj/ilvPzjh7WdnfPD4kg+ePue6BI1WmAB8zs6sf+vM9SpxcdHT1hVDzFyvNvQlh/jOzSKT8t8DQCn4MlEuCM05RyfvoIMjpFNO/YY6nHN69oiTB2/Snt6nak/wTYOE2hrwGFwgzhghWZgeWx718YrZsYA/7844EkcVjrl/9h79l69xWQj1gqo9pmqPcVVdXpZ135zGYABhSrNlOfaml7jraObEKkU13nkDkyo7BtD+96WaxsnNANqYnlGp0VEcdJotZBpoJ2FsgblB3gzLidQkOBZEW0uJRbJFcJFYcFLEx11NDopqh4mSm2+D6lDK6k0qqRglilQqo3CCOTqnCURY+ifF4/De04YFxwtPFUDygsbXOGo6ablKFbET8JBU6DEZh8rXLKUCLyyGxND1kJvSiQy4S0kbJoD3nqZu0aNTE38dJRSS6Vs2zYJw5jipTzg6FtbpHt+KFTfOlSg/8K7CSaQKLaQF1aZCRXlxccnV5d/hvXfe4PxsyVHtOVo0iGSWJd/rqVMehsSXK0esPNu64WkKvBccK3G0UkGAOgSS87Tq+AcefZHrP/O3yRuLgB6j/FPMpJjp14k8wDjjD9tEitlM34EigGvRnxI87WfeQruB9I33C9tR2qQrbV8LgDv0xHceqEAacCt716E1IXTNBgAncFbkpihtzlUzUGnqwlId7Y7XZHnCU2fs1ehLOU1fBSxKYSPHxcxoUt8Dc7M/nV/j9ogylRWZymwdpSwImbGrWXbPWdI/yq2o8fHa83s6qI4sv7bkvV37wGwGlEZgWb5rOEKaM3vGGI1aEY92K9g8KWlI8x0X3d3ogMy6hVgKPNgd4z1Ux9CvMY2Zg0vvXaNsobVy+sbqKnY79nrYTO+eUENuLeL7bhQzL9qsfg7Q5VRnMyA/Au6hN//N9piJ0btrxf3pWOtOULnXPfRgx8jSz5C4OEdevcC9d9/0GrevNuPdekUvKfMcT++Da71VtLvucFeI2/zeWhYxe+1Dd230FnS9YyEz/ZTLNcpBMt4sK+n5NVf/9n/MG//bf4U/8Pv/GOv/5N9H45qts3gDJ2a+FcuLiODxkiwHg7Poa1euRcIW2pVHVfDiWVaeqtGSF9wAYkKR4AiV0DYe8UI3mAsaomQGnLukcoKTBokPafJnOfKR+3yXB8d/m+rRClkGSOBcjdw7ggfvwvIdE373T6m3z3m0DjzoPdkFQlVzNCSGlOlLf24XLc1yyaJpbfyPJibe9z1D1+NjZJEzfc64lHAI/ZDpusEisVEGjGyQUTtYa3aLbJPpceJKlLv5i1rkui/14lCSGZCyklKy9ImF8JisQSSTWAo1PlSEUNMsas7PTnn4xgPevlrxmScv+PYHR/zGB8/56OqGNOaKV8G7gCbh5qZjvYlUPpAzDDnhvImjB2dZffoh0v29MHlTLPPiKkK4x2IpVHKPo2ZFfgu8X1C1J1TtEb5ZQlVPmoJTNhalSNfk0kl8CcsfmT8pA4UgrsbXp7ShpVmcQuwmP0djE4N1pTKYa/FdkZx2vlNjZzycLARQD1rM4FL83kbfRNmdr0Uw3VzJVmje4FK2CC3X4HyLuuWU+k61yPPkhOYB1b5cs0J8wy4vi5mgcT26VLSYE63IM3ZrYmzMgTjnDk3RxPRzNBDgHD40lm2nAFcY002O1PoYqGRAUzWSS10JxUdDKpwHzRXqAy5UuBCIvuJKFSkxBn12VN4RHCxInLbCG0vhwbGnqoq/oPMT8LWE88YK+BCo2wU5e4uIFNMjq5raIv+WIGfC8blwuRGGfsHNOpHGRQFCUseQBW/YHuc82QkSlctn16RVx3DaoGemYecrh2tb6qbiUdOyPL1Puzwmi+PxdktwmY9JvOszV74GHD4rv+nBW3xmLXz8la/bu1DLfqOFJI5dJvfKJP/gbYBPg5Jj2hHlDppt5OjBGX7ZQE6mPSnB+pVYbl+7x058f89cLM66q6+nRRmuLoFoYuAh9zaAhXY32Y9EknjU1yCF5XS+MJsB0hbtr0s6wPkEJWVRNHaaMWp7LN+4aJmDxxHcHcz2pS/u+uHoXuJ2/V9mbX5sxuNCdMzUs8eE5oP76cH9yq76zMaGeD27f0nVx8j+HFJxu3JKZfnVdXNtvqJ4M39jZv9DoPoqkFbG9H3UIOy9atsXYHFuVoZ+uF20w4uOYGZYQ3eNjmkIlw+Q6ghdXyDDendCsc2+ikeb8JFMVfmSQuyWydODqVpgkqt3Fxod+u4Cw/NCyOzz/BYve/aDgmuoTThcI0QTFc9vfZn87BepfssxsqjR7VgXu5P3LzNRAbu2fdC8DsswxeAcAs5bxXxVBcz37F7Q1L0E5mkdZf+El9ffwfcyYk1wVhTidx6z+lN/jR/97/0BLm6eMvy1H+fjtGVLJlB8yn0oDF0JTByLVBJwiDLJCKWyNq680FSeEC2AbnTnRKEKsGwDbWMplC1XuHV9rwMurwiacfoIuod4F2jSwIm8Q7tskHvHSHvf+knbwvEJHN1Dm3MkOxgG5HjNYhE4aQLbZkFoluSUWHcm90MING1D8BXeOXIa6NZb1psNXb8lpYQv7beKEVwmkBlSIg6RVbdhhaNzRs5ITvgqM8TBXKLU/M/H/N3OCWTzTVUMS6hSfFxL6mHnqYJjSCa3NHZxUXO4szoXk+ATRWrPYrHgOCtn9885v3fKvfNjzs8f8/VvP+abj59xPUScBLJYGZzzxAIkRZWM+bl6b3nHTTvTMfw9MXmPE4er0eoeXpa4sCFUayyKtUWqBTgDI5Y+cGzBu3SCu7Y9doapqpj67sjc+BZxFRIWaBEWHyc0i0rW4j6VESKS9vvQRC3DlCHF7j0dAdjKwASgdmZkQQp55BEJwAbSFdJ/l371nH4IZHdC1TyiWryBa45skh/vlwf67prN5jHoQOWPqNsH+GoJk7hpRkK2HKPirY60mBRk/1lG4tWCdlKZS3PpnLKrG5kLYBfz7BiQUwCopi0pbonDmjj05KTU4swv0GFg25kAvLhAdgGcAdOMiX8HoEG5HzJv15G3GuXReUVVB9NSdA7UlVSYyihDIM6cjlUqsiZy2lpe8+TwTY2rFzjnCQtgmUlbYVN5PtqYDBQ5M6jSZcXHRPCBN996h6GPXH2yxqWABEfE8fzqhtxvzGH67D5Ld8aD00ecvfE2y5NTYsosb65Y1TXbfMGX+jXfBa7V86Bp+e+8+4Nc/Id/neHiprg1mO9k0kzsM2kCk0qohWrhySkzbBOaBGY4I3ZK/eX3GJ5fU5205MvrGXDyO1Ylpd3nPWBWRuLRodsVtwIXIHfsMkSVWS8P7Eye48RhwU+IQFyhwxZpzy2ohNG8PmdMdJp47OvBJPhKRnIPHc0+z309Z8ByfL695x6vO5cforTv1wOT05flA/S6LwDxyJi77bNSd6V9TvU0K0dO0F2gaTCAMlo51k+R+sTGsTkY3KufOz/e8nO7y1xJ7NH1c6RIlOxAI7Pvsl+9uS+AdyYe1a9Myiv3++/orvIeFn8Oyvde/R0obqQMR+AYRt/e+fPZe1evt6PNDyvp8HYvK9toGSpyXYgnO48kM91pPxC/8x2q6pi8uUZOEu6t+6QX69sXfelLLO/8EFabyQTJeqAnOa7i2GHCEZTr/IXtrijFxzrPrrNXGp1dF9Bilbsr09m0CBibh8LOVrY7/rBnSobNX/kZ5Ljl9/zx/y7VySl/+Sf/I76zfmGBh6V/ZEYpGWd8kH00skcdDs/QZbxPKAkNjjpYFhonRUDcJXBCWzuOGkcVMhFzbdKUICoBTGCcDvKWpEoW8D7TLARZeGiX6OLY5rmmRqoWlRahWGBcU3KmQ7OoWFQ1WnlcXeGqwBAzPlj2GFsfZwOJNytuVityTqScze7nKyoPQSLZKXEwFrPrBrYZ1mpBl1kTdZ05PqoY+oGsxpshWtItOnqFVJRkxgQMY0rl4DymD2nuApYh2eFHZlNMjcRYTUvAIWVc987RtI520dAsahbHC+6fHPPo/glf++ATnt/0JC3i+yI4CZa6s7SCnM3PdZsVUjI3htcnKL8HQJkHyIPR0SrFZ0txdfGVCw2EFnXBzJxiqYSm1dReUx4x9tiUd6Okjn6P429lAt2ZlEfT0mANT8zcrKo2yeq4CprdW0b5ail+hUz3czAFTOyApB0rUwc2/y1Nz+g2f5qf/7lf4kl3xpsnX+az7/5+ju976pzxEyuroJmYtvTdJUP3nEqWpH6gXt4jVEvG4ArnnT1RKeMo7D2OH0LJAVr8VqaSSzlazfwAFgQj0wA+PnsZ5DWhGktEe4TYk+NAToM56kdbMUkWjDnzhQm0VYobmWVMa6yVzJlXHtQDDxaR4yZTLyqkrnaAaD7T6ug4XgaZbPfN/ZrRz9K1J9bApUICtESONPJWtkjAq5JVoU/KJiZOxHN0fETdvMtiueDx6VOun17jEapK6IaO68uOftuxGgLn7pQHeGOKfU0liQAEhKMY+cGLp5xUDd9Vxx/6od/Jg69+zId/85fNlFOew6K1hRwglVU6DpqjYEFWyZbXeTAAPr4KceAf3SMPA14qdNtZwxvbuAgT+yYHARWl/m4zaeNnCyRDRsZxKOxjWcSJn5073qeYjFOPZW04BJPz7RAkjm1s9tst++Mh4JiVecofXwDyBHzLeTpK7gy7yPHZ9fe0K6d3o/v3mr5668fVMZy8ZzudoJvnptc4f8a9+h1/zsU/8uA5++vdLfaHsJdXwyEwkvGAw2nefpf+en/3Hug+vMbhzcoWO5POGf2zDrDgYbFvbXPQu3eBQ1A5flcDk/XCfs6RnexasUxVlQkXj+9v7xln32eXvLOAd/2uCUmpmGbLWNptcZ/7fvjVPw3XH+E+84j01e9gadRejqz32oYya4P2mwQPy9q+bYq2sffkIVku7VnlSu2gDibQ3iXzTxsfs8wDiBRJu9u6IGPTnNaMByWfqkL2q23+NIcaDePxe28zJtZ/7r+ClPldf/If5+TBW/zpP/Pv8p2bJySj2ww8iyvWOIrVkRKcAzHas7khIaJEjdTAwqQkqb2yrDISKqrKMum4ovLhiiVNRAni8GJ5ucXfUC8/4Kj6HO2JJzbfBf8C8kmReEwwZIumrHo0b82dbrCgwpR1ik13IrjgaHyN9xYA6ZyjKxrV3WbLer1mvdkypoAUhJQi0Q1ss1AFI2tiNI3hPmaGrGQdGGLP8jiUIBuZ6t58E02HswrB2tDsvSJ+CrDKKSHYsbiyHBAL+qxCRQiW/cZXFcG7CVAa7jLz/5k/o25rmmXLyWnL2dmCb3zwnI+eXLHOGXXetJgx3U2S6WamIZVpwoKHXp+f/B4AZdp+YE7wCfN/VGXYXiMp432N+CWuGixtYTCztAETx2QqE2e+X+PyblxCMfcE2YE6mUxchTunmM+lgDw/BsPYoOWcNXLV8qIYWcYRUI7AEQM47MatHYyb/2o0vn1tENni9CnD+lf5jfczl6cXnC4+T7V4QPBlAHWuvIxI6nvy0NOtX9CnSzR7VEy5P4QG8R6zXZssg4iQRzcB1TKOjcDQGM1pGx9Dxkjq+dyy41pKM2aPayp+HpoTw9AR11vriC6gDtRDF5WtKltsReoVM8vmgZbMQ595VCcetAOnbaQJZZAZgb+Ue5QgFs3ZTL1DX3RDOyT2uP6anAdiaVe1a5DGGG3vPUd1pouR+zUMa8dlFIvsRWmc5+zkhJgCVVjgsiP3WySavmjsezod2AyJ6yfP2eSGR+9cUR/f4KoaIRML81WJ8m5eUd9ccNI5fu/pG1z++N80Zdwpt3p5Ll/MFiMmmuEQy2frzH1mwiCKj5ZlZvHF94i/8POmZVmyRVibEZh88WYAY29SdVMf2dtGQCoUWaHRt3GUHyqT1ZiFqbDV5GhgaWo45W9cy+2ho8MZn1sT7P65szjUOZic2NhDMDnr66Mf55jLfI+NHO816qce1IVibdDZAlfCUQkIwZIQaIb1M9i+QA6GSp2Xew9pjT6jOz9JIUN3xeyBb8/ih6igVKMefH/lNiKO3ZfpXjsPujsQxljOHNG43iv7S8t3uB3iRtUyXs2A2HzxOpZRgH5jbgrOweIE4lCCwrA0gaqvVVev/A5MouFTWTMu92WesYOGX/lV2r//n6FanBLe/1nC9/8gw1/52YP2++p60IMCGE4wH0DqCtcWd6OscLVBhzFNQ5myljUsG2vKNx3cbPaypo5ZZPSONi0iuMqoKUvxKhbEF3Np76VMt9Di7rOtCexl7je522ZzTYn1f/JTDN/8kB/5n/1JVn/on+ZP/fn/O5t+IDoLex2y9TOz0o6aryUY1AY1YlQLIvVKFZSlWIpdy3ST6FXJWdh0Hd5lugTbzjRXvEBwUAlUPtMcwen5xyzaLfVpJvsP0VVGbno0XSF5bfJvmyMkdbAEzQ6uLtCbSN/VlDxz1AV4uSINFzNTLvOcM30/MAyRIUZisrBYDY5MxAVv6R1zInhPr8UX1EEekgHXmFD1Zk6fYZyMYhZMq6+qZLiBHW5JCkOOKCa/ZJYuBQfO1zSLhrptqVuLRA/Bl0w5u5corqyVnLL0C6ra0y4rmuMjjusjWv2Qbz2/Yp2crfXEMcLenDO5ANrYR1KKDOkgs9YrttcGlNvrb5G755BaoCEOA9uba9J6TQhLmuP7tEenVO0St1hC1Zqf4zjJ5TIQucZ05LyxauPEIJQI1UPGImcz5SDGfk6g1DPZjbSMuTkgYlI/Fk9WoNTBoG0spXVcKUzNaEKw11KA6jilTqZGT6jO+R0/XPODb3V88MIBHXnoSGlLHgq76dQCdtJgcC4L29ULcnLknKkWPaFu8aExHwjnyWK+nOOEm3RX1h1SNGbXyRi0IIwRWbrHsM4mvwJQdscYYJUSBKI5k7oI24y4DYgjt8K6Ela5otNgjUzMWXeJ8qDOvNVE7tUdixBpnU2wOUdcHqyqsqC5J+dYLPFqbGhhnyRHXO5pgmULyLFnc3VNrM5oXU2oMviKpgrcX1iKz3VSrrdmuvN5wAcI9RKfPTE6lkctdRA23Zp+2LBeb3hxecPFiy1DJ1xfdvjs+ezlBQ/feZuze+csqgaqGn92zm/50d/E5eqSbV/xdnXChx8924GqcRIvpj0FnJciQyoMvQkCmxkB054cQSdqE0Lw1I/OuP7uh/Ze4thRC1DIM9vCnlPdeJibfSn7UzGJjjqIU77s2elj4M8hCFCFtNld79Y951PbiIZ09r0Ua2RUZP/oW5v4XTT63vOUck8BNyN7lWfM6QEzufe3f1NxNbT3oVqW+40qDVYPeVjjOAAi5X7KbgKYDjgcQF79lK/eXgdE7m0jheT2QOtueaivLoZmZHu1f9wh8Jhf4gAb3iq3Gpg/sKfOAKtCt9md4wRiawzSyLIn/dQ6mG619ywH5XoZgNLR593uM3zzfbTL8PnfjX78y4Qf/YPQ1rAe5ZPu6GvzR4Ydahu3IhROFSwjwriq7AcDVhPop4iDW/8S5yzrr5M9M/nL0jOOJVEPsqgtF7MK2vXk6+3OCDQOUyO43HuIEXXuwOPOQ6H4/M+vURDo8Cvvs/5z/yW/+Y//Hn7+p/8av/ztX2Pji25IygZcyjOZB45QBU8TAjgz7YoT6ipxVsNZjninLMLAsu7pukA/CEGUTRfZZs9qsECU4ErmMhTnlLYRlo0QwpbUwMZ5NjdL/LMbpF2jQ2frZ17AgxvkfLA56CKyvap5no+JflFk3Sx7zyQIkDNJhRwtrgCKyLhC7DNRIGWhFiWECAy0jZFlufJoHxCUEBSNyTLwpV3qxJyNuMhZSZpJAimO1qud5dUCPp3ZIQUq7w3c5UxwFth01FQsli11a0lBfHBlnnFMKXqLz4OIR7xStw3HweOkJneJq8sbPnpxw5qEE0t/HBxIYZNiP9B3AykOdP2Wbf/3AFAisN6uyP0l69WGm4sV/c2Wy48ec9S0PHzznHtv3GNxdp9qOEPqJYgnaU8c1khKBL8gVGdloK+teadoE5IECyrxNbjKIrZ8Uxr9prB2jfmNMZPrKZOSSi6RrKNciuyt7ncR3roDBXMzIPsgzLre+LId5n7scW5Bc/o5Hi56luf3uFglcn9Dt25x1RYkmJ+giDnnUtGEE6RRUtrSbZ8aLT4s8T4gvsL5Cu8C3lUTdW0LeJkNFMWkTcBJZar+xYfCBouwx77u/JkyFpFefCjHICZARVGNZnbeamHhalJwdOpZZUvDNah1kDMfeadR3moyp3WkCQnnTc5BcyINA6HyZBfBQYwrckyIerx6TIPMk71HpEaIBA0oFXGo2PSweXFDwnN00uLFxMqczzRBaJ29iZRNG80X03rOmT52xGHF9dULnj17zs1mw8WLSz758BlXlx39NiP6Md/6+nd479e/xg//yA/yQz/yQxx94fvxLhAWJ7z9xd9MP6xxi7fZ/uxj0tWq+AaNwEdAdukMfSWEYJHsKZtcRo5anKWFUI2Rkeaz0vzwF6DrSI+fMEUsOzN9zAHNCP/2Ot8eU1V25wjDionFGxm+WxPk4ew9gkdmbf5gQj0wu+0muduoZOLJ7mR8ZjPdKMQujjHzlIHJkZGca1mOkcOjQsHsmea/z+tj3NIWVh+jElDf4o4eWaCSminHLR+gV1ukRE/vz76H9TDWF3f8/vrbbZDwiv0y+zCnv3V35Dj/CyNWuKPc4097C5XdpRUxS1Kc5bbeDTl7Jtvp3PFeU/s5uO7BPVA1szv+e6iA16je+X3nzXu8p+xAna5XdD/3c8iP/D7cf/6vU/8+8F/+LOkXvr6r08NnHS+/t7godS+CBIfUoaASZdInFfDLykyXRfpGs6Kbrsj/mLD4uJ65q13c/m5WEW2CuQso5mstI3lQzpjFDOwY4HnbfXkrnF6p2PpLiuRN91O/xPIP/lb+oT/wT/HJ/+ff4IPtDVGZsoKZBU9xwQJOghcqL2RngTiexLISzo7gjWpLCJl7Jx3f9/CG6kVgnRqWTc0mVWyuhJhMS1oEYs5U3szo3nl8CEjtoW7o08DVpiU87mgqJXUN3QqyeqoXW9oHTxAfSN0Z6+2CjgXi6yIZiLnLOZMpGscus+hbnIAToQoVW1E2fU8IIFVAvUdDQEMgiyM7j3qLePdJiCUWwUTJR6wijMG4iZKLW8qckRNJIYgF7ziByjskZoL3RDxZleCFUAlVE2ibQF0ZBtjNTaUZj+2qjBuuWL9qETgSTk+OOWobM6mrIini64BzDpHAENWYyRxZb7Zsu45DxvxV22sDSr94l2U6QeMFWT9hiB7nPcE9oq2dJWSo1mQcXb8mDSbYaSLaHcREjgHNS8TXZEnFLGkBDs4vqOoTqqrB+5qjk3u0p2/ZRMAYqOB3jN04ksquI+8PBnd0y9nEMa4Fje4tUabAGEkxBuWo5LLAK5FtImj9BuJalnJOTJ6egaG7QgeP8zU+1Ki3iTO4BmnOafyCnDdTvmqNG4Y+kcrq0eEIvipamKOaviuLfDO5ZomIBLxbUtUL6toy9jgJRQd7NhuMsETtHy0i1IInjxOtXyKuskjoIZrusIPhSLlRx00Uuiyk2PEmyjsh8U7rOK4GFrXR/EPqiYNAHoi9UPlEckLyia6/hJgIssD5Y0K1sAG1yBOoDjgc6mqCd1TZ83yTuCTxZhBOSWjIxDyggzJkZVDzezSTeEtMK2LfM2zXvLh4zsXlNReXl3z8+AUffucxF89XZt1NFrp1dX3Di6sXPH/ylJN2wTvvfV8JyqpoTh5S+5rNtzue/cW/wcgoUCScRp9c0/My3xpfm+SUV0cOjjQkC9iJimqyY4LDqeLvnxG/9V305oZddpfykopZQWb3ud2W5xOC7qK654ByAqd6cN7hNp+RZ5PP3ufDo/f3TaUR2QOTsndE+V9Cifid0Ww5lmdIu/LOorjl1nPcUbZDIDPdPCGaTMi/OTPn99VTK019DMv7sHrCLTb3Zds4f77q0Plrmx10q/YPX+/eUeOJbrdALPtH5/3dZQ5A3fj5Dlx8mM4RBKlqS8GZEpL3Ec7esXc9110s4V1lAZMQmhZOd7XFO857yWF7x87PubVmMvBmhEOi+7mfo/29vwd98P3wC3+Wxb/wx7j5+kdwtZqd/3qNQFDzN1v3Zr6vPbJowQdz73EOqb0FO6RSmb2isbelfL5djbtvtytURHZxCfYLVAG/qMiboTCdO8JkApTst5a9jzsMugPV5cUrgC8Exqrj5v/1k3z//+Sf4h/8A3+MH//JP8PzfjCL3hgFW5hnP2aG8RZAGbziRTlt4O1z+MxpJFTK8cPMwx8cePRJ5KPryFm74mbd4vSIQT1dbyLfubhNSYbtumN7suX4qGE9RK4HqPojfN9RE+k7z/XasY0VR6vAg62jOWrY+AVXNPTSkH3Ae0fKmC+/Fi3hnC1TKpAwhlCdyfBJCOgwWMpG5xicp6k80UHOiV4y6h2ml2mxBq4wsyp5t/STQiwU31MjpMuCQEb/Sgs2dKIE7+hSRPAE501iqKqQEHDe7/6KuXqKwxgXFYU4EwmgGRWP95mqCdR1wEkmDgN4QX3Eu5YKT1KTbdr0lmY5q0ngve722oAytA8JbknqhUTP8rgiDw0aTxBf40NrGoHBW+SbKCkOpD6QYygRTRVZPUOvdEPP5fULLp5ds71J5qOHw+VIC3z2M5/lnR/+rVRnb6GhwheTKzmjrkR668wzcBrw5Y5hYd7hxslKximrMH9l8trL9GEspZJs4ssv0OEF9C/QfAzpfok/SeS0KQSI5cecfDadL1IvQk2D4qGqUBL9sCWmjmEYGGJHXwZ1cR7nKlzxCxVxJqmgA6qO4Be4ozPgxHx4wgiyhUmEXcq1psFXEKoSGSzgBddsqZY39PUFQ7xAszDkyHWXWB/DTY40ceBd3/Ou63mrhrMm4GtbkWpK5NizTRHJPUG2kD1ZlBwGNtsXSISjxRvgF5MvotOmpKrzjIyuc0obwXfw8WVP53seLaFuKrIKF1vHZZ+I6qiCminTVQxDpNus2KyvefbsGc+fX/Pk2Q0fffiEx4+fGwlYJmcvnsbXVKHi4nrDT/3Uz/DWO5/n7X7AaSZrRLjPs7/0n9N9+NRawGGghgLZpBx8VQSvZedsbeS4aY7llIkli44M4M+O6X7u59FYEO4oU6V5llKxpJCYAlfKTXVeAAyEpW4fTM7Mw/sFftVEeTA76x2/vfScEWg7ir3pjt5XjplM3aP/5mjezsU6IcYsTv1vnBPnYPIABL/iMeY/iwDbS2T7Asjo9kXpk7ePv/XTS27z0h/vOOCl58xdESeq8dAd4CUn36qbl91ot+09W5lMxQfLHz/mjD8Ak9M5nwYkD3+bf04JSeaGtLf/09Y5rwKVd23zYXuP5YX4wQcMX/sa7kf/BP4n/o80/+wfpfvdP0r/E397alpTxPTBs95q0Wrdj1Qi1esyDviErjvoY8kcs8uco8i0drnrceef7mqDuo1o3iBtbSwlWhQbYLJfUxyc7ii3c4UgGTPaHNx21CfWAnhJuYBKGH7p62z+zF/n9/7T/wTX2zV/9b/4CS6GzuaUkcMouokpmf+gjZGRKsC95cDbD6948+GaiwDNueP+DwhVm9l8vee4ERYOrrZLnnfKqoOVWprkKAaSV9tIs+m4qTas1bOVRK8N27ygywPbAS4G5bLLHLkMq4Yjv6SrK7q6NmIqual+U7YAneADKOQciSkxDIPhAvGoC/hKCdFcGnxVgQ9GbAyZ1A903UCXEprMpC3OlE1ccXlyXiYZJOvdFondJ6bxfwoZLvzYZKdSzFVEBF8ss1Kkm2xMdVNGqClQy40SfUUJYwwKEpPs803NyfkRD85bXnx3xZAF5wKSHUkS3ZBZr29YrdZmag9CtRPn+dTt9RnKcAysyLlnuVzgCOR0blqCrgJX40plJE1otBRFGswk6rKJosYcGbY93fWG1JxR3e/xJ5E0DGxW12yffcT1xVPutwvYrJHjhFYVqrHY+EfNpIPJq5h+d4SGTKbt+eBsP5UzZRzVxVaT2puwsxtATALBWtsa0idI+ibD1VfQy28g8g7Zf4moFUk3wFCuv+vo1l7MR1NQ06jy9TSZeV8xxJrgeuIQyENn8kjOVlAOMeaLTI4jyxghCNo3qG9MrmnqJuOENAZ2KKqDnRdjkQfI5AQRT5SankCuAz0Dq9XAoI7LI3ix7sluxVtHypuy4Z4baEWptaFyDUktZaXkzNB1DMM1cRs5WnhMIHLL0F8RQotbPJgAvOl9ejO9h9FvFkQzVcicVz0fr4UPnzme3Ti0Hthk4UXv2PRQB0/jlYQjqsPFyHZ7w3p9weXFc55fXfH0+RWffHxF6q1jeu+o68BR03LaLlkcHeFrj8+Zn/9b/zXtk4az6pjNzRqefcLVz33VcvJiAu6HWy7mJIv+G7fiUJ/tL8dMHtSkhVImLBf4h/eIH35MGSmwyGzZBcioGssRNzN2RWeTa2nPis1ouWdnGoYd6BpDNQ6Bx+Hng+1WkM3uv+l6e0eMfWvHOk6eyHNAMB7jvD12HDPcZCCYGHfq7LlvAcfd/3vyzp8GBKcyGHuusyAcIZsg/Pyk2Wu+BaJes/o+bbsTrE4uK3LrmDuKxp3185J1xAxn3FlsiQOEhLpRq1dvHfipj/tpwHL6fveVXsr4vgxM3kXczz8ru3F9jgxzZv0X/1Oqf/V/SvyRf4TqV/5z2j/yD9P/tZ+Hjl3/mrXySSRkvCy7Vr57Z4oMGV11iPfGWk6BQrIHUsdWvOufd2+3qk9Bu2SR8UNCFvZ/3gzGgB72y1sIGGg8UnkYMtoNO9PorEzTUyZFu2hkRSn/9i//NOG9R/yRf+SfZ3V9w1//23+NDY48jntTWzZg6ZyQhkwljvMqce90y+I+XPgSpPXwHsvVDeFbPYMTblQwG03G+4zzjiGaNqIibJOl/q1CZOUyXZ1YJ8UlgVSxjoHnSbnIA9d9hWwXnLcVSTxRha0fCjbZLdpyUoaciSmaPE4fySUXucnjOao60OSarCb0nVJiNURLO9kPFsCTokn3UZhHZ6xhJYJ3WGQ2Sldyc6doCViGGMk5oNmsuaP+dM5mFncl807KGV956pIOUopWk5a6cTJvTWXhMGvEBoECPiiLxYKHD8/58vd9hvU28eHzS/ptJAUHOtAPkdV6TYy5AOKXNNKXbK+fKUdX5HyDdw5ploDgsqLqyTkYwChtUjAGCy+QxYJZFWJS0jDg6sTxPVicZmIciCmS48Bmfc31UUMOnqpeWLeTgJPa6HXAfLvMdFs4xwlAUl7qKD20GwZk8gfXESSKBdkmiv5T3iKph1AmdxmHjoSkFeQri9KOj+g3W9brI/pacKcNvlqQU4lIzRmPYrLryZx8c0RTT1SDVUEEQoVzgeBLwcq8opNMyjhg5aKikhjp89FAP8rw7M8o42A2kOOa2G/YbrakwVbNzldWstyz6XtW6w2xT9SSudpsiNURV/0N6+1zFkt4oB1Vf4G6jIZACi3OH5lEVIqkYUuMa7rNJZvtFV2INCFT10rdVLTtfSrf4EJtUe3kKWgKXwMV6opIbtVz3CTeaiMhZ15sHe8/UR7HBb33LILntBYSkTdCx3VYs6Cj79Zsbp5zc3nJdrPiZr2i63qcKnVwLGvP+fKIBycn3D8/oVm2uOCpgqdfX3FzWdEuYXO9Jv/6U+L1mpworMM4I5SmpuZkvR87ZlIacUjEbSL2ikZjMc2Cq7izM1xt5pP9tqnsopmxtjCKeAvsp8ebRYDnfBCAs48q5hkx9iacw9lGXvI7lICYOw7du17eP3d0xBo/jwFieUD7jCzuoalHhgFw0N6zvNzdBfuZb+blLj39tt12v3B37BM1RlJStw9yZ5cvr3BXe7NXPh1217Xv/vmOMuw9xu6ic6RSPuz8pndXn/6dB1Mdlkn3n2P+251lHB9q2Frebyfm1C+vc97seeYLnTvKdOvzqwDr/MtUnvnseHDsIejcWwmMoBJGyi5++AGrH/9xjv/4Hyf/nZ+g+vwx7ovvwK883QHRebcsl5p7Huzg4KwoCmwjOgaXTZG9syLdWqXcWQMvaU+lUKpoHy3gI6lZ6+7ozrfwpJSxrLKg1TSkCfTuqvEAWia1scdh76Lruf53/wIn3vGH/uCf5P1vfY1vP/mYTTJ5MhEb51LONu6pEUloYlEn2jojTfGfdjUS7uPqLZGeTy4DV9uKx9vMJhfzvveMk7ZkA05dP7Ber7mWlsuuRxjIvkY0s8azcjAUs/CKijpXEB29ZvocGVQZvMc5e/yUEjkPNpQmJSUlZj1otuZLOcRIHyMxGmDuhkQ/RFLqEYcFEKlYdDUZ1bokRMnlu5m3RTNJDRfkLKTsDDSrJfjImswtt7SjrMlAu4eqDtTecM8YWT9qjM4xgBTXCDPtl/khD+CEallxfP+Mz37uLVBYfvMjfuPDJ6w2Hb1aZHfXxzK36XySe63ttQGl9o8hbXFY1KRi0cg5OTIWdS3OW2CJGIsTUzRTh+YJ0NUBvCpVzhZiny2npqZIu2hx2jFsbwhNXYJr2AXZUBp5TkxS+3u+WwVUjoE5s2Yxgkpg57hc9mVN5vtCBI2oFhFj1CzHaonqxf8m6gdvUh09Ybm5pO9PWacjYnKW7D1FHAYes8OE1lMHcSDlRNSE8y2xiWa2kAJQxhVeWQ4YG2urlpywOtJMxhw9BjJNanF5YT4m5ekNhJoniKYN3eY5N9fPefHsiqEzmaCqqnHeWME0bNluOpRIWlQM50dsEqz1OZoc1dDhNw0SNmxzQjuh3zQs+nOqxTEqSt+v6Qcz22vsiXlA+p7GH7Ooz1kevYFvTnBVC64yxlSkgMoeE2IvKSTrjjo7HsWONkbOeod6x831iq00rELLZuM5y1u69oZYPWeTn9PdXLK+uWG73lq7ixYU5BGaOnC8rHh41vLo/JjzsyOqtqGqKionxBjRNLC5WbHYZvpf/47luc2U6MeDGUXHfaUdqf2Wogmax+0IJmfNU5X2828W88S46hJ2DOTOzEtcMUVkToMFQLTghinfd2LU0ZuE7F/FVN7u0dPz3N41KiPMAc1I1YyOIlPvsb26u9tuFi5/OlgQAQ5Nx8jyAbqtEfHQLC0FYOruLssdRR7LMp889wKC9mYEReL2pYDmzp/uwEazO78E7rKHpe+8/q0TZheU2e93Id85mHzFsxxe/qXbeFAqAVGuTOB5/s4/5dpjMesG+m4697W3l9QXgomij4u5obt93suud+u6sgdKtz/1N6i+8EX4vt9JvXqGe+se/MpLrnPHZe/cs4fgDxag7L9iZV68eYPQg08c7NMyVJi0i959+p1NDIU8JPPri3kCk7cft8yT47dcFpWKeVINkZs/9Ve5/6//D/ixL/4Ynzz5iF6EISUEU19RZIqgtqxiZn1MfYa+jH/ZzLLeR06PYRszqoEhtsRYIb62zGjOFlgWUe7MDz4L665nLYlF5Vir5cJeqdD54sceHL33bIt+zpCUThPROeKQyzRrZcgpl9TzRhRYVZvE2tAPDEWdYDsMDCnjEGLMdMNo3jdtyITixZFTpgpSJIcsp3lxrMORcVmL2dojLjMmzEDGtIyF0SwkUvCmO+kxAsz8NJ0BxeJ7Pgbm2FQlu3ZXrp0n4sH0N9vTBQ95iA+O0Nb4JvBr3/qIp1cd/dDhvPl+itytkvOq7fUBZdogGlAsGlulQrNHgscTLKNNebCcozF2WKVRHGvd2FKzmUqdKkEhF0dQcbA8OmF7vAQd09zNIkg1Is78r0YTzW4slpnlaJzIrJNMkd17D6QFFNjkbJ1HGKV5pi7vMpJbxN0Hf4ouHiHtFbK8pulBNx2rmw0ajS1Mg1H4Q+yQnMnDiqHvGFIk4/DtEVVWiKbyGrxFV4kTxFWouilllYFMRbLHQusASZZndHtNqmp8XEBOqI5ySRlyR4o3dKsXXDz9mMvnK8gBRamqwGJxhA8VThyn9+7j6mPa+1vk+hJ/3VFfJaqwRoeevPbEWkjDwOVqhffC/bfepj4+xdWeIQ/E1NNt1lRkQl2xqFsW7RlN+4CwuA/N8ZSaEqI1UNfPRtkMcln08j2LOuHoCXkgho6M8njbcpFqcJ5zP9D6LV6uScMN3WZN7AdQoXIVta+ovKcVx2lbc+9kwb3ThrMTx/FSaJtAVZljctaK4CKaoPbH3Hz8bGo+OSlIxlc7cJSLSXtc7AC2Mo8le06krO6ZJn9Fi7kpotsSSDO10TELzNguD+HAOFMkiGsDkTmxWzzsO+HPz5smLR0nq5066eEm44Q1nTNeZTZFlWeaT1r7oSLlmaZ0q3nWkMvCoV9BtUQW52PlwbCy+9+JhGY/ylg/84nvNUDMpwGQAwZqb/frXv5OMPOS818CpGT2aV/iqZw0uTvMTph9n1nOb9/3Zb8XFnnUq72zwAfn3gLI44D1qu32+n76PBF6c2DkHIQG6bpbx3Nw7J2b7meSmQ5OifWP/yWqf+1/QT6q8Y/OZwtGK8w0LM2uP7XPSYLr9mPhwAULHNVhp55g7WP/heve2bq3Q24dXh5Wdn106ssH9XDna1bMRB7HIJrxNN27gAE4LJNo2aUqxR1fwUF+csHw69/hS5/7IX765/8am82KXnRcYZNzJplYtVkA+8zNdWJ1ETlZUsauLRov8RL50nvKZ84zVzfKR8/gI+/5zo1DNRAqsZzWg2WWc8FcuGKErI6swqAmk7OJiU4DGhxBPNnVDNmy0AySiVJkxzSRc8YV8JtyJmUp2ouZFBPbfmC7GSzKebDxuk+ZoQDxISYTLcd8SFNSgnN4zCxuVWxAOCfTcnQqZVGteOeJ0fCOTvh+nExs8eN9oBZI4vAuUAePF4cyU7coGMnNFtg7MCmFlBpdErRgDIevapZnJ2alq1va2rGoPb/8Gx/z7aedJSn0liwmw/e0UHx9kzfHRlWrSb44qSGAYoEVI9tiGlyK94qIRU8pUkScxcCkuKnAVilCEqhCoKlrch2g35JjMv+/0mhUMi5HSzc30kQoY0KeXddwZTCYSTPrDmlrmeQZr6sZSxNo0kDKqJGpQERdhegCDQL5BPQeulhBWFHJFX5IuLhlSJmYBgYVnPfEfst2c8365ortdgVhwfKkZ3GqtEfHVE2LugrvPE6KxiYZP0a9qjV0xdheTR0ae8vw4SLCYIxf6gx0FgF5TVtSd0Xc3lBr4MHJGUJAvKeqG6p6gWlUCUJGug7Zbuk2Z+jViubZJYsh0etAJtMlIQ/9JMp+fXWFbDukNqftzWaF5I6Hpwvadsny+Jj6+Ax/fI5WS5zfaQ/uzKgeZAlSAxHcE5AtuBN8Ba3z4Nek9XPqY+XtqqKTJVkTrUaOQ0flBnKIdGV158SxaBcmdn66JiQ4WzTcWy45P2o5XtaEWmhqoams0WioCfXAsn1InSr6JxdMvF4etQ+1fIccMxqLjIcq5GxgsiupGEvw9siYjf/Xb5wTHz8nXV2XSXxkJ2cRztOoMDbkOUopf3rwncPj2E2mBzPuq+QfdpPn/JiXfR6/j8BnzsqMgHIEvnF2up+VX2ZXmi/idOrXu0XhuM1+n8/4d7CT01jwMuBx9+PcBksH++/c7jr2ju3lGGi/LmXvjMJeT8zkAR31qmd7XWYhZ8P/obaIbM0vPXSvWY6fh+HggFeU6SXb3vtyDlywduE9zAQRbj3T4XuZQLaW4M1ZcFvZl549I37zfcJv/k34H3wPblmzDEjt/eYEV3kzN8c8VcQ4eU/N1mHlPXxg0YNrvqRi9ovKrZ1lh8qnVOh0qdKuZkPM4agAVm6pzZyah2R+lOMRqpZBrVyk/5X3efsP/ijvnp6zyQN9t53YNifOLGnJEZ3Q47hYeV48rznynS22tzfI5XOImeYo0IRA7RtyWqDSsHaOFYGQPEPOaGVWp+QyadhYJHiqWasjiVnxLjaJNYJvS67qLptFsHIMRGISUuWtN2XBB9N4HKJZT+MwsN1uud5u2faJ9arjZrOlG3pySvQ52yIHLGOQFNc6dfiUjQgSRxCQpCR1ZFVSGkhDj1NH3/ekbJI8OStxgFwLOcv0foIPpfkWxtKZFmcdLLd3FrXc4NPbnRpEsbzaWxtVSHRq/7u37pwjhIb2JBBcS1U7Fm3LUd2yfD/wjQ+fsO0jyU294VPb2ri9NqDMyQCiLV9KxLCUrBfiGBM7GUVqfgYUMHjodC7OkK8DUNNK8uqJMZC8Q6tArYGJvUJ3VovRLDOmGtqbmMbf5gOiTufv/zaCylR8KJkmQpERUFqJteTXFpdBIqp1qRRBwmBsn3OkFOl6SkON3KxW3KwuiP0NeehZnHh8UhpxJvfjF/jQUIUa7yq8D2SM6dwTdEYtwjtuScOaPGyt4XrFOQOQdKmsojMikdrD+fkJp2cnBSiPrFGJIk5aNB4dsERiT9o4Lp9kLnOk2nQ4VSoJDMkSxmvylhrMWYQ3XWKbelarG44qoQkVi8Upi9P71Men+PYYV7dFMmQcyayMVp5of2OksusBy7IU3JKjkGgennP/vqC5IiZhGDq0s+Ap8ULfOzYr04Nsmpbjo0C6l6hjB+ue82XL+UnD8qhmsWgIi4a2ss7pSgpG5zra8/ukZzfkTVdcNsocXhjJlJV+PZC2So7gq5HhdsQhMWwMaO5kHWcgzzkWn3lk6RZHgf/JhDmaI8oNS3T3XsDZnRO87t/j4P9Xmrmnr3cjof17j/+M+6ZpdP8eEkrfS0USqQTdjFSHBLQ6tdzh80AeccjRA3QboLtEdCb2/pLy7ZXrLt+9g1M+jcy6VX137Z9h/Qm0yv4hL9vuwju3Lj7Zy8dadVAt0H5V/LAAPDTH9tsYYXzHveb5nV9arkMglhOEyv76bn/f684n3zOQftmpisTeEq2VKNeXMqCHGG2He0p/gikUebxETmz/q5+i/V2/Ezk52rlP7ZXj7v4zqoTsGF0YhaQ1KpJNjmbu7nu76HN0vM8pzl/LXr2NknDTuMHO5eaO+tittw77/B3XBguWrCxFq+UmTwf3L8EdKgxf/RZn/9R/m/fuv8PTmwu2khlEaYKncoL3haUFelVebD2PL5csVKgGgY3AyuYEcQvUVVA1VMFTO1hWnuMqEEImlUxrMQr9ENlGSw141SWSV5bFFH45BLbiYZPY5kinQh1BvQHB7NUIsJLQwwfzg+9jJPYDmjLXqxVXqzXrbmCz7rnarElljElAFpPw2wWxAcXUT3Yl4YhpJA/DQMyRFHNJYQgmmTeO+Q7JQowK6opvpuGmjJnQVRUvgeDNgjmlQpy9dCcl2LVIBY0EhhVP8C4YaSQ6sZUOC+TRAP64BX+f4CsWVcXZWcvbj8757ifPuN5u6OJrWB9m2/eQy7sr80BhGUZQUCYN1YTl17Y/0eIr4NyEwEdVG1dSzk3GBaWAJo9b1NRnx7i+oloscJWJfyMg2kFJ72dC3wDeBtwpbr5MxrLz9Rore/wM1jHGoUE1oXl33XFS33V7Ba2mZxaK6cCZMLVpVlEAZWTbJ66u11yvNkTdkOOK2tecNDWL5RFNe0rdnFLVC6qqxYciVO4CYQTRo5lQFYqPoYaKXAc01qCZ4CvDanlN7ixy1rUe6iOoTgjNuUH8aVwdQUBGdFTEHDMKLQiLgTN/w/XWscoepcJhVL6myijwGFGlRJaJdRgBUQuAqZqWqj3GN6dIaEtdUmq6+L/qgLAGHYAapAPtgQW4JbQVcIyThmp5Ti0W4kRKxsam1ZROrl9fsu0u2KyPODu7Rx0ix/WCB8cV8eKaJXB6suDouKJdeuqmogkeX9lCaL2JpJRoPvMum29+NMlqoJYNIkabzGNUuhtFB9uZM6QhT5hwkpWcQDOTtXdiZcfJYPQK13LybtTGgnDMtGFRpjswdzc9s/s8J+1295tNWHtf70JRs+n01mR0OOEyu56UACsx6Z+D4Bp1LdLcQ5pjbk1lpYzS3oOwRLfPkXizDxTvZNruGORegj9fOp+X7VOBV/lfx4NHUkBfUrQ7LrEPG15xowkwJFSOYHEfXb+wcWtxboPo9vo17nrHM8y3Q8CYMni18cxbJqtbhZfZMDvf99J3xK7eXlauu7Bi0jI2gA7D1H9uvac5d3BYwUohHvI0ns/rOH7jfdLTZ4Qvv4s0NfSvaBSKLSq7WJ5nDvzHWxvg2mW/OWDvbaaZNx/mFTcK2e0VwUkJdpndbVox6ERa7y9YZFann9baZmXLCjHZ+SkfvNb9xWP66BlyveXR2SPeCBW5zmw8loJZbT7IKRfNd2ETPdddy9Xacy87hk3D9mkB8rkmppptv2DdBfrkyVmoKwh4C+whE6Ow7aFzsB0SSZWbmBicI6tjI56opjEZE2y7jNuqxTJ4jzoFp/jasERwkeDMPW/oB/ph4PL6hpvVhm3X0w2WNUdEDFSWQBfLGif4EUAipGwBs45E6sHVFeorNBmuQMVyZicri+DIU7poiyPRouaCZjSDU2HTR4KW2I4k1NkRYyamTFI1xrTkGc9gyVTE2Mkp7kSNkQTFy+h/a9ZeD+CEtl1Q3Q9UTUV1suDBm/f50uU1V6sNm64vPpivt70+Q9l/gkrJj+uOwA0ITQkkUQNbOZUUUgnG9Zta1hhji0sDLjpJ4+LRqsyEsr13qA+It8CKseE7MWAqkgxAqmJmUxuAVd2UX1XHu48ASmHuQ7nvT+ksrSAjiLQuL+Ju6UtP+xAgouIsf2jRIkw5se023KzXXN1cM8REaB1Vs+D06Izjs/ssjs5p2xOqZkGYpV6UIrAumC7UpEZVVjOmGWfRYBIqTAC1BPHkLRov0HyFa+8jch/ciQlJa94B95LKysBbh2pX9nhUIuIDfulp2i1VCAwxGpOKlEg0xUmwXKWFNfDOnIbFJbaxp+s76n6L1BvzqXcmLGwYy3KB2/vvQG7K2Gg6dapn4CwiXMMClba0rSLynDOiA5qOcGmLpg2VKPfu3bco9jRAdqSYSdt79FfP6C6vceppmoZmOeqlmmtB1/Vstz19ioQvfpaL3/guWvx2NSt5sGjKgURKAtFaQGgdvjJZjLjNJQ/viCDhIE094h3VycKeeR7NPUZwO7cPLCewfzAb782oY1s8ZCOtP0zpEKEsAtOEBky+KRQUvDtnbxvlbASQGs0dcov5ZzbrFDB8Sw/TI9XC2sDmWdFVC8jyvp2+eVFkksapa/z3juwuzKa2GWB9HWz5irn1e9tecp1PA4y3gNBETs0B/+x9K9BvkLPzAtYBX6E3T2eM5e0C3FoC3AWy72L1UNNCdZVF2eb9c8f2vHf6HHXc9eDfQ53fqr+sEOOe0sAeiD0sw133VRh9RA9Zzry6of+lr9D+jt+7x1C+FHyX1cTrPea8R+4Ks8vWdoBW9z6VfQJSGcGR+jiByB2enPeXXTltIWrPerhmeNnjgQFmi1R/+TZdrxvIFze884Uf5YOv/TSuFi5rRSsz12aEFJMFqPgKCZGtKFttDPytG/rHR5apjZqcKzKOqw42GVwQFlkRpwSfzaCWhK7ydEG43jq2KiCeXmqLzg6ejMn2bCM707CqyReK5eL2VcJ5hyMTHDiNaB7YbDYMmzW53yKpyPwJDGoudQbCSr3mEgRTgpacalHmsPYRo9CJI/UG/rJqEYIvWdRG/UmxFZpzJmpu2pWO4IRKPanSIgmaCNQMMROHyBAH0qgqgwUvWQppwy/O272sv4yLD9nDQbsUwtY/XOVYnhwRmoajs1Merm/o+55+iGaNfM3t9YNyukuyb/AqqG/LajZPDrtKmqj/rGOqwoIIJ2SWDYy5Ucyz+D9m8BrABXy9RE/v44aO0DY470sfS6Q0QEw4F3E5Q7WYUhXaQRbVZZPR/vB3Oy3cbgROCFkcfnS4HgfYaWVeTPnTeJ+Z1O3V1Oidt9SCKUdi7MjaUbcV9VHLYrHk9Pg+R0fnLJYnVPUC7wxIjmXLKeN8MnCn1iB2RnfZrbIlGOWucXoGyRkvmUxvzKO04BpwLTuTKkxmfA3W+FPE7Eo1wgAEA615wKmxaDk7Ww1pocxzLrS5KxjV/l93W549jzhXkVHafk3dLAjtMRIqKjFnYBxo7hGtQGqQhEplZcKho8ByHqCYEMYFwmhoMlcLscCudsHpu5/n6J3P8BnNxQ/VoXFF7p/RP/+I9dOnbF5csbnc0g/ml5oGxWWLfFcBqSuGpxfTvJ6zkqOSB8yPp7DszkN7EnCVMHSJNGTrE1pqWSFUgm/t+KHL+KOG+nSBPrm2A8aobrWFF65iyhCjcxPDSMHMJqA9DZO5yWvm77nHPgpUZ2haI8minSWcQAiwfcatTZmlcxPUnyDLh8j2GfQXu3LowUmTUPlYhlyKHWH7fCqL5ZQPkI7t/Q5XSO4Pyl2e/Q6Tvu5939t16znu/Lx/h1ft3j/oJQeOw5seHvva2wGInN8jF3mXqrWLJ1NSOMShu4LMvh8CrXG/zPYfnp+S9S3xtwDYrJndBsd33e/wp5cBtVfsPoxGvnM7fP75Zy3XcPMkAbtj+l/+Fdrf8XumfS9tB3Ng/aryzueMMonLCGa94OoKkpK249i9f5VbBEYsVh3dHf1KBcupa97x3j61/DtL1vzYsb9NbS7Y2Lv9W7/Mo3/y9/DGz34Gd/0d/ELoK8+AWSbRimEYCAJN7dGQ6NRG8K6D/tKCVlSKfI44Nqp0WQkCx7VaFHIwa1jKQhccfQVS2bAyJJMqTE5LPIDpXafCYOIdOWZiUtSZ21KOHU7AO5P28y4jqWPoOjRHvCgDGScO74SoxbWp1Iz3JZd6FvMrTWqsdMp4scw1mjI5JPKgELMFQ3lvCTYwcsw5IRflC4HJbXyU/HFgxIeamR21qO9hiEX3uSOlgVxSPfrgkTzGEoCSimncygpqYu6KlV/NfUMx078XIUtgETxVHTg5WRCHbgpUet3ttQHlMLwAXZBZFKbC2Ag3RsMUMdkptlRLuHoe95kvpROPK2md8jQ6WbN1ziPeIrRUEgbjMqKRlAe67QWxW+NQ6vqUqj1BqhapamNcxiuNWFGL7I/TO8aT3QpORUixx/cbpOqQIrZtO8dJMhe/khJooBZtnpNdx7mADxVNXcPpMfWiQqqaerGkbZcsmlMW7TFV00zgU0vCeHI2QVPMf1PKinkCUOPnkp/WxvpxT8YipyNoT879FE5k6Zx2el5aGq8SLHJdzGdxdBoUTO4p9Z6q8vRDZEidBZgnGyBzTntVGePAMAysrq5ZvVizfn7Fi7Mly+MFbVtTL09om5aT5RGnb7yJlhzvuAZyZeoAoqhsIF5Bvkb0Gvx1Mb+ZC4KKn5gU0Qh5Y8/enkO4TwjHKEelvqyjoCsWb11zmm7IwzNWH/4iH/ziz3P9eG0+OakHn2kfnPP/o+2/mm1Ztvw+7Dcys6qmWWabY+65/rYDmt1otmBJGBIEyQiRCr1JoWCQetC30JO+iZ71IDL0IgUpUqEIQQGKIMBGN7rZ3bf7+uPPNstNUyYzhx5GVk2z5lp7nQMgT+yz5qyZlZmVleafw/yHe35Bbo26ZrJFKeM6RTuJqoqpTSqHC6Y+8JWYNFN3fme+EWYXlQ3E1UB4vsRX3k56OtoYljRtyB78DNK2zK2CLvIeyjiQrBd0IOWg85C9YTmly+wFuvnKJFDzZ2h3dV/eMn2ddmMDirE1FobTma2O3B/+tgvRxA4UF5ojTUaB5KtiAjE+4x5Y3XM62CncypZ6CtM+0LTH0OIxfj1Ij+zbB+Lnx9IxwJKjS2MZR6BYEfCVEb5PJiOA87jFc7S9Nl7Nx+o7hSSOQdf+dShjP09r6R6Keeez3eu/d3TNk9IRwHpnG0bhwniYmfIpU/z4vedNn35enFECOyEED1Ra7PiRA4B3kASkcrjao50BCnGCzALMKqSujDA8WeCP0+Dc1moBo7I7NdaPDw8PtPzrvIKHuvnec3pBg6P7Z3/O4j/9d/nh3/pHbP7x/4VeE84bcwZikrZZ7VFNzColhEwGsgpd8lzfeYbO1s2YFQIMYmTqrrHzbh0U7ywEI+LwBII4GnGELjNkpnXaeUyzVF6OdwZYx2Una7Z6MOklWlgnUkLygGomZRiK9zZi+kLnKDHYBa+CUyGICVkcfuLeJI0c2Lb25SGT+mj80VnJAqquCKBciRVua7gWdhktHugmubWJp2AMMR00s4YcjUEnR/snoQG1ldXU6QUXKPZ8RVNlpqgFB5SgHeNJWGQkZLe7fWV95KTGpWTRD5+Yngwo49Di3aKs/VL+QS4h/mzgWxSWPPTFljKb+L2MVnHeYlx7jwse54MhclGyZCQJGjzSNBazsm4Q58l5IMYtQ3fD6uoLbm+u8NWc588+ZHn2Hs3Zc3x9tltMju2v9tV/YxJ7kRmIKXK9WbMYPIsEbtEj3mh2cm7RvifHFrIx4nvvLaLJGClFPCKBZnbGM1ejQCLhQmVON3VDCDO8r0uscleCtttmOSrayZnsEi67CTgixcZxtIkQKdK50S40AQNKxNU1ygBEVI2NX5zZcMi4eYmbJqHoGF83oWRUI6nvSakiuEAdMtuhs1CQXiw+dTZbUe8dMUVi7Gm7Ldu2Ja/XrL56Qy2J2ayiCoGqCTSzwPc/+Iizv/X3cZdzCLWFpNRssYTFTn3CCu2uydvXdrKvGrSWAjq9OQOhTDGsZYnUL6F6D/yZHV3LEigoqhdo9QGkDte0nP/a+3zX9/zkH/8Bq03L3WZNYkZ9eU7c9qS7TZHCFtV2JWQnhUgYVNSck/J42s3lnDTWaQtbNfP4OqCacRU0Hz7D6UAa4p7n8wiwbN4Y54iz8GAySvc4khQdIxL2ntfe7yilnACYCBo7ZP4cmX9YnOhA4obdJNn/e1R63sJmy8ilNjVsXLXG9u+f5A52yj1Usi+9ia2pgCiqeN0rm/2yy9Ps2dFNjzbmfuwRHgKGj+22j4CjDIg3Ux/p1/fzPQaAHmvb9IoFqZbQnJuqe3JqVDuIVA3iX8D2Gobt16jsRN379ZbPEgdT3z1GFaInbj8BMFUf7v6xCe8EPccg+KGbylppa2RhALETcvl9OolP3/P1DZDw3/8W+uqXR5XuF7z/R6fAGYc8+wY43ayyCGDDns2AB5oKKqPWcyi66sh9ujcEpHTcviRyX3l9mtt/tz7cWzLemR6bHCdOWQLiHXnbsfqv/j986//wv6TVDX/2z/8fDCmRgwWDMIBjql3vlOClnKOFfvCstnNiH0kZEh6yn/KHCtMeRSEXP9Kszuh9xOiCUGeHaVwhC1diTKQsRVhlprgjx3OMSlIzq5OUEJ+MNi4N5JQZ+sigQlQhlxDBkoQgkLJJLENWQi6BRZQdK1pWYpFQKkp2Ss6JlLJxSGclyR5riGYjNtfi/FvoAcyh2ZWvig7GkWmmgBYjPQQh+GA+F+MB/cjjf2cNNDKRyGTLnzWX8WHSzIktwFE0rDYKfQh458gumkr/ielr8FBmY6wvpKTiRk8Ek4KJeEPWuSX1N6T2ltRvkZwJ3uGrGaFZEMICcTUiAZFsErSpE8UA52yB5JGXMZHjQBzuaNtrvnz1S37653/JV19e8d7ld/hrv/PX+N6v/QbN5Qf4ZmnSv1F6M07OybFhp0Rm7OCc6Iaeq+vXaPuK99a3nD17D9/MSZpotzesr77i7VdfMGy3fPDyJc8//A7Ly/fx8wVZHOJqfDVjvhDOLua4alYcfGR6Pic2SJ0zlZIWA1oT8OoOKyI7Db6ASokItI8/CvgQjWaTRrbv9TnmVR/ZOSb5nQpzcmgqhwCpivRnMGNqOvr1ADqjqgJDn6iLTavREChOhNHSRjMMQyYmixrQbTvSZo0fOprK3kPtlVnteBEDDFh/OG/edlkYmQJsbAHdFfnNH+O0RZo5zCs0mF2X+nKy7FuT5i1+hLrfAdfYeNp/v5S6CBACqjXoB8zf/xFu9i/pM4TZOYvzbzFbXTBJJlPemTm6sctN4Z4RclT6TUQCxDaRo054aTztObNGt1cogm8C6J4EZAL35a8CktF+ZQBrBBAjyBophu7trnC4lkwryUE9QgkHV83ten+7k5JqKWTk3joo4mi7G/NyePnB+vef94AkXs15R4qaf4r2k49q1V29HH58ND2S6ckg5qFypDIeTXHQrU9kOF3PIcvEUfnH6EwT2t7ZmBGHLN+DHNH1G5M6iJl7PFjefiNOPZOAhsY8xfVInaWcRiNHffJoHz5x/9Hj5/8m5R2AeEUl2Bxyo/PoPucQO2AJaN+R3r7FLWe8U6k3ThEZJUDKTshTFmtXyi822KCTdYsbH1iMC3D01J00IfcHwb3/U7LJCBTudc/+yzl1YphOqXu/nhokhy9kNDfa/01E6P7HP0Uqzw//i/+YNrX86R//dyQxD2UBfPEDQIUhKt1QTGOzZ9AadQGc25nKmUyQGJWA0GdBo8XDHtTT5RLfO5v0MRYgF6PSpciQcgGeWrZ4e9c7IKeQlUAmqJJyZIhGHTQkirOLvedc7i3Yjhpo8HjGJWvH/oFCcMH8LMfztpgW1jRIVrfmojkr7yDabDbbSSnSQZHiXKNFgmlq8qbxzGc1i8WCxWJJ3cwITVOcea1S1Wxgu4wPk6g7JkdcMdW2quKcM0mlMoHK0QMcwIknOyOU138TPJRu5O3TncrXTlMjXYgWp4gNQdZ4fwfVGi9K8AHnB5zrIG/JuUE1QK7AzxDXTGreUT2tYvZkmrbk1NFuXvP27Re8fnXF3V3L1dsVP/uL/4lPfvor/tF/9I/4wW/9FvNn7+NnF7gQEJdxas4joqNkZ88eTew0l1UZcuR2fcdXn76layPfVcfs2QuiKHfbli+uv+LPf/yX3H11x+//1R8xP3/B8lJwocZJVchLW0QCPiwJzTm+qguNANMiJqN6eZqXOk1Yu5onIDnNadn9ykhcPkbD0QGkBT9YWZWHtAIdQEKpr0ZchU2vArCdIOqNrgEgb9BcQe7ZrrbkXKGSy2A38Ou8kaTaRCr2ld4RQkVKmWFQ1pue7c0d9FsbuAA5c+6F7zz/aDqMFKxr0jpXPNlzRPqetLpj9cWvYLNithD82Qw3D+SqQoKbJK+cv7+TvpZ47Mdr6GgaAMXEgDOQAP2WFB3Nxfucf/RDwuftbgxnU21r2VgPWX3MXqZbR5w3yeVOW1tOwkNmu7K45woMbebsN76L061tWq7Y+d7byBVhNK9wE7gyN36/997jwT074LmPtvY3JYf6eQk6MI79YBuvDmVM7QPYU/v7CQn/fjX3gGSRlZwCgAVcStpCbvc6tzzPONSPQc0DYNIcBasiLXwYccrx570Lp/bf08BTcM0Z1Es0D+zMUZ6QDoDP/bYcgIK43ZdJmYQ5dWYDu1/MU9f5U/nCAqU1CfRTynn6nnIvnerLg2u6d+H4Rb0LdMrhP3Ue9RXi/J6nuu7W4WmdEHOofCSNYO6g6mNDw7GZo61kceoj5VKd4DJoF9Gh7JntYCrv+z3xNbp5f8Aew8NTnWm/H1wdsXXW46ec7j9oj6q5UDtXgnQp7T/+Q9zzc37jP/mP+OLzv+DL648LSDaTtyCeynk0YRquckB2VQ3BQGTAmTl1iYBnkj1T+2qh6YniyFIRs7IZMl3MBaRm+iFZaMSc6ZMRjTtv0sJRvSzjM2lCnDm6xBTpYqIfPadz8dUeveqT4gQahbkKdVYkGSFCxqR5QYRc9vdcnhk/SvqsSilsOOJ0egPOeUTNCdk721+NeHzsapnyeYGmrpjNG84vzjg7v2A2n1M1tTn4TMvnAXAwAZVtkRjbywjezR6z67ek3iLXhaqmqusikDNc5HFfe9o/nTYozEpopdGGbcAFZzaRYhJLpSe7HvURgiKFP8mF2gYGCrIl9q/JqcdJja9e4OsXqJ9b41NnNlvRXnBMma7dcHXzJVdfvuLqqys+/vgVn35xzep2y5vXHaH+p/zD4Pj2DzOLS6U6u8CLqRxHVd2OVFbLqWHH26QqNriGlhw3OF3j/ZzgAtsqg09ElKhCKjYQ4qsSMWiYbBdATAyeLT74FDZcdLeh7y2eMsaahd1iZ2ciyzaFZRqjANlJY3oOjQZOXGeUSpoR7dF0Z+2RmmmETmtMNkChRXpnKwrIjJTW3F5fs+0vyDiGlCcPyFGV6iQTnCDeTkZZE/NFzW3laYfEdojkoUdUGeLAdttxEWqu2i3r21sWsxnSJFzlbZOsGxv1/R3D9RtuPnvDz358xe2Xb6hd5OWl4+xCqBrB+4R4pZnVLH9jQXjvzI68I/i6t4buFlabyC393c/QzYaZO6NZnrM4O0f8YL1ebH92wBkDemMVozVFLNQMwChFtAlt9wwbC+WZktJ3CWkqiO3uPY8e1FOj93bNSV9VKpMKmgvQjPa3R5NyZ2tzYIc4rQIBrS+NkmeScmLObItvoe0bi1P/CJH1dOgZ18d9rHoAXDnIsLuys+mxcTY++w7A7rrgEITu7tt9P3g8wQ6ky/cgfWJrxyn0orvuPlnG4aPe/zxekoA052ayUQI2nLJDPYV99jmtp7OkHFVzsi0GMDV2k3D3ZDqJth/IixgVmwJDW5YdV8wxnljG0Vw7aNrelwdw9GmV7EPv4oE6p3umfdRUoupneE17ZrzHcwz0+BD6SNVTtWUcTdh0yrvTMGnOFtWMMZOS+wh9tDEQ/O736ZkfQssP98GB9h4KeCrfdHftOE3CCadIcbDRPu22lamKw5FtW4dCX8ZIVRydcmbz3/xT5n/v93j/5Y94/fZXxV7QgJtzasIC7017Nva/G80TXInoAjJGGVJHSsmknWLE3+odEcOzXRS6AbqYaYdI1xso3PYDXSqezqM/sFhkGi8Z7zJBlITxWw5RGZLSjjQ8jBYSpolyCgEhZKFSpRn9PsQZ3e54AHfm6CkOgnMkHW34xdTSIwaQnae/UNrl3G5wie0hgvFTDtlMAWeNqferOlDPGqoqUBXu6/2xM46JHZvA4d4w8pamlOm3LXfXN9ytVlAJi4tzLi8umdWzsqXt9pJ7UQYfSV9DQrkEZkZVIxnvotHCaCzGCgNOBpzroc5omCHMJ0qdka4mDWva9Resrt9CqpidfY/6whOaMj77Fbq9RfuWrt2w3nTcbdZ88epLPv7l5/zqkzf86rMbbteRwJwQKj7+1cf82R+dEXzFt9wMV81w0phtgctFLukZvcr3N0LvPPNmwfsvP6DB8/7Zgtks4t0Gh2fpN7yce3747ffYnD3j5YcvqetZ2aDNYUScR1xF0mjOC6lDxcTFrqi8zduwSKXGxch0KBzQ+sj4Astvu48HbZcRXOaIqtEGETtzbAkBmu+Dv+T+zuUKkM1TQ4QKdT3t5gturnu63OF9Zerxck8e6X5yJniPdwHqhtnZGcuLOX0cePvV5yxihVRznAgZ4/HyWVitb/niVz/lBS2umYEOuDwwX9gY0X7D6s1n/OIXn/Env+j54ouBYbXhu5eRRdOTyaSUeHsD339W8R/8b/8qz35DgA0QGelzZG/n3qmFshGy65dsP/kf6a/eENeZdLZimYbCqWkLmq8gayKW8IlGfTQ6MO9tSqo786yg0jFeAAEAAElEQVQinTQcqhCVbp0mjzpxsmeuwPSuKVh+HIu7vzuwpYjZ7OVu4g49AKEHwO4o1edIvTT1cvYQGnuQaBJZmT2DNkHaTMD43kZ0IJHZl3/s2mD2quz9tp/2kcWI5OQwo+pu19aThUzZ7hXd39nmte/odKL6B7+fuq4nsilG+l3VMEUHO0aEj1SxB4Z07/OT2jQY5+rDAPEJSRyUKBxGDRTMDhy1NTon47d87HBRCKslHzm9nHqEx8DvY+ld9+z/vg8mxZFdRcY0Kj73HNAFjfNE9HDO3TsP7M3xE9Xe21rHCx6kMbJu1RJedrTdT+PGDBrj1IyndY9Okk7NI1I8aoXYzHXO9pR8Eq2PeRUJzoBtXbb/pMWj/vRL2537jPScIl2VMdrdpmX41Ze8fO9H1D/9J6TUmb0imKYrGx/1BKWKnbq6QvQ9iX2sjpTVVNcKSQxMJjWS9EFhyErMSj8kuiExpPFzps8juZnZHzrv8U7xDiqXwSsp9Yhm4pDoc6JP0eotHJBGb6f4rJAN7GYxQOvEHHcsEnIB3SJIMDV9StkCiZRxqbCTOpoi3folZ1TFIglmC79s7w8LX6kQcyZjwVLqZJH0quAJvkjWsxbTuSLOkhIaWHMBlgVEyghWra05Kf2q49WnX/LzTz9m9nzBd374PRZnSxr2NJnjcenfhA2l+AXizkFr0IjkuwkNi2Q0dyRtUW1xamJUoUGoyCrkNDAMK9r1NTfXV2xfXyPacP7yOySnhCLijqst7dvP2Hz1EzY3K9atZ9Un3q5WXL1t6VrhbPkc5wcq5wilwz774jUffvkZZy/esygt9Qyh8DCKkMvrHwGHSgZRQggsF2eE97/D84sXzBxUM0dVmX3hjAUvXnyXpvkWcciczRbU5+e4yiRr4ip8NaeaJ5B6su2g7w1MFVZ9O02MRrTjxGICjGOYpUk9Xn4YBwyuAJN9ozlJQIvkFbSfkddfQFR0doVrvgP+BSLn7MjodwDLpLf2Ge1B33Lz9g1tX5Ek4kr0nnEqKMkMjFPGO6GqPIuzS6rZnMhANV/Qba754qe3VE3NfF4X/qxM6DPabvnkp/8zXbrBz2pWd1eQMx5H8Gaovr6749PPr1jHOb2/IPrA/DKxnHfc3nZ0JH76ZkN/F/nbr1qebd+AfwXpFnXLsrjvqKp2CqCE5h6Nv2T78Y/JXaDtBrq3V4TlJzzvZ2OHFk9RplO+UTCME2ukLtKJl7wssxirge4mX/FA9LOK5Y8+QNzn5XrhapxOfXtb1eiJOklUvAFCEQMwrirxYNm79xQQLe0dbqG/xezu5sj5R9DdQPu6LITuyLbvXarto1x6+p6DbemhAvXotxMA69EyJgCboV+dqsSyuWAS3mGDxHYf151AB0CYoypI6ssc2VNN1gvM/AADYc4fvY93p+O+2Um6HkCYCpJ2oQ2n+/V+1keTc9CcWbABZwdhUJidQb9Fu81pu8yjMkTVdH774P8d6WQTj4f/NypkLMuhviG5qvRj3uMqHu8vgFwEJO+pB3d5DsHkww+ne+9txHdSBaQORiFzT0q/u2/84XD4PwRXbRGSOkDljau3T8Z7uz8IynqXs1l6n2wzBjrN2dHBrJ4kgiQFhlGXe7/t5SHNOa6AyjSa5BgH4vBnv+DZf/L7LGRGlzqi2BrpS+QYGRdUNUCfsgEbF8YAJW4CQtPhnOKyKm7qu5iUIVrc7ZxNa5jL4X6M753YrdMpJVQ9MRt1kHEqGodjHBJ96o01Wzw5O5OUoib7GamB1LSN2TlSTtZdyAT0R72yqCI5kGUUbti+nsVU7zqqLAu0TDmZtnM8mKqxAuAdzhvxeCphHUEQv/MS0Gy+JbiAd75oQA1ETo6ZKoWUfOcznnHElOjajrdfXfH2q7dc1krsYyE9HzGdx40q/K+Rngwok0pRq7Y4jRZjOm8Zw6xp6tHcQerMiSZZHG9yRVaPqiOmga5raW/W9KuePGTCbCBcKk6NIFwl0MfI5vaGV598xeu7zHWX2EaljeDFcTZrWC6WhFARgnl5ic9sN1v6dkvsB6qYcT4zipEn27MiJh7Nhr1vaBpHFWZwZkbq3hUBkiiVyyy9MFsA2V541SzxoSpGtyVGdlPjQiKnIpkqE2g0tpVxADJ6xWtx39cJRJoHdgaJjOc2GcUZmrHYT+NibnRKqiuIV6Ttp7z92Z+iac58vmDuvo3/9rfM/tHNsQkrBmho0dxjOvmI6mty+1Pe/vKOtr1EQkAY8EGK0XQmDT1dN5CGgezNQ7+qHPN5RZcC88UzvvWd79O+/hWhu2N57unazBAyDuiHjs12xZvXX5FRVus13WYgxoR3gmbHuh24XbVsOmWIoOKYLRa8uJjhQkvTDSwXPetOef3pFR99+mPkWxHcJcIlWi0wqbHxe1nKaOohvSHe/Es2r3tarRhSZrPesH17jQ7vlUm688TTItVQdra2TNeYvMFtErO7hxHTadFYB8K8KqHy/NEmt48KyhiY5m/Z1GILrjabPefQ0X1gkrocp91F0X17y0J1VWiJbDFUQ8yp3He8n54se5TKPbzDHwPBg+9yKJ2RU/lOP85hepcEbASc3sP80uzlRrODR/Irgpy9BBfM4zl2ZhuOIPXZrkrxaJiV+TSCD7djfsgj8fBhHVMEvfHS2Ccc9dPxWaE884F088Tzj3v2vTwpwuYabZbI/HKXexjQzTWSnwCMx5jdY9kn8A/jJjs93NdIT81+D+cZFZVTZ/ZsJRyejOwDCpN3zN5BfRcC9KGG3AeVI5g8OAuVqavGO7OLtrVXnO7//RqPKJVHljXUlYG4u9be5T7mnTDsw6VbNgNzknIBSdYHsgDxAtvB4ngrJ6eXjHbxEwg13l9EiL/4nPns77GoL7hdXYFLuCAEFSpJ+KT4BFAZD3DC1jMFCSYF1ERRHZsNuSB45y2Iq2YLTZjVQhqixeXHwgLbeVxM0joKBIrErrAEGTGUjA4pkaTmxJORg4BkoiBZTdJY3KxVhSHFQulTeJwngcVuAjtX6OScM6AH+LJmamHH0WJS5fa1k6hxIk8OMOas43ElJrdOJlkjKTlQHH2sLaN2c9yPxlc1Pth4hPK4Yn3koA7MFgsWiwV1VRv7zt4a7708fdDydQBljDiUnDagt5BuSfGOYRjIvYGdXCQ1sVO6bSS2Q6GrFGKf6duObtujKRmnVHVG6swwNCcleE9dzzi/eEnzre9RhznNbcty07NuW1atsm2VfhB8M6eeLwhVQHOkKhtu127YrK/NMyxvcb4BsQCCIlLU08XYtHiejtI6lYBIKATVxVbSQVXlSTskUmJAj+S/eJMcUeFEGSksx4VHRAqPldUnZQAaPiknmQI6R+Ph0XplsouZRrsaWJKMYuBXckb9msglV+sF6zcb5r7neftPeH+xwJ3/Fvj3jfeRoo5nhei67G4t9J/yxc/+hJ///I5t7HChoa5nuGDUPzlHtt0W7TOpHwjOsa3vqJxJcrM6YjugMXG+qBGvzJvErK6Jmxmbqw3NsqFaLsBVpCEy5MBt27PeDqy3Ldttz6ZLbPtI33fEvqN2yqvrgYtgk1HVM3OBXjKffXHN8z/4Ey6/+ynND94y+5FD5DcR92yKaIAqoi3kFZr/gvbjf8Ht25qrjed2E+ncln6+RvSlgcRsanVNWmxhIaK20BWJ42RrvbeIT393M3g6EU6bmBtFGuO03lvk9zaGKQm2Csa1cRIOa0idgcRpkRjFCXr4d2oBu7wSIdnBb3SYwSlTXPcD1HI8+4+R5omd8dQznEqn7ntKvr0WwN6j6v3fD7ohdujt54wA/F3mQNJv7ACyeA+ZXUCzLIc6ZefzX76dvQ/p2V7D9g4B22sY+odB14kHPmGRef/e/b+nsuzlmV7H9NqyeabXS/A7++yvDfz20/47H5+rnqNDh7zLd/oeYjlx7V33T1KvAa/mKJgxNgv2pZD75iqyd31aa99ZyV4jx8t72qYhQ4nbvH8YmF6HHpb4+Essxe/hXR1B6zQWZS/vuzttf+jokHFDNJV3ocgZVeq7du45soApfnRv5RoP18lIxdOXb3G9clZd4LqEC0YZWFWOWU5UDlyR4mk2pxDn/aR1m84hxflz1MwlTeycJGVyIMpFRS/iyZrxPuBzxiNF2JVRdqBOJRkAdZmoJfygmko9l/KtKcpIPm7czMbfHDFhlood6WOOe4cSwxej06+IEBwmP/AFOBcs4Irq34nZW6LFDYAC8MoJOzhH30XjhS7PlHKa4nGP3MNmbqQmkS/jesQTowZ5jMw0AtFh6G2PTZGzszkXF2csl0tms5k9YwnduAOn7xxeU3oyoFzd3gEt6BboyXFNt16xvbuhW2+IfQb1iNSQHQzQ3axobyNDaycLE8AkcjY6jPlsQOVT6sULfD3HsyBkC+8nYYabnVElR0iCdD2iW7xz1CEwm9XML85omgaVRNBIPZ/R9ltub94QY4sLHnEBJ97s44qto/NlIunotW6nkRHlO+8JocGFqpyiTBwsUhMqk/blLEgUmIIwGGhQZJJkjtdNbL93cAfjyhpPOWKWjVmEMZD7eOgZAei4fohkSqj6MqAC6mHwN7Szt3y6+guuP78m/PkX/NrH1/zo936bxXvvoxUoCZcTw+aKoW2pqxlDHPjs4y/5F39yw9u7Z8AGCR7xxnflnDDExJAGo0/USHCOpqq5vrnl7mbF2eUFq27L9vo1khNVVRs1Rso0iwZPxeLyGRpmdBH6JNx1iVd3G758dc2q7ei6yNBnVl3HarthGCKNwCwtyENDqCJ9l4kEtinzk1eR9JfXfPDVHfWff8av/d01Z7/593CL7yLuDJUBNJRT4QqGHxOv33K1rbnZ1KyHTMwtKXYlFBbkVKLjFMcqcrFJyUxqlWmjUEWc4rwz0ve8/9u48GMOW5WH9ACS2TtB7qHU3aYkanyDuWcMYTltkNMKfwQI74G+kUx8tSe1LKNrlJLvt+OgcXJQzmHjvhkQOcYfBxefcu8+WDrx+z1sPjlE7V3jxGONv+WIrr5E+w1y9l45MB6DveIl7Mw2m5zNmbBdwbB+2KZz7xl26HjcUE4A9UcLefzayaIKDyntLZoGZHZmtrUjp+Veux7Ed/tt9wEkwNABahRfoTZqr6/T/mOg/RRwee8lZhyKy12BQ6O2Ysy/A5MuZ9iT4ItTdkTPwAhiTjR2tE+bylRMOjlm3ZtOR37V5boeDf6Dgg++MiRoS1zzPu1UzU85XTz06zh1s/WZmqeL8epO95T1CzVDcm9xXnQ8cDPej5mPrVt0veXZt36E++Uf4jNINOGHQ5CY0A7QClELp6i+lD3WVfpUnMONgp7yyjzmWT5uq84JVWWe1a6o+kddgceceow0xSK8OXHFWMUklKg52GiGTC7EGopH8GLAyIsFZPZuv0dsvZ9s9VPhGCnRelQUr9AEqIKWwFPGNKMYTSFF8JZSAq1ApOAjV+z5pUBUZciZ7HSK4Z3V/mnRgJXBWgaLK+NGp+OAYRyTAKc40Hc9bdfSayQ0nvcWF1wsF8wXC2OmwXwfJkn+1zxsPhlQdq2aMbc0iA9s+2veXq15/cktbz/+jDAIZ8sz6llDnzHv1jajbSZ1FloQMTW0DwGCZ9hesdmsSQReAvPzc2YBo83IkeASPkBoanwzQ7cWHknUodmY7mdNzWxZU3vhvKlxtaftrtluvqTdrhj6HlEhhBoXaqp6RtXMMONfoyYiZ7xUeHH4EJgtl5xdPqean5FwxNiTYkbcnLrJVI1Q1eCDoC6VgeBQXySfGbP7mDClFH5iW1wU80Y0z7Li0U2JeV7E4E5srjkxuxc3yS8cwkiJkMDNyOGMVL1ELj7gWn/Bz1739J3jl68/4//7zz7B+Uymox8iwdfGWxyNuLuNiZutJ+YlKrckzZOBsPdub0DaRDQaKItHOq9nfPWrXzJfzsnB0a7e0MiWy2UNlbd3JMrZxYJ6trRIBH3kar3miy/e8ubtiru7nru2oxsiXZdYtx3r7Yqhj5CFf7lp2Q5Lni8N4N2sI21UuBpY/XzG84uEypo/+OP/ih/+1j/h2Xc/ZDFb8u2PZpz/xm+hi49weUC2PyNUibebnrshMJtVDBI4Oz+DvmwKajYjWqg9NTPZSqqyU0cUqUjVeFxwDH0i9vcBlgCL77ykeTaDN8mm2x4Q3HGSjQvCPtSSabHQPNLIHNdxCoAc7WhTShY68SCWOHsgdm+Duod4Hqjj1OV3XZP7vz26ZB2jz6fc845yTuJjOfwuKHS35DQgFx+Cb6YM41ZrQF1NCrq9QfrNoZnBY20R7nWxHrfjxD3vjBK0e8zDPCNIDDXarZF2ZdKiNJjH91G+qT379R/nUWyCzGcWZzwN0MwhpQkMfMMzxze7TxXJCYmdSXv8KKUc2y17+YZdRCBAKoefVSUYB2ivaHzkRYDh1apEhxtSkZ5NVbATCYx36sli9r9PvJTjnyxoG5FhpzmZ8o9t5353mRzveP6OI1dMK9ENBj6kcEufapZgTjxNMfHqnQHc8VlHcNkNDH/xMc9/668y/8MFQ39rgs+sxN6gnh9sP/FeqBtHCh4fzPs7Z6P68Zj0LTuTPnqUqLmQiQuRbOEGA8QszBpPLNQ/8yqNr2FnooQxctjBL+NdQAtcy9mcL+1VZRMEafHqTo6gDq8mtXSFjzHnQqfnjQ3EpIoO59XCH6r5lCznFc8uliyaxmoTT3Cmvg7B4bLHaBhLnzuxYCjOhFpmCmZS0j5GfMwWxaeIMw+X6KJNLQOv7Bo450jR9tztdkO7WbO+W7O6WfHm6g2bfuDZy6VRENUVI7+1G2OATgqwp0/Gp4de1BlOKlxYkMXTScM6X/HljfDzH3cMV3e8fN7xne+9pLmo2A5Kt+5pr++gE5Z1Qwgg3uhUtO9wztFuV3R/9kdsVisuXn7Acu6hf8OwfkXfw80Ad0nZtD0pRmrnqecNy8s5Z5czlucWK3s2nzGfN+Cg7zdsbq+4+uqX3L55y9Amhmiic18J3ofpxOBEcerMg6ryzBfnfPi9XyPIryHiGBTW21tubq5Bz1kuv83iLLE8j4SqNdW3K/SjJXSkSDAvSmcGxZNxsRZRNEKU4sk1XS82FQJOMjXGSTV6qDlRqknFMp7mBLRGpDanqarmZtNxvfWm3VLBu4ahV/rU0A0DO7Ne03jGlMxgWXsyLQmlclUBsw6RwtuVSiRSjTjnqXzFxq259c4Y9b1Qeaic0q2Fswtvk0fmhGpBJtBFZdW3vHm74nbVs+0tssEwJJNS9pnYJySWE1tSvrrLDJ+ueHbmaJzgS9++6WBzk/hs7YjJ4drIH/34LwnyY967CPzH/84z/vplj3xoEWE0viGtN9zcblCdc3Z+DqJUy0vSag5uJ41EQILYhlKog0bzBFvo7SXEmM0pbCLOHXt2h0xc7ZG+K1KGMTLRhFB3u/bITTQtErtVwwzajzbHo3reLdxSOLCp1N3fe/q4faQ1piNx2l7+exLB4+ZxomuOmvZg2t9cHwI4nN5UD36XvUc9KR164L64he0tcvbeHpzc1abdBtavkdTvGrJf3smOOSzmwCbvOL8efj7G19MtR+1/sC9Sj6RdvHiJPew5/TxYwBHgnlLOaBxgtkR1bg8zbJCH+rncLr44BY1SufQ1vdiP+2b8qLGYKJxQt0/utnZa3D8eEDyyqIz8ok+kvj/ZHNm/R0BrZ1HVRNAYTaNlLrp7h58HHmzso1ODt4wJI/NQK3MED+yIxk85c+nB92Oxb3n1fTKJp2BOOnl/XO/MLwSTTlIVkcZRXGfFuBlVMtv//o+5/Af/Od/99d/nlz/7p+QZVF4JoibpGwTxjroJnJ1VZO9Q58m4EjmmuNKIMKSMeMEHzMnFGT/kMip9EmJ2RDW9smL2jUOKpmVS6x1XosJNgmm14BxmT14kfUUjqKaGMmljVKoccMn4I41T2ZwuJ6ogzElWCyVgKBuzC6a2DlVgdjbnfDEnhGDAW0CdxR/XnNFKdiZxjLQ+RVvqsgXNAJwENAspJWJOJDLiHT64ErtbAIuJPgm4RYhxYLNpubtecf3mNa/evuX19Yr1ysK2NpVQdQP1pmN5t0bcjMWsNi/ycewWKfZT09O9vKsz8B6pFjhX4yPgzlm1cLPq8J3n6vWGufe8kGdosHB62TlWmzXXb+5YLma8eLagCub55XxFcoLPme2XP0dWn5PnFW7YkiUTw5KUawuu3vec1Q2z5Tlnz15ydvmci/NLmvmcum6ompp6bnQ+Xd9AblmeXbK93TBs7sh9zxAj8S7hMBd8dYWlPmfEe2a1p6lqU5u4UE5MmSH33N6+ZXX9itlszeV7H7K8XdA0gar2dkrB4V1FVc/wVU1o5uAqsrNQk8a8b8A1K3SqDDgGTQVH2NLgsODuFQ5fQFpwEATUmZrVO5tGlhI5msGyJMG5mh5PM28IdWV9R2Jen7HwjjwkNA30MdOuV8SoZDeK0F2hOsIW3Bxt0qiiORoPaY52ItQBKdJKh0e9I5ST2mo94K+E5dmMX//oJecXz4ia6YYtbbtlaDtijLRxYDt0DDGZd3rMpBSpvFC5itRnWs1crRKbPjGvHMvGM6uFelETpWHA0UXQTWYxRAJwVzf85c8cv/6nn/FMKpgF0t1bXn3iifmM84sZ8xdLcqzAz+EH36P77DW5N7oP35jR8qDJfLnU0IiBTZ0wYRwyKeY9AZ8twQe4R0HbDrZ7JMsToDxCY2MZU1k6lXkoRdzlk31r8ikE4v697OrZNfGwLV8L5d3Pfy+nPnB9TPvg6F0g4hSIOb5Hjx7rVFseE7Y+0kbBQbXYy3ZUUB52YLKUN5ro3TvYnwJAD1U/3r/b4w/Gih4/8COI+uCneAI8njoVPNSuU9liB7qAkac4VFDNIJUQqSfKwAka5iYl7dsH++HByh8YF5Nz0cGc3Ctg7xA3eenCDtg6iuMGphK93/JS/Dj/BGozqRKtkazodiANu7tP9tko5Kwd4h1pKCraSQJl7dkdYsd3/ghS3++Ig//fz78f/USPgywcrWGmWsqo3+PulL28qpBg+IuPiX/2S773e/8+V9c/YQgdrhEzDRKoe4fzA/W84fLFBUnMI9tsBNMOtBU6nEzGBTG/BG8WuSPNTlJfAGWY9Dwp2z6WC/OCSQtNejeuzCLWFucyEEnJIimNtq8OMY1lMg9vmeycTJqYMRW5k5Htwd6uE2fR34TJVyPMGprFDO89MZka2RUppckMMkgycCrGX5lUJ07MSC5k726i4BMnxeGnELaLFHYYE3goJiVNXWZ1t+bqzVd89vEbfvaLz/jlqze83axxPjCvZszmgYu25gOEJIEuRl4+e8b5YmlCNxFGu9CnpicDymp+RsqD2UCmgSH19LG3OMdilDJN9Fy/3lDVNfXF3GgMnCGiPkbSTUvcbrm4DFw8P2e2qKi9t5fRZ3Jo6eOGFBNVMyfUDbNqiTYwW8ypXODs8gWLZy9ZnD1jMV/ig6OqK0JVFyofj7pIPV/QnJ0xX84YNhuGXjDf/0jKsYToMrsMBbxm1DmC8wRXoszgEclIjvTba7769FMcv+Lu7Xucn18yX5wxXyxwzpOTUs9mnJ1d0MyXpNkZrlqgbkYOFXgbcIonqtBmaLPZE6ZiPD6GYfSiVAjeQ+XEQld6qFEqrzQOvIynrUTfrVlvtqz6RONf8I/+w7/P7/2tf5fLD17Sbdd025ZmPsdXFTlmnEtcX7/ij/7g/8mf/LM/oe9MGudlxkff/R7f+ugjhnjLF5//ijw4QjhjyJE3b99y/fpz4mZLjgkVjxvAaYsPnuiEKngIgY++/X3+vb//D/nBr/8WVI671WuuXn2M/8VP6Nue1aolDwN9SiaNzJmGxIfzOc+ac1KKXG3XvNq2XG+V7QCqSvBKXQdyNoCuPuBCxjczXL/CqZKT8Ml1xf/0z7f81vpTlovA+i7xs1/VDC5x5hvj5MtCr47oAv76zmgsvCNU3g7svdErZdXCl7Z/9tcdLjwxXyZnG8ptU2zfIzqTaYF27MhkxxW75H1wE9E9rHhqh2UPwE61ler38j+o0hjFJJZvVKU8lA4Eq980PYgMH7n2SHum7F+nUftdUzVI1dj3HJE42M9VCRpQzUsY0a9Br7EPAB+QtN1r90MSwuPfji4f1Hev8Efad+r7KZAMSKiL9aKNXXUBaRaQLCrVPWolsTy5hH80h4IntOudz3M0lk/1U9ozdt730EoZhmh4s4sPCmVGVbJ4QWYeKhMYSLC5ol1P6uPRPSfagZr9/SxAXeGyIjGj257U7zE53Jt1D7yEowoO73oHCN3jgjzOr6owJPMJUF9srnfNE2GKba5DYv1/+8c8+z/+F/za3/5f8fFf/jewDEhtTj/zVhD3ltA0NOdnZCnndYyOR9XiXlv1hT8xwEjlrJOWwZGzefJDwHy9DSSbs01BfnsOkEgJXBEKAMQCtOSUyDmiahGOnIg5DGW1w0GJlmPSybEsnRxeGENOY0Koke7POY/UtWks1RXfJ5P8ORGC9zgxCjknJhiQ8ipyztO+kiWTc6ZWhw+eyhsZelYF54pZGqYdBcCRup717R1vv3zDT//yV/yLH/+CT15dsUlKFKFyysaBv1Ou7wK364HVpmXbGWuPqDNs5YyV5Olw8msAytjfkoaNTRpf023XtJsOjYGYTMyfBPpeuX2z4RyHVBWagkm9nJIGC9F3eyusN2uWF8qL958zazxehFBXSOWQlOicEZq62lGpILliXs9Zzs9YLs5Znl/SzJriRGPBzMubIbhAqBuaxZLl+Tn9ZksflYQSCcQhIlQYGWw5MZS41xZr2hUPb7FIkKqQI7G7Jm7f0uQNC30PlZckSfhmYbTpQ0fcrk0hoRmfIoSBlCxSUHaeJI5BlU12rCOsMwxqnmSiIGpxRucolaOc7gQvFgopOFN915LxPuNEGfoVb69WfPZVywfv/Tb/8H/9n7N87zsQTE0xzfxJMqR8kLe8/733mM3+S958ckvslR/94Hf5K7/315lfXJDZcHv7OevrDu9fcrdd8/Nf/II/+8N/zBc/+0vEY5M6W7zvFx9e8v5H73F5+Yxvv/gBv/87f5cPfvRX8MsZOSsXmyvOzp9R+YYmVPj8Mdu7W3LXE8QMhN+/OOM7y3Nc6ll3AxXKkBLrvmfAgHXtA8E7qiCczSsiShfNqDp4T5UjAmxT4E9eO25+DBezzKCB11tP9hGVhqFPDEPGR4s8q+PiOc1k+2zXZKLyPNhcy6o6Xpd9RFUuxruWPFvi6nkpN3PAJYoCzmKT62aawDqh1VEH/7CI7f528fCGs5NavAP5HTdR9N4+drynPx2zeQ5Ukg8BmKnuoz10R+d277Z94clJ8LMP2h7B0Va2ILNLEIfEDtbXaLey9szOkeVLIzv3c8h7cb0fAHcHl/ef65T0+Oiek3jrgfY/KKk9fmGnzyhPTwI0c7ReTg8imiwMZo6Fc/UYTFL6rGbUyojzRoXzddJj7Zw6S3d59z27T9yrBczlQcn9KSR6VHjhDZ7mqZh6FEyaprGYoTwwbUUEqb1F0aptL6IdyJvjaE/3J4cePOTX2e7vl3n/LHM8+bBz7ZAgmRbJ1kbZOdFMXasMP/mE1f/1/82H/7t/yNXbP6HNr3Dz2vZZHXkWvfFqFlocEY/XZIKeIqFEzbcXp+BlZPsbEWyJm+0MGqoJF3LK47Qty+bI/1wAmxPTjJb44TkN5BTJ0dTJEgqAVNtvKVzVI9yW0lOqSghSnIyLl/y4XoxMMpOevfRkWcadN+YYM2dzBairmVpoCTtS7Bgr76k1E1OiCYGmqqjrmuCD2WuOz6cUKaygSWi3W26u3/Kzn3zM//CHP+Pnr17To2RNeBcIwZFcRpOwzpE+3tH1HSn2QC5MOJ6mmpm53dcYWV8DUN4wbK/QNODqM3Lf0be3dNsec6hSoipJHW3bUa2FZrlAVGnqgD+rkexZNIHl2Yz52Zxq1jA/W/Dy5XucPzvHNbWd9sQz5C1t19P1imx6hm2PRqFpWubLiI6DhzKNC5AYPe+r0DCbnZHOnxHbniE5IgnJMwMJboyhaczzIonKCVWzMJsHsZJTYa3PKsSEhWvqe9CIl0TtA7Nqga8aix9auLU0KXHo0JxJrkN8U0I0qlEARGGI0KsYu786Y3FJFkFGUyKTiDkyZCwMlQpRTWVdk6kDBCd03Zrb66+4e5349/7Ov8/ZB98FF6bJtQ9gRuoA75a8ePE7/JXf+VM+az4l9RW/82/9DT74wW9A3YDAiw++RbtqicOSzdAyO3+P9y4rXv/ge8y8pwpL5rMZ55dLzl6cc/b8gvOzF5ydvc/s/H0jTfYBp5mm9lymBH3G5YyLA3Qdz5tr+s2MeYaPmgUL7+my52rjeNu1BHUEgaRKCI6mqmgqi2Eqmmmczc7oKvPGFUGkIqrjep2R18p8rrhG6IaEamBAWW+2DNFz/qLG+wA6gFocWG2NmicNBijDwuFCoN8OxG2eFrWRPneM0nNqo+q+vKJfD8zPLuCrra0aI8DfvRVru6HaUlbiQTHJlI42Bh2h8R4gPbk5Hbb1HgA5tZmdAH33N6OnJIHmBdrfILk/uDwWeIwH7tW93+CvAYAO8NQTwLRWc6SeQ79BN2+QoZ2ES2yvQbPZVjZzGNYHt3+tPnnCDV/DLv7hLvk6YPE4HbdPgKoBXyP9BnJC6oX91G8fbrBianfN+DCz0I/3VK4PpIfafwosT3/HQVUyHEgwFQmBcbqlTdpzpnm4EQrokMmpR7qIOwep1OwLU8IFRx7yCW3/UUMVc4bRDLjiGGPo7FgTMM21U3OZ+22+f8w81Xn7oHS3nu1PrKmUWACW6nQI2oPou7pUaf9/f8LyP/27XDz7TdZvXhW6HYqGzuiU0wi8SghMi2QH6kwrpBgAzMWukGILaeBJMGGfEHMqzlWK92PIYsFVruwFfrJR9s7MyMz+EWJ0DD1oTiY4IhVv7+ITIZBdNlME7wv9nznUjgEvTBjlRjxthyNgNG/DeTSPB5mRe8ChKZMdFhUoByRbTzgodpFmCufU2l2FgIABwqoiuGB7oBjikxLQox8GVrcbPv/0DX/841/xsy/fsG5bi3TnHL7BvLmtIrIqfZe5vWn5srphNqtYLBZUoeHZmVBXvnjiPy09GVDerT4nrq9Jw4rQXNL1Ce1v8NKzXAZ8DefeczmbUdcwXwbOLyuSKJorPBdUwXN2dsny/Jz5colvFmhVEXwgNA2zekZ0nr7v6dct601kddeyum3ZbLdUzpM3iRhhSImz7pxq1lDNG+qqBrSEGTLHm3mzIC/PaDdr3Lon90rbJnoPIp7sHC54cvBUPuNroVpeEJq5iadzsa3IySLgJGEYzJW/a1vSMCBki28tZpxtHt2OIZrDj+ZkYbkmlgrF5UyTHRFTd3t1Rv6eEiH2LGLHLHa42FFhUrQ1QpugyxHpW4aupfcW0qrre+6urvjoxXf47m/+tpEyy30fv3HST449bsby7JKL5xtSP2Px/CXh7MJsoMSD1vjQI9pwIfD+ixfkX/seuV1jXLIB5yskOKQKuCogvkZ8bXQiBeCpZpwmmvPnnPUtqdsSVzekuzXnAsO6J6TMs1DjYoYu0+fEqm3p0kATjMJhHmBWC7PKM6srUuxJzhfOMkXqGtWKJGK2rwNcbXu2WQhDxFUVCVgNHamNaHK8HxUfQrFZ0wIqzdNbky1Os2WFVAJkYpvZSRFH4HZ/vhye+B34GtjuXZNdTnHg9kP5FTB4Ty51GkEd5irRMo5U3bt8p8HkyeRmtrDnHovUUxsx+kPh+R7f43YXw8LKaN/uNewYHD/Srv0IlIcl3/u+X5ae6s4H9lxFkNk59Gt0fYXkQ9tDAWhvyYCbXZqad48qSI/znmrb3sY83bfXpnfhv30v6kdB5FP2g8cqOynyBHJCt3dI8RAeA1NMNoqnp4bNn5zR2CIxfjMh26k26tHn/XCV7qgSBXd+jnvxHvGL12Ozyr5fGi4yFTfdtJ+yAUv6aPbf234Uau3WhnsPp1Nd2sYi+Sq8xMXreh9MPjaVDj25D9tmVHMj7cvjL1em0k7nm2qZwPYJwDl5Bit5taX7k59y8Vvf58vX/xM5C867qXyFEoTQIJSM67cA2QBaztnAJJjDjJoWVMQog0aZobgSkrIcpU3jWCR43tk+jpbrfqp1bHkqVEFID8ntgUg1NKd5Iit3Yu/Ju6JRLDaNTkauyylsCZMD2MjtqRk13r3iR+Hoc7KgU8VeVNX8I0rEcBSmyHtDTuZM4T3eO0LweFcXMCsmSc3QtR2bqzUf//I1v/zsrcU3HzqGFAnBUXsh6sz20Komp0xMA8OQuLnueLNY8+z8huXsnFnV4KTBuT0WiHekJwPKm7tf0F9tWNSm1ogtLBv41vs1S3lJ7YSZeJoQ8M4IROumKuz1RhTqXWA+X1Iv50hdQx2MKxJHn5KRbufAEDNdr2zbjtevbnj1xS2312sEeHZ2x3u3A9/adLz/0UvOnl/i/AXZBzt9iBSxbk2saupmRmgaJFR0cWA1wLozsFcH6BHmOOYqLBtPMz+nni0JzcyAhnakNDDEwRxYMvQpc7PegH+DczUBh8yANCuq7UAeMtIJ4gOzZob4TEo9zkGtFifUITQKfYRBIQ09xIE69WjfMfQdiBJTmghYc4po7Knbnipn6uCRONAPW37wne/TLM/LkC5cWTKKw0uaFpjixyfgq2D2TmoeayIexYPU+LpCpMKJUNVzNJ1Py4Gq2xm2yxh9wqas7tuvqJgkY3HOXBWy0qUV3WZF1Xa0rJBkdiJ9zrSaeNuu2aaeuvK8mNdUTWC5CMwrjxdogjO7paDkboAQCG6Ji62Rx/pgmNgrKs6MuZ15yfWbgaEdqGTGsB32qFisz3T09p4O71LUFCMSH7tyD0Ed7x3jCjN2g59uwo68e4oEhUkPMuUpP8h+pt2f+5vEuFjrYf0nNqcH01EGlQqZPUM3bywuuHPodnv4mKdw7+GeYyksLAwiAQlGPaauQpxH4xbZvn28kafKP/79hBDqneWd+l0xQ/dhg7Yb4/B8APRIe4ciaD1H2rv7v7+j+iK4OBw/TwSBT5Jafh0wuf/3XcItBWI8zBZ7G+fvArkAOZvDwzcBk/vja2+ZOdnGvTkMcjAupa5Nzf32drQA3fOSPS7ofiWKCQryNhogjBa15D4MvN8Tk/JgyOiqM3VtzA9ln1pwn9eygPe9awbwpAChfVOV4/VgB/AOPuw96sETHI9R9vrr6OH6P/8li7/5j6jCkkFb0L2wgZiUz5xRijJZMSGGGDhiCklYpLdomScW57uEsrODgTOmFO/MXM1XFc75Esd7NLlze4+VjdLOZdMqakZzKPuZgboc42SCZt7hbtpHR4DtCqfraDcpzrgup3eUbT/dSZ7Vwi06pWoKVhE1Pw3nCyvM7tEQA6uqCY0R5zLichEW7XrTDg5CHAaGbcvV27d88sUXbOJgGs6UGRIgiVoTtSjzpmYxm5NiZttC3yfabc/t3Zq3tze8t31GP5wzaxpOjf2H0tOdcoJJg5x4nEIlkSqvuagT9cWcoN4QPIKQ8M68rURNnG3nn8zQ96g4pBtQ1wKCy2Yrl3IkZ2GzzVzfrPnqq7d89sUd1zeRbWtiidrdcv7pG370nef8zb/9O8zmMzgrdoY+4Koair0EuiQPG0JTQYAkyjpl7mKm8sEC0EdIzl6MOk+ojKvShWChqQAlG2dVHEjqGJJjM2T0bk3lrqgJgMNVmYQwqCf52iR2biDFSBDBieK94jVTqRLAiNyHiBsifT+UE4M5PokIQ4ykpBA8Qe004TRTS6J2kYqert8gMTKfnSOhOmm3IwcfjFvSPo2Ts0hTs5o3nwhCVbwey+D1IG5u98teqdNqZyc6u1emo7pKkdDN7PQ4F8eH3hV6IuHtL36Gbjb024FNGlh1LUkTi/mMqkksXzR8/0fv8fKDJU46bt6u6NaRITZkH1jMAhtRfG9RXJeXF7z40Q+Zv3yG1IqXbKS9CbZdx2azYUh39G2i224nUmI50XEpZdpVT5h5hs741GwBHHfN3RJ/f9sZPe8ACdO18b5drrJAuNr6P3gjx55ofkbUcfAmpwqnxX5/t7wnjrt39zuTpC2495GL79piv/miqOeO0l41++Ps0D9CYPYMqRZTX4ivYHsF3X0g9mjax8xH9T9kN/kU8DWVJ4AmaO/um7vuZ6bIRdpbU/8+3tzdl1NCq3svZm/nfgpAfmp6pKyD5/8G9UlKQP/OfN+s8BO3PDSYjw80TvYu7j5KXRyr9g9wSHnnMk25EXadxE5YlJxpnByIZR8GzJMyImULjPCuA9N06Qg4HoDL/WfQIzC5y/FgenAM7t265yEvJ++xNHz6Ch8awuySfmgnp60xTKAtuToG6ZmqUO8KnY8r0XBckeCVFzo6AGUxX4cJ2FqDXCgYxQfzVHa+SCeLnaMqSokf7pJJLT2F4DwVoWQuUXIVj5/4J+3Ar2V/Kw0vKm+KtFKnPrL9b4zoY20sbfFmyxm8o652TmKKmcONZx8RM2nL3uOdYJHxbC/QrKhkE/4UM4Q4RPr1ljdXt3x1syICUc3pFUCcRfCZ145lXdHM54Y3cqLve+IQ2W46bu9WrDcr+r4rQpN/A4ByEZ6xfHmBczVD3KL9l/RdS7dtyX1FUgtLFFNEUzSepZgsFmcciCmZPeGQiUmJGbZ9JPYDPqk5eFARQsOQPNdt5G4bud0mNkMuUWWULind9Zr3ljO6diAO1qk+NHhfI66yYOya8b4ihMocdgSGIdJ2A22vZG/hjKq6Me7DkBmyoX8fKivLB9QVq1SfCbWDKOAs8EkbI+vtmuVsTpjNqJ2Aqxm6li61UFX4qmILVD5QBw95IIhSCyRN9DESY2boO1JUEG/B7VWRMJ62bKDNRJhVgdhvoVsTc0fUSLvtoHNUvi5hBzGAKOPJVY+Ww3GBdXhfMdreudEpZcpdTmD7O15ZRUY4uls0yz3TDN8tXjZBPKiD2uHPhLkEXviAqxqa5SWvfvLnrNsv2MZIpxlfec5EOH9xzl/7e7/O9/7qC5oF3N2+5YufD2zfwOY20EVPlgDDgACLi5f8zt/5e3z3d3+fi4++Q7WcAYk4DDbhhoG3bz7hFz/+n/nZH/0J6/WKFAeC7kkNxm5AIQvdOtJvIynq5Al46rQvUy/selvHrtgDoFP3SwXVWZHyeagvoAFJ3YFN3kE6eI3lMFBUKVP5e6069fch+cnhEwlKsLb5GZQQaPbudbrxeIsaqz9egiSu0dsWXXyILJ5b3m4F61cIe5KZU5vpu3DHeHZ55L5pA38kTeD0FHg5As160ImKDIeRYQ6ef7/MhwDGcZnizYO8OAFJNTPHlUL7c6+bHlr3H3qeB7Lea9OJzw/Wofo0TstTbXrKnuWCOfvsN/ChcvfzjCKfvFehgP/OR9AltHhlS/GvyAl2vLIylXn6+XU3D/YGme5/PT4AHTRPJuw7dcmEQ0+h5/sPOfIX7s/zg0Pv3l33pthxceX78eV90HeyJQXrjfaO6fUNertl5s5Zpc9xOR+FrRVG55pxrxpD/mUsAo4UMnHryyJDHg8DoyaMwr1Y8kPxsC5xK3chBO09ahZQi0EuYhLMpOYnBEbwjkgJEVniZDtziI0FXBldzw5EWgft5L2jZHjsuJzTLmCGFjX2UKSW6iYKJ6M1sn4BSGpxyiunhMpReWOmyUmJRQiSs1EW5Sz0fc920/HlmzWt+gJ8Az4MOE3M60BdBeYhMK8Coa7ousFwWU7EIbHdwnbV0q639P1ggU6OzUUeSU8GlPPqJdXZe6RUMaxesR2+4vVVz91nLf1tTxoilfZoisaMnyAN9tChqolq7vl5MFb7lIU2mup0ERxNbZ7QnYskhH5Q+miu+kI5QmjGeTsdVFXN8vyCZn5OUy/N0NT7YseomAFc8XxS6/gYM92QSNmTUZIDr0JUYSg0ND5UJTxjBSUCjveeWR14drkkNcG8p71DciLRg0SG2JE6hw/JgsqrooMSs+KqwJAH+q5FRQkCtcO4HYeBHAdiNxQqAI/6CvHOCGGdw1WekCI5WQi+Ia1o84ZNuyHGjtWmRYZmN4hPpVO7rQguBEQds9DQ1M29U+8k3ZlWuf0d4JSKxkDHJMiS3W0CJq2sKmSxpNH3ucyKSUJrOoSrzS/RtbIMFbPG82u//X1+/a99j5ffP8fXmXAFN7evGdaRugtl8XHM5zXNbMl3f/ADfuN3fo8X3/sh8+cfEBbmgZoxAN92Ha5eMrTK7asr2tcb4tDZRNjh6enUp2phQ4m24NliewSoSt+azfYhaJt2lNgddb8YEFSFal46yg4ctDc7kDjeP4kD9jvbQ3UO3TXs200ev5Rj+8T9tu8Ve/Aa3RJZvAf1vNTvkOX7dsBq3/KgHeWjqdyTI5ojhNpCAY7xj4+a/eR0AtgCOyCw331PqeObtOFEEfsY8hCAnsh/vIPnjFbGZSuKrQvD24cfYR8tnGrMuxp7om2HEuZH0jEgeagdD937lP4WoxqyzT4bFdGpeo/b46QcuuAgcICCu7wkffYGugGjiMNASToa28e4bq8eW9P2Mo4DTQ/fqTzw4vTk51PWjGNhsluTxYi0xTmjGnrU1mMnVLi3XHP6NRyLIe6B4aND0jSFk8K2I92sODv7Nq/WPyalbJ7MpWOSajFrHQH5PrB2kLOBylEiWQDrru9NWijT4X6/vcXxpkw8s0vN5g2kxsBi/Tdq4lyx4fTFB4Md/7KWvyKTDWXG9vD9JUuLXb2UPOO16WBSAleknEsoY5O5DpWQK3PkdSIlnnj5jDKUPccX+iEjTzDVuXd2HMliVEN917Feb7hebWwvAYI4cgh472jqilndUIdQALeSUiQO0Ti6c6broWsH2r6niz1JI/kp0b9KejKg9GGJr5cMQyT5yG2/4pdffcLrn6zIdxXkzGUF8wpcNk+mYTA1chysYWBUCpJLKEI1byknhYtSYBuVnkxfSKNtjpZFAaaTgPMW+N2HGoIRVelkj2GGr94XETPldOrspOIEslOSJobY41yFQ6m8nQJCCDZRg4nNm6pmOZ+xnDuic7jG0L8OSiVCygMpbsnSk1NDzB5xc5xrzBvL20kmqgHlTRfZpEgg0ziTegWn5DyQGRASlW+oPOTUgSp1jgzdHX17S9+2tNuWu23LZtVyt+6pdc7m9so2Z1+8zh5ZrQ1oZ1JKzPyc959/j/nyYuIUu+fSIyeWG+U4106yN4Gu8fRcWpQt1KWIEOo5s+Ul6fkHDP3AkJSYhS4NDLdrZvOKxbMKbZQ+RXwSkoBKRdcJKhXNwpMSLM6Ei/kll+89Z35+QbN8TnP2DN80NtnFWzz2asNZbDl78ZzmvOL2q5XFVC1mGdNGMD7MeFrV3aW8t3BPi8bYTXsL2ZRythjPpd+mBQZF+5V5Kfrafh5WkNa7fFNf6t41q0kRJCxguDOxyt672XsZ99PR5XvOEwqQ0e4a2htk8QGaNqbaLfZNJwGlji17IIkd+HT1FTqskOaZgen2FNn2bsjt44R7T/RE8HfPmmGyqfv6wPh+Xx017hRAP5X/1G/7m7MC9Zm1fXOzI+1+KD26+z9w/ei+gz5+rLyH0j6YfOo97wKfAiqe6GocCd9vHgaTx+UWFgiKc+XuN6H6K79J+uWXaE4onpwwUmwd6xyjmMAkbhzrcRZ6F2QSnOze7QlELexsGh/Aq7u+3z8d6JRHALEQZmWd98jczGQY4qP9vb+e38vmSpmj6v1oYX+oaw/B9NEDDYnh55/jf7NCczbHnAIaM6Ow2ICe7Va2/+cSwcgVdXbWEvGGfCCl3GfYAIrEEUZHpCmco7P441LC3mqJyGPAMpFjZCK7192hfCRER8WIxYvj2TgMci7tUes/oeQTJo3UuC9YPynZUdrsEQpuyILzTTnMQBW80eIpaK7oYkdKSt+ZjWPX9fT9lqGpcQohWCjJOES6dst6tWIzdOYLQQmEgtDUNbPas5xVqCidZup+IHY9Q1F3J03kbA7P275jGAZzfE0Pm/McpycDykTLtv2Su+GKq7tf8dnrP+f1m7f0MZB6c7NXC3w5HSWcU1I2le7YtbuoL+AnFYTuXpQqqRxAtQBDJ0pATOSs5kZf16ZOdsGMb13hZRJvgzOV+NMjBUGZjeb9FUHVwjslYrFVs3pGBnprkyIYeWjTBJbnQgwZGuMcy33CR6UbbqBt8bkmF9JV/ILF7JJZfY4joiqIOoZkcVBH4OG89ZNKQjWSNDKkDbnzDNGhKZn0M0Zi17LdbNhsWq7WLW82LV2XabvMIis3r78gtVvC8owdT9gI6DK74OLjgI/okHn57EOev/w2zBaoD4dgcl/qNX0eQedh2gGrw9Ve2E1YTQOSohlF54QLFfV8yeLyJc+iqQZ8XfPxL3/O25s3/OqTt4Rvn7GlI2rL26/e8NUvIjKcsTx/Tmga+m5LGAaeX75kfn5GHztTX3mHOI/3HsQTpCKwwDno1p8jvtg4xgF3dm5G0Xu8x8Xt8GhBfZddkhbVTnHwGcHo5JksWKyyopLQAR22JZ6yorGz3itmCDtZxf16gOJNX0Pu9lb40zvLMc49KOn4etogaYPi0GEBaYvEm9NNObo2jZD9fIJtzu0VMqwRzej2tUkoHyjreFu+1wP6iIDrcD++X3aYgfNIt+JfS9IHPn/tJObAVi3Q0JjzgipSNehQIadUyu+o88lCwIfyfd3nOYGn/lWSek/yDUlcef5xPWeHTvalZQd1m+pTC1H12D4JFeFHv8bmv/3vpkmxL8m+r9DZOy3teVHtHzSnDpwmlLDLSZGm3e/M010l974JZgYljYV6xHs0OOgeliDpvYlwuLaLE4v0U3mkT6SjsnSXdXf3faxs/gaFB1q0rFvFGUWHTI8SOy0aHxPkkGxvjcDo2DlxRE62mnboczLumYd72K5Ps4VQNg7B0m5H0mEKwahQAGQqkXES5GTk5ilbewvaz2rr+Fh8kUeZ9BADsM67kXkUMPW9yE7SqkWUrPZouBLlxkwvBe/NwXR3ELf/gve4nAhBCMkxbCN9l9i2Ldt2Rdt3LPfsPDVHhq6j61re3qzZdgkzJzDb01B55k3D+axmVlU478kp03UDm21PN2RijCbUVC2hFy10Y9/3dN2Rdu2R9HTaoPYr7vpbbrY33N1+xvX1WyqZkYtDh4UrBLI5+kkeiT+zcUYW4lfvHKFySLKg7yadE4IIEaOHGWKaXqjZbVk0bO+d1SNiksSqeIn7YJuD24lynTMJpQ/egrFXAV9nzhbF7kG8NdbBvHKcLWqaWTOuP4Ca550qIgHvG3wQtBnQ2jPRMjjYppaua3GDxSUVcdR+S9AtlawJoUado4tK7DNmkWEAWMWiBOS4JcYN/dATcyxGwMba3/cDbRtp24HttmW9Hbje9GxM2McQMyl2vH37ijxsUZ0jEmxiqdlixG5DXc+M1qe8U9HMxfwli/klfnEJ1QwRz25V3PGhHZ6e35XKMqa5SPdMOiBpsNjBORfaEJMG+FAzXyzJ8RkMH6E508XM9abnFz+95adf/QnPvz2jmQe624G5XvCj732XixeX+HpBii1Dt2HeLJmdn+MrX/jGyiIRKqbBmY1XLPiay8tnvNVXtD/5Je//B7+NBI/GaIsiOp2+J8nl9He3sDopC0n5PtoQjZLaHVn6SOa9DxBHYL7bZMTXFj4CRRnVWO5QaLHLDXgIM4gry2dLH9Omu4cWH8UFD6AJIcOwQnXYNfs4jz4BcyhAgn7ngCPooU3cQ+U//NPj9R4A2oCGxngPUWR2gQ7bh+7815beBeYOQP40MYNJ04YtrppD7Ew6WS+gvWMScY0bkTy1/79+lqn9D72EU2fIezdzL9PB0HxsSdnDD8Vi7n5b9sGkO7wXcSSMZ1hGAKrgP/oI//wl8c9+cfg4B20ZEcXhpRFhpL6oUvcf4F6HWcoZdLJDPs57/1Ee4qEklU9NZfHHUTQ6o5QZ0r33MwkHVE5I6e2f+LKXsTce5d7ycdgY2XsEweJ9h2JaUNTberdh/uyH8DOh7bdoZ45CXddzd7syyh3nioTUqO68H4nIpbS+eGi7EWAySSJhtI80IVAuKmAX4gTgnPPW986CFZvjqQHKnLL91WRhNkcEqUpSwyzjIcOoflIZEkXLWQD5CNpH9fwIKnd/HYjF8BZv9p2azXxPVckUe1EySTMZc+ax9iUcQoyZbdvRD73tp4D3hVw/QY6J7brl6nZNn5ScE0kTSTOV9zSVZzFrqL0niSemTBp62m6gHwZQY50JwWFCcCWnSCpCnqempwPKzZfcrF/RDmu8Bl6cfwf3DK6u7ri73ZSJamgsZwOYqmrACSOMDt68loKzjgvekUbPZ0eJAGW2j7nYvammAlin4Wvq0hDwwRUHmoCInzy6bNC7abB5X3GxfM5H3/JcPiukNxIITqiqmqryLEPm5UIITW0DXG0w19Wc5eIDnl12tKs3XL35gto1IIpGGDTTx0yMA6RMVMXhWLgtQ3VLv76mqhrwni4pQ4SsJs6vg2MIwcTzsacftnR9hyq0/UBKao4kfU8/QNtFuphoo60ddQh4J7QKJGWxrEntW6TKFifXZ9rtHVc319y++ZIffv83WT77tnnZiwCR5ewFlVxAPTNp17QAKfueDgcCmIPT+vFI0TI4gcKtpSlavONhQPtt0WOEneFzsgU6hEDVLKnnF1SLM8J8wap7y+df3bD5856mqvnw8hl/7d/6Fi/f/4iXH32EVBU5R7arNxCF0MxwZCT3uDSY5K/Y6IkT4wpMA6REzok+9cTNCrRw6ZXxNRp7i+TpuSZ6uvI9BIevPDkns+0Vod8m4mCA3E6p1iUHHuRq0u+deslB7q1fQgP9vkp5fwMSOLCTLatrNUe7gGiykKLFicZU57v2f+M03B3aSx0Mhm9e7LvuP7X/vbPKo+5ByzvICUKDLJ7bplLNIfbYMpr/1Z9jrPsU/njslnubtUJfHLIK5YlurszuFHYq2KNbpr55UiedbuupPPewkRz+fm88HEjp9q/LARek7ud/V0oJLwOOZBROx2mHOHcNGEGH2Hrv94GhQvM3/hekj9+Svnx7WNZRu09rXU485Il+l6Pveqqfjip/16Fdkx3EzTTU6GKoA+5sZpF2WnOIPUjF1Ms4EUdfawNkmhW6BJJ2tEX7j3K81I9St/1MYnVYCNIRISv9X37C4h/+DsM2crddkTolpcRqteXTzzqqqkIlG9WPDxYBxjm8OJP3FHLwEUQadU9xIHWAmse04pHiOe3EqApD8DiU4Dy+RDvJoz1jNDaQMcRhRkmkYqlganVxFtFmYrZSNeFEid2tGbzz5JgnUDmyoYy8msYtKaSotNuBTduz6To7gjhHShHNxmk5Bk9J2bS6uXiIx2h2lw5PwmiRQnBUlTdaQx+KlDXSbzpu1x1RYUgDWTPee6oqsFzOOF/OEYVViQQ15ESfBtOaYtrZugrUTU3T1NRVwItRPj41PRlQ9n0mdQMvz3+ID+8zo6WJtwyvf8Lq1dbc7dW8pHJZpnfGqePgVGqvBAdZhF5NYuRdVYKx7cLODXunQ5MCxWLmYUCnritCqPG+NseSibg0FMNaj3MV3lXMmjnPzpW6ntlL8Q4nnqoQoOIypIGz2rM4OzfHHPF41+D9AlJNNxt4/73f4Xz5XZrqDBxc373mzetPGNIdLhoVwtBbHNisHa0I67Ip5HKWzdkRvL2s5GGdjKpniIN5fKdETJhXfLHpyaoWDjeZJ3aFMK+E5cUMwbPeJmIl+CAM3TV3wxr1Ncotb9+84fbmlldffky6/YLf/hv/gDB7hkgFJEJzCTpHfD3JsSbP8OPFZGdcyPFuM210o1q8qLglW9/K0KPdlty1RV1A2WCKSjwruY/E2CJik9UFRd1A0h6y2IIUleACVahoqpmFLnPG6B+7Hhc8Q7/h5vXnpBQJ8zN8VYMEnHgyHeu3n/PVL3/Kpz/7lYUUG5fxcmK3A4yd6F0yuqdcHtVO8DuwODsPJhn3So4wDHkMugNA6gb6u5Z5mAGrqY90QgFlER+26NAis0vjf8TMMCZ7rNLXKh5lNCr39pufQfXMpPRhYYv75kvkSPj3LmnZw+mE08xjBT21kmNpip68vBtqB5mfXr6UnVE316bqbs5tUV++QKo5xA3a3hXam3+19FQ8dzLDEXaRnKDbIqnYn5+q7B4gfVpVJ/HRQ+XvgQakOK2EGmI/odl3ji0zPj4kGD+F1U41XjBtx766++RzFORhyLtoJbCQf2RG/aU0DbO///fo/+s/RLv+aWPpxNONsWV2pxYtV/fEd6dvPQSa76hrf8oJmEPoCbH0/bOGWt65OZhqO1hc7uOGxWIWVvbsh17mfYCskxMKWaFo9Bj/FXvUuBnY3G3YrAdSnLNtE1981eK8Z5QqBycEH3BOCF7QPHpSg6/G/rTY0q5IMusQjHElAc7CL3tvNoihRMALYiTgOnbXaPow2kuOaz4Tu7LJRZ0b5WOoFqIXGdGJcXyGUOJny45Ufeo+Mc1gHpR+M7Beb3lzc8fb2xXbvjdydhVjdMFN0yOr8Vk6yYTKE4Zkjr1jiGhvkWt8CLhgvL5gQpnUJ7ZDNKo/3OTUtJg3LOY1i0WDDrDpe9qUiUMiDdEkymKuKKES6plnPp+zWCyZzeeE8G+A2Lwbbmmql7z37Pfx1RmeG+Ldp4j76QREdC9UXCovaMjm0WUo3Aaad4JkLScHNRsh2YESLVLOkZ/L3rkzm78iUaibgAuVSSldwDsDDBQCUy0eXM45Kic0leJcNHG1dxajUpTgM+KUnDyL+YL58oxqtqCqF7hwhsgM0YHLyzkX598CzMA3RrjcXnE2/ylffvVT7q6+IElPHWZFImbGxqIO5z1JM00wt3/vfTFQLmLvpGbrV2VispBdOWWGIZKTib5RSDmRFFI26e75xYJtl0mDsk3KLz7+KRc/e5/w7D00OP7gj/5f/MH/8HOqEHj79g3ff/mci/fO+PA7f4XZ7H0D4GEJFJWw7kA8HK2zo9HyvsDMZujuKDsdaUveYitJHNChRdsWHQaz/VEw1bJxuOU4kLYb4uqWuL6jdsLziznf/uiCWCU+//wOehPDd31ks12x3a5o3AJXBaqqwleBvu+5ubvm1ds3VKGmrioLkSbm6b9a3/Hqyy/4/POPufriFe+dvU/YjqqK8s/J1A3He83OqlGJQwLNNIsacULXDozhyUZ0pH1k86uvuPz15UEpU38ppa9aJG3R3lgGSKM6dtwgSwOrS8RfQzNHFi/GBiOLl0WCGWHzChlumfR7vBsznPp+79m/IZh8QBjz4D3HOOZkdQ+05VFAN+nLyq8pmto7dnbwOXXLQ2U9kPmbAfajpHsfhu2TW3Cc65sfIE4UBLZLNosyvBWGnW3VNPUfbdz4Ro/KfUrKaQcAHmrjeCIc37GMO0fRPogHIvXv/S7hO99n/S/+S/Reo5+Ccsecpwfu9JR6cPleDV/nyliGqzwSQtHqmHkOQyLfteVwfDSfvUOrnYnW/bQza9ofdvcqnj6Xp9vfKzKmMjtiGRn5PCVC7CJt26HM6LuebdualC6bGdzoRhqCL2eOnSmReANIQzZnmBAC8+WCJkQLPdwPtEWY4JyF5w1lDa+bwGwxw4tHFPptb8KaEXOMXt1YmEeH2TkiTME5vJPp0OTF+CMtuJkQxshQUrbQErUnaWZwSmoT3arj9fWKN7e39L0x4oiUUJFu5JquyTmTinbXCfjKMZ9VqA8QwHtrm3du2q4BUjJg2LYDEaEKFc51qGa8E2ZVxdlixnLe0GrcKR9jJCfDFGayaEB83tRUVaCqa0JVUdX1iXFzOj0ZUA5Dx7ee/TUuz39AEk+7Ae8/IccxDqapekcP15QzfZYSq9LsJYOHIYMkCjK36Q5CLMAzFTG0hSoaRfSCOrNVGQU2CUeOkRwHcs5U4kGMN9Ims9+Jy70yqzMzVcQNiGTMNiOYe74X3KxhtlxQzeaE+gxfXeDcElygdiYJRQqxtVQ4Ccy371HVc2bNkuvFkm6zMiBcbAdHuqPx5GItyyBKGrQMhoEc02SvGaORm6chEUMgJSOINzsGNUCpineepq4Rr2y7nn615g/+8F/wyzdXPPvo26z1in/5L3/G7ZVxXLbtlqtfveXv/9H/zHuLj+CD91FpIMjupF0W/fHkaal8n06e7C0yo62kHtwvmm2xS3HHndd35KElDz0+BbKrKXGmTI2TEykNaN+StiuCi7x4b8ZKa1KzYLNtufpqYBgG7u6u+eKLL/Czhst0Rmgaah/wVUVOmevbGz7/9HNeffY5t6+vuVw2XF7MUSw01avXb22ciLBYzgixjBnZPasxBowcaHtr8R4OFBXjE3PlkBIcLmAhu4pAxA4M2RYF4BDylI6MayimHQw35qgiwp7LYKk4QX9rdjWuNvX4lAQ0mWd2f1MOZ7sF/qEN/6HtFPYA5GM37F96CBge79GPAA89+vIoIHqgLae2TQuleGmSmu0d0syMUH17dU/y8o4qHm/P1wFJT0nHNqZfI53CAffAApweBCefQ1EJZF/j4tYodr6GfdU3Tu9Eq2A7vt/lLaDE/r/XRifM/v1/QPrxx6S//BUHjorvfIGP/Ca6d3eBaPtSxFFsdf/B3pn2X5uChWjcdCZ9nFUWbaeoq0doKOPClTMylBjlBxL4XYkHq8SR5HNqtu5hSN21aprbGUaBg5SIL7ntqc8vWC5eIG8+nZYxAbxmvHcm9SsmUpqz+SuWBti4DcSUGbIyRNtbw8zhBiP5FvGkPrFZt8YX6T2b8gziYbmcU/UtjQ9U1NzerFj1LYr1jQ9S8KAYfvFmBmDS03IgKSrtKniCCMFb3xbcOQFQk9Zm04SlTERp24H1asubmzVtPxC8gfshDlQZY3KpK3I0KeNoruCL44xznsobBWvld2M173V8ipG2bbnb9mQtArZiV4oTmqZiVlc0VUUrkTEyUdrzVRlBchUclXcEPzr1HrKYvCs9GVDW/jnLxfsmvVEzZh22G2Kb8JgjvHceR8IVFW+KBiA1j3I9SFmI6iwedlIkgzeIzJC1iH1t5I5zPecMbvS6zuTseP32hlev39A8f0a9PEO8o6qXiC+brDjEV/hqTj0/M0lZ2j9ZmXOOLyGaqmbBbL6gquf4sMD5BUINjBxRFoRegoeiQp0FDxrJcUvqb3HiidFOAG6UtuY0GdEaj1URqYsBSBcqcmUUB6JQF46s8aVn2BkoF2/ljMMVx4tt39Mlx5vrFV++vuIXX7XU519C7ZAwZ3nmadsNMXWs1h2vf3JL9TcbXA4oodgdKSWo6IiUmJbGERGM/8Y02eJoWSRGMJYnD26GHokDGgdS35GHjjwMIMkwU4nEI2USqjPqJ+cdtfNczGY87yp6mfHVZcPbLzs0K30fWW02vPrqS7Z3V5xfnDFrZoRmzpCU7eqWdr3i1es3fPX5G/oXz5nPZ1TeJlszmxGLI9l85uFu77St+4sZ06FmugbTUaiqHc2sxOAWCMGzuJyhqaNbRzO4Tpnbv/iED3/3ox0yPdhkgJHnS9VsxIonb1lrd+0SRbS3w1ZsCwjd85Lu19C+Re45/+x9PZH2t7mvBaCOytCjgp5U1hNB64N1vuv2MpRFBE0d3NyBRnQ4t/CcX6O+R9tx1N0nG/dNwOYpjHMKSB+D9ofSbhAffL2X5941B9UMHZ0ZEfCVsQv8mwDSx+0Zsc7xeWzKI/fyTxlzKjdnwne+Q/P7f531/+n/TF5tOPTi+VdIBZTsC0jvDYW9g/jXKXiXW8h9Qnub325UfecRRt7/v0aFdV/G6N5vp8bLBMb3BkflizQrMZJ1iDzwwselpvxNX12hNxu8VqShRATayzT0PT64ItyxIr1n8vRWNbWx4YdsAidN5KGnSQFCQz/0dENPztE8+TUxjBLI4pY9yzXqBjoGbu9uaVNhXRmXYwUwrWVOWhxnSmhEPzr+QO8MAI+muq54bCuZLLlQZZf9UM1TfYiZTTfQ9gPihFS0lykpOUeaypFyIBahUlalGyIJ446sfXHZdLmo8k0QRhHeaVI0ZYY+crsZiGqgdhRshUoJlRCCTCEqzZEnYaZTCRHzB5g1NbOmonIOjyu2k8LXmSNfA1AuCK4ArKwMacNmu6YfRstHAAuk7opX9ggkMxBTpnLOQGNStjHTJSvLOQeeHdUP5u6eRKEEZR8BjWB0LJ9+/oof/+Uv0LohO3ge32N5rtR4XOVx5STtmyV1ukSL1I9S/uh044vHtxDIyQ5xXt004EwkGM1+JylKZS/SWThGAOcCIgG0GLA6j3OhDGqbPE4ogeoNfo87WUZtsR6DzU+eYoXtX8zeU8S84Yo1EIozDs2bK+abjmr2GhcC3VZIg7KsZ8zrOa52BB9IMTH0V8TbHt9lc5TBvMy1SEFGwgMDtAdn4mIbM5IkjHQZ5chZ7FFkNFzOCY2DqcSGHo22COQY0RhJOaFBCM5bPyM47wmhpqobnPNoF3EuUYnHaaKuDEBXdcPy7IIwW4IP5JxY393SrVconiFnrt++5fbNDZvtmiS2GHmnzBpPrGAxVHRDbwbbDtJ6Q5g3hPM5w3qYQH/B9AacyxgUGXvAVBDGT25Ww6B4b55yfYm4kVJm9fErcmhMJ+JcOc3vbyrjjjleLwv/AYBPlk9LhIXhDm2XyPwZu9220F7s71771Tywj927/NT9TnetZxSmPlbWXv5T++rX3WuflHVamjLSrXff+1UhlD99z702PgTs5OhVPta4h4DXI2XfK+tdwO1EWdNwOFGG7uWfvPVPAkqxdTCvzRGgW/O1XtZDoPgpQHT/THTcL7L/fTwUa+FpPCxcqoqz/+w/g6st8Y/+whxa5Lhzn9YwPdHRUuawa4IticXrWovUbpqOX+sAI0e1jeyLZiqV2hJP/UgjcdC0rDsbwlO/7/fjXgPFe6MUSlokoHogbJiKGkMOjmEGxyzZwE4cjENx05pAIMXEEAfw4NScaEwQo6TCDiMY7c2Q1fwtYpFQxoQfEn3wbGJEh0QXjUNRBCTa+ihljx+2Ay4JGgJ9vzU+4+J4Y6Gi8+SopGkU3hSHYjEnIymcmJa7RPwRMI4iE3alnHG6qxdGp5qBGAe812myDTGZQ45GhsYRc2CKSuegntXMZ2dcnJ2hKmzagVW7JnhPHSq8Nw0pKmhMdNuWu9s1r25uWW87hjQwxI6YI3UIVMEXNXhAs8PsKxOpmGd5UarKU9eexWJOVdWTc7NzI7B8Wnp6pJzZuVHvVIGoLcPQE1MuYl4bjeamv1uVzPVcGXTkn4KYDVz2KnTZ4ZJCZTaTQ06o+MkBgpzL5+I1rJmAnV7u2o6/+MnHrKJy27b86EcdH33kEamp3QIXLByjpgZ8g/MNfdeZFjYb8DXzk4x3gSEN+LThTBqC3+CYk10mD5HUrmFYk3VAagOprp4zxMgwbFAdcCHQLJ6ZpNEXQFnml2C2DyEU7kFnROx20jPQqCLFEajQChRwKYWGSEb5OkLCTiBDSmgIrLdr5q9eEZo7aua4pqKqGmZNTaiN0HtWL8l+w5evXzNsOrzK3kKUi4q0TAZ3NICUCTTaTWbYrdkk1VLKGC2LNQ3Qt2i3Ife9eaEVYEnMpGHA4U1q7ALqjdOrqmpCdY6vbhhuO1bbFevrntV1YnPTU3nPfNYwW5zR1HOaqmE2swPM0LXcrm64u1uzur7l7dUdechcLhrOZ75QJ9RI7Km8gxRswncD3etr9OaW6tk5269udw89ShvKSjupe4qH9jAk1tcd/Wxgfl4TU2Z729G3e2HGgM2nr4l9JFTBNpXxlD/afB3QwIwLD0w2kBOo2QeIenQf5u3tKpMOHxBqWjopjdp/x2Omh67r0bW9rycdak7dy+4xAdTVNidSdx9wPpCeTlP08HUhm0T3MZD32LVvKJGTAjBOln0M7PSBvjgFNPd/cx6aJbTFlOJd6bicU2PBvAKLoVjiIKb71wCFT04PvZeHyjoAlro3V6YJR/1v/9s0f+uvM/zX/xzdtCcKebyRepDnnmvLrh3BGV9kCxqz2T16h/YZ7ctBfu+u8VEffuSRwHsHLKd8KR+1618hHc9vVXPYSTs7eRGx59ORMPzoZrVWTqwWAuIrtm3HZtuSVWmHgdttRz2bgRPmwZO9EnuzAzR6HjPvypidZcyOlJWuj4h39LGncVKO8eYpXigocb6EXQS8JPouFun2GF0nEZxMwiyRPIHimDKiUoCUCQhUtQiDijnXKGhQ0zqOblgCth/iGUNhCkJTeYIXUlK2bWRbHKPqwp2dJdHniMvWgc2sZvb8gvfef59FmHN3e8Oba09VB5oqmFZVHEPKbFd3vH3zmo+/fMPnr9+y2vR0/cBquyVFI1+PKF2fqDSx6QcGzQzJWCMyxoPdzBoWsxlnTUNdG7+3d8Xp82usdU8GlGcX36GZX+IL3UYIFVXdFA/kVAKsp3JIlPKCjOdRo4lyY4Yh2dSJWUkx42VH/uwkmO1Z2UQnG51ygnAiJSwRzGYG7N6uOv7sJ78kJaWpljSLJdXMaHqsxzI5DgxxoOsS215MZWw8BIbExSP06HZb1O2BBofInKG9o99cE7d34KBZnCHDFlfNzLEtbslkwuyc0JwbGHQynWoMVBrKD1VV1NaGVEZP4hFMSqHzESjekOObtM+5THKnZojrck/TL6gaY9qPSS3KjxdCZaHEvBfm84a+69HFnK9W17R9z1yLxK3074NH53Hm6B5BeaGfME7HEfTYP80Jhq7YQm5IfV/UjclCQ6ZiA2G8D5PNomCSXucqyEK/7nn96RtuNlve3rSsrnsa31D5qvSIqa+rKuAkU9c1Q1aublZsymm48o6zmed87vGSIQ9IjjhNFtYqJtarNe0K4idfcP47v8btX37KGHILe3Ug5tDjx3dTJplmGFo7gXtv3tf9JjP0+UC6ONxu6K7WNB+8RBZncLe3mU2nvz1AXzYPGzyZncphnNx2EJGyyGl7DRKQZmHxwLf3N8uDNeGxveeB394F4u79pvc/C3tYeUyzZ0izQG8+MUqnU/WM2PsUaN1v41F9j4LS47bs7+r7954qJFTmnNKuiwf2XpmP1DkVtw8e4VCN+1h6F5CcPsveuDqs7x4ofqxfTxwgbL14gFz9sXS8Kb1rk3rKJiZHf48fUnZzSZqGxX/0H+L6G+JPP0FjQtWNsg8eR7Bjuo+0RzA3xWwpBwYXHG5pUWzEeXRIaIqgh2Dy5GPcG0l6L+8+iHxnq/fR6vFjnjwkFlloMvaNg+70ApUzr9uR9nJvIu2waIYhom3P5fvf2xNeWJFDUnI3EEIwW3MJkDP9kNi0W2JORfBjJkVdVHJ2dEPEBccQhd456qqyfck5+sFoeNzI+CLmUDNpUEXMGdeZHeRIVO69CXc0S9n/R81jkZY6j+KRkTITNewgdrjyRRI+cWBTNH9q0s3gwCWHpt6eC3NMzgqaIEald8l8S9Tqq+YzZmcLFtUSF5SYO7SCum7wvmYYItubFVdfveWnP/uCP/6LX/HF62v6YaAfEqt2QyaRo9C2mTs/0LkNN+st2yExpEiMJXypWEAYXxkNnvNSohcacD2tejqdngwoL5/9kMX8A6rZnJg7fLBbY0qFMcA24awU6Zmh/Zx35J5mA6EEGdniMbsHiqpXTXLjROzlQTlFGPv+6PRTO+HZcsnz99+nfvGMqnastgPrbcswdJNhq2omp46+X3N9c82bN5F1X+HDjFkzN87CpjIy1dySuzXVZkvTrFDvEK3o19e06ytyjFTNOS4Ntlhns19TMr5ZsKzniLhiPyqFid8ckgwMO5yvcCEY0FYY6WdMEjaquEe1t907TsLxlaZCCI9koppXeMrZPMQSZF8GPCOoccxCIC8iH9Q1/8Hf/Hc4+9aHRuJNUZ+Oi8ZI6aGgsh/5tdhK5mKbMu1Ae4B0JBHPCR0Gi1E+mO2kSOEJVTtYeB9K9L4MWCzzoe1IQ0+KHTn2DNuezV3k6rrj6qZjs8nUlUPVCFlztDCXOTnqWU1deZI6ZtcbJNyac1dOzKuaJhS1foTUdZASms3ZabPZMPSBuz/6U5b/+/8N7v/+T8jD2HcyhnslY2N70qKVd6cKOSrbu9689rJyuDqbiqd9dc3lRz/Av/yAuL6BXChQioTaJLwTq/50704qPH4vAwYxS+24hfY15Izqh0h9Bt0N5M2ES/fHz9dKRzed2ofedc87U3drdjz78by/aVnvAL0HWfexwclN1b6f3toFqeZIuznO/njaRwP77+UIERzv+VMbTrTnZMUpwubmcOyMhb5T/PuO34FDSTnHfhz/+tNjbZLxf7u16/C57XP9u79LdemQ178g/ekvinB/v+BDkPj4WLccxzDPAJcDL7Z/SFFNR6PcY4oYd+IxyllSJvHXPvA87IBjdftBmY/h4od+u9e/unupk8/BXhlJzXZPx144hMSjBzUpET/5iuAqauepRp5jMQ/ugEc1MCSoBDKZqImomXYYEAmEZNLDvk8MycIC5j6j80ByniGrEXE70xgNcWTZtMN/8KH0rTnshCDFZl9w3sIi+6FEJVPMs7sI352hR2JWC49bOsAV7ZIrIRcrb1pAJ6aJ1DwSlVunVqGEQSwMLxIVdSDeE7PS91B7s99su8QmdYR1y2q9ZggJTQMpJ5pqhnc1aYD1ZsXrTz7lJz/9nH/+55/ws89fsW47hn4gZTMBqCqPDkK3TlyljhS3bNueIWeGPND3RR0fbC+OxYbT1Ptm9mccnU9PTwaUi+VL6vqsGALb6XcEgykrWUws3UeZwidmpMTA1GI7lktjvQGyAiQtfJCiYqLl4AVfdIbiTe07ciWNK+usrnl2cc75Bx/ga8c8OGazhamaGaWekZx6Yuzoh8yQPUkaXFgi9RLXNIRZTRUcqsE8pLw3aWrcEocV29VrYtfiqAmzGsIMDb6QDgdEKutEBcTZQC4q/pyKUSbZBqrz5bR0GFRvJGCfPKuleJ45maS+WqRko6JpApKaLZRVSkbVlEYgajRDkpSqCXx4/oy/8f3f4O/8u/+A6sULtKpMkjotGkXNXlonJaaqjKB3au1OxTI+p9ntFWecFA3c5FQcoTK4olJIRhVl5n4JTUKOmdi3dNs13WZNHLbk2GPLpqcflKFX2j4ymzmaWYUP5tQk6HSC9KEm1JnZfE5dN1ShRnPcEeYiaI7kZDZHWQ2cD9uWYZhx+/lnLD88p7pY0r9ZmcTcgwQxr+29TcSkSjL1jSr0bcJ5M7aeQMJ4GMiZu598ykd/97dRF1DxxblGmby494Hi1N173+8lk0LpcIdko2/Jm6+Q8++i1eIe2Dm6852Y4l1A9KlCtf289+5TkNii6YT68VTBJyp90rM8UuTJi/vXjioQMBqsuzcnwyC+E499QzB3EmA+dMs++ty/X20dufdMe993Y9f+7uOy/XacVNvLwbA/0abTz/Boui++OwDYh78LOyJ/yknQHHJmf/tvo//yv4Xf/TsmLdT90h5u1XEbH2y3A1d7pAnmyDLawlM8d/OO6/Gw5CI8qfyO9SJmtDvexg9fksh9wqMpz94PJx9z/GF/ebmHrW38jKaZZpoqtp6nnTPmGADiwQ7SMuiKswiUvTFnhmFgs9ngpCEHc9RNBdSAATszoxMqbyrvsUGqpmFyvjJnXbXoMDkpfUpI4awUNxR6QCElR0pS1NRF1S0GEGtvnZFyAoLhnKRQovmk2CPekZJR8YwaKO+EobcOkmwawuCFHCHGVCK21eScGKJhGO92+0ifMts+Uwfb39su0cWB67fXhODx1Yw4dOS+5b3zOSAM7cBwe8enn73mT3/6KV/erFjFbLakKRK8MKuaEnIY2m1PimYiNww9Uc1hp+97cko4H4hDJg6ZdrC49mZmZ+PgoR3oVHoyoHSuYhphqpOLu1CQvBTppO6kOXa42QEsx7h4aZFQSuGglN0A3hOvCrv7ZZTisDsdeA/BC3VdsZjVVE2D81WRZdtJKWsi52ggS4J5JvoALhSJYYULgqZgoFcKOXuKpNQzDC05R3xYIqEx+hdX7B6RPRBIOVXCFM9zciMbYRrTiXmEKPvq5tGb28qT0k+Cyi7CQS4TRzWbBDZnYjL1wDBEkodKZLco2PDg5fKMbz17QXN+jlQ16orMP+3Pfqa2HUKZAoxG2yR2OGhXzQgsR4BUDJ33gOdkCF1YAoRE1kL9lAyAjiDROY/zjlQMUZvCKVkVXixxxjmqxaEJKF77jip4vHNkdTuQX/rCOfuuRVqaS6DVvt0QNeLnDXBn9zjBY8AylVe1M0o/nGiq5Yw0rcKH+1282wE82ffMfiwdgMoHtrbUT79ZXPANSJgA75jz6FU+XN8xuDi+/7GiHin7GJAc7F9PBI/vquNd6dSeenxdT144+m7EqQfl6gPZT1b4hDQKhh7Btu8ub28AHgPAqWw9gcsOPhzhgwfaenzPv7b0WNlHwPcAYk0SNnDLBf5bHyI//ww9+xa62R4Ufb/QwzXwZL5jACZQYtZxsIiW38U5iwrz0DvzYhwymCPLY207DSbHxz7cZyaAv9+eU0mPuvqheVcGzbiy7B9UDu+zfpC6gqgmGHJj+y3HeKhvu4iqJ8Zcmm3OrbZnj2wiY/k6eV67vfh5YyhxyDuAu783A0ZF6My+vYBbKervlHWvTqMV1JSnZ7TgKuYzkrOio3OkYII1wbRnybCGIKRkeeLgyFjYzTEWucBUThQlZVegjQN1pCHStS0hO/q+x42e21mJw0C3bbm5a7lrjZvbF9ss2zsd3hmx+6j6T6rEwnOphWpopGQ0zm/DEd0wmKRybyD8GwGUHhvsMQ3EoSP2PakbGPphUt3GbKAjlY16VBGqmmQquEzwijl1AJqKKYZORt6q5pEWvBDLKHfOYmeaCBZc8CAZjVtcanm2POP9919y+fySer7AVxWjV27OkWHo6fpIn2siimrCZdCozGK0nksJ1Y6YHTl7SJkubuj7FaIev1jg6gU5VAZoi0fXZGs3gbhRFWLGrgrGJ8ged9TeSc3U/Qbu8jTRMrkYYGpOJXqOFklkJMbBYmGnjrbbmBg7RoaUiDniB4gxEipTa5+Fit/56Lv85m/9BvXLFxaze+Q61AFwE/gdz/fj5L3HQQk7qea+KKJImaWo33UEl+OJsoQMS2RSNBWn855MJA+DkawOxsGpAqGa0cyW1M0tLy4qPvjw2zz/4AUffPQhz99/weJ8xmzRUFUOp0LMkaE3YOqdEpwS8WQ1s4yUCn5OcVoYxDs8BbimgXV/x+KH32LzyStEIFQenE382JuXv3WT2mxQkwhbPzGBZnv7h6m/vgNfIiuEAMPRNNXdYWm3OrvDko4LHVbIsNrbPCK6/RKpzth36zwFRQ/KFAFXGUjSo83skep39z/04+nsBw155D6519j7gGoCgA+U85Rm3XvePTD74MZ9wm7sG6fHAPVenlNSwXeCnZOA+HDq7l0+AP7f5Lmeeo/u9/M3rWOv0btrOkoiLEPO+O98B9EOv/2cfJ3IX755cJs8dpY5buRBPRNat38aM6rRIrHUwSSOI2VcY/4BeZKO7pUrGJhsjCRbhozIoR32cft27+f4Tcko29jl2Gvm/SfZrRPHWm4ta4Mc37LX7Ony3vgc5SjSVFQ/+Ij1H/0B58/nuLOa8LmnqSvOlnOGqMSY6fqefpAS0EMgl8Ajhq1ATKrnXaZpqmL7mKiKGtqVGN5BzNRAxYCgCXxMkBXcKHGzPTsniFPIXIeoljqL2ZkaX7TD6hLEFFWI2UEmLfuJ9f9otpyKFFLVQTagmHMuGkZhAERN0rlqN4hXqnljYBMmdph+23L1VUZlTZciTQ2XLy/ZrFtcvOH21Vt++eUNnVY0kpkhtCi+CszrmioEQhWog6MSz5AMTPYxkvJASmMfOYakbNqBatPRrDa8aFuGFIu0lnuE9Y+lJwPKdnOLr2oyiXZzR7u5ZbO6LdFsEjk7BpToAFfQuUqJp2lSIe/KYKdEg1ExCqE+mgeuaAnwLoTRnEww2zvD0ki27+IyZ2dzPnx5xgfvX/Ls5TMWF2dUzQzvw068jqmE+35gvd2wzQnZdnRNZLaY0UYTUTu2hHzL0i9xrkZcxUjPAM7sFnPEDcqQM85VFnvaBfMAc+NsHCd4iUUujiTZ1K1l4mWNRq2k5pTjJRfJrtk/JlUGEsSeIfZsYja7hxSJGunbjioLtetZb65pty39YEAJGZAsRR3uySqcVTUfXF5y9vK5ETo7Z3xyOe5Wj2LvM4Ll3UKR906IQokfuTs5FvtJ5QhAloGoKZOIOBUjos8JjREnjXGQqU4nu5jMoFhzxjkIVcXF+ZLm3DFbXnDx8jnPn51zcbYgLGqcd8SkJbpQT7tt6bqOVduyGjokO/re0wUQsQk1DNkoN3HUdcNiccZiLrgOrt98wrf+rR/x5r//42LOgYHO2uP9AAhVJbhKCtVFJrUJ+r2Jovub0u7T9mefo/1gAL5vT+zoxTP7QH8Ek0fo8WKug9lOjpyTExCI0F8flX1iQu8DRWmQi++i61dIf/PwPQ+kRx129B5Ge3c6AXr21bIn8z+17HfVu1f/yXT8IN8QFB2U9y4w/EC7vhboO34J+zd/U/R4qiEPInA4cBT6RvU8goSh/DYeiXek69Vv/AZ69Rn5/0/cnwZbs6V5fdhvDZm5hzO+0x3r3hp7qq4W3UBL3RispkFghGhwGAGSAoMIOYQJ+YPDliMkO8JW2OGw7LA/IFvhkGTLoMC2ZIyQoYBuaJmmB5qe6G5q7Jpu1R3e+05n2EMOa3j8Ya3MnXuffc573nur8Lrx3rP3zsw151r/9Qz/p3P0qjMRdYsmXz+4u1NEIsQ2keSpInExKxFi68HnA2xeR3dV36rfPxiRSI/6tD+ibpV3Ta3GcHjIKgt1bmrE8LEvcxhPGdakQe3d12U0HP3zqv+fTgd2ESGomlc//jJalVRfvOClBxX/zGfu07QdXdvhO0fnIqu2wXce33p8DMkETuUgKV1EmZBiXYdEaF4UKX/vk7RAaZOcbWzSTIXebC4frhP3YwRMMneKfXQfjSltglcqaT5NDr+bBH+S8UcyEVMKNMkOrWef0bqXYGY/4jwPQ3QEF9HWEKLCOcGLYdU2uNAxKSyuCrTRUpk0D5RNnvXrVUfnauoQmR1qVp3jcrHGt5EnT5ec1y2VVTgUzreIpPDStiyYVIliqCqSZjm0HdK5TBCf56fWmKw9bVyHLJKz83rd0ro2jXM0CRzfMt0aUC4WD9FmQoyKy7Ml62WLF4WuKqqDklI0VknqfBEgEFwizowhJkklkRh0kkrGbO8nkYhJ3k0i2Zu2N2iVRGiOIuQpqxXMKs1L94545aVT7t0/4fjkiNnBnHIyxZaTJPmKfqRC7hePgNIerKLxF9TnZ5SrisIYTNEyLx1xOs3CnZ6QPEla29DRuSWRSCeOUk+ZyJxoKwwFOhtRx5gCzUuM+JgAVJAUBSaIG2w70yRMtgo6lIjS+JjU7D46mtDiuzWN71i4SB2E2nuCJNqdE6+4MynommXyAIsRM7z7yUBYYlJ3z4xhUhWo0mbhl0D0qNAlsbLu6Yw2S95mmcjSGMkkqL3+JNvDDiAyjlTeQjY3SEAxfU+idbTGTqYU8wlKaaLr0Cak2KTZgFrn+OemgIPjikIZqklBVSmIAdd1RB2ISuFcyj/6mnq5ZLVac7lYUTceorBQQhE9Mi0IhUpcpKqkmk2ZTyYcHp0wma4xMfLo4Vf5+A/9McrTQ5rHl7jOYVSRTr+lJnYxka7PLLrQ6M7jhnBnmwW7B4Hj/TpcLAlnl+hJmdXUI1uvYVVPi91msxlversbQt48922wz0lX9pbokfoiOwqNqrSzWVzNaPv2Kz/vqcvW3n9jJXe+7mn+PnzxgdO+7h1/7qUve269Kb/nOqzsltOnq4KnvfcNG/vu89dltTnv7i/3+p+uXhzXaV99x8tIpkobaDr2DNzevs0NFJOp1vp43ltl9ev8ln6FPnKLPjomnr9HmN9FxxRN67ryRnBpk/XoPbja9M0vvbiEqMAHpBPi2g1q3P7y5rie96YYiXWHcjY5C7ZuSyq02830h8yt8rdbIqMGym5fjW0URvh1aC8Mgt4xaOx7uZ/UYzA5tGez5Q41Kg8n3D18mcrOKYo19++fUn76JVwMeOcInaPtGuq2xXUdPjuO9Pb5PkSatmO1aqnXHT5GOudxMUWZ602xEsG3I5IsubSKQ8xs12WXdK2Tk43VWFFJW6l1kiCShFYhBxexSiUOTkiqbunHmYHxoxd+JJlMVjlnn4cQk8ma0gYXPTEmXONch8QUfrEnP29doLIVZWGZqoDUDp/NshDBoJEAbeOQNvLs6QXeB5wPXNYr1k2X6xdBJQYDY1MoxUiug0tOOErrxGUJWTKb7Et9G1gu11yuF9RNncjWGWlWb5FuDSjff+8hxk7RyrJaLFgvGqQrKKtDok8nh2SvGPDB431WgYaAkoCVBDYLY/CSRMFJsiRgy3Sa8JFgJItaBYMM818bkycYHB8c8PrLL/HSy/c5Oj5mOp1SVhXWFomupw8ZlOe5RI+EluAFMQGlQ1YltwSnacRQTcFGcGFCkDlaBURSPaIITejw7SV1u2IdGyb2kMPqlKqcYvPgRAkEkj1j5z0ug8MgnigBF5IjhiL1iTEWbaoUhlAXOFfju5rgGzq/ovNrls6zaIVFE+miRmPAOzSKiZ8iXUsIKYB8YSwdAipJDJW2KC1MCks5mSZPNREkBJAOcS0qexn3HombxS71dc+nlVYU6Ts0/8tfe146SU5Cyds7L/oiiPPJccaU2NkBxXSCKi3Re6wyRO+SQIFA8B1N19DEBntssBND6yJ1WBBXkaZu4NlTKAzRWFCaaWkJ3Zp6teDs/JzLxSXrdQNRKFxgGgQjEUqDUWAqODw84vjBfarZAbZ6F5qG82fvcCmXzD/xOs3jz+O7kOPJ5igDKkklCxSmsKDAlp6u8YMEQGtFWRpECV0bBhW+eI9/eoH9yGt0n/u1nfVfRiv5mD9yjxhpCwjsIIN97/1NQGnI2UP9+MWkZTdlubNJvVC6zdrVb2TfTjD5PKQ4RtW3BYG7z/f37Htu96tcnSK7910peh9YvCZd15R9v19b6E0PjNurDcFUmTkibsDKDVmPfxSj8LoCFFb5dKjt26pJQDWvNVcaqAyqKFDdiuL4Ls2X3kXajutU3rtJGVBaEZ3AFQWCXBnWBBAFukD0YTNXkdEAbZ5XqOw1HRAtjO3Ux33SP6n2rglXXzjVd8eVItOPY9X5zlNXbx9f3qrZxryrV3MzMv3qn5nNJpT3LBN7iLFfZXZwyOT1jwx4IfhA8I4u29JH7/A9xzEQQ6TrPJ1raes13vvsoewTp3WXKHBc52iaFu8cdd3RdoEQAs4FGuvovKf1AaUTDkmOvim4RbbwJ5ttAskwzySDPPojiGSEbVRa41Fhw+qSe6MnAlcqYQKlNTYPvwKiTlJU0YYMi0j+u8KkMMy0JrhApz2TqkD7yHxSYrUmuEgwisvFisViybqrebqoUyQeY+hch7ERqxXeebwyBFGs64Z1s8aHgNElWqUwkoInZjMnEaH1jnXbJvzik9bTdbenCbs1oHzv6++jTYnWlqapefTeEy6ednTO0EriogwhhVYMHcSg8SGAD5gYUkhBozA62Uj6mOwCtZgUUSckMOIlpk08c0DFjPL7mJOFTvYUs/mMcjrDlmUKMu8DOniU8cTB8SNNWCQg0lFYi7YRKTpQHlt0dJ2jqx2xhom1NE6oXYlVFXW9wnUtyuQQTzqwjAuetiuMbjmcOmbVhEKb7Nnu6KKj8yEPSIsLHTHTFAgBpRQFYIhYXWCqEk9FUJrQtbTrc2KoscrTxZq1i6zWiqY1uKiJLqB8hxzMIFi6tqHpWhof8Wg8CpsnbmUMR9OCO3dOmN29ixQWHQPSNaiuRboWqUqwKVLQ1oqRF+gkzewXuv6aDKoj8ilS0uAjLiTVdki2lInOKKBU8sIvjo4xk0kqqmvwviWqzPuoLVgIBPTMQEgmEG1oaZY1Z5fPCK3BA1FbVFVhyoLT4yMqG/GupQ4ttTR0qkEAh6WL0LlAaWB+PGd6dML06IiD03tU8wNM8YRAQ9c1vPeNL/Hq977Jk3/4+eR45pOdjdaKYMB3kehTX2hrKCYWvQ7ELtNoaJge2BQRcQHNOsnWxXu6L79F+cr9vAGGESDMO6Mus7PHnk2jT4M0ZrNbDJvHeATz5SugZF8aA4ORFEV2n3neHiw3fr29dO956TYZ7QFg1943/rvv2nXf96RdfLVPsroR9bAZ/vGwjvK61fg9pw77nn8e7r0x8+sA9XV5GkM0BUKXzJX2AKCbM8iHuui2CdWzedW28R8MBm0aVDXBvv46xa/+FHqmgbCtwtsL0jeTRxmFKk16X934xVDD473zYfqeGDpClyVnz5msW/CtpxzbeXeHV1NnLVJPT9uvH2q3I8cvsWz91Je6gY4jLcjzTmhDczYvzZZk06oNoFQKc3qIPprBE085m1PqGaJU2rNnByiyr0CULBUUVN5rQrZbVPROI8l3wAdP8I7ge4fU5KnsXdLcOdfSNi2dS4TmbZfU6nXdsFytE+G3kLBBDicZsmOrzwCKnpkGhQup332ISbKnDZGIxMQ1HVUCYtrYJK1UGtEaRUBn1hoNaJMi23gfmcQCHyQLsnM7Y8+WA1YrJoWhmxhihLIwzKeWwii0SXPNh8BquWbVNtRtR4iJ0UZhksY0RqyC6ANtF7hYLGnaNnmtB0m+DDGQov2kiH7JUVURPHRdTOZkPuC72xMH3RpQfuuLbyXGgJgoay7XNaulo649TdMRfDoltN6noOPZRd0g2CjMgKISKpPsEkIO0yQxhVNSOhmHupi8ocSnMEeD5YnWCd3HSJAkBe26Duc8hW9RRqNdiTEVKDNQNSDZY9t5XBtSzG+r0CbifEOQlqlSSOeIYlg3jqoxFGHGer1EHJR6MsjuEkdWJKqORs4paoWWiI8dPiQ7ydblk5FvabsUbqoqyxRLOsLUGgxp8umyxKuCNoL3La5dgq+pCo82giXFDI8uUqkC7xNtwWFpk31JCPjgEIQug3aVY47ePZ3w6ukRn/yuT3L40kuoyQSUoFxAliuiq9H6CDXZs8P19kj5Re8X6552JK0rGw9uJHn9xxiGCDr9SpVMDzS2nGCqKdgKkexlFpIdpy0rCj/F2AplFAGPJ+ClxVgBH1Be0Yf3cjEQaSkEVus16qBAVWDQnEzucowQOsc0TCidQWuPGOH47inz07uUJ3eZHR+jTJW9yYWu7Xj3W1/lo5/4/amt2dkGIdEdGU0kUq+6RFxc9IbUm/ckxojvAnbSA3Q1SAXC+SXmhz6WYyAns4d8ZgVSdBNxK5QfUf5sbQTjcdoG/1tAsv8zBkvXbd7XAKorYHLnu2TWBxW7WwOdK7d9AHDyQgVdB4ZvKucWe+relPPvhVA3wohdpLAn4xftivF47Rvu52HwbxvY300CBI9R/Xx/8aSUSsEIxO2Z/v2HMV1Q1kEaAzY5Oeiz92D6WpI2jasx6qzeVnzoh+TNAVZjKkXU2YwqO3TGK80ZsTJG2Db8Ha+vN8tnx85Xw0ejUFWRIvC4mGwzhyV2PPA3nEq2PqtBEqfgqvD06tf9JxzVk38nhxj6iG4C+uSA4GuYC0U1w4YiOaWoJBSCzM9okt0ipGAfMat6tei87yfKN3ozqhhyiGbS/hTSvxhC4mD0geBjUqeHZKPZNDVN29F0LZ3rQGIWDgTa1tOFkIRGTUNb10TvszNnchwKMdnqBx/QohMfpuTQmoNwBbQWdIxoqzEm+ZAYpSm0zirwJBjTkmZb4SVLCPMBRXIYR6OoyryHCPSMkMYkYDk/mOC7lqbtCMHnoBCC7ySZ9MdIDBGtLK0LNF2LD5KEPr7BoVAm2YwWZYmVVAMTFTqaHCIzsr5c0l62e+br/nRrQPn4Gw9zrG3oorB2wnLdsGq7wVsrRk8IEe97WpukNq0S12viqMzsQ31eOiaDV4mRVgKORCSaBkcRY8hs7RGNwqmAdwHfOlzX0bU1RQnKgA4zQgxoUckYNm5U8J1rqNfgnVBFy2SW42RHiLFBaY81FUpbWr/EhY5Ve45qC3RxJ9lGhojrWup1TdQdTa2IwRPF0blmMCfsXCD6Du862tZhMFTaUNkk+g6FZVLoFNp5UoIuMzl8mhjzsuJgbjEGtClRaoIKh+iQ7H8MkYkF7zzNskNFcE0yVFbaYLTi6NBy77Tgkx/7KK+88SbF4RGqKBK2aWpkvUyy9hHtTloiepFJH5tbGNTbDJcYYntL3PYCy9LKZNvC8MKZMoUfi0rQ4hHfprCMLlKoAjEKdKq/dx3r9ZJaarRJXJyhTQbCxpDikYrggid2wlRKgjUc3T3kzTe+j/m9l2npuDh/SHu2YtYdcmgMFZ5pEakmU8r5HapylvkG0sHGec87X/8S6kf+25j5hHC2BoQYwJjYs1HR1J4Q1kxmNklmswNPzNKF1dKha08IG747QZJTji02CLTXd4iAiki3TvyU4zSShmxtRmr8+w1pHyj8NqAHNb2DFJMU4eY24f126nDF/vFF6vMhAGGf9nbBC/bJlb16C5zsv3/rWn+/Tjy7L1qNvVLkPXncJr+994z5XW4rTdyXDQp8m70Ox/P3Vg8DER2bTS3HYjFF+n1Ad2kd2Zx687+skZQRetqBj6meSjBF3rBslk5qPfBMkjkiY89KcqXCOd8rgHf7nvE1GT6PPuW50VPTqcImjktjAI9yITuYjHJUO/N6/ILJePXYXUPkBse60b0CfdjZUY/lj5lyz+QY0COzBDudYaxNwjCVpI7amNSH2cvFkkyKRBL1m+6XxYH6p0zDLYlhAyRjDLK5VuZBltTuQUMmMWOSDu9cMnPLzjgxS+CCT2Zq3ne4tqNrUmjp1jtCEIJLpkuu83RtR+ccdedYt02yM+xZQGIkZGDZS9JV9mcIksKIoE3y9Nd5a1WkSDxKEWKKaa6A1nUoZdJ+rBQBjahAWVrmleGVl4956e055283KISQNVcxOykFSc4/Rjm6kDW1MUXGkRjxgCFLWb0nGpUEbSqp74OLrC8bwsJx9vh878zYl24NKKPzyTNYUsSaGHL4xChJfCtJ3R1jEvmnACoJXTsRgtm816pfn/rJkJ1xIknK5mNvf6mG02kKz6cwJk3+xKvkk71mCJSSQiFtJviG9ypKxMeAcxHnMzG718ymM46qCVIsabsLlHgUFYGAiw1d6JAuMs32LdEHuq7BuZrOp4kaXJvY/X3IVEWZ9kaEIkas1kxMiUVToTmYTSiNoTRQFBY7mVGUE7QtEUmSsNIayqJF2Q5jDUpPKOQEFcv8TntC7FivVrhpx5nSWAqsCaiioirgzn3DnaNDPvHmp5gen0JVpoWxa6BZIU2Dnk5JfJqbpS35VY3AYq/qlj6aQL4WPSr00st0XXo7ytgRoyMS2HBIgHgHdU3QGnxLqOvkta8t2ifaB4VgjWFSzbGzQ3xRYIv3cWfv0zVJva6IEMHFiI/QETFHFR/9/t/Gxz7+2ynnd/E64Loz1pcPOdKnzMwc3y6RbkFzeQbRoI1N5K5ZLd+tVzx99Iwn6yfMP/ka7S99KYfXSgtUv/4K0LUBFcHqHGc+L55CAtA+9ETGvdwC/OMzzOEhyhSJP3ODuNN8jQ0Et+Gy60dmjFSG0Uon/ESaL1tcjnLlg8pk6v4KkvpA2FJAmuWGJWCUnptfD7BfAPH0a8Wt63mdMGhfH+08Jjd8H2e9m8dIC3hjunLZlDA5QNYXKQb7c9KHOQu88LO2Sk+420so9iYJOZrd2OHsRZ6XzZTvU95AVe+t3TdMZ3qQfF3P5ujZFJomESIYRkByuzeSFV3OXiswo2ATWkGIhC4Q23BlavU5bP7uXt06emyVuTsuW2dIMoCL2VQg9tK4HfHo6ODK1vPjtPOjNSirER/Axa279s77UZ3SjxnB6sRW0p+4+/fVvHwnBYXIvM39ASX1vEoCHZ36KvW8HmjZes/0vv839HwM9uoqhzdMwDbZJPYy4j6wjUhPpB4QySwjvWhXyFq1jD9ixLt0j/cuM49kO3hJzjTe+8So0rkUAc07mq7Dt11yGM33R+fxPlHZee9xPgm2XFbF+xBx3oOSHFFQWLcuaVwRnCTfLmOL7D3uUTFSTeDg+JBJaemcJ6D52sPHLJqQ5TqCqIhVBpFIEAUhaYxVf70f4yhJ02aS8M4ohdaCMRrfCedPL6GJfOvdx/sm0950a0BpjU5SRa0y31bvgZTRt6QBHt7lPPqiZAgJKPnl2FChJCohnT2ogiTbAJ85mVQWEYtKL7PJJ3kXFM8uF8wuFhSzKZNJydaKIz0LQ5p4Bs1BUaAODVFVlNM5yhQE32GVR9sSW8ywFqriEFSFUpqytMgUfHIBw1JyWB1yZwqdT5LSoEtCdKAjxmis0VRVyayaUBqLVRaLSf+MpbIFSEg2lNYipqCazqnK2UDUmuL7NYhe5nfWouMhVs0IMRJ8TeNAsSJ2KYQSWqVToIE7d0rmRyUnB/c5ObmHMtm7WyK0LbGuic5jDmxWYecXtD/x9qJWSbRLanj7MqiMIXkG5xCGQF7osn2L63Bdk68ng1/nHWq9xIpgygoVHFalBS2GgMahImhRWFNx9+4rTF9+QGcL7r3yGrPJF3nnS9/CrQJSe0xMhuydjyhreeNj38vrb/4A9176RA4rqTD6FeLpqxRiKFVFaBt8t2A1e4fls6d4H+jaNbbrcG3L+VnNs4tLfvWXf5bf88O/k7Nf+1KK0R3TKTWF/kxSjBhJRt+mx9xjv+7cpzvAzX3jPZjN0QeHhHox9OkAGrUeJCnbD/ek/rCtD1Ppu2yVPLwDw0ddog5fgeVD2BeVZpSulXqN90NA+RX4JMEdb0Bbt/Wb3BYQ2Ml7tDddAZlqdIva/u1KgTt1FW22JafX3Ddu03VNvu6xF01XnlOkQ1i72rYN/KD5fcD7d7t0SMFdOTC8cOpx2wfAkUPafXb0Pc3V/K4plb3JN1f10SGqWWAW74H7ROI13HpTt9sXBegiyqdDmtY6gaK+HVmwsXnfr2vYsG0P9/TQSfY2ak8O+VElJMlo41Pd/ag+Y5LzsYv23tqM1xSFKgxi88HW91qoHuJustxktx8UD/fBFvNH8ep92uYZomOiRhqDXQWKRK2UvP578J7ylp33YSBsZxtkqmGfV/RrZB84og8BuQHDGtMD9Cx8SlH6Ur6xX49jTA5DIRAiQ11SIJFA8D6F9s3a2OBdDp6SVPExRILz2d4zq9K7juB8ovhrXKZMcjjX0bSepnO4rk1le5/GR6nEkgJolbRz84OK07snmLv3OJgfcXwy53Nf+hpf/eYjzlYpVrhWUJoCW5SEEFkt11x6cBqU9FRIyewgxe0OGK2oioJZZZkUFeIDq8WKxbM133z7OwAoS6PwMTnTpIAAkqU1PfhI82UAHzoPf3ahTzG9029JFA396Uvl4UzSMZW83uivZ5G/JFJqrOW8bnjnyRn6YEZZlVRlwXR6iEwDMhxDM6GpMkzLiqPphDKL100h2EIBBQJ0QdOKoTQFhZ4haopHY+0hem4pTLK1K3SBhIApK0IBFJEQHDGjgLIoKAvLbDplOplSFlWaCBmIGK0zjUHMxukRZcmhA6vshZ1igbqgcLJCWBOjSjagYZWkaX5N211SL89YXjxLNh9BYUxBOVPcf8liCmFalugYiU2HVimGuKyWxLYFoxMJbHqtR6fBPCZjYnIZRmMjys8ckzLQAkWi75LEtmvxTZ3AZFCI80iIaAzWVkkqo4sUgabrMp1SyPHaDaWdYuczDo7vU6uG6eGr2Kqjc2c0jzyrsxZ3UScC9wj3X3qDN9/8Ie6dfpL57BRlUwgtrSBaRaWTVFiaBu8qCEu61SXBremaFYVr6bqOs7NLLhYtP/v//Ul+z7/zh5m+eo/49JIYU4jIHm9rpYikOdx5GaY/WxhymzxIAeIcFAXm/gPCo3dJTMdq4KDLx3yGTajnpVT5ty3Tgn5HGaVr9n4VPdIugW1J5r60F0xeW4Zsf9zd6J9TL3abs+/6HgB5raRtvJ9ODpFmsQGVN4kbbwl49t2y77dbQzAhhSoN/vn33pA+jNSSm56NO4B8EBRsP3gjfdOHFaleK2kbnQL690Jn850c/aoHBxJ82hfMdOcd3U5b001StBSiIC6ZZCmtk62g6lXE+5DudaBrzKABt+kUNRILShSk2fa2HaLKDRXfNG6fL86mfRn9Za1LHMDk1Tb0EsDt8R0V0nNUDuMQN8uXIqmhJ3oocrd9fRhnVIqGt4n0s/1AOi/IcPZOz2e0GFWm7+nXTj2AyQ37ht58VqnwVP3MMalV8tzOmjaTwaOJ/VyLm5pIotKRTG0kknmY+56RzL8cwuCY6oNLAoyuI/RSy67DuSQQapuWdb3GuzZrUgPOJ0ck5zrKScW9+3c4PDrg6OiIys45nB1xcHDAyy/f53veeczTywvWTZ1U6SENQFs3nJ8t+cajpzy5XCdH6CwwLoxmMtHMpwWTcsrElhwdlsysJXaOy7Xn3fcuOVvsmGHdkG4fKScdIpAIRsBIH0pRZ4AZs51AIKrM/xU3px2jkgluJBvkhqTv7yl+lOqlksnriZg8jxQ6SUJV8v5VUbFsHY/ef0pRaO7emXN0NE/IIkqSsCtAFehihpkdY++9wlF1QOfTicJ7h/MuRTlRBtGHBH2IaAWqRKhou0DdpOguExsoJ4Joj3IGE4p8aE31TnhAMVEFJ/Njjg6Pmc5m2KLIavj8Fqg4aIBdt8a5BokdMbQEcShtCD7guxbnapx+hOMp0QviKrRMAU3wNZ1f066XhGYNQaO0pSyF11854ejUoi14HMvFM3QsqAJIbGG9Tp7LRTm8lH00gs2CtE1S3r/BagwwM4gkJEokickJxrcdXV3T1askao86nfSjpbTlAAi0TbQ7Knro0qlPfMRqi7UTCIJRloOjA6IKBDQHxxVh4ahaw7QpqZoGbSvefP27efWV72Z+eA9tKowtMLGlty1RYlCiks1lDChlKaxlLY7QLgmuyVQUHpHAo3e/xc/9ys/wI7//h3n7r/wdUhxa0kEgnxx7UCkx/d14Y4x3Vxn+HxHcozPqd55Q/fBvp/vcr2f7j8ign+klLFtqb5Oi2IQ6ZznK+9bSo4Bqnjx/D7vp+rVirPyT2v3hFmXdBDZ2d8Lrsu/v01ltJhGMRZUzYrtOauRrpKK7mX4Q7DOGD/uqvi/d5jp77rlJFvYdTbvjsFORDyTE3GCR29V/ALPjU0v/jmTQpewGZOQDmT4+QpGCBsSP/yDup9/bqvA25Bt/TkIMaQOhTYfmnnmkX863ziEC49Patpzv5hbu1kF2r/bnTTaSwN209VzfBf0eNYKyW892AfB7MtydaVdPXMPrKyMNRRgRs+cAJcootLFoZUhR2XLACIlp782CjCRVVBupbO5oyf3ah+QVkno2VSINROzDLSogrwFKbYRRg4ocBno8UZv8jN7wXqe1PdnG99qotNTmcLk59K4VwGRv9Czy06oHrqnfNyauyackhKzZDRtJZ1Kve7xLPiHedfiMUzrf0bYtXdtQVhWz0yNOTu5STaZMigmTScVkWjE/PODu/fus1gvqep1sQp3Hd55mVfP48TkH85KHTy9pnCcoQVvDdFJyMC2ZVTaFK1aaqjIoZSEIF0vHs9WK9qr32bXp9rG8lUJCf0KIyeJBJEkUk8FkliQmw9oeiwwRAtTmjZB+Iqq00fbArB9QMv+UiombUAZxd/JePiiET758wie+5yN87OOvcfrgJeanJ1TzOaqs0KYkSSIritJSVTPiQZ28sL3Hu4amWbJeL2maGtEwKSqa4PCiENGgWmp3ga81TCy6tKAE72u0CKWp0IVF6QqdwfW0tBzOJkzLIpGK2n71IZ1aEJBI8A7Xrlmvz3HughC6TCru8b4huhSvMxaXRHuRPbsnTItDJJZoqyi0pZrOODg2HDSaQ79mIvDamxXFNCKiWLoFb7/7DV479BysV2gLukshlQqrEs+nSerhxO2m80lrswCpXG/VO+fARvaWVeIxRvCBGFzilAwBCQ7XtInMHENhCzpbYH2LkQrRlhgCXdvgmhrfNck73SaOrOXFM/R5wWwyx1tP062YT6Y0paNVa7RSTKuKQk24d3SPw/kxk8kUWySy+KgLjDFoqXBtTXQtvlkg7YLYrBHf4f0a59aZ7ywtCIXRFIXhp/7mf84/9z/7i8w+9irLr7yT29kvdmAKjRJF8BECDIqnG3bX0HS897d/kY//mT+A+Wt/jfDs0eai5P+pZHTfO0qJMqhinlXVo51iAJTXIbzr07ABXN0n/umka4Di1vVrpVPXPaJgcgDFJBm+mwo1O0a6Fbhmo1K+oW8+DDC7Bmt9qHJug4Nv++y3Nb1IxjfdK+wNJ/n8PHtAkdasYJL9uVY9dMrXo2Bffx319GuI88S7b+Lf+gc7VbuuAipRgcU42O9J2AaQangX1XDhw/a5jD7sHpwSgNv+bbhdtj+rq7dslyKjNeuGO/fUbOv13epvZCPIsxrz4AQfPYOd/ghwD0KW3it8nGPGEj2A7mu62XdA6cQ9KiGZAUQBlVzFh3J6Mvnk95jyi7LxrUAlzshNf8kGyGaVuM5azRSoI623Opv2iUSM6ss0WVik0CYBVN3HnVAGiNmTfdSjyTgWRBLtT6YyijnMsveOrusSY4rWmKpiNj+gLCY5RoBiMp1gC8PsYE7bHuK7RJ3k2g7Xeerlium0Yn5Y8fprd+h8IBiNLQqK0jI1JkcFIoHzKCxXDZcLx+VFTeMd/ha23X26NaD0IWbqwb73c2iizLEYItmezqBINgg9CehwYsxfB1M9BLTCSyAEvZFm5duH6aoSlYDBYCXy6ddf4vf86A/xxg98hoOXP4E9vAPVAUqXqVZKkHwqVXaGKo7R0wYdfJL+BMfMtxy6hs61icATwcWWVb1gUa8IoSbIBU3bMjGa4Auii/huiRGIvqFQswRaesAcOtomoLSjMlNEinTyiikGegjpJBJ8i+uW1KsL6vUznF9gS5IXdHBp5RKwlaIsCybTAltYCq2xeobRcyRoJlVgOuuY3lvzWucp5oZyZql9h3MKVy95+/E7mHXg7uqMSVlhMEncXeZY3loPJ6w8yxmWjKz2HksmJavqN2+FQsVEXxRDIozv7Wt98IS2AzRBZzMEY0ALOkyQEOnqVVKPB5+5PBNnV/IfWuLP1nTWcfnskuXjNfV5Q73yaDRVYdBiwXtCs8Z3c4ryEGNsinCjFEoCrbTUzZr1s/dRy6dEt8bVK3y3xnfrpG5XiqrQOKeYVgWLZ+/zM7/4k/zY7/5trL76DklonpzFRIRyYrGlwftIvezwXdxZoLdTf4h68guf480/84ewH/s44dnjdCFm56UoSUKps1TSlEkdahMAv0rR0wPL9O3KhsP2Wr1PELFRH12zGd6wQ+4r76Z0K5DzPLB5zf0qRmR9iUwVzO+kDaiYJeexTBPyYcDzvkf3CWW/HSBuDE63wP91Fbkm7e3vWwzClVt2f3gRtHqduPW2WewTlg1INF0MyhKVpiCMpISS2KKNgWaJM0egTvDffHhtLcYASYSR6dUYfOXGbxwFntue3d+3wqmO+2f3806RoyZfzVht0PnwqGLjtHRDSsVtny6vO2uOj7C9xK+XDm6Vaw3m3im1fBmMydLdUY79eiPJ6Cr178hrPTe2B3i9Q6jS/fc0JpJV1+gE+rTWGZlk0AkbCjvV28D3hEXkMvpSY64LgyRT615KmrBOb58JCiUp8pMan3517+S6cULqY/CobAbYa7kGLKXAUA58z8lBKNtwhoxjtEYZg9YWnYVuaf1OTjQKi9JTvLUYp9N9ukUrMKXm8HSODzkAhzEoa9AqhY6MvkWCELvIet0iAZ49daxWLnOLP2cCjdKtAaWLkglBUyelyoxmvdo98ZCBSBr+PnBK9g7ZrAsiKXJOphFCbUTQUVJA9hgS9yIqMDGWj7z0Bg8+9n0cvfxp9N3XoZoixiRP8KE6fdxWl7gZlUbZjaeTDhETA1WMBO9puxYfGiaTBdXkCXqRQPHMnKPjGpEFREtphOjWiDfYiWVSTinKgqKwaKtRVlOUEdENzq+SWDtPjK7zOOdwbUvoaur1gqZeYU1BVUypqgJswFhFURTYaYkqXJq0KhGhF/oIzQzvdApZWK2ZHNkUc3pSAAV1bFitlzx+65xV6Hi8dnh/yEQZSl1wUB0yOzxMk1/3aqLNy66GkH75ZJjQUP435pjML2X2ys9Dmshek0FEBpkOH4Xo3EBGa8spSmm86whdIjzWaExRYqZTTJjgA7hlRzCCu1A8+eYFy/OWzilCTKSvITrefffrvPT2y9wzkaJK5PvG6mFxQFvqzvHk2SPis28yrSyha2jrBW69IrhE1VAaw6QypPjvit/85Z/hD/6F/xb2aEY8W4EWJGuIjFFMZja9/N7jXRjm3jZhsGxO40pRP3rGo5//Te79zh+i/ZV/xGBeoFT+rEGbREdUTVGFBV2gbEVi2RWSuyoMHb710u35TbYvbQGU/hbZvl/1m9hOVs8DNjdiDbnhpn4HexGwsguelUB0SN4Y8A2szjc3viDaG1dl3wa7u+8/L48XTUOZt0MEV37am0wJ3j030ys48MM05KZ0HXLhut/zpIwCSiM5XGxU4NFoyepPSfGdi4++ifri38Lf/y7UUpDVeie/a2V4W5/Gss8eQvTq2ZvA125Jw6r6vEnTf+zP+Wrnho3zwVBY3lqHB8cq242P4E0zem8VbkyDJrKv7EZnncARATF621IBRRz15wDggd4Bpnfw3SxWKd8evA0GBSNwpnUGjFc6PglOxuvgbvNTHppBga+SpjVpV1OZG0HXKN8hrzwzxjagkM3fe6fKjZ3opl5qKL+PRCcimGAwJlLABihLttcfqfB7iS0qqduNNYRgKMqUtbWacprZYXobVZ36KMTkA5KI4gPiAqZoqeuI6y5wmRFQXgBR3hpQBgHfmyP16ttsJBuHKJhkO8uIHoa8B4eS75G8h6aOSCSfCYQMEz+rUpUkA2uTQ2spDRHLOlqiniBmDmaK0hMUNjsCBRAPQSHRQVgjbgVuDSrZV2GrfGqyKFFoW6K0zVFNSmblCTLXxCic+4YYHKZa4TpNVzfEesXUHmBMwJSB8qDEVhVKWaISguqIqsbR4P2K0DokGJRMUGiMsVBMmExhNjtiUh5SlHOqgwI7jclpSBeooiCaFd6vgA4lGqunSEw2nCUCKjKZJp7HqDQxgvEGOpA7mqWqebZYU3dPmEbNREri8auc8CYUVTJu7e2OJE/suPtOjry9h/VIgDiA0X5yg0LrIvdp8iiOIUAQOlcnO5IYKaZCMamSZ3p+GbSxqOBwscHUFc47FosLVrXj7OmCJ48aJGWFkA4inYt86fP/CMuS7+3WTKop6kSQaoYxFh89znnaxuXFNiby267FrWu6dUPwOdSnTs5n0Wp8YXj0ra/yhYdf4tUf/QHe+Zs/l06YGfd5H4lB0FZRTmzipnT5tKhVihwVN6C8XwAKq3n6M7/Kq//un6Z486O4t762WZWFNEeVRdFAfQmHD5KNZXmMKAuuARoGSqfdNF40r0s3bWay/fcKjujnSf97P3XGmC0D0n3FjO8FwE6Q0KWVwVQoW0CzfH799jVQaVQ1h2aZKKqqRIvFTY45N+yr1946euY2WG+rirLdb8/LY0uCvNlXP1CSYkLalG82st/XxWrn9xfClh8GUT8Pted5lhwwZXDcSA4nKq3377+L+W2/j/rnv4C07qasdkDXRvKk+v9psoSI4RDdP3eb8dwqbKepV9qqtn8fQzAZI9qdDEWRIvxUeXt3MR1Gr7zI25+2/c9vAptqp59Gz2+6LO0LJhN89xGNhtx7J91xr23AofQOiSNJpUjiC9a6dyIl8VnGzAAzVGksdcx7WqYk2th8ZnDa73l5bxi3aVB/505VPRHxzgArrTMQzmANBXEkVVWJ1qgH2Zu82erxtI8qDEnFHbPaPRG7Z1L0zJqSQLjkvxuHIa0VRVGiCBhTIIRhHqUiE6aKIRCjx8ci7c+S9jSJC0ouaFtHVGqrzrdJt7ehDApDQtEWi1EBo31iuEETMh8esnG+SZM+EZL3wdUjWaUtm+h9ZLF5JGZgOQw3aZqmQQ0RGgJfefc9Pvm1h5g7TzkuZsl20iYpXeKYcqjQgasJ9VP86gmqvUh1LqdQHUJRoFQBkiiHtESsicQuEp2iUhNmdsJ5iDTdmi7WeC90bYQuoKXCS0dkRiTikiEdSjnQLsXwtAJlg+sukWip9H2ms2OKYoZShq5bEXygtAVFechkPkMVEW0FY9Pk7AKE0CQVrig0JdoWRF8jcYXIOYE1UQq0OcSoGaWv0f6CSkdcKcQqEMUSoqapWxrfIkWBKm1acHtPu2zRqkav+27qY3anOKYGVFbXGtCRHB7MoIyFwqIKi3QO8Ym7LdRrDKSoOXZGOTtI0R/y+PpmhWiD857VxQUXl2csVg2X5wuC10k0TwrV5SO0IfDk7Am/8Ru/AESKyRF33/gkk6O7mMISgme9vKBZnhPbhugaGhfp1gtWlwvWS5fCaJE2+4myWBtQQeO7jv/8L/0H/IV/63/J3bMFj37m1+ml683aISJMD5Jzk7E6RXxSiQorxoQ8ZdgR0l9jFe7td1l8/T0O/pU/yfn//v+AdM0GOUTJL4hGwhrqBWp+B+wMZSrQK1DtkN/4D6P1bhi7fajoBdNeHDb64UXK2v0paouaHRPrJXp6jLSL7a3sJgC1+7vSSLMA16BFiL5DFSV0vUPTLfJ4TurB4FZeL5DHuK+GvPbdcN3v1/027ied1W1hI8kWY5FiimpWL17XPvUg+tqTwq2zvpLnjRXYh2Tzc0GS6U4hHkNIFzL3nqqmqNkcf/oa9nt/F+1/8pevqfx1Ve+37vRi6UqjymRzLl2Adj9I2xyub1PGqGm9kIoxYNzku7Uy9wih/9j/X5FEU5WF0qY9KbixD/K19RqGYgBYNw3qBlKq7Z9SXx0fYO4eIauI0TbdN4hZN+wXCXBte2SnKiSVsgx2sQnsJf+Mq3BWhkN57pDBnjKdxJTS22T0KuWpRkE9dtNGRS/Dui9q+4kNhdEGmCqtsgpe5f0yFahgUP0PrCoZNCsUoiIiIavj9eYQAygVR+CUHDt802fJzCw5/RitUUXSykq2Ae0dmZLJViQGQxRDEYuUv1iaLuCWHd4La++whYZmDFifn24PKLXChYhR/YTupY5ZRKtz7GXJVARsThEq2z7055PB5kIylpGYCDW1IsTkoaWGaAb9+TM976LjW29/g5/7+3+PRhxvfvoHODw9pZzPqQpDZQLKOBQeQk1Yvsfy8Vu0F08odWQ+O6Q8OMKUM9CTHELO4n3EdRHftXRNQxdalK8xEvCuRRC0LpgezXC2Zb2uKeqWUNa4wlHgUKoD7bC2RGmFixc4/xBsx8Tc43B6wKQ6xdpDlC7ouiW+ayiswdo5tpqibcJpWE+ILSooJMQUWjKNPCgI8RIfz2jd+yyaR/lkNKcq72F1hYQW71qsrrhzckJhj2guW9p2iS4rzHyOFCVK2WyAHPLE6l+63YVEMQ6dptAoLclpxBhUTJuYVklCZ4yhKCpi0eJ0m1QfPpHWE4XJUYNSYMuKcjYjRkdTr/BdR7tasnj6mHfe+TrnqwUuKJogTI/vpljfwVM3Harx0Gm8c6zrmre++WV0dYdXztbcefAqdlqksJjdBe3qfdrLd6BZEELH6vyS1dma9WWD9wqbTR8mAhOt0UbRaMU73/gK/7v/7f+U//G//b/mzrLl8S9+PvdPHzEnSUKiTz3mvCeEXbulHc66EHn2X/8Sx/+DP8X0n/9vsv6pn0o2MVoNqjy0SXQ/MXMBKkG6BbSXW+OwXcruhxdIO8Dt2ixu3l82X28BJgFUV8P0FHX8cpJMrM+2McaLAL7YhyBLX7WvR5vjC6brgN0HSd/OvHbFYfvKAaScJaqu0CJALA8QNKbvn1vW5Qq4vy7ddK2f19dmfMu0A6BFJz2YkYCOHTkUS7oeAvr4GD2fYv/Iv87i//oLuK+/c2UobjM0PXZTRiWyc5JUCrWhv/vAQ9uPhRoDuv7vaO7mcragVJZQDbfnvJQxYJM5wOB5LdezZo5/E6NTG4NsqTo3gG8k38vgtXeoHedU/Y7vwUuNLxoqM09c0qOqD8CLFKt7bF858FH2YLC3Y8z93EsDx2KPGGO279/0jdJ6CxQOks9xp+9MquQg3Hue60Qq3h8RstCUrDIewGSMO/ah6VlEkJiZJ5TK4C53phqGNKvJcz0yRZzSaiCQk/w9Bw7fONBm1pGevD3NoX7/TW3X2mwBdVFCjKl+WpIdpRiTHKCjg6i4WK3xsVfRf4cklCG7uEVi/isjZMjA3J/E0dlmYDSYaRwSOIpWbajDVFKKu+iJYgZuLZ37PUYySFUEUqe13YqvffEfc/74LT7xpR/gtU99F5PTuxzMKl45LjmeQzXRGOtRzTNk9R7PHr6HNI7D2YyTkyNmh7NEgK11AndeUddC1yWv5TZ42uCgEXyt0KZkcnSCSEEsJggtl8s1XnkiFZW3mCIQpaUopkwmR2gmKJmlk4pWWDOlmhxhyoM0IbRDZVLRFJlBgdUoE4h0+HiJD+c09TM8hun0KIPWFuSczj9i2Tzk4uIxUFJaRVdcYu0JVXWE7c5QEWbVhOAtojpQimp2iJ0fJsLzXowfGRaojY2eGv2V0WkxDU6SRuYIBcaiTUQZj/Y6OSvZAmVLsIYQBQmJqqn1jrZZMY8h2QsqRecD6/WKi6ff4uE3vsy3vvJFnjx9ROM9uphy8srLnHz0NY5fvU81KWnblkfvP+O9d85Yny8Qd0GzWvD2V36V+uIRD0/vUc0nzCpLqT3QoqJDmkCzvuDi2SPOni1ZLDtcV2JipIyBuS0otKaIltYGzpTirS9/gf/XX/sr/Nkf/+M8+eUvJL5MlXxpuiZJXntnnfSubIkZhndIENrGE9eOyy+/RffsnIM/+ofoPv8F/Le+mW/tbSr1pu9jsp1U0YF4+mUmZ8qogBdP47V13++j77vL7xUhyguWLygoZyhtiOSgCPNTZKVQrt5kvlu35+3+W5/3VHy3Dddd2M1vHwrZ8/CVR2+LNm4YyyuHhZ3x2eLRjxFcR6wO0lol6eCno+O5RKS7VepByi3u3d8dKs/lcOVe9t5/y5T3HhMdoBKf5zhFMPfvYz/6UZq/9gus/vo/GKQ615e7M+F3TljigSwFjS4MhNjj/+/tp5sA/J7fZd/1fRNgAGkbsJjWooDy2WYwCipuT5u+QlfnqYIiOX8ks7FRnWSTwyA5UxsIMG6IPpwx/wP/LM/OvwDHgDJXJYFqU2T6O+a6zPXr7UDzAyqDsaG1KrW1vxdhkOINqm3ZBpKxB38CZAfewdGnP7iPbEv68mPMdoz5UTWo8Mm2lmkcVFZb93VP5oC9J/u2/eTGMainQtpQHcXsq9LnG4c65U6LG4clk31EzMC/mX5PvJzJw7znw+z7urc3VUqng1kQFCkG+uWqSfbHpcVag7HXS3F30+15KLVOoLFfpPNA9UHaYxbt9gM4zIOwMVQVJBlRSzqw9tyUw8mD3Ol9T0oGqNkWTamNKlyLp10+4yv/+Bd47+tfpDy5g61KTmYFn/noXV59dc7hsUEXHuWXSKh5+uSShTqnXpxzenrIZFqgbBqgEA1tHVgsWkQM0VgciuAC7rLGaktxt6IRqL1m1V3y3pe/zkEx4ZX7xxzeP+X49IjqYIZ3nqVfIQKdyydaW9MVl0xCg1EHaF1i7JToGkQcUdrE1aUUkTUuXtD692nax1ws3qXUBwigtcFGx7p7n9XqIavlIxbLJXVTcjC/x3w+QRtLUDA5OqKjS5K/psOvA8olu01dTXJUCQE2UkmVx1bBXkNwyatIMlZOUsoUg9uiTUBbi/YeHUyatFn9FiXiO4fEFDnAPCuZHhxjbEVbV3Rtw/mjb/HOV3+Tt774ec6fXOByXNNyornz4IhXP/WAB5/8OPOjuzRtx+n7j5mevMuTtx6iVjOm0iZnluac2eQ+d+6cMJ9WlNZSTSq0LQhdy9nDr7K8WNK4ZzjXgRhKbbhbTpiVNp3wpOOwMJxUBavS8cVf/Xnin/pzzF4+oXl4Dii8EoJPk3lbojv+JuOfabtAc14T4zMWX36bBz94h9nv+30s/vJfQoJLi0VwwyKtiMj6CWBQVQVxcSXfm7Dc1qaxvats1evK93Gmav9tN+ZxTV3GtVbVARRTpFtDOU0Sh65FFRUQU5jQW+a9d9N+Dsh9Lga+TT+N/4776SYguluHKyj9pkrd4lYB5VuULfF2OtwlyhKrg8R2EX0GYbcrTG5RN9n9okBMsYnAtefe5zX3xrkdfb6ut8c/LZRMftePIE/XLP/jv4G4HnDepoO3B7a3tQttgC6rLK8Bp3ux484Pu23fgrF73ru9mQpbE2d4TAAvQIq2IiHuaa7aynbLwzt5YWSAsfPM+GCRwd2WZA5AK6Y/9kP4MrC6fJvS3kn1iHErRC0i2USuR6RjySFbKnClNYO8TvXXew3oJioOY3t+pTbgcXR/3/C+B3Sfj+6RRVZtS2+Pm/03MuDtweK4HxOF0Uaq2ZO1Dypq09d1S1lOwjRAtv2VrPru45vTS2L7NqFAk7WBMQN6jUiiKsq7xVAvrUw+YMioPj2gzDHqyUKhGIjB0zUtddtibYFRgrUGa3f4jm5ItwaUPSt8L91WeWD6zokkt/gQ8sQYaR8kClGnEIYuyMazKbUyL6rJcytESWp16Se06sc0G6Wn69YorNUoOpqLh7TLZzgqHopm/d4Jn/7kCR95fcLBcQkGbIzENnK+rAl1B13g8NBibRKVI4a2dayetbROo6sJzmhqsXQXgaZrObgjyDzZJSy6M7741pewqxn6e7+bojxhNtFoq8FWxCh0bc1qvaBtzzg+OGKql1TlEl0cYAuVSc0TsbmxIQmiLHi/xMdzXHjGqnmfNixxHrzWGFOiG6Fe1TTrltBFulZ4+GSFefyUN159icLYpDW1BcXc4pqWerGkPXMcmFOOTx6g7CQt9rIxYdioCzbjM6xtqn/3FZkEK80DrbOUV4PWCUQag1L9Ap643HwItM2aEDzOdbimgei5vHiCLec06xXvv/clvv7Fz3H5bJEJyGMmJHfY0nDvwR3uP3hAOb1P4wJKT6kvIZ53KHEcYrDTI177nh/m/qc+zeTolLKySfwvihAc69UFtpihoya0cBEfMy81pcDpfIpWGh8C1mrmheHedEIMiuXiMf/oF36SH/nTf4hv/Md/nbhusUFwTST4ZDPsRzQj/eq+uxcokmnb+rLh3b/3K9z9zL/A9Hd8mtVn7xHef4dkNJ3tUhFwq7RMRAX2DqIt4LbyG/9F9mzsO2N5XRJTgilQ3erWoOaF06iDpF2i2mWae9UxFCV6/WwHYe1Pe9tzW6C87/ILtDdr4q7f8J9Xj1Hl9xb7AqDyuiJSNtIvmv1RkagKMDo5lohA9DvP7OR1U7tuSgpEJz5YHeud/eKWWfZ449qLqRy1RRCZUvWZ72fyY/886//z38K//3RU0v4SU1bX9eII4clGE7EXPF5TQs9PGJLKbbO39WB/nNneedUj9OtrOkaiEjZjn67sotTxE3nUBXBxGCuhFxzsPNqDydQw+gaIUpTf/3FmP/GjvPvVnyKeBtQYjGyqk2fkpokxegSVhCq5P1QvWcrqXOQqgB0DRjKGGEsp+2spP50wlAwWikltnB16hvL6GvbjHEcTcVQvMZtRRGfu7N3+7Z2HxqEnN7UfdYzalK/6mdG3YdOevhoJiJrUjypJV1UGnD1do8imhweVuE59rLTd5IUmSiJab1Y1rfOYwlBoqKoKu95lRrg+3V7lPfD+pA5WOb518ggO+dQmiahFqd6ENttOJNb4ILn6/RwRMEqQHrlHSUShksMoxSwFy+JqDRiTAsontaOmSOYiiHgkwioYvvLuU3RwhGbKyy9XzOYTpO1QEVY1uNYh3SXdQjGdxmTfF1N4yPqy43wJqpigrOYyaFZdgasd8/fPmb06w0hDOYOT1+D9Ly754tceIragjh6ztHTSsGzO6dqa0DWs10+YVyXyKY8ppyhrMX6Cjy2dW6BCRIUWa2qs1vhwThce0/hzWt+gqznRgY+OdevwbSS0hrauCL5CqYCoFY/Pznj66Mt8//d9mpdePUYpIboOv25YPnxEXFo+/YM/wuTOPahKNjY6G5VAH3KxH5/hxCQ6L63pFCh5QxISqNTaELOkEto0kSURnEvwBNfSuUxR0LYs6xXPnr6HnSQi+npdc3H+mOVqRZAxIxnQOB6//ZCurlGhQwWfwlmKgO9QrkZ1HaYseONj382rn/guZnfvoycHYExyZEcRXIv4QDg4ZXp8nzt3XsasPVVVY32gtNC6SOcFJYZZURJnCh9AY/n5v/qXufuvH/N9f+GP8/Cv/jTrt9/HFoqu9rg2nQ1TNIR+kRhvR6PFRCWt5MOf+03e+GO/mzufeYXye76H+v33slNOPy6KxFOUF9/mMjlR0Z/qNwvy3g1m34837eCmQs1OEd+hgtvey3azkE0Vt66PP+9sjLs4sWcNUID4hp42Y+u5a5Lsua5uuLavPnvrfF3K0g9ivNKOrTz2IYyb+mhfuiVwu9Y5BpBiQjDVsBkrBONrtG/TO95L2HbqK9e146Y6ZYCRAiRsJGLRVKmrd00XrgWJL5D24cP82b7+OvN/5U+ivND89K+8kA3YeG7svluKDzB1+gcMUGl09lRWEcTFjWlpkqpsStljc3gVHF5XnFz7Egxr+JUKjoBhvlON38XhbwY8vXChr4+AmpYc/qv/Audv/zqNf5fZ9NWBgByVzeRySkFREqCLvQRNkU2w9DARVR92thdojNsyAo3DGGcJoda9qntsn9n/TTG3ByoopbK9ZWp/kgz20sRhIxz1WNoPU7s2qniVQ0/GjJyH8kVnm8a+Dhv/kpQ2oFv6NXBo36adSQKayuh174mHUw3Sz7Rf95y7eV8PYSBHT/9MluxmsArJ2TV4mqbFRYWyFqvA6G5Hsnpzur2Ekix0lOyIozbetsm7KO8HqMExp08xppKEkRMrYI1C62yqofLARjJY7cWzkrF6DzE2YuBCoNKCMYnR3gRPVMJ5HXn7yYKJ8RTRE+9EJNsBtA4WdaRtWro2MJ9FtIk5DJJisYKnZwEnDWVpiNWEajahrObEDnyzpCg99w8P+cRHX8YtH/Petx7y7HMXvHx5wvRuiaPm/OKc4D1GKaal5bU7Bef1ObPFO1BZphwk6hkJhNgRfUdZVBTGItR4aWnaFh8NEUNQga5d0raKZhnAady6RNspxjiqMi3e7z06Jyze4r/xI99LdVDg1wp34ZBV4MHJq9y5+zJ2Mkte2Lk/t+yv+tEeqQc2F7M3fr42RMDqryqVw+hmDrLgCRlEppikLa5tUN6jQodbX/Ds3QvqzuNEpVCVxuLxBEnclmldFJ6995hvfe7rVLND5nccQTRP336H97/4eZ781tdhucS8dB/vPF3XUXaBshK0RGJIUQdC1+LaNW29YnV5SYwhRdcxHSihk8iq7eicw6pkmzIrCw4rT9sFxK352//pX+TiJ/41fvTP/1EWP/XLPP3ZX8ttjcQAQQ3vMoOV/Ej8MO7ObtHw9v/n5zn5/j9F9dt/iObn/gHiuuHetLhtdhQVGiT2YRqv2qU9F7SM9vUrlwVol4hvk2PLzj1q997ryrkO3T7vt9ihOr/5vd/j9oDG6wDjtUByN/UAyJZJ7dtHgtht8Oi7aAO2QrUjL+nnAcsXwDFXnlGAzcT2tw191ktOiopYTFEhrX/BJmCpXZNCUe4r70o+o/S8MRYSp62ZpI1aHKI0QReY4K5Bvt+BpBKYPPrv/RtYVvh/8jX824+2pEY3gbGUxwY29oZaGzniB8DCimTbVRpiDiahgiC1H8Bkj2O27Qi52s/5EH/T1BrJ17a+q9ySK+v88Hf/SUgNBxLyoYEtYDTO0L7xEvr+EYvPfZni7hyqKodEzRvMlsSUrW0GUrQbJGSNr2KL3HwIbXtNuwfbRIZ7xwTiG7tKRfLa2NgTisTk8NKrgNU+aWKupfRlZaHKlQNZwkcaBTE54gxOM2wkrJu445vnhurnfXYjpR26CKTnx2Sz0ag0WxUCOrWxd3SSfH8/rzf2kylKUO/MJBKJ3tN1LjmalhYlQlEYyvI7oPJOp4bNqXagBDIa5fPBRavs1ZQcFbSo5LyaN1WJWWwbk1ODyf/QWWSrgRzkXeeBFglonZx+dC/e1gnMGPLhQSUOwYlWBBVABZ4sPYUKTIp0XphMK5zzdNFzXnvqGpQTfN1ibZLOdV64qDWXS2HdOWypOTgsOTquqL1ifd5Q3lGJgZ7A1E65e+cO50+fsDhb4t5eoBaK4rjCeU1wUMTARM24e/oyh0f3CQJNe47WLd4JXVAYZTFFiaiICyuieDpfEOKUIEIXapyr6ZqadROolxG6EhMsVgzYAiuBSkdOZzNOjaV7f8k0HqF8IK4arChOT+4wmR0kG7XeCHjrHN7/29iT7JMWDQv0ZnXJ60UPQgWJgeAdIsk2w3uPC47OtVSkaDRFKKHSCcyLJtEQJRVEjJFIVhER8IvAl//R5+kax/HL9xGlePzOuzz8rW+xfPwU3XkKo5h+9St4M+dkXVNN50Tf4Jo1bdfS1EtWi0vW509ZPHsEsUWcS47VKHwUukxvNMSWzwuHILQhsF5c8rf+7/8Xvv7lr/Gv/pv/Q5Zf+RbdW+9leq9+sU+HoaI0KKPwLqK2DNzzPSiefeEt3Pk55afeQJ/eJbz/LoOKZ1hI+0gPITmybkQE+zfr3Z92Dgyy716VDouE53AU3rQf7+xLYisIPqkkn5MUsgF2LwrGxlP41s8omBwg7Tp5mg/12GS5lbSBckrs1ujbAKRd8P48oL27UapkOsPzwp7t5luUoDS6WeaDQdqVJGt+9pbdZ6Vu0YXXIaqQ1l1vKqAYhiRqi7KTXI8XCLlxU1nXJHP3Dkd/7s9SfOknsd/zA5z/9FeIndu563lwTIb5tAEP21W57TQbPKELm9Ro2dYOSR7Uo+A2Kd/xAWYkQRsOCvRr74sBdNn6tDvD+/V7k/v4yfGr1YOYLedMSDbkWlF89xt0qzPaomEyvZOcMnP/xRjRI77ETXs34Enrjb3g5r6IiM6gKP121SN8p70DIE3l9KTgfXs3fSsbjKtlsIvdlgiqAbCN65xvZFD9E5N9Iyo7xwAqRfdTw7RK7Ul1iFuAt89uqH/2a5a+jkPbct+NhlH1/dKPk+rtIrNvhFYoLEoJWputMelNrBIJvRBizLE1NIVW+LKgLMsrfXxduj2gzJukgmSwGxVWaTSCVoaAT/5vKiLE/O5sBi3E5P06qFKVIrMwELJNg8qdEbemcT+RDb0xdD8hJI9C8jYWjIIKmNrIkyZy1igenytKazjqhNp7mrrjcu2wUaE8hDYyqRSRgPPCsoks68CqDejGEGKLPXK0yuO7Dl+BOWxRxtPUEd8J3gdM4ZhOSqazKXZSIQIFhtPJAW/efZnX773CvYMHTKczjAYdFaVN9caUmLLAFhBF0bmWZd1ysTpjuXpG055jYsR1DXUTCLVBxylGKqIC5QusKA6s5+Cg4l6pscHjFzUQUaGgmhwjukgbCySqA9mIzgcx5UAOOvo78L2OkEl+THIcd2IKESUhB7v3jhg90TtiDATxKV5pcGhtqKymmBiQik46fBdwOQyN9C+Ej/QBDSVEnj18QrNcM5lXiETqdY1vHD56lCieXlyw/vxv8O7bb2NLS9s2+GaF7+pk41tNMNWMcloxOzliMpswLwvsxMOqZmIM3qZXQmuV7WA9bRBcFGof8THi3ZLf+PV/yHf/+i/xAz/2w1z+Z38TUW7ol376WqspKoM3AePGC3ePNhT1o3PW7y84+dRdio9+lPDw3UwhtIns0L9/6XsAMemekS3XvvTcredalHn1vltvYUpv2lgeQR9U4HkZyOjvHiCh9t07vnhD/vsvR9TyrEf3V7PeeUC0AVMgdkL07WhzvDntrda+Cu1+jxHq5XU5XP+cd6jYbnWYcg0U041U4xoAPoYYt543o6RDi9Y2h1nNzVAKdJG8sa8Z2w+dBFRVMv/jfxzz+IvY5hGd+wj1Zz+7u2SNPt2A7ntgrbYlejdWfbdP1QgMdB7V718C+Cw9Upv3d7DLzQ8PY6VVskOMAj7x/97kqL+vvtut3b1jdxFQ13zLaeBYJJu85TYaTfl9H2W9fA810eiqSiwiA2KTDQ+k5OP0AAazsEibLcDWO8YkkvONU8u2GntPZ6heXidb4DBfAm0JIdBLKUdniC01eYazmzx6AJzzFkmCseFwP/RqkgKKTlHXVN5Xhyg6I4Aat0xPeqdjTSIkT8FE+v7qAXbyRL/a7L7szfwx6W8v7FCJ1m9r3CXdIL0KneSgZEsLYnBd+M445Rg98nLqB0DpZNSdWcwHmwQYSM43Jyq12QNVlk7qNDRG5zCOoW/QJp5mls+kyZYjsySidE3UYJRGE8ldR6FgYpOtwapxXCwUR0WL9pF19CyXjuXKI16QTqF9ZNKSB0mzah2tC7RdJIRA0yxYyXuo6YTOgL90VEeG2XFJ4zwPH57z9KHnI3eP+IE33uT+q/cpZ1VSsbZw5+CAg2nFpDhmag6Z2nmKHKDSKaCshI4Wj6eLBcgECYEQn7FcLnhy9oy2fowOa6xUxFCgpEpgTcCoiuAF8VCpgrKwVMZirGBFcC4Fm9cI6/WKtl0RnUOHZEc12KjIhl6hj90t2dYqmXXEkSlDLznL94WQaQlCilfeq5idw/kO791wEIgh4CUiOtm/hspQWFBdyj8GiCotLMakk56PAYUihsjl5YrLyyWFyaJ/rUiSTVg1a1brJY8eP8RLQCkoreLw5JD7L7/C7OgOURQnd46Yn5ziUWgXKaYrom45KA3lNFKHgI+C9542ROq2o/MOF9PLWBQWL5bP/s2f5v6/+ef51L/9Z1h+/ut8/f/504TLdeZNS2C0J5Qd2/FIfuWtVdjoWXz5XU4/8ybFd38X7S/+Qlro4iZqw5ZJwkgKfGuQlodqIy3Li8p1ksNbo8erz4iZwPQACQFVzUB8so9k5PR1U1Jbf56Luz5wfQFG9n7PS0obgtKY2QE0Gto9BOHDiXtnbG7ViL0VvO2Nm9tls0kOKQZ0yOEWx8D9mjw+CJhM17KJiZjhNhs8OnSo6AkYNgZP38akYPK7fzflyycU/+SnCT/yZ1n8e/8l4XwBw/a/5yF22Rm2hRi7jizS/28fslQ7f8ePRsDH9C64sGUgvpkuMki7NnyPJDBZGlSXBDVbEHDPS/J8+eU2YLxa9TEy3vmlB5P9npH/CYKelthX71Gf/RamqtBlibKaXtsqO/MuxJilZTlKGuN8MyhSZECZgbdsSyc3dUi131Ybx2xvqK6ASugj7GQTrj7iUa9t61XUud6S/Ud0Bn1DhJpcmBrPj6Eu/ac8+wbHEbWxqshl9vWO2ZyhB4VknIMKbHhyhkJhLEXtS1N9z2RfFQVGigE8j3SP6cAkCTTrnI+xhqKwBKWw1lB+pySUmqzCzmEWITnc9NyQw/TqJVz9xiD03u+D9HEgLc8Sy97OIIlp+1Nbjs1JAq4aTRRBK482eiBKFRFUgCiBwlpQCiuRUiuWjeOsVhysGhSRNgjr1rHuGnwXKaVgbfKgqzTJuxBxLuLbBCo6BWfvPGKtDSs85UnB5NSgHglt5zl7v8WvNEevnHBSzrgzOeHo6JSysGhgNplibSIPn+gC4yMFBRKFdb0iGA+VxxshoOlWay4Xz3h29i0evv8OFxdPMKamNJGpKanMBBGDE0F5Dxic80jQWDEogeA61sslnW0JQVi3DfiIMWvq1RJXr6i6DoqepypPwQz6JINIlW0rktSyD70ow+8SIzF4JDhC6IjeEzpHcIlUvanX+K7BdQ3Ru+TQoBR116EjlLS03iNRo7Shcy0+SnbsSYcKg0Yrg9cxC0KzbWVIYQ41oI1CW43BEKNj7RpWnUOL5uD0lNc+8XFe+sgbzI5O8M4xP5gxPzqlbtasF3USkwMeweFpnKcLgdoFLtuWNgh1SHOhFAXlhDA94Ss1/Pv/t7/B9772Mj/2z36GT/0bf4Qv/h//KqHtEJKEc712aJWI09MLvaG+skZTTjRPf+5XeeUP/jD21VeS003Y9eLul4D8bl27ZYzMUnpgsHsYJYM+W6Hqsw3YFLbUb8/fmK65wdXpUHj4UiLMnZ1ANYNujawvb6X+3sq6r/vOJp7X9hu8gG9Z5xsEHeN7xNi0dmmLKqc5+o4kcG4MmDJF6vIdyrWjHfT//ymZMrTPv/G6Kt+mj8h9pO3wiJKI9s0QLGGrnBcdtxuSuXOH6Y//Xuw3/z7xR/8cl/+r/zfdr39lVMiuKne7EsN7Md638lMJO8gmD3XL92KnQHGCuLBVxpVJTj9tRqAvRFQLwYcsreqvX4/8dyHXdtW2bULH98jWL2q4WwspHLXVRJMPo0FQKuT9WaEPpqhJCdJRTOboIkdg6ftTacbculpI+4xR6Xelh1Ha9Wwet2rjzNJLEvVwTWs1eu16JL8fVOrs2T1ICHc+J6zSz4PksCt647ya9iedzaVGvM1DuKPtnu+j4gzDrzagNAngQJHV0VFQOZhLAnuZVSVVMuMp2WrvWK2fAGt2Der7SBLIHPg6hwEfUTFqjS0sWhm0tRiTor71mOw26fYSSmUyqXmSCKbJHel5sbVKNmh5hqW2ZwPV3EeJ3Fqytk6rwUSs59Tug6AD2XtJUDoZMmtj0EI2w0l6/vGb2dtcgs7S1EDnHItGeLqQVGdlaNqOddcSfaQ10AaNjSo7FimcE1yXPM8kRloJNCIsfcdlcMxshUwKYgttG6hbRfQKcaCcpYiWAzOhLDRaCVaS7UiMgr+8oBMoJhUYzdqtWbtzVvoZna2TutUJjx8/5e1vfpNnT57i2pqTE+F4pmFSUBVzohK0GByONtQ0nUPFCQaV43M6Vl2L9wEXFV0XaTrH+bOWl1/6GId3HqAnMyyJ3Bwh8TeyiTQwrFtDF/ce4PkEFxPNgISO6LPjjWvxXYtvU7Qh37Z0qxrXNEPoxUiSVF+uWqxrCLHB+Yjz0LQpko4yQml0PiBEAon5PwgYLXgXk8kWKaSUURZrS8rSoDQUoSNcruiahjsP7vHax16nPDjk4PiEyfQAWxQYncw06sUS17UE73m2avHBU3fJMWcdhVX0tFERUpB6bFVCeYArTnha3eEb+pSvPW740md/lv/5n/uj3Pttn+LxL30uzf8MKn2IyZEp70j9Ut05j+1g9Y33WH3jfQ7e+AhqMkNWl/0IbJ16b94FFEyOoVtsU8FIfh9lfLNOoKg5H4DP1uXbpH3ATJJEAd+A70BPETQ0S1S9QI1UTLcGFNcg212JB+xIQq6r6u5m3tdFbS5uFvl8sy0yxVYaU2VK1MGd5KCoDaJ0Ig1vlijf3rjR/1OBmfsA4AtsCkMe++bbNUm0IZgKFQNWAlEXqBi2weR3qPHF934PunmK+eSnufzLP0/3q18iSs+GsJ36d2/fSWsrYsnwwFWYdR2+3sI+MnrtGPlVbwv/tnMZv4T9wz4SMzP5h3k9d4u/+a7tnFK1etoptXF6znVSWmEfnCImIpMWVU3BmN59dgCdvWRNJI76WQ32e0noNgbXY2eafinrgeXYHnNzcNgGohsV+TjP69KubWZaT9JA9iA2XdMZr0QkqBQ3fVDHX8kVkC1S8bGHeeqTHoAmk74B8GuVcefGgUeLApOxjmKrXrmAhJeUIsRA74eSUVrqZRn1Wg/4jUZnzklFJEjABGBEiH6bdGtAqQyDcERpQZue3STNrhj6SqdaSkh2kTEj3MwwlLXjkt+V7JGUWzdoijIo1TqRYxvT25+o5BukHCEKMQZiju0po4kViIQQCZIkknWhWDZpyq6ajtY7NIpOAq2Xwb4tSKRxSfIYBNooNNHToGkD+KCp1wGzTpFhghcsBqeEZ+crHj4+Z340ozCRg1mV7CnLguA6Qoy4tsE5h2tbgos0vuP97gnP3Hu4agWTAuUU68s1j9+7YPEsqQqnBpzVxFIjkih6jLZEI6AbSuuYSsHh4SF+UqAaQ7MM1LWjbjvajiytXfD53/gNyuqA+11kdnqKtpYonig+kc8qhdEGowu0NRhrUcbmE1YiKReSUXn0HvGB6DwhBFzr6NqGplnTNDVt09C6Dtd1eO83BwadSGed65AY0F6wXqgoiBLS5qciSKKjKpQikiSQQSu0EbwIMQohJEHrrKioSovWUOkSpSbEEzi6e5diPsOUBZPplMlsjohBEVitVzx68oj7qxXaOy66FoVGtEUMdKFBjEFKRRkUpQjV5JC6PGUxu8Nifo/H02POphrll3z2l7/An/qjv4dnv5mcARJdUX7ZN/4RpGU2geL1ytO1C5784uc5/JO/F/PgPvFri7wOXYPa+rR1WZDmcjgtb92pS5gcgmsQ1yRwZCtEW1TYdVrYk/XozWa3WvsAmq1Q2iDrc1Q5gzGw2N2RbwMsr2zC+9MHFgoKyemmmiXwiCKqpB2JSoEyjHkrBMAkNZASwbga1S63wj5+p9KtQemHrceLPK8Uogt0Vm+DJNuj6yaRuvrTlQPNLQ8cSimK7/puePY28f4P4n7ts8Sor50u6fdtyaTa6tTta7u57EDRncrAYNN1zflvK8vdjMbfZec9H1/rJTHXDNJ1Q3cTkNw2DBiTC2Vlq0hal6OMVPjZr8IqVGmREDBVBWW1AWeD6nhXXT1qZpa4JYCkht9jFmIMAHEkseydWlKKGeRtrvWS3I3a/Gp/bKuL+/psg7OrNpibGdtj/rSfqKG8vg7kQCw9+pbeoRIhaQaFjQoXEmuH7odgkNRuKqdGkki9qZuWQfKpdN5xTPJRGWPBmKO59eFCeyFeD+qVVpRViSFxKkfbR+i57en/hSSUAaMyl2QkA48kIYrBD+ZDZHuDTccKUW06KEYIMdmPZXagREHUvyOqh91JjanQw8Cikq1gEIULiVomikmnpuwMECXSxoiLbrDJc42jsZGgko2kc4JBcNoRQoHrUpldjKzbSBs9LgptVHRicEoTlEabFC0oOI0RhYlwOLFIYdAlnF9e8K1veVxzyoM7R8jpMTKtMNYSY8S3DevlkuX5OYvzBc8WS94LNbVd48sloWioROGbSLcMSJdf3k4xp+ROdcDJZEZVTQkeDFNKG9AqcDg5orJHNAvF4mzNI/FcngV8F2haT9c41mvHP/7i57noAq+++5CT+3cx1tA1a1zbYgSqouTw4JCDoyOm8xnldIotSowt0KbA2ESmiiRQiE9e3M63NO2Sbr2iXS1xbYv3HSEEYkynOmsNKipipzBVidVTuibQNZ65TxPcGk2rEvpSMaCVRpsEauezY4rpBBc6FhcLmmWDDipxiDpPWU2YTiZ4A/PjCXff+Cj3PvaAcpqDXBmbnIKUQSlNGxxPFxdM1g0zEZwxVNpiS820NJQcchIVT5cti7OGEIVOHdDau1xM7nI+PaE5mBAq8P6I/+qbj/j9/9xnuPc7vofHv/hPIMZE0u8272RaGDc7RIgQXeSdv/vLvP4Tvwd9egfU1xlWlbEURRVIvN4L+1p1sm+R1qIO7uXNU6cY9uUcaS5uZdu4o3naQZg79UCQxfvo0CHNEiazTVN287kJnI4/XycWurHSXF0Lx/mN84oBqZeIsVBMoJgQjB3LlnYeTzahpl0lMLmnD28N/m6Z/qmByUF0Idtjdu39OgHJGIf+1pmo+tr6qc3H7XJfMFUV9vXXMd/4e4Qn3094dE4K/Tv2it5Xgf7aNY27YR/dfaK/dQBgG0y5uXes6s73bWkH8j1buHH0d2t67YCe8RLxoiBzANCq/9/Vdm3VJkpyKgpZ9awVGE3xXR8h6i55tBcFonQW7vYOOBuVcj8meuwrsQPuhu8Dy0Gm9hnbIIqMbDSTZC/5X/RATmU7yk1Lhog6o3I23doDUhLWIEtjJQNu6TFKps6T7M8hudwBqOV/sVfDp3uTI0+SNg/amiFSXcpfKxiHFBrmQwbIGwqivv553KTvsxwLPEfb0VoPwFEpIESCikOoYAVJO0uigNRGY41h5ZJ3fdN5nN8Ja3pDuj2gFMGSQSQaL4kmwmRpJVHQkiqX5ngvOUxdEoWBsNoj2Oz1NOwT+YVLdpNJOmky4FQ6UbdEH0FiUnlqSVLK3Fk+JuqgGMH5kO7pEupWAp0L1N6zrCOdU5QKnA60HaigCQiBdF9A8EAXoUNTh0gbkvWbiRZXR5hoSms4PTqgKkqs0kymJTFL6lzjcOsGI5GyKtKgdR7fOlwXqJuWpu1YNjUXsWURG2LhuVMkbk0doawMOkQ+Mi/59P17vHz/LocHJxTlDIkpRmdZaEpbYstjtJnSHHaclY8J7ZpH73h88AQJKAlEAou25vNvfZUvP31CMZ0QYuSyXuOdpzKWg6LieDbj9OiQu3fvcufklMPZPDHmVwVVVVEUNnFYkewZow90rsF1a7r1Gt80ONfRNSu86+hCR/CB4IUywMnRnIKArC3L6FDSIT6AgbJULJXGK09lwHlHaQvuv/kmL73xSeZ371B3K95/923e+eLXaJ6sUVrTdR2XixU+ag7vHvP6xz7Oy9/9fZR3jtA24NsVrl0lu0ULAY9WDhUdIUZ8gC5qTk5mHBzMOTo9RqymcZHDyxVnT1Y8Wzjeq6ec6TucHd5jcTDHzyaoQrPykS+5gv/Tz3+J/8l/9w9z5we/i3f+xs+y/Ob7aC09xRkbL++eKD552l9+/T3e+69/hQc//Dtpf+3XSA4z/QqWF0Q7SYvKVpBdnr8RK1B+hSwi6uABFEX6cXYXillymvEOFdqkqt48dlVyNE7X/e7bzWYYWqT2Oxlu121ffmr8QfbU47boSvoN5/nPKiRJbINLdEJFhVRzoimGzXpcXR09ql1dC8j3YeUPk76d4PS5yRTJdOI23uwxbCrXg6noeRFC5BdKo/Ezd++gqwJdP8L7bH50ZVKpPY+qK9eulDFOMppIavuG3Zx2z04DwsxA5aaXat9Z6sYkMvAnMqhLX+zUJVon5x8RxMdN1JXdinlBqZg28ixBUlYnc7SDGb5bIpUFm/am3jpPDRnkeqkEbnRvUziolUegDzJANFnat6UkZmOPKUP/CpIBQ9ZXimwB1iHlug/2hZmLcWPv2XetDNpoIW4NtM7k68lRJ1O25/CNEvrZsGmHIAPt4VgVn1T3EZ0FZ0MEut3DRpbixhg3gLmvo8rOqblvdU/xOAjaegpGyJ7PaTTG4ksNhSmwyhC8p20jShfJqfcFVD+3BpSFSc1LjjGCi2C1wui0KUYCSuntaahGBrQqA8p49ZUWekll+k1rjdFJl9+LcH12EZcQIXpCkPwvpoD29JQ7QlcLrhMkpENNUJHWwbIWmjaJc0urIIILIdk4qiRwFpXq6gXaCI0I6whBaayxOapOAqkGy2E15fhgRlGUTEqgq1kvVyxLS1EYeh5GJNKsa5rVmotnFywuai5XDcu64cytOQ9JzT45gNmholBQiHAyg+976YBPvfIyJ/deZzI7SQ4AKk1yY0q0PoDiFJTGmnOiW3F5YDmcaHxjqZSm9hEmQqSiLS1eQ+MTJc7SJ0ck1TqeKces6eDiHPP220xtwcQWTKuSwhrmVcXxwSGzakJRFEj0RB/xXUvoOrquQVR60WL0xOgJXYcOCkLg9WnF4WHFpIBgLEo81A4fW7QWCuPxKuANWALawunJnNdefYW7r7/O7O4D6thRHhxgvPBO9zW61qGUIYqwWq9QpWE2m3N4dEh1eIK14MqCFQ1WQ1kIiOfeQUX94Jjp4YKw7pjPFC+9epd7L7/M6Z1TRFsWznO4WKPmZ9RPHMtHsDD3uJjOifMJTKbZYy1wDvyXDy+5+Mlf4l/82H1+3//oX+PL/95/Qv3oDKN1PjApQp75vVpGSEb3T37pC7z65/8wlNPkRbz1Hue5Xx0DZ2lBVbDPU1ltPzb8qEKNLJ/A8StpE1EGyhloC6pGYo6OI9uPsi+/m9aY3frs41J8zholux9277/NGteD0Q/wrJKA6tbpgDw7Th6oAxDIi3mmEsI/3+Hl2wUGb4ujP1QSkmPYWBLyvHL3AbBxSMR9Y3CLg9CVZ7f3ZMxrr8LyMerglO7z7xIzOf54b1F7C7tFL+4Bj1vfB9yQANEVKX5/fWTDvCVcvF0trr05Lx9JCrbHuX9Pg65UTpRClQYxGlygt2sb8DNq88wotKxITABKK5TVmLvHdCpCYXoFI1dsVQckQ9rLZeQzMeS7IeTWOSM17u4BlKkMiPone7V3zNJJGUkit1XXcWifGv72EsJeoimSHId6u9oEenVfSYaTKjC2gaQHd/149OC4F6uqDDp7MCkCEolKUCaxIIy5B5IZ4AZHkW1Yo2Qe01znXjqr2PSBDOWnflJKIUaj4gZwk4GuNhtaq+Aibd0SladuHc7fzpESXsjLO2KVzo42MX3XScrS2ymoPMmCD4NRq9KSaHJI96RGppNeH+FIBuYaGQYhSqITIhvAMjJCzWZ46XSRCboVghfDso2sWyH41KU9+O2iYuGSut0omGiY2NSZMQpisi0nKXqPF8FR0ARogmRvtDTUoYmghVIMeGFiE5lwcAGLYblIIRSFyKyxVKWFKNT1msXlistnCy6WDU+WCy66loXzrHyycei8ZmIq5jNDUQpvnBq+6/XXeemVNylOP46ujjeDMthYGERZCAxxz5UEJlZhlaMSKFFMbYHTBYuiYGl1sg+MG+lR6x1Oa4JzSMiT3tfpJCCZHsIkqigVk4xNE5KtrE9UNwaF0ST6AauxJvFxEgJHRvHg3h1UqZm/fAcmUyIeHQGzwKlAKBx1m6SHWinoBGsVhY0YA0VZEKKhKKbMj4+Z3ZkhZ0uUaDRZHa8cbbNAhYapCRSVxiuY6hnaWgozASkp1KvopqY9PKNdCG98ZM5Lr93l/qsPODw6JgRF0QleL7GXlroJLDrFQp0iB6eoySydtHSyu1TAhYW/v2p4++e/yHxS8YN/4vfz5f/wv0jk/5BMPUiG2TFuPOgEOPviW8TpAer+A+Str+ZxyQuVSJL2cYgyBZQlFB7axZV3dcA9V37s58zoe7OA5ZOkLt+3Ib5IetH7nwcoPihy+nYiLpNO+EpIat3gQGmirZKmqZpBcFc9mW+RPgg4/I6DyaGg7ZJuKvdW7dgd6+vGfgCMeVPY50ykoKe+Kj72cdSTd5AuENdtJpdmAC79Cnl9DW+o+a5Ua1TBTZ5q+74refdA4mpxLzSWN1Q/hnjNS9/fMiDb0a/pc6JdS+uQCv3+S7/kjOqut3If2q8UlAXmpbv44q38vqRMdu0Oe5LwDdDpAZHeYLPh3qtgLIVH3EgmEwDqHVZ6u9leM7rJb6wmHquFN32zAfpjjfi43gIbZplB+koWguX/+ufYcGBueqsvQG2GIQp9kGqldQpTmcH0MD4qs5qoOPB3pzJG1EZ9EXEDdAe1uwh9QAG1YftmDLJFNBI80TlWq5rVuqWuO6I2OB/T/Lplur1TDnnzU8lLF62TREMyDVAfEkgSqXnur+RtlCOI9I3fnCuSTWVEZcaTTFmjQEgON1ElNG40SAhohMIqCisUNnl0qyxdXHaBs86zyNERpkYxtaBEsQpCF5M4eGqFypIBq8Kh8F5Q0wnlwTxpES5r/DKQCNUDKWKPShFttLCuW3zdMitWzOcztNWErqHQYGKExRonkYOZpbIWQqSuW1bLmuWqoe5afPQ452jaQNMJBjDWMq8POSkMx1PD9776gDunH6E4eB01exmKKUSHSAfeJfWib8EtkG6NW57TPn2KO1sy6wL3vUZFQ6VLRAyXpuSRKfGiWLYB13Q452k6R/Cpn/FdGiul8mRSg3VvDBEfHCEEfAg0XZMi3QTJhPcZfEaPUukQUtoCreAj0wlviuIVEzH3jjEHJZPTQ4rTA7gzZ9W0tP6SybKh7VIMa0dkcX7Jo29+A6+naT4YQ7u6AAkcHh9i0YQQmVRTpvMZr7z2GvdevYdiCV06NJTKoScGWx2g7QwlisIo3OIZF9MJTGpee/Mur735gIPTU7QxuNZjvICy+OqY80qxOjS0coCbFMTSoE0GsZrkbKQsZ+WM35q0/KVf/Byf+RO/n/s/+s9Q/9yvopYJUEaSt55AkqKHtKC1zxasH52jX38D9/WvJg7LdDpIq5wySMyG26ZEsqzzRZKyk1RofZlsBU0xvI1bghi5xYa3C0A/QBrAyNXd6sNl+u3IBwWmAgHjG1RzmW26VAL11QwxmUZoHy/lc9KHBmnf5vTtPkdc24ZeasfOJFMkCi9TJkFBt4d+SW0+KFNg3/wo/OO/TvyhHyF87mwIA71fS3d7kHzzk+MLN7wEWyB0v2PIB03jUrc9l3vwuO/71bpKEOgSJZHqJZBDtUfSwK3ntiVc5v4x+nhO8F0CXb2TDZkLMca0To6XGTUCqKrn39zYHG4cUrL0ED2E+h2obwZjSjPk21/rW9vbCfbOLAPYHHWRZFoekY0tI2w742wwnkJhtw7mCYArxo5SWqsEPEdjk/Dfpn1KCRKSpEHFVA9lTObk7NuaQuyKbNqYvMRTVLmNB3fm0xQBwgDCo/TOU2OHoWwPGhUSFW3Tsbpc8Oy9p3zzm494slizikkY513Edd8BG0qdKxQy4FMiSUKZIrpDTI4nOp82YpRhkmx6RwbG9iTVTEjei+BjIIQETnv1pVEKjM4dBYSIloAtYFIkG8tSJ7vOJkYuW8eyTeDGRKhyKKQ2KJa1JOkkglXJqVOAtYv4oCknU07vPUDmM4ILHE47ypOIiyqphpuOw+mUajrlfF2zWC+oly3vL1fYZ4Z7x0dIiHjxWA2xA2nSZGl1QAVF1waWdUcniXqkLAIT32GbgBYwoiii5q6d8mA65bVX7/L6q69RHbyE1neQoCE0SGiS+rJdIu0loV0TFi2+8bSLlvjYMXtqeNBURD9BvMf4gAsaRLFSoKJnVQfWrmXddrQuRbpRymO0GSZzGsOQyV/T6crHSAhCDKDEEEkx1FP87URw3lMLRdcCNYjCrFuemIL2pETPoXppyt3iEG8V5eUcHp8RzltsZ/FBJ0mxF4LvePz2Wzw5P6d65ytMjo8pJzO0CHdfeYWD7zrBlImE9fTeS5zceYlyVqC0o7QtRhZAorEx9h7KzEAUtvTMD9+gnX0DmQdOPvJR5kcl1URhDCnKTtOyWAnn7YRzPaOdFJR6zuFkQmssXvd0VUk6j0+LwUVZ8PN1zb//07/Ev/UnfpxP/PbvZvUX/0oCJ/kd6RfPGPLBSgKXX/gGJ5/+Puqf+QeUeHqD7cSHNE9SUdZsnXr7dBsxkVbI4jF0q2Q6cfgAKSbQrbfyuRVI6L98iI1yDCZ7ydKtshtVZC8Q2rePvmjdjEVMgXEJTG6kkIIKHbJ2KYxpMUme89d4zH+gsvf9uA+hfRuR5+5G+2HzvTIeW9/3ZK7ZSCadS+HVxkmNbhSFmlSo2TTZYD/4Abpf+o+IXvImP7YCvAok952B9jV3+K1Xfz/vxdiZc0MY5Y3I74Z0u8F8kbPXNl7f8E+O1dEqxI3EcfPz1U4aXtZ8twhMK+b/8u+j0c/oylWSO6ikMUzRPpNauw9sMUyyEbhTOcBGWg8H8V2PlhMAyhLLbemhAjY8lONqyvg+2dD25B+Ha1rrTGOYQJ1S43uzJzQwQrM7AH4jNUzCQZWbN1bHJ4ekhHey+nhkEqJUcnBNWqIEKnsnnn7k9DBmuf6yUXP3IFihhnI29d+0ezMXU47ee1armsXZGe+/+x5f/srb/OpXvs7j1ZqoDAqHeMG5m8PxjtOtAWWynUwnDqs0RiebySTB8yhCes9FUNLbNCY1qWiV7BYlRchRkk1gBNDZPUGSJSbiiVHQYvOESpyTwaeJFyV76SIUhUUZRe2E8zpyuYq064h2agh11Hhh7SJdNtbWOgGf2kMTwPkU+vF4ohLHnIeTg2NePz7EFJpl17KsOyR6ZmVJ5xXV5YqjrsKK5d7pHQ4PJhjxeNdQr1bErkObEueFVe2wRiMhEtqOrosoU6JVoIwws555GehcZGbgeGaZzSsePDjl9M4dzPSAqGwyI1g9I7QXsG6h6wjtCrdqCAtPXAbcIuAvHXIWma0M5QpiEwmdphCFU4baWIwkoNb4LnlxdSlcYggOpQ1KhxxPPdlbqCxhVkpnKXKSVIYQ8DHZlEpIJ9EQwxCOUfoTb+YfXbWes9WCOL0Lp4ri7hQ1KXBtS9sI1dRQrA1FoQku2aJoQwqTFSG0S/yFcNksuPfyK9y/c5+XX/4YJw9eZXZ4hCks5WROMT3AWouKDfhnkMnXFR6iR3RAiQEm+d4SpQ3ldI4ySbKqdcSHBevVJWcXJedrS6cKyukMp0rKUlHgk21vBOUli94TwUxUBZd2zn/11iMefe1v89//kU/zo//On+Ppf/F3ufjqO3kxS4uiUhGTF9rLz32Fl/70HyBUB0R3kR3+ejLhBlmmuYjSqPIgAcGxunqcrggkBJrzjU1jaJHlU6im4Or96sXrdt8+9de0hRxZ6YOmfVhp3+/jH8dVuwJ2r334lskWaN9Bc4kaeTD3+SoliYrJdyjzHXJA6dO2sCt/VlBUKf76C6ilbkxjQPEB0ljLd71Uj6tzqi8zeBC/FU1mq+1ZYoUI9vXX0YVF/a6foP7pL+HffUKKf5ylSltb8rh8GVX0+jR+dsBSij3vldr6s0UzYzW6MGnd7Pp2XdczsufT5perXun72tC3ffx90x/bzUifNq6zDOaeQ5hARk5OCgbibhHUbMLRn/3DyCcOedr9OhT93p/LiRFlNDGGwft6A3KyNE1FegnjmJhbkfeavj6Erfs2PJEbQvTeGUfUtvq5n0xj6aRWKplISY5yGwW0IUpE6x5UykaoMgyDDO6U+eugnh8TsYuoUa9uQOcmrGPuVW0T5aIIUfk0Wnqjlo8xOexIdsbZmcib/LPKfyyJ7X+PA/tHsjFUKjGLrBYNT99/n7e++g3+yZfe4nPfeI8niyWtk81cCAH/nbChTHYOmUAckvRwNAHQSWUbvUAOkZhoq9JfLT2XVCL8Vlmsu+WRFwVRSaXnQwKoBgZ1qs4SMiEm1bMy1A4WjeNy7anriKvB+Ii1qX4ugpNEuh4ieJ1lPj6TrftsiKo7pgctr7x2wunxhNnRhOnREcFoVnWHNRHXOS4WjrIosfoI5YXTO/cp5jPW6yVNuyBKoPaOtnVUhcYFTSeJPqetG2IbsFYQnSRT02LCg2nkbuk5reCNkyOO702ZnEwoDuc4U+CDolsuaOoF/ukz5GFL1RhKUcSVIy49cR0oOrANGK8JzhI8eFE4pfDOETW4GFk4YR09je9ovcfl0ImJSiD9NUaIEoalyPSRBSR5fYUcZjF6IfhEftrbvg6Cq/z2RpWsTtYSeRgd/riAKkABxbRE6LAFSHR0XYsosGWBiGCyzSHRUE6mzI7vMrv3Escv3eXl+y/x4KWPMD1+CTuZUUwKTHEItshB4h34CukKdFeBW0JYAVNQE1RMUXjSKTVyuVwwm82x2hDFgdY471mvWryfU0ws06Ig6AKPpvCBJvSAWjJjf3IEw1qQkoVM+JUV/J2/+Xf5jJ3wyT//L/O5/81/SvP4YiPFD3mNRnj2618m2D/C/A/8XtrPfjad3nuDcBEUIUcqEihmMD2B9RnbOzDbm9/W7rS9OCi3JnkwGhJZ5g1pDBLGwEFZOHwAoUMtn7HlGSybTeq6PJ97SaW1Qo0A760EaR8GTPZquGaxkUyOgdCoAkpi4uX7IMV8qGoKeD+sw9+29EGzu2mcn/PcUO51GfRgMh+uVFUy+xf/EPpgRjyfsPrP/sN8mNkvl9zCsErtLWbfWIw9kDdwbE/lRwWo/rsm2d4bNQqesg0an39W2wBEGX1/XkrYTw3SrO1qy5W8ZPxXyBK+TQ1EyNrFXBOtOPwTP476/ge8//hn4bTEsuGfjHmxiDFR0SROyVEN8j6xAZW5w5DBdC6V2YvVev7FHT7LHvlsQeg0EfsAM2kIexV2LhNFUhmn+NZpeVXZZCsdFpXaROTbhKPI9cvAbbsqm34eR0TapvpRIDr7lyTgGCPE4PASMBai2BypUYa+TPgy5yObiEN93fpdd+x4tBn3hHFiSOEoQ4is12uePXqfr/zWW/zDX/0CX3/vnHUX6GLi8PYhad0keG475+AFAOXwuknidjJorO5DGKVTUIybWKPJvT2/CDGpwKNKksqoAZ05UmPPlRqTk4ICEY+gkZAFxFqyEEYRC0WIkcZ5fEhq7HXrWTceVwvWKyoDVaESATuSHBaBLiPvYe2XFDVNAyo41mdPaCcgpYdSU54mSgpjoGsbQprXTIoC6TqqwlJamJYaFSxuDXghOqHrPAIUpIlTu46mbvAhpFB8VmOs5s6s5CPHE+YTw/FEOJ2fcPfBHQ7uHKIPpgRdUnuDbwIXzzrc2y3lWytMXTE/uostT4h0CI4QaiIeHxydS/REvg20LoHGpVE8U5GFFlYh4DuHcw7vMkl8TyiugLARnSfapjS1E5m4T2EWeyDaT/wYNyA0bl4iEUGLwhnhra7lva7hI9EzYY0OkXr1jG7d0K0aYrTMT15ClKdt16zDGYQObQ2Tcsb9uy9x/MorFLOKyaRiMtFUE4OeFKhCI8aiy0nqee1BR+gskKhflFuBPwRdpCgUPhlYF9bQ+Q7XljCzoEqKwlEUlxzMHMdKuDvV+KIALOsmUIigXFJTkJ2SeiCCVsSiBDXnDMtSZiz+4W/SHC54+cd/J1/7f/wk0ZNMPPJclCjEsxWXv/Utjv87/xImONqf/Gyi88kLQ38aV9ET1xfppF7OoV2yWc3Y/jtOe64p19z82vdv/+6OO3yOWYWwI6UbV2e0nl59/jnJVqjJAbI+Q4Vwu0c/LMYSga5OG8xGyPNC6TZg8QMDsGFxvb190z+19LxG3dSX1wLKHkxqlC2Z/7GfoPrh30H40ntc/C/+EnJ2uZXxVeqcQSZ066T6ckfggCzUGDbrsbFxD25UtsOLQBfAyxAP+pozyU5NN0OsxkIX2b3r6veNNFElh0GT1yYXN4Dnxm7IEshxzsMzedIpsK/fY/ZjP8jb3/jbxJMGK2U6WI1IJ2QUMXAjEYWBXDvGhB9izKwT2c5U9cBRNirwUYjFPo+NXeBmcVE9CGUjnew9yQcpYSSRgeeK9SruDaVbupDO8ZkjoFex53GX0VgPgjVk4POU3pBX7QLgREje20AmMBloXZfK1wUWGKvVZZAw9lLQmPsl9qUOYDMJ8iJDOCPZtDnmedt1jtXFBY/ffoff+Cdf4Z2zhmhLtHh0NPiYNJYMUtrbr1K3l1DmBqbBiRkTZ9GtVhit80DFLJ7N/dwfaNicXKKAD8n2MQJOhGT6ovK1kBxxGITcCciisBokaryLoKHzQlsHQiMUXpgUMCkUZZE4Vr1SzCpog+CDysTqSdJqlMpUNVAZRWkdrJ/iLiz64Ai/WqJCSRccl5dLlsuaugusFyviuubk4JACjVte0LU167NLLp+e4doOiUKn0klLCYQYiD5FhgkA1nB854iXD6fcOS6ZHcw5Pp1weHDE9PCYYjYBk2wJYwuuUywWirAwzKSkUiVFMUNPKpQsCMsW5zz1ak3TtDR1h/dC5wIxgI+BhdWsjabVQuMT3Y+4FI875GhGvdexkjTW/YEh5nUmhGQfGWIPLtOJRiQSQrL5i1ndrfIJVZH6WkR46iJfenTBJ1aXFKuCSedZPTvn7GHNcqnBnhBLSzIqXWMb8N052lgmk5L54YzjoznVfMK0EqR9CLVDqTto/QDRSdompHmooyDeocOa0F2g4xylPGI6lIIUAyVilWI+PWJW3kGZg3TNBk5OXuKN4NDtAQUl0RtaJ1gl6JidkGLmjOzBR1ZlJweDKa/d0Xw/d5B/KPz6o5/lR//gH+H4N3+LZ7/xtcTXKxkqqvTmn/3KFzk7mlD89h/kvgq0f+ezOZShpQ/DJa7FiEGaBajRa7zz7o9BzYtKw8b3y/jH3ftiTHaZyWhqP3gc1++mnXTfz76DdnkrT+pBADEuf0++t+qLGyR/twWLH04CeUPGHzJ94HplALCV0Q11em45zztgDGA+XTT3HzD/oz9B9YOfQd55xsW/+x/hv/EwgwPV4x2GjXZvQZv89p6Pdu/rJa+y+b7V7l761f8wHP4yk0nsHScGORI93IEe6m4kkUO2JDyg7MZzGsj8e7KT06Z9ChCjkCKBl7FSomda2RTQd9ioQTLgkNTc/hlDDjUKB//S72K5fJelespBcZoO58GhTZFAj852f/3+rzWiUtDmpO3M7R2pigdTgdxMEUmOKmzu3bQj/6YiCp3NsbZnW9KW7Sw4vQNxUpsOPYhkNpnYA8BMF6AyfZHkcerV1Qok5muDPWfyIUlq80TfM8iCMxBMxDTZqz2CEPCuI0ZHEMHIhHHqnWskOxQLaoRj+zmzM3OH9S9t2knAkX5PmtKai7MzvvqNd3l0scYUBqugcxHnhc4n0zAlgtYWmzgjb5VewMubgTQ0bX4y2KnCZmL0WoeMwQexcL/P9kBOROh8RKzCxQQoXUwq0yAKL4IRMJEsuk7xuINTuA68EpTyhC6iO5gZjTXCtBCsEUojHE0VttS0kgBl53JkkphsQXsyUJ3zV0oRvWO9PMM8NqzWa1Q1pY6Ri9X/j7f/DrYlye87sc8vM8scc/1z7WYw0+MNgAE4ABbELglLESRAEiRoAJrFEhKXGytyxQitYhkMKUKiVlxJlJah4HLJJbEkIQKgBwEQhoIlQNiBHcxgXI/p7tfdz113TLk0+iOz6tS57973Xs8Amx2v7zmnqrKyMrMyvz/3/TXU65qqWtNUC6TzVMcn3LkN2ic0Hxw6OPAWE9KzZhqCRSnQhWBMRlZMmE6n7B/OOTw6ZGdnh8l8wmSvoJzsgVbRN6/tsOuOIBlNY7l/9wHrO/dx54pCCfmixbTQdTXL5TnVyZr1uqKzHdb6ZJomKpC8x1mP81A7R+ssXRd9N633+BBTVQYRgov+sTpENwYRFYFZ2GgyrfN0ncVZlxyuXfQzI+X+9JEaJ6TVy0sUBupOePFBxZ275wTx7M4tTe149ayhbjV6WmIACUIIGSYr2dvfY7pTcnD9iJ2jOUVpyLUlZwH1MdZ7grtOzu+KfoFAEINdLdDdMf7803TVS/j6BGPehCorZApBa6SItFaZydibH2GyXZApSmfkWcbODiAW0xpcYzheCfciB3sUsvpFTgFmI0WH5CP5hpnwDe/QPPtyRwiOj770y/zWCy/wJ7/9f8PNT9wH61l88hWOf+3jtPdPka7j+AMf5C1f+UX85mdeo/zdX8GeQPVDPwAp33okdW2hk6QVaEcv6SWlXzMf9YJvaRKuOD9sHx8vDkKA8DmmHrwCfUjw0NZXnnMp8OWqHx7588MnXNGnV15/cf963H2etPx2otJwyRg+yWWi8LpA+TqBlpQNpffJvaSNlzb7UULFxdLv1SIU730v8z/+zejbv4hezjn/7k9ib98hKXu2zYo8PFUenjpjvdl2i7dB14X2XnyocOGarVrHHop9VdsPfxFM9mcFiEF0uSYoiZbhzhJsTwOzOb+vd8CGKaFHsNEdQ3rNTv9I/cW9uXhL+um1caPaBdCxHeQZxXvezIv3/gN6liMS/QADMSBWGROFek/Mc03vE6iGez6c8jCxxAwBM0kbPXrGi0T5feKU+H+/4YoNkFSVgEqpnkO8b9gG8j3wJylMBrDS982oXUNbJKW3Vb22cQwqSS5UiQqI8bwMhOAGDSoEvI8WRZfYO6TPHjQ8pB9Ffav0eQxSN4q9oXmS2twLQ8mqDAl3rdbcv3ufF27fp1PxvK6xrNuYqtl1LQqHMRnaxOx4T1peF22QpFB/H1I4egIdKgVheJ8GPfiYphHBiaSI7aSwkXh9n9nGukDtBReiKTsgdCEQvOB8zM6jCIiDXEcydeV95EE0UOZgkgSrQ8oxrqA00fStNWRaU3pPpz2t9dg0SUKIATpdstg5B7n2lF2Hq9eEEKgXSxZ1y6pqYw7udo0OMdWk6iJFkvh4v1kRmOQ9pRHkWQaA9SqB2AytJ0xmO5T7O2TTGflkTshzOgS3hqqrCAi2dVTrc87Ol9SNZeVa2vOa5cvH3DuBbO8moVWUk5IgllXdULcNXSIpt87hgk++KzpmAnIO6wQboqm3dY7WdxF4WgciuOBwPkbTO1FkIaCDis7VIZq7gw94Z6NWss/o4h14F9NhJok2sUwRiCSx1sUX5P79hrPjFqPWdN2UzhtWztCJgU4xLaaURqFMx3xi2N+ZcOuZ5zh46lmKvQMIjvb8RZrlbWz9YbzcRU0OkaOAnbwVl63AzFkd38Uef5TqtQ+g6rvk5Q2K2T7TwxlFDqqYoEyk/tFZTjm/DjpDzATyKao8wOxeY1qtkcWa0xPLTuUogka7Dmc7vCU+pFKgNYJGlDAV4dnS8gfebnjvMxXF7VOCt7z4wY/wa7/5Gh/9mRd43/NfzNH+IW/4wnfw3Ne8j/y44mP/wz9nfe+UF/7W9/D2b/n9zA8P2flzf5qwOKf+qZ9I2iEVwazz411oG/DBtrkprVIP7eMXwGbgwocrQN4jAc7Fax7agLkcUFy2gV8G7C7U/1hw9wRg7OLtnuicq9r22ZaR+eyJTr/Yntd74eu+TuFEgymIO4KP7hify/0f1+GRwJj8PZ/P/Ju/ieznvwNe+w38W/5Hml/8/mjW7dwmoCz14UXQdnnlEX1sNSeMAN/4BRpfwuOnllzy6eFvo/teclYwCsmT2VqScmaDCy8RonrQACl4IP4WwuXt7SNKxrBtDPQ2XbSJcvYBVeaoIgPjyYoyrvtdm1Ix+w2QkZjjuifxDoOfdxjO6YtRgNqYh30YjU0YtVX6ryH2S0i+jiTy8f5cDUF603B/1xi7EKOHe+AFQ5BRynsYgWrPrx37ZyuLTwpMGo6nvpGwAcOSaIj8lp9lH1SU4hUgWvJUukZl6AGExgHog21ivSNBKWx+G1JajoKCtoax13IC1lna9ZI7rzzgbFnRoaialrpqaJoK27UEPMpkaJNjTD7K8PP48uQ+lMonbaNGeciUQoWWXCu0UikCXHB9yp+QtKwSwRUBXMqP6UXotdNdCFibTOFeIiCBIa2iJ1IAGYmAJDjIRMhUIFep030ftJMspalDXZCURiqgJaPFYgOsW49Npm8RoYupo2Na0lxQ4gmuwToB36KaGr1aEbqWqQ7kuTDJIpgtM5gUhmmhKYtAWRSYTCMKrFPY1lPbgPMG6wUxBj0JmMLgg2HVdJysO9rWE0TjjVB1ntWy5vjkhLv3z1itW86qGt1YZm3HG33JLUqmRYEPDm0Utot+D43tolOtjy+FS+DbhUjfIC6RqfbawxSd7Z1L/KAxpWWSF3Di0uKQCHR91FKG5PwakmY24ECFRC+0kf58Uqj1LzkinCxrbr+6ZFaWTEuPKXOObh1RdR7vNcpoEJgUc3Z3bvB573gPN97w+eR71/Ba6JpjumnOvY9+nPbOA9TqGMcJrvy3hL0v4zRc49WzluNXX8OevsKkvkPha/Ki4eh6yRverjnMHWV2nXx6SNA56AJfHhCUwZQzVLlDyEq0DujulP38mIPmhJw1xikmtkMtVuiuBdGgZoTcIJMdjnLhy3aEL3uD5t3Pt+Ryj+CPsV3HSx+6Q6gCL730cfzxGbvTGb/w0z+GcjO++g/+cd75h34vH/+f/jWnn3yFs7/xj5i/8Sne/u3fyLVv/VN0H/kt3Guvbr+XV4GrcMlm1wvtj7j+oRIe+fVy8BcuOXbxlpcBVrnw9+Lnz6a8DrS1dernet/XW0QIWY60Tw7QXjeQ/FyKxMxWXhRe5Zhg49y/qlwlNDzu2Oh+PSNM/ra3s/Mn/hjZL/wD1K/+EO7r/3PqX30Fd3yeNHChd1ejX3k2vo1jfdSTSBej4JsrTv1c+/3x10eQokzMYJMea9ufd2yTHjScYfN9AJGb2O6H7nvJeyYjqXPIe91X6wMET/6GG4Qc0Gu0GHzXRc0pAaejiVKUYsiFjCRw1wPc2Jpo+u21fOnuA3Dr8UYfzR1/G8CckiHjSwoBinVIqjsB695vdNCISr/kpP8nULihVEr3G+fXViMwOYbmksz0fZ5uv2lvfzOtY7a8HiBCukbFKHalNXlRoskQZdAmjyByEBp6gNiD1dFIhg2AHJ+ToPOwyPaA3jtP13TUq4YH985YrhsqH6hbR9O22K7B+S7ybWtNlhcYbQY3uCcpTwwogwrRpBmiD4AWhdEKrT1Ga0T8YEYWEWaZYXd3h9nuDMmE9XpBvVhg1w2djQ1UQbAuupZ6LynAow/SkeRbloZeJKVvjGbckCSRDXqPvJUpVz3BJ7JyDVp7WtvSumj6rrshgB6jwORgtJBpxbzQ7JSaPHd0rCi0Y2cnkO8KmckpckVZQpkbsiz2gYhG6SyCj/Qiee+xrVDXgjihaoWm8rQNdF3ALZdYatZeWDaBk9OGddNR2Y6T84rz85rTZUPdOZRRTHcmPHuwy3TP4GvPfd9xo27IspwsaIKoRNBK1EZKTE2pGGmVvQcHue5fnwgInUts+EqiD0kfrQzgJSUpirnaI21QP0jxxXUJvF5cwpROsRq9IJwuW9SWV+6c87ZnrjMrp+wcHWGzktaGTfpgJUymU46uPc1Tb/0S9N5NKKZowOmAXR1w7vZ48UXD4lWhWXuW/lPc786508w5WXZ0LklvtgPbocJtjvaWfHkL75sYnp3O0dP0lirBZRnBZEiWEZ1w87h+2QlGGXZ14KZveKax+LrFrU7IThacOqHK5sjeAaUXvrQo+YYbine82aOnS05Wr9J293Gdx53HDtFGCK6mbTQhWJqq4qd/8of4Xd/+V1BFhq1afNtx9vGXePUnf4X9L/4TTL7ma1h+13cxbEkixIi/SyK8r3yRH/N99PvW9nuVRvH13u+S6sLFH8YnXQY8w4XvjypPet7jLr0KjH+2pX+GpAHxJiPoMrrJ4pPG/7eJCuhzKSIE0ThtcJIN5jPp/YavvO5zvW/8JyZj+gf+IPpjP4z+0A/SzQ6R934D1X/zzwjWDlr6cdzAaD/f3nBH7Q3bZ1518WPb+CQJAMY+kuO7P3z/EdIOgdBZCBp0XDiDdRG0hFEdAYZsWhfq72HmpQ3sgeIWwNpu3mCK7xGdix09/eJ3UC/v4m2FuEk0qbddpAfSLhpmtYkmcpJlMwwQbsDE/c1k8EPc8DXGJo6BXN+wBEh9An1RvZe0aD34iuAz5oWQRCO0jZ7H9x/PDFHpk+v7RhiPSxh3ddhoBS8GxvSuXhGXboKHQtgAWKUArdBkKJWhlEYpQwx8JrmY+Q1wTf/1fJ699W/zJKMXIS2qURsa+8o56NqOqmo5X1ZUrad2HZ0LtF1D17aDQi56cKVMiBf3l0eUJ9dQJtVyTwM0okKNFSmV0Lxhpyx48xue5fl3vZXrT19H5Yrjk/vc/vQneekTL3By54y29RiJa6Z1YQjWcUESwEwDLMnzwkdzdt+JAaLGL2KBCDiThKBDJN0OKYK70CHyxGlQmSI3cWCNSDSj5watBaMUGsEoR6kt1+YZs52SnZ0JRa4wuYkBSBpEDCFoOhf9IGJmmDiAznvaDlZVx2IlnK5gWcHZumXZtCxboXMKR+Ck8pwsHecrT2sddeuj/2OS1qazGV/wvrfy7DNHlJmG9Tn67il1C5W11F0XAb0yeC04FcF/sNEtwafcoB5HCAqNQnmiT2Qyi/cO0d4mQN+TsCZfSBV0XDh9BKYqyFbu2J4jq5/ycbKHjWQbEu4JAe+ERjzLdYdYx3QyYWe+h88n1F3067TWoXVGMSkpp3tAgaAg6PgytY6qqvnUnYpffEHz8ssl66qldYbKW1o5R8jT3GrxIdC2Aes7Pnn/Fe4sG7oQ2JntkuV7eOsIyeUC22IFlDEDmb+3a0K3JFQP2K+P+bzWk7U1NHfplgu6pceZKa5Z8Uy95N0HJW9/9oCDecvK3ac6f4lXP/kpnnY78eU2Di1CQUA1FVXbcfek5f75grPMUtw6pP3UK4PkLiHw4Z/7AM9/4RcgP/QjcPvBZmUbzKTbu8Zni6Mu4rbtNeDqc5+07odK2FqfHz75ohblEjAZshysjb6WFyvrc56PwdmFTaGve+uniwDySTRqr6d/+vq0wpsJVmUEJJqYuir5I/8vVy5tqxC5+UQTJIEDUg5h0VFiHOVpv+p5H/r9UcKDjP4FyN7xLszBDPPj/xq/rOH3/XHqn3+F7hMvRQFXYorbh6pLmas2psEEQtzDLdz6ZXzBJSVccupVZfM4vZC90cRdfX7Y1O1SUKftLUGh90IchPZNVWFUx4XWjkHypaX3c0xqhrEUNQauIaBKQ/GuN3Hv1V/E2gWSRb/E0LUE69A6IMqAMYjWDPm1L2jThqqv6OeHweTm997sHGAIhAwqDMeFDeelJLq1MKqzv28IJD/IzSIQk+WkkQiAODZpcjZgP21zw+e+4riX9gC2B5rb7R8HHimjEd2nlIz36IGhpCEbs6UMwUshfZekHJOk+El+n4xmmicJIS4Kqd456q6jtZbWdZEFpquTVjhRwWrBWptM8L8DgLKn3onAN73tKf+kqKjiNqLItOb6zj7veP4tvPntb+Pw6RuI1uw9uIOWQL1esDhd0bVt3PB9BIjOb3wrQ5LaNTHS20j0scgVMRhFaYxRmCz5OzgXc0n3L4SEmAJRoDDRv3FaGAKGpmupa4v3Kvl2KpRWiNKE4Gm8o7UOrTpuHWbcONxh53CPYneC0hkeFdvqYloi2o71smK1qKhbT9U6VlXHch04W3acrywrq6g6oergfN1xuuxYriKFnPOBNvXDELxHyoctwrvf9hxvf8MzHOzvMt0tcfWc1ij8nTOa2tM2LdYYenDhU/T1QOWTwEZPGRCsJ2jBEN0BrIum8s1kJUqaImiVAFyILgUx+00yo4vgXKQOgrio9ByljMDmsEkHokN0Mr0TNF1raeqaulrh2o66dazrFud91PT6HZb5Xab3X2SuLHo6p21XLB+8xksvfIRf/bUX+PBLlgenGU1XYINFhRBV9qaJDALWYW2g6xz4GgmBT9y+yw/86M9wfT/j3a6hWJ2jraNZ30WpKGBYFqimJASHrc6p772KOr7LUbukcxZnW5puSeUWNEFTryx11zJRC9743DPMrmV0YcF59Rk+8rEPcf7RB7yfHRwKm9q4mxsKFMpZQqj59O1Tfu7XP8BXfvNX0x6f8+L3/xTdvVOUhrt37tNUa979lV9F++F/yRCco3qJKmmM+sXnspd4BJ4uLf3ad9nxSyoMF788BnSNz38IRF5s25NquJKgI+Wc0KwRb4e2BG2QrISm2r7mEpAaLuubx/XX45u2/eVi3QJYh4QGyU0Ea94iKR3bZyUR/HaWADiLxqKUB1OiXIvyqX0XNppLsP7jyyPApWQ506/9WuSDPwx3X8ZO3wjv/QZW//V34ddN9Hnb8pHrLw8peCQFOQjRqtK4izSs2+0fj/cYyPRz5OErrniA7TNiFTII3ZefcVm9IUUCb4Ddw4br7TfpytZcHJwtNWHMGCMqgpLo79hfJgMgAyje+izqYMbZr/wWzawGE7VqvmmjokIFvNKoLILKkAJXgsiQqnHToGSK9aCUTgBq+wkuRoMPjRehT5gBJGVSIHIVMQDGoccGLLjRFo+BXR/wM/hIIlvxOQN4HPXyxlTej40fzu1TKxJ6uCQJHA69ChKDkCNzQuLaJSBq29w+9gMdFG2EkR4h1RN6iqNxD8Z+U6LRRmHyjKIsmExKOtfSdC3W2hgT0/ej0nRdSwgBrUfcT09QXkemnGTXT1q4kB5Y+skoIWr3tGZ3OuGZm0c8desmB9dvIbkiywPrxR1uzyfkmUoUyrETJCHtgAwEnopAJjG1oiGQ6wgocx0oJgXZpCTLBBscXdsQaOmsG0jUAQoTyHJNZjTaCF4FbIhBE52NATqegIhDgsOGGPGcaaHMFUaXFHlBnhUYVeLEEEIEBG3bsVy2nJzU3H/QcHxcs24a1nXLuvEsG8/5smPVOGrraTqhbqHuAnUT0sToNXoxmpokefSSYpEJOzs5RS7MJgVHe/t0heL4/Izq+JyTqubIZ9jOY0w0MXgFLvFzxpSVccL74AloDKCCjzmqk2bRhw0xeRwVi3dJavWRpb9fmPuFyCWuq2GZlDR5Uw0hSPSJTRM/IzkvSHxWF4RXjysOXjnBNopW4HRVc75eY7Qny+Cu0Vw7uE67vMP+g1uU8wlVe85rr97llz/wy7zwsc9wuvC0VtEld4lofogmfB8kUiG0Dc7GKOs+yu/++Tmf/ujHuLYv3FjXFI1Q3XuBcl6SsYOoGo/QVmua5QnVg1OoKwpXsduu6JqKKjQsssBSa46VQknDm95ccPOtljV3aetTXnrpBT7xy5/i5jrSQXjx0WE8WDINO1nMtT7PFUZbfvqnf4q3fuvzLIqM57/pq/jo3/tneDxBOX74u/4pb/pr/3uyt/867sWPRiQVFL3ZJ3ISjjQK47/jchVQ6jeczxbIKB0XtJHW6koU+ah9+Ipj/bp88XrxHYGSMNlF1QvwFkxByKfQ1tvtedL7XXKfS695VD9zyTmXAGYRMO06UnXo3gzzvyyafOzdRCKYdA3yRBe8nrrZ9E3/2UP+rndh9mfoH/p+3OQm8qf/r6z+xa/SfeKlRMdzCbPk0L8SN2qjk3YySuziI6i8bFoO38N4ng0rGmmHepiP9bFPPo7Cvfrlk61j4cJ1D9csw6dH3z1Wme6n1aDhGtfiB1P6w1dHJQ3kb7jJwX/+Tbz6S/+Ou5/+MNw4JA8xCUXmAhkKtAEl6EyQLGrflIprXI+GAr3yItLsxNS+CdSpTST3xUjw+FtK0UhkDukpCrf7yw2gcfB7dIqgexCYxnPQJMqo/g0FlPR71kUtaW8Wxw+YSHq/rgQKo0dYPz/7fb6Pth77OKaWpjr9gAEY2tb7mQ75v3slj/hEvJP2boHBXzOk65RCSQSoOtOYPGcym/Hs9UPKj3+SdUOKiQhDZrq4T4K3Hufk0gRqV5UnN3mLJ5AeLHWTUTrln0znhKh6LWYl2XzCZL5DWZYEEygyjTGC0b3f4UZFu1mgkrJaelM1FCpyRuY6mq5zo9BGUFowWUaEm0nT5h0uQCcMUebWgfMK5RXWCVUbWNrAsrZ0HrSK2sw8i/xZ2oPyDmcV904dpmyouhopwYmmc1C3ntW65qxqeHDScue44t7pGculpbPQdIFl7ambyOvkQ/Qz7KVclXwVHIi+DAABAABJREFUI+dsT7eUJEzZvPvOBU4enHN2eM58OmF5HmirRQT1WUatHYuuJctKvAjoyNkVEsFsX29cBpPzs/cxaDD0XKEhRYMnX6T4niSpNGW/SdJfSJFtfV7VOPGiZmxrDxQioEymAxkme0Cr2J6zpuGTrx5ju8B895QueE6bmrP1GfMpTLTFh8Czt55iyimFfRXZ36d1FWd37rBaLFguoGos1sWouR4Ui/fJ5A/OWayLnJs+BrIz0YFbe4Z5YWPEfvAoMUxKw2w2I5vMkKzA2Q7EocSglRBcRWjO0HVF2TXMvWMWHEYMs2LCmz6v5Mu+bIfpXkcdTjg5u83Hf+PD1Mc1nZvEJU8lOiXvyMSxW5YUojmYrNnJhZ/50R/hJ/9/P87NZ57mn/zt74D5lPKNT/HJl36dT9z+KD/6g9/LH/62P8LqH/wjwvntCJ50ljr9sveWy39n+/dH7pEmI3j3sBn2gtYjBIHpnLA+20Rcjise3WBrjb4EZA2S/WXNvfC7EMDW+HIPX+4S07XGtemRpO2PK49WPn12wPsykOot4tLL91lm3Pmc7v+40mMRd0kQzgVg/cQ4S134fqEumU6YfN3XoX7jB5HFMeHb/hbrH7vH+p/+GKQEGhcfZIi7UDJoqELvk5cE9WgGD1sE3A83TzbzT3pQMmz9I7/JJ+3IR/XKRcmkB68Xrxu/JJfrOa9+h5PmSke2juCSOazXmvVS5EMXx+v00S6z3/clzL7ui7nz6/+eT3zv/8xq6qMQ37SEzDBXiqnKKIuciXMUyqB0laxWkXO6zyQ2DH0CRnGsLFHSBhJTRjwlbPwqBYQYuR0S4FJaDdgB0iP16RLTHjX0kNeRcidolJIL60ug93UMvg9PTbXKprm9KXu4GTJMFElud/FY4uoeJuVmf9/U01c+RIpsAO/IHzNeF+vvs7oBeBFUP5dDjyMSXPY9vpUNGFWBLNOU8ylPPXONZw/3OV28Sk8HGPs3Yqcgm2w7/iHn8avL60i9KCkdUA8owAc/QtKxb4wSdiZTDg4OmEymGJPhdYfJJAaxKPD02S7SVA7REVQLQ7okoyCXGCwjCSDqNLpaYJIbZqUhiMMaFf+1MSw/E0+phWnmybRHa4/WMcVRroRSG1wuSOdQosl0hvhotg/W44JmWQvdfcvZaglZTTCGoA0BReOFdVNzsq55cNZxfNZxsqhp26iZC/QASg3pqqLiNUY9Cwz+oCm0eihKbVTMznlefOk1nGtZLhbcvD7n6HAHURpTTqhURdVZvHWQmeh+YDYUE72Jxfd/vUeUYHqtsI/+niFpSwk+8oiRTN/OAw6jdfRJVUm17jeN7iXADY9YfCSXxHwlvfY58j1CDKw67zrurg3rV47Rrx7TthVBK5w4ziaeSR7Ic0Omj3nTU7scHs1BDCKRFysvM1pX03Y1tQVNBM89qGxtGMCxtZ5+XSkyOJgaPu9wh8O9HSblLPr9iELEYG1HaNa4OpoC2rqmWdXUVUNVr2jWZzSrSOUUOo3ymtwEru0Ln/+FJU/dEjr7gLY74dMvfJh7Lx7j1jEuCIimDaXwTsiUodAaU8L+RLNbaI5Vx2rtuPPKq9w+u8/bvuUPYN7+LD/y3X+DLqx49ZO/wb1VwVN/+S/QfuDnqX7ixwhtt9lNx4BhvFErE51jrii9wuLSTSkroxm2rR4+OP4eHEFlMN0jtBWiCwQH9erJkMZFsLMloFw4xuZYZAvWUQiK5HdR+PUtD5lrxjvvxYcd/37x2T6X8rj1+PWoAD7b+18kJH+9VbiOz6lD5JLPl/VzAFGKyVd/NSZrUB/8PsLv/0u0rx2w+qffR+i6wYQYRpduFE29NB5iznslYCK7he9c4qvcbtqmnu0DwQhiNBK1EulH2bwsjyhXaRa37/p6enRz/lWvwWNrCMSo+ESKPgZKfSUDUBche9NTTL7m/RRf9k5O7n6Sj/zdv87JB38e7zqqacZpC4uTiq4wTLSwmxXsTUt2q4qdzjMNc3ICBkVQoJ2P++CQTnajDdwG7rF5QcIwZWOiiugOp3UMG5H07vf+hPFiGRJLSKJYi7EEMSBVkAHk9lzB4z7u8cyY03SINleBHvT11r84F0djOdArbQSQMMyXTVBO6EGwPPwyDPNRJLmsMby7vfUyticmkhgSMUrcb70PYEbuACFZlYNFG8VkPuXGc9d533uf5+7pOZ+5fxqxigJlQoybUJISloSH3pdHlSeP8g5RQxmSaBcBZdRuBRi0VgrFrCiYljmTaUFe5rjgUcqjcGkiGVrXRtpSrchyRRY8zkbgpUmclSpqECVFG6EEjWM3s1wrWuZTjzEdSmz08ZMMLRoVAkZFrWbcZCK7Px0UuaG1UVOWG5U0nJrORzNx4zyN89g64FyLdYrGelzw5HlkjfeiWKw7ztYtdevpuj6YK/ldpHe199H0I3JSr6B3uPUp0MjZZAKQGPllEzVCCHC+qnG372OUYlpoppOSaVmgy4LOKKrGRbLx4EAipyJK8OKS1lH3Axj3FYTgXT+AyWwkqZ9IGsvo22lUBI/WOjKjUkS3HyLGvYu+gMM+JX2Efno/JEbk9y+87yXAECmgGhcwJuZSdcogCE40667Di8KFjHplWC1hed4h+QorNevFmtB27M4KXj2uaNsOheBdzI8eidfTXiKgQ8r0kwmzQtibGnaLgul0SlbmiCj8qmX14VdZpvSSrfMEr5Cg6LoW17TYRYu/36JOOvKqY9pkXGtLWu15x6HwVlVx9OIKFc45O75N86Ez9m9PyM7gqC4xQXhDN2XucnZCzu65ZpfIPVZ3ExrmFKbhRIG08CN/5+/zh77xD/P9/6//G/KZ13jmcJdDa6l/4he5r065/qd+H8UXfyHLf/VvsB/7WARzrmMrl3YamGBK0A6xXdRqwqW706VrR7V4xEEgK2JgjIpClzcFks1QriEsT1CPWpAesxNu7d1jgAwROOqcoAu8jhHIKDVs5kHlhGIOXRVNnSGMrh393cjDDwfi/DZgvWGrGO15lyiDHl8Bn217ZIyatut9UqB/1YlXIZzHNGdT76h4kEnJ5Ku+iun734v+ob8On/9VtPOv4Oyvfgd+VV1+6SX3D57Ib+ECKQ0b4rdPHAzKY1QaoiUm0vYkx/1MJXN5iCkML/K/Xrz3pQevkl4i8Lj0msAIcIzA1pX3vez6+EGQUYR4D1jYfqfS/USE4v3vZOcvfiMvf/QDfObv/bcc/+YHUHWFkrSXVZbOLTleVjzQgtOKaZ6zX064vqg4WFbs13vsNQ07h47cTcmdIg+9ydajBl7GHpCFRDsqacqmzTQIwcdUxlrrRBq+2cKiaxYx8LMHa5L6LpGbwyigKTi8S/61wSVwlrogZdKRnrOqb89Y7PCbpWTw8bzQ+1FLGgXcjfVu7AfKBkxvjf94YdpIvf2ZvrfqDtmGUl8lt7Ve/xsBpHvovkoL2WzC4c3rvONdz3NetbS/9CFeuX+SqAZBp8yHEqJr2+8IbZDJDba1URWa3lNPSBM+NtiFQOscZZ6RFwVKRfN09OmzONdhbUdVd3Q+UOaGZ27dJJvkWN+xODtlfb5Cp5RGKr0MWgKFgWkON+eKZw4VN45gb0dRTHNEGYKN2kPB4F3MDGPbjs4KdRuoGkdVe6pOs+w8KwtdF89rnaexnqqDVetYNDFPuA8paCj1p1Y2posMEbD073sgDNrbqAxIfaKjNg7AS3yZjcQApCiJx8AcL8RMCCPtvVKClYD1sFy33D8+58a1PQ4P9wlTRcjBFzlt3dD5joIi+UCYBCgjMJXezyOEGBEZQ7VRPvoyht7P0odBg+9DBGZBRTW59Kk2k9TVR9MPmmp6urFUVzLxew9KQqJ06KXRKJVJAJNlmNxE3kkdUrQaGJWBigFaFkXtDctakPMWH1q6s4BbQqkVhY5R6W0bFXAKhQ9Rw71TBCa5jmZ2L2gVuDYzXJ9oijZgm8DqvEZCR/HJU9r/9pdTdp/ocxJ9g1XkPQ2BPHjmzhG8IQRDALy0oC3qxQr1EyD4CPCt4R32LcPio51iGjR/6fgtQ9RddqwxKvrXvCfcwLprWB3wO+ml+/X7qA9+B9/kPX9MvQ19psg+EH0v127B8Qe+m93pEfvf/ic4+8f/mvY3P5Qib7cjmoM2kJWQTQi+QRb3GYn+jwcEFzedi6VrwXmCKSLIMzo64XuPely6xEfc/8plLBA1rjqPL5GzKO8IKJzJCQjGddGtQ0zMde6amL5y49W+KU+k3tn+OoDPJ1hrL4KfK7Wtl5XPBUwO+1HYLGJXNuxzKL9N9Zhnn2H+p/4k+axDfvj/jNiW8KX/GYv/w3fjThab243m47bZslex9ZqjBFa29uoErsaIagtYJSCZpX86wRANNC5u2sPzXgQBWy25pFsunrcNVB57+riM5lD8eMVkHM+10NsE088j6Unog1AEfeuIvb/wjXzwX38XH/+p78M1a7LgYqak5DIgQch9IPOWqnWceocS4a4puHO+ZP+85Pp6zY11zeFyzd7RHvNsykFynbFdiwomBuMoQYJKwYWpdaHfW2W73aH319/wRsYhloH0vY/sFklaN59gViIwH6h9GGnfRmPag8ke+NIDW2Ldg1KNaLFTStO/2BvycUlgchNrMgTtDOdt5s+gsX1oAqQB7F/hXvgdgojYckuTHj/7DUiNgLl3h4NikiFygBD4Ih/Qec6vfOgjvPzafdZNF4NxPUiW4Uja3ScsTwwo9w53WJwuqOoW5wQbESUKiRNiLAVJQGlDnzVHvI9BL11NXdfYELAI0+mMW7du0gZH01lyDa6uCM5jJDAvYF4KO5mwM9HsTBXX55ob+wX7BwXzaYYy0ZfAthbbBWwLre+oO0dnFauVY90IZ2s4X3vOasdp5Vg3gVUdqLtAG4TWRbLziMaj82yvve79hFsbAVIIKV0jm8VN6NNKJsnFqBR5Ffq4/iG3qNZx2nofKYisD5AokESTNJxhZKUUVnXDulrTdtHXROU5tsypz1oa75lYhybyYEpCt35QF6Y5xkaiU8FFM2biqfLexxRVidRcI/jOxSAfiWTvfU5ZkQ335PDijUoIAaVl6J/gB4skIQRsSFemiGptFEpndJ1FIPnYRokyz3MCmlVlsaEBHD5osrxkPp0zzRd429BZwTuI5CaB/ani1o5iJzfkWXomZdgpDIeTAkRx/+4ZdRfQzxS0v0fz4PiU+/dP6dqWZ5++yRvf8CxH16+hi4K6qjg5Pubk3gOaszWd1zT5LuG5PW687zrz6wYfzjk/e5lXP/0i9184oz62uDpQ1/Ds6S5/+PhZ/vv9j3FfLLpTvHm6x1vmUyZ5xoN1xYv3znht0XJ37elcVI4YLWgDTx3t8KbnbnD9cIe3vv0N7L7lOm4mqI/+KubBf8/en/nLnP1jof3ND5I8qoeJKa7Dr8+QqURuTWWiOXgYMLY0dcNvfZHtuf5wCYjvIheda9A7GbRN3OQmc0K9fnRgzJMCrPH9vYVgN80OUQcR1C5BNKpdxohvGFgj+mfZKqPvDz3exXPDpR+frDy0cT15eXTfP+bCz7F81kraq+bMZXNMwDz7LLv/2beR3f4x1L/9TkIX4M//XRb/+BdoP/qZC5dceLCRMD+cNTYljjDgxmS43bbxc0Y/7H4Hj0IyA4m2sIluH99xG1yGocYxaNzc5bJX7vJyxcsxApN9m7eB8aiGHqSN3GKEUf9If4EQlDD9uvdzfuclPvOzP4ZtVmC7SFQuKiktPIbklkb0BY+0dMKyXVO5jgdNxd1Vw52zFU+dLbm+WHBYztm1DltX2PMzsumELCvRxqDUJph28JlMGouYQjiaX40RRHRKosIGxMmop4ZHS2OX8orTgzzSPrgF6sZoLV43+FNKL5jIxgF0yKKktubRVjR6D4TTVLroE74xrUfGjiFcKClc4oiM91ePyCbbTm8RjVbC3sLYa1wF8ZqeEkn14IKoECyKEjk44o1v1sxmJc8+s8+nXn6F23cecHK6YL1usS4GLfeuak9SnhhQ7t/Yw7mOqrXRFwUd1dAu6gXjgwECWumofdIG0QrtQZNU5Sg6T6ROMYrMJPCgVeSSnMMsU+xNM3Ynmp0yMM0UZVFQFJpZodiZ5kimaIKC2tM5z7IS1pXnfG1ZrR3rNrCuLcvac7qwPFhZzmpPZaHt+qw8m1e9H3BBMDqqfdMrNkw5o3ruql6C2pCMhhAnrTKCUiHSbCjBOQhG4VxMAWlMnOCeaA6O0dgRjvfRazpTKagoUNcuBvakLD+T2Q7T6QyXGdxsSac969ozsw7JFTLLMLqg2BFU59N4CN7aCChFM+0c5QpM6+lVaP2zOx99Mnr6IudDdIIPHj3QNKSeCSnKrtdwwuDC0vtvDi9JTxQrcb501iESuR5zoyKPrPcJlAe0EqaZYVZqSOkg67alaWpW6xbfWXIdmM8yMqOo6qhRDiGQG5gZOCwN1yYZh7slgtBYyIwwMQYtgft3T7j7YM0yBO6cLThdLClMydM3riHPHtHe2mV9NEGyguV5y4NOuLuKmY/IZqjnbnDry28xe9sUPelYLha8+IljPimnPAgLpIyZoNYWjORYCXzaVNyhhUZhXYygn1rDg2bNbbvixbblbhdpniaSiPcl4LOMg11hcmRYHkSfpSrb5fQN74IP/yz7P/t32P2zf4mT/+4V3IMHcSC8ozd/K98R1qdQ7kbNnktppy6W8T4Iw3r7pIBGfAyE6QNzgjLRDeNxkdYX7/kIMHfpd0DwKNdFzfoo4n0IEGJU7yOA5aVtesR9ryxjDHERSF1WzxVteOQtL9T9WQPAR1T/OV172fNfqFjt7rLzbf8p5lM/iPn578B1GfJH/irVhz3r7/+ZCz6m2xJAj5UuCgYRWKSbXwC3g4ZrcDIb1UOImXdSBHnw6T1yI18/4ZJxkQuft9sc6BkxHi27XVbGUPZiGV6Zh+b1I8zo46ulX6PjZzXJKX/XO3jxx/4F7fIkusjg6Xzc/7JEI2OUxijPVBQzFTDe0hIjhb2KFj+7rKls4GRVsX9yylPzHd7cTTk/OePkxdvM9naZzXcoJxPKssDkOVoptDZR29ArOrwd8mx771F6FLDSaxAhgl4SQCMpMNjwZ8fHjAE7MgC/XgBIQakqmbslWuq24m4YBbika3ttaD+wY5P2liZ19DL0ATZj7aj3AaX6uRoeml9RYNhYz8ZKnG1KpRSHQZ9TPAbNhqTMGqaIEvKyYO/aAcUkY3d/zjPP3eLk5ITlasWqqqjqBuvdBT/TR5cnBpQH+1Oq85zl0rCuHdb50ZvlI0p2Pi7qKiJipSPHo/OCNprMGKalYlaCTAVDRajOODqcYZTDG0v5ebvslMIsVxRZDloQLTEzj4rZYJadItTQucC6caxrz/Gq4955y/F5y2LlWTaOznnaEFg1gbaLFGRxXJIEkMhvxUeSdZVIy5WKwIcgA4h0SaoLo2jtft1Q/aSL1VFkQlka8kyjjSHPM8o8J89zjEpa2wR+bM8dlfxEtVLkuWEyUUznUya7+3Qm58FihdElk0mUznRR0BYFTQ5nTcsExVyDzDXZXoEhY5KSugccJkQi9cLBetXy4DgwdxVhtY4k6sRFNFrrBdHRpzS4yAQxJmx1bmT6Se+vGqZrGLSSepjsKs6NlHrTKVLmI8FkUTqNINuhglBoQ66FaQbiG9arM0wGpsjBewoj7M1zbtqSVTNhsV7zinSsG0/XSrpP5PHcKSfsFCb6f+YqBigFobGWs2XNaX3G0noswt7uPjePDrh2sMd8b5esKOm8olutOV+vWa7r6DcrGZO9Obfevs8zzx+R7yqW7h7Hy1d59ZXXOLm7xtcBaaMizY/84LFC8IK3cN60vOoDE6VZrBpOVh2rNuaX16qn6QpJGx59cabTSD/UWUdwjmr/iPzNX4B8/Jc4vP8BZn/oGzn/x98Z/SiQpKlMi6XvoD552Cfmqt1NLvltXC7RhkjwUC/iX+I9g9va5x5f51Wg73ElEHkce83SuFxUFF04dGmTHtfWvmiTwPsV93xUuUJJ8kTlkvN/O8Ek9PXLk0kUl4H0MYIaj0FflDD9+q/HVLfRv/xduM4TvuGvUJ+9jbO/+d2Ept2ui+1pF5IdMImtG0GfTYAFqgdymw390sdJa99wExfAdpEMPYxAW9icPm7P9m/bE+4qk/TnCtgfmi6DEHOBeaM/c+zAK32fxJ9FQfamWzDPuPeRn0fsKloWib79SQ+ZgmcjO0oRAnmI+4YhDHunqHh/6yyny47zumKZVTTts9y9d8InPrzg4HCP3YNd9g52mO/usbOzw2w6hyKAbIJNBqVHaqiI6jMwpuMhPfOGP7MHc9t983DU9AaI9dl8Nv2hUJEtQslDoC323KCujPNtALBJu8mGFFxUNHkPGvLUhiG95bB7pkh4UcPM6bPlxEfwycTuR36cKXd4ov6JQJfEHBNx2SYJfLqfRKuhQTPd3SEvcuYH+1y/cZN6XVG3K+q2oXP20T7wF8qTm7wnGeeTjLPcoHUX/Zx7xC8WTQDdA7IkOQSfcmmDFsVukfHUbs57n5rQ7HfslCW7k45SLckzKMoZM7NLkYM2NpKkuoD1QtVAsxQq61k3LXXtWTWWk3XNedWxWHesmsCqtnQOugDBbYJEeqmk983stYG9JjhX0VytdEi0fpIklrQcpCwyIU1URwQKWiuK3DAtMmZFRmGESaEpUmpGpRR5In032mBM1D72k9t5H6kPiJtxnhnKiTCdTtk/vMHs5udh9o4IRUYwjuXxA5bLDte1UOTY+YRFWJJLBTgyFKI9YgTJAkH7KOw5S0ARbMB1LaYIlGUkiG+dQySqthNj0EZqStJQlIw2kk8ICu/tsHD1NEXxby89xpfMJw6DAMnnRA30HSbLMVn0+XRdjVhHLsJOYZiUmsIIwdbUS4+sY71KDEUmHM6F7qhEs89B2XD3fMWDU0vbpvFXMSWldWC0RhmPdSH6y1rHeeNonEJ0zk5ZcOPwkKODHfYOd5nu7SBG49qOpq2oqhXL1ZJV09FmGUdvmXL4jhnFnsKrJYvze7z24m1OXl7SnFiwID7iDGujQpA0Z5wFawPnxGxB2nvWSzheRw26JtJBKJG0QMc+VwrKsiDLc5Q2dApCMCxvvIGwXqB+5nvZ/2P/F/J3vZv2Qx9K3GZ6oOvoG7CVqa3PVUqA3sw33vQfpUq5CIb636x96LStOi6qaC6Wy3boq867uMm7SPX0yJ36Qv29BuHiHrS1F1+CUYEoCJkCfHQhEQmbwb6q/ZcBr8+mXAKMf9sBJTy61qvaPm7MI8bb3LhJ8YWfj/63fw2/XhDe8dWEW1/F+f/27+EW682YqI0LDWmjHOloRmivF2Kj0NqDpo0FXAbXm+HJRuM7aPUCMSJ67NN0oRsu65Vw5dGwNV1fT3n4mgSM2e7i4Y5hc5XIqK+GiS6beiUxXKjYV8WXvJPFix+jO/8Ume7AaVqf1jKJ9HyiFFoiBWCuhbwD4yPTRNzrieu7pIAUUbigWNYW5wP3T5b8xm+dsTufsrc/58atQ65dO+D6tUP29/eZ7e5SFAXGmATONCImAdUItIKPKR43tuQL/ocDb3b/zP1j9+ObrrkAMrd6WYS4GssWzZUMgcm9P24Y/iUkygA4ZRNyJegBc/RQPumXEvgf+YpK3G/D0I7EsJK0tBvQCtG9zRHX+iRApfYM+CcMIhekvaWvW4nC5BPE5OT5hNlshrU7dLbBWvdwjOcjyhMDylkuzHKhzATRis5aOgJBHEoHsuDIUezlQiYNvl7i6hVOdwS3RmzLVMNzR3sc6DfjnCA4usajRTGbztm7foNrz30ek/1dmqbi9LWPcPeFj3Fyr+LeueVkHThdNyxrR9U66rZj1Vq6NnZ2Z2MQTEA2qQH7MU4+jyoNogpRikKiJi0Tkvndp8w/pAFMpK9KelcHVIipJieTgt3dGbNJSW4USlwMCiQmvldayI3GiCLXijLXmFwTiBFZrrOoTJGpJHWhMEqYqIzdwnB0ULJ3c4aezREzQ8oSufEs5/U5d199hcXJgm5nTkPDcV3haCitUGRCrjVG5QPLhSTp24kHY2mCpbHdIOF7iCeqFC3NSKkQIn0QKBCdtJNJ0vdjbUBc5kR6uoPk+MxoAfeCi2gPbTQmi2kvnW3pgse1LbmOnKHWSfStrQMh2JQ7Xgi6Q4thOjHclBmlzpibhllZsDPpWDVdTGtocloUrc4hN2itES8EbWnrBqccWZlR5hN2ZiVHe/sc7s3Z29unLHKCOIIN2K6jrWq6xhHo2L1V8vQ7dpgdBKxfsDh/lduf/iivfPwOizsNtOk5Q9SidzZsxcC4EHuz8Y4T67E2sFwRTenEJWxYLZKrgEuaF1FClufoIke8xhtwakr7xnexXp+R/9x3Mv/mb+Hs+BT3yu20ugcI9gJQFNA6/usFwF4UjTkrNw1+VLm40T4JovlsdtZHAdutuj0bH7cr7n1F3eP9Nlw8fxTLEyBlKDJ4lWN1BjrDSBsjyh/9JL9j5XcETD6q0icBy33pmcZk81eA/H1fCA8+g7z2m3TFDfTX/RXO/58/grt3HKvq+eJMkqw6n7ILAeFho+4g3wRSoENyvRlArQxg4HLIN/4hjA9ufbz8McdHHpa2NrKPjK6/TEq77NjDUkgY3e4qwBh6U++4ngGDRZaOgWZnp2Type/klX/391G+YlJCu3J4ND1JUwg+WiezDKUM2kAB5LbD4iPfZL9zSNwvtd6Y/IUoD5yvW5aV497ZmlcfnHPt8AFP3Tjgqaeuc3h0wHQ2Yzqdkuc5WV5gTIHJ+rhwFzV+KgXQ9I+Y5IpxHM0A5wSiHY0B+G3ybsfjwaf+SB02ng/j0VQSgWEYkFa6PqWY7CPLxxrz2N/CoIARhjWlb1+cnsJG4zoasnCFQDIID3pTV/LH3NLCD3NiA6Q3UnikRFIImF7zqonpSPTDU/QR5YkBpaYj155JJsyM4CwUIsyN4IMiy0uKDA7nBTd2a2ju4Nczgszwrka3KwoN1649zXwX6tbRtkvWizVdU6NmB8yuvZni2vOsUZw3pyzMLe40L/PinQfcOWk4rTpWtWXdQmM9wYWBHgxSdhiJYLKPEFcjqSkKTgEtfUQbhOTTp7Qg4jEGlIoM9AFBUjBJrgXJYjh9kU2YzaZMyoIsi1HEEYzGYBcVBFzcn531+GDxKhCsxlWezlna1mK7DgmKXKdQEu+ZaJgVGe0qp3OWbLLHjfkBZqZRxQQnMUdqd73j/HxFtT6n6UqqZsGyPiWvHHoBOo/pG5USrAffebxXtEE47TSvnjlWdU2I5FxJidVLQhAchETB45QgspFMJVEFhZjDZViglYSkvYySqY8TJ4LORFmxWdeFLMuiG0Cmaa2nag2rxZKm8dRNS15oyjJnVmiKXFFkhkxioIopDDrPCT5gRJiWmp1JQZ5PsEQXiVlmyDVImZFNMrKswAdF11aoszWF8qAylDZMJgXTsmB3OmG/zCmKDKsCtrMgOW53hmSG6dMls7dN2b0lZFnLoj3h07c/xWc++hLHry7ompBkWh+jIoHWycBXLcl1oDfPdCQWAgcWNtpDF6J5yQvWBlrrIqRXgso0WWYwWlBGk6kMW+7RveeLOPuVf4/6+Pey97/+Myz/1b+l+9jHCM5CV0PX9K9K2iwV6CK9CMnf0vcSWPrct+cicByXC3vgxUXvKgwXLjvpSdDYCJTQu029XlB72X177D3CAXEDuqRqH4UcQSEqi5aYRGZ8WXNfb7M+63LFOD1q+F53/bDpc7lwbHxcRuddgo30U7eY/MdfgfzY/5v6vEV9839J/bP3qX/ug1v1iVaEzMR0isYjnYLOIZZLQWVfxiBrGMPBdHjxxBH4eEQZHlk2c2PTt+OJOXrQq9r2mHOunjnxHnLxUhn+t/lJNv0wtK6PBo4bRFoOPLP/5Ato7Bl3fusnUSZgsmimDQRsCmZRNgbkOBE60XQILvkoeu/xojYaOtX7IvqUsSX17sjy1bQd7ZllsW64f7Lg9p0Tbt484Gh/j729HWazCdPplMl0RjmfUJYlRiftpY5ZYCRoUHEv7ydNpDDswWTSwo76RgYEB0NmnC2fysCWWiWB0DF5+ZCvW3qfznil7/1vw+Y86TUuqfYNOf543DaKGkmZbnw/d2UURiU9bVCqIxH9b+b0hmYIkk9lCBB6qqE43ttzfQwwkx8rERM59xj/91F5cmLz0DLJHYdTT3FNYxFM7shMFk3FApkuKCdTdg4FmrvUxxpbFQRvcdU54iwqn0LwNE3NolWcrmBx3nJAS1kLhdVYHSOBV7bl1bOKV05bjk8rTitH7WJwhYcYGY0f/P6cTxuuioMS6U+jrNCr4SVRB0gCnSIx8CHTkGe9wiaCUpNnlJOcvChRIpuo6ZChdZbmR39/R2tbXNdhG4e4XmHtMSr6VLqyBJGYz1nnVB0xB7mNEXK5CEYZOg+NDZzcfcBy8Uscv/Iaz7/7vey/6R1ke3sUe/uYvT2sOM7OT7jftnSuojo/Q9WOzGmki9H2Pnha5wgOrBNaUSys5qwJONGISsQA/eRPG6qi9x0N+C4+pzYB0f1iJikrQWLSTxM1ZoFhWHB9+k2E9JLEa7LMkGUZk0mJZAZpPa1S3K8ssu4oV1FjXGrNNFeUmSHTiixxWJMpTJaRS9SaOhSTvOBgOqOYTMiyAtERfBbGMJ9OyLWi7hx+taKxGdq4JFVrJpMiBuxowyzTlGWBFHk081jL3uEOR3S0kxqeAV9AbdfcPb3N7U/f5vSVmm4VIipMvKMkrXltNy94FBzSIqFkWCxCooiKa1IUepSKEXnBC6eLmsU65ifXWqGMxpQGnZfkGLwEmp2byLu+nNNf/gn2Vgv2/uyfpv7I+1n94A/hH9yNlbctg1nbtdACeQkqi6AS11MBbAMCRt+vKmHz53Eg6lIw+XrKZe3jit8uXnfl77Lpm4tNu0Q9EIN9AqZbg7PIkAJzA8R/24Dck5YrbvbINoz77EkA/cWK+7HoqftGipurrhERJl/9NfDqB7Ev/DT19K3MP+/3svy//12CtZsN0vkY9WpC0kopMCAuRGH49ZbR8A6AU0bfr3jEvgj0m13cnLsYsPOwRHR1Z17262VXD8eSOX/LZPvIspl1D70ekly6tCBK4xG80hTvewvTP/hl/Nb3/i3q82N00FSdHVKQ+GRe7hCs9fjW0ugJS6tY41J8gtAlbmKJSDIqdIQhsCrtEpD8CfuvTWvpWs9yablz/5zd+YT9vRm78yn7+3MOD/fY3d9hd2+X6WRGWU7J8gKto6VLBZ3SA0uqW22ePaRJKSDJVzL2RerX3jIzcpIV6YEcKaHQ9qj1KX5DyhQYIYig0Ck4fUMtFIKjpyIaKyD7Ngi9L2f6G4QYPT6KYA8bAWI7y46PwFc2FEVjaihG9xlcPWR0zlhLO34+BJRCgiTd55OVJwaUGZ7dmaHURVw7NNH3zzt8UDG9oSrIyil6UpKxxi7v4NocIXJCtk3HsgmcNxnnbcFrZ6d85pVj6tWag5WmkRc5czDbn3B8cpsP/fqv8JGPvMyDBxWLhcfa8Xq18UWICxRkOh7VStBj9fJAGJcCS0hSAFGxaySQ60Cm43UK2N2ZcfPGNcqpofWBRdWxXNcE0dEM6mx6ETxdV9N1dZpcAbyQYdACZZ6h8gznFR6F1jpGXXvICkOY9BQ8Ch08kitUVMOhM4W3HXc+/QLNcsEXTA84OHgGij2Czjg8vMH1p29y7/6Crp3jm2tIt0acINbhXaB1gdY7rIJgNN7kOKMwwaO6Gi0eod1M9DSfQ6IF8sm/Ax+Z85XuJfMwSr4R3zyXPqr0ukSfVYVXYYgN6S0KSgnGaMrJBDJNbj0uyzmuhPOljUoycRjpyBLtQ64j+NdJglEqkCcOsL15yVve9CxHswOuHx5gCgETA8Nyk1HmWaS6WDc0nSc3Hb7tQDxZFiORXQi4FI2c5xn5zg4qN9gQ6HzL1FTUxYJq3nIeKo4X97nz4iucvHLM6myJ72IQk+8ErNC1ns5JnLeyvcBrJJq2lUIrH5lJElXSeC9KWTpZrSynZ2vqusFZizGaWTZFZUXkL3UW7z3rvUPc298Lv/Qf4FO/zPTr/iL5X/qLnH/Pv6T70AdjxW2z0T66FtoQeSp78/cQuWiTpvJ1rCipPHbbu7iD9p+znODsENQz7othm7y46z7qZsK2NuCq0jsPX8QBj6kb322iyPs0WOM2PklREhfUC76nT1o+J9A6OHE94Y24cDMZ/RsfewxAzd76VvL3vBP3A/8nVqsp7vN/D91H7+PvnrClKQxC8h1JgX1ERc4oW9fFzf7yyfVw0x9S3Q3XXPytPxI3dzE6aiAA8YFwqQbnsgd/+LerWji+/ZYLxoUrHp6q2xNwk0EmDogoYtu1gdmM/G1vYPb1X4Z/doff+Fd/hzu/+mOIjdaRLvnF6zSYNgRaESoPdWtptGOh4VQsXRBsUDErzpARLiStYW9q3gTX9Kl8gwcJUXspQNdYXNexWjXce7AgyxSzacHB4Q5P3zjk1vUjDo/2KacTiknJbDqlnEwweeS9VhJzt5MCeAfTM9AvZL2/Yt9rw/etvnq4jyO280ln6/HOResPSQNqMpDIXxxdwaJrWxjMKBdGLKhhCkZLqU5tDTzsuDiaAT3uoZ+TyZlgeN96gLh5KQdXU6J4sNnvHwaTvTZVRKPwQxrMJylPDCiLIscUM3woY6YVb/HO4bqA7aIvQxCN8yZurHT4bk3nFjhraS2c1o7jtefeeced+yvunT5gsahwztHU0LQv8PFPfQIbWqrVgtWqwlKiSqCqBo7ElGyGQDJhJ8WAhP5ziAnRU6cpNRrOpJ0MCU2GEOkQchOiCZHALM957sYBOzsTglEo76k7h9GKddvQNJ6287SdpWobgnfkOWSZYESjxeBEoZXgEJwXWieY1pGLShHrGpWb2AaXTM6ujTySOoJPG1TMTeQtZ8cnnN15lflzZxiT45xD6wmz3X1mh3u4AI3NUd0JUreE1uIbi3cei8cVGV4VSDFFi5C5OgYAZZZF1Q5ScJqfMRLby8BN6bqAziPNjWgdJ3hy+u1LDz6jRLd5UXv3giioBoyKwDozijxTZNOSytkILo2mslEy7omHeklPEzXNmxwGEcZq4DkynrMRQJaTgnyeo1TUbErKfOGsizm/bUfb1Vii1tkGaJzjwXKNlsB0b8KOySlme0gm4Bq8tKjSQ+aopGK5Pubk/ms8eO2ExYMaX8d2dTb6iCoLXafo2pCoR+JS4cJmDmfpDa+TM3bflckStck6RPTFPF9VWBd7Jssy8kmByQzOBxrv8a6jtY5u72ncWz4f/1s/h/yrv4H60j/M/p/7Fs7+QUf74d+Eooxk5D07f59dxxRxo0FFaiEkkoE/vLNul/Fa+XoUKJeAr9Cbhy5bf7mwuV6s57J7hJE8+SRA8WJd8vDX4dJBxXXFtVfd5mI7QtgO5Hmd5bMFkyGNdUxReaFtD6nlLrmRXPh3sUFXXKcODph98x/F/sYPsPzUqxwfH/H0V/wB6n/4cwM4i6AyVROA1tFTc4j0+60MSqWBFnB4jx4FJjfv2tYQjiWW0Sa7VVMgBusAhECwfuv4Zn5cfBmG+OFRCzcdtH1damEPHB49xR56tu3v0Ps/Ru2Jxnze08y/4feg3/NGztozfvOXfpSX/9H3wskrA18jIep/lcSMaR5obaB2sBA4t46lbVjrLPrfZQWEDiVRKI00gH4AJsLIRZukV3Mbk3IIAU+H0gYXJCprGkfbOtaV5WRRcXK25s79c64fztjbmbC3O2Pv4ID53j47ezGQpzBFdPX3cYMXFRfTGPiqCLg4t/pFYbROjD6wnTLr4hSP58Xa/OCPKD6BSkBpHV00fIrS3opKYIzuUnd73BAgFIZ/YQgeGo3tFjhM5yVXpSFNZLKoSuLJHMD9MK8vtKd/zgikYvtD5MT8HYnyXi4bbHD0zrXOOWxT065blitH3Ro6OjAdytQxB6pSSAh01rLuAosWTmvL/SpGZld1E9PctYHgGpRaoIsMj0Wbguff8kZqB3VXcXL/Lp/5zB2qZQcuRPZ7HaVrI7EzlZZERBpfWiVpDeodk9M49RKSBCE3KV94Gh4JUCay7bHq+MH5mlfvnVFXHU2z8ZkwBnIT61TSpyyKATZdSISszlEAq9DQukCe5RS5Iss0mVYEHYnig/J429KJQpzCW5Uy8ASapuXlF15gfuNp5s85WoHV8X3W5xVISTFT5DaDRhC3jjyW0lLgmWYZPjfURmNzgyH69ZXLjkIJBqFzkfppbPGLAT0pM1AA42MWGCWaXoWvdJ+OcXui94oPiGPQc1P6AKIUucnJjCbPMibFhLLrmEwLplMDqokO0umldvRRaZGfMWo6Y7s04ASci7uMVgGdaYzJIkF6ylpknaPtLOfNmvPVGtt5mjZK3ZkWfOuwWtFSIpM5ZmcfijxK6qGhNR11XrNUFQt7zNn5XU7vnbA6PadbRV/Y4ARvPb6LbgJNF2h9eskHlvxoxtYhYCRK8zGdWSADyhSkpSWSK4fUl4VRTGYFusiZTKdMigl5UaIFgm+wbUPbWlxnCdbhj56metO7yD71m8x+/p+R+5qdP/ktnPw/XsKfnYLJQWxMMh6I4ehdDSEDMXEglQEdgG7DBXgZmBzvnaPPY/+toVwCIsdF2uaRxx9a2x4FgMbfL/pajq97HCh93G+P+v2ycim4/Wxh4WdXglI4laNcu2nKY8D0Q+N8EUyOUdoVjyN5zuwP/yH88kUWv/RvWNw35M9/AXk3YfkLH36o+u3bp3V6UE6OdJmjh+izlm3nY94+Z/C97G/0UJvHDzV+8CjlhTYKdn0LtqvooePloFYe+uWSkhQLEAg2DGwpwjb8vDjxwoUPkULNRxP3pGT6tV/K5Jv+Ez700V/jV//+f8fyk7/OQf2APdWhdRR+O5siihP1m4ii8zFzWaWEMwUnSqiUgIJMGTJjcCnDXUhaBS+xd3yIgbcgqeujn+FWz4ZoHYzAJ675/WYSAjSt4969JWfHa26/rJnv5hwe7HB0eMyNm0ccXT9id77HznSOKQzG6MiDrTRaG0IQgjbE3W4E68MmGj06YiZIPQC7EbVPYjDZnjT9uSMoL5vI783xCKzj0KoEABlM41th5EiiDfKDVrfPNtcP7hDk2l/fBwMNp4RhL95c02OfCLbDyD1tfB4pu1BAXpd2El4HoLx9Zx01OZ2l6TrW64r1qmK1WHO+bGmcRrKSspigTBHBpMS80Iuq4WzV0HQOVZSoaUEbAnXT0DUxr2brLVXXoQLMJobnbh7xxs97I+QlVddwcnxAnilefvk+y5M1wYZkrh3xbYUNq3vsvBitTQ+ShG1fggRwSOBEfFwCWhs4PatY20AwilfuHfPpV89ZVXEy6RQdN+acin6vAef7jOcp2MdHsGxFxRd21aAS2XOeKYoiR0i0PrZFfEduhCIvmU4nKB196ZQPfOYzL1Kr/8D+qy8T8oIHZ2e8dnLOYg1aFWidESYFYgPGQVYU0VE6Mzit0VpoVaBuWwqBEk+WtKZNmqS9ZiCanJOJOq2UfRrB+IKpCCqDiy+ISy+ID8k3MEaOaSDY+PKmQ+S5Is8yirxEZTnGGCaTkvm0ZH93RmHWuDZJXqktQ97TXoAMg3GAEGC1alivGprG0TWOIg9457DBY52j6TqaVcXpyRmrRUNVW87rliBCETyh1BzdPORN73obN9/0Fib7e0ge8G0UekKusUbRBE9jaxbLJcvTNfWZhTZuKc4xZJDqrKfpoOvi5NTSr49RehyCxrRiNtGEYKhaj9GB6McogyVSCTx964C3vvlZDg8OmM3mkddUaXzwOOuwbY1tanzXEFyL8wE3ucbx/nPIvRfRv/B9TJ57N7M/+AdY/JPvitxFolOe4g5cpPnCtnE1UQlUik67imUUAZdWjzxpNy9siiPhe/z90nIR4PUfZVtJIDykNNi+pTzcjK06R2vAFvC52I6LO/4FXHH5Nn5Jex5z/PXgx4vw4aoqrrynyNZjBRE6UxKCjtuYEjZvftj0waPA+mWI77JGjX6XPGfnT/4hzK0p5//mb1IvcoIK7P3e3037Mx8iVNXmdIFxq8fvfsQlCRIkTuFNewadzejWYZhUj+z2Szvwsh7f3PDhaXM5iBx/uwT+PXR76RfM0INJuTAPehCzUXv0a+GmwrDZH7Vh549+Fd1XvYfv+Zd/j4/8+i+iTo95RmqmOPJE9VZXns4pWkvSaAlNCFgJrArNsRJOtLBSGrQiywzaaPBgQgJAyb9eDetdlAA23+OLpBLXbh/EKUqnTSJqI7SOQSTJSxAfHM4L68pRdTUPHqy5t7Pgwf0Ft26ecu3ogL3dOZPphEkyhWdFSZGX6CxDEykO42qd6IBkwJDDmAi9/2VUm4S0/8SUk31u616pNKKr77PUscEGvWYxwg0fgeLFdbSfNYkai8BGAErj6X1vrt/WQg6wt79Xsir2VkHoo8p7N6YeiDKYtuPLmRwJQ68AUUM6yNezVj0xoPyJX32Z1lqqxlI3lrq22JRRwHnocHSuJbBIatbYqZ1TVNYSQmAyEaY7AeNspE5xjuCErrNoZahah/HC9Z0Ztw73eebWAflsRhccr0w6zs+OWa1WdOuKzvmonUmd2WvyCJtlKBDb1vOQqTQYLnWQ0pKAfVTtdj7QOiGsOxpfYSpLEzoeLFZ0Nkpq3geCIk2YqLq3HmjBa8GKQyfJMjonRwJqCWmAEJAObQxFKNAh+mL6rqKt10iIoCLPLfMuMCsyMq1RSrFoF7xy/hHchz/JurMs2o5y/4j57hGicpTvMM5jfEATfTCVAJnGJv8j5Tx+Wcc8kk2HOE8mikwHOu8HqU1SthqfJCkF4EIEjsniHQkGZOjPi1GSkWcTeslUaUGCJzOk58pQoiOgpGQ+m7C3s8t8ckrTNpH8vJfy2JbhYv0k7afQtY7z84rlWcVysoqLmwSqtqHpLOuqpa5qFmdLFuuWdWepbHLeDoGbN27xnve9l7e88x3s7h1ijKZpF7RtTWXX2KylNpbWt6zXK07OzlkvOto1+D4Suw3YNiBdZBywHjobu0v3ns0hIDaQS2Qe2J1P0coQjjJWTcfZYsm6bqirKD0WCg72Jrz7Lc/x5jc8xfXDI6bTeRRonKWzlqauqNdrurpBe0vwDd62rDycF4e4o4Bavoz8u7/N5M/8j9Q/92a6j30MxKWAhwJCG7WUPgHaJDQM/1R6AL/xqQwikJdIUz0Bgnry8hCQ7Ku+CPbG1zyuzkf9cBGtXVjMHwJPj0J1T9ie11Muq+tJfwMIyuB0lvaSxPmKRglYXaBDQIWYynILkVxVoYz+BbZ9bIfB2r5esozdb/1G9Bv2Ofmhv0nbCC7fwTydM3vXe1l+97+ImeGSW1PCihGyycM8ij0/cBj7haRNsg+i6Glbxg2SIFvNvXQefNbl0WByM6U2nwa3nou3D6TsPGFrWm7+v7lmHGCxqT8eDCoG3ZTvfzfqK7+Qf/ad/wMf/MgvUtqWQ/FkwaJVAitOcJ2wtlD5tI8aoc2ElREeiOE+ikrHOrPcUEwKiqIAAm7paap2AClRCIwt8gOoHykCfASVY/OtqMiMoUXFwKtEz6J78EfSbkbaS+4fLzlfrrlz9z77u3MO9nc43N9hZ2fOzt6c6d6cnfmM6Wwn+VqW6JS6UaV82ITEj5wsnb1QpRIq65OQ+OASoIyWOueSr78SlDYoldyFYEiHCGw0lIwCbcbzQjYgNMBGKxgkZbvpo0Ygatbj/tzvjT6B3OA8np6xZjNrxvcLo3nS46LIOtKbFNXm9/7voxQCF8oTA8qPv3qGC5EY2lqhrcE5H6l6iCDNp4Fxg30erHd4BKMDRQAfLM4pXBBs53G2o3OB4FvaALvllHmes787Zzop2D/axeIJvuLs9Iz79+9y/7WxrwFshU3RO/3GgYrufpvzIpdf2EyWEc2SDxEE1OsO3QaMMXTOs+58mhzRqbnPmiOAt4FWwGvQPmpItRHQ0ZW57Vqc9VjrGckyaNuxmxuwKmE7y3IVwaXWkJuWIwy7PoI9EaHuLI1b0wbhfL1m1Thmuyuefc5TTGbQWWYi5J1F8JSomGkIhQ2Orotmi9B5QhugtRgEI4KRaDqOOUHHkld6FST0Qgy+T6PZq+XxyX8yyXQhSmRDiEESjpwPGIEicXJqrVPQjKaUDK0MRT5hVmpOTsH2Y0mifRqA68aCGQ0p0bXgbFlxcrZkZ7rGEQNKVnVNVbesqobFqqJuLeu6pXPRR0hC4Kkbt/iSL3s/7/6C93Bw7RraGNpqSbVc8ODeA1paJFic97hgqc8rbOWo1y14hVIZ3jts6/A2IF3EXV0XcDaQmV5aj43OVMzTnRs4mM/Yne+gtKFqLa89MLx694R63SBByIziLW+8wTve8hxP37rO7v7esMi0tqOuG+r1GltXhKbFB4uEjmBbnLecE/ik2iGfP4s5fwX5he9k51v/JKd/62/jHxwDftsv1idNZf9e4fqXKb4sWqI06wN0HWGyS8hBtdUTAa2tctk5V2m+Xsei9tiyvUNf3p5HgNetU68Cob9D5bOpXlwX/Xt1EWldhuUyRJoz3yF9OqPL+ubiM/bfPZf34YXf9LVrzL7pj+DWigd/5x9Sf/Ec86ZryLIm1JZ2cZfi/e/Efvo2fl1Hs2JI0OOi6rmvX0isHelJRs7cfWQsyBB8lrB03CrCJoL34c5itINu6uwBXK+o6BuxDRIf7opxtZvzw9bxcVMGjdPo1zCorcbX9CbbC/6go+OIgllJ8e43s/dnfz8/+BM/wM98/JeYYTHOYojCoUWoQkCcpwpw7gOVVjRGEUpDpRQLJZwHTZNogSINq0IXWcx2JjCZTKjbLrpF6QgMlQg+pQwM0vdCDNYcTLAj7soYoOoTsNLJVarvnLTrpkh1gsOLou0CJ2cNp2ctL79yzKzM2d3fYX9/xv7hnKNrBxwe7LO3u8d0NicvJmRZhjGRaSRaMiWlYwz4oJLJOQL60FMiBY94j/c2Pk9aJ31QUXkksgXeIrVP9LFU6b0bzOGpD3qQPYDK8VyPZNgxsJW0Lqf56bxLQNel4CCXAoDAex2fK+3hoc8TLpv3QAYEI2xIdgVx0GsllVz2gjy6PDGg7DVV1kPjoHKerku0JiEi7+TxgQ8xGCUQ6EYo3RGwtsNrwQdhvW4J3uIlsi+EpkM3LZP8DezszJnN5hR5jvKWIsuZT0oO9+ZkRuhUGNIDb61zMvRNaniim+hXUb8BnBoGU2SQqM20Ak30QEY6F4EQcUHx0kuUiQqHGNcgKmqpJASKzFOUMTOLJxCcovGOxjKkRlIChYr+gAqhJbB0gXMLvX98oR3F1GPbSEeiRFhWFXUXAfiqsTRt9APcnZ8zdYJ4i1EqRl7rQCZF5Khynrq1WB+lrNZFSWunMLSZphVogqdL0p9I1DoqomLKBQbfyv5FEm1SpgIdM+rouEj3/RLnTByQ0POPpYmcSdSeBq2xeGpbs646ms7S2viy6iQh+RD9Wnuq1fGiqdKk98RA5XunS145OUeZgt12hjJQtQ1t29K0jsW6Yd12dM7jO4sgPH3rGl/2H30J7/mC93LtqafJihmdXbOqz7h7/zUePHiALktKo1FkCAapNVIFQmNJsdrgwdkQfSl9SP6UcfHc+OjGhViHgAmRXUBLYHdaMClLVnVN05Wsqpy26ehqz9O39nnPO57n6aeus3+wTzEpsN7SdTFTUVWvWa2WVKsVtDVGOTQamyKOReBcaz5hDpADOPiNH2X/xtvY+y/+Auff8Y9wt2/HhcqFjRq1B5WDv0iP5BXoLEaJppQ/wXX4fBYPd3Vc5CIfV9TIX2LeGcrF9eqS9et3DJ89CRC8eOxi+34bGncVtv3tLuJjAoo+2wZAZht0v+CMpbTLGjlGPY86Z/xTUVB80Rcx/Yavx770AU7/8Q+xqDoehBnX8gJzw6CXltOf/j6O/ld/mv33v536R36J9lc+intwAnXy70xC7mB+CykIIahhze+VTcO90wYflB9yMmBiFGZo3bCobbSY6cGuUMc8/Ogy+j7yluz7sgftW32yDULj3+37hYvfRhvbxs1nG1wipKxACjUtYVqQPf805ee/leLz38zyMOPHf+En+bFf+XEKoyiDYSaWqbXo1tEQEBMiR7GGe8pQTQpWWmMBqzS1gBOV1rME+ZRHpVgJo6EoC8Kiz7YWM7Rt3MI2mrY41UJSumzAeaxTpfTE/VAHSNRuYTD7Jh/EtCMEIu2eiNC6QLdqOV3f57V7J8ymUTn19M1rHF0/YLY7ZTabMJvPmUymTMpJ0lxGn3uFjhnW6OfHZpyDd7iuw9kWl3gclRLyPN8WKmS03iuV0u6Owy4h+m7GeqO73WiOCYPyq682cdOMZlwSSkLSUAa70VAHsF6hlYrgO2Xs6VsYjU8X5mwahsG9bBTJ/Hpg5RMDSrRBEVDS4fB0QVJmj/SC9NorRwqMEMQIwUUuPp9QnHWOUFW0NtDaPiAmgqw+EinXmiLLUn1R69cn8+i6gFIZ1jcDGbeRSCY9yv8eJ62Ok0+ltFIqrZgiMc1ipoVJociy5DvpHJmDMoumWut9jJJOEVQqmUtEQCeTMIlTMPjIHVjkmmmWkeUZrfc4b0BZSOfHcYuqbBPRBTqpq/tVJgTQJpJz+xCw3lO3HYt1jXUxR3nnhLYDoz1VbcnyDq2EzkCuIk+hNUkKCRBjER2h61DBc73IyOSAo5AzqSsagbpp8DYkkwMbFIlEbWPKN6mDS74WkRoB5aMUlybgWMDvc7qSrGlBkbjJNNY6usZyGjqOz2oWixVnp2ecL6JLRBKqSN6a0Xd1cNROPqoJeIYQaF2Mxm9sR+ccmYnZikRpjBGKzERNrbUE7znc3+NL3/+FvPcL3sP+9WvkxRzw1Otzzo7vsV6vKWdz5rv75JMcq2roOppwiy7raAvHWddgG4dLRPvegneC7QAfMBpMBn0UfZ9BKAaFQds0NHUVtcRKMTM5e2WB3bGEObz7rc/wpjc+zf71Aya7U9Dggo3+mW3FcnHO6vyc+myBuI4iVxgdo/tda+Mr7lsedJa1zXkTB8gP/10m7/sa9v+rv8D6R3+W9pc/gLt/lxAUYvIYqOPd5qUeSwihIxgBkxPySSRvF0Uo5qis7FcrlHfQrKL/1JOgJdn+2C+MwxrNk1VzZd19hoLXk0fs9dTfAwjZ4O8nLU98+hjU8eR9EkTwOkcC5KHDi4rZT8ZayfGu0e/xr+cmo25V8znZu97J9Gu/GjMLVD/+t3jxV38We/5G1iHng6884Jm246mDObsmJ5yfcuef/20mTz/P7Bu/nN0/9bvhQUP7ix+h+fnfxL7wUkx9+9CD+RFEG00UGTc5PUDozeMXkNhWGSb6hWMXO+cK0CkSM/qYxHVoPVh/AQhADw+urulClyc13TAs/SNoRfbmZyje/Sbyd7wRmeSo567R5nDuKj5z92U+/bEf5VMvfJiT5QmzCUxDxjwIhzim1qI6TQVUXaAywonJuZdlNCajNTplKIxkOfgoKHoJEBzOx8DcYhKTm7RdN/SDSvxng1lZIMLICIwiaHIDcOn3bejTNW/8EX0yFfeKzD6DXfA9T2NSZQVPn1/bh0BVW9rWc3bacPfOGXv7U3b3p8xnU472d9k/2GF3f5edgz3m811m0yl5WQ70PYKnJ36HBChdlwCcTxRzGudDiirfni1D+uE+AClV1OeOkH4m9HJSv3ik6ddzRvaaT53wi/dRceG8i/uhDnF/j5E0ERB6j0uE59GUH927RKXx8H0D4ogw7N/xXEhCyuuCk68DUAY0SCAohw1R49ZZGXxoIfoMTAvh+l7G3l6OU4GzyrJYO7rWDT5vSEomr4WiyNBKI6LxTpiWBV1X01RLmnqOCy2ejrPTB5wuz1nWNeuuo3UR/WsTaX8yUSitI/G1IXILJtJErTSZxLygykiUqIxiVhgmRqF0BDouBFwXmfe9C3S2w4ZID2SDwvmofc2yDKUTd1aITPIuaf0muWE6KdDa0AVP2eWU0xLnAvh4b53obGZlTI1YWI8yxLSMLo7nLNfszqdRe+uhdV2SoBSCQ0uI6SI1II4+qXwEbQqvBNuvpc6ivGOiDUWpEGW4rkqMcpy3gjEZD0LHg6YeorNVP9El9Y1PjtYqqv/7eRkDmtIcCb32dsRFCQl8giSexRAC1XrNyekJ2giNs9x7sODB+Rmv3L7PqraDdK8EjESQ3/NR9n67kl4AScKqSKI6zmKKy2KSQxMlw0CcBz5p0+fTks9/11v4/C94J3tHByhjcLalbRacHd9lva7ZPbjG3s4R5XQKIdC1NaU3mNywv3vELZ7j7tldPi0vc3t9jNXQKk/to19t8LHtOosyBfTgOGVxcIG2ajg/W2JbG1NDOs9UG5oiZ+/GPm99/g1cv3HEbHcHbQzOORRQdzXr9ZLF+YKz42PaxYKJEpTNCcbQOUddddR1S9u2+Mpyt255ZdXRzGe8+QM/zOxTv8Hkd38rs6/9y6x/8heofuxHCa2NROdtxVYO8L54j7Qx605QGZIViCkISmFVFAK1a6FeISE99EjA2AIoV+3T/efR8bGJcivAJs3BLTH7QpuDyvC6QNnqIsXc5rJHrZtPuKa+Lhz5uHs+QTseeb/hueIElOBjRHfwUWuiC4atcoyjLlZ6WX+Nj4XNZ7W/R/kVX8Hky78Urc6QX/kXVB/4YT5+f8Uv2APeGQwqwIuLNa+sVjx7f8YbZ3OuFQUTJ1S/9RucfPy3MNMdyqeeY+f9X8jON/5Zun/3IVb/9EcJZ4vR4G8aEca7dZDBVz0phgZO3dA9/EDj6biZlpcNzPaAXTp0wpCSbcPQ0msk5aGuu1j7pSUQfdeJDzOcZzQ73/R7Kb/hS7j7qY/w2od/jlNXcf/X1xzTcta1NOKo2waNJ5sW7CoweGbNGXs+IE7ReFgF4QzNSaa4qzVNloFWdETaGZ1SA/skHAZ8VAoETV23MT5AKZq2i3RBLvni9agphRLLoEYWUCb6LSKDv6EiaZVJcik+7Rsqad8kWchk2Etgm1KndxXoU916H8Hlsracv3ZM9uAMJYppWXB4uMONm/vcunXAjesHHOwdMtvZYzrbITcFypiH6kdi3uy2s/jgUaLT/pdYSJI72DDa/bHgCETMosYzIYw+hN7v9KrJwBBMM8SN+IhXMh2pZryLrDIhCc/Be2wKnBVJUeyiBhesITtQAq5DoHgf8BOueh8uL08OKAN4EVofqVAciU8qEMGSCFoLk4nmqaNdbh7t4ozhrFpw//Sc49N1BAISKVCmZQ7KkGU52mQorSMxuolplE7Pz6Jfog407Zo7xyfcee0+p6crQJjNC4o8J8+h1InbUecYUWRGkJT32eSRh9AoTaY1RWbQSijyjFmRU2ZxIJyKneh9lMRip4aYKcW5qGVFMNqQKZ38M1UEkoPUFsiMYHQ0A7jg6Zzb0Or4jfhsdLo+ODonrJqKpmnigPuAwjPNDC7A2noW65qm7mg7T9fZSIKtFWWRc7i/y3RSorygA+RpUqjk8jbVGQUFBRrtPVnTcSOUhNAy0Z6VFmYpCrzzgviAs5GS0Og037Qk36OwAYgAqBitR+IZCzEXeEqxmvYcHx3uUXgXWKw67p+sqBrHsm5onOfkbM3x+ZLzZRfrkBgtXyooMyjyQJnFMXQh0v046ze+U0owWcakzMlU0kb12ggviT4qarLnRcHb3vwsX/S+d3N0uI9Sga5b01TnLM6POT87JctKDo9uMd/ZR2tDU1WExjPXO0zmBV15g6d2T3nu8BnecO05Xr35Gp9+6RVefOUB96ipl9FnNlPEfO4hUUbEpiICmdYQPHXdIMkdwoXI4XawM+Gdzz7FszevMd+dkxdJY+89nXXUVc1queLsZMH56QK/WqALg3LR57FqOlZ1S9V0VE1L3ViatWNVxdSl7to+b779Gdw/+a+ZPPdW5r//v6J4719i8T3/HPvSqwlU1huT9UUAFALKtQTfIc5iy3lcjFI/bzb40TXCJrKqR4SXqPMeubmyvQYPk0wZgs4gRO2qpMiOoDKsngIBdZV2cgxkx98fc9qV5Um0ek+6Rl/s9yfVGPbnKkF6ovh+7wg+mrrHHXpVnRcB97gTRteZ555l989/G5m7jfrpv45/+dc4v7fmo3XOz3PAbUreRlw/Vo3FEVhWp9xdrrhVzriWlRzoglmWUXQNXfVp1i+/RH5wwLX/5OvZe/u3sfw7/5ruhZcG7d7QfOn5ahNwTByDIWzAyaO6d/sDw7TciMSbv3LpLJChn6SNgW5hSxP6sKx0UbLaDmfsz42/jKdt/9rMvvKLyb/2C/n3f/Ovc+8zH6U72OP4aIfTW4dUucGJZSKC1hqDkBthYjRlpthXMG0twSu8CYBhoQ13Ms+Z1gSJa7poNaTL1QS0VnjvCKiU/SxE5ZKzBKsJKBCdtGFEi1wIMfpZkmYCkgYqBuNEi42CBB7TyxzB0ZBVo6eNS0qLpG2Q7V6h90/sfTd7YSLuTyFaxboI5067mkXV8trdY15+acqzT9/gmaevc3j9kP2DA2bTGVkxoSjyqPBSkdFERGGyHJ1lRC2qAp1Ad9rbw+Au1I9hGAb+oXSM/ehLuEDNdvUCEaPjkx+qsAVQe9O4dy5Wp/r1OPlTpoQAXiJuES9D/nIZxng8dTfP8iTlyX0oSbugEkQlzkK9eZFihBZMCs3B3oynb+xjphnnVU5ZQmFgvbZkRcb+zjxxREXCUW00eV6Q5QXeCyYznC2WVG2DdQ1dW3P/+JzzkxXaCUe7uxjJKTNFWWpyrciMQZmMzERTslY6Od1GoFRkWcx/rBSZVpR5BK9ZpqMmUymsd/ElSmheJ8QeufEFlEaUoJUZqH9C8Cn7TZpUvbldov9fynYdUxImdV8f2SeAxdM1NroCBEfbdHHArUcT836frSqq2tLUjqpq8R6UEYoyYzYvmc9ystwQbADr0N5HE7GCIhMKUWRNwNeeumoxDlQTVfbedrjgcEGhdY64KgbR6OjjqUUokguA6yXlQVUUVZgpfi29OmFDNwS94NWvGQAsq5aXXjtmMtGYe4bOQd36GIltI/g2QKGEXEGhYZKEEK1NpMoJ0QlZBcGUGUFnZKZgPiuZlTmTwpApBdoQxGKDR6lAqT1HNw55z7ue5/DaPoFAs17SdSuauqbtWrQyzGZ77Mz3MVlB8B4VPFocmdbobII2M8Qd0bmKZ5tz3nTjhOdvvsqLr7zGy6884MW759w7XmCtJZsqZuuYvvMwL8gIzJRQ5oZM52ilMCRlhPcIwtHBLs/c2GfvYJdsOkFpjXcxO0NT1awWS85Ozjg/O6da1tC0YLsYzKM0y7pj0bSR5qu2VE2HbxxWPPda4SfurblbTPgC7Tj61Mex/+h/R/kf/RH2/4s/x9nf/x7aj38C0TnQboPKfvgHgT2AbVA2B5NFHkmloZgR2jXiRtlfJM2ZfoHSESTTtQwqhb5cBKKXlF6ChhAz/gRwpiRoQaWsVU4Zgmi07x6P4R5zwkO45LMFeq/nkpFfY9AGgt9kEXpU6Sse9+uWItdd3YgxhrpsHMYAVMA8+yy7f/4/xXzi+zG//J24uua40nykm/BrZpfXbMlaqZiuNwQ676I/PdDUDaetZS9bc6gKrpf7HBWKuWgyPNm9E6p/88/Z/+Iv4/D/+GdY/o3vof3wJ0dNi40KPXgmyZFDOsQnRO5JGEJLAlIhrqeXDNCl3RVIGcbil4uyyfiasH3l6HMYHQ9b546v109fY/dPfTW/8f3fxfH5y4Tnr+H293G7c5pySus9mg5vbQR+RhOS+5eB6L+NxhWBstSUKseJ0CayuxicGQ3URmvsyNwcRCIPcPIzdAFUUIBBxEQXGDx9ZrCojEi68N4sHPopFH31QgiIjt7oYbRnSs9d2eMLgX73hOjnr4nCawTxDlE68k6HkZWNkBTbSegIxLgKF4naX31tyfFxzcuvnXDj1iFP3TzicG+X6XyHnXlJnhVkWdxjtVGYohyCXlQCyzHHd7JVq9Hoh000NmwAJSEQkrZy8JXUQp/2dnzNxddwO7NNLzyN2AAEghJE62i+H4D6hh8zxkoAQ0BtVHhs/FTj88S5/eRuQk+eejHLCBIjn7WKm7P02quUakkFUBhE5WRZwbTI8Qr2U6o3N4fpdBoTvBtFkWnyPMf6SHw6m80ARZFPmOzO6YLDNuC9ReclOzMh0xnd1FHojGmpmc1LduY77B8esXN4CALV8pTV2VnkqtSGTAu5ifdSWqMzE0Gg0Sht0kKkErAEnRmMzlGomB0GEB3BpEpEqdGcHtFjZgxa6RTplQYjOY7HPJuMglrCAMSCjxHSwUc/SRc81naIi1kEnG1pbctR3bJe1azXLetVg7OOPFPszCfM9mbk04KgQ0y3aC0ZiRPMW2y9plvX2LoDJfhMce49XdthqxUnbc1x8HQk6gOtsUMKuA1dh0hIxPFxfjnv0GpMZBt6YXLzQiS1uUgv0UI51ZQmamcDlqruWK+h7kIi/IZMort1pgSjUuS8RNJwo4XcZBhVkGWKSVlgigKynCzLmeaaotQo8UiwZBLwSiA3ZGbK0d6c559/IzdvHiEGrKth3SRTQkaeT8jyMpk9MggB2zS4ukI5h1GGIsujAJMHRM3YtXscTA85ml/jxuwmzx8ec/feMXdPTuN4EXjqriJ/UfPOo2usXIW2Mb+rJzq7ow0NkTtzagzXD3eZHe2SzcrB7yUET9t1LFdrzs7OOT1f0HaWLiF217QoG7AOlnVL5aLvresiO4MJjkmeYQSOm4Z/f9KwnE14/47iRn1K+PF/QnH/Nnvf/t9w+v/5DrqXX4VsAqFCelB5cX9OGi+xLZqAapZx3ugskacLJN8qkhUgThS1cSbSGag+XdRIon8CPNDHaRBAfIvuHC6b0ql8C3R40XhdRDN88Mks+gi0cBkSeFx5ndc89jSJINKLBmIqOe0aRvbUR7dlfIoa/db362V9O7JKXoqCLvzVh4dRM/nCD6B/5f9LReBBa/hwO+G3smvc9xonjkxivxOE4DxWRdClvKLzHZVrecCKO23N9XzN9W7OTjll1xRMlND83M9TH5/x9F/7Vurv/imqH/sAYVUNTe2lWN+r/3vfNQ/iPNJHeyd74jZc2+62KPz21CzbHTp+/L77eu/OiKHGnboRwAMPd+nFmsPoqo3pt29pVN7ooz0O/8s/xu1PfZhPnX4Cee8bmOxOcBa8aJzSuABZnqOCQ9sIALMU2ClB8EHDdEaxm+EnQh5g3glla1k3LtHPRK6WPpDUSt/K6DfvQxTmEcFi0GpOCBlKW2J665ogMXAlJJO3D5teHyhyCGMFbwpajckyBjeXXutIP21TgEoK9gyBIZDE++j7GNfWfjNK/SwSI7VV0giGpMlEqDvHq3fOuPPgjJdeusPR3h6Hhwfs700oi4KyyJntlBSTkp3dfWY7U4qiIDM5Rps4C6J5JoHX3r+TpDnt/XLDsLwNGvWxhlGpftcdQGI0+fvNXBgH7IzEqv64Si4KPXCO+7cMQBLG8ZebDF1bZOz9HNy61+PLEwPKiVLRIYyCOuuoFGSxH5PZMVLPdJ2lbjtWrUX7iOx3JzPoAl3mKbKCItdMMo1RCus6xIHD01RLMpOjJzM6C8EUHBzuUU5LfPCcnxxz9uA+zdmSPAjzWU5Z5pS7B8yObiHFjLbrorYq0/i6Q6ucojRoHf0qTQJNymQoo6OKNxApCpIqXusEOBP4FKUGjaTSZjBpI3Hyax1pBiQBSgiDKXbjep2kiBFFgk+bmnd+85LFORkBpbc0bUvb1NRVQ91YVquKqqrIM2Fnd850Oo0aOhUI1hK6Ft/UuGZNV8W8qHVds142rGuPI6NOANE2NXe7igdB0WpipJvN6bSP2VZctE54FYVL5yJNktY9WWpI0YXbBpvNJNw8owL25zlZmbGbG25d38OFlmWz5vadFe7U0bRRs6pCJI9Pa01Ml+gjMwB4MhF2Jjn7+3P2DmbkxRQk+pEYBWWmEfH4ziE+kBvDdDIhywwHe3OefuoG02kJ3tNWFRbIsoJ8UpLnUyY7u+TlJI6T7dDBUWgNeVxAsiyPKT69A9+hVYbJ52QYJqrgqNjh5nyfs4NT1qs1XdtS1msyXfOe6wcs/IR6VVN3lso5nBLaIGjJUMph8oz9+ZRZMYm+MUTwSQh0XUfVNqzqmoDE+elLnLN0bUtVN9jO07ae2nla57BdAO/Y3SkoM6FzAd0qjoPwU/drThrNl+/v8ka1xP3Gv2d663l2vuWPc/p3/2fsuiFMMpRtkbaKG/PFIsTjvic5D4htCDZaMaRPXXLxouGjihNNJbTjHQOFg8kiCbsbhUGOwNAgfA+HAn0IWu+3Fgh4UVg9QYJDEbV8yjsIdnOvC8267PsACq5CCK8HgD6uBGLQn9F40clkL5ff92K7L4LGR4HJy/aLy4D1BTApSjH5mq9GrV4m/OY/5dRMeO2k47fqnI/qXSpynO7NbGrYoKxzWOdTgKNglNAlrdbanXPWVbxanTDPJ1yf7nNjuseOmlJ9/KM0bctTf/w/Zv8r30f1fT9L/SsfjcCy1w6JSrlL01cXoHWITRyrY3PeOLNI/5MPSOvSpr8NDi92+HjNE4hA1qT1METOy4FX80INw7WhN9yETUXRHwa9v4O+eYi+fhCzi739OfJ3v4n7tz/FR37qx9GHTyO7AlkgZAFxYEJAeRVzE4iKFuj0GI2OwbKzYkar93B7OWFuKOjYr1v2TluqUFO1XWRnkR6kOWyQmEo4UdR6onZQKUMTSrTeBz8F3+B1iOsioU8hnuJMRv3pN+2KT6+wA8F50tqpDVCMpvcocPbveIwm74N+ZBAWIhiKJm7nfNQg9olI0lLjhzM3Ez0ET3CKk5OK87Oal2/fZzrRMW94ljMtM3YPdrl+7ZC9wxl7e3tMy1nEIHmBzgpMkccYixQIHKOp41wIwQ0gM4SeFYVBeIg0QxshZkPdF5K29jJN4YZqKKRrNsodD0ENvqu9m15vJA8QU2US0ztH5Zbb7N+ycU140vLEgLIsNKbQSBtoXUZTRyAYU2aGYTNoXceibnmwbGh0RpFBYwUvOShHZx3SAs7GDTlEc3BHYF2BVhl1K+Q7jt3dA6b5DtlkjvWevX2DDp5zawlVTbCWpvHopsZ3XZSOQuSJ8jbO/LzMohYrzzBFhjIGozTG5KCSr0jvsKokcSMaBBWBok4kqOl31V+Tern/3GvqYrqkmFM8UhWNVNqS1ONxHqRFXg1UPXHSJyoEH82beRdpb/KyI6saJFuTT6YxQn1aUExiCkPbtbS2Zr1cUi0WNOsV68UZZ3cfcO/OgrMHHWoyYe/6IcEUdEaxUp67rmGhDK2O4CTPM1rX4SVyb1ovYEPiPY0+fuKjaVZ5h9Ix8EmpqL30ieNHwkaCFB8wSihywyxXvOnZI5599oi6azlZrli1r7FaLZEmSoxxL4jgPDfCrFTk85xMG3bynIOdCdcOdjnY32G2M0PpPC7aPmoiBB/N8z7gnCczmkmRM5tN2J3PMFrhWg++pQueLDcoleFsNDdr0RjRcUr386LIERUBJajI9di0BNuBc1HSs54chc5L8qlnHgx1vqata5gojLTcmE6ZeFh6zwIgOFodF38tQp4b8jJnbz4hz0xMWRb5qgaO0OAdxhjULAZEWaCtDbUNOBdoW0vnhM56rHXgYjrRiTFkRgGOhQ+0Vlhby8/cX3Nip3z9M8/ytmmD/aV/RfaVe+x9+5/l5H/6h7iqxesMrdoIwPrVSzFotMTH+4yLEHrS1o16gaRdU2Hzo+452tIxLSmnePrb58Idq4XGn9ML1QffBNERLAJB+tnkMbZCgo3ayQHgPjkClItfrlY3/faUtLFq14JOaRLHLgSPaOOWY/9lny8C4kdhp8tuEiB7+9sovugLkB/+q6yXDZ+pdvj1dcanwpS113iaSCvTb3qpz733iaYt+Z/7uG6KgNea1nac+Q5WS146P2NvMuXmbJ9b00OOqhUnL32K6+/9Qq5921dw+Oe+Fv+Ze7S/9RnaD32K7tOv4Js2rqVK+qiM+Fg9htsgyu2Hle3j/Tq91R1bXxKASWu55JqQ6cGXOGb8SCadML58TFe0ATVxudEU7/o8dv/o7yG86QaV7zg5vkOgxS/OWPzM97H62GvkdkJX7rI2gYaWs9LiC0VhW4JEjkIbeg5EMCFqrRpdsJpnKD0jn2SYvSlGObLlGfu+ogkR4Nc++TU6TxCTgk99TKgVotXOKRV9w8OU1h/QhT2MNJTGIu0iASiDTfnZe92XkLRoQ+eR3Cb1oG2k16imw1oCIaghkEdQiPebXO4io6FJkeTBD3vvGPwPaR+FZP2JADXyTabAUi+0ztE2Dn/aRGWTKPLyAbPZy+zvTLl27YCja7vs7s7Ym+4wnU2Y7M3Yme4xnU0xRU4IvRmXQTMpfXv9RpkUrZ89m1WvadxEyEu/xl0QzKW39iQQndSS8dmVpoff27M2RL9WIHEeRvzhQiKQjzEyPWWTu7CuP6o8MaCcloasjJHS1lmqtcV1Lu51yeTtgMZ7lnXLedVAnrG2sFrXLJYrurqhECHXiiJTaDyu6/Au0DhLY30ELOoBu/uHPHvraQrJmOkYHR26jqaJmUpc3bLyATGK++cN6v6K6e4+LgTWy2OyrmV/PseoDJ8VqORLhSiUyZL/psFkGYhEoGkMWmcDCFQXQaMSJG3+g0NtP2ElCbyie1fTNCCRtqYHjJFEdXR9oigYzIA9m2fwWGtxEAM1kvQ5wZHliqzPZNN1rKuKar1iuThhtVpT1ZbFYs3J8YJ7Lz7g3gt30F3GU296LmYk8p6Vt5xiOXMtXRDIDJmA1dF8740DXARmTgYH6pgoRTCicMMmkcbHu7S4CkPaMInmaqUiZdT+bMpbn7vBzWf3qRrL5HzCaw9OuZctaCWuxFoJpRF25xnX9qfs70zId6ZkRrGTT9jdmXKwO2FaFmjJsb6jbRts08Xsgf9/2v40yLY0O8/Dnm/Y0xlyulPdqurqCSAITuYAEpQoirApjgZI2wgQsvnDsmwzFJZlhWkH/U9hOhzhf3ZYDoftkB2mrbBkiaRIiwNmUSRIcMDQBBoNoBuNnmq8c2aeYQ/fsPxjfXtn3lvV3UUqeCKybuXJc/bZZ+9vWOtd7/uulBjGiTApEmKtIURDyg6RIzkLm37CO0Wum64jDYHUTZhscDgIEe9c6avtoKDS1npFXLL2XZdxICe16ZEQ9PdhQMYRkyMuCzbmhZvismBFWzF6Y6iwxJSpK4uvHMka2q7CVYKv7A0Ht3hDGqOq/KZtSVmIYSIBIeo1CGMkRWGaEjmVzNYJzhumFAkJDkPg6jiyH3qI6sf5jUH46WtHd/8zfOc2YH7+r+O+x3Hnf/nvsP+Rv8Pwj/7xvBrdbAJye4Xkmwcpr/6L6CY7l7+XFW9GjOZNxt567cvH+FDSXCgoNk2FP1lwH1uTvNoZ2RyW5/95Hh8KLF69Fref/8g3/TN+XhYM6njgRB0lPnR8Xv79Iz/udux8+z591DHyt/r7za5YffazbP67P4z9p/8h/Qe/wVeGNT831HzNeiZJpGKDllIuiblZgrO5a4r65MHMa4Mi6ENLnjFnRkZ2h5FHx2u+Zj7gbt1xr91w9713OPsHP83Fm59kc+8+p7//U2z+9O/iZA/Hv/4zHH78H8MYizhGShLx8uXQ4Sc3QYnlFmp5i+4zv2lW6ixlc7m5LM5qAMscTIom4vlW2VzmwObl8vZya7xj+4PfR/Onvpdf+9mf4Qv/5/+IZ95zdJbzKvLAC6cvBqw5x4aO6huG0FmubMPTCwdnE21jMTYSrGMSLftWRj2gnfOkJjMaQ+tPaasNtWsYzJ6uE+7ajuwtTW/ZDT2ptHUdJwUTogjaVhdVEmMIWJLUHNOK0ZxQ2cCZ6Wnsc0weyt5W+HlFLLUAMHNJ187l6RmAWbLPm8ALgayfHUUWZgbLnqqhakp5aZV485Jyv17iJRYaUU5lGlsFQGYBy0xbEwVKEmrMHofIfhh48uSKb7z9jM22ZbttOTnZcPf+CQ9eu+DO2QUX5xdsTrbU1QrvPXP1cW4/qXGbJlGpKNdDiCp6KlUe5z3e10qvY0Y1b30Hc3M958dsUTV35ZmD0JfRYJa9eRl7xi7XYB7Dmuz9s62XHzug9E45jyEIjXM0lWWqEzkrumCNoaodtXNsKouXRE4jEcMY5nKmo247ztYtXeMRCUx9T384MoSJMUyMQQjxyNMXL/jgnQ94951HfPLTn2B70jFMe54++YDrF5f0h6G8PjJMiTxnL9ZSV46Hp2d88vUHOF9RNy2+9njJkL02nvfKpzRIQR9t4UbacrPN0k1nhpDV38kiJt8o18qNNQuHUjNOi1GCt1HRj7H2RqlmlIup+7DyOpSLaxeeQ0o3paEYswYHKIIapoHjNJCmiTBOHI4HXlzt2B17jmPAuZoYhEnWbO5/CpdqLt9+zGG/p+5PiLVnCJF9CBxz1vZcxmGcBta1b8ghkYyAVbUyec7e1Ndz9oHUAauTRNFXDToNkKWQ5NF9/DhNtL5is6o5Wa/ZbgzBqHVbZS1tk2m84+Kk5c7pirOTDWfbLdtNR9e1+FrFVW1dq8ckhhgi4dgz7I+M/cixCFGOQ9DWoOWa7fYDXXdgvapYdzVd3dF1DU1TUTcNzlvarmG9fcHphS4GTdfq57UVVVXhvMe4RE4BST1pOCBjT4qJHDPEQBoHYt8Tp544TKRhIk3hlsH3nH2ydGwyUVV3vm1ouoq6rWjqCufdsr9nSSRRbwVXeLwpTMRhoj8cOOyP9GMgxEjGIM4SYsAIVJWWjEKITBGeXfXsjhM5qzPC3c2Kh3dP8SdbvjAJx7Dls7Xj/Bf/Jvadz3P6A/8Duu/5XRx+5EeJX/oSxFg4a/bGWujVoHJ+zM+/JBSUm8DO3HqRAea2Sui8Y1Z6OvuSSfqHAqcclR9pXv6jSQMGSK5BjP14gpZv+5ghKEO2dVG2jy+f1Cvn8c/5KTf/P5flPxRJ89EB4Ee95vaBP0q4+Sr6Zgw3BvdGg/K2U2ugP/yH4Bf/Msdf+y/5fL/lc3v4QBxibWlNl7UTVdK2EDGngspp2TRJxpWuEsZZTLrhiGHAOvXdywVpi5KZCDzfX/PFqw+opOLcrXnwta/w8Pyc8/Wak27F2Ruf5NN/9gfYVJ793/gHC51B5q80X6q5Afi8hluDeLdYMAgUG5NcBD56kNt5w/yvZKPjKqPIu4i+5xbn7dUAEm7RhUSg8pz84PfBf+N38BP/3/8n7zx+xqVd83RzB9PWNLJjYo/pLFZWyKHG7ivi84kXNvFkyJw0hnWdWTXCaA1XydHnSYGH0oIxmZpkLUkqjKnxY0XXnpBaR+16fDNxPjoudxXHcWToA8dsmHLEOeW7GskYV7rKGc+QKnpTcXAOlw2VOcG7M5zpIfdYq51dNKA0L0HAMl+fgqkYSmcY0V1USmAjiSJg0ffN7p7LeJ7XVWPJcwebgrxp3elmgsxdevTe6L3PmGWoC4aYc9mPja73RvfzCoczFdlGjlNken7k2Ys91jxn/bWK+w/PeOuN+7z+4DXu3jlje3rKar3WfuLWY7wrY4SCjmb1sBTB5kiOkdliSLJWWb1vCghVwBsz2ydp4DmrvucA81buVLrsyDxSb6a6CLcDVI09ZyDNvhJMfvyF7GMHlFYyDmgqR1s3nG0Tq66hsp66qqi9p+sqGu9pm0bLxrXhGBXNqaxn1XjO1mtOtw3rdQt54uCVPmuIpKwtwkwWkrUM8cg7732VZ0++gfMGIRJTJMSs5TwR3SRwC+k15UxImWvfcxxHQoyEmIiT2onkFHC5pmkNeLeITKxocGnMbDNQLmSh5swDG4DSfN1aq96BZcN7ORi13HAQbjy5VBGmMLiZN9ayAIWshtsxJsI0MU0j4zQyDCPTMDAOA4fjnv1+z2634/rqiqurHU+fX3G5O5B9zWq94q033uD07BSzzYQpMW7XnL32GtOxp1hzLpmQsZY0c0qcUy9P5wmVU6QnlGzNlH6qhVRvrNUAtExgKZm9dvfWxdSVgZ8LYDlFMOJofE1d1Wq3YLQV47p2nDWeh3dOePPBHbYrDeaqpqJdtzRVrTQ7q/auOQamYeJ47Bl3PdM4EWJmnAL9FJlyJieIAnkc6UdHP07sDo620j7i666hbSt8VVGXnulV/Zh1t2K73XByumFzsmK1XdO2LW3TUtfKaZQ0YcYBwqS+a0nIaSJNA2kaSYP+hGEiB23ZBTokHEb5vKF4i0Yho9w+7wxtW9NUqv52RgOsGCZSmJbNTcpz/eHI5Ytrrq/2hBgK2qM8yVwSGyOKikqGadAkxVnofMW2a/j0axe8/topbdMiIfHrL654jvCdq5Y30lfxf/1/hf9N38f5//gHmd7ecfirf434jW+ocXMJTGbA8UOBlHAreHkFuls2YaMoxtKebJ57WfmTc4ceM+nvrx7qow5/++k0qhr0v+pj5hUZg5iKZOzCbdTWoAlEuXr/leLJ+XvcRhS/3eOboYrz+18V29yOiGBZ5+bXiK3IxmIkaSJ7ekL3r/wBmt/9O3H+mvyP/o+8eO9tfm0444tp5Mpr95mYU+GGKWXGeV/K3WXtQNT2K2ayqNXcjLIoUqTiQLVq1uNlo6lrTIlkhTFGhn7gV3fPsek9zpsVD09O+MT5GQ/ef5+vPX3Ef/3f+J9Sv/2Y4fNfVkGZzGtt+dLlGhQQ51Ywq4HDfK7FgRtuIY2vGvwsm3LSMl3BIPQ5I8t7XgpC55J8U1O9eZ/TP/WvEL77DX7k7/5tLo0QX7uPmBUhdmCEKQR6Ir2xtCP45BEx+OyISRgDkITGRza14I0wZY9Yba3pHCqEDBljK3yuqVOL3ztWpiWvE10X2bQt01TRVh27PnCsBrw5MIklidUWy1G9D8dsIK85xJp9MhwsOPF4s6aTC7zZYU3E2blSZQsqbCnlPATlzWNuc/VuBqiBRaNhQDeSwgu9UTabkneWe2Yopuc3x8tZy+zW3qwxghRwpLhrlIQ2CYgyQIlJtGwdhcoZRDSwy6Lj2BQ0MGXH9XVgf3zE00fXfOPiEa+/dofX37jP3TsXbLenrFYdVavCHu8c1lYY73RvyJkkgpspGnaOFSCloPY+c6xR9tgZaVxaJd8CdkA0sL6FRM7/vmz8UACtnAvftNDZC2DmrP3QeP9Wj48dUIYsVCnjvGe7WrFdrTDG0lUVbV2xXq9YrVRwY71lCJk+jFz1I1PMOONYeUfdONq2oa4qYph5Tnpz9ftmvHNIytTO6QnKBNkswaY10HiPy4mEXRzrQQ0728px/+4Jrz24w527F6xPN1T1bOuj3pSq8LbYovi2RTggksmpCAlM4XoULoNBs5m5hHNTBlemrzU3/lA3RNvCbWBZYcq6pjc7pURMgRjUszKEiRiFECLjOHA8HjkcDhz21xx3e66udzx9sePp06e8894j3nt6ydVVz+nJhtfeeI3zixUi4Ouatq4Yw0TdeuJ6jWRFBa77I1NjSC6TnGc/qRF3FkPlVAnuqUkihKiqUjEGFcELVopdgygHRK+NDtyli0HJNs0y5QtXNmdCTkuXo5yhdpYHZ2senp/xmdfvcu/OKd5rKVgNZAVyxIrFJVVCx36kP/Qcjz1TH9QPdcl07TJrJGdCjIwhMIQJ5xy1V0uhQ68eY2pb5akrV5Kjiq6paZuKbtXQdS2bzZqT9arYSDicBScZZ9Vo3COIJNI0EoeRMGgiEEJST8+ClBgyzhtsUkTcFMdzyWpIazNUpsYa3YglZeI0EUKvQqmo6seUIv0wcb0/cr0f2PUjOSa6psIZizUJQy6KKpAQsWJogPO2IdYVjXfcPV3zxr1T7p+dUDc105Q5GMPT62uG68w+NHx2W7H+tZ9g/MKPUv/Xvp/zP//n6H/2Vzj+2I+Tnj7RefFqEDnf9Pl3+/LvBhTpJxcU0utyZAvnSKGLcpwSyLlaEadXeUS3PvKjH4JJ460T+BiPj0QChdluR1ylFBrFVHTcLSr1W+fzzxpZvoruvnqMW///0qG/1Vczt35uB6m3I5wFugOMJ9mKhMXYivVv+262/60/STX8BubX/u+MH3yNJ9LwNXfC41Wi8h3bw4gfB45jYghxcTua6zAL/aes84Ij54QxohZychOoaeMEi/cGWzqChIUvp40hrDMkb3jSJ97e7/jSiz1njx5xvm64987X+YqFH/6f/4/Y/NgX2P3oPyLvDtqBZSl/648RC7jSKrVs5tHcXJdUvG7nWPRlgG152RyYzp1VXg6Jim2cM9iTNdQe/9oF3Xd/ku73/haGB2s+9/Vf5Wf+9l9ijMK6XS8+il4SFiFhGCKMIWH6kTy22FBTO8vGeFqfWPvIxkc2DdROGLIj+RaHR9IAkhX5FUclFdXocTvN57Zrh20NaWsYJsMQ14QsSGqp64rjeFXEdZYYDJI8fbb4uOI4dgw97ILgrarNV3mNq86wMiFG95B5L9BAqJiTl8VjVh/f9mfUMrEs7XWFQq0il3hhdr+4USTnnNSbcRGigIjaCM7Ka2MVeEJe5iQmAbIK+KyzZZ9WpBRTxFVeuelTFLCCkLAlNsEZJBle7Hp2+4lHH1zzznvPeOPNC+7eO+XO6TnrzQnbkw3b7YbV6gxfV1jrSRLBOlzd4ARtGVrmTnGQVMTSGLSTzc2E1+9gX/o9zx38FiT3ZXRSt2+lZMQ4MY0j09hjDFRtQ92s8M6qmOhDC8o3f3zsgHISTyuexlW4SlGeunR72awaVquWZlXTdjVYx34aeb4/EnJiVTvdCBFijFxfX3M4WHIKHPuBw2Hk2AdCERWkuUwhuZinG5x4DJoluHKhauN1oFlPyg4rntPNiu/41EM+86lPcO+1e2wvLqjXqyVANBgtgxczdVP8mtzMwSpcAmNt0WOUm2pM6WduirWBLURafb3c4lXehpIVus83nk5Z7XFSzqSoNjBjVGVujJkQA+OovMj94cBxf+Dqes+zZy94+vwZT55e8v6j5zx+dsXuMCmP0UCbdeKkqMd5/uKS7XZF17Z0J5vS+SdhLMTGk5qKqmlwzZp2P/L0esdunJBgsd5isi08U0eWpAGk08zPGoP1LN5kGL1Hhc6s0cXSJ2vev5Twe5wm9oeeoR8Rp3zT067l7v01n3rtPm/cv2C1qYg5cTgOamGUEk6E2nlMSsQxkI4j4/7INMSyAOhKX2NIWais0eA1BqZp0j0zW4iRKQa8tRzHQrZGk4Tae7pa0faq8tSVx3unvEkLXeO5d7rhzumWVVuxqj1ta2krS+2KkW/MxBCIYSTGiZSyUjFES3/WGZy3uDR3oABjdSGktPTSHtlRPULDSEzCMPa40pVBKRJCHwKHKTHExBhU1e6tWl1pZq8c5Sp7mtqzchV4wySJiFBX8NpZy4OLLefnp1R1TRwnRYtPOsZD4PFwJF4NvF6dcHfVYz7/n5O//PdZ/e4fpvkL/y7HH/079D/90zBNH0a9uBkfN38wN8/PkY1kTRolQnIqxJnlqcYUfmQ5kHUlqLu9QH6MBUyW/3zz4OulYM7MuxFLDWkJvKxWVdJQ0EpXvJqdZlK3Ast/7mDyY/xtiTHNR8abL733Jaqq8OF7BIDVtdRUZJQqsvmTf5T17/utVJ//f8M7P8+hPuNZfY/HvsVuPa9J4nDoaZprjgdDfZzYD4Z+NESjxbaYFH0BYSWO7w4b5eORIYAZ5+EwIyk3a+t8wikqLzMXwV3ImaMP7JrEAb3cPkM1QBUGvvg3foL/z69+jR/67/85Hv6Bf5P4/vPlniwbrGg6IGlEdoHpq+8r1/l2zfCb3ADhlQAJsCcr6u94cybKffg+tTXudI1M2j/98eP3+Cd/72/xhS/+Ele7S+pmxdnmhHW7pq1rgnjuRE/lLM14YC0T2zHRHMGPBom1DjmXWcXMxeXI2bCnO4Eolja0XAWhETBiqSUTp8AqB07HPSf7hJscdg8yHXGbPbLuGYPBH2u6vefQZ5p2jTOGOA1ISqRgSMkSjGc/1bwxZF6fJl7IFcY4HIlzSZyMDRv0vc4YOpnFQer+MO+TauxNoVEV0Va55BpUzvuvFq/nAHO5M2WeqohSk8/bQaYp3HzKXhmLGHhGPnRkzl3/fBHqek3ITdnzEWIqamjRjnl6yFy8qqXs7QlfBEXDlHn//SuePbtms224e7blwf273L1/wp27dzg/O7LZnlDX6q8sBozTJNVqxl3WxnSzbBpKIuZuvjsgknhlsS0BY16ERzODz6C2f2EaSSEQpsA09QzjHmMNjWyxrsLaRpMu+xGLzzd5fOyAsm47fOVoGkW+2rqiaSo2XcWqq2nammbVqv+VtYiHXT+RkyhqM0WGbBj6Hk8u8nWhn9RbMcaMtxWrVa28Mqs2ADFql4dVXbPdrGhXNd6rItZVFbaqyday2w+YBA8uznnrrTd58MabnN69Q7vd4kurRMpgVAsgu5StNUi0S5l6zpLMTBovA84spuRzNn17GXdQrC9u8xNySsQYSKWcnZNCy1OIyv+LkSlO9MNEf5w4HvbKibzccbXf8/zZJY+fXvHe4xc8fnZZgqzZLP0GvhZQNKsc0xi43h+ZQuZku6GuLW2n1kd+1bIKE/uupWpaqlXPaAP7xz3GlDK+nbsAGEzROCg1RT28TLGUwBTCMje2SCommCewGtCCGqM/3w+8uOw5ebFHrGUaJu5tNmw3FQ/ON2xWNb7ykFRtuK49rixEFK/OECJxGJGsxrtYW8YT+KyltpwSU0jkVJT/JaDIxqrnp7GFlpdJWdQI31rGytNUnrpusQacU0VhEC0hfNA23Dk75WTbcLZecbLynLUVJ11NVVu8MeRyD/Iij5tRaoOU3uaLglHmUgtLP/CUEzFOxBiI00QymWEY8MbjfK3HyUIIeu4JSGU+MPPSSjIgUZWmzsKmqqhbR/IwiuBc5mzdcbbdcHJyQlNVmE1BRXNkOB7pn++4fPaCy/3E69eO19ctZ+YK8/f/feTBT7H9Y38O9/pD9v/Jf4qM00fHRK9OFfNKEWV5UxlkMfKS30lWRT6zG90SVL4cWH4IGf1mz83/n195HhQJLWVtjcQKgjp/CVPemCbm3N3MXiTGoJkWen7FaeLbopTfbr1+9f2vvH5Bx26/7NXX3PoKLx1neZ1DjCOLkgN8nuh+/+9h83u/i+qf/B9Ihydcnn6GZ7Ym1C3rZsWJNaQcWbWOtoKrWjux1N5ybQ3HUZHFLAZy4tIFXstr/vTV/VsnBt/04piP+qIzIqioTa4g+1uo4a17a748cvUX/6/0b76GXXcLCjYnBvPXP8HjvvMT5NfObsqCKd0ocG9dr5szMsxAOjO6mgae/xc/RT4cb59u+UhdD+OjZ+RhRGJCQuATAp8AhE1ZF44Y298yvDbLrXIFEDH5gJFLFakYeMtA3oN9pM0XjNVd6pMcCaLlUStZXW9FsHLASq/2QoWqJN9IGJcVdRO4mwKfzCXIMupaYkyj30QKL140kIl5T5Aj8ZYNkxHBmoxlhZV6qdIJ8MLGsh4rv8+aUsIVnUtZpFTEza0uMEJCtC911u9nnV14fjOnf6adze0QZ60ClFxPZoW3WRqppaz2OVpwsDfVoaXbkc7huTwuKALqrQGJuhcjpeUk5KSd3jJCDIZpDOyPE48fXfO1bzzi/GLNG28+4I03H3Dv4h4n2xVt2+HaWn0tfV0o6h5XokAp53HDecxLmXseITMCO18vHT7zm3Ohogg5ZzW9j4GYJmKaND6RYm+U4uJPqXvUx8+KP3ZAOYyKUkURjjHRhsQ6RHJWxKdLQpuEpko47ziGnv31wIvrnv1xYHeYGCOsa8+m0k3R147KOHyytKZj3dacrDu2mxZXeYYpcX11wJA5WzWcn51yeueUplb1ra0rrK+ZcubR0+cwJe6dbXn48AF3Hz5gdXqubZKs0cDIOkzp+a1ZMDdcx4JQzkarLOWsW5tGWRTUkkAWiH1u/r74jZWbFkIgxUQMEyEEYgra9ztmQkhM08Qwjez7HdfXB66vBi5fXPLsxTUfPHnOB0+f8+TJJdfXI+MUyaVsaJfPV8RQjAbeOWmzeGfVPxNnGGPgen+N97DZntBWFb72NF3L2emW9vIaefyMq2GHe1qCP2Pw1hGtI5hZSAQxKB+vbT1t67GuZowRYhncC4+uDPGCSlRGUcOcDY+vD3z53Uc0qwpv1TdyW7Wc1Q1d6/HelvdYNm1LspFKQEIipYnDlEhBs7Gmqqi8YL1XrknOyJRxppS1biFFplyvWeSRJBKBkDJJwJus46S0yhzjvPnoeEhJg+bDYeA4Brb7hqdNw7arOF+psOXOSUdXg7f6XdVeSJHpRZFXNqJ5szDGIRIWikSaN0qEGAPjNBJyYpoCptK2ZiEmhkHHlkGoa0/VFHGIyTqWsyZLoZTrbAQTM62v8StHnwLON2ybFW3TsGrVqNcYIadAihFvDC5mSIlnY+Rrg+F66nmwDzzY1mye/iryI3+B1R/9X8MP/5mPDirLxvHtoTpz658SnM1RkMQSBBgwvvzM5M3E0t/5owK3j3ru1SDz9r9zyX15f0FMJZdy+0dEZpKXcYWtbhBVKxSX528eVL56Dh8V8N1+7qUI5yN+n18/P//NNEgfOp+8BAEmCfWn32Lz/X8c86v/CcMUeLZ6yM615PWKVd1SuYpkhJwq9SOUgr67spZaB2bkMCjv3Rjh792/4u+HHSJKjUlJSDFqqDCXokuiZTBkgyb+hSungUwmZiGkzBgju2Pg+pDox0KvcwZnBee14tD6Cvvoi8SY6ceJaUikpEGSNdpy9i/W38XjD77Ez35Xx3bdYrPwtS99hXfee0QSofLqUezKOuOsVlYSxYC7zF1Xqh1zHChShqYkEsolX8ZxYzCdIlF5DpCApvV0rWdVtUp9cZ7KddxpO05XnhPpWIUN7fUKc2g5VIbn3vBkHbh/94pT+4z1yUSwK74e7vO5cEHlJ06G52zDU87TwLmsOOm3XAznrHYVtoZ05ynVw0vM6cjh4Hn3+TlffrTmujdcrA7cWR84XU0Ylzg6x+Ayu0Pg6rDiveenfG635VHugAqxYHNPLdfcMdfcM++x4RJsxCEcSCQc1uj3TpLxFoxzWumSIpwpc2qOkW7i65tAU1sFSkGzde3UAlkhXBWUEqPHTCWglLJPpaJeN5LVjaQ0PZBl/1CugzW20OIK1zNnKmswOGIqHMh5WqXScYiIGCkAi/ZGvzpMWg5/fM1Xv/oOn3jjIQ9fu8fZ6ZZuvaJbday7jqppqKqWqvJKkbL+5Ulrsp7LrSE1LwiLYrugljqtZk9oDbrF3lD26ramrmuaptNAtVRggeLe8vGFjB87oIwxsReBMWN9pPYT186xOnht31QPVN4qymGgT5GnuyPvP33B5dWecdL12G4EZywVjuw0aMwYqqqm9p66qmnbFVXjSGbE+opNW3O+aTk9OeFse8KqbZQHWdfYqqaPA8cpkvqRdVPR+loHXIqacRlXSuazgxU3F20JgIq3laGIBOZRVxrEWzTLKtmZwoMJW/C5lJPy4ET9I/PM3Ru1202cRhUUhUgcE+Mw0h97rvfXXF1d8+zFNe8/fcG7j5/yzrsvePJsT99PSCo+vYW8nguvYxZbzBlJnAIxJnIqamBbY5xVIyJjaOqapm5wzhJzxBjtYpJTmYQ4jKnIBN3DM8vAzBhyyoQo1M5yum2pmwZvPccUubraFQW4uRncMnMqWcxbjYEYhK998Jyubbl3sua8q6laz6qq6KoK51TsI6WrhncOk4RIIoREmJIaKndNWWRsyRBhCoE4JWrjmGxpxxljCfiVRK2eW9qvdEyRECftXVtVWDFE9SqHkJbszxhVSAuCd5EpCcMU6PzAZeV43lS8WB84XKy5f1rTNgZvK6UHpoxIwgTlSoYQiVYW0MugxGdb6B04i6m9crGKlUSQ2f4oElJijJF+6InTROUdvlJhUQyRKQph0s4/IYQSyOv9sEZoqoq6bnDoHGnqispXeKv8Y12ulDTvxdO6mlRVnHYr9iHzIjgkNRxe7Ll3OHC2PtL97b/I9k/+b5agknF6OVCay8fcCvw+FDkVyywDYjxiPSYHjIRbr5uJmCWwFAMEQOf5stzOh74dWH2rYO6bPubPMizIJbCUoWZOE/Pf3U2585Wg1QiLvuPbIpavBpAf4xRf+s633/vNgme4oaXcCpJNztjVis0P/RDp0S/z/PnXeWY7sqtZbda03Zra+sJDU19hnCM3zfJZibI8ikHMhFj1tRtjYqghGwVvQ4I45+mARZN8SRlrhDCjhLeV+c4iVpGW0SUOVWRoIvskHKMm2zapv3gdAq1TdC0jHPLEsPQ41vGyShVBhFBZrlcWt/GMu4mvDgfelgGwuBSpXUVVuvs4k7AUFayxZbMXbHlerG7Ei3VauVZRkr628AnnIAhfBBViMHHE9SP1NOGtxxrHuhkYqsRROqZmxfmmw7g14DjieeHh0hs2vqb1jk1tmKqKfd3wzr7lUQi87jyfsolMosqOul0z9A0tNdJMmE2F1BXiB3LXYE46OJxwfT1ychyJ/YS9O2K3AhXIxlKvVkRfcz20fDB1PI4bJDclmWowYnkhhskeuSsHrFXbLofBFvGLsQZj09K3W3O6It4RezOs58ScG6eUecC92g2GuTOb1o2LRzQLAioIxlk1jVgCy1z4/FpsSFGUZ1mQPiOyVN40udCytibu6lpAKkp2Y0gE9WQuE95mg7F631MWhj7zaNzx4tmRr/7Ge1zcPePO3VNOzzacn6w5WXWsNiu67Sld11E1Nd5WZY0uvpFkXSfLPnsz4Uvry6XGXRI1YeFPqr92g0s6nwyWLEYreikUYFNn5b+Qkvfj62uSWILohLGmqFW9o64MlVO7nrood7GWwxh5vjuw3x9I2UA2xBzJIbFer0gkhinTj6rctsZSB6EeddLHZIhB2IWeSoS26xh69f2zo6qeqspzjCP73Y6rJzvyuiVlyyATm+2WqqoxVVU65FQKJxdFtnX6Rax1GOO0vaKzhU8JusFZxOjz1oIU9V/OGnWlpK2qUoGJc0patcsQYiRGIUXoD4H+sGN/OLDb7dgf9lxd9zy9uuLJ0yveef8Zb3/wgueXR0JQeFrp4iwel9o3WwdESprRx3mHyKYsvtoNwHu9NsYaqsLzDGEENFCKedLKXQl+h34ixbQo3J0xpeRrSdESi1GDxXDSdVBVur03nuurnU76mbNSxozyW3UmS5o9vYS+jzx5ckknmXO/pVl5Vk2li0WWIlDJmoQVikCIgSSCrysdX1D8udQXNeZMzom6coVmYAnJUlnDZGARfRmd/DFlpqgwvy8LUhZVkNqkprBQOD2SmGIg5kzlK6JEUq6YnMM4z7XxXF17jv2AxC0PLtY0VcZDoSBMdCVwz1kUoY5R+aGiGaUmv4aqqek2K3zbIBhCToSk5xliJITA4TgwHHrCpOUW7zzeV4zDyDSpqX/OSZGPEKnEELKOhdo5Vm1DUxbjqqD1gF73nHRhjPnGXzPpdaicR2JmqCvy5oT+eMnV8yfcffGMk7/2v6X7of8dmB/m8J/9Z+T9AShIrC/Z9S1/SH3ITQRYLJEofrHJeLXiosJKYDE3N3OUj86OWTEqYVHUfmSQdRtYfBUNvP17nsvtN/w9fY8S8/X1KgDEluXTlIB4htEka1ZyyxpJXv2sV9fobxY4zuf77QLHb/a4/Xkvqe1vocC3rovZbNn8mT/D2CTe+8JP8iIFmtax7TrabkVTN9rHOSdMVuGXJioOQ4NYVECTlLJ0w2EDFz19iETRtWrxF7UWV76HURCIWAI/TYhyKfHB3PkjZ1UH196QKkdqMoFESMXvL6uYJ6YAWF2nZ/666O2x1pDtTdYoIkxT4PLqmme7Q6EOWXB+oRE57wvHzyzfKxH1/SUsnm/4nEQba7A4KhwpqZm7s4qWzRZsMn93EXIyjDkSTMY5Bybi+gzeUddCYyN1JfgTT06WMVlCNuR+j6kiVTVhclZ3CTFcxYkHVSQ7TUizEZIT0toSxOJsojYZ0wdwIymd4aSG7EBaqmNPdbXH7p/TfGbF1HakqoZNx4nfsjp2VPuW0qEBjTgFYctgDZcmsakta57gZE+Og6JlokCbN66stKLClmVcK83ISC77knIol/2wvGZWJMtyzVVRjjGLpQ7lGudc/KCN1/ufEzlH9Uu1CjjNmJL6F6ei8Nd54pzVPal8hi1BmrOmGDwocijIreYCAjZDyjdJRLEkGkNmvDry7PrA1995zMnJitNNy53TLecXJ9y9d4eT0w2rkzWrbkXTdnhfF/oDlGI7M1UP5rJ4EeZkNIGZr0UJ3DUXKoIfo+9XZ/DCB+U26vnxHx9f5S1wDJFj1ExzimoibQFrhcoV9R0GpSII3juyaHlXxQaGYz/yfHdE7JVK5p0lxYzNwqataWtP7T2NNxjvGfqRMI6crDruPd2xaiuqyuKdxXtP7R3JwvP9gWePr9m6mk8+7bn/ZMdq01DVjqqq8ZWlqrUHs3e6WZnCFXTO4WyFa6rFCkjtgLy+xuhnieLyS5Sfs6h/bUoLByElWYQTIpmUM30/sr8+cP38GU+fX/L+0ye8/+SSR4+vePzkimdXR3ZDJM775by4W+XciSnULBGtuqGkYEmGXAZRjLq4N5Wj8U55rm2NiAYNKU30xyO7GJYSizGOPiSurvYcDj0zuXm2U3DeYaMG3iaaxRzYisXZimyUm2PmMo/V/VjmhTKjA3aG+qzOK4tgYsKlhM+J1mmwkVPW7jM6T7Alo8yifWWrtqYu0L21FoMlJaHKTjcOq6pRo7kLo2SqYPFJF5CYsx4rC1OKKjayjsq6ZYHP5ZwN2qtcjHZ/6mPUloUx04oAiWAVvRUxNNYxTKNegJx57WyNrbwKCTLFmBxMEXAYm6AyS/ne4HGNY32q/dld12KsI8Z0q7c6aux/6JmGqXCJoO9HwqTodz9MOEPh7AoSEgOGwUYaV7FtW7q6IkrSlpO11+9ZbF4kq0F7HgN5mohBxTbeeXzlmcLEmBJ5qojdXS6l452n3+DB/gPu/d/+He7+6/8e1Z//d9n/1b9O+PUvI2HSrkl1qwFEimWQpJsAbSnj6I8lQwr6vCnohCQWP8T5xQtKUaOKsYliWKcP+/Lhud1k5nYQeRs1FNEXKsGaOam84XPcOsDt34td0BI0z2Sr25/37dDG+aXm1ktvB5O3j/Nxjre819xcu/mcSyBvrIW2xZ6dU336szT/8r/MUzPyT3/6P8Lla07WLXXX0m7W1N0a5xo9vxQQ0aYBVhcF6tqRqTnpVshk1aRcFK0TazCjIrrHsTgblIBBpCQSsCSesQR/GKsVlzkoKBWGmXJkDdTekiphjBaJ2s0le4gIMk1UXr0vx5AJcW4wwcI9XRw5sMQgPL28YjcOpKyoYzYGvCNmIYWIL0GBtXYZHoiQTVGxy7wG3iRqSw/l291PbuVH6dbNzFnK7dKuX2NK7IaBaK4R3yLGY5uOtq2ZxBF6Rx4ieZqoTE8VD1RuwzpmzqxwWllOu0wTNaAx2RJNJtQwkamHEY5X+HSJ8Uds3CG7FSmscM7jo6dZbViZHXE4YrKhcRVp5fHB4DutephjGWPOILnCiBCs8NgKta/4VNVQuQ8gXJMmXbMQwWTUc/cWv1EQjM3krH6keiFNCdLK/UOKepubuTtPnnn/LEFmSMqVzCLaqCLp3mLIyz1xS1lbNHBk3uMTcbb2STpn1Gfal31UIKeCXhpdQ2eX5luB7pxgzazOmSWjSQlMh8BwvOYJl3zVP2Z70vHGw7vcv7vl3v0LtbHbrFl1W5rViqZWlxKNY1R844xbklcp4k0Ak2/U88vSZwzZaKBuFwHQ/OZEThPmpTX32z8+futFZ4liGHIuc7tcSNH2dnovBaxTJTCZNmtwmUoNxGSdaFMUJonEIeK9X5C3q/7AFHVTd0BTG+0XLmCvjvzq+y90kRGWFn21s6zbCrGZKQVsMnzxg8esmg4xCk2vuoa2tqU8qL6ZGpAWRZU1dE1DVzu6VgVH3t8EnW3V0DQeW3mML/5smaL0toWmJCUT0MFpjXpGTSlyvT/y/PmObzx5wle+9gHvvP+MR093XO9GLYtKWfwwRayRsQYqZ5VPc9rRrixpmhiHyKEPBCuE6cZFJaWsvo7OUllLXVmcySrMKBD5OI3s93umYcT5GmOdcpCuj+SUsVisyTjrkSqTUlBYX2RpszykxPWhZ4VFvCrfMEqJMMWWaeGZSL5ZAMqG4Iyhslpa8EY7gIzDxJ6BIQjWOurK0VQVWKscJZHSxahYPZXrHmNCpghZaRS1qZhnzJQTfoS2rggZxpSRKEubN4DaeeWUurIopajZeyo8HHEEyUxZGKdMQrs4SDZMITMRCVIWGckcxiNZJlaVozGG021XaBDz4qEKb19p31qCw3U1Vcyk5Mje0XQ1ddtQta3yZrOi4N45EAgxM0xBM2dRn77DMLLb95hs8N6ybmsgE5PQMxD6iWC0F7pJhmru34osrTPnjNbMZcZULIqyBgR4VdJXlSPGyBAn0pQ49CNvDwa5dHzXs4Hf9n/593j4fX+Ck//hf4/8ItH//X/I9PnPk3d77ctdtXqP0qQ/c7n1JQQuLgKCpXNJCeL1/r4SSRmDBpQ18EpQ+UoM+PL7bv3/HFjefiIlMLfK2i8Fkcuq/fKbP06QN7/uVlD4IfDxNpIKN1zIb/V9Xv2cpVSv/5qmwV1cYNZrqk9+Cnt+jn/rTex2S2497z99ws/9wt/ha1/6Oe6cOR7c8dSbhnazpVufUTdrLNoRS5Ejo5Y0JJ1TomhTU3lWq4qQakUDjS9dZAJiMjEbghhcRhtSlMRwzjoFIRktKd/0AVcKjCvlvZQUpBBjcA5abwgVBF0OSFGDi1k9rIm4BoDZiA4Rg+49Zm7KAMMw8vzqamkXqPFJLibZGkiGlNSfL2uTAbIGGSJCMuCXgEFRVVccQ2ZBhSlBz9w5DQzW3Nr8KRCc6PlJNgzTRLR7zMGpK8Tasm4Sxm2wqxUPR8PplGhTpB4Gmjxw4gJ3bc9DEzitRuqowhtbW5qmxlaaMGfJxDTh4xGOB5ha2J0z7XXPjmaiWlVUYvGuYrKeIUUmmUj1mmlltWe7aDKgGzSQLWJqQnXK87rh4cmau+2KNj0n7l9w9fwpYwwkigCmzK2UBZtz6TZniGhDYlV42+JLSUksuHWtZxqBKUCx8nBz1PaBqVAU1OZRz9NZU8YAS8C1TJ+ctY9dKYlbM+8LUvxTC30iFf4xBV1dSs03Y2s+PwVsirgwm6X4oX76KmCTrKbxx6c7Lq8OvL9ZcX7nCed3Njy4e8adO+estxtW2y2bdkXdrqiqGutEl8G5oUjWNtSK7mqL04Lr6DkhWOdvkrUUl7kNChZJTqVa9/EeHzugdLfphjZjrcxSqoVrp2KUiKA3LaPBUUaKefjMp8slIJwJt6p+liykIKRUbAGM8iDm0m7MiagVOe0cIwbrhc0Y6CowTkgZnvcTeTwyJEMoWeys2AXBuFsTFoObS/jW0FSWxleLP1blLbVxtE1F1XpyIfa2VaUG2VXpsuM9VeWoncdVDpvVFiaSeL478ujFNV999zlPnx0JQW5c6OeADcAKFi3BrrqKu3fW3L13wvZsS5LE4XrHsR+RZzv2BLDCuM8LAVm1AULlPV3b4DzUJTCeYuIw9pqVINqmME0cj4GxH5nGUAjyhRskxdMrR7W9KlLtJLDrR+UyGd1K5olzgxjdyr7mdaKUBJwRaqudcZzANEzsMAzHRFOPdF2L26zIVrmoKWWsRa0Vip2PsZaYtLw1I1hY5eX4qsIH9dtsakdbMlMzRYaCJjtr1ZnAepqq0p63OasnnVFOr5mtKpKKYFKmcGK0FDyhargxiSZMIkyxx2M4czUrDGEKNI0Kj2olSGpA7AUvFXXXEJwhjAFBW4pRxqF3lslI6bhhsSj5PEsm5Ugq2ecUAschsNsPtN5xse24s+lwVrjuR9IU2R0Gng09z/qB837EHh1ihewtS39wUX6QBrCxLEbF81JhW7KFyQiTQJ4iY05cT4nHo+ELTwJ//TryPRct33/1N/nNP/tfcPe3/k7Wf+gHWf/Av0b46nscf+KniG+/qybldQfSQJwUjUxzOfxWSbzMwQJb6GDKWZFIzA1fcfaztHITbL4aeL0aPL76mtuPDwWaH4o2v3Uw91FBpfmIv32z//9Wz83Hun28V5FQOyvVLcZ7/Jtv0Py+76X+zd+JX1lMvkauPsCGgenxz/H2Lzzmb/3c5/i733iCW1f85je3vFZtabsNm9Upm+0FXbfF2RqRrPSXjM6RpF6OEo3yk0Vw1lLXjm5VEw0ERqKtEJMRq2IMW9bcySaiaMUlFVVxzFI8fXOhyWhwZ41utrMJc8waFVoD3hlWNao0HrWFdsqKGubSsi+kWZAhBUUrnsK2qIxT4nA8stvvkVIVmPmaWZnkWo0Rfc4AJqlvck6yjNVsdL5oTl1Qfzu3C5wDHrMM77lD2lzyXsqu5bPUu9MSQ+Dy+pIwjQxp4NwlNpXHdo6uyXRTQxNPQUY13E8TJ6ZnmyfaQiGwgFgh2Ij4DGcNsW6phlMkHMnR4FBRbLe21JuGk9ywEUcdILmEpMA+Bp6HzGDWPA0j+7FCpOUmE9KSrkkGEc9gHNP6nKrbct9v8asVL0g8O+w5xEQMWuGTZeOgBAszklw0D6LBkC3rwZxrOje3MLYLSJkL0KJVw4RZ+IC6jpNR718zxzBG+4NbitBGNDmwgjWKbBnJeGvK8bU4rJKLmeqQF8T71TaJy3Jm5vOWpZSPsaQ8t0sUtFuOYRgzH4x7nlzuqd62nJ2seO3eOQ8e3uHs4oyL0y2rzZrVesWqW9N2K3xV6z4uuai1ZdmfRcpn2Ju4TZkvBdKyemn0OpZq7L8IUY6eUMZKoirkaS1FlJu/wLpFF10EBcZr8JbmC2m0HKlO9paUNYAyBSoGnehJMj6bZXwucvmSMYgp9jRlAcoaU+gQE0Mosn1ESbBYHXhZIEdZ4GwNxAQ3I59WsC4wG+rnYqQ+V7xE9PONEWwxAjcKmS6Lm3VqeeOckoOjZA7HzK5XRTGaSNyUWgw0lcF7g7M6WR7cW/OJN+7y+mv32Z5vOISRx48d/sWO3SGSjGOKPXYw2jO2ZBmK2la0VUvVaZALmen6gLcObx37lBnHRBhG+uPIcL2n3x2IeCJ6n8IUSSGTQlp6iuvdUeW4G8HaitlE1VAEOFkWBeVsyQpqo2AshTTesqornLWEEDlk8C6TxVJ5IU4jgwQEo1xQX1FVXsuzXg3Xc0yl3696dPlSrpA4LZYR3jmaWpiSZUoWiemmf3elQhRnLU508cjeq6I6RMjKu4qo9Qkor1SrGzPHNy+lFKXOGa7yyJfzE3IeeWPYcO9sw6bxrHOlQWMpf6Wcij2GIdpMnyJThiBlYpfVJ2ZVOKfSgSRME3N/4JQzU0j0Q2JKsGor1puVJhNWGELGGUswiafTyDcOO7pdg2ktbVdRFb5sTto33jkNCnJO2q0pxPJ5WQVlOTHFxDHp38YYeX44cnXZ86xPfHkQfu3dnn/8vOaPPxa+74Of4TM//w84u3+f9e/5I5z/W/8641efcfzRn9DAsmqg6sA3EAPEUSH3pQxRFp95AgoaPJauWGR7K6i0aB1LbuC+j0L1vlXg9nHRv2/1vm/33m8XRH6z98xBZKnCL4Hk7XOYy/QGMBb/5pusf+BP0XzyPvbpr2B+5f8B148hO0ZWfONJ4Md+5W1+8qtPeT8bqq7mk3WNqxy1bzltN3Tdhq49wTnlxaUYiSEgIRbnhURMUW9P8Ro2KM2mbTwxo0KxDKnOSzUnlwRmdMKYBWOc9qbPQsiGGA3JWKJMpGxIWf0DLVpmTGKKuTQ4J2olh6FFy8cmKAc/JcW1k5TeOyJ4p/uKdULTaSUpxcRxmNjtD4TiE5nLfnKj3raFZqKJly2JdEqpKL0LH7sk5XN1W6TwwTFLQwzmVryluidZK2WJmxaUs1uIcWUzSkLOI0eJZImMCBdeWHf3kVWDXa9J4R5TPxHDSgPINLJGaEMiZ08W1TFINSCyYsLDqiZ39zFHkGPDlGpc5zntINrANk604wF7CGQCkioSDS9S4nLa8XQ64TiXynLWC17aDavQKJGTYYgrOhxnzrDtLGf3HJvzK16MEy8urzgcr8lRg/LZFmpBJUWKJzV4W2hPpiiYZ49pq/uypILAJSHlovYuqmZXqATz4mCd3p8Z/EKUljXTicRqbGCtggzKP5v3W27Zv90qKRuY3WEW+lf5d+FySr6haZc32fK+TF5QWISC4FpCn+n7HU9fHPnae0+5e+eENx7c4ex8zfnZKduTDevtCZvtlrZZaaKSBLFh0YrYooWYe5nrUlEWFq9oe2UsUnQX8/j9uI+PHVBq8KalRdAWb7ZAynNTcWMMLhtSOd25hp9nPydzgwY7q/0yKaxLzUJNCeQKIlIyDLcI5opSSck1GgxKCSKdLZYzFpsnJBaVoQb/zIKQGRG8fTNtuXGaVRjNaPIcICt/dB7k825xu281MvtmzVmqKVyKiJTvOUVDlDnb0uN5A3Vl2K4bzs/WdKuGcRyxwBv3zvn0mw957eF91mdrXhx2hDAShoF1p8TcYbLs3AFJukB6Z7Rk3zqaylDVBmMhjBNxGojjSOhHxuNIP0T6/YHrqx1X1z0xW4KBIKKWOjESoirH58HtyoI8jkLlAsblBcUzpYxqjWPRXZfJlkvg7xy0tWfV1tRO78cxgE+RVa1E+xgjh4NOAF9XnGw7XBFfOa+2OTmpJVOYVEjknC4YVjK+lJRdZfHZ45LgvVB5oUqCtaLHKypwMweUUoRAQT87TLmUfLX0koXSvrD4PkZVqc5TTdBuUseUiH1Cnr7QcoMxmE3LabTk7NgfRvog9FOgz4lDDCQi2UwY74hpLHGCzgFV5ctis6IG+SWRColxCgzjBMbQVJ6urbHeklLUnzhzRjPPpwPPw4qT1NDUNX5VYypLzIkwjmRryTGQx4E4ZVKIxBgI48QwTUwxqvAhR8YY2B0Dl9cjz44TV2Nm1Kokv96PvP2NiR97Yvkjr7f84eeP+cwH/yGnP/f/Y/u9f4rz/8mf5fB3f4Hjj/04kg2mbrUU7mvIE4RQuJYzil+Cy5RvAijrUIser/MpxxIBRJa7Ym7m2vI7r/z/LaRveek/S4D46uu+VZD46vncenys2HKmM90WGM3HLTohEIyraL7397P5k38c9+7P4H7qPyAPE7k5JfpP8LVnPT/581/kb331bd6ZIFYdTe1pip98bQ1dpd2kqqpSXlZWBC/FiTSN5BB007UOrM4drNPxU7hYlbU0taVNNUPKmBRUSFlX1POWYA22JEjKnQSfhICWOhGHkMgxFY/WuHCSc0b9i6uioHWGFq/+r0UVjGgSN+f8rmZBR1Vvo/tBSpndYeTyek9GqOpiBwO33DRmJwFTAhztMKa4RcEVvSlVmzkHKu+ZESqja9AMTsxarhmxAg02LNr2zzsV8ri5HbAV8pSQNJJ5QTKZROLk4g59swazIprXuJ48YTD4KLTOUJkKyQ0pRYZpwIQaWff0lZArS/KGuLmgtg3HnEmNZe1HXB057wPNPlC7FXndQFcTbWIvlqdDR4oNlhaFc8s4FPUi0TnsiJNl/yQxhYbu/IRNJZyua3xsaNuBbVvx6EXieDiSRwUyNGyQJQSaL2rK4J3VdsDOYq3TIGzmAJoCIqEl8Dn4d07jFFvAqEwq3pFK/bHWFnN0Fj/lpXIjMm9pSzA7o9Rz8w4Nzoo4RgoNUG9o+bsCYVkU+RTJZFt4oEIB52bOJUVEVsCaQv2y1hBC4up65PrqMe+984KTbcfpace9+6fcf3CX+/fusGk7qqrSxLJytF2j7h7WKz80o5VaEga3lLVn4HAW2N5GWT/O4+P38nYWG7XNnDOWIIlZ5XZDcyoQbp4nnrwUuTur4WMuGcQcyFlULZ5DxDpTMjtZLvIMFZssaAtG7YIyeyUl0fJ6EFNsdUrgOrd7moUWRRBqykWyt25eST5veP+iC4J1RqN8fUrbvOb5+DM6YsoCpYFdksLlSZCMEMWUThFGO4SWhOB07bh//5SHD+5w5845WTKXV5cYSbx175w37p9z77UL6lVNsoGmgXVnuXNSM+WK41jxYjdwiBFXeeqq0bKw1y4vzqgCepom+mPP7sUV15fXxJAYhpG+HxiOg/IvjVc7gzARcyDFiRgSIeWC+rIEVjlBiALEAogoT/PGhBad5AVVk0I+96ji3JdNJKasiKjXfuApRQ59ryr1yrGt7HKd53JIAsYpcDj2TFNUpNcrd1BEkT9fGbwHGwzWOJxRE29fgSnE9Nr5YnKvnELBEI0Qs1MupSSMA4mmmNiWLyGQTSF5lyQjz2MUw5QVDXlqIuvDyOk60FUVY9AF4frY8/wwMQZR/pALrM4OrNcBv1pTt3lB8ufFj9I7diqUBGsdGEWHhnEipqRNBjYNVVVptwXroBpp2o5umnBe+Y+rVc3ZxZb12YZu1eKMqmHHaVQD+5iQOBEn5ThP48QUIkOI2gYTijBCgMSUJ4YMxzgnZMKElsZ/+Zj58leO/Hjr+ROvr/m+mPmOn/wrnH7xp9l+/1/Abv877P/yX0aOL8A3mKoBV37yBGG6CSyzztglipKsr8mJlwiTMkdWhZw8R2Dz0CwowfL4iP81t3//ZuimeeVvH/dx++DfLgC9/Z5FYFTOfwmsy49RTpZpOjY/+IM0v+Uz2J/7f8HlV4mrewzuhF9/54of+aUv8ONf/ApfHwOp0gYPVQ5qa4L6kNbe0dW1thjFKhUmZ8iJGEbtLWxFdw9xJUAz5BhAIhJK6VJEk2Zv9FiTB0rbVdFxXHmjiVrZsEMSnGjZUaxZ/OIp/LgkoiXIEjukxNItrHZ6Sl1VIQSk2KfYbBZf2dkm01kVXqgeUAhj5OrFFcf9QS208HiTmVJEhYUF/HC6v80VsZgTRqzalZYgxNhcbL6KeMeYJTFVnh+6eNjCAVRi3jJMpSBVxmhlwlrdg0wJWq01pBjpD3uct1ybmkdGiJvIplmxqTZcj4HsjXJaTV+cTOrSvjYRn+7YXwXCastwuua4Muy84U61xp9XWBGqnKnMgJuO5MYzNGfs11sOrWNaRwKeSS6o2OBydWuwSkHyLMY6RBJEeHaIvLtPfHLIrFeW9fkJr20qtuHIru7YbNZcX1+SdiPXV5fs+yOjRGZL4Fw8ElMJ0l3xO9XAfgaAtCpjvMOWeMGVKqIWEe2i9leUuvSqzqLCP5R3a90cDJdADhbOaxK1HbIlSchowpBzXnJfa3RPzDkvPF5JLJlMmsulRseWlZkyOINw6aZTlLmNfmqJPOUM2bLvE4fhmkfPLvnaOx9wcf4+Dx/c5e75KauuwVWGzcma8/NTzk7PWHUdIlXRIghifMnbZ4pJiXWSVtBu80o/zuNjB5RzhJxzWjaOeTMVUQsHI5pPzJmhseiFmYOvPHMiNStQ8rFQlcQmO0UHnS0on2gAZ0WW6N2XiafcGEHNPQsEXkyhVGlc4GVKdoJGvdlCZZSjaef+nOV8bSllzyimphizDY7BF4QVz3KxZwGoRTda68rAKEGoKtdM4cXBybpm1bYYK9w7bfjkJx/yiTcfcn5xRj+NPHpUYdPEg9M19+6ccXa6wVSGTd+wbRrYtKzrjklqro4Vx2nH2/0165WajYPQTyOHfoQxE1PgarfnybMXPHv2jLGfyMYTYi6CGoNxtthpFL6rFKU6msXMVkWaq+nF0mBflJOBIndqw8TSkmrm4BlkmXwWyCkQS0nZlQkWs7AbBu3haytOvMWX8lDKmWmKmGSYYmbX9xyHEUmZxls8M6lald8GU1T6UcsuhVrgZJ7QmmzUldfN0EJIhmScogfWYr2W8L3X7FGs8sZighBEA2HRc5cspdvCzaYVIlz2gef7I6vKcQw1Odc8uRx4kgdCFIwJrLrMnbOB+mLCbwyrlaOq7S1rK4OJghjtba4UIy3vikAIuuFsVy3rTU297li1W2IWom/x9UjVdZgw8HrX8Mbdc+7ePWVzssZ7R5q0Y9M0JnJISOkuNE0TeYxqwB+Lz2rJvJqmwlthigZjE1MKhAWLKbFfyc6HDL/SJ9756jX/6HnNH/nkhj9YH3nzb/z73P3T/ws2P/zDHP7KXyWPIxJHhciqElw2lQp3woiag86PssiJgISbwHEuOxiH2IbkFLW2adTnrWVRsb0ayM3Bnbn1p49CKr9dAGhefaN55Tgl4/y4geQ8aeasV+BlNSZIdmActmnY/NAP0XzqLv4X/1OyWK6r7+KXfvkdfvIXf56/885jvjpMBAzOe2yeE2pF70m6sXVNQ1V7xDmN5UPEOKdzOhkwXv1SrdHrGQMebQtqQiTn8QZRMYbaGWqnFQHnMsYomiRG74kp3oRi1bM1zqie5DIXK03+Q6LUBzAGvDfMeLRko2i1VXu3ugQKowGikJ3RJD8XhJKyt6DBW4yB/nhAyNSVdkpJBenUypLawWWjEbyggLmzICZjilgpFSqUNYaYEurjm4vlHJCl2LPNiJYU4OMmIbvpZa33P+bZ0gZILO4UiHC83pEmiGHieDKy3Z5wslqpqtd5bCWcWrWEmWav1pw1aI6R/fMXvJ0G+mh53Td01ZaTVYWrLEwjsT8whICp14izHE5O2XvH0AimN1SxY2O6wjQpbi6SMNYrZxYLorygvct8PTlenzxn1YbtLrJtHKvJsF3XnKwqhhPHtO95sbY8ferZjQOHcSCkjNJ0NVDPshgNLZSBWWToCk0KDCnoGjZjnTFq3CFlD5tJGNbcgEtQmmRIKT3fmr9LIDt/dpnfUpBR61yxEiqokzXL35KwMHasVaAii9rIAWTUsH9ueiEl6L0ByEzhbc6tnGcLrWJHlxzvjdc8enJNW3mapma1rrh//5wHr13w+mv3uHtxwXazpao81uuPs+pqozFdXmI7TXbch5bBb/X42AElvJxdaU9VvUFLuUhuUCpjpJhUF29EWAjVBl2HZrMGR8Y7S84J7yCVfaFygjczL5HF9mMhxRowaNAS44xk6XnNyGjlzQ3s7HQpUHRUg1uZOVdodrmU3MtolXQTNCoXQ8/BeacLQUGtTCwBaymXYx2m8Amdh83acnHecud0g/ctpMRb98/4xJv3uf/aOScXJ+z2B+K4x02O023LZtPRtS14YdV2bNoVfhuw1hNyxfro2R8uuXq8p6093gnGqV/hbr9HUDujw7GnHyeiqAovFcGFrzyuqnCSIJriaDXzgywiqSgP9T5l9BrOAlxTugzMi2XWFhSLkIoydfVfyjgpiYRxBcHQThjjFHRBsIZ145ZkI6dEmIL2BRbDYZrYHXumMOG9pfb1ggzMlAQBELVp0EC5jDMDYLHeqS2SU+QkxVRQMJ343jnmpmuNcWACKczJlPIe5/KHqlM1io6gqsCiEZlSYj+MXB0rriZPko5HV0eeG4Xz1o2i5Gdb4c4bwEp3OEU5ZmV7RnIgFvVdzgnnHDkNalcVE01dcXLScefilPO7D+i6ExKO9TBwuDpyst5Qh557teHevS2bkxXtutPrm4UwDYzTxHQciIPyJmOMSBTCpGPAOov3pvCDHTmrEW5MKiaKi2v3zUN/06DuvKu598aWw+mGXzoasutovvQTXPyBP4vZ/pscf/THSO++qwKdcESmA1iPsRXLKo/c+v9b/5o8D8DyXIIUtT/ugka+siy+erpy619DSTxKRCe3/rAc5lYpZAlmzbzy33rt/Jy9SVRThDAsQrcPndf875ztlHJrISkz19IE7b2dfUv1yU+y/WN/mPrBGfLr/4B3X1T89C/+Bn/zn36BX3v6jMEKxyREozZqxlqy1Wvni4+ks+oQgXFYX1E1HcZY5UiKGj2Pk1oq6WakCZgVh5gRQyxJ3XzNtApVWasofSVMIZGaiphz8TrNRThRuIRKilSkXCjeuujMsgbjzGLt5cRQFzCidqYYP2v5G2Mw3pLReWML7UqKB2YGFaaVvs4pJ4apJ4Nyq41yJ0WEHDXpyikvdnJSkMIsxXZGSnmy3GJTgn8NJvXJWQkuJZG3WVGvOSiaVd7LmC28TEUqiyBIMUqMAZfR7mvxihQG4jDRDwd23ZbTbkvXthjnWXkN3gcD1qhzgakMPmoQ+PhwyR7hor0D6y0mgm2FdmOU0hA8QRziYXSOy2g5jA4mz1pqhmgIYU74Cn0Iva5GHBhfRDGG963wtml5yzsuxiPmSWDlPd05nN6pyaZh7BNn2zucrk7ZDQPPrq+4Ohx50R8IIS3B5Eynm+kSWXRtnCeRKbZWzCBJVi4rBVBSsc2tulqpOs03RIrQagGP5korN6Ib9Ued52i5bVZ1ILrtWBYOYtmTYJ7G6gEpqDVRLgBNufU3qDgsdD0VSCqQhikIthTaoJGyNDj2IbA7Rsyl4dnlkavrI0MfOO4Hzk43rNcrum5F03VUjc4bW4JKU07AoII4ublC3/bx8QNKQ1GZqmIqiTCGpOBBibo9FEGGiltypT2U67K2zv5awk1WOWMaNoNHqD1kT5nYSpx2BYFcIP8suLLJ5Vn1bRUSx2g/ZS2bUtR33PIgnoMcLbNii3DE6msWyHdZGUCKFD+JEmQ1s5UCGevkTwuXppif5oyroFtZTk8tp9uaO6cr1puVZtcGzu86Vl2mqhJRBqZ4wNqRqkpYDzlHcorKWxVFP9u6pbKeJBUhC+tuRd04vNNxRhIkR2LQgCPEyDCOZAO+8kgQcoTaOoITbScWChclTqXNmS663tkC2wNGsKIZVTZKLzBJbhBaw2JBo5P3ZuhIFqJA5SjfQ2g8dLVTLmCK2uXHGJqq9H3NGlv0w4S3mUxmCsLVceQwqJN/1ziSS5hWB2gIkTGopU6MkKJa3Kj4SjdnJ2pB4Z2W6m6r8hSdVUS7cl7VqxJIqcJYYZzLH45ZgFgsJXReSNbfs7eLD+QQhOtjZBcnchYOx0RsRf1WnaGxkbYWNhuD31wwsWKcKlJWD1djIBkYhok4FN824SW/sbNtx8P7F7z2+luc3nlA224gW4YpctldEVYdzbBnawPVymOtu7FKMpGUE/0wsN8dORwGhnHSVo/ZqFee1XJ5V1e0tQrpQoRhEsaYGZNZylIvP/QibZ3h93zqjO/77Z/gzXsdm23LxdkZab0mvPsPaX7b76D+Hf820+d/g+Ef/RPil78E/UF5kbFfjlOy1ltBW3mu8HeXpEA0/dF+2996Tbs5blmUloBuriVbRepmXzlRo+Ubrqa5gUnm81wObm+CwkWhkfS9Cy/0w5dM4bNSmy2VHRU7zKhHhbgK1lv8pz7F6l/6Xqrv+izHd7/MF37kr/Bf/uPP8VO//jW+fHXNMRcOmDFY79QObbbfskoHOelqLrqaVeN1zE4QokUCSG0Y04QNQpwSY0yIq9W5wHg1N88sva9TzIV3nYvwRDdgB7TWkuoaRP17GRNCUNQ/K6VCzOzlWyzIKHN0DsydxdgKkx2V0X3AOIe3Rtd9UyhZKTMmXTeSKG0jpax2oZaCRgjJ6L2QrLw9AcTfEnE4hy3K4zjb4zmj11C0tDk3RLm98YoRxM5VuDlX1b/PZtsxaTBgCx9QjCyATBYpLQldSeBFgxwjy5yUVJLfnBnGgRAj7TAwro/kkz1izmncCeuNlm83qxprElWMmBBKXCL4KuOcJw4T0U6YusUkaJtEWDkMZ0iCvj/w/DBxmTumtiaOFqLneR+YyvXUAZwwUrrGUFTMOCRnemt5L8EHA9zNcC81dGcW1+6hPVL5I317pPV3qFzLenKsz/ZcXT4lPH+f/dWRNAYNDLlZe9VsXuekwSrNyRott5tZ2KLzSESwVQk8y5xaCpLGECVj8pxsZGyxFyy1cB1T2RQuL1g7JxoalM0BLIKKjEqSkwtYU9QXxROUYt1UBMKUExEKYppwFm0MU4TG1szBrO6tYiAWq7SiK9IxWPaP/T7xla9+wOXzHR/cPeX84oSL8zUX56ecnJ6wOd3SdSu61bossRpIq42guZWUf/vHxxflzIGWaKk5psw4qahiLK2uiq4IiwaDJgm5MlivRO9Zyj6vvTkJkgzBCabWC2bEUHvlWVZWf0B9xLQx/I13UxRZLl6JCXVAyQ1Kqr5MNy8yUlBWM3tm6d9t4YNmozxQ6x1V4xX6N4pa5gKbz10dMKofGMfM0CdS2ScEFUx1LWzPHadnFadnLXc2NeuNx1ZKBchuYJIdQ7Sk/Y5h3BPyFVkMQWqmeGQ4KBIQ+oE0RfY79Rusu60aWodEUzulCaRMGiPTYYDSBUacwWOojSFgyEV57pIleVcWaKtK39JNwhRfLklJA0yB2W7AFqQypZI4iKIAWmpSXocp3gNztpNLdpdEUevKq+VS4y1JItOk/XmTZBovmuFKUi+4YIjeMkyBfT/yYj/STxNt5XGmRjpffNRgipFhCkwhlZK+/oQsqtQu6KMztthWaVekuVNCSKksUCx0i9p5ko0ka8iFg5tntCHflAbM0turWBNVFKORQiUoY61ysK0867qidcrbGceKECq82WDtFu/WIBbvBWvUiH4YRsZjUGTZFZqHybRdzesPHvLpT36W+w8fsj09x/kaydAPI95Z9liitfTjnuspUvWB7MYi6ovkJExT5jAErq6P9GNgipFYOjZ572kqy0krjEE3yH6MXE2R52PiaswfAbZZ1GFQeHjS8ru+8yG/87d8ltdfO6PbbGiaBt+usL5ieP/nGfoIdx7S/hs/wOoa4q98ifClL5Le/jr58hKZuZQyR2Az8jcHcfNmfqscPD8/I4zzfndrzXjllG+em5EAozjbUuSa0Ubqm0D0dtQ6/y63Ec2sKnaJLOVqc+vz5kB2PrYrwWwux5ktgFqPPTvHf+pTVL/5u/Df9Z0El/n6r/0yP/O//4/5+5/7JX75/cc8mwKDyBKrzj1GqpI1z13BbOWojXCxaXnQNWxXNckkXlztefL8irNNe8Nnj0k7M4klewjWMxnBZcFOI3malHeddbMNIZUNHuUKJp3X3hgq66icMFqtA2QRhhDo+5FU7quR4m9cwIpZhDMHmWIM1qv1mDfqv2ssysk0hip7XEzkPDEsZUGW5gVI4cc7o3xN0OAPrYqItaWK4bQBg3NMKSpVJQc8KkgSjCYbBahQxbCiRJqQwmzUvfT9zuW5wvtjKTHKjccihVaZ8qxxUcFH+eNcOVKHokJHyMLxuCemnpyOpBA4vxfYdGc0tWXbGbzxVDnhUoVMFW4aqf3IauNoB0vthNZOWBOYzKDOXtmRkkHcmjRqIJlwjL2jHy3Xo5DEvDy1cgaTEatOMIoMqsH9B2PmSVDhFS24M0PbRFy1w9pfxdkVqXlAsz1nPLT4XHPnwZqpbXgi73C9vyLFjMlz/2xF2iXrfZ6moHuc1aDKOo8LUS+q98RSaTKLq0xhzZSAL89Ke6NI89yrXae1BscWbeUromV9Nfmfg0lw1i/WgKmIj2cKh2SlbMz2fDN6ahXRuslPS0Qzo9OIQZxVoTJzSVxIpV+eQW2BZs/LOe+WbElYHj/tefGip1s/4e75GXfubLhzd8PdexfcubjgdHNC03T4utXzJFG5l8DXb/v4+AEloguDVT6XPqMihAjqio+iP87MTLbCYSpcgZmvQC4rcUm8QxC8K6XCYh7qSzDplptZ8j/RIBSjlztm7apgM9SNwTqN861VywhXELQseiy4yQLnfq5aoVbEs6obunVF3dUloNRzwglu7rhWLnJKwn4vXD4fsYz0e+VqhNLUo6otq86x2TScnrZUnUV8r9+xwNy7IRKe7zEGIplJJirr6eOO3eEpOUeMrbm+7rm83PH+B48hWk7OM5eHA2OfaGqLyxrsTiEwhlD6dSu52KD8QWe1Qb21GjjHxaYjUnyyl049tlANFElWo+B5/5wdXMTMgx2MZ7EMQubeooXXjGY5zqs35Kau6VpP4wxjUWnbpGWmxvtSfisquAzTGNgPA5f7gef7XikFORPrYu1TxCL9EOjHyJSydnMqwWQuCIFyZO0SXxRyWkFy1YJn3tttEQ9JVr5TyumW9UiZEYJyt4RCujcLDcI7w7arOd12dN5R9Q5rDCddg7Swqrwi3UbYv1hz+aQhuw00vpTLNDC3pStDKNxRMDSNJ5pM3dS8fnbGZz/1WV5/+ElOLy6omhWmCG1EHDFk+ubA9U64uup5lEaurhwPTyfaFpDEMJae8+NIiBPDONFPkf0UmJJunLUznDSDKvS9Y4yJS4kcDAzp1py6yY8RdA5/+sGa7/r0Pd546wEX9+/gmw6wVLZaEOTc77h+7ytcfe4n2KcVzd3fxJ1/7fdyd/v9tPsejkckRcKv/wYyqr1QfPtt/f8UyddXSAhFNTdHjoaZWlE4NvMIvrWwvXLec3l5gSxyea99GX005ua9cvP0MugLT/zDUeyth0Gh/arC3b1H9R3fiWk6Ftg/Z+zFOf7N18EY7GoN5xuGwwu+8eUv8k/+0v+Jv/u5z/FL7zzish8YkiIxyjLUddkCFaYIFSrt+mJnTz/Dums4rWvudTWbtub5OBBDpD8cGY5H1pXFIko7iUkD0gIohKE0TUiBHEY1eM6ivpIYpR7lTJZAzgZTxJreGapsqetK6RJT0kpFzIxRSjAwt5KbBXvFIizfIH25YDrOKHXJOodzRmk0VoEPb1HqTobCzsKgjgw2GqIVqHRDD1m7Qllriz+gnkNT1br2pazzIZeOObUaRmvF0yyJxEvDjxlpmpXKOjMk3/xNRZE3c+bmGAUlKYHkwtMz5ZVGHUScAaOdNjBiiCEyHCas7LAu03qDO2lpV+Bqg1nV9KPHU2G6mpUF12l7zc5ZWhcxcsTKDmM9Y70m5RrTdDSuYZ0M+1SRgmUcPYcxasJTiEIslchbF0J0XgiGKUR6EaJJZBuLiCVTmxGRD8C+jsmBECoudw2HdMqqgTunFXeaFfvrJ+wv9xz2PddTr3ZmBe5J2RSaQ6HcGdQkXclIN7oHTV2KDqOAS0U/YYSXzLzn4F3IhaIFlOYjMqPbJe9TrYaFufpa7nuWGVVMi0J8DibndUiKUmgW3tz0Ji9JFoVDOufGtwG1mWYzR0tzIiP6N+3eA2PMTNeJw+4ZHzx6wcnpivsPrnn9tSP3756y3a6o25am8sV2r8b7+qPXr494fHwfSh3val0C5HzDuZtfYDGlpCjFSLysu6KQTxawmaXbwfzGlLSENos+jJQkvfw/RfWqjvMFZnZaektJPc4kC21tF8f6XIivxWOW2WqFciPs3HbUaUeeuq1pmoqmrahrR9V5fOupK7CVUa2A164MptKofwwJUw2E6EijYxoSkg1u9t1EP8d7j/Vqcm7EIElLK9loSbpPoyKkVgd75dSQfdcfmULNMBx58mzH+08ueffpJTU1k/UqihBHZT2S0YmVE/2o/ELnLL5kKxaLdQ7jzNIfe5wCISXGGFXdbIrizWjZXNdI9e6SgqbMmVCekUvDMlkysNh1FBTToKiAEyXKb5uKs3XDelXhSiknWKAWau9ZN46u9moBVSyXxhA5DhP7fuI4TBijiOu8b4eYiFnop8gUEmPMDDEzJjUAnzmjbi7JZ+3W4TOkqNlokEQQ5W35YjUhQlEQphtkvXw3tZeaA2wp+ZFubt4aNo3jwemaN87WrCrPCRXmEk6bmlCngrwrne7Zo4aeFXdSx/puTbvK+KrCWVUTamVGCDFTldZRxsGdi3vcf+0Bb7z5FudnD2g6DSazyWTJ6rlpPcYZkiRGgadXA48eDzzattw9bVg57Yg0HAdCTGXZhCkLV1NgN0WOUe2PLrqas9hxsm713lhDXXu2K8tuTMuGvaQextDWlu9884w333jA9t596rMLnKnIkpAQmMJInEb6sacfJvpDz6MP3udX/94/5IvvX9OHDb/rt383v/tTd/ktn32DO7/rk7Qeqs0ppv0+5bu4FdN7l4y/8DnGz/0C+fJFCQgXKEp/Zv/Kec1aYIcZSby5vy8FgQYgcWsAMOfGL6OU5lY6f/sYt48/9x8XTNNS/7bfRvcH/yDV63ex/fuY8bKISyotr02J6YMvcn11zTceP+fnf/nz/NyX3uYLj694ErVTUwzClGcBoFIkFBMpIKhVZCPK7IxRKCB4rNFWrY3TtdPlTEvipDY0OWJjQnIihxFXOZzXJg5jjKSUirjRUDddKctHQIgWbII0ZdIkKvjKc6KqIkVn1R9isfERLZ2njK6VAMZoGT2pXVUq/G5nbFl7EtkmKnHUmNKVp5SMU2aaAsOQGKcbIDiLuiyFAMGA+IKAStTVzUBK2p9F+0RTOroJeZoW/tsUAs47beULpbKj6NW84SN6Lq4g0jO4omuFXfajGSxJMreynT2KTaGZlcqZnVfgMuxmJgVaonXWY0SbJexL1SWHQJ62nJ5vqHB0VcskNck7UuO5UzWY2rDKrZbgZcRzIB3eZ3KW3j8k5g0VDS4l7qzXrG3NlEYYEh7RDcIoUjivhUbkFk3DQBJMipxZw2lrqAj4BhojuBSQacCgY99MwvWLxG98NXN0J7x2OnCx7jhr7nDnwhO2gd1w5Hp/xfPrKy6PR44xMESKSFKrXvM4M6iYJs+VAyk8+VkgZmerwEJjKAkJJX6ZxUApqdONJpp6T2LS4DKGWJB4pyrvQosRoxZvc05qYEZayhhZni338+VYZUaxjVEB22zqPvtul5hY32/sTSCJLShpaSOcWZxxokAcEsdhx+OnO77ylUdc3Dnh4WsXnJy0rNuWtqnYrDrW6w0f9/HPUPKe7XkoHk83PDmdTHpdvFOeWu2gdFUswtoiilgW8eLlVJCeeVqY8vcU9eysZalqqdO9kmU1INX3FxAMKWq6uUSQ0kxSne9XGTBl7FtnqFaetquoSzDZNJaqsfja4RtHVZUMtBJcZai8Etdz6Y3ZVpbKa1lTB6WWdsXoXhdCYpoChwM0jS5gxmRSabbobIVDbWy8d1RVhdiKHD37MXM5XHN1NfL8+sDl8YixDe1qQ900UBtCjviDV9QBbWEWk7ZCy9mCt0sXAWO1P3cqTewFSseZqOKSguLdThRcQXycqPApFxWaWgipuhnU7sO4gtBZUe4IkEU5r5UzbJqK8/Wai+2Gk61BolIg2pKpdVWtdiXeUfm5l6p2qhljYipZlhNtS6kmwpkwBvqY1dpmSoxJfR6nGAg5aYmj7O+mLPBZIjFriSCkTIjK4QIQa3HOq2AlaevDELWvrNoRwNzeaubwaFyiY6Wt4HzV8cbFKZ+6e05thW7UzLSrHa4ySBGSpOx4sY+8ezhyZY686SYuKqFuvBrdO6eJgXelD71uctvNhnv33uLua69xcecBbbst5xNLgCukFBnHnuHQa8AYItl59uJ48sEVp88NF62ltYKRiIRJEzSB/ZTYTYlnx5FDSmWDUx5es2ro6oqV0XZv58eR/VEIfWYq83heNLbe8fr9cy7u3aE9OcW3a8gZM0XG0DP0e/r9Nf31nvEwMh4n+nHiGBPP9kfefnTFF77+Hn+5Nby+rfl933mXf/V3vMWnH55ztm5pGagqT/Pwu2n/1O8lfv8fYfz8l+h/6ieJb38DUkKMxVjP0qt0WQ/Kqeab8/1wW0duL0Af/hvMlamP+OO8yZbfjeKFyTiqT77F9r/9A9QPt9gPfgH3638HhqDlqQj73cCjF5d8+YPnfOHt53z+6XPe7TPPRuVETwUtS0nISTlZ07y+FmDLFbGAcaWMbm/OJWVBjLbx3A+OfV1R5UBdOV4/P+HOesVJ09BaqwIa76hXHXWzwjctCbsk9+TSqjNHRAKpMqTkSSEQhonRjEAgTYlsLJXVhHAIk87HnElRrbrIpWuamUFiDSZTFmKMxKwiCF9oK4IwxYlkLOIEYiY4RTX7cWLXB45jZsxznxvRTa/sZ+MkSMuCFFlYeP6piCKcmR0nFPnNOZamBloet07pMN56wBYOeOHFmRsPQh1ixeC6oE853/jcgibf1t64VkhJAm4QWxbQxczKQCMYcSzt7st6Ok2ajKcwIHFkOI5smhVxA7XTFplSGUUlbUvnVrgopDBSxYQZB5JzHM2RZCucm9hUK1xVsaocTjw2GLYzH3GeEKJeuULCSlnrjIAkWgJvrGrurQxbZ+lWYDhixogNHniA2AcQtfHCFC0fvD/iriyndyyrM8O2qjDGMaxqjusN12cXvLt7wbvPX8BxUr55jlqeFrfwV7Nk9aKcYzjRa2y9BhkaDBYevbjl77PwRrIaoKu/2vJVNQEQpYqlqC0L06yvMMJsOyTceGDO40HpvDfla6VBSAmTbtkFFaGmLceYle4syOe8DCnY4QtPWo0KctGr6Jo0OxPMvcVTgutd4HL/lLfffcJmVbHqWlZtzenJipPTFX/io5a4j3h87ICy6gwE3ZzT3M5nGUKmfBFFC71VJbZ1RaNUghfNXEzJzgp6afXOpGQWK4UZ5dUFq5idF77aXIqNoh0VkpTg0syXSxZEE8zNwBEWJ33jNFBytcdWDaau8Y3DekcqBMkpBEzUyN46LaV7rwu3cZo1TlNkf5U47jSQ0RZ8YKyU9pCOoRd2u4EpJqpKoITASZRH4ch4HJvO0jaepq7IHvappx96+l7YXR05jhPGW7Yn51xcbFl1NftxZBh7HEoGXrUdm/VaW56lQMoJJ1ruso4SUNa4PGmbSlEhDMYsFjKlpoQ1vugbEmrzUaB7ezNwKQkGBQySUBbJYnhePMJxBrrGcrHteHh+yv07G07XFcMw4RAOvZaaVlWtpsfe4b0uBDFzQ4af0QwslTHURoMmIxVhSkxjYkqJIWSmoLZIklUgo2XATF3K3CkHYsxkY5iK+bd+H8OUBCuJnISYIuM0kVIuikJTENwZAJMlZjBG9+2TVc3rZ1s+9eAub9w9xxOR3Q6Dco0Ft5Q8Jmu5HhJXMdLsRl6PcenMsVRqRXv9VhXUlef09JQHD97g9OIh5xf3Wa22ZQ4VVX6e0ZmRmCYy0LRrVhsI1lEBU0w8Pfa82PW0NtOaSGMSVoT9FHneB54eRnYhKa/YGUIShiSMMdGKsO4ahhA5WTds94n9kJdS64xmnzSOOydbuvUGX3UYW5PTgRgGpuOBcX9Nv7+iH/YqLJgCEjMmgivUCUE38g+mwC9dBtwz4clpx+vrO5zUlqq/pvrir9L9xq9y9uBN2u/63Zx9z/+M/u/+HMcf+RHy1TXIiApiZhUjJQnVgEtjyVdRSXjp5i4BZVmgXkIkKUjnHDje+rspohxjMXXD5g/9q6z+4Pfirz+P+dLnmMbM5Wh58WLk6dPnfPXZc772oudrlwPf2EeejZkYPDlHhpzJOUBJHEKS4mKgyOQiJTIUDqDHeF8U0srXEQVoyGhv+KfDiDOW85Xj03fPeOPeKXfOzmjrGgmBOE242uF9RbvaUDWKUGe1UECmgCQVwsWYCJUlWfW1NTaDDUwSlUJi9OS8GCpj8WK0XbpotKZjX8WcU57XyxJwiWNuyZhyKi4fej8SmZFQHDbUTeQwTuwHYYhqkp4p1pnMWqhCYUGT/5h17XYhqthoNpz2HouWUFdNTc6WMagVVYiZMEU9sJt9M82yyRsMYhIJgzGa+M/G2BqIzLGJLIikKV9YwaTZfUSDCN3PTEHSZBFlzEM0RlVTm3IsSYnd1UgaA+EI41qIvVBXAyfbNbVbaTVk5pmKQWxNDpYUNwgdMThMnojsqZsN5txhTIX1AeugsR4zE9oKv0jM7OkoCyJvEV538FvWlrfazFlnwQWyO5DGHnO0WL8l+y05ebrGc353TcoZ9+LA4ekLZHvF9s4Rf9ExrCqa2mLaE8ZVR6hOmd5/n8wBG0blwmJKomLItqD0xS/aGRVf2WI1ZWzhzxYE2pkCiBg1l59BKRVfWmZqioYMSXmmUoLTklAU9ZCOgyyEObgpASSYkkTMwc9NcjN7WGrMY0tFXkf8bNwuBoxz6rDCbUQTBd7K65bAdjbIv0UYnzm+CsI5Lq8Tl9cHrDtQ2eesVv8CSt6f/tQdpq8+ZT9ZbCrckayO7/lWcGnLwNaJNbMotBSunlQ3kGFmhpRvStRCWZeFIv5BjW/LlJs5BWrNIiWKL2l5OYsssmTjkrWE7J0idKpShmTUWDdbw5QFCUnVfKEU/QqqehumNuV8o0RVC2Zh7GG3CwyTdk3QMaEClnHMuKNBbMSPxay3DBaHomC1q2l8DcEzeo+RhDfClBK7MXB1dWS/71lVFa89uMt6s2JzslH+YYhqQGstjavYrju2mzXdds2UJnKKmCw0TY33jiYEjkNPP1WsstDHK9zo8MYiZkYyvSr0c+kUkeYlvWTcooptjFoGybyJCYthrM16r5xXG51V5bjYNHzi7gmv3z/h/p0NbeVoa21/2FYjWQRvFY1zhe+QkpRuNXMmaPHWUhlbyhi6CY0pMISo/JAkDJMKerQ70tx/t9hVlbJSEjXHVwBWfUNzSiTRiHnuSjNOk1pVzAEks22JLgjzfLXW0Ho46xyfuXvKd71+l0++dpe7F6ek6UhoJ2BSx4IybhMG2pr1umOYAtEX7mYJ2cMUSsluIIWAxbDqVjx47XXuP3iD7el9um6DK1zE0lkdQQqqk8uCAtZ7Vqs12bqyaQm9NRz3wu7Y41OgNRlrhT4kng6RqyExCYoWJ0WtkyhqOweujfesqpq26mnVE16vc5mRtbesuhVV3eJ8BZKQGAjjgeG4Y9rvGA89w3GkH0fGMGrpPWni4IwhFUW0tRCmSL8b2F8fedbUPM0GCREinJ50tB88YfP8Rzg5/1nO/6U/wsl3/Nvs/uO/THz77VL2uOGraQBpyWZWcWbMbdn1nN3O6OKsOl6CSW42h+UNhpeDzVuvrRtW/80/jvvtn+HF13+c5+9+ifefHHjn2Z7Hlzsu9yNjCAwp0ePZiyF5S5UTxkRCnOhyJIvy1UVzfPqypFq5tf4WsR3ekQun0DntiGGMZ+7jm42wE0OVM2fthvt3zrh394yzizNsGAnDiEkJkww2l37WRQFtijGrpEyOEZMiNidsSuQYkWkiTxNxGlWIkNQA3IhgsiAzZ6TMQVuCpBkcqC0qMrBaKvXGaoHAWlIKGlwlRZSctSrScFmNykNiGIUhaPevuYvvbAE637qZJpAFpqBtHGfgwRoVrCUS1lcYDL5yzBaDxiRSDkxRcDaTjXoBaGtgWCgWog3v0kI/mlsz2mU4SQkWDDpE570QKZzOBTAxzGDOzE+HgoaVoAWZ96oZOjf0QyDnnbZT7Q9sVhVZdtR0NJxiVo4QMg2ezlWIOeGYhX5q6Y8jLYZ4HDg0B567U/pVwzA6XhwSY6LYnOi6IkW0YIwFp6IVY+HUwm+tPJ+uEm9dGM7XHrvrIeyw4yWmH7F1rQnlFKi8oang7KSljgb/biI9ekJ+5204sbRv3YM377E73+LWhrW0bPtTqp0hBqugSkpFK6CqeFBRi2pCNGl31pX5khHnlJKWslYdyxidlfk3hBHlQ840amO9BtJ5pjsoGJUL53c235lpEEuZu+wdCiDcCj7nEndZg0QnSXEVuZnjijoUNnEZ1Ckl7R1fxtfcH33hXJrSBESSfo5VkoZBirOIJp1JtFQ/XE983MfHDii/4ze9wTEmjtMLhpSY+RIz6mIpwg+j3EnndTwnudlABSEbtQFaQjRR/uRCcbJl0hcoWaIs7bFmE1FFGW54BhrV36z9yscRqspSe6jUz4gMDEERqJxhnKKee4jFkzIzG5HOgIQt6V/KuXRkMORYMoCiDh5HIYaCpjIHxcr9GUdFxmxVzkGgdjXeaOlSik1SnBK2KNWmKRLTRB8i+0PPOETMZg7XLZKyBnOFBG+NUgwab7Emk1OkdlaDcafcH2ssq66jqmvaGDCVx1SGJNe6+A4R7y3WVVpeEjBTUIQiZFKUGws8QymLs3CoKBuczpf5+qlP3Flb8ck7J3zmtTu8drHlZN3hvdOFNGd8MbS3pYyVs3aFCTEt6KFyOR21c1ROO2yAloymJPRjYEyGPgSmqKUjVUUWA9l5vGQNUFO5dtkqBzMVAdDMlZSC8oUQiXGeiKaY0FpyULRSRTiGyho2LbxxdsJvfv11vvOt13nw4DU2245wrDjWB4wJOAuDlG4IlaO7e0FyWspuO4N12rlg6EcO+yP76wOH/YEYE03bcffB65zffcj29C6r1QneVirAYSZ/6zVJRbGfs+B9TdsK3ieM04Uvj4HoR6zTgO0wBa5jxFnoY+ByGAgihRp1o1KlzNycMtkmREqbuyJEapImmbFkvJURurbCV3VZ/AJp7AnHA+F4YDgcGfqJcQyMQ1BD9Rh1IUdg7kBS0NppGBj21xyeV7i8125BO0U4dncvuHh4F9YbpumSRz/3l3j9s3+YB3/+3yJ9/jeYvvRlpt/4KunFcyRMzIrruXxYRtmCRJa4k4WPuVS65oT1JnA0izWQWVShkgWaDroOVh2nf/KPEe+3/PoX/3Pef/8d3nn/iieXA1f9qKXJnBhDJOfiTpAUAWls1LntG0yVsVkR+P0xYrEcezXfrm+dc5aC9hWaiwq8Zk5g+d05soUhGQ5Jv3trDOumwppMnAbG/kiOCR8j4h3ZNYDDVjUyRfIwwRQQCRpsSlJuzDiRC7qZxqiWPDmDceotHwUjGcmxtErUgNIbkJL4O3PT1STOW0ZB/YyxSyOGhdPN7JurorQpCEPU985I9yK6MxqAz/rQLDBG8IWLWtcaNKs3buHXQWkA46m9wVinyu0pq3m2gNiMc9USEKZUOp6IbviZIjiSkhAbs+Qkc7tfLX3qcj/byZji1zsj//OcVPii9LsuozPPVmgpadBh9Hsfp5F+6hm7lt1YsZWRNYlODCF6vF1jfaNrbO4YnecQEuMhUaWEmQRS5vo68vaUeNLD29eZt/sEOEzS+6knWcAiSkvcTvhMU/PdG8ebF4aTbaKyCTOO+NRjxxf4aVJARSyyz0Qz4qKjNp4sE1hH1VZ0q4qWkeG990juGrt+E+kaVmcVn7Cvc9xvCOOO4XBAFJHiMA7sDxPjJEr3SEmrqd4SkxS7w0I1sE4dOjJKmVnCjNkX1CwxiLWuJCp5KUbMXsuzZaFaGsmybMzwF2VPUZApz6lBGRO2lNtnMCcXtxW9y864wo/Uz/JlPBhmWoYw6xlM1vsx96yfvVlnqo7JidnDeUlyyEuSc7tw8+0eHzugfOutT/Di+sDT5wNXx8TBRDyZBi0/O1BLCK9t74qYHsQslj+mMreUUeqNxmKULQsBefZ8FICoC+XMl9GI2mCSrvoz4jeTU3MG69V6qKscXVPhfaUTKkwMTCQyQ1AXfBNvyvGmBEU5m4VuNLf1sma2RVCPRimfrZurzliFwvX8tSeo1UBTBJtmbowhOQ1cnYUpG8I0glNCugjU3tNUFauq0XJLNTDGwOXxwOawZnB65/rDkTCM5BQVOeoPxK7COUuShDeaUXtrkBgw4umahqZZ0azWrDYbnF2T5DHxxSUEtAVUFCSFJYjI6eVgEhSFXMrdZZXLyz6r4iVrYOUt97eez9494c2zju2qpnbqd1c5B42nsoYUUgnWhSAqogkpMcRAiGroOrcv89aoYj8nYlBEcorCmLS3dYia4ShaoyIk8s0il5P26Y5Gg5UQM1MR9sxBU5RSNo6pBJpaDkp53p1mJaomUZsW7mwcn7pzxnfev8+bDx9wfu8+Vdswek9uLzHsIWUikUDG+Jqzk1Oy8xjJXJytmIIa0WcRnj9/wfX1jmEIrNoTLu495N6DNzg9v0e3PsXhyTmV/saplOSFFEsP76gdNZqqIsWoJfAwEUPQhabssEYsQcqGloQp5IUjOiPPAtrebM7agDFMTEHVv3PW3NZFcJc1Mbh/1nFyusJ55ZbFaWLqDwy7PcPuSF+EVuMYiFNclMHOg6vAV0abJ2SUl5si03DNcLC0psZKpA4D2AhicW6ttkk2ke2Bd37tr7F77xe584nvYfVbv4cz80eRR1eEr3yD+Ogx4d13SS8ukXECSskrlk1xzlQpi6rVkrHxHms9pqmx2w04S/3WG7iuwziLf/MNWDXknGC7oc89eMOTZ1/n3V/5WXaHay6ve55e9nzw7JpdPxbrrKjWPMyOel4FbcbifKZziVXlaX1NHg1XzRFD5moy7Oakp9wdbc85l95nlTFQvOVs6Y6RjZCxHGLmeugJ08jU9wxkxv2eOI0YMeQcMDtwtqayFa61Co9GnVsqaklaXixofw6RaYoMs41XVr/ShFYeKLxLCto9G4TrhjlvZGojnZK2f5z9Xuf1WIUSeeGC2XLfsoKnik6V63IjBOVDOcHN+FbRUJgUkVdHDzUH0gRN+YzWGIxYal+R0kRENDG1mRoHcztGY5UmUwIUm8rcKnudKWKJ2wjljBgtgUfZ42wpHc8VFVOUTLcrahp8miWZzJJwviiWjSGL5cVxog5CD2xNxdbUTPFA3TqkyUjytEnIxjNkR+MybneFl4hzAxuBy6Hna4fEB2PmmA1q1mwwxQwcB5KTuq93wt17LZ++6Hhwt+ZsnfHTRDoeaVxGxowJE9I/h2HA5wNNXxH3A1e7zHVM2BcjawvVuoaLO5gV1Dbg8geY4avY07eoT85o64ZuvYHoyceOcBipKsdR4Ol+4tnzHYfrK1IumpCkeoaYC2/fqIoaW3ThxeJWaX6lkx8az2TRTXBuu6grhY5RW7h2M09xwdCWYENffROw6R9VtDXXdwyzfM4WafccNKpRvF0SEpZYRAdTNjd+mEo7U+RSu0yZEqDakiCZEpOhQei8z8u8nswKxG//+NgB5d2797lz8R6bzWO8Vd6AsxARaqNQfGXLkLY6KSgXV7PmkpGV0otizdrj0+WbyTA/ctayoxGrljWegvOW+zHXdkTZG6nAwcZbnEd97rwD7/BNTYxo2c5lwjSSSlqYZyJuuuERxNmMtARNtpRuvTUYp3+bzbKNSFFUmlIRK1ljQToXEUfh4BmMCj1KtxQpHmKrrqZddXSrlpNVy6ZdEZNw2Q9cV1dcXb1gCgOHQ4+RSNMa+v2B4XgkToEjI8+ePuXY97jKQ0o0rqL7/9P2n02SLVt6Jva42ioiMrOyxFFX9O3bAgM0QAEQNBppxg/8A/zT/DYkbYYzBBoNoLuvPqIqVcQWrhY/LN+RdZuN6QPQuM3S6pyqFJGxfbuv9a5XHAaGSYPhQz/ijCeMHd4FSi5Mw8jNzZFlW7mco04ubEFyvSKwKkB67ez1jBXC65pjLzMszfLJwRg892PHT9+94au7E8cuQCnEdW3ZtBXXxjamIamZ0pBBYVuaCr0IsQgxp4YSqLdZLoaaMnPKrDGzVY2xqrWJtwSqKJnaND5Wqoo251LwbSSRolrXlM9iRXPWrGw9aHitLaoqzxsWReeF42B4e+j44mbgw6njOHkO08Q4HTEhUNYF63zrZJXvVQW2ZeMmFw7HO2recNayxpVPD49cLmceni+UmDkeTrz/8ms+fPlT7t99zXQ44l2vCR5trLN72EnZraMi67ZpJndMlBipuTSHAWHLmfO68TIvbMuKQ4UrNWe8c4Sq/pxg2ganm1kpRe17IhirmcKl5Oszjmg4gWQVTv3ZT97y9u6kRZ4IeZvZLme2l4XlsrDMmyKUa4Qk9BI4OuEQNkZnCE497mLSTfkQDF9Nnq8Plrsbr/e5BjCO0lWMfCSmJ+Xj+o04P/P0998x/+b/iQsD/eEdw/3POP3FXzD97/8lg/0/YhdLnRe9x0sk/f1vroc4n3foxmBCIPzsG0wfMONAHWkcjw3jF6RGLj/8moff/s88ff8DDz888fjpO2QcGG9OiOl4Xiu//v6RX/3wwPNciLGQWxTqltT5QcVeM1k08WccAsPUc+w9bw4dLneM1vISL4xPhXPZUT2dqhgjja/I1V7H7IVQ6wprE7iIbar+deV8WTg/PRPqSIwbMSelpfgA1jYEXzt3244aNWxuI0B55Tymkik5k3JmS8ojrDWrf2PJIKUZxe88MeWC1/17V+W7C0LnAqU2G7CqIo/d3seaStoTbwoIlZQV2dyFpP/wEhTJ2f1Td/SyINDq5AL0vbRkndLGaLuwU8WYmELK8sp7EyHGSAhOTcl5FTDKFT1V5Mg5dxUl2Wucn1wLj70QUR1CK3BbfOgrumnZsUpjWs/sWmoKhd2WyCBq0t4gslwzl1knXetm6e46bpOHlHDjhAsdKQtrrngrTIeBm5qRAINNTA4OrnJwhpeiP1c+Q7qoqmp2fubw1vHVm57De6h3FpkMsnbkshIPls52ODymWHCBsmVczNTnyPOnxPci3OdK31m600A4TsgA9IKbHzB1w/uNrStMvWUYPXE29MOIOw44E5jpMNNGMj22GtL8wrItZBGNljWVlFVbYa1mgRuj54gDjHHtzFeqhzTfp+uZgWlrX/Ctybn6UrbCcaca7c0C+/R232PaWrTm2hbtG89ne7A2HvvnGGu5ImlG+Z7aYlS42jVq/XGdJMru1S3XfaClfCj4gu65+5PxX5Pn/eNtg5wjOIsVFSzsFbFtJGJnwBnRxJa969vRxgLGGULQXFbtPjM5KV/JGFo3tfMJUL/LrBw4vMZcYV8Ll1z3zlFeO01vEa8FZm5IWQVSUp7LnIWtauGJf7UF2H0ThSYEcq8dcHAG2xnGvuMw9oSho+9849AIcd14epk5P2+tO1Ok0hkYxp5uCPhgKNI4M21hqfJ7ZVsSUx+4ORy5fXPD3Zsbpt4xdD3VGtzLhUxiWZ7JaWHdZkLoscEDKqKQkkkUnp+eiOsGjV842sDlUQU51nvG6cjx7T23b9/QTSM2VVxV2wdvPZhMJZNyIq4bKW5KCHeoSmzf5FrXJKIjKt+mfQ5N0+mscBgcb8aJb+4mvrw9chp6jIEtJi4xKyXB7vGVusZKG/XFmNi2zLIV5pyJwBwTMWWsGII4ildu1Joy55iJtVIwKsQRIAvFtOKtasxbsXJNVDKmcZqasGb3CFO/OyHVQi06ZpXS1sg+L2tdXd8ZTqPlzWh5d+p5cxzxTkg1sS0L6XLGBE98ubBtG51AP1smowpZL/D0//4dN18Kd4eem2jxtiPnSH5YOM0Raywf+iMf8pGbs+coG+5TRuQJKbkhz1rtmlqxKWHXFbtu+HVDtk3Hu1vEpUhIiT5mxothiJ7b0jFnDY20IoipFMkkG4iu072qjT+mKtyXiZvFMRS147hJgaEOTMZw74RSdPxiA5w6x7863HN6EOqvXlj9zPrywPzpB9aHJ/JlhnPEzYljMvR1YKyOmAp9MhhfCf7MSxGSETpj+CZZvpo97y6GkxF858AMFIQUPcE4urTh/TNpfYHHhIknnPd4tyH+96y/+o7U/U88+gkXBrrxDYpqFcLxA+HmvoGwBsFhXGDnRdeysP7H/8y6fAJTcSbSDSOhCzhTyNuFy8MD508PrPOG2YQbTuTUwawFmVky7ofK/aXjlPfmpSpSXjyxJGJDfUvRQ6FPcC+WL7qJ9zLhDdz5DpxH/Mr7DHvKVTA7QumwNqj7hnHYqilJpqFJSEP6m/H015tlfMi4Q8LXHlcCXdUjIkSH2xyyJMrlhWg1YKEmtQ8qNTXnCuVT1i3i1opfCmGr1CsNBbDgqyFkS9g8YRWm1RPzLhQQdnGKEcNWhaV2zNlzSfFqG+etVws6W4nSGsBa1amgGJ5EeON12vAPTjMt6AycsE2opwXlhlK3EKHP7TwSLVa913NMqlCMsuIMMISAIbUwBai1kJKO44NvR6ygrw39wc45Rb0aXaIimLr/PLlyLdnPWLMrcksTLunkoKmvrtOyz37FKzcvtyKhXr1yNU1OSmWbN0x6Ya0Gn4SxnngpC8ZnDfVwjmEKdJ2ly4XNdgwdfGk9depZbOT31lKdTjfkM6Q0uI1f3qzcnYSvDoZwuuOxDwySmV1m6mCbKvZ0S7I/I9AT6oyYja0ahqPn3eA5jhPvF8sX33t6s+LqSqiOeF6xtSLZsdTCczlTTGHsHCVFnARO44BzA6M/UYdEKh63JVKojFvHuiXWFDV6tgo+O41ortAH3yao+r45r4k1UtHQD6O83brXIlcuZGtYGodWi8edg9lU3KUVoQjYfeZjrghm235a82QbulxfC9P2FaWUPzamaAW9gpa1cZL3wnMvSs31c/RLBEq9IuCvP38X8PzD5+e/fP3ognKTzLpG1vNMrcqdUvP5RmCGK7n4c4PO/Y3ZEwqc8RRRnklvoJjKJpWSlFi8F8bWojezcUOMVVGL+o9pJy7eIKK+f94ahrGn79VDKqM8PMmWap1aw0glG0PtdCNyih0jIri877HaZRjRTt9ZTwiBcRg4jiPHw8RxHBn6HkF4ennG+UeMPHF5XsEocnt7nHhzf8PpZmI69NfxhYha9Tw8v+DCMy9y4TQO3J0OvL0/cXt3ZBp6RTKlMtXEeO7oukCNCyKRYCeNUBt60pKQ3JG3hTVvpJIRDFkMnQ30zuGDJTjPeU08ns88PTzQH0aSsZy3xHpZ9f2vsG6RbdvIOYJIG10bVf9JvRLOjTbbjbupRaUWXEKwhlM/cdv3fHEceHsc6IIh5kRM0hDBcs347XrlU+ZSVZQQI+dlY4mFeYtsVYhFbX2ccSSjgidxhctWWHJuSIRpyvCKLTRxVssibgikMeZVsLU/3ApVU0sml2b43uK9FUl1ahvUtsoQYOrgdoK7yfF2HLibOqagHevHx0emaaJkjQg7v7wwPz7wjQj/5g/3e3+KYKjfgf3bGedXrH25mt7qeE+FAM4/YN1z477tuwANIf7sYRdpXOI9jWoniMsV4dg/XbmWAal3VLm9fv1n30y/9lWGiilgXwz2rA/7/lJqVbFOHT77FkYznA//90D9f/0NH+1/1lFM1TxyX4WTCEcREC1ymu4VwfG+vuGv6i2JSunkGkzjVoP/W4P71W6tsm887X5aowR5RkRurnYZ1/HQ/uuZijFn4Az8oHsOUPhbitlfx/62tPHT3kwJTHBtDvfGtaIo8SCVXgTEt0Oj3TBSQ++Ef11HRIb9/Li+4/thtDc5rxuwwSWDe3Y6CRGQOpDKPbkTaviHOzbXMah+871roxEHr59y/dOu0D9buv+w4XziejB99jkCzHy2CK/Iyucjd2FA6CscRBDx/PHKAm0/9b2trVHd/39/zfvYTdp3V4So/ZxqMIk/uvb1LaAq9ok/en//4Xuz/+ExvNRM60MpqLVQKqhIs2qRWKmEhvBYsdcizxirySg2a6NqGuBRC7VaPahNQyBln5w0DnotyuOXfVTNdQy+v/Q9EMQ1D9F69VPleqjUWjFOf5aYplhwehbbhoDVWjFWQyO09hFyTdT1TDGJZypdEXIYSKOj6yzTNCJhoFjLi/MwnfBDx7Bljm7AN//i3S9auVC66n8yZf4P7xaOh41wqPyuvOU/bT1fG8svrOeLvuf9uxsu0WOKYKthyM9YM1PsAXMwBEkcbivvS+F+cJzOQvj4e/xlI+WM3PaInHhM8H1eeDIvHEpgNB2TDSAO8YHQBQ7Fcd9vdKeB7I9Ev7L6wPPq+Wic8oVrxGLw1pGzClQMFmsbMt1qPmNDiyd2GFOooueuNJWmsXofNHfcvj7LosW2WEUud8soaSItK6+3FdQT+bpW5VUcJJS2rlTx/fleocWk0YnBHt7SOGvWOupna1fP7dcJ5L4udU+riLzuFT/m+tEF5cePD3z/8YE1lSa13w8q5RNcFdoNySlJrn5eqrZFN7KiySPOOB2hmKKkZ6coqO+cpuwk5RRlEYxTLqDzXtU+VJxkihGG4MkYPI4pDAyhI9dC3CJLLogTxBR873DG6wjLouale+FrLQbbJuiVK7+hatHZO8/QDxyngZth4DT0hK5nS4Xz2uO7XrOw23sSnOPN6cjX7++5f3vDzc2Eca5l1FbmdcMHT6mV7bzivGHoPYehZxw7jFMOZo/lth+Yx57LNHCOKyD44Bj6nuId67KRNi26U2m5zElYo0EkXo3SR2/pQvO6/PhI6Dz4jmId5wRL0siqkgs5pWtXVveCS5dZI71rkemdcOgMJ2cIVtgqbKI52Z21HPuOU9fTO0uupY30VIFZSyaZ9r63RIOUlAO5JeVdLVvVrOiUWXMm5owzmd4EsnGUbNTAPKnXYJGiNkN177AMJUYVMDXU2Bh75bdYJ+zm1/uBtZvslypItUhtnFmzF5PCcTTcj4b7Y8fN0HPqOsbg8NbivacC3338gYeHB9ISeT5feDqf+Z+/ViGYxpd4ZtdzEQfO8MufvOenX79lnHpS2nh5fGZ+uRCmE+9/+kvuP/yU8XCLMZZa9T6n1OyMihqZp5hZl4VlWUlJ76X6eCaWdVWBQOv65nXl4dMnPj28sG6RXAq1FI0jk4KIJg7FnIh5QwocPNyOgduxo/PqzQeGp8vGty8b3z/DvOlqGZzh//yvvub/+n/5N/z8T3/OOIzEyzPPP3zP88dPzJeZbd1IKRNq5iBwNIGDGUnF8rwmfpMSf/3ywm8fvietmdNw4O4wcds7Tn3k0GWGwWF7XcfRF6Z3PXf3AW8LD59+x/qYGc2Jqb8lhA7XKbpvncfg21hKrcBc6PD9RBhPOD8oh3ZNzOcz8eVCTDMpbY3L6xhPt3TjAWMKaXkkPp9JW2VLEQ1+cBTjqMZjjCeVypoLL5fIVioZSxRDykIRS0yFmJM+A9vGtkVEDH3fcbo58ub2xP3pqKPWNfLx29/zH3/1O371Q+T7pYkZDXiawb7bc7tV3W2tjvFeU3929bAmidwN8L/76oZ/+6dv+OnP3nO4OWJdpx6RuaoIELU6k+azI0kpD6VxlDVIIBNTUgBi2YgxtehT28SRllQLqVTmWLjkymWNmvCVK4i6OATvqEXYSmXOiad15bJlFcAYFcXYFrlIqdhUiGshLcKnCg/yag/0j107hR+B72qk9ciKLtHGuLkBlLIjVFnZWtAETjrpssbR946co+oFKuQixBSpzuM/m5gZUBTSmlfhRWuMpNZr8S9VbW327XfnSipK6RXFbDxK28adOw8ziyKZZS88rLTxpzbWiH6torKVuq7k+gmbCkuYWFJH1zuGdaUeb5DxQD901A6SK4y9o0uFLAZMpxxmazGikrzeW/7Z0fKL08b9TSSHwn/aEv+35zPvjyOfxPCvxTEEj5l6JFdqqnQlYW3C9wPGC/dTpQ5nBpPxOHos8vtPpO1MPdxj7z4Q+4lZHL9bNwZX+eAd5J6VkWfpmNzA1PUcS8J4y8E5ijHI2HHxjtF6ahgo1lNzJOVFp2Wi6n1Ms/ZRMJgGnzXqnroeCJoMVYxonVNr49fvZ2ZzVWjjcpGdQVEbyKH8dC30GvDRRt27Gfk/HD1La2Bfk6N2gLFxaq8NtGFv+LT4rNcYz1LqdUK81y/KsSzUZtT//5eR93e//45PT89Up36NAtDg/899t/RV6v/ba0GpB3qJmdB4l1YczjdCqeWKGmmepcG5jBkNo7UMQ+DmMKKRcoZiDUMwmN5iOktWZix99dhqkSVhi6qppRfs4Og65VlhtSBWYjXNn7EFRVo1v5X2kItoXqitymOpoVBcZC2WvGlCheRIWhfWZbvePG88h2Hi9nTki/fvON2M2v0W9fH0F8/ztmEfnnSEIu1hb2H0nTF4p1ybMRhOh8DzYWA7D6qGTYkpeorRmLMk0jY1wybCnOF5EVVWGi3+x67QdakhR4ah8/jQY8PIUg1zqiw5E7cNiuCsLg01Ha4gGlcGquI1RugN3Am8sx29EaKHRwPRqY7fWYOxASkob1SqLmjFkvShzYUtGqyzbDGxbJF1S2yxMMfMOamVylYKMWd678nVUnHUrCk/MXPl0eaq0XM7sbkWITck3XdKtN4biX2UpfxWfW25qLtMKeZKUrYod7bv4G6y3E2O+8lzM4yMoacPHoPgnafve6ZpAut4nheeny88P5+Za6R4HZXhLEwd67Hn+y2zLJnl/EjaLO9OJ6oUHv3M4iPTyXHz1S3yzTvk5o02bKLctBxXclbhQ06ZZdm4nDNLl0hJMCYoIh4Ty6bryWIotfD8nPlhNXyKhtR7tijElCkxY6QgkthMJpNZ6kyuwq01vA8di6903tIF5RV99Bu/lpXfJGEr2vW+DZabPz1y/Ms3DD97pyO+Hy6subJshcVX4qjjSVMLWSqxoWwld9TuRI1Cv/RMn+D54yf84UDqes7BsKaVx3RhwtJ1I6bv2DqL/fqW7e2Rag1bVzh330F/JNy8wU1H3DRiOn1fnDisWHzwuK6jG46E8Y4wTFjn2NaNcl4wLy/kxyfiywvb5UxOmYLDfPMBDgc6J/gV6g8XZMnEtRDnymWFy1qoKJXBBo84iz1YOhcIxmElAIEqllAgbxv5fGF9gWUTsJ7h7pbu/o7udCA7R6qJ86eFv82P/E184e9j4bui6VqCcr6C1exuUw1WOnzoMNIQLDHtg+tY1eAY10S/wVfScx8iw0m9eslC2orGy0nFu9aQZ6jRkjdt4gQ9LLPo85Nn2Dph2TLNHEEP56oCuK1UYjHMi7B4w7ztRu16IDqnz+/LlnjMG482MrvElqMmwGSLC9rYdBi6NhJei/DrCt+J/BdKyderMVhUuPDZ3+9CHtcmVRoPbJo+oJJzm95YRbAEBUm877AmK/2nVHKFVDMqG9ACspWj7IIJUGTItMJSmr2Qc3vSiY4id0adakEqzvs2dVHeXS1FixzbisWWOmF2VI1XQLlikBbsUExDMlPi5fyJxS28bD1dGLg9HnF2BW85dRaLBQmMdmR0XrUIpY1QjaHiwVdOvfDGVj64wocu8yCFvhqwnlQdMRvWmIjlGbEzdXKsYWBzHdYfOXQnCgFbF11XJmJ5okqkfvgS5z3mwy9I/Q35aJBu4XfLH7gfJ96kI76cWKMnCfjRc3KB0IHrgzqwwGszYp06gwQhZatJTSVTIkiqOp1rSKDWl8pbtY2TKo0LJUa5+uLa3+26iZYiVJs7Tm0odBUt9j+/P6Y1ehiu9IE9m3s/jw2aiQ66Xq7JTE11drUoQnCNs7uvdmNeV8H1b68IZ2Nb7366NGT7n3yKXq8fXVD+7d/8LblUDjeBKXsOKVB8Jr4YJKEwt1PLjFgqA5bcbFmkiSZLbvFEVqO1EHvl/ezjJO+gisGHjm4aGY8j02mgc468JeKSVWU5BeypV8N1q8kPNWbqnLDWk3PGWEt/13G4PzKMo3a1rmJtxXnX0Amrlj7I1TS2VM13LqVgilC2pk7dNn44PxGXB+KWqLGSLpn5eWFdVcBgjWC8oFmf2u/mRlQvUlt0pSKQvlECbOOCOqsJEt4IzuoGZo1l6gJ98FhvmWPk4fGZkrSjPs8z27IQdrsb0Y5oy7A1D0lvtJaWaggBijWsVfAFkEzCEGMmbUkR1qDD1JIz7EVWu0/7QhVjGYG3JvClsYzBs1pDHyxnC31nmTpLsBUf1Di6FBqJXg+TXCoXMhmDIWn03pKY18QcM3OKXLaNNUX1Pmy+kbUGlpipCGsuLLleObCqclYHAWndvFS1HALUUB6D+/wBLKogl4KKs4paC+02WP0Ah9Fw6hy3Q8/NGDiOnqFlBwuKzOwJHLV5NsZcmbfEUqt6nSLNRFs09Sd0OHEsy5n/8e9/y69+9zveHAKdNWqf4Xv+9PheUxtwqrYVPWRLUfQxtyYgbYllWZjnC+u6UaoQgkbClV3eKnJ9VkrVDcV5j1iPc2qqv5mZlBNSUW5ZrTpqszvHhta5vs5/1KrfYNr96Rx8cz/w5z/9kuPNAe81NSVukbjEaxQmKOHf+gq+kr1GCRYDZuq4yZ6vFxjDiUe3cHe8haCK+DxniJXuzZHu/obaq1F+tHfEPCHeIzaTnEGOH3DvvmC8O9H1k6I7RaAI1lu6fmDojrjugB9GrPWUEnFiMavgOvCnDls9ec0kSWwCB+sJ1oFzODsSfEc3VgbrSMAFw9lqXKALmoKFsRQspVqS8Sx0XMQSq1CqZbKGARisYdtGTocDx9OBw3FkcBZXK/My8/zDD8R50b2j+f1idM0KRvdeqo43s47jnPMU29SorZDJVRHr3Q3hb75/5Oc3lq9OBw7hBjsYYinEdSEljTL1PfSDb0lVquyupcWTmsYhzFlFGg1xU0qnrpdUiiZztY8A+ix1PUV5VEohEOGSi6aNFUHEUmkm5aKvN1GhWAbrtfjLQoRXWyD+S/hkeyTan1o0yB99cjY76qvq75oNJQk2KBpbm+eQ980d0hqc7ej6gVoSXYUtZ5YtUnJuXqDhKoaiIcr6GOkeornkr+pfs9vLyWskI3anCrSCEdhtYfb/N4C5FqSv1/5vsPPGX39hEVUJx7oQcyT2KghzrqPvK2MQuiC4zlNny/PcU8ykpA4xep67jOkMh97zxsNAJRQhSOFkLV+7kUN1HC4bfPzEkD8x+kfqwcNpJE032O6eXDy2Hxi2xFyeyDkS4wO5N3S3X7CEA0/HL3keb1h8JZtnRhI9lqF0dKuhXjILG/2xY8yOUHXnd27ADJrq1JnM1BnuqoXJslZPkRERQ0xwfjizzVE59VKo1aC+CKoN2c/oHZiyLVmu1kIxDbwoFRHXhMq7pV6TdbYm4sqRVc6Djr5l94Xk9XPFvPJ4KZgWUSgCtVF1rLHqhNAsjCxW1ze7IEC/36vxFCAqZr3Shtp4vV4/4cddP7qgnBl4/8sPfH0X+KURvn+a+f3vn/nD3z3z/Lsz66czdc5afZd20Ah/lHR2Vd212YxGGenr9QEOp4FpGrXw9obDuwM39yeOtxNQmF/OzA8b4BneTAzvDoTRY61uoNuyMH88U+YZa4Rx6Ll794bjuwOHu2OLrRPEVxWu2E7HDKaoyWcFkqJmS4zkLVOXolYe1SBbJc6ZdUks80qaM2WpxKVSm8rSd6/WDQApCy7XK8+lCNeuMhijHIlacEYIVpEF30w5dRNRH87QBVzneXy+kOMzy5IAIaUFmzX+y1kLply7EEGnTN5D6Cxdr/nQ1rqr6bGA5lmjtIXgVDxT0Ti0XNREWYHklgBsdUx2sI53zvLBWO4IrMFymCyzt9jecRscN50Kb6jaWOR2yIlo8koqOlqVUklrZNkS57Vw2SLzFlmifuSirKtkjKpGa6IYYV4Tse5jCBpHzyDNg0+a8tjSBDjmlScS9oeYlrxRtejGWIIXJg/HzjBNjuPgOXWBY98zhaACNateeHE/sK0WkPJ0YUuJJSZeloWYtfg1RpHoLPC8bly+/8hiPJd55eEyc67C44tRJMBA3yW+yVCMCiz2grlKIWf1bNy2jWVZKFtiWWaWdSVuSVXsbtJxSyseSil/hFqELtANHTYVVYbWQLCOlJJuiKUwW6eZ6GVphaN6yer5YUi5UK2h1qKoDIbJGf78Z/d88eUdw3jCWEtJakeTU9IRaikadUahukyxlWQqrnPavJgzx+MbrPfYBOFkOY6FcJjIrhLjET+84fTuLW46EEvl6ftHPr4ohcWJ8PFjhXrEuhu64Z7x5h3H6Uab2zVSc8J3nnE6EToV6FjnKTkRlxfOzy9s86Yj/FG5WuINKUUGKbjOEBzYulLjBW8MRnpcyvSbga2oun8wuKEj9D1OnCZszZkcN0LveTNN4HUknrfIbCu3NSNDYOw6brqgFltSicuF/PjAm3XF9T1l7FmGjcdYedmbKoTdT7e01CfZJy+Yfeaqh0qjARmUB/vdnPi77xe+e3/hi7uFsWVDm2JI80aWTKkWFwy+69WoHIdJAhmkqI8mVdRPVDQG1lhFg0qLZN3dL6wIQ0O7rQnYDGXTSMg1FdZaKK0p38d/XehJsl0t3RxG+b254gQiKnj7sYO6fbfcNZqff500AC5XIbYYKOsFzV5oY8xa8d4jxuC9J3iHqa6pq9WuJcXU0geL7vk0sc1+H9otkfa7uiZY3A/z19hFub6u/XvoJEb3/dKqcR3DvhaMasau937HqIxVMZY0ZLu2n6uNdqamjW0+syCs/UT0BWdnvK3Ei+HTeUKsx5hEtQaxhtA5pjFw5wzvhwG3OfLZkIJgOktvhMkoLcq8LPCrv2Py3xN++pa+vGHhnvrmjjqMuCC8kZljivjzig1CkUD1gTSeuBwmnn3gIpk5Ou7Cl9zWwpsKxxJZ5kfom8H9VIlF43q3YBEzYEOH6zKHGqlrgg02eqrvKDhNQEqWml6oeSNFDZeoRR0LyIXilNfug7tyLA1gG1K8K+xrfWVT7+ez4ZUvb6/3piGDemy1xmO/j4pwl1yUXtDiIPf8bgvajIhp7BJp97IVstg2Bq9X1FH7emln2T5tru3MVmrEf0U9+eMLyrdf/Jz7n91x/PqEdIHTOdHfn3GHRw6nHzh/9x3zD99z+fSk3WqVK1q2L+JUwGX1ljOIwr8CzlmO9wfefbjHekeOCe8Np7dH3n79hpu3J4pkPn1biGvEVsfp0DPdjgy3ExZDlsK69ViEel6IL5HDoefu9sD9u1vG2yPVG7UB2GVRzZRUaDy9GDUvNmfylshzIr8ktnNkvWxcXma2SyKulXVJpFiQrMirAqyaKKJq6HpVrudCq6ybeIGKMbWNTHRTdUbzrr0VtUpp4/Gd6+K8xVhHLrBeNratqp2RyYwOqg4jsBZCgOPR4DU4nL5xNPu+azyqtklZ2w4atfAJfXcdnZA1Aq9m2ohbC0p9jVpUnrzh5OA+ON5ZR+o8frRsvUcOjtNhIPRqnaB+dBVvHeIcMRfmLWlBovU2JWWWbWOOmSVpfveSCjGjIycDwQkpZ5IV9X4rhZjrlYsk7IlFXoUeFYJVr8u904N2f9C0gFKz8tiqrsu+E4ZguOsN92PHsXNMg2fsPMF5vH3d+GMqrFnvrRWYiWyrsKWs6GrcWtcHxuhj/JILT8bxuCSicWxZY722IgQMXaN09P3I6XRL6Husd82wPLdCcm3F5ErcImmLmoKRMtsWdf30fUsdaqpRa6/o4G574bzXhKJ2MIbkSCkQYyKnhHeRIQzktF2nCBhzpb0Y77EUOmfwDpIYPpwc/90v3vPu/R39MIEUUpxJeSWXiFCotWihYxLGFqotFGPJOSmZffkDZX4g5UpXL3xxY+k6VSDXvoP+HjMdGG/f0Y8nlhh5SZ6//fV3PH5cGZxlXmbe3jSxTzVIstSsdA3l/Bm87wndhOkG8AEpQsmREjfSy5kcI3hFNjpvMaejHgDBQs2QZnJ8JJQzjorLBrM56gqHbmS4HSmHAXeY8KHHZMPysrCWH6BcGI8D09t7xts7LT5LocZEjQlZVJkvMRLPZ9LDA/HhhUNc+KrveKwjtttY+sKvTeSlvo6xSpUrR7BKxYuqrzW3uImJbEPfmvTaGoPxlmI7TBcIR09/e8BTkEsiiqNsCRPA9hY/dnhnsb3HLhtx2bQhE0Vlqtoj6OjWGBUnmEbtUSWCegb3gaEb6IyDpRBt5mPMzI1TvcZ0jZd0Vqcd4gKGrA0zDl8dvqgNWdNAv6Iv/8T1itXQvlIv1+q52opUi8FWwZa2jZgKpuK6QDVc1ddU8E6b91dxQ+OO54rxFVtK85FsvLxWzMrn80+re/qOUu18TYx59eyU10QU4DpGRUwTqL1yNNW5pF558Nc9IZer2ONqJSVCKZFlrRjJfPQVpGeaDd3seY4DywbLltvL1TFtHwJHWzn6ijcF5kKumXJyPKXKQ6isNTFF4ZvpgPnwBv/tb/H/7m9489Of4YuwuY4yjLipQ1xkWyvISjiryDHmSrLCahLnJDyshdz1eOvoS+KGQp9WtBUp2JrY8oIpHlsc0TiEwNBZ7Ljhfeauq7iHwioDcriFMBDnlXSuzN1CIeOLJeeGDDdv0ZizFnmiz5u3zXrIOLBO/WqtFp+1Jq3s94FRA2pE9iZP30eRV/9rBaF38dc+yd3BE2lF52u+t04IhD1P/tWC6LVNuqb57DNHtyvTXy0eK2r2vr+qH3v96ILy/uYLDocbjocb6ANFNrb7O/L5K4byA+e7E0/3gvt14uHXL6RZayhDUz5ZTR8YB9uMz2srUqAfAu/e3nH79kSxhbhYAo6b05Gb2xOnNyeyZLZ1ZnlYYHWMIXA7HekOY8vYNqxJ/fbSw5n504y3hr6zDNPAMHWfPVDK58tSKKkiMbNtifmyUl9WlpeVy2VjPa9sl0i8FC7nhWWNlCjEqCa31tKUdZWaDZKVXxgMOFOxRsnrUosuAREcQnB7dCBXeFn9ippb/t4htA3EA701BGfogmO5ZFwudN5jnG9KbCUPd8Fy6hz9wRGrQdN7BO+cbv5tQ6YhFlnAi+CaSKi0Ilj5jXo8WcBY5capka7+nMM0cHt74hA6eq9mx3cDlKGD0dN1qihctsRlTsSknXwIji2pgvtl2RrnSs3GUy6sKZGq6EFShFSaX1xV4U7KiegtsRQ1Ey6wD7c0Ys1ALVqIG9tsjfQQLa2YskZtgnYfRwDrVKgzdJZT73g39tx0npsQ6IMhOBU0iNHGYI2ZNVdKaXmuBiIJEfXEXJPmqdud74Jys15K5mNKXMRAN2Ix9MGxroVsIJZK5xzTdGCaRoZuwHmHNCR1bYXkuq7kXNR2piV15FKV41cKKSWs7XVLuG5G0tBoTUoxGILXpJ7X5AVdp9kZDAdi3hSRZacLaNrK/rnqk2dwQb/3L76a+MtffM3N3a3yvEompY2cN4qkhgYnkIT1BRcE63d0SDtkZzYckeAto1Nzbz843GlEbu5hPME44rsD3vTkCgdvOXrD03kGERUOVY9Lgl0jPM2UrPdvnldccHg3kLaEExVw1ZTYljPPDw88PT5ScqYfHDllCD0SAsY7/NDhTUe5bLBlTM2KuBmPP4wwHulvbqlvbqmnHjsOYNVjL19muqHHPz8w3d/z5qtvuLv/gr4bAV7N1VPCbhtpfiF++kjtR1L5lvJosEvhIWei6fmtXek+Q55gp3/omrQi5Jw1VtB4bHD44BqKYq8qUYuj95a39wM37yeGtyNyUogueM80jPjosN4wTgPd0OF9UFimCCVlalR/3dIMvAVztcIpRT0RKW28CoSh43R/y6GfCMWwyYXtObKmzMOW+LitPMdINIJgr8WQc145bChq6Yqhsw5ahvRrvsk/fe1ozX5s7vVc23KufxdrVe1JG93b0OAIqU3op0psTbHRAtN2gd3XZTUapZpybvY9LXSi3aj9gN/tfmop+ju6vSj8zGFD9JnewYFatXDXrb19Tz0SmnhIkUgt/D5DLlFEv0p99U6+ptxUcolc1ozUlZoOHPsJtzxzYeI5zZzngUqvme2d8vXfhMB9V+myOreYziDZ8BwrH0NEOkVSf3F7Irn3YL/E/+pvkP/0HzgsK13qif0Bc7jHH24ZQqW4gF07qo0K3FjB8og1Pc6OwEBOgrEFJJO2DYswuQ5Xgn6IVz1AFPKWSBh8B7YzuGCZvMWlFqpiHcH3TH1gGHxLN2MMI5QAAQAASURBVPIson6xe5OgN6A2EEN5q642e0Xr8M1/2Gl9SU2pmb7LZwV8Q5llRyjb922w9e4kcfX4/EfWdpU2gWtVqKLRV2MiruWjUf7s3pxUaYtEb/i1Ddt7lL2/+bHXjy4oS4HHh5ULHtMHtg1evgcTLV9+fU8B/nB/xvuFEhM//GbTV1IqODgcLG+OgcPYhAJV+TYxJnrvGJ1n7A1m7LnUSlk0aUEj2CreG3zn8MFD1nxhawy9d3TBNSNMmA49l8ESgo6IaqnEdcMPrTsuCbKOeZeUyWuiXjZezpGXy0J8mpkfF5Y5ss2RLRbipgrlWjW1owga/dcKrF3r7yoMHg69VbN3KRh0gVur1goWw2AdXQhY75VTl6qOhIVWfFvMTj4V5VIOwdMFHRvtKHbf9Xir5s9O1O4g9APHvsP0PeL1d27peITmEVrrztcwrFlYk2aHx5iQrFyRWApZoDbBlHP669bWYA1Dz3BzJNzfMN6ecJ1HbMGRqE7vRxZhiYnzeWaeEznDOPaEGqniNFpy3Zi3rKT+9oDlksm1EnMlp/19aWbDAlvS8WpsfnOl5flaqzwSQQnzRirOeqUDeNt4h6WNHlQprdm3Oi4IVuM6J2d5EwI3znL0nt5busZHwSgHVZLOz21R0r63Wmi5hvoalIiv6lp005FKLJVzyjwvhWQ8loT3mlNqjGNNFecMXfEM45HpcIMLXUNKMmvcmC8zy7wSY1IVd8qa+FAqJRe2GKlFnQ66rvvs8HjdJIy1hNAxjQbffM5yzrBGSsytALH0nWccR9LWYctG8IHQ9Uofcc2HEEN1eth9uA38iz/7kp988xWH0x0uONZtoWxRPTNrRWrBSMF20A2ObjI40hXZT0kUfUEV51ZeC2DbDTDeUbuBKo4SC8iCpMSpD3x5O3F0DiOFQ+84DJ4pQHr8gU8fv2c8nbD9xFrgcHtH3CLOboRcKCVTU2KZLzw/nVm3gqTM8vzM88N3mNBx8+4dw+GgCSnWqIBrEcyl0OHppneIf4Md7/FvbuA4YcYREzpwQRvRbSHd3ODmM+M08vbd19zcvlNla0OeTBFqLpS0UZcL+cMz5f03pPtfsf37/8j2179h2AoBg3NGuVxFmlH366Ejoopli+g4WiqdCYShZ5xGXLAsa+IyL+QKw6Hniw8Tbz/cMN3d4AdPTS3xJICLHkzBBfeKdopthVRrCnZe3k7sr7tN1WdxpqlSXPNwtBbrLK5q5OjTeebb5xe+2xKPMZPa75FFPf/EogIIo44gthmOW7sXZfJHApt/6vrHDsz9Hdz/rU0GKRViBBOgNjcOKeqBaxunGa8KbO8s3gZFj3AYWbAGljWp+rt6rAkYIxjntEhtNYPUHVDYvSPN9XXZfTDaigYaermPrY0YqLsCvCHGDUHbLZr0a/XfrNN7SDvOdlHQ6/hf1Af46YXLWDDJcfGZT1VIqe2LB+iPhg/ec6JiamSNBek9Na2wVGzWpBYxlvPRsvUdEg4UbjDPJ/juCfndR2T5geH0BXwY1TGk64i5p9xtmEOl5IjUBzq/cKqODoOkjrJVqsnMkgkxMTiLt46DHRlyh/cdKwvRVp2EXCp+aOdw1o42eEM/OlJnKXim0XOaApsL2NZwx9rCSaS0hJzX4q+KPrdWNNVNvH29d0aFQLIX//XVMsu0Ol6kXEELKdIoDTTz+zZ+lt3A7hW1NNc109Z0i19SnqVt3t+tqNz3mH0NoCipvU7xXr2ha3Nv+LHXjy4oXy6F33//CXtMGG94OkfKarh7c+J0K3STpTveY+sL22VhmyvPP0SkQh8sN5Pl/Wng9kY5VVkqWyksy6YK5Zypq5KfZRPishJfOtJjJI4rIVSYsy6aRZifN/rbmW4wmM4jFkrJqpJMaltRI1weLtjgSKlgXEZSRaKwrpFlTmznlfW88PK0cDmvrJeVuNTPYGdLP6pVUCqZy7yxnCO1oXShU2lqXDecFY4jTIOOLB2ClISXDmdEUz3QUZCl4q3VtBFBC4qGzkixDUXTLORYKyk10YgozO2sZegC3mnUpYkZJ8Jp8hyOk6biHDqcb56E0FJjlNxu0PzZ57XwslUeXi6k3DqhZk9hg1Gvw9btqs+eorJ9F0hDz3aamO96zNiBtSzLzJoitSmPtxSZLyvzeaOzHh9UcFWr8rpKFS5R+ZJIG8uIGgOnbJtRuR5KRoQqiqikop2ijqp1VLR7eO2IoPK2dq5JQ0cAJGNM46ca7cY6r91h5yyTC4zOMQbH4BzBmGYZYanYRrLWQ6v3DleV3+mkahNk1eS/D/bqnZeBlOBhSbyshctayUQG53SEWqEazeBeo2f0Dj+NhK7H+oC0+7WukeXlwuXlGZwjl8y6rGxLW/fLyrZu5JoJfaCL/VWQZM1+D5XA1juPC7QOpfl37jyw4Ol6r00Uhm25wFZxJrSxf+N1ikVk0YbFwy++HvjLP/8Fb774GjeewOjoLMV9JK9irDD1DLeB05sDPghleSG+PLTNOFMK5Kz2PNaiIrtUcMZiTcC5kWwq27pBbh52/ZEvvzqynWfi+RlvMtM00HcW0sr8/MTH3/+e/uYd5vYeM95gYqWUFySupBjbaDKzxooZjmA31suZx8cza1x4fn5mOh25/2LD9SN5W6kvGbcIh2miO3ygu3mHvXkDQ490HSZ0WBdwQZ8RN46EYSBtM9ZY+n4C5zGuwzqvCSqf4WU1buTbO9LpFul7ylo4/+ET83ZuZv2JsM9n29d8/l8ZLSqDgC8V44TxELg9jXTBce6V3nB+eearU88vPpy4f3fLMN1gg1FUyDgET5UZyQkphpqb/17NGgVn1JKNphKtbUKiaZpOrcJSoWY1wM+lsl02un5grQsvzyvffv/I7x6f+W7dmEvBeodvNkOmivoEi/J4ralYNGRCHJTOU0jKZTc6si7/BUTnn7o+l7JY9iEjbQqgo0RtGAuGqiENlb0qUH1AVQFiCE0QWBWtLKL2YakWXFFhn238UGm30cheJOq114VG9vFk44ezN4htzzNafIjRf7cVpRm1d0F1Cy23WppHYRt5026d3aMi26h9B+JiLOS6YFLPA5EfiMSSoXdMx46vb+AvpsD7DsZV6J8NwVSC8zg8Y+8x1jMFx5tcIBTCCOUM+e4GZ76iy29J64j5Q8L8vGLvJ0J/AjvwWF9IROyQMSGR1wVqVqV07ql5w/uKlK35W0dyFMZV0VbbJXytdMERh0BcFp7miHWCr06fj6HHTI7jyZHPhbcXKGfLagZ64xh9YjHCZSksBSqe2Cgc0gQv1gglq+F+kYh3/sqXtcZibMabxrG0KgDep0O77F/aWSVCo7ztwRq7R4qu0t2tQb+0OaiIBl9/HiG9G5uq17b6SqtmeG88Vdhqd+pY0TW1axB+7PWjC8q//ruPPMyOcFBFkzs4vvhy5O2HhcNdpOsE3/c8P94y3c6Md4VlrqxZrWo6Zzn0PfeHCeedFkm1EoeBUoWh7+nFtdgrw5Irl6cLvX/ElEI/eepaKfNGfNnonWH5GPAUcmcxzpBKgZcVuwBbJeXI/GnFmo5yaRzGrbDNiXXduJxX5vPcvPs2UtH+rxsHReCGjmHo6fuBWAqPLy/6gOdMTsJpGnEhUKrG+Nm4cBgsXaDxD1T9WHJpIyWNo6KqIjhYgzcOaTpnaRyo3SdKqkaxnc8r25Y1dgxF4izq0xacwRnNazU50wXLcQwcbw90p4Guc+zmwaA8OTU9qKwxMswZ87RxWVeWJWp6kVjGfkB6YUtJRRpoPqmx+r1irUQLeQxsYyCcOnzwSBAuHxPzulG2BEVIReH/se+4GQcOh4E1JZZN88JjUc5krdqhG4SUGyRf25C2NV8lC8WrAi8Lze9Pi17XRjbKIdFirhodi9s28qkl46zGvYXWwTvRh9pZLSYnG7gNjoOzTE75gtrdKxXBiaJ61jeLiIwaIBehVPU6c6GhJs4Tq7DkyHMsPMbM85pZVsF4yzavhOCw2BbpldmSEMuIs52iDaIbQEob8/LAy/zA8/mCtYEYN14uZ16ez0jRuNLLOlOlEkJH320M/QA7X0YMVK5GzDoteE1bCEE3vy54pmmgYFhr4eU8sm0XUtV77/BI0Xu3ViEXwxc3nn/1L37GL/67v2S6/wrXnyjrTE6FXCqlqIOCAOOp5/DuhvH+RE6bujgsKzlmsB5J6brJqUeakNIFmR+x/TsQtSxZ5xWTKsYPeD8wDGqPIymRXp7ZTCJvleXpiedvP/L08Mzbn0+8e/s11Tjl4K4b+fzENs9kMbhuxE23+GEiL2fsyxNiPKUY5vNG3IScvqWfjpSUWR+f6I3gDgP9NNG9uSOHQUf3Rd0maGIHY8EHLRy7rteINu91wy9qVqMZz64la1lM1yuKjWWdZ7Y3R55uA59eMrMU3AjTaPGrpuv8Y5d89uGMYQyeu9OBcfQMMVG2FV8Mf/LhyFdvb7m5uaEfJqWFuMahNgmqOnWUrBQLIyq+ybm8Pq9VFLnOQm1rsopmXaesWfGpqKJ1+/TC5ZywzrJeIo8vK485US0NudfnqGYoatZEqag9nFWfXGcduWY2W2HyyCUyFRhRK3n5bywq92u3FHLtW0grKreoY0xnlKagPMpwjeMztYJp0bzOMQ49PmghYZwhbUrfAdfM+JW7pgOoJvJ0uw+gacXd6xB+Z8zKPsoUdBz82euGZgbT/ts0Eeg+anXW4py7cjH3AemeTL3/vqoe1iIzb4VLqSyyIn7Gh8JXPvFXN5ZfHgtTB2ThOA0M6YBbK04qt5OjS5m3IfAza+nyTJLMFhPH2xMpdFj5EvfyBikBLhVZAsl4zNLhysQ5Obay0fmJ6o/ktEGxHN3ERKGzBUskERE0De28XJCS6GTACtjgMYeO7BLJqwDRFUuPocPSV8FT6LrKsd9YjaU3gduTIR0M2yHx+En4dDYsQGoTV2cNNAHc9V5JS8fCsseSqgCGKyVLXAOKaM0An9EP0PNw1xkA17NtJwntI/L9eyhdYl8dRql0LQqbPc3I7IIgGujyioQa03w3y3V1/ehn5UcXlL/5/hNWThjnOX7RcfvTzN2HmcON0A9BjVwXg/cj/XiH7xNhymyrepCloqKDlDPOW3pvGbynlEARYeg6DlOP7bxuzo2vFXLCPM1IDJASQ20clSUSP11Yi8aveWv1wZ1hyIFD6FliZH2aqVFYO1WX51TZ1siyLZznyDqr0fQwTtwfjvTjSH8Y6LpA532r+uG8bkx5IsbMRRaMVKhG88GNwzlFFK2xeBdAhGWeyUCMEe+dchDRBJzzvDGvUXlX1uooc93o1kAVp0VbSpwvC5fzQooZYwzjOFDjhlSNcnEhYE3Fi1NTdjF01nMYOoZJC8rXZdGS7o2iljF1GLtw2TJjsDxTsaJpRtaqGt27ZvFiKmTdnCvC07wyPp15fDhwO3rCGHCdwwSvUJXVQrmzQjc4ptPAm8PU4gkd59VwWRdCb8Aq/SG1iMPdDLa0YsJ5u092MMaSSkWcRi9WmlnwTlhvz1kpgmYF6yhMLW2aHRMo7cA5rDGMViMbnbWM1jHawNRZTQBqhymt4ze0jQCPZuUatqwmuEbAe4u1DmlmtLFWLlviMWU+rYnvL4mnJKQKLutrrKWowW+beaWSeVkj53NknheWecX1AzlW5nnl+TLzcllxNrKsC99/+sgPPzwgVc2st6hj9HHcmGIi+KAjagzV6CZWjCK26xaJKTXl/j4KN4SuI4RO7a28x3pPwTPnit0SqYkv1pxY4kYfCn/+J2/5V//qf8X9T36Ju31LMZ4oizYfOSvfNWdA4x2FtnlV2NZKSg3VEosY25qA0lBrocQNcz5jjwXpPEkMy1JZHp4YpszpaDS8oBhEPGsBg/KYNgm8zJnzZeWNkqRwfkRsTzGCuB4TKrYabJjox6OO12tBXKCIJ2VHzIJLmVIvdOeMpMj6MtN7w+l95gYAey3QBaVpeOvUwL+Zi7u29lyrAHPJIAtYfa+dC+q1twskciZm9WJ9Thu/i2ceZaYOhmMNTOeWL29eD6J/eJU2JkulkLOmR3U+gIEvbka+me75y6/ecn+aCF2vfMViWui1KtDzGolrahZA6lJRc6UkRdA1GKCoX3D7SEl9RkvW0ICYq1Ke2hQo1w1pcaQuWPrJcxKLx6iyumQNtEiQs455JSlVJYuQU8QLzKZiguCCZcyVewObCOv/D8VkmwBeEco9AnmPFU4ZTW6zGVNU3e66DlB1t6BUGG8qXRfw4jTj2hk2U1hX5VSaxsenIVlVpAmQWmTirgy3quzdFdyuxfmWlmWu4/UdvVTgQAMNlJKw54jD7mnYRujX0an+q2sOFvUqBuGq1BcsJUKVBfjEIXZ8eMl88S5wP9zgnQYNOBFqf8CEQHmu3GD42nrunOdGKnkTzgYyjhwc5u07FvMFxY6E4umeBPOUkK2Q/v5CPGc+SeZbyYy3lul4woU7TO25CxV/cAylYJLaNAmGrRZWr5OHADjXkY2Bocf4gphMIrPkyiI9x2rpjMcIat92vOHuzcBlfkHGjHgDqzAIuBR4jokklpcGfOw2Xq+Lh+Y/KUC9+nZbq6/Htj3ij3mSiiKb5qaiY/GGGl89lPcFKtf79hpqo2Nq0wAXMXr+7R61NH/wV3SycagV6v5sovkZp/NHXj+6oEyLYbq1TO8N739mef/1yN29JfRQs/D8dObTx8T50VC2HmNHfN/jDzP5XHhOhW5JVLdwKoXOO/rgCM7pCA00V7rCwVqk7xCjXMkRQ4/TQqM7shWPd5ZeHGGr6qeIeia6mGGrWPGkvLLFhW2OOKcP+Lqpz+F5TSxR6HvH+/d3vHv7jpubE/0QcN62oqQ066PC0HXEoEVdNTpmzVnhfGsU4cpZWJNhyoZ52dgqmCUqid/atikYqjW8bBsvl4V5TdiaOc8Lz+czYir90FOqMG8bMWakqJDnZhpxYnFy5uXxRc1XRTtbjNfBqkDwHmc0blGRDa2yjHGIFJrBAMFZahEua+J8DMxzoLaEo5qVG7LHUFFFDVONIqnbVnh4Xvj28YXbu4nx9sCAIlzDGCibpyMxGcdp6Lnpe45Dz9BpLnKqWS0qoHVjjd9TAKe2HMbQ7J2aWrJoMVyNEKt6x5XWRXlvoH2PWrmKbqj6vYJR5Cc4Q+8Mg3F0xuODU/85r5txZyyT93StmLROzXx3EZnQPO4w0CIZvbVUI1dLHhFRFbsUXrbC81b5ftn4/hI554qWVNrgVLV4pJRMcHtUqfA8z/ztb7/lF989cLx/bCiWUCK8zBuXeW4IdlKl+ZZY5k3fCxfw3rPMC3FStN05p6OO8krSrjVT2+YlDYEwRoUF1mr+vBTBC3gfyM6RS0WWwhojIpqkIzHz07cd//Jf/JKf/dk/43D/DXY4kFJhqY6ldmwEoqin4ZoyrAaWBbNO1OpJdiBNb/A3dzjrcLnxj1OEkkjbRVMsSsKkQoqVS7Ys0fPwHCkPF07ThbvTG0Qq58uFORpm6zDR4rjBDG/ojiqMIBVMtVgc1o+UwWLCBGKx3tOHgEhTGa+ZyzmxLmqlhDG8PC8E6/ECthYSlfnxiXh5IU5PuOmIWI9vqTzOeTUD53XD1ki3ZmPVPGrJiRz1cPEhNNeFyjbPzA8f+fSbX/Gf//5v+fXzR7pR71lXLUNQ9wXZHbn/kWtHv9at8Pg0M41n+qAJMD+5O/And/f82fu33HYTZivk/KJs4BxJy0x6ObOcz8qv7ZxSTJwGDKSYSCmzrJEYK0usrKkQY9GGq5k8x1SunrGlVpy3OK8eqEag1I5xc7zkTKjCmsFli8sqzIpGwwykqjAuS22IW2lpMWqT5q1mz9+283Bt78H/UsH9j10Cn1m4vD7/pjWYuQjOKxK2i/ZMVXoToqbWUgUTlOvamUDw2uS81EXfkxzJjV9oja4J2n5l23RmTzSxdt8YX9XdsntNSptCGG3KtVlr9kFG0ahXJJLXwhK50oX++BfX30FEKTuKuimKKWScLJhtpmPmkFe2h8APcs/d7Vu643vM4CgMbMOIDB5bDe9TYSiKdG/FE41Xw/+6kC2kPkBnCesAP2RyvxDuJsIPIN8Vvl9m/s4J0/ue05vC2/fC4IFyZnJaRJUlQUxsq+GxWMwhYHqHr+CprAXEKrgQLOB0+lBlIIljWxJmGBjcidDf0X/IHPPMxifm+C2EQN4yw+zIF2EyhmyVqmOMUVV7E1Tttd8V+d1RwdrWjFUE2nqnX+MdUpt4FC0kTRPj7LfEGHtFMavItcn5PJLzqhcyyqutsqv8wbWKsrCjqNIaV32dSqu4VqxXIdyPuX50Qcmh4+Zd5s3bxN0bx/EQ6J2qbp+fVx6+3/j+95Fvf3VmeYj0ncd3A2HskbSwZOHTHMkV5pzpnWXwhqELjF1PCR5qJURFJ3x1rCmBFRYnxCWTRMhrQmKlOohlweRCag98EUi5EJfCuhTWtbBtWxu7FIpIGycaNfTFME0H3tze8ebmhvE0EXqnXnklk6vyfUSEnAq1qOn3eOx4iSuXy8xIxPugfCJjWXNhXjdSUwS6vmC8axw8RR6NNWw5sW2RVDKkwmWNPD4vxGJwbrt6JTrvCN5xcxi4M5bHcSXXwrbMeuDvXYV5tWgqKq9vhF7bSNsCooIPax1GKhbB3YwkdGNOSYjxiXyJV6W3NL6gtAVZssLvpQjzsrFuGzFvTexSGYNH+o7pNOEGx8HAwRs6b+mD8sMuayaumZya96ey7NtoDKgwDqhNi7EUyXog6RPQOja1Y8oJOteI/6YJdACsHi6pquDJOTWMN4DHEoylb1Y3+wbtnaO3jt5ZglPF4qvHl21jJX24GysRMZYoWWMviyFHHf/NIjzEyOOaeVgzD0tSzo017fWruq4WfbCz0Zgv6w2ShFQL/+nXv+Pdv/9rhmEEqYQQSEukxMplWQDLFiMpFV1bFVJKmF6bl2Xd2NrosdiiRa/sqUAV5zwhdM3iRMeXpY2/q1TWlLBVx2LeefrpQNw2tlLYUlTjaipvxpG//Pl7/uqv/oo3X35NNxzB9sRyYa2ORTpW0xNtT7IdEgwynEj+DQ/rQOiPdHc9xw+erg8qYAMMBakqzKvpTNpeWJZIykcqjlQsj88r56WSo/Dxh+9x9nu6oMlYfprwfkCsJVGp3YDrb1gukeePj4TjLUM90YWgSJLp2oRI2OaLCnQeHnn+7oFP3z+wnC+kHCk1gxGOw8BtH5i8I1DZ/vBrlsPA4AyS3mL8SN0yZYQtC9Z3aqpu25Jvp43aOOmirrVSi/7Ou+K2bImXh098/O2v+PVf/w1/+9f/nhqfeHPydN7Td/5aUF5HYP/gekWi9BmKl4X1+YE0CMdTz8++eMO/+PINX7+9pTOW7eMTkYp1yuteLwuXlxeWdaUihNJhcYhVd4YYM8sSiVtm3bJGpkblQsdcSbmJ4Nq+kpv7QXCOqeuYpoD36v5gvcFuEZNzmxAZcslUY8HVxqOuDYHhWihlqXgMbWsnYDiIhhwkEcr/17vy46990OyaWIUmgpEm1AwO3QONBikY0SZct9/XcaQ08/LgHEMXdJ+3otZfMWKt01hcuEalqobCNDsgafu57vf769lLjp0aZPYM6M8QVdgV3218XfehqE6dzB8NN1/RShTQp6L7vpjEYC+8zbrv9dsD89OZzR2ZSyTFDV5e4N2JbrJ4EyjB89b2/LMzPMekU4ii72EXPESQmkhVqRcsOgmx64yMifJYqUuCbPj2cUaWgTfrRGLlp195Tn5VYWu0fPqkcbO2BHKCLQl+SwTrmTDElKFE+rHtgaYnG8tWDMYFTBFccQQzMfiBXBJOJnURkJXVGNxU8TfgbKJ/KdwYA1kbiTUXNnHkpnK3zUkB07wHjLka/seSlTPdRMoKM7cITWuQolPD671qjYKCQ3It9l7TcXbg8XrjVUzbPE2Ndc2BoV4N9NkR6D8ab7+uBLWe+3HXjy4op5uV/ujpe4c1hZQ3Hp4Nl5fK8yf4+PuNh+8uLB9XclkwbqM7KKxtOkNeVZ0VfE/XB0LXkWrm5bJQn86MxnLwgaFz2M6pgXKLCktNmeudx4i0zanqjWjjSs2KrmxbYUs6on5ZVtY1kwtI0XQFrOCctAdLGvpmNbc3Jba0UXMi5XQtHoJXPpkPHdVCLDPLy0rdhJITNMNQ7wO5ROYoDNZy6A3OgvWG4MP1wS2odYZFC601Ji5rJNULYU6MY8/QeY5T1/4cuDkdsA6yF47zyPIy6CZRlMdXDSQsMSojs7BvYNIgdasmzLYVCNbhnKWTCl4jpHKENa68XC4aZ1jVYqlmLaZLqorMisEjdC272hqn5GDrCMFznJQr5EtHVzK2ZvY1eZk3zpeNx5czl1VH+bv5K0bRyMPguDt4pq7Te5r1PYqNbFyaajRlPVDwyu3Rzb0JaIyh5DbeaQr14CDopxOc8vJsM4D1Vk1vvdHIN+dUsW1aAehaV3h90NqGnquwpMzjpo4AWMtmKi9Z+GHNfFozlyhsuZEOfDNy2OEO3YeUWyfSjOe1AH5aLvwP//4/MISemjOH08SyFdY5arJRLk01q7mrOEdNQk7qjbbHWJZSSSVpcVJ3ArhuJp3f0TP9nXeCuIjgvFffvC0yhI6b8cDqHDmt5GTYtkJnDW/fTPzFP/tzfvLzP+d4eofzA6mqIGvLhSgQjWczPamb6N+MTO8+ML39kv54wg0dve/oQ4cP9jUKdSegV907Uo4M5xe+//Z7fvjDM99/PJOWhb4b8DWxlIXH5zOlZqahIwwb41q5f3eP847iA6u1nLfC8qvfs9XC/fu3dMOASCK0Ak+9AJWv/fT7H/j4n/6O73/zO2pc6LyOJKfRc8owecvBWTpb6ZeV7W//Iw8PnxjefYOf3uAOR/zhAP2A6Ua875prgjYAih6UFvagDgcprZQtktdEXC88Pz7w7W9+w8c//Jb58QHnhOkUOBw6Ou84lsJp8Aw+YeJ/mTPojHDTOX72buAvvrrjy9sTd4cDt7cDP/niDT9/94a3xxNlWXj6+ECthXFSNXdcEpeXC5e4gjP0WZ0FlJpl2NasDfyiiPmyJWLKxJTZml2QagDUMdcZIXjP0HVMQ+A4dtdD1FgtpPMsFF9IAl0yrQh32OvPFTpjiAaqAVN1f81twugMjGgx6U2zHvtvuK4iV9HxNp8p2mlTFTUPEaTma4ajJo0IHt+EZRXjLVILzsJxCvjO4DbL5hzLZaPWRCnqKWjc7qipVkW1iprnY14RqLYXOQylWdno66pX03NpyNWeoKJm9Dp9uv6O7WzadzjTYC/lTSq3fW96rIkczYPG8/qMZSblhY+XzGYqgylMMnNezhzGO079kT4MHJYXftFZHsTxQ7QQLDVVinUtSrDZ+lXIl4LJgnmuWmgmKAFC77jrB37/sPDSB8ap8uH+wpuDx1EptmLe33Bx8MMPC9KAoC0Lm+j+b9RIGFuEuikfX8Tp+NiphVtnDXYzjNJh44H58sjlOHEavyaUd7jew3GhfvsDL/UBmaETi02ZrlheCpyNKLDU+IvW05BFfV817aZxg/d13+6Mtfp6jHfYKnrPqtIX1Lx8f5aaKEe7HBCr/FepTdilaYS2rZMd+bzqKYxpRe+ORO5IalvDRs+EH3v96IKyOy0wWjZbeVwCD6snzsL2KDx/m3n6mEhboXNgzIqUyNe3lvdfv+XdaWIIXrvXNVBsxznC87Iy18LLZeYSLetQmejojG6yS9biMOUCpWhhk3RgmFt3WsSqnczuoVg0c1raw19Ky2Vu74m3ysFxDa1a15XLPOO7QNfaXZGEc4YQPP3QM07qA7iukS0HlqVn6Bxr0tSDzjk0GzVTREfDwRtupsB4muiPA955hZ+NIZbEy7xSa2XLG+scG7qo4qSbw8TQO05T4DgNjIeRvusRUzhMPaebkct54nJZrjmdxpomYLGI8YjzGgnnNBVHlbs6vHXW4J3DeY2KGhoR+GXe+PjS0/WO5/OiEX1JR/kpq2VGauv2ECy3p47D1BGCUyWjUSf/oRsYQsUni0+JnC0lJtZtY14Kn15mHp5mnp5nLutGbtm9Q4Cx63h/03MzdHjrmGPSFKN22JSidINS9PUgmmLhaCq4NpIpRUU6rologlFOTO/UHkGauttZ08RRmgnsbEvtoHmHGTDGNb6RNghVNEZTBJZa+T4lfr+sXJaEWEMUOCd4XCuXrKRtPYAErwmd16JR60pdz4oCK82jFGHb4Lsfnvh//E//jnnd+PKL92Ata1YD85iTWiy1wlKhC0sqmSCBkivrvJFuoppqo/QNjSVVoVEIAdvM2o1pQogdURFhzivFgPWBIfQ4hNmqGEek0AfLNz/9wM///J9zuP8a1016qMeNtK7EdWWNkSSQTEC6A+H0htPbr7j58BX94YALHc45vPWY5rtnnWvosL3yCHOJ+JdPrDUgP8w8Pz/hquC9x0nFO0uRohncKdNnw1Yty7pyO+lrr2FknlcuH8/4usD8PeNhwFA18tRbXOixBfLzzPr9A6G88M1doIuOyTpsEQaEqXZ02RGs1QaqGsIm8IePXH7ziVwdpRuQ40S4PWAOI10/MXSTcgbFspZIJqmlVIwth179cZfnM0+fvuPx4TskRToPb+8M09gzjp4pBAyKwt8MGwevbgOlIQ6fX87Ah0Pg3/7yLf/2n3/DL7+453bqCU59YafDxOl4xGM5P2WeXjZSTvSbZZqcPr8xErdCqpXFF/xSCZ2afecEcStsayamyrzquowpkxpXz1nlRer+6Og7z9Q7jqOnD7qxWOupkum85dAHtqXQG0N0GleZa9G9qygUJ0bwRteymKq8cqN591qiGJzRQiL+6GPxj68dVWpbn44I2fcGLThzoxqIr1SJCFVjFtthX3DKCRenTaMBFzyHoLSk1QeMMZwvC1tMhKDq7ypq6SPNW1CFvq3w+AxZuqKfu5E20vh2r6hWvdrKmOuY/NUDUT/3ddIp155EB+T6DOpAp4DMTB5qzVhT6JwlVnhaLmxSoPS8dB02/YC/ecd0+orjNhBsR+8dJgpmuuGmdJSoEyb19/baHaRKfs4UPFRL9Y6C4A6et+8GytJSgaKBUgk2c+ouuH7h1PV87H+CO6zUxzN/eFqwWYul4gw+OIKx1LgRsSwCyVU1JffN9gmHWaD+5oJ9coxOyD/vsW9UXOe7gWBW7Jc9a914/MOFMYNL+h4lC1EsubgrqCGiiCXNkcZZh5RdiGObngKs1fUVnMegSlV1E7Q458k1qfXQrsj+/OMzMoPwKrIR8+p7rY0BzX+0oZJCMzzf15YWnDvV68deP7qg9D4hBdZZuCyWNXZcngvbD8L8vcLj4yHh+whRCBn+N1/d87/9l9/w9v4tUiq//u3v+e4hkun5/izUpxcu8xOpJHo/cPPuhrfv3jFNA85VXuLKGkWTQS4L88vMNs+klChZlYJbzuRsGpq250W3N7S2cUBVJMlgFM4O7eEXIefEvK24OTBKJnhL1zvGqWfoe7rQNf6empN3ztI7x9B50qwjW1X16TjCWov3jiHo50y9KsW74ClGeX+dBHIxnLuoKIzowz+NPXennptjz/E4cHsY1FTV6yHrg8cGYYtJhTopqQ2EqPWQDR6ReuWl9l1H1+mBsReQ3hu1cWg+gqCF+E3NnM4Lw+jAFipKtM9Ji/JaFfl0xtAbNDnm1DOMHT6EK5/IxIQFUmmelilfifxzzHw6L3z7fObhvPC8bmwlYRAGb/SQ7Dtu+47BeVW1VzTBxmjxa6mkKuRqrkKYogJ+alG08XPoXg8yHVV1LtA11eTuKRdEx9ne2utYGymaYdvWkTXaIZaW3WvQzrBax5ozTzHxw5pYsv6sJMJcDUvVYnJ3EzGoxsEIGnlG6zwbWg5KTXBmT1zQovI333/ismx8+YcfuL07EYYB5zqMqVdj9lIaT8Y5StaUJRMTW4zEFOmyB6Ojxy1uLWLNYExtG4eiEM4pOlhEN71100Iip4ypwtQP+F4pHJecuLvp+LM//xPeffMzhsMt1gVSSmzrwrYtxE3teHJKWhhar8bBgEEN4dXCpBHH2t2romkTxnqsDRjrcEYLk/Pzma73BFuRLVJrxIjgbaLzFTd4gnP4Tuh6R9/3OKPUkRIyQ79hbEXKynZ+IZSAFQFrsdOAdSdcdXgnvL8/cLrtIX5Jd850S8bkillW/JZxF8GuypEzXbPiMKqcLyWRZWP54YHohOIq0ffEfqR6xyUXHvPMbDP2eMCPB1x/wPcBciXnlS4IH970DM4zDkrF8F3A+UAwnloqw1Y4DIFDZ7FG/WP3tb/7Mh6D5Z99c8P/6X/9c/7NX/2Sn3z4gmkckRwpaVPvW+Mo60Yhk0WLwloNNSeobY0VQ4qQ1oqYjdBbRbqqaTzKqkVkFlKs5DYGDM40jnBLvAqWrrMqHuw7nKfFnlY6Z5j6NvatAaQQUyQiGBHlJLY/qzSrfWsajUQbJl1dejkaMgX81/DB9vdQuYOvBZcxu/hFn1dVxdJsgwXtyZqQsXE2a9urbBZscO25N3ShY+g6DCupKEVgXlZyrkqB6bS5VbTRvDIaGv3Gtb83BgUPzO5PqMV0zrkJeXYvQ/sqxrkm7Mh1QrSjXdJ+78/9L7UTNghF76FVyoPsUZGgXpzzmcftTMkd9e5AP0fm50hwbwn9gYDnVCu9Cxwd9AzY5EgN7RVR5bP15srhLLlQs6VaoT8K7w+Ol039Wo1Ugr0whu8YfMR3N1zcgdW/4eBmvuy+Zzl/QqrGKvddhx0qyVXWCEt0xBQ5DpXOdRhvWY1lFMt4NriPgumhSx1WerxxWHfi6fJEZubdmyM2PTN0gjxH+mghG2pUgKKQdYpo9UNMxmCRooBG2Ys5adwCUTsgY0oT+ZhrQ2MteBtaGmFufEerIjspChTQEOe2Zutn56HeV53o2SYcVRpZuRaZtOdkN87/r+Ec/+iCspqNLQUuWyIVz/xUuXyKLE9nvBhOt45+0s3DVcFsCx/693yYbhiHA0Ll/s3EGoVL9PRWCMZCrsRVuL0d+fDuHV9/8yWHuxM+wFYyMSbW5xdeHs88To+8PFrOLytmzTgriEsYW4mxYkRw0h4eaaacVjlqdX9A3OsDB23RtrGANdD1yuPa4eEtRv06q3mpinJ5hj6wdFvjGeYrv6Wzls6H68N65c/Uooe1UUhbk3Lsfj/xznAYAreHjndvjpxOB7qgMZXeK7HfO+3qYza8XDYul5ltztQGX/tmkGubcXrfdwyjowtelb6d5swqR802Mm/Bi2HMmanv6UPzGCz1alC8L6fO6GZ5Gh1v34ycjgP90GOsJaaKWTKrqVxKJqSEywmzJdi0aH+4zDzMK+clcVl1JOaBcdDXFWygc57eWbZSOG+JOVbWUpG9Wyua1R2zopDtV9As3x2N8FqsVRFyS9cQ63QUKFaV4dW0h43X7s5IU9O190e4ipBoaRJiDM7oxr2VyibCUguxGuXqeOVy1iLNgFiuP8e0n7WbFysiQ9s0aekeFsRhTCX4SkqQSuHh/EKpwsuSONzcaMMT9D2pUhohuz0D3rE1PtayRM7zytCPlKpRl1tMBPt6oNSa2F+kM4oWaM56Yl5WLpczJW0MwXE8dXT9xLb1XA6On3/zjj/983/O7Zuv6MKoRusxsq4z6zazrSt52UirFqWShW1ZWecL2zLjQ0fndOS1v0dWKmKLdvNtVKeAicIY1jhszYy+QAeGDFLpQ+VucDAGvPO4vscPHTZYhk4V1b31HKcjsghHa7idCsOoPoDOefrTkX66VR7CVEnLytFAj2d4FNynRJkTZZ4hrsh80RGWE6wXatAzwRiLaRYd1TnEOoo11MFSxkK1mb5sUBc1+787MN19wA5HNXGvlboulKVSl0JvoesclQpW0S5vHKUI/eAZesextwytoPx87G2Au8HxF9/c8s//5Ev+5OffcHv3nlAtaZ2J26x7YK3aEVmwwVAk8/Sc6ZwhdCoqcT5gi1H7tVJYUhubtXlyLipc2tEOYyE4S+cd3rsmsIPgHUPfMQ49ITiMa36zRegHTePRiEzPGgsBIRhF9rw1FAtZ7PWg9BgkC5BJRijkhva2AvC/cdy97887SHN9dNszq9ZF+raVNhouRdeqEOlNIGfTLMGgWK8mccZhqBRb8M7TOc8x9MioHNrY0F3rDB7XBtSio2HdSK4FpHob7miUvsj93NktgXaUcudI72px2+glGC1u9uCK/YCs+89q55aYJqJECzkliRqobR9CRasijsdPK+tc2KbCaRT6LjGME4xHDsNExyOuJqo3LN6Ti23ggcHf9PiguoN8hvVFI3iztYSD0PeW+rJR8eQIXd048B/x9Vuq/9fcnL7mWW5Zy0jHQrDPxPUBJwvZOfpOcKGSrIHSwh8siKyAo2KJ1SPWUqyoiesQKGKIf5g5Xs5M1rHdjnx1+oJDGPC5YrfM47fPyKcLEjNHW7RAXStFPJsUEk3zoFIHzRdPCTGQc8HYqnGlVELjwlrrtZBFF7O0e4hUSguhUEaCNnelURS8s+RaP+uMGuBmrHIzpe0Q+3/v1IZW/3hvXwuAH3H96IIyvmTW6snVk7fA/KIJErWc6frA2I/4DqrNbMtKnTO/+u0LP/viiQ8WUo18+9vv+O3vL0SOnFNgvlw4ny/EKHS+483plvfv3nF8e0PXe3JJ1BRZbkaep4HeA1tEYmmZmpnQDELpNIKvGs19pi3MXdxi2gPj1dHmOrYwVs3JfacFl7FOH7qKqnabDYBrcH/noe8909Qzzyvnc9JEHKtoSx8CnXWq1ttbPVEfOuesvkCrdg/W2ZYSYRSZGwfuTgduTwdCMHS9qkSdU06FtZZgDYepMh0G+j6wLoktJYwYirWkquibd6qiH/uBvgt0nSqajVW+I9fuw2t0oAuKghrDYCve62jZY/BB4fUuOLrec3sYuL8ZuTkdCZ1/tZ/JGaGoiGJZyctKmTdoRvMxF7ZU2Ta1Demcp+89h2m4ZtNaseRSucTEOSZSMRgTMNSmnDRNgCXKPTRtpCzgjdAZoKBbsFHEKBXawSdYKhgL7b0Kog+XFi+vRHTTRBO6Uet7geXKKZJqiMVwyStLKZw3YY2Ca13nzuPar89pzQ3oxNGUmk7aejQU0XttTEv7MTruSEXTKty6UK2lmIrU8WqtU2omlYbONtpFLIUtJbZtVbGOeaUMOHS8Lc2QesuZGAul8d5SUYufuG6UHOlcYRo9X304cX9/T0wL6/aGP/3Fn/D+i58zHt5gTKCkSFoX4rrqNGFbSNvahG6K2qV5Y74cOeV3qP1J66arHlLVtKQOm5GSoetxIeh6TYmSEp013B1UEZqyqOOCdwTb4a3H2Q7pAn4IYC1dZ/BYjqcPEAK2bPDyLXb7ltAZvDd03YEwjnSDuh2UstKPnmHs6BiwccWeIadKEvV0IwdMozEgu3WYa9GsHqwWDfQZc7Sk3uCPDjuq6OKUDcexp7ubGG4nXH8AY6nxQpFELgXje7zbuaW0PHZpyEOlD55TF5j8LmbaG5hmpgwce8eXd0fub0emcWgCA40qpYk+arO72kem6xJ5fl4wAuPgOB07+qGnD4GchZgzMRWscYBpQhn9hmIKzulhOXid1linudbWqW1S13uCt4ROY0VzLq2R8kCmeBXOdQ76AFuFweje6mqHbJoMVUWjGH1vIVWC7Vk+nZF1I7eHcDfy/m+6FCijbQFt3zHXA/p1utAmH1FwQc+NbBMYg7dB7YGcjij2CNwqlex0T+pCYGqN4bKpl2+Mgg0deF3/pu6xeDutQa5o01XBfT3zPptVtqrYtlHm/nqvyGNr3q555E20I40+Vg1NHKQqYB2zaAZTbdyjSlWHJ6vOGc441kvi23TmZRX6ydJX8PPKu2PHm5sbqvdEMu5wIkdLWQ34ihwMMjmqc7gpQNqIcWWphmgqrlsxUwKnBuIuJzpWuvodpf6euv0JL+ZLnoLnOE6c3EC1LyBRD4gS6b2HfuR5zRqraS01GdKSNTnuUjHFK69+CMS+ww2OPhfc328cMOQ/7fFfvGc4HQk2Y/LKON7RH54IP/wOxgx9omzw8skyr4FkKmvW4I3SJquEnihVG7KsdI0+dITeU6WQtqz0Duc0QafZOokYjNXnz0htz716RqvpeX1FsfciVBeE3v82/q7s9YD+m9TWOOwOAj/y+vFJOQ8J6oFUOuXLLImaLzgTsaYoTE0gxcyywPzi+O8f/8C8XHh7a3m5PPPx6UKY3tGfPHPeWM5ntstCcJbpMDCMA32v5HzrmnWJEWQayDHRPwWGoePl5YIxrwWCKnIt1RaKFUxVHqV6776+uYiiWzqJ0ILTWaeIEHow69jFXI2mjbFYhGC1Q7R94DB1rGtgHAPLmilFUSdjm6WMSof1QZPaCtdmkeN1fBuCqhrVCxG80Zzu0AU93HpP6Pxr5FxT51VRW6Ch7+m6nsqFmDdM9RQg9QqTd51j6JX03vVqhbRHcRlrr5w7EWn2SEoMPo4TX7y/Q6xjiw15MLUpMjXPd+x7jmNgHLWoNdYQU4LdXb+osnO3C8lrRLJmmqsdimWyXk3Ehx4bbIP4dZ9aYkG2rGPXtll6C1ss5H18RKvVRcfIYlqgvYimziioSFWfdmKp2nD0hSE45Z8YXf57vNWu6N55RcZyRYR0JFubUETH7XMuXLLwtGTOWVgr+CoE39BwC0aMviZoOeOvqEmVXfCjTc5+aIkUbfzbx/5IxxSJ24qzjbRfFOmpUjRr2qLJHbVSRIgkcujISc17u74nEFoijiJRwQZyKS1WTCNGS1XhW83tfnnHcep4d3fiJ19+wRdffcWWVwThpz/5GXf3bwldR6YS08q2zcRlboXlTEwrJUdy2sg1EfpBn4eqZvC1VlwFIVN0jqi/X4VKpqaoHo5Ylpczy8szJq9Mg8EOgZItthvopxsul2deHj5hqoOuI3T6fPUGQjDc309gPaX21LGyfFzAZowvmODVMswKUhI5rxjRxCcrG9SI73StyqlnnpsfbXSoNb2niOatV4WJWkJWxZpMwpCCwR4LdixsW+T8acGJwx8TpiRkPZPiRlmeIF0wZELweO+uqKOpSpTX5kHRscFZ+mAQq4j6Va3ZKsyht9weesZxAB/I7fVdiYC1WRdJaaPXzBIzj5eVGDOH3iNVuKm6d/XWE11t8aU62jVoMVGp2GChejoLU1CHB1zD6436TXZ9IDRKTy3KHbtaM3tPdUKwmjo2FSB03B5OEHo+PS98vGzUJSO2w3mHdx1BhBwzwzCxPDxTns8s+X/BS+mfuK7PK5+hlKalqKH7lROuRTht76FAsYK1ysmvRos1HUw4YhJ89Y2ipW66tnHdh75Hmvg0pUIi4W0g+MDuGvRH55fuGuyWSJ8XjPYz43KaV6XsY8y9vja696khd+PatYJZ9vegFI27tIqcXW+kNNMxY67inz0msGIoWHKqLOVCyIVRCpP32K2S12fq6YTre0I/4KZR/SsX9TattyMlWOy3BUalWsRsOeeZrr8QwzM5OGS0iJxBEkYq3lwI9aIC0zIR7ZF3t/f0YaDEyu1Q6NwDlIW0JLrqGDHYWjDSEaMHFyAr+KJNmcNLQIzD9hPWQE6JkRHr4XAsnI6WGhcNw7AHhvE9K4+4cUXmjekiPGXHLFU3/QLZVJ1omYpXuAPjDX1vmA490zgSrOHh4wvzkqBKE1cJtfkWSANFdlAGpAld+aP7vXMmdSj2OdrRJkBXUc5uIScNsfzxz8uPLijn2WNSK0BMxfmIJWNqwtrKGlfyi3qpLRfLklSN++++feTuITF5XbQ5C3WJXFJlmzdMgakLTIcRP3TgdgucZt7aTlUxjmIcGUOulRxXcqwNjlc+Ed6SjPo2ln3eIeC9ISV5TR0pioDYYLSzDYZp8hwPnRpTGyWQW6vCDm+bKtE6RR2kkFNkXlbOc2KZP9uwPiPI7mNNQFXEvllaWC0eNevV42VH0yzOWUVLg2NnA+ZarykNFVW+D0ELSmcNKa4gASuWdctN3KEihRB8G9uCXL2mdMOppVBSIeVIzuqD+ObmQPryLXc3N8xzIhbNOPbe0/te7WOCo/O7qMcr2uUcWME63beCmxhPE8s8sz050uWCLUYLh1YojSHggiMBWZQCoYKh1iRcTWK1S1bltSoknW2FYxsq63gbolxj3a/jG2nNdM6N+2QqvW/ZpXav3BqKbexORLg+WDvyYs3riHytleeY+LRkXjY1KrfN/y4E1MKiagTyvnd/hhdcuUr7BmCuCEDRjrIR93dOVBE99Jdl0YO/GmLaVGkpqi61Tg9qLZYhpQ0nhnkamOeNMHSEvlMLiaJqvyy7Kvh11EEjf9tWwAxdx7u3R7766h3vv/iS+3cfKJJxwXJ7f08/TgiQ4kqMK3FbiZuik3FbqDlR8kZOG9UYQhgI/YRxPbVaStLm4fVe63Oq3bOOYotUatpYXp54/vgtpI1h6Ju3o2O4/wmxOm4Ob8jOEy+LPtvOYUXoOss49vTDken0FtdNbMtMuvmaeP4NbE8Yk640kFxUcWtqblQHh/iV1VdMNUS/UXtBvC4uff2WWJV/nGpSkYgtdFYwrrKNQng7UMeEkEkIl2XGnIVSO7aXhT4EnK14V9rzFZQ77FxDtIRqGnu/ajSl95bQWfre4c1eku2XwSIcBsPhNOCGHnHu6gxRncF2gZoqphTlv26RNamP5CVm4hZBhN4lKJbDUFXkJ5CMJctusK0InnGuTYi0yO0tDL27Ev0L4IIjhCZmsjqWs3bvrECSmkT3wXIYHdKPfHH/DnO8YYuFm2llfN7o50LtDohz4B2OiomFclo4Hw7Uh0fWjx9hiVh2pffejv64a//M9gi2aYgihM6axudU2pIYadx007LLG72q7hYy7TtWbVZtSz1xDWXyV4sWo0EPbMoJzVEpPd7ifbMwa/Epn3Mdd+Ps/fxR7qb+BuosUDDy2jTXhrZeM6PR8fbOtyxFm0qL+tLaHShB0S71zVQh5K4c3/l71bRkGEHN2NNCeozUaQBrSGmhSCW/uWfoLb2zai83eOJWkL5QBgiTpXgwvqe4wBagTgsv/vdsZuESBmb7RF1fNJLTzfR15oZCpePJ3pGHnqnLpCQ8E7kx9xzSGVmeGeJCiBFvhMH3GO8aJQFEKrKCuxiGj2Dngnm02NphgiBjp2i0nxnM73DdA92bA5iR6fZLLvKW4SDU50fc43fYbWYoUIpltZYgmrBkfFBgoWSsr9zcWk43B079iBcLW2W9PKtuwCS9Hy2d6Xr/WhAL10JT9sTFthaaVV3dmw7Txua80iIayKRUcEeh/jHS/U9cP7qgXJ8DwVX6o6EfhZI31jlTolBjZq0Wd9GKPq2gwoZGTG7G1IhQ0kJiIGcoUTMtu8PI4WbCdR7xjlwVeaxV+TgxV7aUlYBtnaJ7oSMguhFiWsZvBSkkKziv/oQFiFF3Aak6qhQxBG8YOsvbd0d++Wd/wk9/9gUuGJblmZeHB8oGzqgiT4tJJZUryhfIseNl7HjsDNvKVaHV+YB3Fuf2QkD91rx3bcMBvCWETD90WlTu3aDZa9CdT2YbytqyWdmfTrXcGKeRbtB4vi1FnPHUqgk78McGpXuGc92LXXSkn2JmS4mSKp3tuPv/0PafTZIkZ54n+FNqZs6CJS+CAgroBma6cd09Myu3crt3J7u3si9PZD/1vZi5obuYZiAFoFiyiHB3I0rvxaPmmT0yslI4kfGSlKyMjIzwcDdTffRP9wdpFlmixIAkaTSpFSz2A4JXJcnfGI1W6tIoU0hsDjt2u2twjmkeWR7ueXj9HfP9EaYEUaOqRSvNUgqPSyTGzH7oGLxr5fXlIkYvtV4WPGMU1lacyCnF4V2F3pM5RKIYpI1C5rVUkJMdihihdPVi1JEWFjmVa60vgcBN1d6Qng+n9VwKIUlU0MMSOQaYk/z8fafonAYE5SuxYnOjE9tQ3O5tUWleQCTVgthX5nR13tULaplo2sucIGnKciLk5oSuNI2uGDa8d5cM0Rwi3gqqpK2i3/bS7auMbFRtgRHnt0wEVknjTo5gbOHp7YaXL2759JOX3D1/xmZ3oJSE8wbfbUA1I9ASWMaZZZ6Z55EwT5QQKSGSlkxOci2nEFhOZ6bNmRJF/0Ot7X5BmISURHeWMmGcmeeZZTwznY+kGOgGw9XtNVQJb+63B6yulLolqcKRr9EZ0Q47x7DdMmw2FNMRi8LoDuUNQ7en2x6Y3/0BHe8pVUxEMQRKzvIclCU7Qz14ikukOUnzV4TpMYCubEyPK5IfGslEFckqoVQl6op24lRVuWKDQjnNfDJM34thSE1H6i6hNj3dxlO8FeOWLZSs0aY0t2+7f7WS170WtLY47+mdVLES14VETm/WwM3VwOFmgx02aCMacWU02ku/uCKT80RKURIGggzJocih3hjXkgcSJSU658DKYbIqWnqEbsYbQUmN0Xhn6Qx4t7a+iO7PNgmOtebSW02V+z3V3ALKDd5lNtazvzrgrp+QTUdJma2y5Kixmy15f0e2hoIg7bpW4jzTHY643YbSGcLrdzw+Tn8a3PLRoyLD6Lq15jZ45QzKyAG2XlzUct/GpivVFJJK2CqgSDWKqktr8MrCgplykbnZFlTqjKW49fskEmC1b2xOGwBrwTRzhdISEZNyC40rjX5e1892il1r/dbzIx+tTZLn+eH4e1nrjSDzpdX41Y/3kabdtNpc0NE1FUN0psKG1CIH38fHRxbjmJzlGAqPyrO3nlvt2OeE7ytu2xF1wRSRPFUq1lvKYKnDltg/Mm8ir5fXvHDwppzYh8wz04P2aAV9KbiqOOkrvik7nrtKtpbTMTKXd+zD92yL5WqIxOkNfj7TuUC0C0pHzKBR1qFyYfluwebK0GvyqVBOEXenqRtHcTOdXdimv0Pze2z3E+LhS5S7IwZPLQVn7nj6kyf02+959+YNx7OwQTUKS6VaIktXPcom+j4xDJX91pBHAViU/jDUU9eSERkqq9LkEBqbKYUc63CoGigSU7wMj0CTVn2YEf4JarmygOVPu19+uMtba3Y3lu66kFUgzQvznAihQJSAZ4MMUcZWfCdtJL0DpQpLqcSQqSpg80Qq0gkLms1uy2EzsOm6S91UCpEYA3MIzNOZ07Qwx8ycM3OtZOuJuVBUxNSIM06ym9BYu1JocqK0naAHOUo6vSx8jp989gV/9a/+gn/2l/+M/c2WpWSm87e8/eNv+far7ymLxP9ItMx6cix03tI5y6aXCISTirLYVBkMOi00VG3DpNZN0G5kyqmq4Jo2UlmNsZIdVRWX+q315r2gR7qKVqKCsZ7tBg7XOw6nPceHE+c3RzKJaZqlQSdJKHupSiYvrVvOldzsOWehVJJUsZVccdazGyzWOnEHb0TrGFMUE0umQe2N7lHNQa3l5zZUXLfj9u4528MtbthQQuTx/i2D73ngD4zl/oK+xVQIc2KOQrP1udJVqCi8c1iXKVE63y9RBwo5xTs5nYfUgOgquKvVGm1Ef2hMQyIyUNrgpFqQeEMPSsnQ5AiqnfZRzRnXtKZStahkSEQz58wpKM7FsNDR7yy2ZJytdFZRUmrIb5LNoCEBBvneaqUgmm5FqXbQWan2jw6Eqt1TopaTgSuEiCKjlKeQpWWjKml3sYoaaaH8haUu5Cwh5LoWbm6v6YcO1eKCSm398EYIF+8VUFAlk1XmZr/nxYtrPnnxlKfP79hfXdH5DUucm5GikGKEGlnmR5bpyDyeSMtCbD3wOUaJKwoLYYmkOaKWSjzOWGtY/d2lhZinlC7pAOfzyONp4ng8c396RGvFy0+f8+KLF0QMzjjQilQqt09eUrXHmg3bbsPx3RtqKQybK26fvaLf7DEqUWOEqtjv9qhqmNGEfqLmDDGSwySO5ZCxptJXLc8rR3IJIifIiXEZOS/HhhIanPJoA7pUbBUBfTUV12uMt3LKGQvKGmJWvP3difywQXuDqpK9G2IlT7KpaosMf0jAuFGCvisD2YAIhgGt0A46p+jNmt/Jha4brOLVkx23VzuGzYCxXoYCmt4TCScvdQ0hL+2QISYv6XV3eK2oNROKDDymSp4mKgsbYs3Flaq0w6DoncVZYRdyqZR2QLdWgAGtNbUxRtbIgCz9xko2J63wvcfu92TvMM6TlsAYztz0muHqjuXqE4Kx1JSoSQLRyzawGfb4zYbcWYLWHMtr4nH+k53e62MlBFP7g1ZcjEwliTHrckrUAmDkLEO5rQlsxSoLSEd5qQZVBN2zSDSQ5AIWDJqh7/Hek1JkWSZizMwxgFJ414ZI9WG4pNYPMXLrulEqVZVWqiGHj9IYKrWuQe3riGayoXKsl5a6DCQrtX35/FpZJV0r1b4ecGVO/YAEV7j4GpSCKUmPuc+K4/dv2C0T480tn+xe0vs9k1d0vaMGsKVgOkOIiWQsY3aQHKNWPPiRX9eZJxYO5g5XXtCpG1CGKx3Zm8Lv5spvquU7pXk7RV7PC7/YOX5EzyEd2WjNsBlgPmJTxOqE1pGsm2s6V+oE4euR3GRURhd87ymDQtuEVxM23eNNAPUdO/8Zc+gw6pYUFffHBzozsPnkinp3w9vv3hEfTqioSTHgNgasAAA1KwajcamgloiOCpPBi1dWGJvGRecV7VYZ04yjknYihk9jLRWR7LHK21RLTbnobeW6ra0xZK1xzPVPQyfhTxgot88M+0PB7kaiihxLaQNBIc6yiBmT6HuNt5L+X4tiLhBCajEYhaoSmxSoTZRsjOWw6dkOPcYJspLnibwspBAYw8g4jjzenzkdR1IB0w+YaihloiaFRnRkut3ySld8b3Hag7XMMTIeF3JElE6mcnt94K9++Zf89V/9FZ//5Av2VwemXDid7th1Fkvl/XevyYEmoJEXWyuFzi1TzWn6TvR9WoEzMjgqI/2tSpnW2Vs+dLUiz2F1NHtv0a6ZbpQsNOIykwVBrxqZhhIYrXBO03lDKlum847T44b3j2fiXMSIERJLEFecL6DWvMMKOVVSFmdYSlkQ0Kqo1bQWlVW867BWBEFKSeWekHlFdCpWYnWUKpj2+miluLq54+buJd3mwLAdIEeGoZfNMEe8rjAGSipMMRNQbKsiZYk70o2utdbRdxWUDBm5FFSpGJzw11YG8NYKKa9VkytoJUiJ0FHtBmyOraqktnEphVASvmhySXhjkTB9QUabVZfaaIHS5Ac5FaZUeEyVY3ZEZVC60HcO69rGYSJLHrHaEGvGaRlmL2zXRb7QqHAlz780ATxqDR0WHecayqz5sKmVLEhfqZWQ5dTqjJFkAyvoZyYzp8A8nwnzIr3zRXF7e4PbSbd0yQlojlE0zgIl4AC/2/Hy5RUvntxyd/eE/c01w3aDxrBECEvA4ohmoqrKMk7EcSRMZ+KyUEOgxkCJWRDMEDi9PzKeMn9I30sEhtGYWkWfkKTXvMRAjTKoJgXZO6q32N4xdJbeWDwGYzymH/BuoCpFihHXW7a7AaOvmM9nSsx02xvccMD3AzXPhOnEfHyP2l1TtBOHpXFkFDpH0X4uC2FOFFNZXGGshTRm6jmTQ+b8ZmT5/sTj2zPO9kR95r2LLTRZ2ACFpD7kCmopmJOgvvktnM6ZcVI47TDFECdNDopFi07TWkEZqo5ko9FFsVcwGIXZGupWvq72Bqvl0Nt5Q++BkQvarRVc7w2fPr3m5vqKvt9ctNRr53OJhZwiMS6o+iGndkXIKTIEDms9opV4I+0sWiV0fMQoCRuXaC8xZGitRXLQtMmoDEqcy0Lnt48buW9LrtRYW6GAGOes96hhS7HiBqdMLA/v2BDod1dM+w2nYctspASBFCUcPAWU9RjvscZilWXKsNRvOJ3i/79A5aWGUUgP+SI5t8ay5sQ2Wj6xNFRODm0KpS1oR1WeonyjipvJsIBWllyl0c0ZQ+dlHQthwTvHOE3SFBUkVcRa27T1XHj5D4NeM+gUOYzmhjrWxoGuuspSRHdemxSgItWitVRS+sCuaaM/0KLtV0qprWflg4a0XXgXu0+7Dst6WK/iPCaLjGQOAafOLBbui6JjwGw8RRUGK5pv3VnmrjBGzaI1EU+JhlOyvFOJUQW8MlJVmq64rZ6trmzswj5Wjsny21me0fuQOZ4jX739mn+xO/Jn3chLo3HVQfJSQLEk8AGbZ1BdE7nLIbG2TC6FgEJZgSXTpQkdp7ZVBywaSuXh/cJXv9NkOp49k1zrvO159qNr9LdfMZ8mBgz9JtC7gloy53cFFaSqMgTR0dYlctU5dHEsKRJzEg1lbtIJxOBDbsi3MS0Gr+KNu6xJ1hpSEmmLGCLrRaIgrVjtAFDW8Hz1p6hD/oSB8jaw2VlMD+kcSFMgTrCMhVosvld0g8L3Gd9JjVCeCudQiHMlLYUcwVtp2cGINnDbD+y2G4yxLXJkIacotNg8M44npnkhBoFrnevoqyYk0DaRlWTleS1nrYxEMmz6Ldv9HVEpTnGk8J40LihVsUbx6vktP//Zl/zo85/w5Okrhu2ebZlwVqFiQmUwVnH//QNximKCq6ILVapircNZi/eOzksGo9VAqaSQ0VaCa6U+S4ueryqs1a2hJtN5GSiNleFn3dRLy0bUqzlBSQuPMeay0GirQe9Y5iuOx0fev3/kIQeMs4RUmIO4v4dcMNaIhgWJE0i5kGJpCJ0EI6fm1E6xNNoRKs3xWRWUfFk4rF7F4bJx0BawbthydfuUYb+n3+yx3mJUjx06skqQJkycSPpEjQWmRdolECe2ruvwJhf34L0Ejmfpjg6tuUeZii4VhzTcVCWoJlqGNqsN1rgLNXChzIvkp7UPN33J6mqVR273j6ZF6CA5o0rJ6S/kwjll3k+R42JJyGCmtEGeuZWhT3ucyyS3EKIkANR2SGhP4YJE1rbYrnOVUPfNsdd+psqHFotSJcA/ZYnpWDe4mkFX0a8ZjEyiaCILj9MC3393OdTccI3rnTR3VJGEGKNJKdAZzfUgAfrPn91ye3PN/upA1w8YZYT2DzPLNGIaNZfILNPMNEv2ZAqRkhK1JFIKqBips4jT0+NCHCO6ABSJGSv5Yl4yqmCVFnSrc9hdT7cb0IP0a+s4Mb5/h9sN9Lsn9NtrfNfj+46u74WOLQlrHSiDdSsNBLEoxulMPj9ScsL4DShI4wmWkRQWSknEsJBihZQ5k0ihMD1mwjGQlsz03cjy3SMpQI4BTCabQnEeBdgCXYZtznTItWFOhTpVCX3A0NELKhSgaN22KQijxHxUlQF5vU0RfV2nM+66Jz8xYLXUWFahRUXasG7lcrUYKq/uNnz68o7D1RVdP7RwY5FJlLgQxiNpWcjThMoi75G1TBztKGlfou9x3qM315jtLXa7x9SJeiyocL4YfGrT5yoaSIUkJZR1TUE30+VH+ataEjCMNaJbrhm78XT7PUFbTNVQImF6ZDAFt3cE76mdY7GabJwwJ3pN0mgJSFrhmo50LgnMxFdfvedxauG1f+pj/Sfqg1knt27oHIXREiKoQm0yHKvRzqNsD7pH2R7TmtNELgCaNZi6ZScj8jFjLMNgsTGglCGn0yUSTK3GCa0vzB58GAIueko+sEpatdiYuoaiy+G0tB7qC3uiVvRTX9bPi8nnMsSuNX7ra9Io1kozpX3UxtMGT63FUGab/lJVqFGiAeuc+TZBuS3E/YErs2Ggo/gN6vNreDSMufB2ycxj5Xe6423X0w8TjsCdDlzbwLZoOjWh1YIriTEqfj+351kiyhkWBb95/C32KtD1z+g3PbbsCClQkmIoqq1PFdcZlLfkvuJCId9HqJZaNCYlTDpDeA8pUh1oV4SpqJAXx1d/P/HHt2c++bOeL37cs7lSDBvPZz/SPLz/Azk/4F1lMBkbAnbSTI/CAoRUiVnh+h6XwJWK1YVcJnIOLCGwzIWaKrkWQSsLEuFULB6LVuIG16a5wZXEEVZkGK0NZS4lrxsSK1VX+WCU+yGPHzxQ9puM8YolJMIpkY+Qxooqiq63DAdNPxSMixgrcP98rpzGxHwuzKO8wDd7jSuyuNdS6X1H3/WyuNRKWmZCXCR/chLjTclcFguNkST5phdcsmiteuOlK7cobE1shoHt0DHnSsDTdR2jFUTHKcOPXrzk+dMnHPbX9N0gSJC27IYD5foFlYz2ls3ma+6/f830cGy5UYbSKGvvHcNGmmVqEEF/yFlCpGtGxUhKEqWglfQLOycDTFZK4ki8xzbjjNyDa2wMLSpIhlBvLd4avJfuXmOlSWEaZ94drrjZPzAdHymlcDwv3N+P3N6c6PsOVE+1MjzWVMmpkNowmXIl5kpoiGZqF2SbERu6JxSY5J4h9XMtkL02ga/Wlv3hjt3hCX6zo98MGCNO9lIicVmIV9fU0z1jSIQyYQ0MTmGMJ8Yqhq3SoHYqXlUJZi+KUhLzkok5XQY+a9riK2u30Hi5So5hihTTIi2KoKhVVbxRounSFmdsM+CoD5thpYVq6w8oj5YhLhdYcuUYM/dz4n4uJC2JBPWSESRVWVoZlE0MO4WdxaE4tapI1ehv/dFCrFokR6kSS2JoP+R6xG9D44pSSoyRkvD3duuHmDFLlCzKpqHRyuDchlQjx2Xh69ff4ax0Ue4Oe/ymRxlDzYUYZkpaGK423FwNPH12x83tNdvtoSFbDkqlpCSDV15IqWOeJwlBH88s80iMgRSlKjHlSC2RsswwL5glsK0V7S3WaBkQq+gDRbAiWZ5aK7Q1+MHSbz3D1mG3njkXznMijRM5JHzfMew2KK2Z5xNhOlNK5XR+pN9uyMtCSTNhPpHiBKWQU8F1A367R7uBFCZSHNHhhApnSlxkaK+VmDIlR8KSCGOVerwlU8+BEiuhwpgDIVpyEeR0UE4Q+1zIVTU3pqHWKIu6BlTCNu1T4YMr0yLogjJaqKkqHJvLVeJKDFL5utSWLiFRIs4aOmOkCaquvdWKjVX89MWe50+u2e720t4CMuiHQDw/MD2+o8yJPCcIEmAvRg4xSMWaOM+BzkWM66HbwN1L1O1TdD5jhkp+8xUqL6IwUUK4lmYeW2UkuRaUEeRVNO8rarbSpQrjnGjOrMbsd+D3OKOxuhLPAe8sSkGohcV2ZGc4GRnJPphRrBw6UVA1qlQON9e8iDMpnlkeMyHeM6d2719yFP7PHysQuP5eVfuXlQt1XEslFBnINRVjFcpalBswbotxPVpblGqmt1UvrkR+Y7SiIvKIXCFkhbdK8igNWONZVGGJ+aKrN/YDxS0vtWqO3sylQ7yUZjCsaN0iiLRCVUVMWcZ80wZkWs6hQK3NVVwwjZVaCwaMVeTmWF/j+S65lm0MqU0WoPQHw8c6xK7mJa0UNSTmdCaVTIgzjw8Dt9sr7vZP2LpK3XWYqx0vo+fdVPkuKM4hU4wUSHxTMr8K79jpe3zeUIxGmVdsBuhOivqYQVk2G8+1jTypHbuQMfoMKqJ0T9GWxVrwHb3xGBy6GIo3uN6Sr4UaNoOGtwnmijpPlPGBkI94taOQycWjO4mks8qRy5l3x8Sbf//A47sdX/75luevAk+vb7l65Un1DUo9kufvSccz2mms8+QiwBK1km0F77Gp0A+aiiZVSwiW+RwZ3y/UJDmlGmQ9L0lirCrNPNVYL6PRtbFmcvkJ89hMcx8fKFbG7Ic+fvBAqQ2EKTOOmfFdYnmM1BiEft0kNgdD1wtdm2IkTpU5KMalsCwyLGw7jVKZXCJlkR/AWyeiYqVZloWqEktcLief1eWNFn2O1mJ/r0q3O0n45tUNLYJlWax6b9EVYl14zAnj4HrT8c8+f8Ff/9WXPHtxoO/FMVxLFr2QMex3W7R+gXMDfb+jG3a8//ZrpscTNX4IoN7FgfO0sN0sxOVIQXpjxxixCWobaFb00Rjd4ns0ndb0XY/zHVqb5iqXXyJYl4XBWXFDOq3pvWuVXBIF4axjM2zYbndshg2dHykJ3t2fePf2yNPbM13XU4zG+DU6Yj2Riu4spkrJ5eLs0k2sX0qRzyvyS3SSYj4RnZRuWXgFiqLrNlxd3zFsr+iHHu8dxogTPUbpW3Z+IKOIpUg/u9F0yuNQJKcY58h5ToKuLgtaNdS2QXelVOalkJLCOYVtLvzSYktqiwZSgNKp9XA3zVYz4ThdGbxhsIbeiYFqpZ5y2yxWTRDqo4G11LZZVMaYmXJlTllaD7ImVYVO4BTcdJpPbm55eTvQbxTTMvPd2yP/+P0jr9/OTKG2Gja5AUVvJLpSGearJB2UtuF+dEJsDKR0wJa6ykNRFZaSIcykYjDG4pzc3kZZlNVkFOd55pvvX1OUImvFldE4b0kpEaaJzhQ2nWW/7dlvtni3EapOadHUlcASFsIyUUsmhiDMQIwSFbTM5BhkoE9JTB4pkuNMTTNORawreG9wTkx72khuqLdG7hVHGzzkV+/BukguMyVklmmhqD0Yzc3dc7Y3T8k58f77r5nOjyILMeCHHROZUjK1RkqVKKRcMl3X0W+uwA3ozlPTDWF5TRwl8SDmTAGcVuKetpWuFHy0pFKYYmAqWcwZBlwp9EXRoei1Qi8Fnwouy1qkdBJUS2mqhjWL1mgj2YQWcd8WGUSVgWol4F5Vwb17QOcilZbBU4LBpLYpU/BWMVjZ8GMVvPN6sHzxbM+Tmxu6fiPXUkqUMJPGM/P9PdPDAzlUVNbSQBYKS5CDZkbqV+cQGZdINxQsCr09UK+eYOwtqrfolMj3X2NKaNINgdpVUVTdottokiElkgzVBp1VH72yMdVo7HaD6vdgOzqjqGmSnmXTiwynGNB7rOkwyl50JEo1Y1LbMNEKhkShcnP7lGXJTKfMGALfvDv9SXrKVW6i2h8qzYwDH0laRHMd16HYOLpui7UD2jrWKqVa6kfOfVo1nuhMUZq6ar5zJVhhHCodxhVcMcQYqEWTckuoaOgfasWUVnRyXcAQjqNWSjYiWVKN8jYyMLRPQ1CsKrWK2koN6jokFnFvm0vE2mr8KRcK9YMwvNHhcMmuFKBijaSplxczKagxseSTRIyNJ47H9xzDmefPKxtvKU7TD46fbT0Wxz2aEUVNhbMJ/JbENZaDmjH6QM9IMSNXmwF9DyUnnvSGn3eV57UwWolm6nzAqUBWkVIMRlthuIyh2IraK9JN5fETIGW6peBmTQoR93iGwyPK92T3cwoKaoDssTljJXAIZSWY/Os/zihl8LrjSsP2umfrn6G7K/LmCZM78nA6UlKAJVGXs7BrCopKDL1n2zuMc2Aq03Qmm8RpvicEQ3KOWisxRqYlkmNqBjlJZVk1keLQb/OVFjNhbdeQyNoa9f0nakN+8EA5HTXpXDgfFaf3hfmUoCq8r/QbjfVQdSYlWOZKPMM8FmIWF6bWYK3EmmDq5QYsSsJ1Q474JKLvWhW5CCoUa0tbahep0oLwWWdFG2g0usgNsuk31JqZg0QtTOMZnGeZZsiZp/uBv/jRJ/w//u//kn/+y19w++SWfnBoFcSBWoQYsGbDbtPh7B7XDXTDjs12y+O333B6+54lSJ3VbjtwGhPbrQj445IlHqKufdPyZgiCoDBOf9BYVdrCqzDWyvBkJQLEGhFdW2svcUPGGDrfkvIVgvxc1M9I9qURBPnt6cwfvnvH7e2efrPFbges4hK0XppYPOc2PrUcKq0UprRO7NYko7TkcGojLnXbmoVSbG0YDUq/urrm+vaOzXYrhiOtsC3gXaHR1qO7Hj1sUEOHqxlXhLpdlkxKgubOYWEKCzEE0Z+WIu0uS2ReMiFAipUQwLnaenFlucup0ozLsjDqgrEKpyXOx6q1OlNJnywfTtSSyyl95EapFjz/IVut0jaotviXWhrVJZWctI2xN4pPDjv+rz97wS++fMnupiOGma++fs2//s+/5z+Y7/nq9chxETRyRT5tc6asEU+lcvn7f8I4qA9D5YfhtxkEamWOiSVmjI5ycHFeZBzWYrSjKDjNC+79A73v2LgOXQrzPJLmke22Y9t7Npsea1vklKpiRpkDWSmWZrjRCmKMcoIOCzHMoutsCGXJWa71HFE1YYhoFzEWrE44L7yk0hVvFZ1DKEPXHPdWS/adiYB8DR0Lc4pM08ITPNUOKDPgO9gcrgnzLCHgzSLYedvQnMJmf4AKKUwSbzQdGYyRuspuR9pcUc8DeY5USqsM1HSdwqGJc0LVTCpS+7ZkKTugQg2ZcpxJMQlqvUgO7rm08K+W+1mb5tcZT+cq+15x2Di6ncV1EnFkmymlGkNJiVIke5IsvfbBFWaTKKngg+i5JYkCeivmnbWK+bBzPL87sNlusc6jKyxhIY5HwvGR6eGB6ThTqlSP5lhZ5sw4J5YsOuNKpVTR58YUpZHLOuhlbSnGUufmED9+R1W56bJax3yVyjljrFCBWsxztYU6S0RcC2VXCtd5+usbst+iTIX5BGFm227kQoepnpQ3LMWTSoWcUUWjWiNPrRVbIdWISh3FVIbNhtvra+bzmdN44jguPE7xT9ow29v9QX6iLrclrPdllY9UbbCuw5lemptafmAmSYRPFcRQ6VUHLiCLbOKCusZSiItEKFWg6E4KOJSYtWrNsp4j65IQLTKsrYZOCSuvrO7wnPNlQFQtuaTU+rGH5oJOib5TdKG1UaKlVJRdW99kT6gZYk6sCRkyA6mmE/84yqjFCpVKrlUc56peskxzyox1ZlGaznWU8ppQCjcYrm88zsCVgx/Zyns38BgX5lopWfOQEt86x99bAzayd2deqyOL3qOsxhv40nq+cIEbznybZqIudHbCFAtMUDy69HRUOqVF0kVhrgtp40kxo3zFKYUOC/bxnnx8i731zLyg6g2aB/JSqHmCuOC14uXzLZvB4m3m/s2Jb/9T4uUE2x8l+m0iWUuwV1S95/oVmO49x2++x0wyUBbniEjSh0kag6Y3Hq80xcx0dmaukckZEoVOa0rIzAVyKm3ekOYbpfUlFmqN+apKWpRyTo0tFj5sZed+6OMHD5TnR8V4D9MRwki7EUrj4qUTsyRFWWB5UMRTosRMTQ2hUxVvRYSvkb5jue80RctgGYtUqKUkvH7JQreGJNq1VGvLzdMoo3DOoH3XeERIsVUhNs52jgvzODHPC74abjrP58+v+fLTF1ztb+hcjzWKnCYJNJezt1DOzmKsFwqiKOIcWbYLcU5kjpAKfT/gfWAYesZxJMeKVTLA1JI/0pY0XYvSlIRswA3x0kaLbiiDrtLj3DmkAnFFNY1up0CptcpRbr45BOZ5IYTEktYO3UjMldM083icWaLEtTgvPd+S0bkaWsQ5TBZBOEhTRSyX5V30Xlr0nEaL23UdaqoqKGXYbvY8ffGS3dU1ne+piD5Tq3WhBG0tZugx2z3d7iwI6TyDqoRRtCApJ2KaKSVIFqiSizmkyBITKVVyXBFDiFVRtGhiBUGUhWpdSrUS6slZ6Jxm7y0bpxi8IMJoEavHnIVWYu03bSf2wge4X9Xm1rc45zE+iwY0g6tyylMGds7xZ093/Ms//4yf/eKnbA57lnjm7ur3ECvHceF+XBhjaj3wtFw7eb4yLDZ3PmIqQldUbuhIg0hW+nu9iVX9uGJUuuutnMooGXwzWhijKTnweH5kePCCJI8W4gJlpm4lKktbhElIkbRUphbjo5FYEl2h5kyMJ3C20dwLNUX5lZMYcpaFHGaoC8pkzIDEPjmF9RplhFrrWuSNc0poQmObIY3GQIDDEHREnSIPD2fmMeKUlAEIbWhxrocascoS5xPzeEJpx/7qCYenn6CVpx9uef27X3F+fMAYj+0cJVeM6YX6qmesbdV0KtIZpJVlTpRTYrlfOJ0CCTBNX1ZKZTrPvD1bIjNzqsxAMCKhqEZL6DgSt+VVYrvv+NxofuINd/uCvbLoXureLsNAtehcKLlSqiMGRaiZoHML7i9NQFuwprC1kjIxFkHwn9/03NxcMwwDxihBf5aROJ6ZTo+M88xSykUmsaTMFCtTaof5UtBVk5Os+etQYpUc6nW/g1wph0+o40jNC8yPlNjaxhpqZZEhT5AS88EE0iQzytJkIprucKC6AbRcEyEGnAHbObJ2ZH1DUDuW1LMsnnmBEBPKWqiGYr1QynXE5UQpYu6Bwv6w52a+5vl05niaCMtb5j8xGkX9k/+XPxVWVkNYIGvF+KJ1f0ElJbKtDXNZavaMMagGuORGS6/DHgjiimpyJSUHLGl0s5KKUDOFyGr3rMiCItfuSs9/yCNc637FwQuqDXIGieQrpZBKvtDTVsu1pVQzirZaTaWanr6dhBWipy9VdO60ogoafVradQ9Qcr7Ewa39gymvyKb8m1SzRAzWTLIDU/2aHCtPnr/AaM9QI7fM9BTmqqjFEXLmG5ew1qNt4ok78uAXXusTueu4dVtubeFGTegQqLXinMF3C13sSCYIW6pHDBPK7cidJ8+WkhXLOBOq6OF31WLmBXO+p5y+R28d2r4iBysXQwoQE2UOGA+vbju2V46hgwF4/w9v+fb7t1z/7Ts+fZHZvbolPj3gnCV1G/zdS266DceN4927BxbvcFqhlkydM6Y4zElhiyOTydaDUlhniEkMN0VBoFCUHChKbsOhluIFrbUYYdvVJvS2vqDOqv2X/oRigB+OUN47wqioKcv9YQuUREiKPEINLRk/ZOKpoJLcUNZZCgudVpLRqD5kZTkrOWVW4JUWWyBu4pQLIUmkTEiZ1FpXchFi0mrD4DuCi5QcQTfDhoZaJWIglLnF5mRUzSgyZYk4JJbGWKk0Eei3kEskZUFJZVOP5LhAShKnuYb3thebWj4a+FRDvyy2DYzWyRsSUiFlBMJGTqSpoU+r066isVqJ0UcbrNI4bSTrsQmuYxJBf06VmAKnaeLhOHOeE8cpsMRCzHLBT0toQ5g4tyvqMtytQvl16GqVLoJMVnVBji+dra1KTk62WtBR2SoY+o4nz59zc/eUzWaL1qZV/QWoq/Bet6y8gX53IJ9GUk4SfzLO5JpIJWLI7HtPbzQ5iis75MokqsGLvq6Wir+YSdoFqtsJvU1ZSolMw1nYOc3BO3a9p7NFTpft35VSqG2DW5sEjP5wmq6IPKAWMUslKtUaXO/YhsrjOVBqRGVBhnad59Xdnmcvn3H19I5hsyfPPfn6yOe3Bz7ZbPlt73lrM3Mq0qfaJsFLM07TOslwqS4IbCOt5H1rP/a6Fa4WgzZ/ypC5aqaaRkxfhNaKEBP3x0e0NaKjiYFeVa4PHXEOxJiILqCXM7ZYQhUUaHXglizh/pRMjZacEzkESlgpbqFbwjyL4WMe0TlIK4jXaKfQqzbMammQMurS6a1Vbm+iSCtEgyVvbCbz5nHk7/7hN/z4Z7/Fdx3WO0qMUDM5BXJOjOcHzu/f0A8HvPE448UPaTYoeh7e/4ZlOnG4vsWowrRMzDGSm+HB1ITOgRoi+ZhYHgLn9xPH+4kllobciAlDAXOtfLNEjlVxVJXRKOYAQSOSCCvVr74zdH3mMCTK1rG91uzuPJu7jm7rMNawOi9lz5UBPseESj0uJhnc1FrWIAdRazS+kwxcEvRW8/xuw+3NlmHoMKpK4HpJxBRYQiRVhKHQUnk6xcyYE2NKTElYDWMVxjvssMEOV5huRzUySOSqKLZj6XaUwzMMC3bu0eM9OsySsygXH8a03mlaA0v50OqiqVSncdst1Xi0MnTWUOOELhnnLRgP7ppgnzHqK07JcSqRkCTBQBWoRjSoRhts8kSWdhlJCYPrew6HK+5uZ47HM8fzxHcP5zVI56Md7yM04PLnxgkIV38ZIjU0eY18mlUiyzLWtYGwybFyQSH60Uu9aPv5JStc9LYXM2aVasPaoEJxi4um0Zimu0dTW4tXqRlTJYYIVS/rY2sBR6MuNPjqBLdWtJyqoYgZea1KyRcJglIfwrBlWVSX4bc20t9ZI0ZLNNQPcgZxh5dm+Gg6Sj4Eqq9RaYJSfqikVE3KtIQZNT6gjebhXcWqxPXdNf0wMtRRBt4kL3zVhseUue/gj6aQ3Zmi36N3nt1yzRM016pwlR55e34vZikcznS44lhYyLWiCGBm1FWg2j3hyjOXRLGCDmpVZX1ixsQTdnqgPCRMOVDNHl3vJQN6PqPmiDKO7cHz/IVjPzj0sTL/9i0lRHjzSH3/G6ZfR7h7Sffll9w8fcr9dmAeHMOnr3hye0u6f0dakzOyQmdLmcNlz6hZUZQE0Tul0CWBKQQF91kkh8ZqElmc6oomparolk+dGxhj2nUucw4XmvyHPP6ELm9ZQKwtVKRbMqdCCQ61aGrL8itFFj/rC8YWTKpkpfEa+s4xOENukQR9J80rAFW1PMSaxSQSC0sUtDK16IX1VJtzwSpDbz2p64g1o+LCGraakqCZtrNNAK4k600VvvnDH/jN3/4dhxefgtvga8U4RU5JWiJCJoVKjpGUF2KaGcdHzo9vGE8PLIu4dmMSejHmRE4Rg2LTexxCEceUqVns/bkKPdYgWUjlMhhIqK2mxtJ0nKXp6dpN3pp9ahsKU8yEUBnnhftx5HFcOJ4n5pBQVpOCvF+51ubo1UKh5tb9a+RmvcTSZC31ljELJVNL84HI8EpVH506m4am5TP6rufu2XPunj5nt7/CuY4YA9M8klIEZXCug2Y0sraXruJuR7EnVE44lzlselzNJFeodKLBnSMxSVPHUi1LtaAyxldyloy6nFdRfGmB5G1toWlBbaU3sOssV71j21lsq+tUjYpXbXHNaEJKWC/DkVFa4jTWwTNXxlR4DIkpFxItS88aYsxYDU/3nj9/deBnnz/n5vYWv9miWxyNoeJ04TAYnu07xlR4NwaWJBvK6jjXSFNQzg14Vx/o+hVIuczRMktc4ofKihQgWLuxFuM8xohSs9aVSu4oSvM4LYT6VhCEZWHnLVeHDW/ePdDvekEzSqE66ZPNKdF1HdZZckykeSbFgLUWqOQUoSRSkuEyhUU6vc8TaQnYGlCdRG8YLfFeVSuJoUJJOK+EqLEmCwiFtx7kRIaAgbkUfv3VV/zqP/57xuXE1e0tKkfSMpJzZAkT4/k9p/u3zOeF+++/Y7t/gu06apwJ45ESRqqaOb07U1Nkuv+a+fiA7jdUY6k1knMkTIH0sPD4dub125HzKXKKcuDqlMIqLfFcSiozQ4WyMbi94/qwYbjZsbvasT9s2Ww6+s7Rec/W9/S95m6rudl3XG08XWckL7ZUchwp4UyJkRojPkViCPio8aEnRZGKiBtY451h8BZnIprKdaf4/PkV11dbfOcvAJBCNI6Se2uwShI2liUyxsLjtHBMiXMKVKvZ7Xfsdwe2T55id3fU66cUv6EiHfAxVyYzwPYJG6dx047OG9T4jhyDVNgiZRJrEUJ7FmK01BptDeZwQPc7FIKYl3gmhxFvDWgLfk9yT1m6Z4zmQAiGmmdcPVPKkZzWYUyQzbrGD7WN01hDqY5us2W7P3Bzd8vT85nTEjjNkf+6m7X+k/9XyDqu2g0oQ558mlarma7FthjXhrGGElYZGbWVoavWpl1v8T4r82O1oEallkuXci5ywCot1kmYNNvWZ9UwAdE9gmpMS2P66ocqzlKKHCCU3GNFuh9bnqjCtgi+FXWUYfCjasUmrSmNJkkJjDVQGqpVxVzUBFMNRV0XK3m9ZAhSFyp1PbyvP6+AAtLypoEcR8ZToTPwaCqlPlBuA0k/UNUCamGdYucC34TMYSikrtKpEz99ceBlP/DkMXI3B3Q6keokoeVFE+ct09JzjIXFGIyv+EOH6TvytUPVgXKu5DRSTolqLEpldJ3J3GPLPeoUceH/QBtLVjPKP0MtLymPkTQbwikxPxr0VMiTRlePwUMxWBMxy5Hy9Znz/Xfoz37C7mc/Jj/bkTdgth0v9ze8fftHkouE+w6zKLp9Ty0QtCLmkViQ7MwUsDnTo+h0QbVi2FRTe5lqK/QQkChTLvFSSgl6vd4L62Hwhz5+8EAJEW+SLLJKInzSoqlRasWMS1incF7hNZK9aCpFG5YoeVamoVWlJrSx+GEjvb7GkEpu/duSIJ+iFKWnlFoQt7iTS8pChUsQGJqKKhWnJZi51kLWCaOM5JYphRscDuhLJZL5/Vdf8aNvvmazv8Z2FlU1MSyM45Hj6ZHx8ZFlnohLYFweOR1PgrSUQIqJGArTlJnnQFgiOUkOlNGV2kR8Rjs5dSEISy2FXBKqzf9tfZK3bXX45tK0C3JSSFmaQiRGxlJyo3+XxDjNTNNCWBasVlxtt+RlYV4Cqig2my3ed5cTIIij65JBp9a2gwItgB2MoJRGnlgtlVilq9euQchGBmJre54/e8Gz56/YXd1iu0EG3pKYl4kYFrT2dF4W0LqGhFsP3YDuDkJXoumqYtCaFGQ4D1Yx18KsKgVLVRpvFGeXGGNiDokYCznVtngK4qiN6BFLQ/usgn1nuRo8gzN0zrTwcishy7W2mkLVjFZtUW70MUUEyhU4zZE308LrJXCsmiVJXZwycmi6Gwx/8fzAf/eTT/jxjz7l6uYpXbdtA7hct6ZqbvY9v3j1lLv9zHfnM79/f+bNsR2GShv2tUEpQbOlTUOMFoYPQ2X56P/XP0NdI9NkADMW4yRuSnRUuklVFMZ5VDEsKUsl4hw4nxc2/TuG3tF1Fl0VXO1QvbiWU8o401zJUYw2aQnUImaaEoXqLmEhx8AyT0zTifPpSAoBbxKpGjwdUPG14JVkEspmK9eqVNHpRtVlofwR1Mm0XnujBalcGNGm4HSTJFRDTgWje8gDcdhyPk383a/+Hd998zW9Hbh/8w2n77/GlJnhYNEdEEfKeC+vkbrG7u8kq+/9a87v/8j8EHn3Zub1m1n6zisMeh2LxE1vShaj09MrfvqLT3nyk+dc311xfXPNZrOh73t87/DO440I/5U1GA07bRi8lxBwLetFzQspTqTlRJkfCON7OD2QTydUCaic0U3LZpNh6z0bpxmsGNBe3ng+e3LNdthdosOoGaFbQDuDioWqFTkKkzKFyOOUuV8iR1O5+/RzXrz6EVdX13SHa5TbkrfXlOGarMSRfQ6RiMUNB+zGM+w2OJcwPkkdXhBjgGTEyjAm605DSazDXt3g9ntKRVIawkQKk1TH2p7sdyT3jNE/ZXLXJL3BoBiKUIwlBkJZUFRheoyhWEPUSmjoKveVsQ7nPfv9jpvxmtPtmcfxRHpzJOaPKlYvww4fnjcf1k/ZjIVNKDIdSpQaDWE3DnHiFnRuQxMtvL1lBa65juJwlwSNNdZrddvKQVAO9CujXpPM1x++phPTYi3UapFdMbc049R0Mmv8y6pX+q8Pz9ooav6IpeEDQqVaI0+prTKyiiGz5ibPMKbNHu3AUPkgMkXeB0EuG9Kq1SWIvdZ16Jb731t1kVkZNGEeeV8ruSay61h2Ew/lnsUtoDOUKEfp6piz5MaWUtl0mcP+kTxobjaa3Zsz6nxG60ivDSVoSvKcz4p5dCydZdt7Eh1VeVSnRRNsFSYY0mkkusIyJLrTiOWenBYp00gPGFXAKmLqUdNIeRyJZ88pLejiedCRhzcjtYocq+pKVtI4Vm2PDRPh7/8DS/iGUF+hvnhO9o7OZJ7kzJmJJRmM6/FOk4pmLo5atxAnsgnkJVCzEeNmltcxNdBEqwYsoUS6pAW/1tq0BAxJBqAossmXuKgf+vjBA6UqE5gICM2VU2UOsvN6Vxk20A9CVdkWA5KzbPBGGzrv6YYtnTPUMFOTxnSeYsR9GnKWXL0CKUrl4qXRJSZyLMRZ3N+lSq1VTJEQZqgRozSd5HDI8GIMfnA438mSH0fMMlNU5f279/zhH/+Ow/OnmMFhsiWkwPH4jtfffsX9229ZxqkZVzIxCUIYcyKmzLQkpikzzkHMKR8JmoUGaFWIdr2528mwCLVMo7AFoVxNPKJNiSmLHsjoFimyjoPydzEG5jkwTTPLtODQMkzmSk6BEDNhSmyGDdZYlBM6UStpq0BJhMB6BtFWKBhFgdQ+hyambycZ1sBxRAPkvOPu7jkvXn7G/uaWYbPHWRkSxGEWmKYJZwail/Dr9aRqrMe6nuwGcoo4lwkmoFoshkJyRGuWKjWlLYPV7PuOKWfGGDhNgfMcyTljANfiLVAiC1i1XoO17DrDvnf4FqK80j2lfmgEKO3nzEoGStXyRqW9SLMUeBcrXy+Rbx9HzkvFOAvaUCgMneGzqw1/8+kz/uanP+bFy1d0V9dge2qYRJIRKx2OV9sdO7/lxZPE7+8fQGnmlBjnJI551ehsJf+jimxYVitKrmQlMRDrdrBqKS/L9uWQYi4LgVxfzYXZKDDbeYzyqJxRVVM6S46R333/HqsrnoRTlU5lvJIQ61IyMSxQJQg7p0AMIyUZ6YcumRrCxaAzzyPn8USskW5n8FaLTCYuaCylrhmi8hyoQpEZtMToKLkWVK1tIRRTgLWOm+sd3d1znn0iaPB+e8AQScESQ6DmROcUViu67swyB95991vu3z6yPJ4hTMBMf670G0fnFkgLyu3xrkf7HXM19DaxLN9zfEjcv5+Zo7zAXmvk7tbkWkkIvWS3judfvuCnv/wZn//8p9w9fcp2u8X7ns53Uo1mrGjnmsFQUinMxVyj28BVc2iv8yLtPcuJNN7j77/m/Por6ru32GBIteK9wrvA4JzohF3h8+cbXj6/YX84oNcs2iriCNXoT2ONHN5TJsTCFAIPS+Q+ZPrnn/DiF/+SZ59+wW5/wFhPSppJ9WTjScpcXPNKKZzf0A9bdND4/ATHA4WZrJUcqEq9XKdaSZe4MQa3v0LvDyjl8SZT4iQHEOux3Ybk90R3w+KeMLobFrNB6vUSrjpKGaAE0foZjV+ZAyOSlboOM0phjMM7B53n7upAnCdSODO4yvk8Nd2gkuagNvwYLbl9pqVKyGYM50m8A9LWJdeFUeCsbuYagErODTVsUilBJj9y3K7Q8UeUe22oLrRUh1wuZsN1P9FtvZJqReE3qtKkklijX8Cga2mszOWLs1L3a2PSmjUp+kZZey8610r7eq0+s1YoBaOFbVzp+5py+x7yvXVLYlljoS7fc+0Yrh+C0tcDdWliz5SSHCAbk2S0ZZknXofAGcXcz3w7BILJkpCgJOVDlUJVha/HhVfbyBMV6brCUB85GE2vZqpb6OhQ2bGMMypX4nlimReC3pPSQJ0c5AnvLaWMaCtNVObgKEUTelA9kBzGbnDDjuoseTrh0iM6BTid0XEBNOMIv//qAa0UcQ5ca4Mphp6M33bw7AX16gmbkFH/+Ct4+Ba+f8P56inq5Sds/Z5h2KDmmeHKo9SAMh3vcmIZDVOwVO8pNpFcZlGQ8SyzRuVKJlKrxjlDJAp4hZI1QSk5aDYZQmnRaiXnS6LMD3388IGSE6VGQelSIseIVhXXKfqdoj8UnC9YXVFBEcZKiJBSxSnNpt9gfEdWRWJOtJPYHyN5WzUItZ1yko0zSbtGiILCpanpsWqh0PQqVFKMmJpxDryWi90lUYUM1mB8q5eL6pJdiIZYZuIyS7+lMaQcmMOJeX5kns5MU5ITZsqEnAlJhsm4ROJSOZ4Dc8gss5hFzkskLQsmy8KRSmTnHLFKaDdKsqNUAUSzTKblvTVKepwD45zYzImkECF0oxpKhhiTdBovQer3KnjboV1H1ooxLUwxMpWJvjP0nVBgElIcUcW0IVehiwLpBgHVoks+SjrMiDhDN62MjJmVWgzXN094+ennXD15Rj8MeOcvF2KlkEoS+owTnRsotaOQZMEpgpxhO6ZqmCKUJIOgqwqlJDTdOQM4eq9AWVKBba7MwbGzjsklUi6omkWD27DEUKRVx2Bw1tJZS9eGyRVZKhhB+HLB6dajrTXWORaUSC5y66/VmmOpvMmV9wHmhCDpIaJ1xmrDp1db/sWXL/iLn/+YF198wvbuGXbYtABpoYpTkXic68PAje8YS8ENhikGQk68PY7MOZJWCk1XMXA1c5Pgx22YvNDfDdFrfyfvaDPmlIJOEstTdEErifbQRuMHL3oy49Ae6DdSKhAjSzrx9cPE1t2z3xgOXWEwETcMaCClBZ29VCOGhRykirBYgypSr1hCYDpPnMYzRhc++clTrp/t8JuBvCziLH6cKFMg10CumpTNGrDRDGxc8uk+ji1RaLrNhlefXfHqiy/5ycvPeXp1je+cUHAqkqsma0VnPL26ZuPFNGdzZTaPRCWShZI1D/czYQkctiPOygHCl4RVCqWlwel8Trx7GzidCqFtvJuV3gRyzUQqsQJKcX2957NPX/KTz37M9ZOnWC9aOqOtZEw2ga9q4c66hZqrS1i1umzMINdPSYEcZ/Iysrl6Tn94wnHzG8Zvfg/LjHKZvjcMXtN3iutk+OLlHU+fPGk9545SE5fjh1IoPqASISTGOfMwwX1Z4MUtT3/+17z481/y9PkrOteRU2WcEzYbUqrEZubRSmOcYrvpsE5h1Q6/u8YsO+r0Ft2qZXVGhr4qQ4WyGrPfYvuhoX6JuizE00ni0foDyR+Y3Q2zv2VxeybbUYzHolHGQSeBX7p0VBWpSuGtDOxZrz33sn45o6i0WLaNwqPpasfWXvPqSgnjkyIpRkpOpNIQSm0uZlKrRVySMkypcn9eGKdMWDIhCXVrnf2QjlFpEWBCZa8HXznOS5VuVSL7oNXj0Q7vSmmRKpW2abAOlLJO1xbFVPWKkgv/VVooetWVnISpUQYgicynrlrG8tH+Lo81T1KhL2DHmo9Lo+hrFVp0/ZerYxhqG8Db9fwReGGtvoBEa33jipRKO3Bel7OGYKpmzm0Gnty+b8ocHxemm8DXp0r0LTVFFTHvmYSqnnMw/P3pPc4MfGqlhe7BFMzBkqrC1B3baOnmM+oMdZGQ4BIzedbkJZCWe8LD32OHDvPkitRvKVfXmG3POM7Uw3Pq+Au0eslMB12H3x2pb/+ecr6X5Ie0UEtEaYvWUvBCtVg1Yw04Muq6Q7/6FHX9HF0zm4d3lHcP9PPCu4fvmK47cI6Du2HYv6L2PcPuGuM8cTpx/P6RcD+RtKb30tC0VEfVPYaKz5GqIRZHUVEkF05e55oQprdJGdAaVbSg202DLxKIH/b4wQNlKSMlJOl/DiIw1lZhBoXdVLoNdJ2RDu6UWEIlLHKy6r2j7zq898Q8UxDHYDsikdZ4kSpuxrgkUkhMy8K4jIRlIoyRFMRtJAcf205aGefk5rVGKgJr0wSEJUrbScvJWWv9tocdLz/7jOFwJQn0nSecJ5SSG0fMLWJ8Ged0GShzTCxLYJky4xiYQmKeAzHG1lyiWEJGWXDWytBCJZXCEttzbxtm1aIFrUV0ZHMTwh+XwGaOeCq6GY1KEfRgXgLzEkkpXRZy32m6zqJU5TxumfqJ4qOcpo2I7FJOKOslC64FsOu2eGmaMLqhpkU1SrReVJarYIhaK7urPc9ffsLdk+dsdvtLfZpC4l9Uo9NCiKgy4fwjXdmAriwhyElba2rXk/uNmHe0I1eJKnCreUtr6UWtiPuwKmIuBK/ZWMViW0tDQ0eKVBi1bExACZpAVcSiWAqEqolIt668FwZVwCiNyQqTRasVc2bJkZQy1mvGCos26N7jciKrhZIyVmm+eLLnf/pnX/A//PLnfPnjLzg8eYrZ7CRWJUdqyeSwsISZomHY9vjNQF8KsQRQz3j19Ia355lv393zx9ePfH86o5R0A9eqGqit5DBS5WMfsAr+CYvVSCVCLagsIedFGCiMkXYnpRxGWax2eNe1AUfiRFy0lOXEkgvHx0fGg2XsCi6OosOJlb6XIoJUEjmLzjCniiqFEhNxTkzTSAxnXjzb8qMfP+X5F5/hdztSCoTxxHg6Mh8fCUtu2W8OrbQYGkgNnVukBzwV6WeumpQVQz+wvXrOJ68+5e7qiq03VCIpLajSkJga0DXRGYXqHbp66uCYO0N2hilZznMghgIp0KkZtwFrKvH4Hb3vMLZnfP+G4/sHHo4LYyisJj6j6mWgLBUClbnA45hYTglPJ+Y6TEMgV8pvpZDWOKH2Fip1oZeUboc8DVVrvJd7r2Y5yPvhBtMfMG4LaM5//C3UhABvlcFXXvU9n3/6hOvbA/2wQRsvOr0mwck5SxVnY13mxfA4Zt7OEXW45cVP/zmf/Nlf8fLljzkcDlC1BOfriAkV0tiQMllL+s7Sda5FBg+Y1GOMl8M/Lc5Gy4BV0Chn8ZsBszlQtRJzV86E6STMynDF0l0RuhtGd83iDmTTg+nkAKnVOmOhrUN3Q7sdqsiNVhNMKzpQCpzS2BrBBLKLJBPpOsWw84x6YOo0YTEsyTEtC1kpqII2Kq2xVkusVdu/QobbbeTdceR8CowxN5OT5LbWdhLMtYoj3goKL4f4VeIBqpoP10ab97UWI2SSWBDW+DKlV9NNQ/WalnE1JVbAG0sqWZBKITRJuQEDqqAxVJpbu+Uir1IS3ajQ2rixNT7IamENcpL1VhtZdGozCompRt6PWgrGGLz3kk7CB/PVmjn6cbxQuXyfjyj2ioA3KrZCDYV0AsjAPC6VKSiIbfEz641UWibewteL4nT8lu/TO368uWKfKlntUH7AOSf3527LUAqTCdSYZIBaCiUsmNMD6qvfkeZI/cufUp++ImwOsPeYzmM3ihIGdApywF4Sdnpgvy3U86/RZWGr4frKQ9+z6xU1Fo73E4dk2AXol4RSCbUxqN2WnDJsdvC6YiYFM8zTxJFC3w243Q3WDjjfkdPCtg7cXlvuHxOPj5GaLF4dcJ1cU1knsissx8wpGRZbqS6TlVQZg0UFuU9UhVTzxXh1YcH+hBSEHzxQLiFJcHTVkLXQzBaMA9vJQJJiYZkzYYFpytQoOoHBWkwp6BYeHkLAKocqyPBoDVULIhlDYpoC0xyY5ln0eGkmLRGjDN46et/RecmhLCVjTJFFRmkqiaoyS0yEacbWglMalQukzKH3fPLFF9y8/Izh6oZ+GEg1sywz4/GR92/uuX9/JqNZlsg0CwU9zjNhioISLoElCvWdc2aNBFLOSy2isaKn8ZpUIKTMHCRf0zRBci0SXquKakMCpFRYQmZekkSMFOnbziUxLUJnpyTuUm81vRc622hwWuO1xVuJUMlNQhCCPGelldxoSkJlVwcfSHROKZmYUjsRy8YDVRapIn9v+54nz19x9+IVu8O10GgtBkOJmleQCGsw3pBSYlmmiwgpJXm9tDb4zpPyQC1i3mCJlHSmKjkY2M5Cdm0TlEiNkDJ6qZAs1os0ouS2mCHUYTVCQaKkmnCMhXNWjG2YTAqyMgL563b9qCqvXbVoLEstLBlCCnilUUZiOryHLhdylQXzi5sD//Nf/4z/8a//OT/+7EccDtf43Q7lPEIVJXJaSDGIVGDTYzuP6z02Bp5qxe5whTKO+3nij998x3/QfyTWTDhNJDG6y4am1u4T2TT/y8fFsMMHWlFy/rIYX1o8les6um7Aasca/myN5KBaa1FsKFSMFxRmPM2cPDhnqSmTgmLsPK7zFCXIsFa5acoKMWQ5wAya53dP+eLTOz757DnPXj3H76/IBokDWSbmKRKj6H+U6lGul0DvUhoit5DmI+l8Tzy/YRkfSG9POKt5/uSGl3c37LYDWglinJdEDHLYKilJGHZRUAquJFQSChkKm6GHkpiqDMWlAiVjSsTEI+XdHzgvlfMfHwjvT5xPiYgSmruub4QYCxKKqVTGohhj5ve//Zrvv/oDzz95wrDd4t2eav7p2yazT23SF0G9Sq6twlOJMafqDwyCRlAO4zDOg/WUDPNxZPr+LXVesNrQWc3WKja3PS+eHNjv91jfo5QB4kWjlmMmLZFlCUxj5vEh8+44E4zn8MU/4/mXf8EXLz/ncLhBG5EvocE5LXmDWrcfQg70m0HyTp1RdNphF0Fja21d9KoNREqhrMLuBuywp7RBwMSFNAaU76jbKxZ/S+huWNwVs9sR7UZeG9WqABSSw6sBo9DWYX1Fk1FVTJY1RlSJWBUxGkye8HXCpZm0nJjDmRJnbFrodIvB04gxVEuQiUiPm0Zby3tmtcF7zVY7Nn2h7zru3Yn7MRALVAy5CLuRq2olHKt3VgbF+tEAhSoXo916IFxpx1Xnvuot+ei6WX+pNpx+/DniErfCWpRE6/tsSJ40keVmgrFNy7jmQ8oqstpGPzzWwXMdCq2z1NqUXKrJt4p4ATLtufNhrylrW08W3a5sP6pR9fXyeqy/64tms5XWJmEXi6okIOcAC6jchndXwVWqXqipoBbFo7L8qp55LAt3aG7qkYO65ulmjz8let9xveuZuxNTspRi0MGQo0jxjO3I55Hwx3fM6cDZ3PFdmNls4K6z7Pob7DizqDMqJ5bwSC07Du4Jtka8WjgcoNw6rvdwd1Do4jDvDbfvR7ZvB5h/D99/A3UjbOV4wuaKWzxq9sxTxGwq2XRszR5rIesTixoxwOAMz3d7ymlp5lqRCpghoYeIPWgmZ6j3kLSiGNea3eqHNr7m81A1E5VE8K2Hl/8mGspxKm34UaIJUVVif0yFnFmmjC6aNFuW80JNEuHSdZquM2gyOS0sYSLFRUTiWRLdz6MMGjlGlnluheiJXEQjp2zTX6EYup7eCdIiCmBB8kwRreWScqOZCzGL28lrjS6Jg9V8/skrPv3ZzxjunuP7HaVWHo8PvP7+W37/u9/xu9/8gbAIORCWxDhFxrAwTvPFgLOERGouSesczjlMrXilcdoKOpqS9GbX0rqzxV2cjVAe2nhilL/TuvXXZsmRXEKgqkJK9ZItucQg+ZENrRILtiKVTM2amCulUQqiYyyczzPn80Tfe6iZ6u1l8NJaqIScIJXSxNHttCkSmcvJNueE856nz17x7PknHK5u8F3XKAyJe1FKNejc4l3PZtuT5kwqGULANMoDPqAx1jpKvyFvJTEgTQVdI8VIib31kntWaiVG0QUlbTA6kWlhv6tbUK0yCGlmmBIcQ+Y+ZB5CZlEGjMMZJ9RIWzxrEXQ1UNkYiYCJWVpnQk6kJdA7hVXglGFwHlUqtzvD/+3PfsT/+Mt/zpc//Sn73TXeWUESFaBa9VyWaxuj6fc7ut0W4x3qfGKrDdduoN/uGeOZnVXcvzvyzf2Z+zEykyhK8ljF1Fb5iKWS1xJYP7weAzTSPCO4vUIpi/MW33cSl3XR0ArFE1OQzbJoqhbdajGVrA0Piya+W9j0gc4IqjWnShc9DUKTzcxU7KZj8+yGq7s7ttdXbLzlqstcXzvJmfQO0w8otZeIrlRI0YLet18DVZs2tpnW1hTJcSLND4TzG/Zvf8/p4cTOX7P1FmcAJRukOIYtJc4tIzNSs+iyVAp0unIYJLg854IZHCpr5kWCw3PMJJXpO+hVweTMvGSmh0WoKrX2NMtVlpHhLFTFXKog2bXy7bfv+M3f/5ZPfvop+9un2M32UoX58YapQJA5VramuYZLwZiCxkrMjpHN/VKspzXOD7jugHUHUnHMs0SEoRQbp3l1u+PZ7S1DSxpYTRgll+YOlwKIcco8Pga+v5/4PoL74sfcfv5zPnv5E24O16gmSYotJommZzVaS3SM1QydtDJpXdl46EvEqARamnYoCUlIVBSjsYc9ZncQeQtispxTwfRX5OGKPFwT3TWLPxDtQLW+1RVK0PLHaHytEiemjKa0PaWWDCmga8SbjK0BHSe6uqDjmRxOLKdHjsczj48jp9PI2A7sYU3vqCubJMOSMUrWJGOpzuGUBKjvOk/XebxWKH1iClXqa5FIpURDDtswKgixoLZFwiUvg1dFy6FcK2imJfhI803TH8IFsVwPNo2EWL9Fo8A1BU3Jq2BGtRWhIh3qmpwqpUSqlkrhtau7KIgptwG6pUQ0GvQj0krigowW/XQpl9pGiuztpRRca28RNkwjzP8aWkPbw1cz0gcUfx2QaxFtrLjABV0vKzyeDLVo0A5VMpCpWn4nCp0e8HyVz7zzlidqxs/33CyOT9ULXnTPmJMiD44SFDqL/MsqhykW5xzaZJbHE1N85O/P7/hPrzKHm8BPhsjLvuOmdqicGR/vyXNmoyydHYg1YE2m6xK3TxU3h8ptv5CnwKYzXF31dHZL/dsR/vBb/FnYrPr+G5Sp1Gwpy8A0GVROjLmycYkpvee37/8Nsxt52n+CUncMneX5leP0mKlFQKKuFaJ4pajJ0i9SSRyLkmjGUshaYZzFIiYmMe7I35e6SjP+GyCUYV5PDS002mgohjRZqX+yRhbvWcwd1misrfSDxrpKrpEUC9NyJhfRlI3LRNIBOxVMQdDEKpQ1puKdbFrWDlL55wzOSQeznFCbgaGI4NtuDUvIcDxTmKg5iw5FKZyzfPbZS37513/Fs09/xLC/RTvDOD7w9vV3fPW7X/Of//M/8Oa7R2q15ArjGDnPM+OyEFqWEw3RMc5hnabrHF3vsdYK6pUrOUiHJkrCa2NILMuCUp2YPgxoBF7PtUHKbTNJUfSHsQjNHoI43XNK7YSom0NQFpqYhWYZp4VpWZiXhYoipMxpjpymhc20YHTX3OKN6pVUCTFAVS4ZnlxOqYpcC3NMeGu5ub3j+YuX7A9XdN3Qnm9q14Q4FAVtUXS2I3Y9U5lRxXwQXVc5BZUsi49Go02H7rbULhGWBZMrfS0obUSuoBS0GJ9YxbRglSYrOdUW1cTFbYXLtTDGzGMovB4X3o6Jc8ko7/FOMSiNLlD0GuMhJ7SYCuMSMVaqJlOCWjUxSKIB3tEpSzUW2ym+vNvyf/npj/n08y843DzBqrV+rK3sMi2IRrZWtLUMmz394dAScaTppOs6XN+xsXAYBq43AxvrJa+1LdqrOeDj9IaPKaJ/crsrMenrVSGlFE4brHWSsgCEENpzrDLwt1o0bzq01VhjGbPibXKoZFBe42rCm0znRDtqdMAaJwh959k/ecn1i1fsnjxjf7hlf3WNBdz8DqveQZFIIdXQCqsNxoD3Hu2uwewpugPVUemougMtg1DNgZwX8jyxe3bPcnxHPT3gXYdtG2+umZICKUXifCLHQI75QhOrELApszOW0vekUpmA6BdyFm1zTYaUE2koxJqoAQiKdJaDbWrvhTjs62UNCqWyVCS2A8V5inz1m294++1rnn/2BbvrivYCUdYWyUKFstbXtYFStYxelMhcSs5oV7F4lLEyfDYEKIRICInpPPHu3SOcZ1IKKAVX+45nt1dc7a/ouo2gky1/smTJoAwhMU+B42Pg7cPMm3GCpz9i/+mf8+zV59zsb3G6I2YZJMNqUsmVkuWaNlS8swy9u4ALg6vYmDE6E3OQe6ANGdoa3PUVZn9DtTIglGjJuVK6PXm4IfkDyV+R7I5kPThPbfeCohlCUO0gWDGlDRktqqZUGShzDugS6OuMikeIJ1Q8sYxnptOZx+OZ+4eRx/PCHD5k+MZcWVrSiGhc13QMsLlgiDgX6TuJBLLG0BnNYdMRU8KoQMyixY1FgbGUpkdPDb0MUTIlQpX7UxKB1Ichq6GEpc2Uq5xHtcRI9dHHKkjCQq24RlmzahRVaVILRSmqNXvR9jDZQ3Ip6FIJquK9owkfRTeqDCFEci1N/ykb7rqOKC2HG6O1HK6acWfV/5aWbyh7hBQF1FZCsj53av0gv1Lyeud2yLqEnysxx9KMN5L2ojDFELJCFfOBcq8gbsYKOVDjCfSOVA2PiKB0qwvneqTXHRvd40rAO4e1oD30W3Ah48ZI1QumL+g8kx9H3vnK/+fbd0zvvuPPD0f+5nbLF/aG69TR9T2u2zGPj0RnCKJIYTMUHuOEq4mOM1UtbN2AjwvViaQine6ZTyNaFSyJbBXabdFqR4qV+2nB8Egumvfht3ydf02qEyGdeOH+ks3+jqE6agiU7DjNiH7WeIrSzBS0K+xSpYREKopgLcEXtM64CuFRkbLFqYqtwihWpQj5v0Ax/k8eP1xDWcF43TLSxGwToyBJJEPV8m66WvDWYHWWxguEfs3akButGqPolrQ+c9V3/PjFU7787BPu9hum8cTr+/d88/aBt+dI0RqrW7qjkRvBNJhWGy0gSSmo5kaIeUYphbNyEjLW4r3jp5+94L//l3/D5z/7M65un9IPHeNy4vs33/Db3/wd//F//xX/8PffkPMaqBoZz4FzTCJAVg6lNJ2zGGtxnafznr73OC9Vg6EspCKUmtOinREtj7TTlFplQW5XfsyZWCsUyRILqTLOETst6ChVVjnLIk6W6IK1PLYq0TuQZBg8z7NoFJO476rKxCKI7RITNlgcSioaqRdHeSm1Nd4AuS0IGFKpzClRtWZ3uObli0+4uRHEQwwuEpSomujn0uVKxRlH57aUokiLVPCpKmG90kGdCSEQk+gUawVlPdVtyEoRVCLUKtaZUi/oSM710vEuC1e9xFKsaNGc4X4ufHea+PYUeBwL2oGpgVpEaqO1QlURtStyQwqEPkslf1jMC224zJfaOGssg7d88eyaT18+YXd1jbWWmguUBKoTPWqbBgsFdKXfDgxXB7r9rWzq00yyS8us05gs2XOC/ghNaLRQ9BqJJ0F9iAUqHw2THw+Uqjb0oAo1pNWaUaeoGYKKrAHHuRa0taAt1khYsjcWow1JWe6DpShNMVILePCK/Say7xOdWYQ26Qb6u0/ZPv2Cw91z9td3bHbX9J1HpxkeM/HxLSWm1qKzCIIS57b2b0B1KOUxSmQZVRmq2aLs0NpPmoZsE+m214ThQO2+Q4/3ECT2XsUEMVGXGZUiurYg/5Kpi0gqVEjokPGxUlPE1kpnHdFoUoIxJAwLmyGgui2codwHfCx41UxpNMSqHcjWGT81lFi1m/PNN+9488fXTH92JN4uLRXgQ7apVDFWVmuPoNrrFzRyg9ciTUUVTGka8xQJ05nldOLhm2/4w9//mm+/+patnbFOns1+47m72THsBoxzQkXmQA6BsMzM08Q8zcxz5nFaeDMFyv4JV6++5Nmzz3lyLZFJEuNWpee9JGnaaNFuqjFUm97TtaSI3oEhoMhQAzmNcv0ahd7usLstdnNLUV5aurLoTstmB/6a4LdEO5DtBqVdy0/VaExbp7hQwFJbG+U+zeXyq8SACme6fIZ8hPweld4TlzPj+Mg4Jh4eJ+4fR94/ToxLZElRfs4iNa+x1JZ9XLCt9MFag4lZzCVtLarVUKuAClrD4C2xudlBsW3GK+O8SEIwzLEQnCVlmI0ihiIynLpq1kozwLTw97qmHbRRssmjVhpS9IVNRlXrxbW9Zn02waKsu3UdVpWg+lUuWjH6FXIJ9IMcQA1W8mVLJYXUAIcVQf/QDpXrRyknrK5h+Uliyq2QQKNUEfAHGuXPR6hkG3yRj5VVtrMilOug3bKQc8nollErr5OwYxQJt5e7cEXXFhmwa0+pimOBSMXYxLtypKuO5AaujSNvLYNxbIYdg6uoKVNUQhuNy3INzgVOunBeTvz7N19xXBz1+sf8iDtu7ZZ+v4X9nnN4wPd71L3FF43vMmp+x/n4G8b7e/LmCXFR1FTI2hN1IKhCoeCLQtmBc+0Y08B4zhwZWXbf8ugfeVBfEXVBVcuyhUyhtx3VJYxJUCqlWHISP4hVCqLB2UqvNR2WpODYa+Iwsdkd6W3iOPS8eTuQk8XVhLGakCsn9d/A5e0HhXKlmQyEFqxJ+qq1K3QbgzWFTmlUTa1KrhAXmHMlq0RICyFI84h18JefvuJ//h/+JT//+ZdcXd1hDIzTkdevv+Hb7//Im/sH3j6eeDwF7k+BjMJZ6eg1ulUvtpNWTaJBdL7QOS+UFonDtudnX/6Ef/HXf82PPv+C22dP6Xc7puXMm7ff85vf/QP//j/+B/72V79lmgohiX4ztKq/ogzGabrOY62j9w7vrVDd1raTntDTH+B7GlpZ0N6TU2ZeFtk7tNxw0qn6gRrPVbHExDQHjJ9pLANZjrVYvUL+ulVOf4haWEJgmRdiFOTPO0vnvcD1usWaZKnvIwt6mossEBUu0ROlLVilJtHv1cp+t+XVy0+5vXvJZnONMa7F8pTLwiXZoqsGRhYw6yyueNKSKDWLg9fKCTjXSogLJTbTUa0SjG89qRbmonBZnJamRGKMxJCISTazXEuj9huVUwu1JpZceXtOvB5nvnmIfHPOlKrwpTLYAipgE5JZqu0FFQZ5PUsol4iEdeGsIm4VPRgG5zRXneP53RW7qy2+74VmrRHQaOeknocPqIN1Htt3DFdXuH5DijPOG2ajmtmmUFIitmG505p9J3lqqSQ5ULRNxGi11mr8Vx8fy12EnpUTfkEih5QAG1SkBk8bi7YObyzOivPbKI3zXmQl1vJYDJwr2lm2hwGzqWz7iB883fUzts9/wub6FZvdDX2/wfoOVKbGMzVO1HkE46mxkuYAZKGlU8K6hO6MDARYUkho8xTdK9SgUXWQgOom2LbaomqhzI/kh+8ocaEoI/mFFLQSmYHKBV2gpkqNiTJO5OOCOSbsaRaZjG5h0b5jTIESxfwzvjmSTUG9q5y/uYckOjVbhZnRtWJrxWpNrGJky3UFpsW0cTzN/OYff8+XP/+ew+2dmDJcB0boT9kGDVo3DdzljdMrjSC5pBRyWeTnTJH5PDE/vOXhu2/4/d/+il/9239PPh/xN+KR743lZue5vd7Sd74Z8zKlSoNRXGbiMjGOM6cxcI4RDnuuP/kFV5/+Gc/vnjJ0vezPzYwoecBFtNZJTH5VVbxdqW6aJKOtfwhFyfZKUGhVsa4jW0eyGmMGSnWcYyb1W9JwTTUbsutIxklTjlZUhOamsThrFmPOElVUq7xnObX7tCR0nrHphJ5fo5fvKcsbpunIdB6Z5oVpqjweRx6PZ8Y5M6Us+1Oq7Xs0hKxJmpJqEqAsyFxN7XBWM7VMxJTpU9eqcVtlbkPqrFV03rLpPRhFLorjlBiXyhwKJkDUmjnAnAp5HSTrKouQq+nj1JY1AWClgxVI9zutTap9TPrf5eAsa0AV+ZCCnCO6iFadFidUq5YUjJTovLAZpQgyWbQkQKwxV+3YBEgyi8q5HV715Tmu2s5aBfiwqJZYIsPvOix+/FjfX3le8h1y+rDWyWtSLtr/khVryxtFQ1bye3vd6iXzYqLWBLUjF8to5Hp9YOFgTnRdQO0sQ9+j2aJMoCZwvcbud7BkbHeDnrd4Co4KfYdyex70yPcP79mWjpvtAbDo655y3TMPkbRN3NSeAahTRoWJ8d13pIeJjd7xLgRqt2VxlilBLAmTMoaOOWjePGbOoTC9Wwib17zbGNTmjOp6nNfozYFcOrIypJTRpmc8B5HYFYVXiq4mbMsK7gaLz4lYO5YO+sPC7T6x6RacgvPJEopiqIVBV4KuxKj5oY8fPFBuNgWcIiyVNEMOilyg94phq+k3GaPayaFATYUQM7oaYs2CJlZwQOct//0vvuR/+1//F778i19yuH1Ct9uiNCznB7aHHTf7HY/v7/n+3Vtev3/gj+/e8+50lg5SrbHGNL2aXLwhJkLMLMvCEgPLMvPk5op/8cu/4G/+5q959uknHK7vcJuBaT7z+vtv+c1Xf8f/99/+W/7Tf/o1ywQxZqaQpSqxVIxzeCsIZN91EkhslWQ3Nuq5NMpF1MK1fbxKFVKW1yGaKLFFcdWfVNCqVR3KzZ9LYYmRmCNLjJj6IVDUtIFN11Z5VaSBQOkPrUMxSO+ydwZnLLvtQN87Oi/uwqwhhyjDl1pveoG/VFsEShE9pVDXmu124LNPXvH8+Qu2+wO2aWHWUGKtpQEHJadc0WLK8GO0bQOLlsJ5pVqPr7jf100KxCGsdW6fa4jFMOZEjhnVKvxSFNPWmk26fo2SxTWaq+IhJL4dA9/cL3wzZsbSNu6q0KECmUVFtEcGSri4D5VqAcUfoXd1/fuqJSaoKgbTcd11XA/XbHdXuK5DO5FgoB3VeaRTMkmNRC4obXHDhm5zLd3zlHY/FEqMFCu5jTFGvDY82/VgoDtO8HjiPgsSU7IM/22L+K8qW1QbPJUG3WJJWH+2XKR7t1XDWevQ2krMRxXEKJRI33U0GAQQhD34jjezwb8DXQ0kzY3bsbfX9O7A4Hc47SghtvaWQJ3uyef3pHkBq6ljQGeLwlCTp0ZNJpHce7Q5UXJlGSO5vKO/mXB3C3o4YFxHNU42tBSp00ydJ/L5KAOrtkQsMSnmcSSfHiX2ohaIAbXMglCmRM1ZcvtTwbiCU4WhHygpE2rAJEudK9N8T3oTWcbCUrkMjLpUnIZeScVeKa16D9XYEhkup5T59a+/5nf/8Duu727R2tBtd4JUWkGDdalN2iZopVJKIjpq0wg2mLzkTI4zy3jm+PYd777+it//w6/43a//jjideLqz9IOl84Z+jlx5y37ocH5AKSdfr6iL8S4tiSUkppih2/Hksz/Hv/oZh5sX7LYH+dxaW4xbO8SV0koDBEW12tB1trme1yDspm1TGt1v8LevIF5Dmi76ToxlLpYxOlJ3g+r3ZN1Ttada6W9Xq2EM3YCnDyaP0ujtkjMhV2rM5AykiM1HfB3R8xuYviOe3xLO9xIHFOSw/jgGjtPIFBJLlrU+VklOKLXFbmlEgVxX2UhjBKogYNZoClILm5ZIbNmN2shaqJBsXG8UO6/ZbQzGO6oy9F3iNGfOY6R3lnGJWF2pi2Ju8qP2rcRkCNCGxZWi/nAAERLcqjasNQkGZZVgiiY+V9lfRPMeqTWidCWahDOmGUvF7R1CAmayE3RRN1o/p0TKCatE17uyZKVk1ozK9RpWl3VVtdeukGtr7ykV1H+RbXhZa4SFuWRTlnoBWS+f2r6mKHba4FizvGnVQFo/Z/1HjX3KAZUAX6nWUqzh3iSu7MKuFJZ9R58cOhV0bv9WKXJvsZ1GqSSAWUgMU+ZhC503DBX0knjz+++4joqbJx7zyqJeecrGUE0mnxR5PpLmE9P7M+/HyJQfMDpINzqGaDckpRljoJiKyoV0nggh8VAiRVeSzrA39FvQHdhDT5odi1M85pn7N0fGRTOPmiUj1Z014lWmtxuyNWA7khz5KDbSm4G+6+n6zH7XM2w8NVm6UuhqRlFbhN8Pe/zggbLrDHMLp05ZIOtu0AwHxeYKvBVIO8dKGoWqzQlMLZIliGi7Bu/5Vz/9kv/tf/1/8Yu/+it2L3+E2+6lwzguOL+w3x6wN4Gt7dgOW3bDW6yH/l5zf5wJWejkXBWqUaIhRpYlNkdt4sXTa/77/+5f8Rd/8Ze8+uwztjcHrLEcx3vevfmW3//uN/y7f/Nv+Xf/9m8Zp0rFCqWlLdqC0wbnWrxAZ3DG4IzERigrJ8KyhoIqoVIlIFQuRGts6z8VAbyuVeJPNBeqtl56xot87COX3FozdznFNVrDmIbMrjqV3DSJtV4CfXvv2e027HYbhsGjjCLm3KDwTK6CdGlj0G0wbMlKlCr95L7zvHr5kpcvPmV/uML1A0prcQxS2qlwvdnlrl9Db9esMoW6iLwv9WJrvEOBHAuloQuliJ4v50oqhlKamDxmcsyUIMNZLUXiK9rGXVUhFjgvlfsp8HqaeTNm5qY/sgDtWkwKksnEpFBK8g1BEI/19J1zlhNyFYpFI65EQW8T3hl2znG939BtOlQnKB60hVArCcheRvJ4Is0jUHGuQ9sOpa1sXqWQgmjeqtbEeaLEwMYZPrs7cBMGrrqBWgthPnLMFYkDE3F7RvSq9aPVdj3vrxmVioqzEiwsiLTYxrUyUKq8/lrq+4w2YngyVq4LraX5BYXGEkthKvD97EjvI6d5YargD4FdTORJ4r2oEVUzqiTKdE9+vCcnwBkJyM9iulHZCt2zjBwf37AcH1mmmfMxkKJic/0P7F79iN2Lz+hubzC7Pdo5VA6U0zvSu+9gPlFiEJd1cozBsEwJPUcqI1Aoy0yZJmmcolK8BjxkoWLXhIROd9jqZBBNiTgW4mNljPJzpzaoawVOVXql8AowiubpF00wldwOue/fnfm7//1vud73lHBmc33AdhtM1+N8J53Txlw2c5nfRT+Zc6HGRFwCcT4zHe95+923fPfHP/D6698yHl8zeHhy57jeeTY7cXbG2NGZDYfdBmtFg96sdjKIVWkgCykTlWa4+4z+5Z+ze/opm+01KENMrTwiFS4dzsi9oZDXzWoJELci0cO2gSeVhpiZntztJXUhWqrXlKpZlkwsjtwfUN2ebCRPUiknGZ3aUqulKFpurAx1ogGsl6SInBMhZVROlBgxaaYr7zHpPWZ+SxrfEqajpFykzDhFzlPk8TwxxsxcpJ5XG4UrlVAKqWYyFWsktkxd1jSRvdRcwFqRKeXa4oMSMUh9oTZOkNr2PjrVQQMVrAdjwG6sDJsazlOWAa0mchGUcIqF+tGJUekVhFjv9fZ7FckXWg6LRmmqbrIL4YfJtRKLpC6sOvwYA7ksKCWHy6Ba3BsFYywlOzEnuYTvvKSVNHNOWSVaFpyV9iwZKptBsqyHBv1PEFSqDLfrz6EbNQ5C2Vct2mFjTJNTfFjQZM5UktwArJW54uXIXGBQFFSpGSYDITf9OsLqOagugs6ge2p2JMTZ33nDtR+4TtcM8x5T7ijJMM8Fr49oX9HLRDce2dlbBl2o4ZHUPeL7yJgfuL56in+tSP/wnnyfSCfHcmsIOwjjyPu333J6/Jb3r7/n/WNgVpGiZqy2WCXSDpRidpqsC0tZmMJCnhaq0YAVluD9xOjBdJplu5AOZ87+O1x9yzzNxJzJWVO0zNh7b9AGMpmiDFlp8mApRlFjZYkDp+UppV6zRI/tHNksRGuxOVBqwuB+6Jj4J8QGlUIMlRQFyvYeho1iu60MA5L9FWOLyKmUUKCA904GI6DXmr/68af8v/+X/yc/++Uv2T/9pAVAt4sxT5AmalwwVTEMW6yzQCbXCasqVineHhfOSRoIaq5NJ5iZpxldM3/5sy/45V//NV/+7M948vw5m92OCjw+vuHbb//AP/76H/nX//rf8J//7g+cxowcniUfzFmNs0IVW+uw1ogg2zTNXhVBeHv/qbW0RH8xhhRkcEq1suREKYpeS8ZhzkaQx7YISBacBLoaY/DO4qyE6LoWDrvq3WQBF/G7VRpnZQAOjWLuvJcwZqXoe8d2OzSzkPj6cnMUplVcX41QhFWJzqzlHXpr8M7x7MlTXr36jKubJ/RbyZus5YPAvrZFopLbCbBpKtdhRoGxjq7rqUAMgbAEYsycp4nzceR8OrMK0WPMhAAhJAyKmiTs2mQDcYGY2mu/Dr6tc7SKiP4UI/dj4nzK6KrYaUEZ13mrJIiAMRVnJE6HQnvPmokly8CfMs3tXi9FmfIjJkq0eFPZbjt8v8UYL3EmukDrlC/5TJoemB/fEMZHlJW2JpEbFNI0Es8n4jihHWCMGDBQbLxjuO0JtTD4I8ew8OZxYgyB1DLqzIXM+TBMtnSTxnCvTkndTvD1ImvILfN1NYMobcEoiq5CrWlNroVcMkuKYCzGCBKhqXij2XSKbWdRsRLGhXB8QGepeivLCDmgydTWRa2tJS2C4JkidWpWWWrJpAWW+4nv//Ad798+sASh6upX31H+4/9Bd33Ni7/4Bddf/JR+f4vSGXN+j368x8wjOUVSzExLx+v/H21/0mvZtuX3Yb8xi7XW3vucE8UtXp0vycyX6WQqKVIALdGCbUoyDMEqYAKEOoYbtlv2BxBg+RMYarhpwIAabhiGDbhjSYZkw4ANmwIkUqKVqWSyTGa++t0iinP23quYxXBjjLVP3MxH8Xa4H+JF3IgTJ/Zea645x/iPf3EJLNeNO104xitjaFALrKuNvuOA3k90TVQ/WAOVKULukTZUWnmH0qiXznbt7tRgAoo7tfCEKcIxwuDo99SM8+on5y1ydV4a//Dv/ZghKI9vPufVJw8c7k4MpxOHw4E8mMG87qNK7YYErRvrPLNcZy7vn3j/9g1P7z7jenlLbyvHMfDtjyceTpnTXeI4jaQh+2g2MKUD4zQavx2sSK3ueVs7rVRaE8gHptef8vDqG9zfv6BLZq7P+dFmWaY3Ti7gkabCNESb1twaKRtJrr2jREKfDFWRSGEyukpVNGaYRkhHVCx6kpjQYMWviHnOVoUadgEfz5OJLrdI3toasRZyXxn1wlAemep7tD7S+wKqlNqZ54V5bcxrozZAxdJyxKk9xfb/IMbJTykS1RTMtVXWrVnMqtgeiYcUGM3Cfq+szahIquQolK2wtW6+vtF40qPv88cckCaEHh1KjEZ76s28jKu6EMnxSdXbsx72+omb5v+56KIjPdDVKAG1VrQ04+qXSt821m2llEIIEKPtdyFAiOaW0mKzSeGq5DVzOByI3kx0OrqZiE8QQvTxf1cXSO6CHRP1qBtTerln54cDC/vYvmHnZsJG7ILRxXbARsTcYgwYNxRZumeQo0g3JLXviKQ0OyB8zG+FJvuBYX+uC8RGaIEpNg6xcUB5Nb1i6C9gPdDGTJk6/WOoZWa9vKPWO+bYWemIbszr5/y4XDlNr/j2N77HNDwwpQPzD99RngrXbwd+Pjxy2Z747M3PeT8/UVuhh2RWdHvziTdjrdnUHqWHYF7GuplDhARqVZbrRn1qxBxY31W2t433aTFnA5y7HwYkDawpk4ZAeDA+rIZEC0oRoUqja6Nq5Hy5o5fAMitLK/Rh5LFBiQdCaHYgfs3X1y4oywpaI1rNty+PwuEYGUYf/bbOMhfWBXq1FZBSJCUbAQ/S+O3vfMq/8S/9d/hv/Pnf4eVH3yDdnSB7ZFtZacXTa7bq3oimeh5j5KPjA3Ut1KLUJlzfPrK5AXmrDbTzzU9f8+d+89f5jd/4TT751q9w/+oV4zRQysq7Nz/nJz/+IX/7D/6Av/Vf/h6PjxemcaJ085XUroQYjIhMIEbnq9Gfc6/FOkBUkWYO8jFEQxmzIY8i0QpdzMoAsU5f1oJIZMiR3ryJKoXNrRXMCDabPUW0HTyIcexyCKRs5OuUM3HnLfq4IYgVgirG8ZoOI+MwEMRsf8Ac73drIJHohQN0J/+nEEgxMOTIq1cv+Oa3v8OLVx8xnU4Ej9Xz89J+6AebXa22CShEiUiy8bl0s/2JJTDXyvl84d2797x584a3Xz6xrYXj4cB0mJBgisLaunFBURLRPERt/mf4j2I2TE416E1ZtsbTUnicK3WDgwgpZCqduVU2zM4lVsjNDXq7oZL4Zte7WlOgCrIT4x1xxRTttpnB3TRw93AiTxalRwwmDiqFtq7UZWG9vGU5v6XWSoqJ2grbfEZbYXnzOfObt2zzyhgPt5FRjInDNJLzgSqdrTfuvxyZsnvYBd/Eu7vVfTAKshGQfxa4OSD0bj6hXewzaDAB3b5RBzARjAjBTm5qqzRHSap0BoRIZJTIx1Piew8Tn748cjpNPBwPZG3EupnK+vIEfUOoiCghjYbcyAA9Iz1A65Rq8Xrr9cKyLqxul3W9Vmrzw1SEeZ3Jr09w/8AdwTxnr0+cykaoxbiFS2W9bGxzYrkUki7kvDDkDq0izZqRmAJhGEjDEXplXS+IViQoMU30JVLnwPXN55T5wtJgUeHajBd9THAfYQrwwg2utTkiFdU51+JqV3ve3r698MN/9FO2+czr15n7h4E4BKY8mMI9hluikvZqWbqtUtaF2jZKXZGujFF58RqmaeI4jpxOE9OULJkmpduZOa2R6TCZXVAyIaGamsVsxTxqqbeOpIl4eEGe7ghhpDRxqxgrBNZSjO+ewm1txRg8XtDiWEX6rThABZXMtXS0NLYlsm0ZJZk37Wjvp0tAw2hJN9EO1xDzrRnfR/34EARcpVyV3qo3lYK0TtbCgZlRHxnlQmD14kIoW2NdK1tR5rW4MXtkzAkpDU3CVjf2GNYUDRKzEb41Zl0Nne1NLUwDgEBpnYgSYrzVKpRGFKM4b3RWtaImBZ9qBSWnTNTIEISaOkNSWoZDha0HqlrEr7JneRtysY9/d4fIGPCCrXPLyVK5TXBKMdFjK5112+yZXhe2avtYr2pWc4LTkSCFTgxWLIvslnXVpxxW5JmpeUSKFYHiaGMX86V8HmV7EbQLHrU/f44gXlSKGaUH8+zcz5PdqxL9wD4IQbQRiVTf4G7CG/WUnaBuHaTQbdqHNxB2aDnauym0wnAJSH9C7jLx2FCdgQOFhSXaZY3jS06/eqJ9Urn+NPPF28Q5X5Ah8OIuci8Dr08vuE+vGU4v6Cmw/Owzvvz8M74oM2+PC2+2R651vonxcjYEMYRsAqQOpVjy1FbrjTpi0ajJ930hZwejCmi1EJHz0ww6uxbBJpuSLJXrkhL1cOTc7zhMynDo5DGjUeih0VhpLSHbQF+gtczWIitQfcQ+TUrblq9bJn79grJestn9VCUkJWQ7jXqHbbbx2bqI2QtVTDE6BA6D8M27kX/u+9/lr/wLf4nf+p0/z4tvfZf88ALJ2R6KXtG20tcrdZuRGMzgVldTkdXGGEc+ffUNQhhpDS5z4bpeb0k8v/q97/Brf/ZX+e73vserj7/N3d1LEOXp8R2ff/5TfvjDf8Dv/a3f4w//4R9xd/+C17/ymuu8Mr1fePv+idWtHHxd+wFtxeTeMWmUW9eUfERlaJ1FtO0O/wFIaSB2KOuKAm0x5aP2aCbfrihs1TzaUjaE0hBZU73FgD3cQU2x7hJfkWARfaqOnnqvmozHk1K8vZfSOtGV2BL2cYGhIqpGPh+SIaJDitzdHfjmt7/F608+5XB357zJzm6uu+eyfjjS2NEDVEyg0gu9V1OULgvrWlnWytN14fFy5e37R96+e2RbC8u2cVw3xnFiqxtb3dDa0SQMahnqoam5+reC67JpvdO7IZvXtfPuWnk3VzrKQQaOw9HQgfVK65XVx/DF8+Vt9/L7bDuZF5MCRDc9774P2cESsHvy4nTkMB2IyREW36ta3ajrE+vliXo931IlVDt1nmnLSp/PPH7xGZf37+ldbrGKatAJ4zQwTBkV4XBOTNlj3wJo8Zxyfkkx+cEPFeNgqPuOSI8eXdbZujKkTAzOsBLPcbdBFMaZrBQRDw5IVK2gjdSFFynwnYcXvHp9YjoeOOXJBBhtI7TKEBMS1dEmMQuSmH2sKZbzXSpt3djWK3Vdqb0hMZLGTDTaz/PNCSbyGBOk2MgRUq+U65k+P9Hdn1UqDD0a8lwuhKhEfb4eeyOU8kgfJ9BKk0JUu7bTmGgCcy5ct0fmizJXuHRlFRga5Az3CQ4CdxgX+QoUFeYqrKpUP+huXFe1YgZgyInjKISwEpkJGs15wfedlAQd+rOXaR9Azfc0JSFlYRis8RzGwUbEUZz/p5RayGMknSam49EM0IOlCDUffbbSKR4RGPKE5COkA1Uja7GRqF2pwLIVmsLR7c5iDsQgDNE47Lsp5/M+6F6TYaAFoeZEj9WmGrL7xIrRiuJgRu3JQgNUoj2D0u1itF05jNsCdbedaZRiHMqshZGFQ7gwxTNZZ5uEbAvXy2o/rhtP88qylpv5v1Fw1BxHuvGxcwzu3+hofAiWNtM6tQkiTgdxilPvnRytkKvd7XfEmhbB+O61FpYtsNSRoTYm8s3iCFWLjaWTpDMOMDW1oj5H5lIcvLAickcju/s9qmfBRw10T/wC49Mv60ZvhbXu3sWNVjZKrzbmVft+VfG4UJBm3OAEJD9TQoClVtZY3FLOJ3UaYHDKmdOdsju6oFZ474WgoYvdqYw7CLEr123X6a1Ruq17xMU5frbshScinh1uFkQGSNrIn7Bbbz3vi6JizeteaN8maaAlIBelv2k8XWd+dvcG+WaivPySF1k51pkmD+jxnqAHzm6LuI5H8svCdP77vLzPfHJ64AHhk/QS0Q1i4a1e+Wz8BT/jcz5rF7ZLoTkVKkoi3vQXkYawubi0tY2tFHM+6WYtFlwAJb6X5jxwmCZv1g3Rnst2mxja81jRxc6pGIWyLLy9rBxOkZcPJ+5P90ynTJMFTYWoEak4Yt6hi+0FougYYDgThn8aBeVqW3L0A7A3oZWBpSdQoZRG2TqhmWXPNAZejcrvfO9b/JV/9p/ht3/rN/n2936NF598k/TwAsbxRiAWOrSFtlzRbTWOVRwslxfbKFMcOA0nSlHOh4VTnpli4eF0z6//+p/h+7/6fT7+5je5e/GK43hk2a68ffsZP//JH/MHf/B3+L3f/dtsy5Xf+sEP+OY3P6X1xhfv3kH7nGXOtiF3q/IFHL1TQzAERxGME2iQvmeSi43wdqNsSzeASDICvGeS177S6kRVt8zwNJtezeNuiBbtJaLE8IH3WRDv/ILlcKtStblC29BiS9+xwiZGT9Xw7llsjgnsPC25VR4Re+9TCiSJTNPIp9/8lsUqnl6S8/iVQvLDYpLbZmcbfe/N+UQrl8uZp6cz56cL1+vMupgYYF4WlrnRenJCUWNeV2tK3J6p0a246n5Y9sgQEto2LxC6j+itw76Wyrtl493Smatt5NI9OzdHpjwwr9VysJ3vI35y7BY7KUZDZvzhVdUPMnR9o/JDZAjCcRjIw+DX1i+FNlrfWNYzpcyOBjrXtlZqe6JvG9vlkafHR5ZtQ2Ki9RVdG9syo60yDIlhGixRSEB6c09KMxaOPCvT99euwWv+a8HslW4+br42kXgTHMnNf6rbPrTXb4iPxjeqBLJV7QhW8KSWkLaS9ETQYIdjWQElqK3FgBAlQog22hHLQe7bQiuFbZlZ59liONUEUsfjyQjqOrNINYU9tlGu75/YPvucUDpbL/DFz0jLW05Dt0QMCQwRjrKSckOkkIM1VHYWuQ2JJ0EEMfpI6xldVyJK7BYTSxXWc2VelVnh2oTiqIkIDAGOAodoY/AKPDm/dGUvJm0EHnCLpwbTOPFwf+LjV4FpSMTUyTmSsiPFsvMprXk0FZa6YXRjj2SMXkiEZN6UewFEa8RoPr3j4cg4TcTkAjEXPKhYxnkXoRGQlG7fo3VlLZW1WGOo2tjWauI3j0JNMZCiMORIinaIB6dOoDuyaaIdSZEsYgJC7+CkgwYvIFMmOrXIMEFcmyFWVMJtz6nOKbWQCCvIsq5MzBzkylGuDGpepdty5fx05d37K+8fryyb+UI2uAUgxCgEL8B2EMGGQsFtyXZ+vAEjfatmFRdNKdt8MjVkC7Gw6YUhCT3AlMxBgmLJTZd5JU+ZqTRCCogr1rt2L+CUKEalWGnkAKvYVAlHf3vv/qPe6AW7n2u3TFYPxrCEtNoqTW1U35vJMBTjVXqZ5c+7XZfmS86uk/06Yp7JMcotlcj8g4v5yKaI0m2fqh5tHHaR6m4LJDcRIWBpUH7lfXJve1czVvhuJSTsAiTZd1j7c9k/O2iv0KO9LzUSkAQxegvB/CmxCZxotNF7AZ4U+bJTPoP3M0wvCj9ZHjm/Hin3iY+ymlbgUNDDRwz5BSFlSuh8/3TgX10/oYxPZFVSU+IiPD1+yR+dFx5/9pYnvuA6XJnFC78mRAwNlmb8/KLGy19cnFpqYy3F7AV9FNYcXQ0hgFsTHoaRIY6UZu4nQZ5rj61ubqO1O83AXFfmpTAvsF1n1ofK3TISD404wqAw9s7kz21NidUL3RorjDOtv+Xrvr52QRndNwy1dJWu0IsafKtmKSEilsiRlPsU+Evf/zb/g3/+v8lv/Lk/xyff+R7j649IxxOSBu9aA0hHW7OYtXWmrRuSkxVI4pFQKRLTgEpkPFj2dgzw3W99zHd+9df45ne+w8uPPub+5UskCF+8+5yf/+xH/NEf/j3+y//i9/jDv/8TUoe//C/8s/zmb/0ax9OR2gqlzfw0NCwXxzsZxdEv65iCuKIZOxRvUD3+5Ilt0CFGU4c3S3UZY6IjlFJYFjMbTymRaiZ4JycIUWyTHlMihUgKNtoOwR7EFCI5GkxtnZildSjdeFsihJgcOQ0u5rGDxeLa9hzhACHZYyyW850kMDhBPKXMq49e8fGn3+Tu4WPG8UiQ6CIcR+8+LGRuiQ3GLaytsCwXHt+957Off8ZPf/Yz3rx5xzLbQxKDECSZ5U9rpGGAHKlbNb/NpVnRppYHLSkhKZCqR9KpEupOtBZKh+vWeJw33l03nubqggCodFKtPkbz3clvl+5Q1Y5e+cGxI8+3nS/IbUQTdrTPiwXpjd4K6opJ7Y1eZuq2oNWVrsGQ5l4rrRa27UpfZtb5TFkto1y1s1yfEBGW2a0q7iZSjtRmRtQ3lJH9wIXdjgj2VsGRFf85YVyuBubtSKM5HzTYTkyzbuj5+Q6GFDQEyZGKglbGFpCitG1jrZ35rnG+DBweB3pT6mTWMbvrAeDjai9u1RCSWjZ6NaSsrBu9WXEswZT/jDaSu4aZIUaaK9FVYXv3ni9+7/eBiF4vvD4ILz+Z4GFEMK5WlGhcyAEkJ5KA0swE2Zui1gqlrMTeCEMm54EyV+py5ipKXQrz5YnH88LS4aowd0Pd7ICDUYQpwCRCVWUBrl2sAXUkKYoJdlKE4xFev1ROx8bdwXiHOQpDEsZBiDmScrbn2vnawQVvewQlulNu3LbFBXtge4j5bapPRgLjNDHm0ZwM3JfQVMCBjo0HJWaCJHPkaEoPSmuwbb7eRNyyRa3pPRh9KUdhyIHk389cFKzg9ZRqu1a2NZl5e3AFbrQJhsQMyUU4uL+kO2X0bjzHDxXKVuio/7C9b9CZgzxxkjOTXJG+Mi8rl/PMu3dn3r2/sJbuhtu+1kWJ2TKktlbMH3bKRIn0qq5wNocKYiDeHDEaY87kYFQomqW/TDmhvbNshaqVoMnyoXO2ONNgE5h5q+R1ZdoiQ4qGXjZT0ZtJd0W6tXjZi1sRQYMVldqbG3pX8x4Wmzrd0orE09FaYWu2n3btxsXDCvBSNvM/1tv2h0tBqMBuQV9QB/Isqz42zBoomO9o004LJoKNqh6Va+rtGCIi5m0sfg7h/GBEPnDUsP/bgYmbp67uKVG2imKMPiV6BjR2L05REwLth5CEYOijttsGb0eUf+AeoQR0KYR3Cl8o/S1sVfi8NVqbCeuV/PaJu48mXj6MDH2lXt+xhcbp7iPGaeRh3fiduxNy+gFP7z7jPH/B+5+94bPPvuDtWdmudi4Q1I8QG0GHYNPOEM3jeaudeSsuorGJW21q1CkJ1Lphfqvinp+N42FgnEZQqCVScsISpQ2cSincRMDdJ3h2blfqRViuZy6XhYfHA6f7E8MxkHLiIQunaOdalUbJgaE3WrD1tt4U8//k19cuKMmW0lA3g2g1Ci0FLMtVmMYIwQ6JIcCvffzAv/gbv8Vv/Ppv8ul3vs/ho48J0wTR/0kxZZm2jpaFMj+yzRfaqkgrMPgh3hXVwLIulK1wff/E+uY9H7/6iFff+z4vvvltXr7+mMPdHZ3G+y++4I/++B/xt3//v+Jv/ef/P374oy/QLvyFX/0ef/Y73+X1R98kjZkvv/wJb989cTmvtKoG6bdgD/g+6vRDwgQp7KQ06+hdxCLSUVdJdzVjbDPY3uhd2GqhdONP4odr2BU9KBogJuv2Ukoe79XNDzAmM2iXbkjD/uB5B7OXA8aHs84u+jhqf4gREEmoBFQ7OQZCVBcfBQJmRH833fHpR9/g/uEjxuOBmOOt0/zKeNXHHDt/xaLcKtuycn4888WXX/DTz3/BTz7/BU9PC3WxjbCVinYl5cQwugWM2vjCmsjmnFmLt6Qp0jNg4hyKIMX4VaqBrTQel4X315W3l8ZS97GaUimsTalzZZVCVct+vXXJfl+b9hvSGzxDekdJ6B4VFuTG41JtrHXlusxs60zZZtJqNkClXul9s+FxMGPq3gtdjc9Ut4W22Voz31Glr5sj/8YjD2m0A7k2+rpR1mpK29Zu92A/DALPBaTCHnZzQ8X2AnOR7mNV65J38Y4P9qEbF6erUNQOtlAttjGIQms0Uba2cq2Ny0V4uly5Ox5dDZ658bjU1rSZUAtkG4P13qiluBWNRe0J9iZSDPRWoDfaVqlbYVmbq6oNOe9bQa4bQ2ncd+FFuuOEmS+rdRBIV4ackTHTtSK9eJFlOEyLzTb51JHcSYdAHgeWMjKvX7JdzmzLxnk5Q96QCbjylSZj7dBbxJUZqMDaYHZkJ7mv6ykLrw/C6xfw8CLw8vWB737ryKuHxOkAOSVEivEnsbWPuKuB7tQUX39qqI7uzgpqe0fX5gi+vRcbkQvxMDEc7wiH0cRgGm6OGLUHeohoTBANxa7rxrasBB3ZSmPZdjQUtm7uE00V8VCJMQWSJ5vs7/GGd0nwGFBX4GKWZyZ8i+YvKZYyZvvVbpWk1OaK8v1wrdWQydpuyVq7mXnuZ0admbgwxoWwXdmuVy6PxtF+/3ihY84ktYvH2NpbtKI4UmrlTgS62Wudl8pazCPYOI822SlLITgaN0RrBlDlOCXGPKBdGUrFtCrWIOVx5DhmDiWCdM5rY142roeRHCsDlsDVW7fP6JBgUBu9Zxc+GYpl8b2tVTO1bxtKY5oO1K5kxQQqaiP82oye1D1Io1WjNTWU0vU5Xc73iCBWwA4SfPz6jFI2gahWWIZmorGUxIJJqOa3GfZzLCBlMwQ0RPJgbhbRTbEF42q6es3tpT5ojMXV6zeRinH/VZ7pEmYSIq72hgmY/Tm0zyMmErJDEhFPpfIzTIp3iefO+AipmNVSXRuPXxbkupCPF94tymktxBcPTPcTQmFd3zAMJ05ptNSot0J+TPQvO08/fcf6zlJszMM3EMeMhm4CxGAcdRttR+pSqW1l3cy+ay+a91dv0HW3yLNzPkZzwDyOA9KUihBjYCvVpkxisbOtCzka0t7VmuDWq68h5bxUlvXM8TpzOg3IsXAcM6chchwT9zmy0cghUDVRysQS7vm6r69dUIbcaatSNrswcYzkITAeEjl1UvJIxg53ceDPvv6Y73zyCaeHB6ZpNAVqq3Z7Y/Yb3aFupnqdZ7Z5pq2NNAlNNtbrme06Mz9dqKVyvV54+/mXbBp5/d1v8+Ljb/Pi5bc4HiaW9cqXbz/n5z/+Mb//B7/L3/zPfp8f/egLVJUpB169fs3hcCKkgaWs/PSzz/n8y3c8XWa3bFCC2gXpe+qA4JAy3onbwu07AR17oGLvNg4Kxm1s+0bJM9cwpug2QF9Nl4nZEJ6cM0MOxKDEmG0TC9ziFlXwEaaNPnd1X+9mpWGXNd26PAmBW6KBgEonBbVM6hgZYmDMAYmRHAdefvSaVx9/wvF0bwbkt6HEXiT0W+XSuyvEWqNuhW1dmZcL13lmK5UYIy9fWERjL8q6bJwvV67Xmcsyc15mck6kPNjBIkAM9Gp+ZbU1G+Op0EOgFaFtQrk0erXCdCmN87bx9tKYN9t4xY+xC0qmEYBNlaUrZV9xajzKoSspwM4B6rr7u31YQPcbKd/I38paCk/XK/P5kbJcGIcRDY5EKuAoknZTwPfeaHWjbWbU3poVVLUaakff02wiop26bNRlZblcmBeLhduadZv7zhi8yPF91NAg/ero2969nxp4gaiB4AKN3Y+U2/dyGyvsz6SLUQyCMKTEkG23mGNiLnZ4oQ3x7xm70kuz0VU1cngeGiEm6KYSLc2UuaKGusQQqdXMzpfLzPnpwny1dCditvFWsmcshcSrNPLxIny0DQxrpurAloPlxG82zpZBiKIucOmEZAhcxux50iSkh8hwSkgLRO6AO/s+hxV1Plq6u/Di0jhvSt3M+PqhV4aspLg3UxZnFzq8uBdenSLjlHg4Dbw+Bl6eAg/3E3cfv+LFiwfujokYtptNVXDfvh02F22IRnBj7d6ro6tOxXFE2TwvuxXOBKdwBMJwIr74LvnVD5DD9+j5BaKFxkLrhcaREl5QM/S8Ulvi+nSGx0fCQdiKUIvHYwaPMxTjcqVsStHkVma7QMPWpP0kHn/XUS8ABNVgk6g4IMltgSQ4P9DTbTo2om27kvsDk3G3ReutIbWQypWDnjnwnjHMaF0o28x1vvD4+Mj5ckVi4HQ0h4512agXo2TkbIbdrXWGYSDmfEu4LvXC7JCdhWYkVANrscSQFM09I6dATHA4jAw5ox2mtTAvneo7ZoiR42EgTKa43trKslTOl40pjHTpbMVG0X3n0rtvo6ifJTEg1UbBZSs2sSgbpW5mwdY6mvq+S908gNtuQl9d6V02swpyUYzhux8MYhzxo9v4vQo3rqn6/wk4F9KmlOKiGfH9s5cGKVFvItZsDW03Ctz+v9sGi94iaYGb1Rx+FJjae592GDof95GHmkF89xm9squb/Wt3Ko9b8OEopaiiW4BZyRcYViF0Gw1nFWIPrEvlsV35rKxomdlK4WUaOT4coBfq05fUAhqFy/nCuy8+5/3bz9kuV6aYjGMsQshCzoFhGMjRmmIb7grr5meQNnIdbFRdjCZQ/XPvKv3WbSdPe449Qq823Sx76mCt9rWtWzMhuPOCT6SiEGMipv1rTDx4vnQu18I8bpRDYrsbeTge4BTRYbLJKZFBIffnSdY/6fX1EUqFUuzmhxg43CWODzAcGjlbMUYR6mxFy0myKaZFrLvaZrNJiIk4HKEJvW2U+ZHr2y+4vHvLdr4QJUM15fV8fuTp3Xuu78+0Wnm6nnl3uXJ6/SnT/WuOD6+ROPDu/Tu+fPsz/vjHf8zf/YO/x9/6z3+Pn3/2iKpYARUiMWYjx795y/vre370k5/z2ZePLFula3BEpqPRuGA7+dq4Uw6zWwWHafC6wfkO96Rs47nQ5dlz0bupIMIQDX20ZAm7QcHNv3OKDDmQs5G6Dbs11Eewh11CRH20hI9o2R++ED4g59vGblzBjtBJUl25J6bkTpCjdeIpJk4PL3j96Tc4PbwkTwcI0fwtffQPrhD1/25u27FuK2VbLcZtXdjKhoTAaToy5ME5pGYoPF0n3r97z7v38PhoqRVDXBnzZEhNtGvV0ZsyUMR8xhrCpSqXpVHWlW01cdi8Vbb6jNg59ZoN2PwQ3v+7AoNtUYaIGq8AMAWpOirZun4wujUlY/X13xHW1nh/vXC+nCnzlTpN9t6bvZEguAmzG/voXhy0mzBiq3ZA1GYFWEyJ2AXdVuq6gnau88J5XrlunaV0dvGiBKM87BZBHZuSfIWNgB8C1vewOxTszUbYi15HY00t34zWwnM8ZcgRSYEYM4dhIozQpwwpOfWikklGAdgMTWqrmfWmnKx4TUqrMG9XSt1sk+xmlxV2G6jauF6urGuBIKQUUT/Uozhtg4Co8JBH7momzJnSEj2ZFUZOjdYKmjo9FLQViPaelIDkxBgn88Icmnm55oy2kboc3De2cTrdw6uPSWsj1G52U6Wwbit5mzn1Su6N3Cui8N1lI2uij4lwSBwOE8dD4jAKx+PIOJ4YppGcIzl2Wt3oe5qH7KkocjsE96mC4S0Ahk7ZurUDUnpwSk64dUCSRuTuG+j9n6Wc/gzb8DGQkL6xlMZlhcfSeFR4jJlrXnnaCu3xiXn4gnTXKTpSjSdDyMlpErbWcoi2X4RokyW5ySD8PRjm3ageWyh0DYYQxoxIIkhEu3EQ6TvqaM9A7d3oMF5otI4VTc5T0dagbgzlyhSemMIjSTejmqwzl/OZeV0gCKe7iWkawKP64lLIKRgKv1XjVnuK0JgT5/NMCnA8TJRWyTs9p8OQI00bh0PmdBjJCcqGxS1GyyEfxky4bmhtln3du7kCDANbLSyl8W5eWZbCNS6MKbHWYgKb9uw7as9eIouQPRpxF/HUWmie1Z6zpVqFIGivVOdbNjWeYWsWq7usV1QtpMMx9dvesL+E53u8U4L2rWRHK6MYpcPOG+Ofqqrzzo0+JU4nUK9CtTVCiO4ygYdgOJPTbYP6zrX0tLv+AUc/iAMit5G5vVfjaUcvnu1scnalP1MWO2xPVMRGj9VEESvIWckXIbZOcs/QGAIpDYjCWipvW6Fq59ze8LFMvK6B13cnZJ25ns9crmcu5yuPj08sy0ySjWEw4IdgLgZmmxTMlqp3FBN5bcWKcbsegGfFQzBLL23Uuhoo5edzHjJ5GOhVmeviUzMDdEp1ekOr1GYgldkb7meo+ZPu9ylGQZNSi/Fkz6u5AbxfV15thbF2xrvAOEzcpwkqjGH4GgWivb6+KGczU9w8CMMpMD0oh/tOmuyC6NypVahFmUvlzdOVp/OV8/mMTJmhHiElwjDBWix9o8wslzdc377h8vYL6rxwyidYOmtdOD99ybsvvqStxtG8LpvByvlIJfJ0ntme3vH2y5/z4x/9Mb/7B3+X3/+9f8jj+8U7TSd3N+Xtuyd+9LPPGN6/54v3b/jxj7/g8XGmEW6cjeSGV11NrBL9qZJ99NSNl2LcymBj6hDMq1KE6tZDEm1jbC6IyENmzDYyCV4JBOfA5Ggippyjj1+NeJzSzn20cVKQXe0WPIO7s4+a9k6yiy2gTvC4vk4UszTKKTAOgRyUJJ0Uk29OEy9efcLdi9fk6WQ8S210N7Hv7o3XqsVR7oVlKRvrtrBtK+tiXXAp5guaUjKEKiZqrOSUSTGSJJriN0SuTxfWeaVsGzmNDOOIRPfdDOZ5WbUhbm5cY2ALgUuHuTTq1ve5iV8DE4ztiF3F9pDGM2Lnwxlu2bg74hMC9L1TxlFA/xuO4O1m7ddSeXNeeXq8sl5mxvvVOkjv+y2tQm/odLd4EcARp777QXbfEO3eNS+Ae6303jjPG49z4XGpXFYb2QUsRxis6OteUO7F5C7K+crL10XTRmnqeJbF9IVgyJZ68kWMRknIeWDImZDNzkVzgsOROAhFApcizEvnEGdSa8Qciagdjm463UqiF0VipGlndaRkXZbbZ0wSSSH5IWLeakktMqzj/GWxw0pFKdFGzLoq4QLDZshzzyBjI+hKjyutFerSqG21BitmYhoJKRvfWTrSLYghB+E4ZTQFGo10HJleDxwlM4oVzq1VymppRn0ruMM8UZXvduUTlDwIckyEnBlzwrW3tjqDIU9Cp8UIOph6w/eXW061qN8T50ZiPrGReLM1U4wKFFuyvSIGEKWFAYaP4fQ9Sv6UC3esXdjWxHnunDfl3IUnHbjIxHs588SV7dqQzz/jVCMh3xMGs8LaecUpRXJyE/ObqM/oPjuv7UbF0R2ZjO5CIagkmhpKzWpJRV2b2/94zKE3RHsUrHpVY3V192eoktqFsT0y9idSnEErWgrLYs2KiDAdRqbpwDRkt18qVjiOI8tafe815GYakxlyo0zHA7HpLSRi2ZqZeosyjcnGgsfMYQy0LdO7UPBm1s8DQVnXQqmGVY5T5qFPLGvnuhZqKVyuM33Mxjt3GhBA8Qz7GAzBT5J9D4JbzG2ydLg8jPaMpoEg8dboa1eLqS0by7awtmeij8ulgJ0e7vZWvkf4vPCDwSvsLK/di/Q2eu5G0RH1s9+pCjYRSzseaZOsnVvpnPSgxsnsbtum6M33dF9DNgWzN7ULc2IMBq8EkOCNFeEGpxq/smPkpu77+N7oJKgNWZR0VdIVkhqAZEWWNVHRqW1zF9ZL5XGdudTPeZoXrq9O3KfANl+4nN9xfn+1mMopcsqT+z0HmgZDUFW8mISybtTa2aqyrMZL3Fplq0bvUJ9mivOYdrN46TCOifvDERGhbBtdIYmdoYJxac3PEkJQendLQbAzvFWbZwpmixiEHiClPXVKmFXZ1sZjvXDYOg8FHg4HhqFwPwSGWP7kqfKPfX19hBILBEmDkA7KcBDyFCF2tGMGxWunrGbz8dPHJ370058zvbrjRZvJpwNNjDXfw0AMmXm9MF+/5Iuf/pT13Zfk3nkx2ch13RbO79+yLRspjDSsWN2q8vbpzNNPfkJ484Z31zf88R/+IX/nb/8DfviTN8bvEAjNR0NR2aj86Bc/o1CJY+Tp+sTbd492uCbb2MOuTk0eL9j63vPcxqG6F5IxkGIiueov4KkzvdNCpWm0TrvZN45edEZPBJAoXkxGxiE759EI1nlP73CDcyE4V83fS9dbx4cGGk7+veEZGEdNTCgxJTgMdiiMUchBbpYQEhKn+1c8vPyIw+GemAbvIJuTe6vnaFsxUOuuvuzUWqjVc7qDEai1K80RWON+NVJK5NZIkslxIsbEOCaejpnHpyvn84V5mdmuK+MwEoIdXF0MqdopBipCGgbGoqCRmYVtsS193yy7bynhVmdaoRn8Vthv+ca2p5P4hihO3N+tg6x293Gwd8wqidY7T3Ph6bywLiu1FCT5sF2MBr+LXW6pQW550v2a7ITpG7+n6/MIUJSincdl4+114e11Yd4atXlSRBTjlzqSYNvprVSm3X7HPkPCI+T3K6TG8xGxTTdIJKTIYZrIYyalgZxHs70KgUjgunWazDz0gdiFn1wrcmnUF4FyHBgOmSFaCRQwRG9RSGkhDYPVEsvGPM/U5mrgrjTpVKm3KYA9T2pWJ62xOcc1eqExt8h169RZyEnhaSO+BsZOmgohzUg+U7eV8zJTL4WyKSEfSRUkQSUQi6ItMaQIupFCR4YOEQYypzhxNz0Q850hHr2i9cy2XClbvXHTpm7inB7F1LDjYHdC1RGDDe2NoAV6oWshKXQNQHbE2CF294OVmxmzGbzTgiPcO3S0FwEdiR1JRskIw4TkV5BeUmTiaXbbmrlyXeC6CZclsmyZecs81czCyFpmRCpcVw6nE0IhxEbUBKJGTXG6Tghht/KzhgxrgPeGq/X+zNdse3SiqU+1g+ge4WjymhvCijd1LhgzNXKD6mNhbVBXhn5l0kdSP0NfQZRSZuPnum3PdDgyjQMpCKsXKofjRL2shLJPcSyggRhY15UehDwlRoXMyPl6ZS0mmknZiuhpTNwdR6ZR2KSwrrBWR/aDKd+va6U282+szaIqY4iMw8iYG9d1Yy2WmiZilme7Y0VTMc6iKEK22NAbtQCz3wqZNGTSMJLyYJ6PTd27U2/UgFLMd/JDZp76HnkrUvciEWu+94LyT7+cL//BftOaT3HC873vPm7v3VKD2j7hUkUTRrfxz7KLbfYp245qBhfhyY5+C88Nizccts819wyN9qab84tpiDSU7nnr1kD3DroKMleGVcjVBD2DiKHBIZFCsmJuX4utUtaZd49AnIl6oOYItbJtV8YxcDoeiEM0m0OfFOxcz9oCS+lsRakhUNrGdVtZSnNBVqe4DZw1VYbOVhfpKIYM55hc4Ku0FAkYatlas0hhCWaWrnb9gnNQ6WYJ1+gWA70X7oLZvInxdIm29rRbytXTeWZdNtrpxDCs5FPicPilC+OXvr6+sflmhNw8KcNRGFJGW4AWqGulzMqyNKQ3wjBStPF2ecv79T1xySSZzeblMrMWs3zRGOkZfn595PzuHe/++Be8nk48HCfrwrtyGkZKrGzaeH+58ou3F8rjDJ+/ZabxRz/8MT/5yS9YrtsHj4+jAl5ZaFfenJ+4lpWco3kK7qrU2gg5EYMVbVbZ708OgPEicJWbhGjwttt8NH8AogSyBmJz9AultErSeMs+BStO6EZcHoLz1GggkSEmhrSPxjHU0nNPu8cSNm3WUanzTG4KxmcTWAjkGJiGwHFKTIMwJnHbi2gefTEwHk68+uhT7h5ek8bJuJ2tmVfdVlm2mWWd2VyVa12Od6AiJi7AHs6QEyk3hj6wZ5PX3fC8KSU3hqGQh8h0yNxPR06HM18eMu/fn1kuK0vZGEIymN/j/3r3DUKM2zRNo22OvXu+t29ze6ut1lUPEmhB2bqpccEJ6AE3i3VeapBbiowhlfpciGKkcLG22jbH3njcOo/XhWW+oq0iPd8iNcXHC+bJZibVxvV5vlfdEUujVJgNjHS3BmmNeS28vcz89PHCL55Wrqs+85raPr7y2657Kfmcaqk7MoaSgxivJwgpckOaUh4IaTL0bsjkmAkpk4fRhBMuDCnFRyvFCe+bFYT6tNHPnfpy4P5h4jgCvRElsG22XkKOJnVGqG5Wb2sXWjX7K6KpILUpbetsS2VeN7M76VZUHQUGEcaeCVugrxWlwRdKvL8SE/TDShpmJF3JslGPhcuXT7TrSouzqSDHhU6ENlO0kE4nYjT7jB0dkNaIoVmRGxOaErSISCG+WZj/cDXSfLO1ljDhFlFQme0GtGaeqa1A3dz2Z6cX2OFq9i/i4q+d7xwNlXVZizaBHtHGjR6wo3mI0URISk8QfjCh4Q6VRMVGbWsJXJowN2UtsBZYitlrbSqsDa69E1pj6NXG+G78H7Rxdxo4TpFxtNjZfUoiN6No9UPUaCO9BzP1roq6E8OyGbqh3ZjI+1Bh50+LZHu+wZsuH3O7fUqvCttK3C6k+h7Wt1S9ErNx1XdOsYhyPE4Mh4mYAnUtbO4WMUgnhoVxCGybbRAhCJd5ZVs3iJlDCBwno+k8zjAk813Jyfwn7+8O3B0HcjJa07yt1hyh5CwM2Uqy2htrNTFdbVYURuddhmK56Iua68XiPsTW3Fm6TAkR03s3i/8NZuXSBdIQGUPmmCdyNKld3zOztDk/2/xmdymhfvADnrdJPvj5lxeS3L6oYwKd5/ZVbvuRKEgz1NCmE4Wlt9teBOYe0WN2KzP7Nra37w4M/fnsUjOBdyThBp6o9t0bHW2CFB/RsJurF6MZ2UjAik0wmlgTWDthUcIs9pwn25pSyIQwkoM5z3jX76CLckiNpAGtC00S0s0FIGbb13o3VwbTMjRTXXuxaIWjpb/VatQf8z5Verfkm+aTuNo2F2fpM7XFXemaNyBTHiDYv9l3XqNalK6tP/V9Qih9l8WZldt+v/ZmPYj/uexrQOjVC+Le+KI+UcaVtR8I+k8hejFFM/aNyXgK2ypIi0hTtqtSZuu0H44jf/YbJ/7SD77Dn//t3+CT736b490dTZTL9ZGnt2/54qefoWRefvpdYjxwHE788dMf8/N3Zz7rVx6mgZeHgRfj5DXCytO28NO37/nhF+95vyrXIry7nplnI1zfXHBvU2LnfYjdjEZna0ZgHQbzXltaI4VA6MY3MycOi+WzlI9wg9vFiekhJAYPSxe35LBuwr+2Kyl3xqGSYmRbKr0aDF+qoTF5MIuggHUJKQo5WXxiHiLDkIyYnaK/Kev4lcYQsx8wwaFsccDHeCQxKNOQmMbE4RAZc2D0SMd485PrSEy8+ugj7l68JB8OFgHn/I11XZjnmXm+spYVbd0V6BYzqF1vKs0UMyF0s5AIyTtW77RS88KvWOEWIcQDMcDgD2XMkSGNvIvvuZ4NbdDeSWrCrbarO7sdyCGqRWcHK95u8YNeUyZgSInj6UChcl0Kbats+wYYnsfddg+c5xq8HQk2jiEG9zyT56fOX/O6cL5cKeuM1rqTDp5H6L7+TMhkRaLsStwPRuKIj2/2sZGaYOhxKXxxKXz+WHi6NjczF7PSUSsW4wefWcTVq8ktJpopycVz31NIZC8mpzwwpkyIIxIHNFh6SYgJvLhVdU/G1vx9NbbSWeZOSgOhbohuDFvhIIoGpVbvimujlGINQA6EITGmwSkFzVI7SrslWAzOZxOCoTu1Gje2mDdZ0MiByEDguJnBuGhHl4K8iYSTIgO00M1cuTfqcqXPZ3RbWa8b1IVxudIPA4yAHAnTryAaTZE6mZuA9IYWw2wkqgtJjiZgV+HyX/yCt//Blzz2ak2K7o3MV9cHN4QcQkrEF/eGMJ2O5B98H4mBYcicDsfnv7IVtn/wQ3Td6E8X9PGRvq2omh+gfVu5fe+dDScKgwrrXzvSf+0eyQfL7HUenfngughMm6NWenOl6JjowWgiCmpE/yEkUp44HEbGcfD4N2tSxIsL3degN2Dadx/ecDvAxnFwDmBDxKYyKTlNCHPHMETXcrRVMe5ktUALLRuUGdZH2vIlZfsSyUofRtu7NbGImOAmjqS8B3BUaqukIdGWwnEaubKScrxxBS+XK/ieOR4HDlOmPG1m5URgDAm0E2LkNCVOh2xUk9odm20u3oFpyJZe4+VZdU/IuPM/dfdO7JTdt7mp8c7FUrpC7yZMCcaVj94EZjcvzxIZ0mBxxrb5mfvIvrGwo3T9tif9yVfXDxpP/ScVk89LeR+4iVhxaR65wDOwbOKqWokx+PjcgBaCp/+I0yZ8zYQg0J/5mbuIxiJwxfni4bav7l8jYcfG9wLww5JZQWyU7gcEbB0WJcwQNiUENcs8yUxxIjGQdEfKzXliioHDmDmNyZqqGNFq3N7elXmxArBV4/427fQeWLfqdlCWLLfWxlotK37dCpuvc8sdNwfW6gb0zacQloZrRu5dla0Uqz0cRu4d9xvtnn1uSKaI6TXKZpqJhO1bWpwfC9CN7tGqPWsSn5vUEATp3Cytat8orRHqPwUOpR2uZuHSlkwOhk5tpaCbECXz0SnwO995yX/rL/w6f+F3fsA3P/0Op5evyMNI75XrNZH7zPr0jsfHQl0uzMuFN49vWS4XahCqChkYm5JroWlnqYU35ws/evOeXzxdefu4sG5ub7APe58ReG+cd96Hj2eCd24x3Xzb1mIGrUEiKTYimRCjqaOCIXoiZvNhPntGto3RxthWHNgIJYjZ9QRJ5k+3dWJInDmzLNYriHSGPDENgxlYD5FpzMZLzdkSMIboSSbRHi5socUQ6MEOAg0WcdjEuxXt5hkWYRozh2liyolpjOQIOQckGk8zRnvI8uHE3cuPmO4eSMOIBqFuhXm5cj2fmeeFUgz1TdEUniEmv9ANmllCGO8xOA/M0IXWIMiuvLbrDsZxTDG43YZtZSlEpsE288f3F67vLzZKbtWUjwJgMV62bTTMNFztkJZ9HdhDM4TAi7sT03SiUtB+NuEV3PiZISY3VLZXFKHti+e2NdmWJcGK9yI2mqqtc9423l0sjaPVeiOPq3Nm9xHQXlAaMqO+gdtGGF2FvxdwOwBQGpyvlS/OG2/nwlJtJ9jHV+r4o4DTLUy9ero/koZM6JagUPx7DzkzpsQQgheUmXE4QMp0iXS197Ftpj7v1YoOEUGcl6V+SK2LkgYbn0dVvpw3ThchZKWTUFf9o0pMgeNg6sRSKrU0tzUxCyTtTttIgnQlBuvUm5pN2M3kuNsNPtTA8aokGkUasW/EVSzveQjMl5XHNyv3LytTeyJ9uSBvKv3RfBSHPiM622GaF6KciOmOlB88B7uaxFXVRkRu40LzLbIkdLbwg//Nw5Xfm99xfnridDzxne99jx/98IfM1yvT4UCeRqbDZIhtTPzWb3+b+XohDZV/+Lf+A37jz/02f2Zb+Z/91p+9CcDi/WsO//pfgZCID5/SqnL9yT/i7e/+TZ7+wd9jfjo7Tw1PubK1Np0j3/zhSOMlfXhJyEeL13RvQu1mj6VehHZLT74VCcbRDDY61U7vJniIMXIYD0zjZHzaEJyfu49vHAcX8fGmUhsmkhNrtrPHEtItASpIfPae1IZWQV2U01qzXPZWLbrWDd21Luj6Hs6fsV0/Q9sT6cWBkBJjTmweLZrzQAzmC7mtK1tZnbYSPDlsdFsfaF24nGdUTZg45MD9aTRebRAr2FJnHAa2dSPlgZSzCbkU56eLT6sSorAMewmlXgRYok/zSUp3CyQk+DOg5hnZu2W4AyE0Qg5EDCFOwVFhH92ao4IxoMNeDO6TKcRdG0zhG8SjC/TDuZ29bKr2VeTyH/faC0rfEm/3M1SjwHUxUc2epKPdGsyb9U8AUZsKNh//BT9fnwtFExUFHxsbYh2MliTidm7uj9nNB1MUgheS9ij4YX9r7n1fbaBrNf7kbL6aOQhTSOSYSSRSyETFXVXgkCLHHDmOA+Ngtj9l6SybURm6mvF5a/XmRqBiU4FamqcTmfvK1mGujdK6R0W3m4m7NvMdbQ4m9IYVgiEwJpuagp3vpfn+5NcYr3F6N/N0qkekijAOmVA7IiYc3IoBEeZpKh9cL18Mz3RuA3fVita1dL5seqO6fZ3X1y4oJYihEOxRWW4sKnCIwienyO/8mdf8y3/hz/Hbv/UbfPTdb3F3Z6phUaVsM0PKnA4HXr24Zz6/5fzmLX0YadcFWuN0HGnNCpgqwuNW6NuFt9eZz88zb84X3l0KS/FOxZ4YHIi0AsM3S1GDepN3UBGci6VsdcVMx5UuypgGQgqMo/EoSC64SVZEWqGIm60bQrJnfatCHKyCDxJd+Wd8ntYTykAahKDCISeOo3WYKZvFyDRGpslQnJiS8SV55id1HxNo75YtruqxgJUcIsU5mUNIHAYbT42jbbaHIRGTIawpW7HcukLI3N2/5nB6SR6OxBCotbLMpmJ7Oj+Z5UoIDDmZ7YBbPd04PYKP7Gx0IV5EoerjCW+JukHyMYgjPUqLnl2eswM5RrqPXRgk8fR0YV6WG9dwV8yHGKltQ9xWY0gRdSPmW8ElgRQnpjxRXFyl/sxYKkV8FuX459lZA0Hcp09BxG2eMHR0h5wUuGyVLx5XLpeVrRSm3om4ivEGQ9rf6G4Bs8cQ7pv43vB0P5jNJqmzrpWnufJ2LjwVhZhJUYkoW29mMgyengPDmJju7jke78h58pzsyoB1qzllksAQLXFoHEfSOKIpEtTMnGsxwUBzwZC6CEYUghqipf5zBEIWgiqpw+PceDjBWOzgK9Uudgq796nRQrbWWIodFNE5vGbCb2iLGskO2Ytwb5R6tSJlu1bWOfJOCkuoZDkbEf6N0B4yc+pc5wvLjx851YW+NcqqlNnyz9nUuI6pE482OwsJ8miUEiPHJ5BKF6h9ZigXhMlQ6rpAtxzk+PKBX/mN79F64+XLV1wuF37ju99iHAYijTEGLu/f8gd/6286yrDy5Wc/5nR/z+MXP6fXH9De/Zjr3/y7hjTtx7Z7yYbjPemT75K//1t873/4VynpwOd//2/zk//3f8j1Rz9BejDLEwnUYPzidnpJOD4gefCpjvqe5PQHsQN63wtRdfWnIU2iJgLQZAKFPGQOh5HDOLhf7d64y+3w2dG47hzApTbz542BOBhtBRVCmhx5euaJqZq63w7lvaisZq3VLdecUmA9o0+fsb37KXX5DDmYBdGQB6IotRVDPnN0Y3ZzmwBTQzcs8rKEyjGMNtpT4/SOY2aIkYeHI6cp07bCmBKnCRd1Dl4ERufSClutbNVGmaLCELM1H1hzGzwvd2vCdalklG0t9NrtYBahdBvzltbsebGjywRpFBuTh0wK2Z4JtaY3BbOgoYsJMG0gzM7b1r0zDeaVGvVZ1/3ckHK7fx8GJPzSM/+DX9/OWEfBUL2ZEoA5loRgYI00iPvYWsNtamVim92xhBtfPcYMt5G9uV4EuJ0nu3ZgF1RGvNHs4Rli3bvUGw83ABHdCrJBmmHcIIuSJTBIJBFJYib9OSqHGEhBmVLkMGayCK3Dshaj/nS3OxIDBEQiHwosDylTCZTonGeUVsyerrpIVlx93ZqNwOsOpN62AOOSD2n08AKzjjJwMtzcW+gdkX6z9U63s8rG2SlGIBO1EKfOXMDtfilVb8Bb8n9TsXoqfLAcoghbhbf9H79G/uTraxeUp9OB6wy1J1MSpUAKMGbluw8Df+nXPuYv/8Xf5Dd+8AM++sa3Od0/EFO6Sdp7L5YuQidFQfvG9bFwrcKby3tEA1NWdBASkXVtPJ4vXM8zb64XvrhWLnNl2ToEoXp3rb7aQ/DF5xtodP/hPVNX2Lt6pdROaSadP7jcf0iJyRWNIbrvWozsXn57N25we7KkmRDNMsELhVuKg1sB5Wwmp9u2kSVwHCZSNN+8mAOHMXIasxkG58EOYMwueI8A82rJ+Gu93wQ4IdghmHx0cBoN5RsnzwRPdnCnGBnyYA1BszinYbrjcHrNON5ZvGLvbOvC+emRp8cntrKR82S2Ld713CIbb7uS8aRwtHd/r/12MNimuQs/VGwTtNPL0zPAVcdCDpkhN9rh+XNfl9VSZXhWy4YUHUlIxl26G+i181Q2llpv92m3EZJb4Wt2VkHEI+78Pe1brqsNgsSbctqEO/ZZt+D2DF1Zq/LuuvL+fDEOlm90N5GBF6heiXpSSPugMNsrchxBqjS1Me+8Vc4VrprRfGC62/0KbYx2XVeWdUMxju3xdMfh9MA03ZFCNuscsRlHirstVfY8d6WQ0B7MSaNWlvlKLZVSTHFoSEOnu0htJ5qLmIvB0qp9X5SBwONmPqDHLTAku68WK9qpxT0GS2FZzfRYELIdCUjvlL7cTPfLZrGMbcNdL6xked83Num8U+FY4JQz+T5xONrPpReyJHQcCdwxLht9W5hCIKVOHJQUK6iybkKsA1MciXkkROMHiQQ7CcXEQmt7pPdCLE+IBmReaXUBlM8//4zP2wUkMM8LaCf3wrvLI2NKzNcL6zxDa1zOT/zkj/6Q6/lMrQUR4cf/6B/yKp+J3364BRJ4FWircblSf/h3KT/8O8z/6X9EfP1NPv3n/1W+9b/4d/jF7/4Nfv7X/++Un/+CWDohG2UhHk+kwwnNmVixZI5gPHHjw4oLY9xaxD417DGw7oV4UyffHzkeMocpeX6z8RTtre6Quvior1NqZ60NSZYGMw6ZMWVD5bt75DUTp6FqnrOObO6FRHfBTmsVLQ1dZji/pb37BeXxc3JYkXR3SwlaN3OWiDE5QrrR1MQ+eRoAuY0YpYmLFsw+aBgG7oPZBr24PzKlyKqmWh5yZJwG6mYis9YqpQTmRWm1crluLFvzBsDpT2INUszJr7Wwbjv/2LwhjYpiljRmIdbce9C+QQFHJztB1ZpshT16M9imgnYPZBBT9KLYtb3dHkOk7N0Je4KTr7LnX//XFJP4+pBbSbqfb57E5YV52ptxL6TFhau394pP8FR49h6yBJ+Yot8PF+3EQNnM8QIX1e7BF131VryaZiEZ31iro+07Qrm/c7UIxgrDJtw1Yeid0cGXURJjiD69EcYoTNkcV/aAgk6wVEACGpUclaiWWx6j0HqkV2xyGM0HVII1w6u7E/Sq0NQQw65IN3T7FhqCnctWSNoeOg0jh2FgqzalE+wsrZgK3dqIdvNLjfiErBuHtosr+DWSIkioaLC9vWBc6uZpVFVNx+F4nJ9jiiQ7M0MxGtbXfX3tgvLj+9ecU2Vu5g04ZHjIgR989Ip/4c99n3/2t3+d7/3Kr/Dio48ZjydT/TYTJdRaqGWz8aBzpzQobx/f8tmbBcZMmiJNLNXh8bpwflyYzxfeP144L4Xz0m/B8Oqk5n3h7HB78AKSAHuGw94FNVWPrzLibCm2uR6GzMNh5PXdieNpMFVf8AOGQJNuC0JMHcs+TtgV3nGPRRMfT5tdSKMzJkjHETkdScGKmZASKZup+JgCk1vsAHQRzLe03eq2XdlmCQGmUtTunYoYzG1jzMA4wjQYsgqQUiImq6xL7aylUiVyd/eS6fSa8XBHCIFlW7hen7hc3rNti3mhhee0HeOK7kKPHWHDi8fdwsi4L3s+Odj1pkNt5eYb1io3oQ5qKRBGnTE6gUTjAo45UjahBKil+vtwVCslshtYJw327xUhXN3InY5ENVNdB36iczCtIXj2RBPZhT9708CteFUACc/ZwsbyolXlaVm5riu97IR644PJBzu3qMU2thgJaqqP7qrMPerRDlHjLNYOV0089pFFjTejEYYhEHKm1IKsM8O2WrxfnhjHI8fpgcN4dGX+9mwpFQIhZRuB+prsvdNWI7CXbWXdFtZtsShEz30WV3A2X5P7+0SVtWI0imj2Gm9q42GZmXKma6DUQi128JayIcH4OutWUJSUIr1Wgpr6cR/1a2tsW2UtG2uzyDFTCFuxQWv8PMM4JU5D5+6uc3zRGQ+FwzRyao3xFAjjwHT3QLxMMEfOpaNhI41Xqq7EQyYeXpHGF6RgUJSN03YE2lrDFpUgV4IUqFDFosiSBP7Nh8D7X3nJ/V/+7yE//H3m3/3/0NcrDHbAy4uAvBTgiHAC3sABoMAP7oAnvnM0scfzgsH3M54XkAKt0D/7Ied//39H/pXf4pv/0r/Fp//z/yW/+P/+33j7n/516FbE59Md6Xik+N4XbITALhj0Et6QESzTO0lgNwQxT7uNOCZePLzg5d2Bh+PR/A4dzZcPi14wPpcfmuvSbLgjxtsdY8bDavcny/xS8WYBM2bvatMJ86Q0o+9eO20rcL3Q3n9Gef8LerkwHIMdogLLstK3YvfNTlh7XmQgSyDFTKsV3cy/sTeDgnq3KN/pMDL2TAzCOCRvuIqpsiMchonLtlBbZ67FSrKuUCvX2TLPU7KxpnabChAgSySKAQLrqsRWWTf3nO2WR67aaMUN3KuDaviZJh5UEQNBIhklx0DFxZDBPFqt1zOF8H7dWmmuqG8+xTTe9c6VlA/W114iyAe//nANOg59+/XuUylYcfc8ibHzVf2/ows/9ymeyw+9OTSbm47z97oJmqKYWXl1jYGJE63Qbi4ECwaz2/sTK1DNPugD6yr/kOJrU5pCE9LWGVdlisJBMocwMkab4k0pcBBrIlIKzlW0hdo8aCMFS8HK0d5rck9n6RFNwrY2LtfVpnzrZql+tZlNUKlUNbFaK5XShLV7nGjlVk8EbE81jvtAFHFtzjO9xUSioM2EOq1WgsCYItnPqeD2RXRLxhFcMBytogjYaH8pUJpFgHaf5mo3nYNgbgNVjM+ZKl/79bULym+8PnG3VpayMWb45H7gN7/9MX/x136VX//V7/LRt7/D6eVrxmFCPE6tt+Iij5WyzqzLyroUk8tjbvFLK2xnhUVJ48BSC2/fX3h8P3N5XHi6FuODiRCD4Xf72lLPvN1NyG/FjuwPgI3o6FYKlGajmd7NC2tKwt1h5OXpwP3DgemYcacaS7zpkIMZNMfb6MiSZm5EYq9YgrjiU/TmuTWp8dhEbBQfo5hlUPSNMURyTOx+breOUQ113FWMBDx9xCB4xbgpObroZowuvLGRVu9m1wPO7+imLmzaOd695OHlRxzvH4hjYuuFZZm5Xq9s2wYipCFb7m7ceS620aG4t1inFU990f5si7PnkjpHyJAHTMVW3am/VrNKEedRqXoBAr02kgpdTEVvRbyp5lTFDzazn8jRbniSgPbIdt6eRS80Oo2UIuM4kC+LFYOCcWRzcvRmPyD9xkrwa9z9+zxvhGbrALtl2rU0E/zsiTEMVkzcOE2eSOG8HtX9uvTbNdwFVdqF1oSlRd5VeOwdjSMpN6Qr4zAxHQ+03kj5wlo2IjAMR8Zh4jAcSBJYa6GVjbathJCNv5Rs82uebmSfodO21ceDs/thWuRnigl20VewKYDIs+9gU1hDI7nx81o7x/PKMQy0aq1ULaZy3O9H684PEqWI3pqr2szeo/fOVp2wrtAczW10ttpvvnU5ZYZJ6CdID4npxYgcApIjPUWqdFpI9OnAME4MS+Awd0q/oqmQJkHuXpBffEI+vEDycGvapPlBqWbD1VGj0ERT6aYk5NESr/6tf+1f5/DX/jXe/8f/e+a3f4P+nYTE10h0R4e9EbPjjw9/2n/xodDgT3+N/4eAPfy2D5Qf/R3e/R//1xz/uX+F7/5Lf5Xxo9e8/09+H37/x8hhIowjor5PiiPjwQ71Zh2O5dtLIeRAqoEUkhkut0pohdOYeXE68uLuxGEYbKJyG3E6rxhDQ2jmc7du5kygyRqrlPJt39tHkvskSbuNvpvbxvRmZn23tI9S6bWj20q/vmd7+ox6fcM4NLeyMq63NLsm1ffF4BQlktmqWCNbb6I+ezYD1dHsnCIePmpG2aWjzUUlg3G8S7EGeF0a9AXRRFkqj9fVuI8MrGuh9kppxpkf1BJKWuusVQmterZys0KyVdquYP9ArSs+vYgihKag1aM5jXLVFafvODfbMCV2ezezcXOzdN2dIPTmNekmBrdXYne9EOcBPv9Z5AOkSvz5UHzcapVlV/+eXcw3tnVC6KbXl53zbmNuceTLrI8sycYKR1NGp5j893bRULRmtrcbH9T6Bitegz8qsqvDg7p1kJ2dCiYY2jpcG2lWwmJnxZQyB4lMMZIC3EXlEAWNFuAQg3lKa/MUHulkEYagRI+NjGLobFOlbIVlseCDrZrKvXcLrijNKeC9U5q5H2yb2fNUcfW27kESdkvFR94ihuDuXp/mLWkiHcXU2qufPb01ShWjRoVowIuRTP2MbuCcXEnGsZUoDE2YZ6NvNAVtdg9CsveGo+J71vrXeX3tgvI4ZI4p8NHLB37je5/wZ777Cd/59BNevfqUhxevme6OxGwjbl29Kywr2zqzLjPbfGW+PjE/Xe0GrJWtbpyvF95cOjEPxLwx14XrsnC+rMxzpVpuIEHUFz/PC9KLgZvjfsDJ51YUpGjcArw4rN0rckerYhCOh4H7+5G7u5FhNKVz64asmWmwLeDBDwtDVKx7qt02I8V4CEGdj+DoVvANADpDSqbC9cSA6N6T4tySvUCVvaMXO8z7XigHsRxk/7qY7P3nFJwHYRtNa8owDOzGtbT2PGLIAy9ffsqLl68ZDwe7LtvCulzZttWQ5yEbhyXs3Bfjyu5j7VYt4cIKQ1PE2yUxtLKUchu99OZ5wb3eIsFMmFFu+bOBSEyW+5o1Q+8UcV9PP5SDiCUCYAhQDNBjh2AUgF4jKWSUYAWOk6JD2FWlka34wbMbsfkTbJZEftV9JKYejWOfQ6xQCGZj1HqDKFTEGpRevfh87vX35mCPxRMRV9vadVA/ELr7Nqg2ahfOVXhXhKctsBSzm7AYvExOE4dg/KGSK6rKNIwMeTJuKZ11q66orZaR3Y2z2H303rrHvG2Fsl1pdbUsYR9Fm51Nf7b0gNvBsa8FESgCM+bvGlvji8vGy6BIDYzRin/ZDyMR59vZ1W5qB7d2ZWvN1Iq9U7RRPC6x4/e1K5tzhs1cOzBFt7BJkSZWtK+90dfGKDAdE0sU4pgIR2G4VNq2cXj5iruPXlCHF2h6QRpPX4FsjJ+13z8f6ZmSy4pDiYTjyIv/yb9O/pd/jbf/p3+X7Y/+NpISMQ+4sevzhrlTG/x77pSK2x/fxpD7upGvziM/+GKbFChogFaZ/8Z/TPr0e7z+Z/4y8V1i+b/+lGE6IHGg14aaXvtGr1Bx0o8f9JbMlGjSfOLTHGzq5rd4GDiNAzlb4/uMTu530d5yrbbm5rVRsQY3xmhTG2/oLanFLc98zL2LcHo1JKpVK8hsgtXQWuh1pm1vqctbVFcT9NBpVZnnlTz46Fdgp9Ds/LogUPpu3WMWLbb+TfBle2jws6ObIK2aKn7nFprVSzMx2Vaw+VBlmZubUxvaVmJi870tYFx56WaJ1Ws1HqOjvzbB0dsZ1NrzDdduAlOtLu7JAmIuCzFk4k543e3i9ulGq7dEsuoxfODF6gfY44clgWDagikKKe/oYL+hvcKzo0uOe6yhN9NqsYdbsXOlth3BVLpUqivW5cZVt30QDagan30XtOJrZKvFnxd71gO4ZqM7Xej5jbdu6mlL1dFnjdj+BW5krpsi1048K/FJyd1ScaR3YlKidHJMWKbJPg/8wLPR9+kgdk+p3UFQo8t1tebXgiFgGgYIxdZ7iCAVok1aWjOuZPf3vU/7bvemP/+oVV257XVNCLfmUJyLZcEjhkJ3VRP6dGGrnRiae17b9ZDgsZX6zLHVaJTA2sxmaNns14JYMpgC0ZBZVL/yXv9Jr69vbF4b33t5z1/49e/za7/2HT765DV39w8cDifyMKKqlHm2FIzazAx7mx2ZnFmvT1yeHnl8nHn3+MTT08xWlNIry7bR10LtnU0r69qYZyPki4sQ9ngmUPdjc34G6t3+szhHUIYhMORAEuPraFNqha62OQZVxmyCmMMpczwONrbeYWJfx3tRclPSqSFKqP0bYKNwW/eWQy1+IxUlhUBOifwBLzPGQHdzU2fZ2TPhKRk7ITliBec+ptdm7z0mG7VbXmgy4VGMpka32YB7yfGVAvb+1Se8/ugbjIcTIUZKLyzrzLquqCpDtgSGfUgl/jPdDpvuOdS3wggrDPCNzYqrdrOQsDzSzm6UvhPwu+4j5mCqthSdR2KIbYmZlJRxGCil0GqzFCZHPqMjiar7weXvTWFtjcs8cz8NTtQ3hWuHD4qDcDvHb4ii7gUkPuLVG2pphUbwDcWK/a7qxu5mG/T8PdT/DT+GFbMNaXvMWvfiJdwO/drhvAW+XANfzo3HuXJZN9a6cRxHzx0eELo1MK25eMvlW+KGzRhfSenUVhn8Pco+emydcDvQduW5GkfQ5jjuWmCHNc3RTVW/Jo0Q1HhrRCQotRae6spTahxDIk225qMje5ZE0g3pE1M2ltrZtk7RlXPZ2PpzjBpYXdZ8A7YiwfaA4qT2sXc3kDY7s610crSCIYfIMApxmgzpPnZODwOvvvMCOUzE8Z7aRrQ+c7J2pFxbQ9R8T43SIVRslBlbJf/GrzL8hb/Eu//Dv017/BGSBjTu8W47Er2voX3j/OrhcXvdaki9/V1rVne08ivVJXuT6W+Y9b/6T7j/wV/k8NG3WUO4JeYo3FCMXdltZsiZHAa2VM3Dz9dm7UpVRWolBbNKOR5GDuNoAjZv5k001W9rvHvSx1w6VWAYB/KQbK26OtVMzg3VNsqLq2PV9gHt5ipQm4/Om1kF9VJo8xmd36PbI/b0RrR11nUz7mBsHnEoaAvOXzZ0q24b21IoW2fdjN8Znd8ck6GXrRlnuO379Y7kRaOutFIptdyQ0+KxOOta2baNJlBSYl3NQLw1TwZSowIV50hGVeg28rbklF3kZpWb6SzVC0S7482RO+m4UMze/15c7VnwO92oqTVk3fms+5P0vId/FWG6FZSTZU43DZR1s4lecqFpghiF0zSQojX8DWXZNvdiVOZ13+vFlew2fQkdZBTzmPXF3l1YouG56RIfLe97kYi5C3Sf7A1psGfS+ylrUG3/o1jaTdSOakTdDF4RpHthvnTGBaZq08MpDkySSIhbTnkx3RVJ3bxjnd7UqyUlta2z7Ke0OtKNCc9cWUjIgUKlrZV561w3Za6W6rV1QyZbM6/W2q3dC4hHXtrzNSZxup0BZaWbwK214hQ8D0vpNu4RrQSM872DIzuAsxYDSVKEIe9BJoKoUI1vQm/mUZwm++ulYCJlFROKdTGLSJQYf9kG9stfX7ugbKWTNfCQMy+mO+5Prxhzhl7Y5pleKqVu1FLYVht1b+vKNi+sy8J8OfP09MTj44U3lwvvHxeWpdK7sNXC+TpjM7rAthq3hG4XQzHrHFFxj6sd3nZEwdFL6Z5PPQSmyZTU9MbWNzfs9KfJH7TDOHGcJoZxsAseIl0wMuoHKOPeLTifFpyzYPC4/9sSKNoJyVR4YBBzCoE0JPeaNGh/7xRE8PxNuRWWuwWRwC0BQbtB8GZNFEjRRm8mmjGUNDo6YMXFc86veNrF8XTPq1ff4HT3wDAeUMx4ffVcaUMuBkK0kYSfTPZw9Q82K/+1t6uIOlxuEIePv8XftxWTZlr9HGDfFScnW8eonu5wuS6UZTFekiopZYZhMKSgmnWCurWM7KMPETQqkmyEHVDWsvL+8T3TODDXyrKacnc3iUd49jzz0VtwayLjYPqGR3CVbvfCMxKCZf3uhuLBlf9WrYbbFv7hHn7jTbrQqvfdyBxUAtfSebtGfjELb2ZTeS9lA0zxmFICsdSRGJONG33Ttc/jRWZTt18xL7hWKzVYzKKII8bV7C7EK47dWP12/KiP6uk7uOZelLsJsaK1szVxy5/iaS2FmIXDIZq7QIAg3XhO0ROmAvQCywJtq9A3utpoKCIWTZqeL58inuv+fDCJADHQxJKLtqakGAh0au/MayWtkXwQxpRJWZDhDo135Gkkjyfaxdaxhv05K2jbaG0zI/rdRDlYhKBIt0IzCSwL7fG9tfj+jNnhrV+Fgf4EIvlLq0r96i9uQMwHoOWf/ov2fesXP6G/+8JVp0IPVvibd6fxX3ekdedCI3sYJFjKVoCY0NKgVLLAmBMcdo/fAADrIUlEQVTjOJizhbcit33WQcpeO6U0ltJYW0eipXqNYybnZGtI1VXdOw3GRAtlR9Fqp5fmiTLq6GSllUIrC7pe6euZvq5EuptGd0LtlFLNR0+MRqTDrRWB3qnrxrZtLGthc+uekCL4SBk1wcbmpv3aV8Yc3XbHGsPq73nni+7o5VY2SjNF7O3raBadiPkf2r/ZaK5cpjVH1PaCEo/Z5CbO6OI8/C4WN4sS1Kcwod+oR905kbiVjv075qqx32e9PcPP6+qrJaU1xntTAZVxDDf6gDrtY8rZfC+Tizq7jVRRZcydEAtlLUiz/bPv7iahs5VGwgI2ghd63a+XIG7ltDfe9ozv7zLGeAOLbg4Q9qYJojSE2COp7PSAAJpQ7e5WoOjWiFtnKjAhTCkzYmKcIZiyOyisS2GunXFIjGNEok3ebhz/amsgCM4PtWe+lUalWaHdlHndOF9WzvPGWipFMXPxZmd8qfZ1XRztR915RpmctjaMyZ4f9sQdOzNa3+sFQbSbuLlVt0m0+sAKyOBAx+6Xaeusqc3KohiFJ4G7aCgEYZyEnGHbYFmV2ux826qS4g7Yfb3X1y4ol7Lx+P4953fv2Z7eMY+BbUzQK70V7+jMFLtshbItXK6rmXmuheu88nS+8nSeeZyvvL8sXC8b22pjrq0GWlUC7dapmZxevZPbEQC92U0FcZPWXaDj5uvDKKQkDCnQtCONvRU05C/iWa6DeULmgZAyexyfcZ73x9BGJBLlxi25xcRFKxiFvUh6jmmMYWDKgy3wbKpvfATUutoYuJuie0fHFIghW+GBjSyKKz1izoRgG34OZmsU3M0/+Ya+nxvmGSl0MZRoGCeOp1cMx5fE8YgEsw9S39RRIYZk3CcR+5eb5ZY3H23v9gjqCsU9MgwwjpCbYLdm6uHOPoKvlK2yrsU/9z4ub2gzb7a1bCyu6DblZrF/q1sRmHIm1caIWifVGkGfuV2KQgpm3o2Ngp6uM9dlZVNHYBS0mdIu7Kie4Y3GytRnJMk2VbkdoOL+o/u62zmSkoL56tmCYreteP67jpx2bgpa7Xawt1Zo1Ua6lw3erpHPr43HtVDaRnMz+BjSbXSEvxeRYPxhDK1o2iy7d93YNrt2AYvHtDH+sxrXB9nGFYzJeUqGftl4ch9xWNG1r9nb4VTtsAoCA3A3wad3iY9O8OpV4uEUGCMMSVwYIrexDXTKZqjQMMCokaN0YmsMKXCaMqeXE9OLF6ZYDpF1K8znmad3F5bzTE5CSkpMjRiq8TGxwim4WfByVR7jxss7U7eXFdISGO4H1mtlm7tTRGzNaiv0baHX1VEyG2dqtA60i0eYeY1ovK1ws4F6RiYdF9o5lF4EPNeEf3pj/sro6yvf64M//2UbunrCyO5V1x15blb4i09SDJDwEV331Ka98eh+WLlgLKXANCTGZOrQIHaQtr6Pr+37l62xrpVlLTSFYchMh4lxtP2juS3QM+q4TzecH6km2OvanA7Tb75+pVb6tiHrhbo80mpBtUKCdU2+Lo2HvfPRS4EYLHmo13qj31Qfqe/85eBjcTN9r6zrZoiaRIs89HSu3TB659R2bzCqX+OmZozd1Z+mGwdUb+k5vXVv1DHK0c4ldWBDnLt4u7Xe5HbcW3gHMUKFUInBAQ2ez4rbueHNuU0/PlB1y1dwgdurA0tV8qYMrZOdUBlFXPxp3+95n7SzMIhyGMzqbYuVaRy5nq/Ml9l4zo4iUh1BdUTVCplw21dKt6lhDPEWCLJPefBnGT/X97XHjuqJ/Zm2RurBU5tskqcSrPHbOrIqeYXjJowYAJSj0cOijbVY68ay2QSn+2a/7fvjznEFUjT/XjSwlc7aGlupdII1D6os68p1K6zFEW1VtwqyTO/mUwOj/gs4z3HKwjQkjoeJlNJtXfQQ6LXSHEwj6I3fGaMajaT325oIhmcAkJL/m10pRajVzNwH8YCaaIVp6+6BKXZ8DaPvWxUD9dQs2+SfBkKptbDM8NkvPuOjh0gpZ9I0Qm90JwSXWti2jW1bmK9XrpfKpRhva9kql3nh7Af9pVSelmKKuVVpZTfpNjWv+gjCfraFFoI4aV68Yr/1pbYZByEeIi9fnnh5f0eKgffnM2Uz5RvdYHzrNiwP2HK5B1LMN3TPPrDjNN0OY9ltgvyAztkeEhqoJqARozAmG1Emf0BTSl7Mim3qrXsMXeQ2WsOKv50TZGnoagbm2bsKT5gYYyTviFJQT7CQ23XI0ZRqEgPBVWt5OjI9vGQ83hNSNqKwowGGfEYvUKNtHt1Qul2NfPvRzPettYY0vSFWVjj2m13Q8+9XWiuGSjTjSZVaaaWy1cq6mmBr8UJo82Ky78UktpnFIJZw0dXySUuxYrTvfbjaWGhIhKXRu7D6gVZ9cSR5JrU7W8e9Ra2LVDWVs7nrCGAc1524r86Ps4PaOH45W2yhOFol/UYXtyWEsIuT9muJOuFbDWHZqvJ+Ud5cK08rzOvGUmZ6KaRpIoTk27myb+0dW0+q3MZy63JlXeeb/Uql2AHZAsUTvpvqzWvSNi4bQ+H3Cn+/t6xjXMHv1yzsm5eY1cTLMfHN+8B3XsA37uD1vRUkUxbQ6uOaCB80hhqFMEZGEpojE4mQldPDPa8//YRX3/yE48ffQA4vQBK9VR7fvePNz37OFz/5MZc3b5gGG8fsI3oj7u/UBovbvCyNMXbGZHn3776YeVoq0xTJw2AIMSaU6LXQy0pbV/aTvwO1gvrUorvb754Zcdtz/E4/w4v+097d8aeH3vrBz/+1vb/uK3X/Xr/sq/2Y7q74xfaP3VDfCuZny5JdBMOOXvrhrRIMyVOw3KB9CKS3ZgJVaqus28a6VsrWCONgVJ5k0HLv6vF/+7o3VbNoA3X0t3V278nem+8p1dBzvxexXqEuhki1jtTmosFAq4uJIjC+eIyBnKLt8QSGITrn21K6qiMU0zjehCDV04MsvMGCKpKbYe/ik4p6wejovpoTg4npbAJT1RruWgtbLZRmXMq6mW9hcCDDbrYhR14f2RHjzZwJweyadx/F3prl1uhSiWng1tXutBQ1ihTdMMfu71k+XGi/5NWB89Y5Jqs6hwGGHNwOx7DpWzhIL76mQHtjGIxjC8IYjwidx/NqDiVNITQ3wy6QMoRkZx1CcnqNGZnbNX1OL9uL5M5t95e9gPbJUXdmaO1Ei3fiZq+HAR0UOGzC3SoM1UCGQSBjjYBgBuTbZgh2TomtOLofoDXzPgjBYpBTzJRmE4CtNipiTglNKD1QtVH899xFitA7odueFK0Psv3T7bf2ZmhMYvqKGKG7kBVMh+ELI2ATSfFfd43uBW48VnMZ6FQJVix6QxF9fN66uZysKF2DWwkZ7YEGWzN0ToIyjsadDWqOHtWYLl/79fWjF3sjhMS6rbx/fLR4oCQ3qxjz6ipG0l6uzPPCPDeum1XCpTWua2VZV+ZSeCqF81J4WgrL9pxXqd6eWQ53eOYk6a42kh304HBMqDg5fFGmmPjG69d8+1sPvLg/cb1eWcvs45vmtgz2sCUxov9NOLNHL0n0Amn/5DZ6DW5kHkPyn20hNzVOZwjJIevImDM5xZvxOioex6S3zlIc/bqNKLQB9nX2mc3rKsZocYwxkpIdTbvJbYhmENtc4BKDLbyYAzkncs6EmJhOD0yHe4ZhMmGJW/vUaiTi3UMxhueu+6bS9mJRfbG3nQfom1lzbmD1orO1euvqa63UrfhYyxWXnoJxa9lF7PqGCBFiipSt0Iutq9KKX4tISh2tyjCY/5eWZnA/jZiswEu50tdG5RnJ3g/u4FYcRq0w4U/bKjsZfC+WxO+LURxMmb8LGm6dMkoeduuXfQ35IMm70V3IZKOp3exdbzxKRVi3jcsCb+eNy7WzLlfqttC6cx99Xe4q+tLazQKrNxPglLKylQ3dVjvA8cz37ve4u4H7jor4ut7N1J9f4lYUjrRrJzi6Jaq2WWpjipGPjplvHgPfPSW+cRd4eQomXCNQqo/g0kAYEpIH41yKECYhHeDQ4IXAcMyM90eOr15x99En3L3+NscXr8mHe/NaLZXr5T2vP/qIjz9+yRc//UfMb57MCiNgcYs0838LASNwQRoO6DjRk6HPXQJSndtbNxAlhGqFZCmUZaUXN2jzEirFSJFg2TIhkHpHciKMA72dQXdURfa25oZg7oXeP4ZB+UtfXy1Sn1+3pvpDXuWtKPnq/dubjv2Lwgc+ufaZgyE5wfjLErBkk9te1pyO4gi4GzGrI9zVC7t53ehie10arKAUR9n0Az7xTZV8+z4N6bsytT6Pup2q0bZC32ZkWwi9meCFTuzB4xSvlK1S1ZW/80KOgXHIgDKMluVtI1go24bEdEPbzGmi3UQxm1uc5WTPzbpWKzb7rkS3S1GbTWKaN8toMFGNNLayUluxyQuWU9/7B+jaDRwxtNB6K6UT3CfZzr4kYmFNeMyhKtKDf61Rg8xkPXii0N7w7VMVG/mCFYwfSMR+6aupjTXptg5icCFjMMgqxEDdigUItHqzt1NtxDA4BzJwujuwtU69bs/NUjfRYesbeVCSRKcHiSX+uBBGbt9zt197Li5Rs+spe/MrVljWWg1lr931DH6oN4ESkNKIizItQu4WOTtINM4inUA0caKq1xn9do9DFzPnFyGERJRE68GCGWq1htjPhBSD8US7WbJFJ69KbEhrIO3GO499v+/2CA8pkAdLw1HlZna/859DMLFqAvaOw+4x0BUJBztry+b3eT9/d6Dkq01FaVZz1Nbdn9rsg2KwYtvqDgucyMHOwVTEvFf16+9hX7ugvE/KaTC4dV1X3j+eEWmoE6pL3djKxrJWrvPKdd2Yt2ZS+m4qotZhLY25ds7LxtNSuKyNrZmaOe4XD6vkTXCjjsgYQpJT4OEh8emnDwxT4t3jytvHK8u2cRwGvvv6Fd//1muODxNfPgaWMvPm7ZMbrhom31RdNTrYBouTgpXbmGNHmnaj8xDEikTs60p97t6NjJ4ZhsQ0GP8op0gXJ6Fv7sGocrMt6n6DW++2kUuwYqA1MzlNmXHIpBjMqytyS2JIKZNS9Ieq0dW8psacyWNiPGRCSAQSMR84HF6RxnskDbbC2rNKG4xOsKOzN1RiR5T0+Zj6k4dEd5FMdyRit6zo3ZSdpTQf/z7DN0Hs0E/BrBo0Z1ISH4Ekaiy00UQltVbitrLOFnG1W5hsXQw5orCuxTZUV/4epkSrHWlqow1xUQCGaDRHPFI0lavEaJnB+waHoTW1u/rb9yuHAVy97Osn2sgm3GgL9QMLpe4HiRcX3nioC5dEDC1cVzNKPl83lqVRlpm6rT4CsshM7S7Mas0QyVqpxbKKtRZaWY2G4NSDnc/XWvVnyXme3UsftYIT/zU+ersdSN18yXafQJf7EBTGLLw+wMfHzqupcX8w+41SYe6dkCpxyIR0Ig6BYRyQkMzcXs11IURb4+P9kfuPP+Hw0ScMd6+Y7l9yOLwiHw6kPBLCCKpMp3um04HD6Y671y95/OKnXB8fnTdsz7XIjqp1xnFgOj2gKdFidGFX5RDFVoEfXrUVtC60zTwNu6uMcRSqRS9OQ6BIov39PyIMP+bu3/ifcv6P/rf0dXGU9NlP83lW8I9/yZ/8Ev3qn/1yYMka0338eBu2m0Ox+8s51LVXCMEuitEykvEIQyKmTKyFlBIpN7Kqr6vCtprBfmsuAHOkT7tZ+mzLyrpuFIU0DgzTwDgM7p4gz64Pjhrto3Ibr3vYQbfxe1GPWWydVoqt6bLQy4aWSqq7NZlSqEgz+tG8GV3IcCwl50gtzWhIQ/ciDspWqM6PMzN+oXhyVak2Fakd2tqRUIibNc+lVfcNNO1Ar9xy0SvNmJJd0SIENV74utXbmLQWpVTbM3ZWdXChmdWXftagztaSGxoYxQVpvt9Zg9rNtDAZsNL8jNrBia791hzuz4FPwb3J2VvIP72yqloPVrrFEoZgQJHs76srZbO9IogVgHNdKbUzDQNO7yMPgVRNJWz3GxR7znszTm8ggPP8raFVF8H4LuluIjtiuT8PKWAUFKyBEnfbKNWACl/9iEYoFb1WZIG0KgPZ+JbRc8QxA/zq3qeiNqUJWDRh83M45oGOcCmN1ovvLN0T9Gw9KULYKqFCj4IOA6V1C43w53JHvPeit7dudLycGIcRCA60OM1tb8i6XdPsIEhRG8WrCJLMyD+IO712F7t2cyvAi0OrK+zs2/kPRWFrNvbOdjtIUXAfc2dtqcc42gLatv8aqPtPvL52QflqiNxPkZzEeJHF1K2IFRClVC7LwryZSnurjbUUW/wdILBpZ+mVp61wWSpPS2debeQowRoMwIi4TSEYqoIqhzHx6vXIp5+c+PTTFzy8vGPeKvLZezatvH+3kWLio4cD3/z0BfefvuR4vWdG+eHP39D6iob94TY/rBgNUdwtVqyJVitO/LCKKdxUUujefRqKFwIcxmwRXuPIOCaG5J2YJ1LQrZssrbE2pVfzTqvt2dTBOk7bFHIMJLdIGbIVsTlFulb79TAQU7TFI0pIBnPHEJimiWEcXamnIIE0HRjGe3I+YibeRorf0a1b4bivuX0cexv9Y/3xDcl6Lip39A95NqO9cbOq8aVqs5FTdwTiNvr1h2wX14QkhGDFJWIWSdApZWCdVpZ5YVvMNqQXH1sni7ncLKzG/i8mcqpOlreNO4BzTxJdnm0wRMV9++yh3m/IjghZcd1Bk7fwhvZ0Gkkskzxk/zPdi+pdiODFiR8g++/v60wUSqk8LRvvlwvrVr3qrY5EJBNMBFeVt85WjE6ixXjHpRWqJ1AZMmGkevO2MwoFwUdj/n3UmzRx1e1+7OxqfSu7PeGBTpBOcO/IwxC4GyOvj5HXU+I4mh/rvHZKhXHKHKc7ekheOAqETMpmByUuHMgRxjFx/9ELXnzrO4wff4vh7jUxHQjOGTXeriUfiSROp4/I+cjh4SNOH3+DdbkgCHFItge14skRngxEIkuk01m2BepG6AXtM9qvlFYI1QUTrdDaYjY2LoQLCNKMo9iiHXLtzVuWf/ff49X/6q/y4n/07/D0H/571M9+6Kf2zp3k9pxwK/u8UJQPfq37r33X/qDKlNvff/46f/Ju6zzcv0bGA9v23htb0/w3bTc0MwYTlaWYGIdG14lNg43tpVNisACDuJG3QKuN8+XCeV45HI+Mvl+iQq9K36qNCVtDcmY4DEb72K1d3PvEBFzNTbd3yowi/uvSbDzcq4nsutuKlWLCKCkrtcxua9UdWSlITMylsZRC7wGLGFamwRSw0wiHZnzGXpRlWZGUnC5ke2YtmxXExYrGrRg1QJaVGAOrW/tAYN0sf9nMyytL3axIdOue5vGHpRRK3W2J/Hp1v/9uwq3e1O58xuATN0Nz3d5OnNZl2SF0tedO9bkQ7wjtg/Vg6mhDLUMIlhTky2n3o/4qav3VVwM2hbAZuUeCEnwMm0KyZhtDrtvedIoY7UiEIUXykJh6IsbAtlWWpXhRafulnYX2LHS1Z8sAD0MGJcrtmev7NEeNt27DJEe8u7rROaSYEKm2Pzfjs9M7lEYocFiEQSNDyGRJDCFaooyIc+qNO434kEaM2tR6hZCpHcpaHdWPpl8YIqfjwHE6keJA2SpL3Gg1kQUSnXNrFD8La3Uxqx84oqaiH4f8rL9wKtVt8iTPrjVBDTEWbFRfb04YCr3dALhGQMS8kiUKODLed63TvqMIN6u6m6jN12EysJWuWHpO6MZWiCaM/rqvr11QvjhMHIeBQGBZNj4c3bXe2Epj3gprLWYI6wujdgPfO5XSO0vrXLbKeW3Mq715gok5TIVvpO4YhZCF+9PA61cTn37ywKcfPfDRixMP90fSNPL+MnNdCz/56RfUYnYy4zjyybe+x6vvfgN9+4bju/ccXtyhP5ltd472MOcYyTETox9gCsRwu9F5CB5faIpVK5r3kUcnxMDpNHEYM9PgG2vyx7wbj6/UyrwWL8AbW+s2Bmh71qd1o9WLuJyCj8wt53oY3LU/GL/P8rh3b0uPEMSK3ePBLH9SyhCsa0Uy4+GONByJOd+K/50Av5O6zXKEW7GnvonvReWtwPSxrjqfUlt3haHD7dU90YqJbZp2swhpzybGuo/+u1sjuOAnIJb4EUckRCax0UJrlW3cmKeF6+XCMq90sTFlrS4aoTvR3UfZORFLu5HcrcsVF7nszYF1utr7DUWxz2Hzgp3Ps6Oq+xhm97HMMXF3PJGGkRCTddf4mLzv4qVOcTpAdQJ1DHbPuhfXS+mcN0NkA0rOSp4SvUd2lZWCjWW3jbZtRtZ2mklTN0lXRXrzgtHEXjdOkhcigqm6zVqoeeGGXUOtiFb7HD7iDmIH3ZBMaHM3CPeT8HJKnHKyEV1V5q4cUuZ4eiCMR5BID4XeO+vSWZcLvRZiMyZUTAqniRfhNYfTkcPdA/l4h0q2DbhZWooVyMkOyzgwngbCeGC8f6DU2de4TQHE8+qncSKa4Rbie9DYu9MsCtquxHKmrI/U+Q39+p5GojVF2wIu1GlEeuwkFbTtB1BCl4Xrf/Z/5vi9/y4P/+N/m+v/8//C8l/9dbRaBjO6z5rYa8rn15861eVP/Pyn/+yGSepeYHYkJo7/4r/Jeb7w+PRojopqBYb6GNEM/DFqhmLeiCGQu4UshCA0Gch9MdsYFx9ezwvn85nTaSCkAzmEG91lKyZGUAnkcWSaRnLK/rnV1z23/cAKob2J8j2hVCu88MLBG9vWDGmv20raChFDhsxhwHiCrRpXrJRmE4zmaBbdR7bmXTjPK4KwtMo4TDYq7Gr/9tbY1koplsxk0Y/FjayhqKIuUjKu6OqWP54/vjmDbg9GQCi9Ok/TOvPuz9rtQBdzKFFv3IM/08GfvyamzA/iY252kQo315EUvBH2c8PSavDhh99/51HqBz8cr/7HFpT4n29dCdX8L2PvhGouCIZ2WdGvzbDxnDMfLvIUA3enA7V2tmxl7DwXrI9Vup+5qO+3oRp6FA3YEQ+R3qc66m7f5iUcn6cnPqHrzrHMIZJJPgVrSKuwNU6rMm7m0JJisn1fzFOTD5TgrXajpQUX74ZkLhohUauxPnMMxATHY+bhMHF/ODCNA6qBp1Zp0tlacX9MJe6TVh917zaG+/0YhmwJdjGZuMu1GQiIF6BgVnqKrU075206V/a930aktzttU3FTKO6jdfFgboEbdWNHgZFdO+A/ok2AQ4QUgz/HRscYduL413h97YLybkgMbkXS1cQVO2i1VeN2rXWzIqHaJtB8tCRiHMm1NS6rFZTX1ehK4ryg4KrCISnHh8zD/cQnnzzw8at7Pnp9x92LA3cniwObDiNrU97OC9d55ulSLIkjBF5+/A1efeP73L/6hFfxyOuPfkF0EjBiD3oUYcyZYdzHQFas7WrsIRvqN+T8lRGvAmnIpGAL4ziZTcaQ0wexWbvCr7GW5pzSYvC8F3B7DKF5HVqxMuTINHryjaMs1p151viQ9+fXPocXPUNKHKaRcRoI2UhRuy9jDAeG4Z44mPm1FR96I/CbZYWjHl1vymzjYthGf+u0uo+mPC/UfMcM7VRV6mqFjvGidsV3NTP29lw41mJQffEUC1u57n3mhbAtCTuYg3f8qI/6c2fQztabbcTOi1XvXA297aQx0ebCbVq9764+btLOLrm7GXnDfnD7mE8DuzmzbUgDLW5IV06HkdNxZBhGLxKbjwjarVBvqp7bq/75bOPcU2dq7SxVmatxXyfpFIWsQm8em4m66r3SNjtwWyvsueI2U/GUXT9Nem+Y+Fd2AwS3l7ELYaRtO4iDuEecVlNIqt580aYUGJIypsAhCWMS7nNkCIJIp2tCO8ScOL58yXD3khBHWqksy4bUK1GUKZpoptdGTsanGsbIcBzJ48AwToQh0yUSNBovd3OT82Ixk9qtiBmGA8M40tsdvRdqXynVDClzOnCc7kw52rpFVYoFHFjSUUP7Ec33jNNHTC++Q93OrJd3yNOXrO8+o1/fwbwgrXnogAsz2BcN1Lpx+cP/B+vjP+L03/9rTH/xr3D96/8+2x/+Llo2K0j2GMu9KfgAgXw+3J95l7+sqNwj5W4/I6Tv/oDDn/9vsz18wt/4T/9fHL6A76k3ARjRPwBZkjkTYCi8NiVqYxpNSNBqZsoBOqxYE9VK5fy08vaLd5wm87cN08E4j9WmThoScUgM04EhDUTc0QArcqrbBNH6Leq2+p7Qdu5bqWzFk12K8azLNtPKQlk3pBWohV7t2Sut367cPkXatmoHcko2jajdnqe5mmpZ9ihUO85rqWzrxnxdWeaN1U3LqwtgSjWV+FaqTXK0s6ybpU+p3lTfqi4E031/Mx9ZBVfPg3ogxd4E3JLTxVCgfRzUtk5pQkh2dmR3mmh+r3chxyCB1O083cRGx3uSiYYAYmdTCJHWzSJtX1H7vfknvbqaCK03+RO/77622tg9GGNMxu3PyWMrjb9eW+PSF9pk13ZeG60KIqak772bN6wCETM/D8kKShfn7L6gNtqOzw2xU7JiDEQiRTdyD4yaWX26olshrsphhqEJKSQDEzxxLQbzwdVqzULrbnPUunP0o+1fCGixZJ1p4O5+5HQYeMiDcXk3c6e5XmfmxW2CqiHe0pWkncET91qXm14ihWddhPq17Q55JE/P2ykeaDfPVMw1xeIsIeyNWHfkXM3IPHhDhRrFy6ZvPEPino0u4tdeXfzVfNSfjFcZFGI0Vbh9qQmEv+7raxeU5pnV2Lpx9vaMXXtTzZDJD6xk9qpZULpaoSM5EI9K6IWwNqJikWdViVl49WLkGx/f8ek3Hvj41R0fv3zg4cU902FkGEeGFM0mSILxNM8b5/PCdW4Q4MWL/z9tf9IsSZbld2K/cwcdzOwN/nyIiIxMVKIKQKMbJLulSWELF9ySe3JLfhp+Bgq/A7kil5TmkkJpaWmQbDSAqq6sqqysjCEjfHyDmareiYtzrj5PNCDwogisxCsiPdzfs2eqeu+5//GGr37xJ9ze/ZLj7Q2LKwxD4Px4odVmfa1WL+TUKeqjZxgjw6RuRR8d0xitIUK1ELXoiUhPFp4hesbBW/9noDWlL2vTzmyNQdKbLNmpumtaFKnrRktFQ8chMA3OwthV6C4i1msdd53n55FJIjbUHmbGYWAYBnCqVVXafmQcr/BxwpuzW4cPi/PIinBhOsMmz8hC+iOhfFXntRlqSqtarZeTDpk2fKZto5iurw+KxW56jRWqOyuteZSdeu/HeEU7S9W4AkXAlQbQKDfrqe55nMEjMZKzxYEUpT9C9Ar/EwwRsUdWLCBInjd1HagdtRV11bdKtFzGhtNDCNiQZQH2RtFfzwOHOewGANVPFjRT7Y+RXY23UtQQlM5PufC4Jh63ytYcuMg0CPhGlsyWuwi77ekJKS8avVKKivSb0cjtM2SiDx+laK5qyepAB8R2ml6XKTTNXSz6vgXtcI0ejsExe5hGzyF6DcF1VRuFW9XIK/tchjEyHw+M05FW4fLxEy0tjNEG00H/jgSPjJ54fWB6eUc43UCIVAquFYtm0lE3RI3ISGsirQsuahKBDxPiIiKQl5WyLCxPT2ytMs8nYkFpxJLY1tV+TtOiisoVmji8P+CGF8w3v2S8W0nLJ7aHd1ze/8jy4Xvqw3vCesGVrrl81i3q/dRIH/+aT//v/yPTN/85p//t/4764yfO/8//G9tv/yVtW6CaU1N6EkPf6Z9HyH2plk5/693Zsz9ba8gwE3/5jxn/k/+C9qt/yl/+5b/gX/6f/098+PAT//Tmn+khaUenzQ1qiKGvjTVbdM9aSOdN217OiW1d2NKmDEou1FR4uFz4of7MOAZiiDaYPpcwuDgShsAYo2XWPr/fvn7UpshjzYZKZm2bUY2kOrlV+5vZ0kZeVtblAilr7E7JiqRaqUDr9B1tZ0dqMTy2KQOWcibkwLpsVC9Kz4KaD0lUgWXZ2EzXvyyJNakRKVfNkmx2oG5N1+llzWxJNck9eqjYwbMq9GSH888vpClXqpokS9cno7mDu56xNts7deglqDHCG57VdaDeCSNwhcdXz9l7Hu15X6odTrFhrD1HW/Xz45cadO2sSV4rKSiC51026r1SpOGb6lVFLJMxBEIMjNHrflpVc5tLYp4UxUzZjJ214MpnUiw0oklqVWOL2Dpc7cDr+vOisrf6GaPUM4F9SsRaEXG0IrCCXyCsECTgcEQc0akWMQankT85s6Rqz5zSyiEM9HQV5xTUmcbI6TBzOoyMQagps22Fc9ayjVr1z3oxCV3Vti9nenNpakyqTeP9xhA0G9d7PRDYTVNKsYxpNel0YEfQCECN3NJBU1BpdEdtqyU3dDS2lga+7sa6StWDgu1l4uxw6XSmqwZ2lNxRdd1vhwEGr96GfyuB8u94fXlskB6Y1EiwJX1oSvmsXk9T23dEr1VdxFEcdTpeEadAKIX4YuZ4u3L/6ZFPH1fWc+PmNPLrX73h1796yS9+ccvLF1ccjwfiGFX/ldlDXEspPF4u3J8Xnh43ytYIIrx++Yqvvv41p+tXNC9seeXh4ROXp1UbH5wFwwqEqEPk4TjrwDp4i79QTaUOfxrE2molmpPROaWlB9MWaPuD/tpS2k/KyUwUuT7rV6SCNIXdEdM9joFp8MxzsAFVtZ3OuT2mqBpd2kUOTmAcR+bjyDgO6jTGWhZrA/F4GRnGAz6OiH2NbuyoRpd2mtcZtVGyLuidTt1d2/XZxFN6BpfR5r3Te9s2u+nrc0xOaVZ/pqf31ltIuunTem2LtWYoCqALWC7JULxC3qyCzFa91k9g4iw7VOyEpvdeiI7aHHnwpKzXpid79nYGZ0Hhn4e2dvOAs26WLonzXvMNow/kEIDEPDniIHvTxR7irhyypRXowqwKf90oS1GjwLom1gJLQ+8FSwZorXAoevBpCCUpI7CkRQ8A5pKv6KlEWl842J+9vkWUUjWOqihyJeiwRbG4nFZwTbVtzunpN3qYQmMMhUP0TF57bIOoOSVYJVd0EKUSvXCcItN4YBxntvVCcJkYhWn0zIMjhor3oxqw7l5wevmSw/U10/UdEiO1ZUrd9HN3TjdV5xCvNY+5aXZcFTWfUSqSEnI5s75/y/3PP/O0rByOR+TuljE48nLm6f4t9+8fqZvHea0m3FxjvB559dVLrt/8kjCf8IeXDMevGE/3xKtvGG9/xfL+e9aPf0d5eIdbF5rvWZoA5TNCeiN//P9yfvpLwov/Eaf/zf+e9pDJ3/2W9c//a9IPv6Ve7qnrYrKRfnm6evL5/wsCPiDDSAuO8Opb4p/8xwz/5H/KA8K//sv/D3/x//g/8PDuO2KsfHv3gmt30UG3ZMiFgrBuaojbnhaWdWVZLjydz1zOZx7PZx4vG3lNpLKwbok1KY2sJjp4eqjQMmP0BKmM06TVcaLoZBwGC9vvQ5ve63qgMQ1ltSixXMiWK5tL1XrNlKkpsaWFNWfSulA2lcxsadO1qTpGF/XwXK2DvmG1nXr4Fa+fXa2VZkaNNW3QIpe8IcHjF+0vhsaWMud147wkLheNucmtWS1ez9pVKUgpxTSUmeCsnQs0Q1FnIBvilGHph+BnElJfGsMDlrpjl140cNp+pl4h6Cw5pGcmA/jaOAbHq+y42oT7CN9H1SSmqoawImIJB263kf89ZgBbGTs1LpSkgwi+7LIbHzzRhR3JM2G6fi+BED3bmhmGwOEwsSaN6GLZ2EqiWr1iCA1Jfv++Y1TtL5hu2kAfDKjCTKz7Ot16G1jDlUYoRZtxUkMWGBdwi+CbY4iBbih1zmlMVM6suexVx75av7hTyYdr4KRynAfmwTNHwZViRquss49ozeg4BtYl2yCWWKoatqhZCzuqWDyPMqLHYWKMg/kDdM3OSffV4AOZzDRNiGCDeGGwcoXFBvPdrLQH47Ozjt4p+kmtxvwa4+TskFP4TB6zk4AYW49iDwK1sVahRXZt5Ze+vnig7FSoCk0xjVz7I5dypw31ntBKJ7xnPBw43B4Zro688p6tJN6+f+T78Ja0vaesK7fHiT95c8c/+tXXvPn6jnFWypemg+SWG8k6R5c18+Fh4+2nCx8+rVBhHjx3L245vTgR55kSMjVnnu7vuTxpnEHp+oQonI4jp8PMYR4YBo3mES+7BgHMHeWUbh4HRQqVUhKl8GzAzbuuR+mZLffQUdsm7EShGvKqwlzLnpoGzzQOighE1eJFH7UKr3VYvEcqFYIPHMaZ6TARh6jwedM6vNJR4eaI45EQT4RBWw6w0OOUEsn0hxr263faupSeEWc/W+10Q9sHxVySitttwNyytVvsdYzW3lDsM6nstVxOcXZFALKe0L1zNFRsHt2gN7l3+jmmBFum5JUtLZw36wCvjej7gyV7VIb+mFWRQNcPCJWSusHITluiJzFta7BBt1bbNPWh0GvWo5mUe3eO/TR6mlSWoIJxyz8z9LflYgiNxaBY/3mpSRGSVfVfW64UlC4iRrxXR2SURnCV1eQlUipuswggywHtukknovpH11EbUJUcYAgiTqza0qJWeqB3s9DcYsj3oCfXwQtzbERXCNKgabXf4JwOm7ExeqXA59ExOe2ydcEzyki7GpkazEEYBo8PQpgnwu0LxjdfM55umcYZNzqqC5q5lwrNa2PT8+Smi6JINVbB66k/L+TzmfThHZcf/paPf/c9Hz+tiIPz6wOnG9Ufvfv593z/3Ufe/0FIZWKrnveXM/EK/hf/7B/xn/7P/ufc/ZNIiF8RhiPOR3AjbrzBH28J1y/Yfvwb5OMPkKzJy9YEZbUF5yNOgsYrffwXnD/8K2q4wb38NcP/6n/N7G5py0b79JH66R3b7/+Sen6gpY30428hROJXf4L4QPzFnxF/+WeU+WS6tsz3f/gbfvh//V/46bf/grzcM47C61/opj1MkdNZ7+2SMnlZeFor52Xhcdm4LBrTdl7OLMvCtlxYLyuP68aSNtJWFHVJmTVV1tSMnl5Z8xOHK08Y4KW/Q/yIxICPUfNa+2fRkbCuvTaksjTtalcWQ3uyU1YzTM4qBdp6u1pJpJYoeTW5kMM1T2DCMeBdN/c0ZcGoaOzlcyqDamCV1bjkwqVkQolcqpAs8m3bEpu9h7VkUgcMsHpRjL1psBUdJpMdcoNpIRWB0wVCjE2pRj/tFHN5HuiquWZ3XZuhla3JLjlqaOWvb2pkq62be/QeO5bAy+a5asLBVEIuQOr0Z8P2CP2uzn5psuwXUt6orFHfkhi9KuC0wEP17YquxRhxThicooylNkJrjEMgpcw8jbQiPJ1XXG34VDX+yTqoW1D5liAksjadiewaUAXTqgUWKB2nKTBND3aoCbLaf25bg60Szo3pEXzG9ryKRDXjYAecbcvWemR7pQ/q27dghNYKUxiZnGeQQMuNS1ooVe1Q8zQQx8joI2ntVdGbRllZYYeCEDAFwDvGYWAOA9FF9UxU9R9gAeriNBLL+8CWEtM0MA6BrJ4henVyzsqWiNM4sGh4dge0nAEDoIOsx7wRrWDseU9F09Gq9fuRfU+O5vDPXUU1CD3v+0teXzxQ9sqkYsYNNVyooaEaHatXRGfvJqoFcWEiHE7E05Hj9YkheqVOQuRhufDu/T24jegdh2linqY9X3HbGluqLNtGyo2UG9tW+Ph44e++/8BPf7hnWVZihMM88urlHdM8IyLksvB4fs/PP71lXSwiRTRiJEbPzfWB25sTV1cnpkNUCtFu8o70hOAZppFhiERDJ6Fo+K5ljbUqOwWTiw1Q9ll5p2q1PVqnNY0MGCZi8FpzNniGwbItA4Q4Et2g2jtD+sSMJEE88+HAYT4QxwCicRnFROO2zhEH1aWFcURctFOMOhxz3kh528W/xShjHRg7gqaxHppEofR0DyIu1j7RjRMtV6Oxyh8hyIpM1L1arbVKNoheF1ejP3Kzh1vhQN+db06oYrVpNIJTo4e2XGykVPeNjKqCYqUi1bQUQ0AGp8BgWyxWt0Ep+BgtFkHfV0ckKrZJWA6Xs8GzOe2Y7XV70cHBauZUj5lpuUAqSK7UlKlr2mN8StK8vbSuqtsyuqS0pmaSICa5U/0irVGSfdaIZkzmTE6rOsWLbtJ913FeP0fs+vduad3nCpJ1wyuiz2izKki933URCE4DrUcvjA6ioDoaqQwBpginwTEPmXGAYxyZY9cRN7xYtEgcub57yeRWom0WcRiZXrzk8OoN8fYVcZoINJMmOHIq0AolrxrC7RR5bhXS5ULNGTdEnFSoG+nykfO7n3j4u7/mh7/+Db/5i9/y+x/P3L28oqY7fLwmjJVULqy18eNl4eePK4TA6YXDt8S//lf/Ardc+E+94/bXQrh+AbHhhoEhROJ0YDjccPYzFxcoH79HvAYetz84yl+YmS1UcsjmBHfQMo0fSeVvedr+S0r1lDzgDr9AhhsOb/4nuBtHON1y+s8HaI3t/oG8LVw+feC7f/Vf8vj2bzXsO1+oNTF64R+KJ/g7fBKG6gibR8KMv1dx/rYtPH18z8e18WTFEcumzNGajVESBz4QYiUCrWXW1naj3NYSOa2kvPL+aeWnhwfutsQVWsMXg4b4dw1wqzqB9MNJrc9xYjmbkaVUtt6JbXFBOWlFb7FWnJI2atbWGv3VgIBzA/NwwNdEllXvZapKn62EoYlW/onXJrLSyi61aRVcLRTv1dBg5Qpb0s0/2SCoeYx5dxMrHWfGuqbRZ84OyvaUmarFEMna0eaeF4pl0bY951hnPdnBikozdFM37loUge8FAN0o2ipIrURpnGJgbNDM8PjkKyvq6vbiULL12YTz7zPj/Juv1ro7WFHBgOxMGfJcK9tqBR/oEXu+aZd3N4WW0hjGaExSxXuQLbFuOi9k0SYdsthapT9vl5d2U5J9EHowtrzK0pR6x+vC5VvEr5o9PJ9hWiGKyiU6GKJ/T8GPZdtMl9lBnr5naDvWPEbG4AgmeVuWVXvVBQ5T5DBODEPQCtmnhaeHheWSFFipG0JjGiMRjxsCPqipMDhPTl2vXhi8zQZe56GMamRdK2jckidGjQZU/b5QvUo7aBor2KxkJedsOt+Ma5b3KzZUNoHoEClWLapJNbmwV7SaKIhKZctVEzpEqxhbg2H4crz7ywfKXNTwYDcdtoC0pqe6Tn+I6ImxGtqDDzQfCIbyzSFQJTNFYfBtF75rNlhi2TLDmshL4WkpPF7UPV6KVn59vF/46cM9794/8OHjmZIbg/fMY+Tm6grv9UZelic+fXjH+58fqcmgbBtkroaRu6sjt9dHDqeZMGi0T2ti0SZoMPiog2TvyS62UKprGR2YLCS3WTSCiAV122BTgZ5NqBpJxzjoMDkMER+i9XM7BkMTS6nmNs3qSLOqxXmeGKcR71Vf1mM5sml7tC/ZE/3AEEaNVujtMK23UqjGUWmlTu8202Nk3chqseDstg+HZW+W0JaatOkQqc02ipBqPJBuXh0Rrf2oYzKIHnpOtSiJ7rrDUcjkpBvEmjaWbWVLibTp5pNMo9kjiKgdFag7xdyjiMARojDUQCGQa9m1jObFsZB3RWQRoeSK86b3CVa12IN2cSoiF8cgwtVhxMcAru7dqilnjT7piGTJO+WXNh2Ga3le4LSdQIXaGc3O1EahlWVdWM+Wl1i1hUq1NqJoqDFjzzyarb9GCe3YhG3+bm+6MMmB6N8PDoaguWQhNAYP0Wtg+BQah0E4DHCchGOE4xAYI8xRrDYM/FjJlw/EMOLGI+N4xTzdMI6CCx4/Hhhvb5iub5hOV4ToaTmR06JDdc3UZBtzg1r1MFZzYnl6VEnD6aB3yLqw3P/E/U+/5eGHv+Pjdz9x+bjweD7z8psrkEbaNu4fP/Hx4Z5P541PqfFUItdXjburwNVUmFrl3fe/5bf//J/zqwrzN98QTjPj8YY4XeGnG0K8Aonk1thqpd6+xR022r8U2r/qZouESPoMVf03Xi3hW6Lx3yudzH9Nd2LLMOizt277dTzSOALa1HTar+OO2XY6U+x35ZF6mlhD5eHpnvskND8wHg8Mx6A0cNbO5XRZSNtG2DbGLbOsG3FLXM4LTTRbstWMhMjV1cx8ODEMBySOBMsTBUx/reugCv/LbjTcD5PZoruShoSnqprrkhO5bDpY5k0P50URfWrdkxmq9/h4YGgroa74mpFSrH3Mm65VkcQ+gqSs22MtbWdQpHiqlTbk2g+5OpD1lANtGGqqN/78WVFbsw5BrTvKO1tks2B/zMxcWu15U52hmA5bB0X9I9o/Lk2lNMV+joaQq6YqdL08VSw2T+thA4JvlavWeFOES3ScW2GxoFzHczwRny0Nf59Xs6pAGQ2utOisWipNPD0aTUq1LutKK47gPdu2mGyHHW0M0SO+4qOntZV1TexZpQKuaX2i61lutdm9rc52h1j4txmVuqzO65rsc+XYoC5wfXHMVbT5Dvs7tYATUlJ2rthar30UZmKzuLExeu3VDuptWJZV12xjs5wEWhEujysPDxceLyuPW2JraoKcB88wKGOhNcaA13SCkhtIwQWYYzQQzqsfpdT9ACHoe3bem1RN9/jgHG4YlA6vvYzEow6nqghsfZ4JvFd5kI5XmoYhHmNXhZwMKe2fKXagsNSPEJXlSkksrujLXl88UNIgpeeSe3B4ULLSdbFt00m7Os4VLlUYB+EUIuMwMg2RaPrE4FSrULZMy40taZPOwzlzaQuXvHJ/2bi/ZKUflpXHx4VPDxc+PTywnBfypun9rsE0jcyHGbxnyxuXy5mP79/z8f2DDpJeLOvXcZwnro8zp8OBYQgQNKCW0ghD1EHPmwnAglibQeY1V9Xl5Wq6HrefinsTgPN9gcs7zTqM6gYfo2eeInFQusBJ0BacGPYBpyNHTkSjFYIah6YYtTqqlX2Y7VFGCLjomMaZ+XDFMGqcTTMeZqc4e9yMaSO9E1x9vplq7YJw7abtG0SyusSSiplvyq5J6dE7qs20I3dTVBRBF+rPREQ7jV4r1F53poafZF973dZd09gsBN01j6cSgqc4daXukUR2fXJFH0apZlwKNAa2pGGVmg3akGAoi72p3gLkq4a61qYnQ3EC3lmOpV6nY4DDNKrWFaBVQ27LH3++2SQEn8dFmX6ndkoLtyP+rfbh88K6XHg8Zx0QsRiu1npWta4jdojLVTenWhri9ZC316ahi7aGKfcec6U2QoTRwxhRN/cgTEPjGIRpFI6T5xQdp8lzGIQ5BoYgjFGYgi6+zmXwjq08sj78zEgjjNf4MCIxEuKAn46E4cjgR/yuq1M60blKiIKTAtamowG9mfPjPdvlicPxgFBoZaNc7lk+veP+Dz/y8ae3nB9Xhjjy9TeRq9OAULg8Xfjw8IElLaTlTD4HYm28HEZ+cfTcXAvlUlnO8Nvf/A2rG3idF1786lvCeMUggRAGHCPt2EgvFranB9J/tjD8+h5PVVTYa/2q9xFxXiVldMTFmJpurqk9Sqc/iwBpH4w6o+G8PifOOVyI+tXEzFM5mQi/ghcKE/XwZyzzn7Ld3LGVRjwcVK8eB3KGddsI28oiwXIGnx3nNVeaa2QXiK4SfMKdDgxz5M3XL7n79hvmFy+I44EQtRml55Nu/dCr9IdRwc9ovA6X2pKVjL1oubCtmx4OqyGSxfTHWXVnxbIWvURSOLK2TKsbkhOyZUXMnaJKuanDuvR4Iq2XIW2FbqrLNeuhes8afja80Ic8dNMt/aDWB0d6vJuhjphZxDZih2rod32fPKOCbk+QMCQX1VKKN01g/7LGPRYbpMV0dw6nOYGqtjFdv2aLzjhuC5y3yoP3LKLNYN5pg1qpylZ9Hpr+b74+T7bor46ytmasm7BLGIIVQFTvyORdi4535KQuZGeaUu+Uao2DEONAxZOK0suPD08sSyKlAl7bp7w0cJol2rrOXd/JrpcE3Wupim5KdcTkOF608Yz7ypzUeDMwGICpMisBvT9rd8MrW6kmP31uBxGiCEEE12BbV9Jm96MN/Oc1cTmvXM6LsnhUxDdOU+AQIoOZk3RzUYQzVwVYqmvMcdhR0ZR1QM+l4LIgFaqoF8A1RaUFzRbNJdvtWk03qfF2wTmGGMnOQdai0GobS8mN5spuQu1pB3tJh1oBDIdQ+YVY8Us1xsKJlgfUwhe/vnigLEURlV5Er3oVDdPU6y0UGmuFS67c58Zj8dyM8Nqp4WCYPNMQWbeV0jLbtrJumYawVcfH+w3/0yMMK+eUeVxX683UfLT7hwfu7z9R8qamDKPGgheujjPOGz243PPp08/84fsfWM8Lw+SMmofoHNe3B+ZjxEeHOHXdSoNhVLG5CxqqKrZj7w0lKZOz5krqbi4Wt1C76M76Nh29D9Z7TekfgjCNgXGMxKhaTB/VTR6MTtiDxu3m2d10Ud1hfbMtpe3IWhOY5wOnqxPz1ZEQJ0QmCgeahH1Q6SedZy2k6ehqNqe6Uc/IHilAp69L2ZtrUlEH+LYljYYyd7a+bXVr02zTQ0/3SgX2uB4VLu9uR9EKtFY1T7DT3+MwKhXdnl2XOpiZVsVlNlYSiVIdteW94UKqdpzHqLSBc9NzbZWIDv9NTRa1dsR531noY4G4nj2pA5svelqeg+MwTsSguaytqKu1pUTdEi1pxJLB+So5KHoQ68Nkd7A6EapUBE/dI4eqIp61YHeafnayMzj6TFbMFY1l8nX9kb6q0SU9uHbwoghkEIYoDLFxCDBHOIxwGIU5CsfRM4+BeQycpoF5hHlQZDcECya3eBrVPDbVv9ULZbmnjgJjQ+oR5ycNH29OI2JagR1RGfT5SqLDZ4hoNEmhWCxVrRswU3IiXSpPn97x8fsf+P1vfsvjz5+Q6jmeZq6PI9d3E4frSJaVlDLtCfwlcBRhCiunog5Qd4msnxrrk7C0By5/+7fIV7fc/PIX+KAtU606pfn8QJxvkeNrtvAjzPekmhljI4wRiUJztat9oOd7WnSQEyWTxA52tW/uDTTupdpirpRfsGfFibPDrGqyisZo0pojl07VCXl2uMHhgmMaj4zHW+b5gFRhIxtyUSi+UUxO4pzHu2xMRzI9VSWOATcGbl/e8stvv+EXr15zdzwxRY0Hqoaw9WrFUorGA9mBuhRzk5femtUza3XNyFuy1IeeWpCgPf/9ZjuXDhQjxXlSrEjdcOuCkxXYGIJn9I4lZ5IUk9KgXwtHLpZd6e1ZF1Qn3FRn6bE/W5/pamcGuoLq4L04q7gV+hlZ92bLhyzq9BZ7OMU1lIvpzIuuNa7p73fjw/PFV1AlWzRZNdlOqZVoAE0rFrGF29m1IGpaK65wjfAyNe4HYSnVdKCtr8LP71n+h0Plv23ILH2tETFNXrOBuLOPYnt/oSTd71sIOoS0hmuO0iD4phmRIVJbYpoOpFwZhsw8Rt69+8TToqkgVRx1UJQS6VpKjYPLrSqFu0MsinoW5ebxyXFoWvfqHx2+eCQ4BueRWvGtWdqBMWS5PssP+hrc9GBgyb2K6pWqWaVFW3W8j+TcSGlhXRaq5ViPw8D14DiMgcOga1dtsmdv6x4e7LnxO5ydayM1zeBOuVDP6x6iHsQR7BDRU04wSVarxfo1DIzRkVPzV+1/tVrw4i1rupEVk4Km1Z4JqGbGcQ1a6p+FEXx9FatYFF+f777s9eUu7/p8EbTHWYyO06Fma5VLgYet8ClV7rdKbp7pAFCJg5pbondcWmXbNpbLpic84LJuvP14JoUHcnAspVIxLUP0pg90vLp7wekqsq4LTx/PXD4kDmHm5e01MUJOG+lp49O7P3D5cOYUZ4bbSm6N0hyHIfDV6xtOV1c6SBWtlfOhny50gmi9AWjb9qFKoWtznrWmxfUUnNfPwhSxULV1IIwjLnimwTFFxzB6whBVn+edRs6I6nFarvtmKi3rAOO9uco1U66UZh2uusLF0XN1+4K7V19xPF3jhlGpkwTr5lUDUXvkjtKxGgjcN+yG/yz8tBkCW0qjJkPTbIPoppJaC5vFB/Xw7t4DXGvdH9psqGVpdXeWqZHkmabWJcLoIsE2u2IPuKJVuRajnKrFoXhc0M2t2qBb6d+v2Em/MkjEN6+bdBBEIinr9S0OnNEGndavLeM/exx6+4cTb6dvCNJYmzCPkXkctcsVXZz3+jNDIXMylLL0Bh2LRLKf/Tm+2BCOKnicBtgH/Rm9E7b6mWOzr6v2jFejhqQpmu1aH+h1tBevG2LwjTno+5+jcIgwjY1pFA7RcRzhNMJh9BxHmIfAFAOHcWAaI8Gr9te5TqWodKBPr+Ico7VDVa+rVHp6oFFxw4ivjrJVLnk1IbxTxMt7RALORUQiSLSQ+MK2rrseOeeN5f4d5fzE+x9+z9/+5jf8+PERN068fnHN3YsbDrcjt29e4A+RZT0zXp/4w+++49O7d1ylTGDguo3IEliT5+nB8XCG1dCO2kzW0CxEvlUEHYTVkHLiUgeWS9UA5cHiqrxYTmbdh0VpKgMS52neI/YcI0LA62HTKiNbK12Lg7RsPboNoWhLkbM6w6o1oTVXXPRI1Wy9IokolSlCOAxwmHAuklIlOk9yGnclNl0453GimzCoRjCVDe+Fm8MV4zzx5s3X3N284nS8ZhqHPYgb0571gO7aKhSNFEuNfX2sXSZj167lRF4TW3o29HXXt6uaGFHFNIX2XvVZj2xhxrdrxJ9pPNDETBNeM1of7fNuVu5XiuXptopUre8taK2rRmzp0BVE+5dzK4o2isV0WVKGc/p11CjXXbTqeC0KZKqZTBrB6TMccSZXwujFZ5THVxtaKpb70gyJtJpC+1BFNEi+FX3G5qo5lL4HyqL6toMErqVxLXBbKw/Nsclz8FQXSvzbBse+Zv3Rq2sVbR8oriOl4BVL2tG+noHsezd3lwX1zORxQGjEIRItWP7kA2N44uyFlq9AHljWjZw2AGIoJikyY5J5B1S9Izvi77w22lAbpIAkGC1pBQehe8ZFTSpRVNRVaqWJghzZviZZGTr9XgqJOTzblnbGCsTc1YlcEiE6otdM7ikIx3nkMCryWKzu0MdAcJ7RDoetYRFL0fa0xiUV1lw5LystqdE4m6nSjdo7rgdq834AW2tIaUQRiqIhCn6JIKOjLKr1FDvM1IbmTPaq415H3PTzaeE53Ly05zQaZ8i5IPw7bp9/5+vLXd6mc2itZxhWkjS21kjieNwan3Lm45L4cC48pUaIkauUdgSQ1tgoXNaNy2Xj6SmRtYqZJWfePj5yjyDTZNlQwjAExNK0ro9H/uwf/gPm64F3777j0/gjj2zMcebu5oALTmu9zgvnp0d8q9zdnFCBtwb83l1f8Q++ecX19YFxGsEemmBhrQqnA82icuhh380WFqPk+kPoNWOuWaVRz8kapxFvCNk0BqaoA2QfEsUWlU7Z9lejmYvOuo/NDNSqGI2Ugcbp5pqXX33NzYuvmI43+DhrLWDJLG2zbtli6Jgu4uu2kLZFW2bKRi2Q0ZwzQbV3uVrupIW1qrjeIoPMofk50qmZlUDtjT+f/b459Lrupdpp8bkKzKiN1ulA/T19VnRhLlUpD7259d9rLbY5Zb1+vuGjN6G76rRSVrrDyUA0lNd7C5o1E1LLn33+rYJreo2C06D94EGs7KyqNsYH4XgKHA4jcQhWZ9VsoKjs+ifshFkzuWaN4UDvQUwr6J2KrUWsg7vqBjbEgfkwsKaV9ayUi+fZmdfba3vFmvSNxPR1zgvRNaYoRBsmD7ExBDhOcBrgNArz5DmMI1ezMA/CPBilHawXPurnEILTz9Lpsxh6PEkwNB2liiqeGh1uiPj5iD/e4udbCCMuBkUKnKJpOB0gRTyCh+ahOTuoOpCAjwfGuZGXM5/e/cjb737k5+9/5P15YRsPHG6u8S+uGG6vuPvlS65eXuPHgVNpXL36hhevvuHm6rf8+fob8oOjrpHHj4FWHKkKm1TcaWa+e8F0fa15rSRKuZCSgBusSSuAi9yvjfIxE9KKP2QkCHP09DjGXhlZzYziDO0WaTTT3zbybtRoovSgWKaoOtkrXp4z6BClw/RLqfzD0WhOnxHfVrxsjAGG4ClO4Y3i2HV9YIcVOhpjyHzNNDLDFBnjjB8mrm9O3N3ccnU4cBgHgrnua0f4aBaNVXfENZtbWgdF01ZnMypkDTFfcyJbvNiWVlrJhO76NTF086JCqqYDdx+Kck3IeKSFEUkXohcOQ6BK1UNd0XpDZRZ0GFdDZNciYohIp4EBLNPQlqiKarlt2SHZAbBUGzhMb6lGGxsIeb7u/ZCMKCvjjN3qDGP1OiT2Hbrp/PYcQm50ea4Wc9MEL5pLGZq1qZiOs6FauMkFbgXu08bbCo+6KZnR4nlt+HcNlZ+/pDUCKqURQxx1DWsUp7mKegexD3RN+oBu3gHb21pTsCNExzhGCyNv3FxfUesDecpUTtw/PLIsK2tSltL7pjS0qEdADUpVD3UiFm+nP1kTdCG0uaRrWxXwypp7K6Ynt+eyS02Kma4Ex5Y2rZhszg6GJuOw6+zQDNtcstLp0XMIgSnooXscB0U9nRAFRiccpolpHJBm96U4DvNJW3haZdsSj8vG07LhSmHznush8rAlBahcUNOoi+TFmtHMh6FxdnqQrZYXHZxjEE/xarhZU1Jsq+n1owo48w44cNZ4VGvDRR00DQsxsll24EclHv8BNJStabxMdzKnUlmonCs8bBsPufBxzXx42jhvVUvgQyNtK2lNlKVQVn1al3Xj6Xzh/LRqFpco9XrZNrY1MbjAOKgYXp2zmegDX7+85c/+wVfIPHCaKj+nC58WTxDP9dUBFx05nSn1jCuJm8OB4Zdf6YZnN+rt6cDd3Q2Hw2iuUf3QnJ1aemLhHqDaUHq9n/Z0x6ZX8elJve2IZgiBaRyZhkAcNWJhHALOnG3d4NNaM4pWH8hq2pHeiKOuNr15qlHTFQjjwM3tLa+++parF2+I8zU+HnThyCulZLIhBc9hw9bqkFWfpwu/6iREHGlLFv9gtHZKlL3ezAT2WYPbO1pbi24GXRKgGirVjHb3v+2nmrnWPm/BMZ1G115WRTrFMsh0gFeZRR9ES9Vw5LTpibY2zVgETPMa8aHg/EY+64FnQ08rbhwZQsR79qgJDRWudj9rO4N4Rxz0EOB9eH64YKclpzHy5s0dh9sTvtfOYdfHXK7SUNe3zan9Huk90fozPS/M0uN+RJGSMQZO8wSWv3fe1NiwP+T2TDqx21FMk+xg8I1xgMnDPCiNfQiN46gB44dJuBodN9PA4ThwGD3zIIyjZp4GZxVloKkDoVPbuqA7G0oUna92f2r8k1TBDTP++IJw9YJhvsEfrvFjVBmIM02TqBhda+SsK7jZBlvQQ1qYGA8vyC5yvjzx4+++53d/+XtyEYbrE69OA7fXN1xfHTneHojHmfFwYL46gI/kXDle33A8Hnj46Ym/+u9+ZlkgNqe6tdExXF9x9Ytv+OpP/yF3X/+K+XBt7RzJFm47xFRF9B8fE09vL4R0wR0zbvDa8uMbPtpmt19jPSA913bqVev6MMGyDcVoSukyi2KDpeqnRCJO1FmdygZBF51anMWcLGQWAkmHFNGIFR1wmlGfNoSIuWdtk43DwMF5bS8ZD8Rh5ub2Spu3YiB6peukdRe3usb75iatadQMzXTDZT9M7nWKOZFTYkubhptb7BW10LJWHnaEXZFUp0HWzmhrGchxhukaGa5w9QKixonqInPU1psOr4jdS2rea7h+gMfQ/H3obMTeKkbBoc1IXVVdbYgXQxn7LGiKPNRU3l3hz8dj7/S61NqoTpGlWtpzYwqqQ22u56pqxqupBwzZtie86eUOXVbjvQ0Veh8FHLM4jiIc6D+jELBZi67V/fe/etSQ7AuM3kTVtLFejNIveo3UWKOUckUZuX44ECrFVzZpxBoYR08QWC8r0xAph0JzwjgEfn73kU9PK6VUxkERvCLoYibPkXu6lnYNpH4+LWuDWY9Gw6KGNOFJD75edO/ocrLedoSxq85MOdL04Fdz3muHg+t920JAGGIgBuEQPYN4gjjVlTrVpR8PkdM8cXO61nISnLb1iCN4zVTNW2I1Wr/VwpYc8xRZSqEKbNQ9TzL4YGkFSfeCpvFbzgdwaoRqFisYxTHFniqjkX3S2NNPtrURo9igDb19ThmHtiOStTYS4A2c8K11I/4Xvb54oExJ6bu1VJamWsmHVHgolfu1cL9m7i+JLVfc4DX0OagublkSD+eN4ZKJ1XN+KlwuC9tm8GSDZkJr7xTeF2mmSVBB+uwC3775mtevbqkihHxiGQfyPDJ4z9VpJnggL7SyEGrlzYtb2tXzqdI5zxQj4zgqlWTImzRF6mqT3YgToqdnkHeOp0cX9FCDflrXGxLGaWSeRoagWslhMNSThliel/fOAn8rewernfJ6C0GtPWbCjBwNkMZwmrh79YpXL7/lcPUSP51wfrCHK1HShbRpfVlKVbWFpqFKJVn7jVGvJog3GxjOe2otVvNXTINR/uhXyvnf0EUpAtpKNndj1071929Ut7GjtVqSX2vWYd7zL03128p+Au1OzT54V5NZxDjo8Ok00FZ1SpUahWDyiVobT+eFlDJOvP7TqcPee6+ZgVUHgOZ0OHXO4cOA8wFv/Wj7QGA0U5PGze0tb169ZjxdU73XoTclSt70M8mZnJK1G2CIduu70fOhpNX9M9PP2yhEKmMMIJMZxc7Uh8b9qhtdtIc2iGpioq2l89AYoqKP86gu7OMgzGPjMDqOo2McPafZcz1HjvPEGCPTqMY51f6KDTUK23hvA2tv2rHF2+0HL0XdnEDJHlcmmG9xh5f4wwtkOkCY9EeXtlNL2D1gtIV9NK2z9Zo5F2dCnMgh8vTpZ95+WLgscLw+8NWrW45XA60UtqdHLqEynGA+RcYS8HHgeH2Nj56Sn3jx7de0v/jAmoE5crg9cfXmlsOr17z65te8/tUvefHqDYfTSU/sNOsZjrTaWEvm8vDI47t7Pr79iEsX3NqYDgPTMeHHoFH4RgUiVYdiH9TQFYIZ9iyqquqmq4jyc6i8UtwaR+REP1vEQa4WPt9oueCa64EGqi8sj7i2AJlsz5zOA303tkHLKZISQmA6Hom5sqaMxMA0HwlhYBpGovOMQQOSq3Tnfd3Xkl07WeoeByRFTwO6Lii13bZESRutJtNQrraeVU1R7gfKZqicd0jzNHFk0cNHE0HCSIkzfjhCHimiho7BN2Jwe994bYrwtx2p67pwM2KYxjI41KRTCt4O0rsMwfIh+yNb7eS3I5t2IMbuZ/tw9yzybnjrdatVhObaHsju9EHSn7l9RiWbWacWPWIhpqCqhoC6Z3kNzhuKVhUVa3BsWvnXaWuHDpV/n1efPZ+XqAZOh7PSwFXr07LJs5m8yIk2QtUiii42SEtSk45VXAbTGJZcmKYBnOqLhyHSfnjL+bLaPFCBUaUk8fMIHJWVqWm1AVVRW/v8sUGMpgey4LU0UER/jh6Z0z0CpdjeFDqA3faBFdjHc/U4QAiR6IUYNIIq50SWQjD26zgN3N4cuLu9YR5mRjepIUk3GMsLrqwCNW8MQZiHyLKtmqrRGqMohNhaL5FwuMMITY8HQoOqCKQPgRY8PrhdeiZOGIKHceQhL+oGh13Hv61q6oxRB/UQhLSpZNHr8kTN0BviVKspWmX6ha8v/qNbqaxJQ2PvS+IhFx5S5X6Dp61w2TK4xnD0+LHiQ2GIlULm43nh5/sz/jDTniof7i88ngsp60OnDuyA9wPOoimUSrRTUG3MYeD25pZpOFJb4oIKWI/HyGGIHA4BTyEvj7SyMIonXt2oOcQ9D1aURs2FpV4UWSvNOqhVGxl8ZJ4mxuOk+SmimhGHICHYrSxmalEoPXrHMA9M00j0jnEcCMEZnYXqlpzDezGK2KIimi32hgztLq2qFEy1hdH7gevbG1589ZqrF2+Yji+tgk6gJkXH0oWyXUiW2VmKe3YcG03dTyKt9MWy7tyInsoLnTaFLqsxqstusv3/G2JGLRosXvrQpKgTYPqvZk456fOlbU76hHe6XxeDShMxHVbbqZeO1rq+iLWqtKihoCKBITh8qAgjgjoql8vCtm1QOwLS9ase5zWeyInoICmO4PWfxaxtTbQyS4JuWNEHXl3fcn16gQsHcnX4rI7klDJp3di2lZyShrknRUmq0SylFluI675oeVH5Q83FEEd92KchACM5J+Y187Tq9RiAwcHkhTHAFIUxNKYBrie4GmEeHccxcBz036dJJRfTwetwOY2Mg/XQh24OchiepdSR97o5t7rTpPrf7Np7h8QIPuKCpy5CzEfa4Ro3XiPDEXVtexsii/5dFCEQsbaMpjIA8X0AMnorRGIYTFYy0nAcxpGvbw9cj55aEk/LhegibQ20Krp2tIC0QE2VWLNWcopHbq+4Od7w+pe/5Pabrzne3XB194qb2zecrm85XR0JQ8AFaBTEzjjbtnJ+eOD99z/y9PYdl/szUjaenOPx04XDzcg4j3sXb0Wp6ibV6P2ov1wX5VvlpW12CspXG+LNmd+0MUSfkKYDqmSEZAujM3OWVn5KvuDTI7WuODJFwvNnaYdRPITmcfOEDwOkSm0Jn2GcZvygwcveCdF5gm2EVU9o/4OBshmtX5rm1EorOkjmTYsOUqKklZwuyiysix5iLfrL1W7E02e+Ob8jb3onKNKn6KgDPyHDibJNLO2R1SjI4zhwWTNrygSb+oJzWhVoyG+3VKu4QE0KlUJUtlwzAvva2ykFbN0357KTZ+a8VD0Q905vRYN1rdZ7uu3AgOo59ZCmubZmNOz3u3teJ/uhbQeQvOByR25NJy3OMH3939EJkzimKsziOIuonvVLN/bPXrq3QTTEzhlKCRiTVrUtrFTVKgajlD/7Zg7RZquA6vRH1eBvW0ZE+7+3WjgeZ93fJfL16xf88NMHzpeVlKoaxaIoaIA37aOiby54fXaa6mMb7KCOvl1FMIRG8B5pmdwsA9kukSA7QzcMevG88/s9Yhub7QkwOkcQlfWMzmuUXdW8yCaNwxB5cT3z8uaa03xgCDPTdEBw5FQNhVeEUe9JbQhqTXR2iYnVOzskmAksF82onkakNZ4uC2pRlv3PaKuTGnGqCFLVcDcOkVIbj+flGWV3kFNTk3PT0Sb4RojCltreUOQMVd/ju1oj7qv/v//1xQPlx2XlkjP3W+Gj/fO8NdbsSK3QpCptNjTC2DDJB1trfFoyP98v1PgJnOPhceH+nMgFhklbaoY4Ekc1I4j0+AQBMoN3fP31C+5eHvE+ULYzl8ePSGqc5onT8cBhnpC2UdZHWk34Bs4HqjQqjkqnZTe2bds7omtSA8W2raSiMS0vrl5wrOCmYNVJ5rgMYvQl1KQLYgiBaR6ZJkU+h8GbWcNc3t2t6dQwpHFDzfRLDW+DdG0bvUKJqkOY95FpOnB7d8fLr77mcPWKMF/hwqgLQNlo6ZGWFgstL6S1UvLzCfbz+kSxYYrUK61UQ+haJTRFLUu2YRBdML25HJ3YERwxRMkZIqmPoaPsw18p1RA9UarGHnr9l7Yv3KWUfSF+poP74iUanWBrvBevQ161Csnatad6Om7on5mGEY9uKKVl1vPGljMhqei+x3B4Q8R7VqcY8obRlaDIXLCBqjUNgw1OaG5gSXBZbFjPK2nVOrttSxqxlLQzuJuoNPuu6edhFA6mi2217cYlBT8aTgqzdywxMvoLJ8uuO0VhHmAOjXFUivs4CKcJbifHaVYNzxgdx1E4To5pdIToGMbANGn+aRxGNYaZvU/slN9BF++C6XDant0Gz0imDMJwfYs/3Kqz8bHg15k2n/CjxneJQKDZoNfzPruWze4n5018b6L5VqDp31WtcQAfiOOIzBOnYSSaoePaqzZz8B65VLZPK8GfKdumtZ7rE5/+9i3vfnggnO44ffstX/2jf8KrX/6Cqxd3HE4nxmFkiINFRVWFR0Vp/pw3lqcH3n73d3z3m9/y8ccfSY9nvKuk1avJbUmUNVGD0KJuRt2kpRl3zqQ0JpExulds8dZhkl3/JaJIXacctYUpKcpXsyJ4/dBXGrQB3AUpZ6grrmZ2byF6ryOChEGNKalSW6atZ/K2UOpGykGzK0fXCSNa1Q5g8dak1QfKzyQvJWdSTtSswf5b1mGxZA0rL9tCyYll07WppI2SrJXK2B297WXXjNOHJ/rnlu2fnhQnwnSlzSehUlpmiJ55CNqwY2/eicdJJfo+fFRqU/zdOQgV8J6UkxqbmjN9YCYGr4gwkCrPg17rsK8+K+qo7ZE0IN6kT7by29nRXnr4LUXlAZiO2tJ1dB226+R6WoCte6P4PWDcYWYYsNFCgGLrmqjhYv/9jrV9+at/X0VrMWmLvmcvlqkpmdFHW8q7Y7pC1cazVBptiFZ80mgPZ6iNYRj2zFENukebytDEiBcnHTDXLSsIAFQZmUwvKJ8/Q61LbiwGrSkYUkSjDHvDfG986+xQNdxjl1SZlKGjv6UVeiC6MynC4IUpet2D0KSKZrWIVRrHo8aVXR9n5jAx+ZkYp+eotlIpKe3yjpISrWo4f/R6eJtFWIJjq6rhzaBpKK3igucwD6r5rEItiRihblVNmQ1oFjcohRh0LxvHqODVknTY1G1WKe2ishjnumQDclawyTthaI1NFCl3wJa//F764oHyD8vCYyp8So37S2bNVbOlAriok24cGn4w+rQJrQaqGzhv8O5hZbWH5vJw5nKuDMPE5ALTNBDCiISB6rTPVwS8q0zjyDevX/LrX/+CcY6clwceP/7A44cPeBGuDgdub66YgpCXJ2pZAEUdS1ZkTAO1iyFzFnBrwd3ZQtPB6eKZMg/nB6rTyBHnhXEY8IOzbLFu0fc4r9T2OEYdKsdRBxXTKKnrLUBD9UO7wUW/QozBXGZGE5uertTCdJy4vXvJze1rjtd3arwZrrQRAtB2lifK8oGyPJG3TGqenKO2LjRnOkT9ns2+B80hTZ2UzRCHHnPR2y6A/X83o1z2TazrI3eqUmy9fKaadqmCLbDYoNlrHfXUZNEYdspU8Ers/eiG1oy+iT2fszVbjIzakWddazOY1YlniMKBZk5rFUGfc6M6R2iqf9IHD6LXSA7n/d4IoZmiDm90crJg8trg02Xlw2Xjp/sVaEyxQl0o60o9b+RLZjsn0prZUmVLjWXVATOlvAurK9rR2mylc7bPeFFFrTQLYW+VQxRkqurIjqqHHD36zwCHQbg+Ba4nz3GyAPzomEdhHBxxtEzVEBiGYe+sx06jIhhqXvfKNbfnypW9NYJmpgARhtMLhtuvadMtOUNtRYN3gxrEWi3EUBic6quSDVGqk0xI29BmVk+tAfyAjhEVcYMOdogaoeKBw8s3pKyu0nx+1PtfhBIDHpB5oZwfWZ22YTz+/IGff/+Bv/rtB94+FIa7O+7u7rh984a7119zvLrdpS+QqG2h5YoriiSWkjmfH/jpd7/nb/7Fn/PTX/01j+/eMVnUUxPR9q6kDVS1OIrjGX3sJ2ravklpZmPWX4ZGOKsQ3NFhbEBxNhzsWtX6LOxriq4rM5pwbUHKgssJTzW/c92zU2MbAM2sTXVhWxeWh3se3r2jBM/VMHM8eMy0q4c+1IGuGbWWx2cJBr3RpjQ1IKVU2dLGVjbSukFaKWklpY11XfZDVjXJTG2mtevu4GZDpK1tvTq01rpT0FttNDwvjy94Oc+8nBJlfeTx4YnrOkKF85bJTXV93nly1sNbqoq0B1uLlUa1f7qmB1fRSLBaqsqubChf9sOW7JFdnUbssV36tm19tKGQJqpRdzrc1aon2Z4z2eA5w9IGYam6DlY08igEGJpnbDpU7gNqM+SWfotpXqVvDt+C6kF5du1+yauPoMYhKMEq/Y0ZgioaX5PA7ks14oDssh1wiug68Fnzd9ftQauLDR1oNHK6MMSwI4NDdNxeH/j0eGFZNtYt0XCq0YwOH4OyGs00k1KN/an97KWDNRrVFMQZAKFrm2bBtl3OAJqAobFyyhI5Q5xBpRNjVOPNFJ0e/rPuBblBrpVxDlwdJq6nick5QrMDnBVUdAav64XTslp3d9p1zqEJo/eMEcZsTUdm8Fy3lUFGQvDEoCblWB1lK4Tg2GoGzAiFtgrWlvFeB+15ijSpXM6FnHUv80776y8ZWqlMQXbwrxRNP/Ci0qoiohKav8friwfK7542nnLlKTXWpDRCHB1hqLhoQb9OFA3MKhofXMQRcBLIeB4vWYNvt0JtHh8jrQo0j/MRCZVxbgyTMIwjNzdXvLi64c3dK+Zp5On+E2m98O7H71jvF47DzBA1kT7njSYVXwMNzWlMNlA2ozC1jEHhco/aneIQCLZoBirFwqpz3thWDdfO80ScouoVqMjgCT4SxsgQnfZxW31i/z8VZ3tKbWZy6caWDGhNlYiG7yoFqigWCMfbO1599Ybbu9cMh1uG8YDzkUY10bCiFmV9YLvcszw9kLdGbYHEiSzeDDRtP6Wp9tHWB0MZa1WdotKsn2XANUUus2VXltrpIPaDb6MpmuDUiY1zhBi1gceGUfaB7zlWpdWuS9ITf/0MEdSII/0GtW+w+2CrJ1sDMXASdtREUJo8BKP6QN1486R1azmxlYzkRKnaA94st9EH3RCCE7yajNXNbILuVpL2HK8bWy58elp497Ty+/sLl5yYKARRarUuiZYqZYV1LZS1sG2ZLTXWrZKSXYCq48PTVjUqog/cAmLCcs0bTEhrTF64vhKupsb1CKNvHCZh9MIcPMdD5HgQrg4DhzEyDgNDgGESNdVEpfhVBzTiov4ehiLvBwbR66E6U0XZfc00ozQ7J+bigBuuqMNL2nBNlcbWHmh+JA4DwSs6FtkY6oXgJjsRa7yTJyNtxdeKw9EItDKQ3UBzAwFHGDCYzeHGa4arV8imqHW7X8gfPuDF01wgPa68f/eJj78RzsvKx/szHz+eebzAwgGOoznX1aE5eh2ovVdTjEY+bRaT1TRcfr3w9sef+Kv/9l/z2//uL3j//e8JdaV6AWdB5r5nhxZqTlSNhzD0FfC23tRsU7u6tKRmoNKq0Kqi+8960r6x9YNcgWbXoAsndwlKs8NHQuoGLenB0es64pzs+amlVup6Ybs88enjPe9+/sDjcub41Svi8cA8DM9GOtjzG3VT1I5uLD6sFe2VTz1KzaoUU9Wyg7ZlyrZaNNxCynqY1kOibpYCOnSDIbJt/5mF53Wx1sK2JvK20UrhdHXkm1cHvr711PXM+7c/c//xgR70+JSSGYU0cy/nRpRqjTPF9KmWdagQsCGXzdgW/X0d4jUqqH8mqr/T4a1JA6fh6hrJb8uj7DOEImpNh9bq27421aK0J11+pFM8ITqNXcOesyp7qLmmIvfro9Ou1sW2XSvaD/PSniUqX/rqUVDFhiItUlCat+sY+6iZa8ZViKIaYzFHfS3qUE6pmNvbK4juUEmCd5bpq+8z25oe4sQQJiQXwjjw8f0958tmSGWjieZZRu2DRVCNvbS2l3p0LaFD2R5192tVZqpWdbtLFaCKDu36uevA5uxruaYH3WmIHMbA7B2uOc51o3rHuiaCF26OM1fTwOgdUgwUyFlnGtFKRWkNqV3+lJUVKJXi9B7tSOVxGPQwlEFS5lwrORWcy6qbd8IUHbU6crGcUFCW1Ad1cXtLWjBzcJdOqbS2sK56b0dgo5EbbHYvqh3BkU3CF+xQVHleE77k9cUD5dtzIVd1m2JRIj42QmxIxKJYBF0rPVRH8dBGIUsj1bIPEcoCaWxF2vSUfyXwq6+/4hd/8jXTPDLPM4fjkUE8bSus9584p4XLwz1PD5+YYkS8sKSN8lgYpoEYAnGIdgKU3fhSew3gLrbWx1CMrvX66RFoZLfZqXsjbeZiroWhjoQQGKaRUVSnMEZHDOqKFVv8gg+WpacP/Lolck7UlMhF0TFtxVFkUqFxDTj1ceD29o6712+4uX3DeLzFD7Nu/GWj1RXKqpqJvJLPjyyP91wen1iXQpOBFiIlBorzKvLuBppudqndcGNRQKVRStoXIGfoMK1RcmFdNzPy1D3PUiMY2DMod5Sw1X047M05vWVIqfJqkRbP+qmONu6VgLYYFBv8d/1WU2G1iECxgZXnAdh7hVeqoYuBwDg05mliS5llWdnShgSleGvxRs87fMTob3PgG+/USiFviW0rXC5nmg3qay58WlaWbYFc8CUjLeNqoSX9lbdKTY2yiXYqJ8jFBNq1UaXwKWWeVkXHTTSgC2inaEomCMxT4DoWrifhNAhjUJovejgMgcMhcDx65slrcH5wxCjE0SHe6KswqAN7PhKmA3484OOEhBnnJzWPeE93Nzv7LKVVWlkpaaHmhbKeaS1RwolKJKXGp49nHj9dmGPAxUbNG97B6BqhPhLqhdBmipt0GKvayRx8RdDw8lQ2UopIPCH+oOYN5aPwzjMMM+F0jasZzy2Xt39geXyktUDJjcdtYd0Sl1S4VM9aIYVIPFZO1yPHqwPjHBFfqC1Ry6L3nGvUlshb5nI5sy0bl4dHPv74lr/51/89v/vzP+f+u99TtkWbP4aAj44wgQ8aM6V0fjaas7uslbsKTsObe7JDK9poIa0gpgOmqcNUc/AUmYQuW2mf/ZJdi9z7h1srYOuC5A1XMi7o4UD1iNUMexvr5czjwyfuHz9xZmV4ecvpxUuujieTEm02oioC41ujibq5td5VUz72KtZSTMajBr66buRt01SPbWM9n7UQwuJNajdUND2M1qYVc539EGm7w7bXOG5pZV1W0rJQtifyaeLFizu++uqElMJhGvgw/awB/uXTfgjWXMlATY2VCk3p1cEkCB2toWRja7LpyZ8NeRrvZShjM3e1Lm92ldQoUZuFR9sALg7TmCt7UwSTtJiUx7xWVRq7Y9EpIoy3HupiA2/VkgbpX9uoyNqHytasAEGM2Wvakd1zib7w1fECpz+2MT5tZ5PMr64Dl0CjUmpG+2iaob36s/nmNMe7NdXaoo72hkYgOQNehiGYSclxOhw5LxfSWBkHxw8/vud8zqxr0nsdDfkPAao4DG+gw7SlVLwdBJqhfwiYuMLMX9CqsJVGRiN+atMf3lHRlDi952PQdXQaIocgeAJpKywVci1cHyaubZjs0pJWMjUnNX01rfEstbAt+oys60rOSfWPzT6TmvAIY4xcOxhLJa4CayZRtQHHiUmtHMlpqHouGV+hZdsrvfXNl6yH4toIzmuhzFBZN5VGtKwypGZ79FrM4OlthXGOXNVYRlbG6u+jx/3igXLddBCQwK7JkmAXGEgbtOIopevZoIUGrlClsJYF5wJDCLSskLzW4+kP/tXLO/7pn/5T7r6+Y5q1xqjUzHq5cH584NP7tyznJ45x5PZ4o73PrVK2QkqOJRU1xWR1SkoPnbahKSeNYFG4XO/EkiutbNhv6Ik0OLS1JLNsK7VWJjdTz73PU5jmgIsBP0Scg5I3nDSUUVHKNCdFSPV0nqBWfIgMY8S5/qCrzqKUwjgdePnmNbd3rzhe3zKO17g46UKWV1p6ouYnE72vlO3M8vTE5fzI+enC03mltoE4RdxxgsHb8LhpnhfPKBQWcaSbQyKlzU7nejITb8aYHhOVs2WyZRPlW3uNDYIqrgboG4FGw8i+eSjtUEwY3eMbOi2OaSd352AfMjvN7mQ30BRzZPf4HZpTDWQfxpw64wUY40AZFU0ppbCtGp2EBGpAh6XcCFk7T6VC3A9Hddf8rNvGZVkZp4FpDoSoAcqPm5pwtjVTU4K8gXWkF8vylNZIm+aDbVkRngDUmlhKZbPagr3Jp4pF12Q81qVdC6+OkasRDoMGVU9DZIzCPHqmg+PqMDCOARl0mBy1kBt/POAP1wynO+LhJcPhBWE4ImHEx1nNDm6w+Ayz+knvBa5AVnq5u3fzQt1WLsvGw2Xj7Y+fePeHt5SnhXLKUANhPlBcpUyJMDdo2vaiIfER79Ch0iV6flynpaR2GYR81siSWR4TsjnGqyOHuyskOj787q/IH8+aSdo8zZse1XvGODLcHLj6+hU3b17y6puvef3NNxxvToQIQlY0Waw/fVlYHy68+8N7fvzt9/zuz/+CH3/7V3x89wcmaQw+MERhngIxCtPoiLNXBNjZhmvDXqUYjeq1MtEFPaSgqFZ3ByjFbJSq8Z1i9FV3skqHq/qRrxkK1VHQWnG1EMqF3BK1JkUtxQFqytty5rwsnJeVrWbiELg73OHnE6+Pt1zHia0mcjOzhdjz3xStKH2tKFZvmrVWNKWsmbY5UdaVsinNvawX8rJR1t7bbbEtdm1FtL9eZUG2FIo+U04qrSQtB9g2ynZhOV9Yn87U9ZHzfEG443C40uaxSRudPMJ6SeRN17Ts1ZjXolMUWBQZc0ZtNhuMgtF6zlK8Gz1VwkBmr4NUE9n9On34sjeul8UG/v7f+8DX179WbdgTQypt6KlKGdn+owNdsbUwG6KW63Ob0j4otUaisZTC2hqL15+Vms1x//ejKj//067/b+nd5JoqIKKfh6ProXXw9OLVZ2DRPRrkr2uJhv8rlRu9OqRHi3AbomMaI85pBu7N1S2PT0+cB0f0r/jhh3c8PCXStgK9RUuZDsRMVs3MN2B7WtZgdBFrRLKro+QAucC2moZQJzId5CWYQVLHJ+8qQxSCQ2nxqhhxqoVxilyfJqYYjF/RoUxaRUql1qSxfU0NSXldVV+crXijoXIPM7Zq6oB6PgZ75sbB8bgmttJ0QHeN5hyDdySHIpwirFWfcSGCKNqYc2UrGy0GgkS8NKZJgxVy1vtocEIqiq6n1nTQl2cipRfOePn73UpfnkMplTAIfrCTudOHLOdGKULOusCVVMA55tPEfDMzzIPqVNDTOeiDPY2BMUx4F5jiyC9eX3OaAsdh5DCP5O3Cer5w//499z//TFlXi+MZGGKkusCyLDb1Z2KMmmU1DgqtD95gXwdSDL1Sf5yahnVxK7Xp4NCqwee64KRUFI2ld3Jb1WQ7E4JjHkdkKBTL7ROLUMnZFs9SSEUXZREYxtFobrvBqYbSFeI48erNL7h784bj6YYwTvpAloVaHmnbE/ly0YzNbWVdL2zrA8vTE8uycD4vPDxcqDJzur1mmgq+NssKs0Wux1gYhazDoubDbWs2CkEIoYI3Y0HRWrM92Lz2gPdq2pTyRzrJtlNmYkYPc3HX/t8aPQiyRyD0v9ep74bSGNLfbNfvWC5Hp8B7JIoeUJ8F2q0/rE0362EIzHliM6f7tqiYOkhDxSMW1J8yadgYYrSAXh28U0p6KKiVm3lmGkfiEAG9H7Zt5XzZWA2dqabxbDlbdNDnTTn6uUkrRpFpA5Q2LalrFkOWvHMMY2SgceMHrmJjjhCDUmDDIAyDOrjnIWg3/AAxDITDyHjzgnj7NfHqNXG+I87XhPGKMBxwYUBitA5q/SdmUnKiiz9Zhzp9PzrctKqSjW15Yv3wE+ePf827n97z/d/9iE8ZcmHdKsM4M0XPcBc1rolIdd5O5trm4GWk2adQEJr3uBDxfsbHARcUbcilsi6ZP3z/gVjh9tUvCXevuTq9xL14xeXt91weHpjSRqJyPXhKnJBxxp8m5qsrrq9vubq543R1xTRFHIV0eWKzKs/l8cz9+4+8/eEP/O6v/47f/eZvePvD70nnT4yD4AbPPHoOs+NwcgzBczoMHA8TwxiR4G0QVLS5c5Y9BcLzjBhWHIjXwGPHbi7QQUE/e+dUL9ZEmY9mQ16TBt7t2ubW69xahabxPB26EaNIq+Ws9oOYmpBGmhvgcGA6TAQvXFLZKUP5rBdb74O2D5OlZPK2aRe7Ud0laa1i2TbS9sS2XsirGnO2sll2pWW9NtVKlqaacaTtRpSGat/ztulztyYuT/csy5nlaWFbH/h0cmxpRRyMhwk/3OGorOeFF+/uuTxdSNUEMw3GYUDtFI2A+2zTNOq0Mx+1kUolNKgOihN6CYPYYFWl09pt/4Sl9TVMbE/p61/XHdOPuntoOO5ZO+l9Xz81NsibuYcKSYRNGkvL+/curRmC3HjKGw8l8dQSW9QBw+20vKKXf5+XsfCGDehdKba2i+uHGD0UaS6yJp0IatyUqDXIUNEkBzuUVsz4ooenlBLSGbTWmKeRHp92dTwhbcE3T3vTSN+/Z13VqFOlQRu01S6EPb9U2T4z9DkFFdxOjWsoOxZN1ZqozOgzjULJJndw6vjXqlp1dg+mEU05s24bydDJwzhYbq+igBjrVkmmbDH6OekckKwMxrY8ehGGQwfkQTTpJdeGl8YYAlEcl00RyrWXw4h1eAdhSRWcsYQ16aAYnJWPNLYtK/Ia1CQ9jDpQp6z3u3NKmTcRtgqDKgpUZ2n6XkcjmmnpS15fPFDGWRBf8QHENXKFVoRSHLWoAaQPB2N0XN8cmV4cmSzOx22JUBquFObJcXN7y8ubmWEcqKUweiFdPnD+KDx9qJzPjyzbmU+f7lmfzlxNM/M04AKkasHbuajxYVPRKaJGGec9vj0jBjR1yYXodtdza6YPbHWvCdQZppKMwhHRG6wbEVLRhpaSMm1LXL84MR4PxGuPRF10l5wom2as9RPuaB3hPadud5nVwjAE7l695O71aw6nF8TpYBvBqqab9QPpfM/69Mi2PJEWpYDW5cy6Lqzbxvm88vBwoblMmC/EaqFnKH2kdBL0eiwnHnGVRg8yz1Yp2chZQ+D74lhy3rVPJZVnfVDr3I85oC1cWAe8slMRfcHt0+y+xrW+iMm+oQpitC+6AYqRLa1RxTLQKnhx9Hq1fpJ+dqHTra0gGgp8mMc9OqLVlbzpwBcHTNNZLQdS/35tlSFowHwp7JqvaZwZ4oh3XgdAQ5ScV5SxN4ao87XsepZaNBJIN363DxPeO8bgzflYzAwktKaBuqM4Zu+4ip7JqwM1eqXAnFcfi4uaK9acIPOB4e4bDq++Zb75huHwmnC4JcwnwjDh46hDpA+IC4qq2T0h7jkcSEqilUVbmfygFHkM+yYo4RF3Xnm8ZH7zl7/jd3/zA7dXE7cv7xgF3n/8CcmVQV4Th1umeIDi2aShQWJZtU7eGzIUtEnGRXwcEVFzREuFdF64//CRv/vb75BU+Qd/+kv8ILjjFUP8Frm5YU5qkGq+0YKj+oD4SIgTMUSmODLGAVcDdSts5zPLeeFy/8T9x/d8/Pk9P/7+R374u7/jpx9/5PH+niCZKWqsyDwJp4NwnB1XB2VPjoeBcYr4QQ1P4hUlF0NupG/uLmilnNM6V8V39LMWb8OgGEolotmVYpmVKGomPmgU3U6uiMIaVZF11XGqE5yO6qMSnO48dt7ho2d0M7kUXJyI04EhagxMM52ka82GWKU1e3SXrlmasZpyYt02a6RKeyxQWc6k9UxaLnue7VYyW9EDuaYmdFmKSktqa0qamjazpI11WdjWleXpzHo5cznfs1yeyCHjD2/UwOkDMY46nJ5OXF1fcXtz4ulyZmsO5wsxOYqAl0D0nmDyG63Rfc76G1JirbBmWHKlFHVNY9WXYmtPS4XssQlRP6H9euzrGRYorUM/ruso6zMdbuuxLlHyRwhQRydFFJlcSdpXXgtBF3G2Vnksifu6ct8yjy2TXNCUjdqNfXb4/tIN/rNluQ/Ppeit2Hg2P+6DdJcGVK3v3HX5RmnrF6vK9tgwVFrbG5DWJePcyJoyPmVO80TaFmpLDGMkp8I0jNxen3j34RNLruSUEWDwI9Rqe7Pf73Expl+n4gq1qBQC+9yroszOCU3qXplc7PeboZm0pvdFCHoUqaLtfmnDebR6NwZCg+A0Y1YH+Z4trXul+ib0UKy/mu0xtu42HbCHoNeuGILdqumwYyTiVC4IrNLoMW6atSmkou+51ELPCQ7BkVZFQ9dcVKvpG2OAMigyqaZdS36xG7DSB03Z87Kbk72e+UteXzxQuqBUMsBzWLPC4WInwFKzUY2BYfRcXY2cbiYoG2yOIcNE5NU88ss3L3j54oR4uDyd+fThzMP9e+4/fVI0UCqpFi7LogOWnWRLq1zOK9uSQBw5K4Ssnc/oUOm0f1n2JoNnSjsEj8RegbQiLRMGXdi2XKhZzfJNNChVrEqqGO2QSmVJZ5Zl4eHxidubG/Ja2JaZMI3UPeJAw1XHMe71icrkNoO9G/Nh5u7lS27uXnM4XTGMo9EjG61u5KxRNOfLEw/377g83LNdFnWMr4mcK2uqPD5eOD9ewAtXd0n1FfSTkK1c9qsHJjtDTLQCspLKSq8g9MlZnJHf9Yul9s/AIjHsod3bB7DgahvKisUf7ehl7SHG5hasprU09E77a22cdJrd2d91q+yJ/857pD7HGlXa88+CRq6oi5TdPTpEz+mg7kPf4KkupFyRLStdYqdvzacMhBB1SLSfJeUKPnA8HTgeD1rhWIzq24PcNbRemko1cNrL3VGC1iAYBeRtHgjBiPpitZo2MFcLdI7BMQfH6MUoJTvpi1N3qQSlQgbH9OJrTq//AYc3v2a6fcVwdccw3Roiqbpe5yLPyEp/shs7lycNISPbPfnhZ7b7lRZfUG9fMt1eIWHQ+BKJVBwfPj3y3/7Lv+DhfuXu9T/FH4+EqyPeJR5//IH1KdLKQQeRWlnLQqtCwBZPBz4MiFdzlJAoxYEPSCq0vPL08T0/f/d7fveX31HWyj/+j77j9us7TvMNx5uRdLyY5ESQbrgARXwaek9dEvcfH0ipUDfh6fHMh/cfeP/2Z97/9Jaff3jL+/fv2LYzgh5u5wiHyXGYHbfHgRdz4HgQjseB4D2H08g4RUK0aCAMdXfQq2I1283WA3mmeEMQWjWZiOV/NunHKs3uRPyOTFrauaIRFkEj2BBjD0ljRdh2rR5mxBPR9hbnPD5onIsPA2EYmGOE0pmH2r3VuwynUqnizNWNVipuG9umA2UtWem8deXydFaX/fpEqhps/ozK2GCaNdpF6Bu7GtFSUaNSTWkfJtenM+fHB5bzwnm5B9m4efGSw9ULWjwgbkJaUKmRBIY4MIwjV6crCiPDshGWwFYVofKWyakrQ6DVQsAxVIejDwdaWRoyDNLIpetQIVVNiVjzc1z4bmBEKxKd9AQQ0xmafEHsknRKG6zG0cmOzJYq+3m4mYGnSCM76MWtGV2DH1vioRU+1Y3Hkln1lIeI00GwPR/chb/HUGl/uP/5fkbvB4v+S1NBpC8ZxuwVKPozaIGHyVWy6WRrBbEee9Fh+7JsOO/IW+HCwhAj27ZCTUzTwJYK4xi5uTnSHi+sVq5SdIqi0GthnRrHxGh3BfK1d13EDv0Fb3NL3oBg18no+DVXRqc94h3e8NZyU2rjaU1spTBGx+idsq0hEg3J7FnJVNR4nDVVgcZObXe0tzbLjzRWrQNNmktcKU4HcO9gCAJJGMUxSmE10EuqtgQpIKw+kSK6twbv8U6HzS3pZz1EMwAF/ZrrpotH6ykGbScMTQOqrGBp5pj/wtcXD5TeG6zbmmk7LO+qanBnRyeHISi96zxjaEQywxgIk2NM8HI48avbG968umE6Ri7rhfND1QHt/ox3XuN3YlDqcp6Rql+3GhL2+HRhedoYxhHv1V3d3brO98oyZw9E27WD+y8gDAPDGCirmlxaBZ8qbVl18AhRNR8d6gcKFYen1Mqny8r7+yd++PkDN9cn7m6uOV1dMR4mhuPMdDVbL6ktOlVRvd68cDpdcffyjuvb18yna8Iw2wJVbaB1IIEinlQal2VVtPZxoW4mkG+Vbaucz9qNPs4jfUDoC1l3nPbTly5/KAHkI3HQDSVV1ZGVnLVH1lV8CIhXgbiidIYmOmhiCEh3YxYdNou5LXurQW0KaZbymW5yR7OfFyowp7NRc9VoeYwm6o7GnXbB9LDYk9osnqWjAHsThZp1psFBOyraWSr5Yd0RnDgMtuEGQgj4EHAIqWzkmtlK4+o4c3V9tXfT1mymoaYLiTq1PVV0w6ZmqjRbXANSG9GZGUDs1Cei1FGtey58RTUwaqoyak28yTTMlGHu+obgxivGu6+Zv/5T5ld/wnz3DePVDcN0xIdROTT0cEXR+k41gNiOYXo8caL5fjxBfcv2+Nf8+Ju/ofivePVn/xkSA4fr475i1JRwLfPLb19QfwHf/skd169uGIcTfvDMdeF4LYT4iYnCOXvSU6Vt1dz1juYdJc648YBqbgXXCjU1Ut0o64WPP/3Id3/5PT///pGyVn7zr37LfD3y5hdviPOg9KFzDN5TqaRWqFk1rOlyYVsX1scLDw8PPD6cefi48O7dOz58eM/j/afnXmlfmWJlip7j6Jli4zgL14fAzew5HQKHg2eadV0aJghje9ZQfgYHaWUgn93bbnfN9+Bu53pgM0pv26bdD4F7PJiBPbKHVToNXTfq1okOfWLpEVKbrsnNcjWdDdreIyHgasONmoxBrRSxw3gpUDR9I/fGm9a0NrXXr6asqOS6UbZEyyt1OZPPC9vjI+vliS2vpJbsOWt73BfYNBUHxFsL0H6oTNR1o20r20U1k+fHR56eHliWjdQWXry85fbqJeN4RYuzBlXXortgarTciCFydbwixMwhbRyWlUfLh82btgJtuWkbTTbmAE198Hii86QAMQmbLzpQouHnS62klghOzZ6A0dxmZKnPa5LYYQmjDfvcb0orZVTsPunyHS9CKbpCN+PlxQvFOXKDhF7TTOGhZu5z5lILiUJtDnJVZ4XdX62zN3+fl2Ev/bBZzQnv9mlDKN7hg6Dh+qIpCbWqvEs0AcIy/XHOq75PqmXR6r7Qm8lcFbZlYxoirXjWWnB+VElXXXHOjDvhwHCYeff2g0orSPa5VkM8mzIETeOyOitWbdjXddhT12ImVGPGqh7wxAtbraRi3d36aOzMTVo2LptKmYYwaVVmrXjTP6u5T7N3GmISQANYbErzzdIAvNfIJ7GDYdODRWfiGlhMmx76N3svoVUm51ldJouCE4NvlODZSkG8sJbnimPvhVAbKWvQvABEvS5D1GD31fIl+76uQLvS36Gnnokz6d+Xvb68enEp5GJizR7SJWhobAIz6zN6T5wGwuBxTWvpPJ4xCC+OB94cDry4vWKYItu68eHdPW/fPvDwmHWo81aPJ04baPBWYeiQVslbZUmJc84spTJNI9OgkT4hxP3krsNCtSBx06Y18MErpQvqgIozZVMnYnNCKkEfeK/mBzCTCPqA1KbxNNu6kSxG5uePj0T3EzeHI7fXJ775xRteff0Sd5poozrPXex6wsrVzS13r19x8+KO6fCCEMddz4ehFI5AkQgu0pon5cZl3Xh6OlOSUra5qftyXa0f106OYkfNSl9U1DSglIQm/Cs6FBkGXSxqY+/gTUbXSk6K1jnpKL0+sDRS7pWQ+v30RGRU1h4F9CzqLk2jUqhtp5nFKBRdy/o7VYNGNylqGLjpr2xztUg327QdWv9l6JSdOGvTLDqPbuytNtww6vBsDtLlrEG8ZdsI0TGK3d8NUmrkmljXFalwfX3F9fUVh+MR76Nl6tlJTkx7Q+3QLSB4H6hSdUgXXUilKfJQUTG5dso2XLP2A6PS1lxYnYnI0QU8VK3fCwg1eNpwIN58xfDyTxjv/oTh5huGwy0hTLSmsUoqF8hqNTVDgCJmznIAm4WVq/PYyRPIPd4vOP/E3/7lf0OTGT/dEOKMrwN5u9DyE/Mw8h//R7+mNri7e8mL62t8iKQIa33JfGyMseFkgy3B00o6X3TDjZEaIm6+VdRNIq46KhmWM6UW1vNH/vC3f8V3f/V7toeNUjx/+ed/4Or6SF02hilS26b5d61q6HAp5DXpJnB+ZLksPD4+8Xh+4v78yNNyUT1WTUSnJ3Y/OA2Lj545CtdHzzQ4jpNwfRo4ziOT1cnGqHKDOIOPWg/n0CBtzcUrhp7ruuFMGlGlI1r6GODZkU1MT11tEKAluy7ueRgTDzxrlpsZexSh7/rkQmtWj9r0mQ9iejAf2FohDA4RdYk21CXegLVkXK0ES4XIpdiGXMibVrJuaSNvC3VLpnG8UM5PPJ0feHzSLNxSM7UVcit7xatgKCsWXl4rXszc04S8XajLxna5cL488vj4wOPlzLat1Jw5HEaur19yOMxMxwNrcVwumTRrwkJdEmVJWuU3RoZp4NhGrvKRJW2cl8ynTytyfiK3izbVOM32FIHBBaVGq6KloxfWrZCLdicnUeOCF83ETUZLdomMLlw9Ak2HcATNGMVMGB19tHVt17QjSNDKx9osrHozHWCDLTRSE1KtFNd4yJnHknkqC0vbqGjPtK86tEh/D1+6sX/2Evgjmlzj9XQ32QdE0Mi11oP5reGoaU5pkoSESM6NYdC2ODXD6tfM+TkiKzfl1D/en7k+nnC+aTuOC2zrQs6ZYYj44omh4l5c8f79ve49adMDadXB29VGdDpoKd2va21DmaxiTGWp+sxQGlId0SuDJTQSlQhQKjmrqXaVwmWrnC8r0XvmGPA7oMEzQpjVYFxaI+VNZ49quniUUer7cgzK/Cko4OzA0c142TJQBYne8BLL3y0LQSCKkEUBAGfzjnbCC8nYQGl6rzbRvNQt6XcJHoLTNcMiUhHQbNHP7gEHz810/yEGystFhZo2mylSJRqHUKueFqbBMU+D5lNGQbLSDa4Ip+PMy/nEy5srptFzOZ959/4Df/jpI/cPC7kKMQQkeH3gi0LpY/QMMeigkTTHsqGbcCsFEXVQUwPeBaIfybuLOemwlQvitMEmFE/Jnmka8DHs2rBSNRTUCczjqLooO2ElE4k3ZEeNlMZW+iMXOG+JDw9vGX5+x89vP/Krd294/fUd17cnpquJME1KyVydePn6FdcvXjOfrjQE2i6lwuB9U7EHWwDx5CpsGdatkBbdOEtTt3XJSptqfWXY0R6MIum0ci0dScTQOw0+HsaRXAvjNNJaYy0LJekGs+WKD/45F6P1YOPn03enGZ3DApc7x6MLB+jNqzFD0GlAXWzNlGODu9jprJiOq9MSGnLudspchyHswXYW0dHjfMU61Pmjz9EhTMNAOhz0VCkL65JISyOvhdVd8EGIflEkomndqLjI9enE6XRkmidFfcWDFBWFo8jj3lDiHEXD6nZBeiu6yVfRa+tEh/KSG2u2z6VzDwi5Nc65MKCmodE1Zq+GuFEaxUXC1R3D7dcM11/h52tkGOx+KPiWbPHUn70oGKn3hjiVsEhARFsbICEsSL3gXMVNI8PtC2r9iT//5/8V49VrpXbGkZwe2B4+coiNf/jrr3FhpFQHokO2F4jTCTmMhNOobsrlA1t+JK1n3TgINGakbtS84jZHahZcnxolZ54+feKn777nfH+xQ4jw43f3/Ov/5m/4+fc/EGMjbR+pNZNq3g9+rTQo2IGqmdJcCDFyPU94V4kRhtCIvhKkEKRynBzHKWqu7BT036fAOI06REY9jDrn8ENgGic9yNSNls9QkiKRZu4TJ7sBQ4zu7q0cemMI2ultm54J5YUGecP5aOH9VvdXm3UQu90xrYimDTGtaixRrahrVRi8I3vHJWttozhHSkmNh3ZQK62SkrbGUPRZTKkoNVcqZctaDrBdqDnRTOeYzk+cHx94ujyypQ2yav2aBZf3lplmB8vgg97f1YxCpbKlQl438tOZp8sjj5cHLucHSk7KFI2B69tbpug5jMIwD5y3QiGq9KZpDFQxnWYIXiWmMTB5T6OxpsrddeLhceTxfObpUTOGa9X71TVnLSSwFT3IeXFsSRFMTfNpeKfO+WbIVA8X73pCMZAliqM5vf+ouv/xfNWt2tUGUMtlabZO5T2/UhmNEjyPIfAxQ1vPXErmsSTObaOg2qOG/gyuyG6k/P+nerG/v30V6vIRu8+KaSKVpdKDc7Xr69AKwJw1WzYET85Fo5kQUk7711OgR5+JVDRCb10/Mc8Tmv0p5FTY0qb0bdDyiRC1ASaXlS0lmsBApNXAGEzG1Zu82nNlrHdOhyzbELrD3IsCYHNQt3bLiyH7wpYr50uiZHh8ONNKZYgD0TTztmObNlrBIpV9WZaySTo0vaADUnqvaNbxs3xAP2JR05ETpIoVpAgxKgpMaWwiTM6REDZxCEUBhuipRQPNVcItpIxlreoFTVVYVpgGnS98ELy5vE1Ig/1RQPWzPvQt7Pn+/fe9vnigPC/Pp56u9nH2C1HaYIqOcYiaUUnFJYerjtMw8Ga+4vXVDfMUOV/OfHj/iR9/fseHTxeaRD2JRAdO2LR6Ax89h0lNOyrY1mq7YidHsfxGsamh1gu1wGGatUu0ZhbLgBKnPaJjDMiksRitatxE817RSbP2D32IFUd2egbsaKcIqo0Qp7R5BSTRvLA0DdZ9/PEPfP/zT/zizSu+/fo1r1+/5ObVDa+/fcPtzR1XNy+ZTje4YQIJ+7G1YdKBrjes1TKlDGsURxPPllZrrNAhJW3afU1zZrpQOLE1rR+srZhwH2puFqvz7LTOWfPwQ4jEUGhjZS2VtKzUJvji8TE8DzzNUOpOjewLkEOkdnZkP3T0jb27/vrGX43n7cHuWHSOGomUwi6tn2jtAQY1SbW6n/r6Qq507rMWCREzwZrPsha8E+ZxQOpRHxaLnlq2SsoNSRCkMPbqMeB4ipxOB46H2VDFqotKNnTVNrOeG9hq2X/uvohhgur6mWYl16oVjptWfrrg7IBW8eLJRfiUCpeSmKhMUThMjuoiV+MV/vSScHyBH69pBHXI+s0WtqxEq1NKB22uVNG+07KB4BviGt7pwcyxIG3Vw9d0IB4OHG8mfv/Xf8V/9X//v/I//i/+l8yvrskk1uUTh8Oo10s86tiulLyQlkSpjjbeUuYXuFBhcYTpE4GVIEm1q15wboN2hnVRacTTE4+PZ5ZNeHxYWZ40lknp3UZe4O0fzqzLhemQEP+ewpm9mg2Hd+oun4aBIQya+GDrS4yeGD3jODAOkWkeCNHpwHIYmIbIEFTYPsSg7Mgw4M3s5+zQ15uUpC5Iek/NF73mtQvnnTEdYW/iQGzWdCpjeEbn6/NBvRStg+vovXM2bFiOpd1j7Pd+s006Q8tYzLU+j2a0KnklJU2yqNkOfL0RyqjnlpWyrCmrQS3rgbylwrau5LSS1kWd3dtKWs7k+0ful0eW5aKDZ/us87sUnO/5sP55ALFnUWohp8SybFyeLmxPjzyuj1y2s4Xxz4QGQxzwLhKAq8PIeJy4fvWK+XQCn2l1o7SimtSqyBRBzUzTFBjGiSqe8iJze5m5nBcujxuXy5m8rspgbZWSVD502TR2qTXNpVSzYjEDYNtRfkezwy87ytOaPgW7nKUZbS0a/7NVOzhkXdJKpaMzFqkm9gvTXMPqHZ9mrw07pbKlldyyoXsNWsFLw2WHb01ZwS/d1P8trz5MKnii93PtALmeXvYDfm3Onjk7SfdWI4v5QZSdCkGfzVyeW9AyDe8DrW56n/rCUguDV2azVt33WlmZxsgQ9ZmejwfER54uFwVXckamSMDTfNSDlBNcMeNK0/B59UPo0DUFtJlmFC3JcBCdB9FZo9bGuhU+1EdtacuZcYwcDwNm+sZ7Rwze9MbVwuk7TSwgdT8MevG7i7zLlip1l4vpUKmUs55/3S5XC07AV82vNhPnWdLu5O9lqbUpce0RMyIpWtpriZ2Iei7WRhwwAERZsb43CzpcFtTMmps+Sj179UteXzxQ/ptfc3+opOd5qdhcmmUabUq3DQ7mELiaR4IXzsuFTx8f+PntBx6fVuIwgnhz3+nX1cBviD5SS2ZN1oHZGsuSWC5qzfeiF3M1WLYK1OWyp723VlnWlS1lYtBuy1YHfAjECDlVxlGdfiEEsO+9bRs+OOIw2JBkrkwn+2lyS4XS0L9XoWVoA+Aaa6t8XDYef/8df3j/iW9fPfCP//SXfPuLbzjMB8bJQqXd8PwIN124Wum9n4WairnFVEsYh0E/r6DC+OWy6Em3dMpMIfQ+TNZWaDVTWiKXtEca1VL2YPNauqZRbxztcK7gExkhpQQpEbPGNXhvLUCmC+06qy68x+ic0griFJKvVRdKbYvreky3/7sTbYToSMvuJpZezVd2XaQ3mu8Z6dHBr+waT3sg7TNoffNtIN7TamFwDhkHbKojLxdKqiSMSm9Kq3unX+N0NXM4TIzDoMawUvYPrCcA5Prs6BagWpIA6HMgrdkCa25P61JfU2FZk8aaNK36HBB6y8MlNZYt85AyU4Tr4vHzFTLfMh5uidPRhuZEzYGiwaiWsxbwQV3dwQ1QFmQ7IzUjOeIkaF+s0x1OnFYCdset95U4Vuax8uNf/0vW9ZFv/9k/4vD6FVeniflwYsuig2zTe7jmTNoWSnX4OBIPd4iruOmJMERctgU8NpwrNNkUXRZHyhdYHtkenkh1Zl0zOTfy2hgY1JTQCm11DC1wPXumeURitiFA6dySKuIbMSSGKAwDjJNnHD1ThDA4xlErKuMwaFOG9ZuHQSOLfFBTlA8DzketiHN+10AGbxV3tVCzV+mPCqV1P5F+APK7Jk6RcjHWQdENTT5QdgF0MHSt7X++p0+0fnyXrtfT99EjffragT0bLgRc8eTLxrJurGkll4L3mgWcqzVk1UbdEm3baIKyO86pOztn6rKxbpo9mraFdT2zrivL0xP54cySzpSmvd45V1u/7V7HUBmnzt9WNE2h1ErNK9uycj6vnM+qv8yuMJ4OxDgpSl0qx2nWgyWZ6Wbm62+/5eWbV4wz+PqAlK7x1rWkFA3Ndy7gcMRxZBhnGo3T1YltWVnPC8vjmfXpookZ55V1SVATuRQG50lUFl1pdIUROwzaKhOcxfyI5Vsaqoj9jTHoAJ1qY6FxMSIt9YNzfh5OWxOqZd8KenAiNIYp0mLg4qG5ShsbLJWAGqZo5uhu4FsjlKyJKl+6qf/Rfv4ZIwZmdG1Iboi6NAwFVxZKh0gdondN6GfXfkvaDiUhkLMYqqfofYcHSqu7nl6asmnZFaKxid6h8UC25o7TiHeFeRw5HgZ+evuJtCa2dWN0geKVifENhuBNDakH/mxSkHHU4XK0NUHnMT0IOO8tP1I02F8yUoXRCacxaLZ20/1WfMM30b3ZhsxdWiXqCFI6WnbJXTMXeLU0EPvoFP0tefd+KHvWSUF9vzE6huSJ3jNGz1Iza6lEJ2wYy9fYtblKZzuV4dCTH9Cc8I2dIRS7bq2154Yu9PtSQYLgnx2c/97Xl5tyOr/YT7/93mv65vrCQWtIroQ6amRL9JzGkVoz5ycVbX96PIP3vLi7I5fC5bySDZlqeKubUldSKZV1WRA0HPrpsnG5rDSncR1gNAJCobGVAttGDRoRsW0btWkl2zCo4zqlwuJWohfWdSPEoALyWvYA73a+MBmaAEqrqtNc3cBItsECdeuJJ0hDwoCMCpun7cyH85n601sOU+Q/ufwjgrj9s1Rqwz7TVqBslLxoC05WHdi2PLFeLrQCwY2EoeDiQvMbW1Y6SiuvHCEOuBh2WkLRv6KIQsrkVK2BRAXtpRQT3CvC2ZHDHRmxXKyaNW8s1IYMQU9epT8k9nm1ntPZ7wtDTko3AaGB3cWiJIpulP00BuzOdASac0Z725/rrjhRhHA3K3RawxCfPqD2zVbRA+lPCVAsZ9Jr0O2Y2E6ZS6rkVXPB9LAtVNdwwXF7d83t7S3jEKEjx/bZlVrM+OARr0HpilRYGG0Vez48va3I2XPTqobrF4sX0sosRYOdaJtCssNK2SqpgQuON+OBYb5iOBxxIYBodEcpGbdB86ihKjgkOMIwElwgrxcuH/9AevyR6ThySFeMxwMSHc5X8xc5Sim0fMG1hRgL8yhMsXC5f8v9uxdcvX5FHCK+QZBIaoKYHKYWDaYWgp6iXaR6PUHXLSFlo/cnO3Ota694IAZHSTA8OdIKLTfWJVE3YUSp6o2VNTVaCxxC4/XNDYdTgJBp6HPX46mcV5o1Bk9wFec2dcuHxDRUDhOMQyWEET84hgP46Ex7VQmu4FhxVfVYjv5sVdpWNAS5bbTyZBmQWZmE2lFz00dJlx7Y3dqgm8Z0d9WvqRiVhjKLSSWUWehmBva/sy/xykWiJ1097YrTQ1/ZlGV4etS82hA9wxD1OS3K2lBFw8nTquuta2SnDWRpS6TLyppW6rpQtjPremG5PPF0fyYvK7lu5LqZtKavZfqsezdQm27wJnQl12oo54XlfObp6cK2Kbo7zifmq2uCBFrKpGXVNaEW5hfXfPPrX/H6m2+4ubrC10dYoWwaZm6gq1LXm7pk26z93dOk6RlUbU+afGBynkWEs4BkjUPyTu1TRhRZeoVpm3vTGl2W4JCg1KUXry7vUglOyzGiUyPWkiulbpxTL4PQa+a9QNbrWJquAzjrFg8ggyMMARc8lca5VlpU2rflvifp19MOawit4ak6HPH3azjp91Pb7y0bpU1bKt4OPmKDi2BMjNv/vt7rWuna4/dApR4R8E6LQbI53HcvBir3wol6HPBMoyeaBjXGQMkZ74V5msjrxjSO0K559+6TVpnaYSKLtlnZV90lSaBJEpNXGchx8gxxUGCjVFzwRNE66NSAVhhj4HqIRCA6raHsXyuap6MWSxCwwHy6wcZ7tQxY25I+s2Layj++F9wfJdJgOtyqqDtmfmoaczV4zxAcMUF0SmV/vqaU2nadbmdtdO/VauFSLKVF+iFCr3blmfoO2BcAcuY/TGyQxxxJ9t77wOD7zWf5Uk6E6BwROAbPHJ2e/JIiTY3G4TDr5lzgfM66udSGuKhrYi2EUem0dVGqJvjIVhLnZSOVzBA0pkVpxC5w10Ug14yvbkflSoFUVN/hxRF9QFzmvOrpsmRFAWs2SgjHtmVSOqur17kdfWjwbPJx5sht7FB58B4ZBAme9SKkNfGUFn786T0//PV3/MmvfsnheCRKRCYxM05G6kYri52SF3K6kJYn1scPLMsn1mWhbAXvAsPhxDkVZEyk9YmSitIC85EwjFrzBqZN1Ie7lrxH9RhzoQilgdylJqXGbfP0EvF+QLy2Yqjxx3CSFmxhKTtKqZ3aujFqiLLXwaTfnO1zZKXD/5jrEdW96X/ZHyp9s7K3yIhpKFuH9W14rLXt1181gnqY8F3o3J5jNMxriUeYhkipM0stTEvW5qRqp34FJri5veblizuur66IMZJLZtsUOatmUuttF94LPgRK0Qq71sXMzilq1ltOqCB1lwVU08a5pk/6RtGDQb+Ghj5vFZJ4/DwzXl0xHE+4OKomEn1ucqv4JprT50c10jgt28qpsD6+5/Fv/xXr4Uj95lvqy5eMx4kwWAWpZGo+U8tKyYvS/xF806acdD6zPN0zTZ5pjCCKhnkn1LxptppJNLTyVOUAWmGZmEpDIjqsOcGFgTAG4jCSs+d81sNmKlpdmVeQTRWXzg0MtZIr5ASjc9xdnbh74wmjul31XszUqjpa7xXtAD1cBXG40AihIu4Jz4qvAUmBdhnIi8O1hnON4jXNAtu0nVPaGyBGR+3+q6L6xNrMEGDItp5hdNP0WE5f1UgyZ4fKuqMCiuC7/a/sAg9FIfeTmj7POwORUd2jZCTr8OkaSCnUNbE+PfJ4fqD6YIyL2LqYiCHQamNZzyzLmSiO0CoblXVL1G1lWxce14V6XqnrhbScuZyfWNOietei/eEUu97mVAhN9ZINq8AVjZApW+aSFi7rE9tFB9Q4esJ05HC64eZ4SyjwmD5q1W/zzMfIm1+85sXX33B9/YIhTqSHT7THR+r9J7bHB9Z1tefaUzbVvcdpo+WM4Ahh1PDnUsFnijgCHle1p9k3Q2trtR5ruyat1+ShOaKiLVQ6qHiCHZKdU2PW4A1R8qohFFsHen4vTpk816B52SPZxO4P9/9r78+aZEmy/E7sp5st7h7L3XKpqm6g2QA5zeG8gA8khfzO/AIU4etQhHwDOTKYJhroWnO5SyzuZqbb4cM55nGLMxhkoWTeQkVSKjMrI64vaqrn/M9/CSr28kndGbrYZEdEf3geQTxuXfFixtWic0kvVYU5v/RS/59Yyuo1bt0+8rZnwKF6Cc3ABvyLJVQTuWoAwO4dCwRJaaRpwLoKkMILPWXnPWICxegVhXNoSIOgn+cwRVPlO+ZpIOfCzZxodyfOT0+W4FbxHmpz9BCJQYfAviufMgbHGCJzihympIpuF8i9UmpnslhVaY4g+t/cTiPSFDXdkcJxiszzyBA8LkSb6qkHpLcLVsRdERJxpoYXzMbP28XXDF746tk2V4S9zvI2ZesGvoSoftox658XpJuS/HrUUJoWiD4Iviufci9ocV01L52rnZXN20h2XXXrWCuaJPW/iCjH6NSoOcXLyNs7SNExJK9RSjEwOeGUHKfBMyXP4aD2PsOo/n6tNzLwvFxYt+3qYyi1guscD2oErptZu0wcLNvGltXAU0mrCuZqhqihfl2LT+c8vWlXX1rnkvVi3NNralVV87plUrTix7qG3gWhqcjk0klJuRL1xYBRL9LgSQhbrWqQXhUlGYcB3xrDNLP5xJI3fnz+wn/7//y3zCnwb/rG3d/8huHmDX4aFA1kb9ZsPCaN3guCjo6d1+xzBOZhpkzCOha2eKYVzzDdWfb3ZMhGpV0FNHt0mlw5lM3yzXeEslo+ryKVZvpunZSIphvUqmODVhspBuswd/hc/+qmAtmtmvYEoqtPJLvRrBGZTYDUbeyxWzQ5rP1mf96ukKTxTrxZdJgg5qtLXPrOX3n5fbt9lDflrCAQPMM4cKwT7dQJbWFZijZKDqZx4N37N9zd3zJOAwSlSVS0aalWLO3QUbcLCWeouUAXt2s0rIvsL5+ZdEXERWio7ZJvnSJ63A4xXJWjzgpzHzTXepiPxGlWNNOKnJ0YriNaLSid5Y/rSLZSSuPTj58J7WfqVqBX6G+RwxE3BhqZ3pSj2yRoSIBlbjuLKetto0lGrHh3+BfVqyFk9E7NF5bzF5wXlme16EkGQfvk8UkRVLW1iVB1j5bSKFkTmvJWkB6Uh2TIV+2Np3Mll5lxCtzeD0ynDoaKaHMTzFd057np96ToUlc6RlQkMaDIkA/lSpVQyuMLur5zxmhqJdK6J/qIEPXSl4HWlQoSg+BCM0EO7J6YCn11E3MYZ8oZ1aV3HVHpBjcFrZZI0nVc1izDfhfV7QpMRYarxlk6USuqCud14fPzI43KPB0ZUqLlwrpcjNtVqDmzXc7kbQUXWFoj90YpmXK5GG/yTF02tvOzhilsC6UrzaF3Qcoe5CC4FPESr89pzvl6HvRSKMvKumXWy5m1nIlz4PbtW4Y0M883jDFppnjV5v1wmDmcAvc3t9zN9xwPt9StsDxk+PEz/eFHyvMX1ryq9VFx5Etm2RZ68hzf3KlQMOr34PapUBN66bQi1CrGQ1fu6P75vpw7+gDHEK92UNGrpRzi1SeyNxqN3IUkivLn2jnnzlqErQkVpVJ4r+NSglDri5o2Rmd7AnWDqJ3cM51KGpLy4YZI91G5ddtKb4UoAnT15LwWBf9lq+/FILwo0vcmxb9Mn7r9szbO+qdFj+7lK/qlo2MpRWk8rqvJ/44k9G6CjxcufDXrpCget2XGcTCxmjbr67IatzDiKOoEk4I2LQIi3RT7Tc9Rh90xSoOLyasVkSHIpTVyawQXGL06Iiik1HW8PHqSj8xpYEiBOEQmc31Q4EG9i52zghnjIVcLS/FmpycqrnxxRlGqFtf7TlFbNVLvdOdUuKaVgaoQdl4uyt/0zsJXooNiZ1bQ5rt2bQDs/zIdhv1ZzplDgVz/7P3f76AZDuqOpu8S/V+wfnFBuWPiOyTuUFJoSjBOnsMhcppVGTmPkeMhcTyN3NwcGIaB+TARQlA7ilp4enpmuaxqQeAw4+/KYZ6YxkGL16xG6SEGPYSyXvYpJSPAd8vINbSqa/RQCu76Wmvr5NpUwGEXYC5VX0utZOMsdLvoW69U09D7oJuk1KZoKN3I2TYW7LpR1ZC14JxyPXqtBB+oNFz0DAz0kvmPD5/4v/7f/lt++PFn/k//5/+a7//+75jefUM63uKGGXwgEAg1UwyRi3FmmhWV6XKhLyuuw0xkXiIXH/FemE9HxtM9ftTf00tmj1hszXiTVXTsXaulAZknY62UXKnNYghrY8uFWop1W3qo5JwppdKnCcZEGiJyVX9boobI9VLeC6GvhTLuyreR67bqvV8/a7dfPmLKuB2tsb+EnY+jq4vaJTgU7dmRzb3I3f/ZG8p8tWGxDRJDYJpGihHFJQitVJKDm0Pi/e0Nb05HUozX9APXO96pEXQ1dFaLcn3NzegTYu+765s09EBhiH300aVf7YYUrdDCIvhOsexvunqeebtoUppIaSIOA+GFYc1OrgbzOYQXlJSO9w2XQHzgyx9+pK4byXVFH+/f0I8TLgJO7TW8JFz30BpTCtToiUmIQeO8XFBfVuc9YhSCUooaaBchLyvPnz+C6zw//IxIw6cRPwT8NJLGCRcnfBi0KC66vy5LoVZPLVWjMktn8gN7mELFcbnA5UmgRcY4cDqBYG4MZkgdhvRSTIo+m34XMgS9vJ3zao3i9z2j+0N9Jb09091GRN3ygW00hT4btTu2OtDaQAqVEBt+HJAhwK658w6aojCiczL2+6Q73ReOF5N8nQWEKxq50z96fXkeBUMrHfp6UR5lz5odfF4ubFKZ5gNjTLjWWM9PbJcnQhpoPlDyRlkWtqdnWvRk79laIa8rUrOm1WyP1HVj3RbWvF1dHmpXoaK3iYIPHtca3kf2sAKdGGm8bV4XWrlwec6UvCIx8/7dr7i7v2NOB6IbWZ433fMBptPAuzc3zLPjcJgZkhZVzyXzcF7ZfjjTLotaqRBxtbI8PnF+vLDlggyeuw8LrRYkjkYM1Ge4lUIpG6VsVEs0KUUpT6V381rWwt97R+g6xnbOX6NZnY0YK5Cr2shp1roWUqUpBzoL9LDnl3fjgxs3NmjSVTCUEbPZyZt5/AoqJm1dM+pjUsP0MeJ6IrVmY0uz8vnz6/ovXv2r/92b4mhFkJpd8zKa7VoYXgsR2Sdg/To1ugIx1YR4VVNYdp58aw0nZjHojQUvQvfWYHVhGgeqKMjjgmdZNk0NEjXdGVJCoqqeY9DpUuvVOMrdeI36vOr0dncoUXCrd8+lKMo+x0DwMA6eeUocDgM388RxHEnJE1MkpESK0T5rDQ3QSYWn10qzZCSNfdSp5w407CIssUmbFpVaCHbs/gxKvdCwGDEKjyb6gVrUBWc8067vOUXdi4hOOaJXlskQFVVuRVFkRWoV16iyC1kNqZT91OFaVHa+nh7+59dfJMrZ/3KG5novxMExjI5xGDjNE7fzyO3xwOk0Mc4T43DQ4q108qaZyM9PjyyX1Typ1Bi41sI8TxzmGZHOtm3kXPUib53LZSPnoibmPqiKNSbjpxlXwsE0KNTdmlBLYSuFNVfSMOmGduqXyKafmI4NnJL9tWki16qHdBPjO2pHqSN2KK0YD0kP/+BgjIFaC2IIS0pmfdNEE1NcpAt8XDL/9//X/4ePnz7xf/k/PvK/+T/8G27iRIizXTJK1g9eR/rDMKl+30XScEPtQi6Z9PTAsi58/vgF8cJ085Y0HwlpuI55d9xc7MGtRd37uyEbzQQ6pTVNgmjK5Sml0EpGeuVrVSk+kGuDRdGASUaiKfPFbE1AN3+wQrI79Vjbe6HuXqyCrobz3vK6bQN7581n8kV55pyR1r0Vnk67PlWGv3RQV2uKr8YI+9ofkr2m1DpPSCFyPMw0OtF16gZeHKejKrvHIeEwK5X2MmKIcaTWTQUxxiGrpV07dp3Ti6JG2N+zNztqftta031kFhNKqDatbuv4XpWwLiYWyoWeC7SO757o0zVlRfY/Ex2T9a4JJHs6EinCfGR4+5b2ww+sj488/eE/kIbMwa84eUuYJpqDbW1sl8q6VLoLzDcn5HjL8XZmHqMpN40TGoIiBgePq/raWi1cnh+o20KIQs+ZOEy4seOmBIcjbjoR06h2YZKpW2OtlXVttObITchrI3QYnFePO9HEkNyEz18Wnp4mah20SIwekYh4ryEHKereMMhJLzJBxF+VkAY/KpHeoRwo+YpLLGL0CrOnMtsPJbol/R67Yy1BCe/DRLo5EG8OxGTWVTRTY2oWupjdlNb+NqL0QdGHr/b9Xmbqtbkbwe6WQfqzPljR60Eo0FZ6WSg10aQwjgMhHpAK6/mJy8MnastaUNdKWS7ky5nt/MDmNQYu18y2Xmg5syzLNeZ12VHAqlOivNvumG2XCgkcvQm1aZKZtEqrVX9+u9BapnSNDb17c8e3337H3c0bRgKff37SpsF74jww34zc375RYdU4a7PZO0U6XzbhDw+NUD1v5sTbYSR2YetfOJdMbo2hVrZtY8srMQwqJDS+ZCtVpzWGTpbayE3T2Sqd7kQZsU5RnjEGwlf2P7sAUC9inSpcqlCKXK1Wip2JLugd0Y22sHNqvYcY9L+PwdO6qoERbfxra9QumniSO8OQYAgMXg3GGQZc7dD1GfFSCKhf5n9xRfn/d15ehZYi7OFhOxXj6yN2F3SAXLmE3ZBFEd0Pu3tHc4HB28zTUPf9jpHWNSJGtCByTti2zDgMqqr3nnEeaVum5krAEUMCF0herudP91ogBaMY+f25FeW1twadRvcemrCVSkPpa/fHwM3dwO3tzM3tkdvDxGmalDrj9lQc2GON9VxRYKuJU9oeDV/1DvaigMYeodhFBcQGLAOiNlN21zo7evzuI0l78THdi0IbwQenE5FhCJSsXuHBvjzvUBP0IAw46mbpQuhHXPr1+NPXxU652QVmfznS/YsLysYLDB6cI6KeRjGoGnaIkSkNHMfEHKPK8PGsWyGvGz5oobHljWVZtGvwHucD27YRY+Qwj4g0LpeVbcsKSwfPZdnYNk2zSTHoZhXlmCjU78w/0qKQPCyrqru3Umm9MTrN55TWUU7zLru3gjS8jCxb043lnFC9pu+2BsOQ1Li0qT+jtEa0LiyajUDpOupAGt4HctdRMd6xlUZwnS8F/h///e9wQ+L9/+pvOd5/g5tNFWfEej2UVPjhOxzHG6Yp4tJAaZVp/sxlK3x5eKKxMt++JYyTKplFAK+XqdPRcquNWjM5KxxfbdRdStYOvakJa7Fs726RgupV6U1tDc01tlIMXRPGMRKTKmD30a+7ch7lepB6F7TwMf/K2vZEHfuZr1BDLVNsEOL0exLDbNxeCNhDAzuteC/U5EoduJpA80Keb1V95EAVw/ufNYbE3XhgA2p0BOd58+aW6TDhjfJwHTE25Y6W8nKharGhnKngd+Na67ZRAYpHEUaHKl1Lbfp9tEZwLwlP4tR3UKSpQa+9xuA1iejh4RPr5Zm6brjjDSEGtVdSrNNsR/R1uqgiAxcCPkwMh284ffsvWB9+pv/wA327sHz5E25UUk1ob+lhoGywnFfO5w2GmeN3B5hvmE4nghVK0rvxkLRLL13975p0tarZVrJzHA4j0TnSmBiPncP9xOH+qFGj6O9qZiS/rBvrph6aeau0omO8GIRB1JS3d1jofHqu/PRT5tePldMdjCfjqAaPT1yJ8q1h+elWXHZ/Vax6Owt2g29EeUsGQV5TNxQVtOPVCj4tPsNVlCV4htu3jO+/ZTjdqWhQ9pOz0cuK71lH7jFePew8ah/i0OKAXqBnpGaoKz2v1sg0xHdFwUQ0397rc+Kt4aJnaCuCIwbP6ZBoDZZl4fnxM8/Pn/ExMPcDvWyUbWFbHtguXwwlCWxlYyurJQ1l1m1jXTdyVb6kPj5qwl2tgPA+EsXb3lPedRdHXVfN5y4rDcGlkdNhJsTCt9+/592b99xMM/l5JecLQmI+nXh7d+DNu3uOcUaacnOldDX9ro6nJfNz7Yw18DZNjLO6BozHI3GrUCs+empvlHUl+wHfoW4bdSvkrbJtlWxOAqXJtZhswtUHd79fomDfsXHPrckroord2oUqqjKuTScXmPBkp2S5r3wcd49E7502HiFRm1q71JoBbykzBgp4NTeXvNGTuo/UEOjzgbk3XClIEUKrREOa/kvH3tejlV3Tro23c4qE9WY1X/9q9C2wJwc5K9zCPqExjmi1uy1YgzdEa3RFdQy7Z2KnEQjUXkEs871vTONoE0vHcDywNCh1Y0hRzy7piGsvdAWjEnivnml6LXidzPVG3+lrVtgttTAkz+nmhu++e8OH44EpJmvK9LU67wnd012jdaOv9KI1hNqifzVD3ikEhnQHa0xt8gA6efg6/U2uf+0ABFduZAie0DTMxOGuWfS+OZz5G7N/J87OBft5bWZU7Ne7WDSljb7lRcDVUOBi/3m7yH/x+sUF5f5G1YFd/9ImKTBPI/M0EmKyy7CyLI51yboJvaZMCDpu0C5GCbl74Xg8TOA95+cz25aRDvM02MhJk0N83E3INRrR+ZfcyjkFphiIHmrTbvNS1DIjpcSQlDjbBbxZDBUr/Jx3JJx+GU3HvXhVDYs09dV0Qi9K8M656EjEbHf8XjSJdp7FzMND1AOo0XHN6VjZCq5hGGgCz5+/sD4/Mk0RPyoaGeJITCd8qkh+VoWbCwxxJoxHRgDxnO4eOd79THQnTjeKTiq5X9XHu+ADvHJJS9XEi6wWTNni1KRrNmgu29WEVd+7ZUzbyE+MJNKkU3KzQ3dkwuMSOmbdrZ+kK1TfBUcwL9FuBZamcGhBYlwS3FW5vYNKbh+ZYxsPvhrh7geXFq5aQ7/YBu2KaufULUBR0I4zxaD6rPWvylFtipCJOUWm6Hj35sTpNJlxbDM7HeWfNtlRQX0/Ii/Ht0jDOR1XOwFvRtS4nd6BKphbYyuZXBuTxT46K05VCS1qR4UYjxZ8aHz58jOff/yRd2/fMs4Hxpt7fEz4XlAbGaGUjAtRxXIp0sUT/Mh8eEv58K941wvr/N/hls/6esqZujwicaQ3YVkal3VF/MD45hv88T0tzEj0LEunSQHfabKC2+gM5FLoy4IsmXxZqCUTUmIaYBoC8wjzDRxuYToW5f5IQnqgbJm8Nh6/FJaLcmvzRahnxxg8g4t0lwk06J7iPY/Z8eOPG48/F96+jcSDw6VAdHoJOTsc2WMLxXpw43rpQW42TyJXdHAvIPcUChHBNb0Uauuq6BctCULwOlaaOjGMjHf3jHffM95+qzZDasqhyB5i4o2Ei4NSIr7KyXW28Z1UpK1Iz0g94/IXwvqJdvlIu3xBlmckL1eEHw/dAZxx7RnfM9ElpqS54OuWyU+PPH75icv6xGG+QWoh15W6PZHXJ1q+4IMnN/Vi3PJKWRe2LbNtmVwKxZ4B5YsZquKU7qA0ia6TDXQi0nKj1I3SMnjPNM4cjrec5iPzMfD9r7/lze0dhzHxp/OZ3DPDIfL2uzvevr3n9nRLAi5Pj+SlsDxdWIaF7bKynBda6zxunYdL530UTmHiMJ+oN43aiyYpdWFdM95d8EC5rOTLhZyzjqlbZWuV0qCK6KQGb1GoDidRG2OvaFM34VxDL+7S1WGhWiOsLCidkBHAJ9S+y+soFSd2oSt1J6VEHCIdRyjgXaVV3Wul61A0jolo4tBookLxjoajeFELsiGQ+kg8XxhyJkj/qwrK/c5XkRtXo+1WdwTUCmY7QZt1Yw6UKmwF5U4K1cdKpy4StFguCNFFoxEKUvWzrUHM4ko55CnqpGHbCofjTPAK/AzTRNnydQoE4PZm/is/zi5KGfNhB1cq+KDiKCvqpDZcV6rR7Wngw9t77qcByQ2pwlp1kulDIJrg53rPdGtCBKTp8+gaV258QwGi4D0BT+8BKYXizBgYbwjqXkoa1clhThnKxXeuET1sXtN25h5ZWyXEgdArzmel9tmZFYLuadfUzzIFKEZN81a/0RzFBEN6D2qT6OSlOPwLJt6/vKBM7gVFj15HFn5wDFNimmcbJ0POlafauJwXMFTw9nRUpRyNKIrg4ALLulFr4+b2hA+Ry3mhZBU6HA4zMQW2LV9jwnDOcrYDzmmO7hAcKXimIRoAISxrJhctDJ0PDIOaF4t1n46o6FRRFC6UXcn0MppsVQ2GrxdKCtD0y95KtbbMUXq1TkxMkae+hB3orVw7tNZ0Y5WswiOq8ONPX/j9P/5HptOJ95I53n1gON7QgyfEA+Oko49cn6jbSmieIOAIUBtTGrg9vUMOI8ebN8SYroKa3i1OsnOF43FQm8amKdezU3NRzlBTm4LWFF1obc9E1cNjL77EYcIq9alj9YocN3Xs914bCjUt1y26F1Z6P+tn7bwzjyx90JQBo5173W0VDF11Joq7Chvgz0bmu6hLL3kM7dHun314eC0+tbgUU+or2dy92P2EQPKe02HiMB0Zh0k7aDF7pa6dYG/dPMv2p8KxM0P3rm8Xqez/r7PCVqQZt6yzZr3MQqkMSUgpWX68HlSuBTUjD5BGGJLQ8pk//od/z5vbG/UlJTCeTviUrqho643eCt1EJKCjtzgeOL35FueEcZ5wz/+R2B5x0YGPKhKygt8NJ4b3MyGdiKe3bKXx+PiJTz9/ZE4rtTe2ZpZL3bMuGb+uvDtMTE4vH4Xam12DKyl0pskxzVo011opG6zLwsOnMz//cGZbFPLIzw1fHMcwcHABkQBkowY4lg5/+rjw009HPnzvObxxjNOA82oL1WsxJMBbQ6T8xd7alYawc7ZAC/beYCdoiF2G0h3SRIn1rmnOvas4N+BcJ6UCccbdvGV+9y3D6R1+OIGPeJcgGA7rAhDxccKFQVF0Q3Hc7lXZuu3Yqg1YL/i+QrnQ8zNp+0g7/4ny9EfK059o5wd6LTjXae0Z8he8OxNjYmSi1UJfV5anLzx/+VnFR8NAWb5QS6Usml7UeiE3vWzzUtlWtfNR7no1i6x+3e96mWpRZMxhWsu695q6Qmw9g4dpOjGkkWkcuT3ecnM88PabG95/eMv9/S0e4bicuF1uiMPE/dt73n34htvjBLmwPT/z+PmBn3/8idENlK1wWS7UBmtzfHpY+V4SxwmOcaZPla1tOjTJjcvzglRzhNg05WdZV9ZS2GqldE0da2gkdq6VPVbRedMjdxVF4PyVL5071FYp5ulL1++yVmhdz1tpIK4zehXKOe+QqHfY4XhUoCN4alM/WvCU7sg941NiSIOaesdwpWx4/2Jy351XLmzbz/0BWtVR+J9BS39BVbD/pNGmHFwdKfbfJIYudaV/G2r/tYBQ6U17n61UOQWDWlGQRnu9fTJj06TecAQaYub4qnUIISBRufWH46icwrUqtSBv6gXam7rF2HuoTYvtWrW41vMaxImCXEFFOGuxhDZzXzgeRu5ubpiDZ2ubigOXove4dGKKHKeBadQ6RMO9bdrmVCB3dVG58h6jciAd1+nfTrXZp1PeeNUd5eQ30ffSjCerzWM32p+6WKQY8U1ILlC84tKdnT4mDNHTRBQ7Fa82dXaXekMog2kXmk0bdnHrC9zyy9cvLigd6g3pUN5kGGCaI9M065sK0Frhkjvnos7u8zxyezshODUC71UrUedZlsxl2zieDuAT50umFOWhnY4HUoqGqKlNi/f60DmvWbpD8MQIQ3DMYyQER6+d87Ky1s5lWQFHjIFxSH+uAkbFKACuedgKgxGmd56LKty06/bmz3dFaZ232DJvSnQdTees3K6SCykELWJNxVxqpTYxX7KV7hr/+LsfkFZ4PFf+m/995pu/F47SCZManjvRPGjEsa0bJZ/BKSE8t0ZdMtN4gnAkjge16rFNXJt+3oqqgd8PM9HuL69Z0bbazQPRTM9N3X1Vj3ZVgl8Rxm7wfdRIqWXb6DUztsIgAylGhdCbeZQ5d91AWlx3nJjNjXlQaodrilr5ihO5j6ytffpasb2vXbnt7O+dmZqDelOKKcx35aEKOyzpxEYCvTXEG4CFw4fEfLhhmo/ENCA4/Qy60QRqVVGNeevtxa3sRTfOzKZ5KabtfexvTW0uVI3Xerdc9kpolRBGG9MoMXsMcJwch8Exj4FpcKyXT/z23/2/SU55kq29Jx6PDINGfO4j2NbNeqI3E3/AOMz422/Yhhlu3+HLR6QtMIy4dMCHkRQ8h/SWgUQcT/hhJn/5zA8//ZF//ONHQhXymtlK5fH5zJoLkcB3h4Hhm3vcMEBHCeB5o7qO+AVfE0GiWuV4LThq7SzPGx9/OvP0WKjZa891aYw9MhIZfGBrGEcskAlkhI/nzB//uPKbv524/xCYTkm9OVEaS+tqeq43iQ6g9pGcsz21RwJI38dkSqgP3kQ0Iohr9n0kfEdtviQh4UiIiTR/INz9C+Ltr4jTDRajA66C1/AG5xIujMqX9knRSUOkr89J2qW0+xismLCgIW0j1AXZnhm2L5TzH6mP/8z65d/TH78Q20IrPyL+qLncHPAZ2J5oy0+4csGnRC8LWyv0KixnnQg145HldSWfM+u6sObVzi0V8e00khgt+WNP73HV4nIbvWVKKzSnoqj5cOIw3TGmgcM4ckgTh/nAh2+/58M3b7i9mdFmJ2qs4rpxezzw7nhUn2AyncbDwxfiHwNRoBbh6XxmrYVM52Fr/PypMU1wnCDKaDncRrd6LizTQIoeJ3rOra2xVk0Ey61RunLJ92F0wJtLhj27vNjpiPN01Omh9KYNsB1yvSuik0UsslE9DKuo0AKvFIk0zZxOtxwPM84Jz8sFcRutO2L3hKZKZp/UGzGFyDgExkEYzWex1s6WlWPZvdnkjZHYB4ZVbcL+AseXP1uCod77X7yUpIK7UoD2ZaZtmELMBgL9zyy0ejNUoXXq9eca3ihjLyhmu6qatbA3xLcLqamQJgZPzZVetPh3Xm/nHUQQQ1ZbU8pZkReP4eA8KTrmIdh00e7xKoQgHMfIPEYOaYCu4/ZK43lZ2HLGO8d5HLi7mZkmTd9yGGJZlTrzwobc+dDg0BqmNuXm7pZBzjuSeQ/7aC4ooUKulsDjSEEAfQZ3WsnQPIMPJDrFiSU3idUoL2dbcEpTbL4zDo4lC61CCHL9enfApRplbL+z9rv0l65f7kMZII5CTA4fHcOgGbcxaqHSa2atlZ47Xjyn48g8TQwxsm2a0tBF6CnS1jPnNROHAecj61YoJdNK43hQ25tmHdtWO9kUhTEpRyGaW/wQhTFFUtKR4tYqW23GwdILYUiRFBRd7CJqeuqwYsBRDY1RZEL5Vd6bJ1ntZjvUiM1fPQ+ldXppyjnq2kkhQq7CsqnQBUFjt5qmGihnrrPtBYnrNKlsv+9s27+lS+PfHCYInjEnJd+LkFcd8VzOF56fn8llwxknslWPnzTDGSt6dHynyja1BMpKkEfHa87rw7OVrGhpd1fkQUUiQjXkrRQtlFUR3szcV09Y9bUUSu5svTGUwlQKx3HSotJGzLq0yMIZ12vns5hd0s5JAixNB0M6rejff8uOJH1VWDpDMfd0nJ0wDTvB2Buy3f/MssEmNlrk2mt0BLNXCLgw4NMI3hk6UxV57fsB169pQ1fhEtpB744Bom25HrZ+912D3UfIe434a+yiIzFFvl4+rmcOA9xFx2mAwzQwjVEtb4KwrT/wH/+/F5bLJ779zd9zevsNcrxHDkeCRkHgfCA2U2DamLIBYZiYvKOnCenvkbboZxg1C36IkTBqgoeToBZba+G3D1/4tz/8ib5txNIom1qvCMLBj7yNjrqtdN/Vv7UK66USW2BrC5ehcTgV/KjPpzRHXlYePq/8+MdH1ktRBD53yJHYA0NIdsIJSZRCs7TAJpXn6vjx48qXnxrffh85vtVuX50b0EawWzlgzdauhn1xH3Bm06NFZbc41537pQh9s/GdoqMt3jDe/gY/HDQmcPqAP/2aML/XcTbGiZTdZ9L2mKjiWQUGFmd6dSLzL5sdp0EAXhs0J8rRld5g1gAEd/pbhrv/Nendb9k+/XvWz/8DdXnAL79jiA3pN4QV3HnF50fmYCPMnClto2yV5fmJmjdaKWznM5dl1ZHyurKVFbo1P92iTu07Dc7bWLEYH60ivjHNI3OakZgYhxOH8cSUJiafoCllZJ4H3rx5w93dW+5PKsKcxyOH+cBPP/3EmDwildaUMhSCQzw8PD8xfvwTrgeez2f15hTPl9z5p144xZGhO3z3uCwsl8yl6kjUR2GcEnHQSVqrnbXqfdGakOlk0dS15vw1J9s5TxXl3VdR/nztkHun9GpngCm2sTGnFZC9azY3viP2jHjphCHi00AaJ+ZpNsG/M/spR2kF1q4m315BhWBm3PdH4f7oiV7IW+BpdTyu6jWYe0CcY3BwP0TSuvG05K98LH/52sfYtQuJXYSio+XWTcR2PTfd9fzGGjJNf7I/V/ZD3J63XbiFJuKF4HFEpQYJelZ52P2efYga1dhFbZtzJaOaCmdWYB1VP2M2ay+vFwV8dush5xmTTjYHryhd9Y5saW9DGplSYB4jQwjUIVFLJw0FtzhKqTxdNnwMfF4yd6eRu+PEcQ5E4/r3hgE6+2vR+6c1qxWcM+Bkhx/0jvFDUKTSgVgks+a9e8vqVgCmeOVHDyEyu8oaPEsxGmHwOENprxO6JLSuhvLOm+jTfveOQHv7q8OfNSE7H/eXrl9cUI5zwCdNkkhxYBxGUvSG8Ogm2y4bvQmnaWIYB0L0Olq4rLTeSWmglI1lveC8Z5hmalGbmrVUDqOahGpnUSlNOK+Z2tUyIXivyJ8PDMkxJjWn7l1Yi5qea+GqhUTwqPdaVfFFiN7GBc1G2Z3m1UaoBaXUClWTZBoaJyYeCZovrue+s3Giefuhh0krwlYKW21GBAYpimTtqmrtmis561hRujCExh8fn/l3v/0j7//pn3BJON5M7BY565Z5+vKFL58/s21VU4VKo20rnkQ8weRmhrHgYyX6nXWoBUptaj6sr9ceXPOV2rPLe3spjGtt14L060SYWjXbtpvVTTMBT636ni7rynEc4QDDmBinUccBhuzq7+x/NoVRPzLYbYIEdx2N74Wrc/shY8UkXFHHvbD0NjbRkbb9Tns4VCikdIOdG6f/nxC8v6YEiVNFunivXqsuIFaMNLOe2BFWFSo51Nais6eeOOMFq/WRUyGSmDekVbE7CttM8TfFRAhRM8JRU+5WNbL0lDq3Cd4MnpsJhqT0jhD0sPaxE/rCx9/99zx9/JF3H/6Gu29+zXz3nnQ8sR0PTIcDeTppJrU3faYLuKAHi3OaddzdSBeHFP0CalfyequVul04X878+Kcf+NMPj6zF4wvqY9lMClR1lFeMCuB6wzvj0dFYsiP2wvYMy+NCPI4MvpOz8Pxl44fff+Hjx2dybcpnbh6pytUtLkLwCE2TSLqpaMWzInx8yPz4w8pvHgO3ayfM9lnvB7Z99r1feRM6iTcRmQ96JmgzZs2PvOxVRVg83Qs9OdLhjsOH/5rx9lcwRHw6EsZb4vQGxkn3fCso7L3PRNRtuImAz7hecH6AMCgq5iLsGSciyq3ykT1IeU/eccErzzpNOlY/3OKPbwnHX5Fu/4bw8z+yffkRX34LbWZsiZQLUz+TemOtgeXcybmxLpnL5cJ5WTgvC8+XC3nb2NasDhBSwZr5Jp6KdmHBqTefCr8qQiXdRX7zm+949+F7xI3k0ukrjGHAC+RF88SdxdOmGJjSQIwJAYY0k0Igb9nGyRUtJzUC9+7tHbXrv6cUTUDr6ke7dPjDWvjmFHk3JA5xpLUFeSycLxtLKUioDFNgnG2ELBorW7vy89cqZBvTdi+atFbtrMdRRWg4Kp5Ko0i1hLCvZrpYw9b0Oe9e6Ty5YI1mJRKZRhU5OptC9d6IMXJzPNI65AHm0e4xr0kxMTjmOfH2Fr5/GxhcZzl3Pp0DMQ3k7snSWNes06yycThMzJcLX54uLFn4C2tKdgpkaebfGl7GybuhuUOLOud2MEAx3l2NfD1w9UnSs1GPTGoV6I0kaMqYgDMXEI/e9XhPiJEqgo9BU7W6PaOoX+sQA5iHpKs6SQo4xHybHQoexBgYY2AIXgcBvhNCtAmX/sYh6UQzBo8zjUgpkXEeOLSJ1dKjnp/O/Px45niYeH9/w7vTyGlOjD5cxUqCvpdiE0OLm1cMUdyV3tV2VBBFTwlOIyS7ciY6Jv4NOvQYY0S6OjuMITGGTvh6GGicyG60BB2PC66a7iU61qz30D6lQfMtr2hl5wWl/PrO/s+tX45QToqopJSIccS5aNn0nVo1uqtslSGOpKQj5su6sq2FspUr727dzoh33B6POIHLspG3gveew5zINRMlkGtlMX6fVu2OOQ04dGM4J4pwdC3kVAmuVhVaYOhmAeW76WtXdFITTvqLpYF0etGQYedFD7SsCJ140W4CYYwRekOcp9aq1inerCeqcCmZrfaX6DYsh9opgVoMgq4VxKstymVbGVLg8fHMl48fuft4oJTZ4H/H82Xly+fPnB+fKQVyEUptbGuhtczYBg7xRBjvCanikhZW0szWwlCzkjOt6CUZQtBoPqeGyaUWWuU6/t5jFHeOYDfe2T7CbV1V7lqMKe+y1cpTU0X9SU4gMAwRCVwPXS2mtHhToM4QI+euEV9cDyRsXKLO/tcDTtT+BV7G3aBwgI4odYCJ18xVjxLY9dly147Z7ekPzuGCWRLJ/iqVq1tqJbXIbowr2EUvogeW14uh2MhLtBPCmSCo25hFunbcV9WctSaBxuAqcwykFPC+ElxlDJFjctymzs0g3E2Ro4bS4LzyPGMMxIhxkTqlPvHbf/7v+Off/hPTzXtu3n7g/u1bDjd3jDf33BwPhBTs8/LXojy4l3Fvk27UBlO9bpllPfN8eWKpmX/++AOfPv1MLZnB0LsXnqFRKbryN71rTCnpYVo21tKYosZbLo8XppuAE2FdC5//dOEP//yZp+eMoIhvbZncYZNK6hVkwElj9I3QK8JI94nazzytnR9/2vj8KXL3NBCmiI82JpZ+3TfOCAn7eFKnSf4FHWyKZO6JT9hzv58J8fgNpw9/g5/fkt78a+LhTq2QQkLigEsDuIhIw4dkQhzdU1cDLPOlczRc6NCroej6PAY8vW3gE3484tKkozEHVwxBtAB1MUEaSPGATzeEdE8c37LM/8jy8Z+ojx+ZnOcUHXVakG3lx+fC588bny7C5VzIy8alaBqOenjqc6973D633qhZ1KfVe0gDEYeERG+Fw+3Av/xv/hX/1b/6r3hz9w2tdB4/PvP48YmSK+fnM2Ur1NKIUS3kailQC8u5EaeJ4zxBuOFwOvP09EyvjX1WOJ4OfHszsJaKXzY+PX1WeogPFCmUnMklU4YD3MxMp0BLmak84C1StoqihDgxCpBOatZaWHtja0LDG0LTFRVy0L2CJR0x7plQWkZE+bl7qILv8uINameD9xBisjFwAPudaRqIUc+wZV0hCuNhYHYjEiOEMz4GzFVP+bWitkFz8szRMcVOlEb3I20Y2Hqi4VmGjTwuUGYuNXOcB46HgR9+euRprX9ZUSkqUov8uS2QM5s4sUJTz3U7Oa9opDVsmKAELXScoZvFGrba7JyezFLd7SCC+WpqF6/FJWJcQz2bYwxIU2tBtdCK+NDp26qK76DWTWNMpK5j9cGEuyl6tWHSk8AmVsIUPWMMym0MHkmqDYgpEmJgnidOuVBK4+my8NPnRx7OG59OE2+PB+5PE/MQ9NkxQetm1DJn5mFOQGp/sb1zWiz74PdZngkzTU7fOl4c0Xm1shVhiAO1FkLspFI0/9zpZEWsmOzNHAct4ck7bVk1ENOK3uu0r31VkDoTxNr3+BdsmV9eUKZITMlihoIhMap8pmV63fBoYk6InmXL5GcdYwcfiOK4LBc6wjSNOlpqcN42au0cp1ELPa/2IVstLFn9/WII3ByP+GYXtHQtWoFty1yWzLIUuvmh7T/jrKjQK69fx/NVzKolKrop1oXTFUlqosTs0ppxvRS5ceKgq7hB43NfRsyXslKaWsEE75V7UV9I7L07umVeazcnygmr6qv2vK18/vKF79Zv6bESnVopfHl65uHxiS8fn+jdIySac2y5UnKnpQJL5q7qWCpakdaM3OxCBDZN8mlFx38+EtPAMDUbJXRqWZGuoqGrT+TOn7QCsxYtjquNu7uN+jHUspZ9BKzfwdgHLeK92v/shudY57YfWoo2qj2TsHNzdjheL/4dsbxyYa0oDd7bePFlfKAPrXzFN5Fru7VfJLo32vX14LARvfaq/fq9GT+qa9IM1kE7cZZb3a++k7sYyGEKbSP6eyOo7PZSvTR8bcSWGV0hedGM2dgZQuWQhDkqKnkYHKON7cV5fEjElEhmwKvIgYPokSDkBj9/+oE//fwz3ifG6cjN6Z77N7ccDgc9fE3FGp1T6kJrVHvelmWj5E5unVwWlr7x5tu3HN/c0UJgGPSAou8jfJvtOSEg+owKpGAcpQZly7SeKRVaDSoEOxdqg8fPCz/89sxPP26suTKYc0Ohcu4riyuElhnDicE70vXQ0veRJbL0jR8/Lvz848j77zPp6BkPIOiFT9/z6+15NK7tvgFF1OfRO+NX0ukuIAjRGxoz3zF9+w+Ed3+HH+/wx+/wk6IbYoWDeE0sckRFE73aml1vcWeNDF6Rh7ogrtvnOCJ0WrlQLo/E8ZZ08x3BJ/RDt9crlqO2/0IJmtceIi4OMEzM6Qhhosm/wz99xE2FWFZCvpD9yh+eH/jDPz/xeG60vKngjKaigK+ex+C9csil41tVQ3CvKOlwTEwHOJ7e8O2vvuNf/8P/jn/9L/4V70+3LM8rv91+z/PPF4ve1NeqNk56UV2ez5znCe+F6XTLkGZCStzc3pOXRi+FJWcInXff3DHON5ReWH7/I3/87R8V1enq81urnnd+igz3B+a7SJgL1d2Qo5CeHLnqpCMayCA0cu8srXFuneIcVToNs6930H1FxNPRvVN6pwo2BVNUqKOokWiLoJev8wp2eJRyEiJeAi5CnAeGeeIwH0lDpJTCPM+c3tyT0si0roj3pHHASUTjVBu1V1yA2jxLVjsiGR0pDBzTROgDW1GboeSgh8zsErUlTvPAECJ/+OmBh3N+OXv/M0vrR3s+qgLm5r3+wtETiyf1egx4awLE9tFVk7CPoZ3ywr1TDn4XLdbXTV0fYgzWtNuYVwKuq3WYi16dI6Kmf+WSScGRLUa0VW3gYoy6X62IFOn4oOddcKJn05gYY7hS3XrTOyyGSAqJ3f7NJ0cdRK0Om04VD8OIHPR8/3zZ+PR04fPThY/zyrubA29PiWkIRFT0uVUV5+706A6611qz78vqqWtxZ2DJPkszUap3IMETXUKkE4PWQxop6YnO6+dX9hE/uPbibeuNrsA+YWtc7YRi1FG5NMwiij/jUv7S9ctV3nHQQtIHg4j16nbSKTUjrTJGLSB6b1xWRR6DxRflbaXUwpCma2Temi39pnvjmzRw6gi/lk0LpOA5HQ7mDacXfnQOqY0NYdky52VTRVdQRVMXYR5GHdc6VeJN04RzUKvaX/S9/Bb9EFvXJlI99PpVoLGnc3T3lWddKca7VJPrrejIfsuFUgXMjqDrbtVG21R4siuY9y8cWLeMBM9wd8QfJ8ZDYFsWlpy5rJnH88rjslCLAxfpLtJKp+aOi5nhWOitUmvBV2cjexsbxJGcCvhVkR/jDAUfmKbJyPSGTEnFWbbmlRtkhVCr+s+td42E6rYZjf+4d6ilVZ4uF0prHNvMPA+KqBmadyVzO7gq17DNazYMNqHEhgMvY0teUMndwHxX1onT0ZF3eql7t0fkaSza7gVn//m1KNXOWwvS4IPZQKnNUu3NiPmCa+2KdLXe9RIRMbHOi4r/hTPUX5wDRL/zwD4X0nSayTV8VCI4GPIYhOQdU9Qut4nmd+cCISV8DyAJh6aG9Kbj+BASLqhoZZ6i8rxK4+n8yMPjF37/R7Px6NCr0i9a1xST3CG3zvOqptMORxoGhuPAt99/4O/ef0+cR26HkdvzR3748kwXVW6H5JgPE6NP6kM7RMKYzJuwM3ndT67rodklUDfH+XOFKDx96Tw+dMTpe/fdrH5Q7tq5bgy94ZkZSCRxWri6gBAoBDaEh6Xxpz9mvv9N4nCv/rg+is2BOiLVvgfrvO2c8EGpIftBv/NdpUP01qakI9M3/8Dw/l8T778jxIlxOKGGYEVRKhzSvdJpdqVkt1jF1q/IRMe4k+xof4auvnvUQj7/wPnjH/DzW46/Eo7DiE+qCFcrs4gzJJ/9WQGdzKQjyUeci7QOcd1Yn57xbIyxcZyEu6lzEwsuP/L548VMl7Ug8INOgkI0b9Gu76u0glTlrIVp4HR3y5sPb3j3/j33d/d8+5vv+fvf/B1/++Fbpjjw89rYIz97b4TkGQ6B3gPTIXG81bCLbV1pNbPlSkojh5sjg08MPvL0+Mynxy9MtyN/c/obbm/eQBB+fLjgY1Rhozi2nNnyxul4Q08HqlcRy3Q8cP/2nt4T0+GRZVnN+FmbqZwLTYStdTbEnCUs+crZPSCd4jpZGkX6tbD0+0jQ5oPOQRUtRDsOHwOEoM4gNo3wIeI9HI4HjvPM8XCA3ogpcby55e27bzhMB84Pj7StatFiIcs66i+IFNbgeSLR3UAMAQmRIURK7vjWNJIxBRo6rZmmA/M0kUK0c/wTz2vjhZ3+n64Y5Pr/yvXvBVVqa7Gov0OvZmvl5WUcvv+Sa5Sp0ylda8rF9GbS3zvkKmajpd62Io0UvU2ZbOrkglIPqp7LXkCaIFGnayElM1/RVx1M0NJNzdztzsErbUizvlHqmuzjcWuQm1zH0oMl44QQqXUhr4VWO0MYmJMnZ8fzuvKn5YnH88rPs+f2MHA3DoxR77wQPbhw5Z7uII0Xf/1ca+9XUauzCZDzHoJ+9tWpwHb3ct75lsk7phAZQrPsb/ue2ktR+JLH7V7ep3393ik660WThPaf332c/2e2yP9o/XKE0iVVHRMIgo7KWtWqXZSw7aPO5ba8cVlXcq5M0wHJnZaLjaITdLkKU2qtDGHQ6tnytKveuQSvlkNDctSSDV3TeMJWOmurrKWqgi5E4ydwHXU7Q2BCCASDzGutmrgTowWxQyn65Io3BJK9MlfysGFqSvp1+uJK2dRCx3lyaeRSKbXpl0hH/I6yKVcPUH+xLjsH3zhVQO+8v7/n17/5DTdv7hkmLYxDaYzzgcOpc94K5VGzzGtvLGulF4fEwlQ6pWgudG2VXlVsoyb0gRgV1cJvJvpQaybvHPMwatfpAtIvml6EcpO61CuHcDd9Fzt4W3sZjcNu/aOPvtDJreLWFaEzDjqae4njssMY98KH/Oog0rH4TsrezyUhOT0Urw22vIA/YgjIzo0M19QZ5YcoKqzGwjvPEnv9weI3RStGGzfYd7YbsHc72ESR2p0G0Ey1tyvl2RFPe3b1jNU37LHRPpnoGpPvii56wAdq80TxGuIkjtoDrms3mXwktkTwjm1zzDgoeyfp6b3gXSD4SvKOgcZxdOSwCwkKrVUd/UlTqysH3ic0jzZopGnT5+cwz9ze3PL+5o6T8xzGgdoWynkl0QleP+f3b+95++YNt8OE69DKRnIVFwpD6oQUmYeRnouimUWoa2VtBXGV7RG8JD68u+H2FCnrhiyObdFLR587YQrChLB6NbgAb8begUUa5xr56cfKw0+d2/eFcXIMTmMJlSqpjQUvW+eKwjtT3urt4mw36aHegTC/Y7r/F6SbX5PmN3iv6Kj0DS+ZLhlX85VqgVNUnlZp20LPqx1q0F3ExRHiqI1lzUhbyWVDtmfOn3/Plz/8jj6+5328J013DH4gDCc0LUeRL6Tr5yMCYkgoDhdG4nhHuvmedP9I+eNPfHl8ViWr6HQpRR3h4R1lE4Izj1Pp2lS3houKgjnnCKL0oPv7W+b3Hzic3vLm3T3fvv2Wd/d3/Pqb7/j29o45jIh16kNyzLMnDgN+GGnuBMDxMHNze898nDW04rzx+csDNMf7b94j3VG3jY8/f+aHjz/wm8N3hDBwd7qDXnk4TJxujyzryvN545JXxuPI6c07ij+ylpHLU2eMjjRM3Nx6fPSMaWW9bFyWymXLlgim1izNRuLgwEaETpTqIKXSXFHOO4JQ8aEzWKNUnerHxHtKCzolM96feOP84cCr2PMwjBzGkSFEtqJRwq10UhgY/ET1G0MclJ6T9KxLIoySrLEWVgKujxwGpUME10ktU8x9RKQpvSUkpqQWOzEN1NbZlkwrTyyt8+LU+J9eTUyogfI9pTdCMADGhGw731+MX+7wL8WIA9dNmCgmdPtqYuOM190Film3RRFc8tTWCS5QWyd5DSXpO/K5c9pt3wffqVJwSb8/6R3XduObvbFvxlV2tFaRYK+bBkHV5R6HazpJcHZfxRA4Ho8sm+AfVi7lwpdnRZJbFWhQClxK4TkXvlwcx/PG23nkZowMyTONCbxTwVlTkEvvzoCITiwdVot4p2k31+W0hlDmgyKW3ez0goOCToHNk9sHHWTUXYgUHN2pRgGjaOh7lKsHdO+WCPaVJyV2R/8lPIlfXlDiCS5qrqPv4ButWiZr68SoD0ypha1UlqXqGIZAKWorMXkdMVcRpCrKJ70TI4CQsyqZOtqNH6ZE9MGyVm3EgWNtqpjWzk2uiEDrndKUa9KbdmHi0PScoA9bsct/ihrf2EVjH8GpPZB1J17lYSpCsQ3ZujruiyjHrErH90YumZw3g8+jIh5di9Bu/Bv3FVKlvCwtWp3AfEpMo+fN6Zb70w3ECOIJacZPhRomzgVK/sx5OVNKZ1k3WnGMh8ayFWrTSDEfbEzbgb1AlkCMg6amRI9UobWCKs0U5u+xU4dIbYk9q6aLp0lRP7GkI0BxDtc9MVhcYoiW/SkvIhhAUJNkcqE1tbgYoqruwYjZhjy2JtdCbC9S96Jy10b43Zic/QDav6MOYRfD6Ehb7BvbVfnOGM+D99Z8mA2F0we1Gv+yNSG4PTdbr49meaz6Ou11Gcq677fW2hXB3QVRzmEj/H2cL6akaySpBNcgQXOOJpHabJzv1ZRbYqA5tfzxztNQgYcXT0jKxVVRjRb0MQjBNYLTvGrnNWVnSNCCCqha6HRptic8PkQkBLofuVTPJnoheu8ZBs9x7JzChf7wBx4uA//880cuzxcmVK1/Mx74m/ff8s3799wdT9ScWZ4fGLcnvK8MEeY5QFVbi7Y12nOlbEIfiyrpxfPujedwGDifO+vZcZHKw6OK8ZQnNDC5wOQjvheitvB6MTCQgU2Ez8+dH/5UefuNcDroeEuLopcc9x2d7u7FbNnto2Pr3pshBw3BhZHx5tfE4weGwxuGdEC3uQqPqBekPVC7JlD5OOOHg6Ih+Zly/kh7fqI+XTQ2MxxgfocbD/QQoVb69qx52c9nHr985vy8Ijcb7cc/4YYb7qsw377HT8qplD1Gzmy36DpWd3uKTkqE+ZZ4/JYy/4bfPf0OyZnApNnSLbFKpEmgo01jxFGK6JlkBtA9ut1ul9t3t9y+/5757lvmm3tub07czzMf7m+5myZiVzGRuMYwBk53E+9/dQ/eMw4T3Tuc19zlwzBzmg+0Unh+cjw9P3K+LOS6MsaRzx8/87s//Imfnn7kb/+3f0v0iSF4WlEx0Ol44vmwsW6ZMHiG6cDheETCyO8/V/rjM98cOmNSAGOIAyWonMa1RivCWjpr1bxtAdIYcEPBjYGQtEBYl07fOmWtuNTptTKIAJ5e9XIpvZPPHlcDRTwVr2izqHjJOfNEdU4zycfIOEzk2lUUta6Ii3w+fGEdV/KiXswuqAMENglx3mvx4FTN7H2giaq/EaWVhejVZihOeBf0ZzwmboK7m3u2pbFtQn14UmujX3D3i2A0n5e0l33CtENYSkUz9M2aOM9+9hoIYLRY5wWj5L+MwkXPemMB4lxXTqHXoq7URkT0ny2acf9cFGzTz6Y1qN5pPjgvv5tuKCFakLau4EAgMIZABLqJVXffUSfhOsUK3jNNkbv7A8/bwtO68vC8cVkKz2vmy+XCZlOswXu2Wsmt8pwjh9FzJxPDEAheR/i1qhiHrjQL5XU5mgViiFOO+Z72s+sQnFfHA4LgvewfKM515hiYUmSoQvGdUh21y3Vs7b0QxMSFXimH+l2pZdLOJVVATa5gzl+yfnFBifcqbmhKfuyt0GvWCxeI3ityVze2XAEtYrSTUMWrfpGNXDoRf+XUqKhA+TnRqqwdYs6lsm4bvQvJezWRtaJQOyCF0UtXscpViIOO0L0VlCo+qeSiRasiRarw3nedGLzuHZpBjeHF9uHuLvutKV+wmPn3VhWdlO5MGak/j72G1D0hiFp+WNvmnI7UYvDc395Cqzx++sL7X30gRpinI84nfNiorbMsK88Pz/iUqHmh1IrmCCtCVnKh1oIrpiDuYqM7I8B3GONIHjQtR4yjqbxHfeCdV6QqJP8y0vAaQN96o3pPLV0zzsPe99nFbt0chgbVBrvJeqHpaYEhgsFfR9JacKsgZh8h7ZK3fZy9k7SbPVQeM4P9esP3F14luF1ca5xLt8ea450zVZ3+Gd1Q3F2FHjz4qHvZI0q277sQ6EXUsyMb3dBbbWqMf9J1T++RWvps7ObelroRuvmvqWWF956YHFPSAylFtQYJQUnik3cMEVJsxIgWS9LNBF1IyThHzg6a/ZNySiWoPVg3almwwYMPbEUpDJ1IJVC7Cn1ihCFWxD1SHj/x05L5snboMIRAiMIhJW7GiXc3d0xp5NI6WZQu4UNnTInotEiPrGxNx0VlG+hlYjjOTKNjvgt0SXz56PjUhJ4jXRq1vfAFXdffg1hkplQ6KkTIooj+ZWv89NPGd58Sp3tHHBw+chXDBb8rvvVZ9kF3i2iwr+4zUHstjJEbBvz0ljjdqc2ZRZz5HRl2FakP5PULOTficENsJ21Uzp/YHv7I8vPvWX74ie3LI1sOuOkb4u0H3DDTBbZl4bI8cX5cubiRdPOOm3Bk2zYevvykXG/fGOUNSW5xaVKUdK/29nGHXcQimjM/Ht4wv/2OnI5cns4kP7C0SvbJfDJ3vrIK4mIyao81et05DjcH7t685fTtB6abN4zzW7yfCR2macYPA90HJEClMYwDN+kOPwSGmyO1qBWLT5oa5L0nuEAQz6rQF5fLhU8PD5zzwv3tLZenlYfzF/wcOdzfkYaEiHBZF2prDOPMeDxwRyWeIzEduZmObLnyTx+fuJxg3Tq3qZJipdfG05pZt8JzzjxthaetcS6w9IaMjdO7xOEuMJ8UMao58/Rl4+m54ZZGvTplaFqO9EBvAb8JSwvIlrTYcWo47UyhfOU+exjGgTgNIJ51Xfn08MTj0zPntbJtjePhYGegMIyRmaRTAxMVhqTm5iIY50/PGtAmeRgSIZxUVLfpOV/EIn1xhDhwPNxwOq1c1o2+6dj/fw6p3JnGbidH+t0WaB/A6Jm7m3DTdUrkgrsWi9fCxMQ5vZu9TjdBk8MmCdaoi1DRhlAjUbtNG/V37P+8OzyGlOx7UUs01/VZ9/5l0ihArY20R57ayeKNsx0wJA+xAqsjpYNL2sTjGEPgdBj5cHdLzc2cTjqy6jg9N53OVecoPdIa5NTpEjkMwpor3gW8vcfeuTa6zcRjTarGWgal9bgm1++6O7UwrDvo0psJNCOxdVILHNLIpVS2AKWq6MnbYMbZVM77vWjnSv0S4UoV8Lzcv1eK0C9cv7ig7B6kV1xvtF4IvdAtti/EYPnFVSOpeiUEVVB1EWopSvD2neYaPiYc+pCJfZBbqYQYaWgCThThsqzUUsm1AY7BKbdCf/+uk/dXf7AQLJjKxDj7hknRWypMhd4ZxulaCNRarNbZuX3uqurDK/Tda7vmQNfWWUtWA9MmZGnqZVaxwszSOwO2YYSAkselv3SE0alf2GkemaJjGiLnxycePn/m1t3TnadUNUmveYXWOEwT+dax9U7YMi1rtFNpjXXLTLkgO5LXBXGiXmmlmTjJkUIkDZGaC9m8JUuv+hCLMq5dUK6Gi+G6GbW29ponHKxgQ65jxMG6xI6NlhEwZb928mJjT80gTTEaHK/FYhe7xOw7uFpS7GIEI3jvmz5i3BC3jyq5Hpzsr2FXInt9Ay+PhhaFPvhrkaqesQ5jShk6qkz0fUzfO6jlDriuaSugo8duKQy9NR2VSn3hfSIkZ8Kk3vFSSaExWRSjhEaKhTEmxhSYJgguk2LAuU50WuiOQyRGTT8IiHEnRW2ErgUsplqHa0HpXookj95z0QrkWjvb1uh9w3ltBEPoeDrOZ1W5+s4mcHSB502RiBTUcN2ZarlIYVszNWv2vA9qwB8QXD1D/glfF1wXWhvw7i19gugmRXL7mdubyPrceXxSEr30RqATAzgqivl2Q6qrcgURQygrRQYenjqfP3bu38EwwngEvCIZzayp9nLxatcRLJnIRsm6VyxxIsSr2fvuE+msyXQhQNezTFqj5opzjRAVbpFaaVum5sy2bVyeFr58uvC8fKLxH6gucKmeJWuT7eaB0/d/w4d3MylNRCK1NNZ1IVzO+DCCi5p6kSacBpYbUtSvIQEvaTaRcZp5c3+L3z4xxIh3A4fniTdvb/jh5yfOqyKUYrdvcJrC4YJnvLnhw3e/4vu//TW/+vt/yd27bxCJfPr0yLqsjLcHppsj4zzgkyeOA4f7O3zwzDe3hPnA+nzWPWnjUY8W+LWo8XbdGsvjysOnJ3waCGmiCfgkfPjmLTenG4Yh0mrhfHmyWFzH4TTjo8O5yDwcCNHz/JQpPfC7s/C4wofJcTdH6IHuA8984VO78HlZeDxvXGqlusZ8jEwHx/Rh4u1dJJFZLmb87B0xObYCve1RCAp65Oxpjxo9WSRooWM/o2KtHRsUxhiYx5khjFAby/OFL0+PdOdZauWnzw98eTwTYmQcIvOU6MeZaRTSGBlSJIgjmoVWcw26Usb0hBGOxwPShMtl5fFx4fOnB6oU0hiZhlGLymHgeHPDzZrxwaI1W6P8T6CVu5jQG6dYPPRqQjfr0AUdiztRBTFd+xwjPSoKx5VJ8jKh25v/Hcy4cumdecFCpjMkT3cvtLEucg2cjpaTroIWjWmspgwKUhm83h9FgO4oVc3v99fjDX30fg+60LxuLWxtvNz02fJdVdaTj9xOA+V2ekGGvScNkYdlYbM7VapRbLyeWaWpa03wmna3p7uB5xomYlPX4B3RuLrO0FVEaKiTxg5m7CPsviPY5jowRI1ZXJ2a3ruqARPBNCZmzKEAoY3avTfQZc9ZFK6T3/9lCsrWVNXcKlEq0gsgJraIBoGjLvCgyRI05fa1RnADJVd8UvNVzX3WgrTViksjrTQtYETtgJrxHUtvjGmgNBUsaGi6dRKieap4p003qLze6bw02r9s9UVVtY9HFeVTJKL1BhUkBOMR+muFru2TUvC3orm2e6ZzrtUsdGxMbNYjGsOoh3vdkaG9YXOaoDBGxzdvTgyD5/lyZl1XHn7+pAX6OCEO8rpyeTpTLotyOaYD+diopZGDxkCpLZB+Vs0pryTYpVlbUR9OU7N77xniSEuVPmlH1NZ2tVHaGedXDiN6GbjmgM6eh76PD3r/yorHtl5H0Q6RjifRqo4Xcq+GAjtiKGrhEIJ5g77wJZ1WE3pBuheC964Ad4F9mHytJfv1UlUT2es0pnftCneBhaj4BWn2OOuD1N2uE0ebC/sdu63R/uc3U3heC130fTqUr7s/fCr2scLVKffJeSGi4+lkHLiYwMfOEAuDF4YYGXzTQwVTAUbdL0N0pAQp6BhDTOWdzFYE40U6p6RzbNzem5CCquid62puu4uGO4x2E+iYSXC+qt9rjLjoyM3xKOCL8XLFE3xV1B1hbernV6Qr4ksghMmeh0pfn+n5jPSiXMbWaPmJsAn+0JnHSK6VS20MkzCmxugrg1SqCElQug36mUZfoF0Q8YjvFAeb6yxSeXiGn392vPvgOZ20MAnJEM4gdHuutTLUxIodKdZC1Yzqr5SKFyRGw7VU0e6DNS/B49KI8xO41dJvNEWltmLBAnpGPG2V561zycLzlnlahM+rsIknxsQhRMKS2R6/sKYZJxMhFuqUafNGSyuBSOuNUCtEK3J7Q3rlRcUOvRXaZaFdMq55nESSCxznmbu7W+7eCu+/b/TyE5fHRZFJEyr4ITHf3XO4v+VXf/cv+bt/+Ac+/PpvOd6+wbnO3XdPPD+e8d3x7vaGKSmvPcaBYTwwH2YOs8YwflxWyqb0mmA+ra1CPq88f3niy6cHPv185ulpIR0W7m8L0UdOx5nvvvmWw3Qk+UBZN5bzM89PF7Zl02mVT8zzDVNKLOuFIpVhGqF1PtVCqYnzFpiig7DxGDs/88ynAp/OC+d8oQfh3c0tt8NImo+0UNVpwU203Kl5U/pGDuQNRPSZaiKU4mk98bw5luLwPhICOB9xPuBc1P3lHHNK3Mwnkov0rfL0/EyVThpHfIo6MRIVnOwAwZYLp8PMOCamITKOA5M1+t34h90ADO+dIrlV6LJwPl94vlyovZGqmnOPw0iXThoSd3e3xBTZzmfO68q5VMp+ZtqZul99KoTUfxeu9+J+1r8UIArgvBSmvb+IKsOOcCqUdBV8hp273O1+7Ba/W8wc3YlS4rpAsEYNK8papztHa0pXaSYQzV2IQa4F08673+lVtXbGGK+je1wnBH2+vX8Re4poTKQj4MUxEBUUGwZuDpP+eU3w0kiuM0XYpNErNkLWSd44JGKadFRvheyeMiSioFGvhlB2ufpf7kbtO8raRF1kWofNkvdKB7w2M+K1sA4+MCahtEbbhKo+QUTfCc5RsSlWtybAiv5uUzu3f59WbP6n8ev/8frFBWXNWaFk2UU4e8B4JMZI61ktXPbRJZ3WVX0NGoEEnnFMWiy2ir92HkLdNrp4fAxI7eA627apyisFqBsO5UcMg6fkpvzHpsKYGKN2LWNgj13ahfd7XB44Qkw0dGxbc7kqlzVKS73GWu1fCUT0g200StPx+576k7NmuioSaRiYIVnsHJH9C7TyxwU9uD3CEB1zhGEYeHi+8NOPnzjczvgUOb67hxgV/Vk21mWhVAcSmcdEno5E83CQ3ig1s6wXggzgPdFOhGq5tH3njPhACok+jqpyrBuxheu4VpXY0Hy/IoatNXvQMQmuUhVweoi2pgjyPuFQO5qGWm4IPjqQRimN0js9a/55TeprqupmK4GdosIxWJFpqOlu3xPs7/fOT22d9KISVGUOXIsBbwKdF78tQ5escA7ODmXnjDj/Qof+umdv5in4tUdns72ttkG7Ol25RmAHcFC+oTcxFzb+d4YEqUm0Z3RB7SMM7XJmXaL9k/ndofQS9PFQsnxvNB+Mk2skd1Onqb9htwACsdxybziy+YhWBzY+0c+kXo3TncGZ+sl56r7PTWXsHJoxPETGOCE02lmRKEHIRfeIqlX1AJMq1DVT+Yz3hYuLOH8gThOBTgqdQ6zchMLJN/V+8wEISsin46TiXdstQXEkTTmRTmmRh0+dp0+O8x2ksQPeUjuUobXnXzrfDc21PWFImnPOnl+hVRXdiCVqBTsTahOiAx8OSLqBccMVM6bvBWmVLa8s68Z2qSxL57wIzzlwqYGld9bqWGrg0huHCLM46tZ5+PmJmgM3C1AbQQTXC6yVNj4ThkSII3GYCCmaq0NTP13Ri7iWxvr0wMcfHvjhY+ayjFyyxxPx8S3TYWS6bZzuM8t5IUT1jT2cbvDTCX+44/D+Azfvvufu7gPv795wc3eDj4k7ecf2dmN9emLqQBdK0QxrJ2q7EoExRPyO2psPY8mN8+OZx08P/OmPf+K3v/s9P3z8zNo30vMzT08P3B1vmceBwzjgmqPnwnZeWS5ntmWlbpkQHQkhDYNSoGqjbfB4fkCouN7ZnuBpiry5PdFa5XEVvmyen86Z3z88qq/lIAwSqf6Gta9I9fQ+MIfO8QS9Z5xXvnL0OtbszVNbpNTImiNbBfFBU3CMA62JUxEf1LpmPk2EIeEl8Hx55Glb8WFQ5NsNWqA5Q+jEsdbG9nTmsm4c55HDPDJumWncSGmypCUTWAQYpok0TCoucU90J4zzhCsFQJNp8oYAaQgcTzPTIbEdB4anZ9J55XFZ2a4i1B331FruxS5oF8TYGWr3YxBTbAf9b6pNmpTDbmxyO0571QmQD0YTEtPCO3e9c5z3Fi6iZ02MKmB0u3G4waLNQZUKdLZWEe8IzjEA1Tmk16tg0nkVQBUnZN8YvKM5uy8Cev7am9fRt523WlUr3zJNdOk0VBxb6kbvHrpOj3JzpsTW+ymMjnFOHMfIYUjMMRKdN12IUsJyqUjbX6eeq7Wl68jeOU9z5iTS1PN6LUZXaC8pOE3s5yXgXSUGtW9bqk73olOxnHi51k6t78Wkfjfem77gqyryL0Eo3V8ayfS6Xtfrel2v63W9rtf1ul7X18v/5/+T1/W6Xtfrel2v63W9rtf1uv7T67WgfF2v63W9rtf1ul7X63pdf9V6LShf1+t6Xa/rdb2u1/W6XtdftV4Lytf1ul7X63pdr+t1va7X9Vet14Lydb2u1/W6Xtfrel2v63X9Veu1oHxdr+t1va7X9bpe1+t6XX/Vei0oX9frel2v63W9rtf1ul7XX7VeC8rX9bpe1+t6Xa/rdb2u1/VXrdeC8nW9rtf1ul7X63pdr+t1/VXr/werU7yP/fuY+AAAAABJRU5ErkJggg==", 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" ] diff --git a/demo/image_demo.py b/demo/image_demo.py index 2e2c27adbf2..1f994cb40ea 100644 --- a/demo/image_demo.py +++ b/demo/image_demo.py @@ -28,6 +28,25 @@ glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 \ --texts 'There are a lot of cars here.' + python demo/image_demo.py demo/demo.jpg \ + glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 \ + --texts '$: coco' + + python demo/image_demo.py demo/demo.jpg \ + glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 \ + --texts '$: lvis' --pred-score-thr 0.7 \ + --palette random --chunked-size 80 + + python demo/image_demo.py demo/demo.jpg \ + grounding_dino_swin-t_pretrain_obj365_goldg_cap4m \ + --texts '$: lvis' --pred-score-thr 0.4 \ + --palette random --chunked-size 80 + + python demo/image_demo.py demo/demo.jpg \ + grounding_dino_swin-t_pretrain_obj365_goldg_cap4m \ + --texts "a red car in the upper right corner" \ + --tokens-positive -1 + Visualize prediction results:: python demo/image_demo.py demo/demo.jpg rtmdet-ins-s --show @@ -36,11 +55,13 @@ --show """ +import ast from argparse import ArgumentParser from mmengine.logging import print_log from mmdet.apis import DetInferencer +from mmdet.evaluation import get_classes def parse_args(): @@ -60,7 +81,12 @@ def parse_args(): type=str, default='outputs', help='Output directory of images or prediction results.') - parser.add_argument('--texts', help='text prompt') + # Once you input a format similar to $: xxx, it indicates that + # the prompt is based on the dataset class name. + # support $: coco, $: voc, $: cityscapes, $: lvis, $: imagenet_det. + # detail to `mmdet/evaluation/functional/class_names.py` + parser.add_argument( + '--texts', help='text prompt, such as "bench . car .", "$: coco"') parser.add_argument( '--device', default='cuda:0', help='Device used for inference') parser.add_argument( @@ -91,7 +117,7 @@ def parse_args(): default='none', choices=['coco', 'voc', 'citys', 'random', 'none'], help='Color palette used for visualization') - # only for GLIP + # only for GLIP and Grounding DINO parser.add_argument( '--custom-entities', '-c', @@ -99,6 +125,22 @@ def parse_args(): help='Whether to customize entity names? ' 'If so, the input text should be ' '"cls_name1 . cls_name2 . cls_name3 ." format') + parser.add_argument( + '--chunked-size', + '-s', + type=int, + default=-1, + help='If the number of categories is very large, ' + 'you can specify this parameter to truncate multiple predictions.') + # only for Grounding DINO + parser.add_argument( + '--tokens-positive', + '-p', + type=str, + help='Used to specify which locations in the input text are of ' + 'interest to the user. -1 indicates that no area is of interest, ' + 'None indicates ignoring this parameter. ' + 'The two-dimensional array represents the start and end positions.') call_args = vars(parser.parse_args()) @@ -111,6 +153,16 @@ def parse_args(): call_args['weights'] = call_args['model'] call_args['model'] = None + if call_args['texts'] is not None: + if call_args['texts'].startswith('$:'): + dataset_name = call_args['texts'][3:].strip() + class_names = get_classes(dataset_name) + call_args['texts'] = [tuple(class_names)] + + if call_args['tokens_positive'] is not None: + call_args['tokens_positive'] = ast.literal_eval( + call_args['tokens_positive']) + init_kws = ['model', 'weights', 'device', 'palette'] init_args = {} for init_kw in init_kws: @@ -125,6 +177,10 @@ def main(): # may consume too much memory if your input folder has a lot of images. # We will be optimized later. inferencer = DetInferencer(**init_args) + + chunked_size = call_args.pop('chunked_size') + inferencer.model.test_cfg.chunked_size = chunked_size + inferencer(**call_args) if call_args['out_dir'] != '' and not (call_args['no_save_vis'] diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile index 872918972f0..aa307cf6963 100644 --- a/docker/serve/Dockerfile +++ b/docker/serve/Dockerfile @@ -4,7 +4,7 @@ ARG CUDNN="8" FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel ARG MMCV="2.0.0rc4" -ARG MMDET="3.2.0" +ARG MMDET="3.3.0" ENV PYTHONUNBUFFERED TRUE diff --git a/docker/serve_cn/Dockerfile b/docker/serve_cn/Dockerfile index 510906432b7..894e15dd714 100644 --- a/docker/serve_cn/Dockerfile +++ b/docker/serve_cn/Dockerfile @@ -4,7 +4,7 @@ ARG CUDNN="8" FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel ARG MMCV="2.0.0rc4" -ARG MMDET="3.2.0" +ARG MMDET="3.3.0" ENV PYTHONUNBUFFERED TRUE diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md index 4d48a0a0d22..00ed8f1c1e4 100644 --- a/docs/en/notes/changelog.md +++ b/docs/en/notes/changelog.md @@ -1,6 +1,34 @@ # Changelog of v3.x -## v3.1.0 (12/10/2023) +## v3.3.0 (05/01/2024) + +### Highlights + +Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness has led to its widespread adoption as a mainstream architecture for various downstream applications. However, despite its significance, the original Grounding-DINO model lacks comprehensive public technical details due to the unavailability of its training code. To bridge this gap, we present MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline, which is built with the MMDetection toolbox. It adopts abundant vision datasets for pre-training and various detection and grounding datasets for fine-tuning. We give a comprehensive analysis of each reported result and detailed settings for reproduction. The extensive experiments on the benchmarks mentioned demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny baseline. We release all our models to the research community. + +### New Features + +- Add RTMDet Swin / ConvNeXt backbone and results (#11259) +- Add `odinw` configs and evaluation results of `GLIP` (#11175) +- Add optional score threshold option to `coco_error_analysis.py` (#11117) +- Add new configs for `panoptic_fpn` (#11109) +- Replace partially weighted download links with OpenXLab for the `Faster-RCNN` (#11173) + +### Bug Fixes + +- Fix `Grounding DINO` nan when class tokens exceeds 256 (#11066) +- Fix the `CO-DETR` config files error (#11325) +- Fix `CO-DETR` load_from url in config (#11220) +- Fixed mask shape after Albu postprocess (#11280) +- Fix bug in `convert_coco_format` and `youtubevis2coco` (#11251, #11086) + +### Contributors + +A total of 15 developers contributed to this release. + +Thank @adnan-mujagic, @Cycyes, @ilcopione, @returnL, @honeybadger78, @okotaku, @xushilin1, @keyhsw, @guyleaf, @Crescent-Saturn, @LRJKD, @aaronzs, @Divadi, @AwePhD, @hhaAndroid + +## v3.2.0 (12/10/2023) ### Highlights diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md index 9e3c1a7852b..f1a176e4d04 100644 --- a/docs/en/notes/faq.md +++ b/docs/en/notes/faq.md @@ -47,6 +47,7 @@ Compatible MMDetection, MMEngine, and MMCV versions are shown as below. Please c | MMDetection version | MMCV version | MMEngine version | | :-----------------: | :---------------------: | :----------------------: | | main | mmcv>=2.0.0, \<2.2.0 | mmengine>=0.7.1, \<1.0.0 | +| 3.3.0 | mmcv>=2.0.0, \<2.2.0 | mmengine>=0.7.1, \<1.0.0 | | 3.2.0 | mmcv>=2.0.0, \<2.2.0 | mmengine>=0.7.1, \<1.0.0 | | 3.1.0 | mmcv>=2.0.0, \<2.1.0 | mmengine>=0.7.1, \<1.0.0 | | 3.0.0 | mmcv>=2.0.0, \<2.1.0 | mmengine>=0.7.1, \<1.0.0 | diff --git a/docs/en/user_guides/tracking_dataset_prepare.md b/docs/en/user_guides/tracking_dataset_prepare.md index 2c38569c9a1..56a4b77fc6e 100644 --- a/docs/en/user_guides/tracking_dataset_prepare.md +++ b/docs/en/user_guides/tracking_dataset_prepare.md @@ -92,10 +92,10 @@ python ./tools/dataset_converters/mot2reid.py -i ./data/MOT17/ -o ./data/MOT17/r python ./tools/dataset_converters/crowdhuman2coco.py -i ./data/crowdhuman -o ./data/crowdhuman/annotations # YouTube-VIS 2019 -python ./tools/dataset_converters/youtubevis/youtubevis2coco.py -i ./data/youtube_vis_2019 -o ./data/youtube_vis_2019/annotations --version 2019 +python ./tools/dataset_converters/youtubevis2coco.py -i ./data/youtube_vis_2019 -o ./data/youtube_vis_2019/annotations --version 2019 # YouTube-VIS 2021 -python ./tools/dataset_converters/youtubevis/youtubevis2coco.py -i ./data/youtube_vis_2021 -o ./data/youtube_vis_2021/annotations --version 2021 +python ./tools/dataset_converters/youtubevis2coco.py -i ./data/youtube_vis_2021 -o ./data/youtube_vis_2021/annotations --version 2021 ``` diff --git a/docs/zh_cn/notes/faq.md b/docs/zh_cn/notes/faq.md index 8268bd11562..2b4237c7411 100644 --- a/docs/zh_cn/notes/faq.md +++ b/docs/zh_cn/notes/faq.md @@ -47,6 +47,7 @@ export DYNAMO_CACHE_SIZE_LIMIT = 4 | MMDetection 版本 | MMCV 版本 | MMEngine 版本 | | :--------------: | :---------------------: | :----------------------: | | main | mmcv>=2.0.0, \<2.2.0 | mmengine>=0.7.1, \<1.0.0 | + | 3.3.0 | mmcv>=2.0.0, \<2.2.0 | mmengine>=0.7.1, \<1.0.0 | | 3.2.0 | mmcv>=2.0.0, \<2.2.0 | mmengine>=0.7.1, \<1.0.0 | | 3.1.0 | mmcv>=2.0.0, \<2.1.0 | mmengine>=0.7.1, \<1.0.0 | | 3.0.0 | mmcv>=2.0.0, \<2.1.0 | mmengine>=0.7.1, \<1.0.0 | diff --git a/docs/zh_cn/user_guides/dataset_prepare.md b/docs/zh_cn/user_guides/dataset_prepare.md index a8bf32011a7..1caad856af0 100644 --- a/docs/zh_cn/user_guides/dataset_prepare.md +++ b/docs/zh_cn/user_guides/dataset_prepare.md @@ -305,3 +305,58 @@ mim download mmdet --dataset voc2012 # download coco2017 and preprocess by MIM mim download mmdet --dataset coco2017 ``` + +### ODinW 数据集准备 + +ODinW 数据集来自 GLIP 论文，用于评估预训练模型泛化性能。一共包括 ODinW-13 和 ODinW-35 两个版本，其中 ODinW-35 包括了 ODinW-13 的所有数据。 目前数据托管在 [huggingface](https://huggingface.co/GLIPModel/GLIP) + +请确保你提前安装好了 [git lfs](https://git-lfs.com), 然后按照如下命令下载 + +```shell +cd mmdetection + +git lfs install +# 我们不需要下载权重 +GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/GLIPModel/GLIP + +cd GLIP +git lfs pull --include="odinw_35" +``` + +下载完成后，目录结构如下所示： + +```text +mmdetection +├── GLIP +| ├── odinw_35 +| | ├── AerialMaritimeDrone.zip +| | ├── AmericanSignLanguageLetters.zip +... +``` + +你可以采用如下命令全部解压并移动到 `mmdetection/data` 路径下： + +```shell +#!/bin/bash + +folder="./GLIP/odinw_35/" + +find "$folder" -type f -name "*.zip" | while read -r file; do + unzip "$file" -d "$(dirname "$file")" +done + +mv GLIP/odinw_35 data/ +``` + +最终结构如下所示： + +```text +mmdetection +├── tools +├── configs +├── data +| ├── odinw_35 +| | ├── AerialMaritimeDrone +... +│ ├── coco +``` diff --git a/docs/zh_cn/user_guides/tracking_dataset_prepare.md b/docs/zh_cn/user_guides/tracking_dataset_prepare.md index c99f1885e05..0db495b54c9 100644 --- a/docs/zh_cn/user_guides/tracking_dataset_prepare.md +++ b/docs/zh_cn/user_guides/tracking_dataset_prepare.md @@ -90,10 +90,10 @@ python ./tools/dataset_converters/mot2reid.py -i ./data/MOT17/ -o ./data/MOT17/r python ./tools/dataset_converters/crowdhuman2coco.py -i ./data/crowdhuman -o ./data/crowdhuman/annotations # YouTube-VIS 2019 -python ./tools/dataset_converters/youtubevis/youtubevis2coco.py -i ./data/youtube_vis_2019 -o ./data/youtube_vis_2019/annotations --version 2019 +python ./tools/dataset_converters/youtubevis2coco.py -i ./data/youtube_vis_2019 -o ./data/youtube_vis_2019/annotations --version 2019 # YouTube-VIS 2021 -python ./tools/dataset_converters/youtubevis/youtubevis2coco.py -i ./data/youtube_vis_2021 -o ./data/youtube_vis_2021/annotations --version 2021 +python ./tools/dataset_converters/youtubevis2coco.py -i ./data/youtube_vis_2021 -o ./data/youtube_vis_2021/annotations --version 2021 ``` diff --git a/mmdet/apis/det_inferencer.py b/mmdet/apis/det_inferencer.py index 9efbb00cbe9..ce8532eb786 100644 --- a/mmdet/apis/det_inferencer.py +++ b/mmdet/apis/det_inferencer.py @@ -313,8 +313,10 @@ def __call__( texts: Optional[Union[str, list]] = None, # by open panoptic task stuff_texts: Optional[Union[str, list]] = None, - # by GLIP + # by GLIP and Grounding DINO custom_entities: bool = False, + # by Grounding DINO + tokens_positive: Optional[Union[int, list]] = None, **kwargs) -> dict: """Call the inferencer. @@ -343,7 +345,7 @@ def __call__( stuff_texts (str | list[str]): Stuff text prompts of open panoptic task. Defaults to None. custom_entities (bool): Whether to use custom entities. - Defaults to False. Only used in GLIP. + Defaults to False. Only used in GLIP and Grounding DINO. **kwargs: Other keyword arguments passed to :meth:`preprocess`, :meth:`forward`, :meth:`visualize` and :meth:`postprocess`. Each key in kwargs should be in the corresponding set of @@ -366,6 +368,10 @@ def __call__( texts = [texts] * len(ori_inputs) if stuff_texts is not None and isinstance(stuff_texts, str): stuff_texts = [stuff_texts] * len(ori_inputs) + + # Currently only supports bs=1 + tokens_positive = [tokens_positive] * len(ori_inputs) + if texts is not None: assert len(texts) == len(ori_inputs) for i in range(len(texts)): @@ -373,13 +379,15 @@ def __call__( ori_inputs[i] = { 'text': texts[i], 'img_path': ori_inputs[i], - 'custom_entities': custom_entities + 'custom_entities': custom_entities, + 'tokens_positive': tokens_positive[i] } else: ori_inputs[i] = { 'text': texts[i], 'img': ori_inputs[i], - 'custom_entities': custom_entities + 'custom_entities': custom_entities, + 'tokens_positive': tokens_positive[i] } if stuff_texts is not None: assert len(stuff_texts) == len(ori_inputs) diff --git a/mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py b/mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py new file mode 100644 index 00000000000..c6059780da1 --- /dev/null +++ b/mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py @@ -0,0 +1,13 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmengine.config import read_base +from mmengine.model.weight_init import PretrainedInit + +with read_base(): + from .panoptic_fpn_r50_fpn_1x_coco import * + +model.update( + dict( + backbone=dict( + depth=101, + init_cfg=dict( + type=PretrainedInit, checkpoint='torchvision://resnet101')))) diff --git a/mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_ms_3x_coco.py b/mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_ms_3x_coco.py new file mode 100644 index 00000000000..c02c3237f81 --- /dev/null +++ b/mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_ms_3x_coco.py @@ -0,0 +1,13 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmengine.config import read_base +from mmengine.model.weight_init import PretrainedInit + +with read_base(): + from .panoptic_fpn_r50_fpn_ms_3x_coco import * + +model.update( + dict( + backbone=dict( + depth=101, + init_cfg=dict( + type=PretrainedInit, checkpoint='torchvision://resnet101')))) diff --git a/mmdet/configs/panoptic_fpn/panoptic_fpn_r50_fpn_ms_3x_coco.py b/mmdet/configs/panoptic_fpn/panoptic_fpn_r50_fpn_ms_3x_coco.py new file mode 100644 index 00000000000..25ebe5d67c4 --- /dev/null +++ b/mmdet/configs/panoptic_fpn/panoptic_fpn_r50_fpn_ms_3x_coco.py @@ -0,0 +1,45 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmengine.config import read_base +from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR + +with read_base(): + from .panoptic_fpn_r50_fpn_1x_coco import * + +from mmcv.transforms import RandomResize +from mmcv.transforms.loading import LoadImageFromFile + +from mmdet.datasets.transforms.formatting import PackDetInputs +from mmdet.datasets.transforms.loading import LoadPanopticAnnotations +from mmdet.datasets.transforms.transforms import RandomFlip + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type=LoadImageFromFile), + dict( + type=LoadPanopticAnnotations, + with_bbox=True, + with_mask=True, + with_seg=True), + dict(type=RandomResize, scale=[(1333, 640), (1333, 800)], keep_ratio=True), + dict(type=RandomFlip, prob=0.5), + dict(type=PackDetInputs) +] + +train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline))) + +# TODO: Use RepeatDataset to speed up training +# training schedule for 3x +train_cfg.update(dict(max_epochs=36, val_interval=3)) + +# learning rate +param_scheduler = [ + dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500), + dict( + type=MultiStepLR, + begin=0, + end=36, + by_epoch=True, + milestones=[24, 33], + gamma=0.1) +] diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py index 044efe4cad7..670c207cacf 100644 --- a/mmdet/datasets/__init__.py +++ b/mmdet/datasets/__init__.py @@ -12,17 +12,22 @@ from .crowdhuman import CrowdHumanDataset from .dataset_wrappers import ConcatDataset, MultiImageMixDataset from .deepfashion import DeepFashionDataset +from .dod import DODDataset from .dsdl import DSDLDetDataset +from .flickr30k import Flickr30kDataset from .isaid import iSAIDDataset from .lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset +from .mdetr_style_refcoco import MDETRStyleRefCocoDataset from .mot_challenge_dataset import MOTChallengeDataset from .objects365 import Objects365V1Dataset, Objects365V2Dataset +from .odvg import ODVGDataset from .openimages import OpenImagesChallengeDataset, OpenImagesDataset from .refcoco import RefCocoDataset from .reid_dataset import ReIDDataset from .samplers import (AspectRatioBatchSampler, ClassAwareSampler, - GroupMultiSourceSampler, MultiSourceSampler, - TrackAspectRatioBatchSampler, TrackImgSampler) + CustomSampleSizeSampler, GroupMultiSourceSampler, + MultiSourceSampler, TrackAspectRatioBatchSampler, + TrackImgSampler) from .utils import get_loading_pipeline from .v3det import V3DetDataset from .voc import VOCDataset @@ -42,5 +47,7 @@ 'ReIDDataset', 'YouTubeVISDataset', 'TrackAspectRatioBatchSampler', 'ADE20KPanopticDataset', 'CocoCaptionDataset', 'RefCocoDataset', 'BaseSegDataset', 'ADE20KSegDataset', 'CocoSegDataset', - 'ADE20KInstanceDataset', 'iSAIDDataset', 'V3DetDataset', 'ConcatDataset' + 'ADE20KInstanceDataset', 'iSAIDDataset', 'V3DetDataset', 'ConcatDataset', + 'ODVGDataset', 'MDETRStyleRefCocoDataset', 'DODDataset', + 'CustomSampleSizeSampler', 'Flickr30kDataset' ] diff --git a/mmdet/datasets/api_wrappers/coco_api.py b/mmdet/datasets/api_wrappers/coco_api.py index 40f7f2c9b93..b2d11a122e1 100644 --- a/mmdet/datasets/api_wrappers/coco_api.py +++ b/mmdet/datasets/api_wrappers/coco_api.py @@ -92,7 +92,7 @@ def createIndex(self) -> None: if 'images' in self.dataset: for img_info in self.dataset['images']: img_info['segm_file'] = img_info['file_name'].replace( - 'jpg', 'png') + '.jpg', '.png') imgs[img_info['id']] = img_info if 'categories' in self.dataset: diff --git a/mmdet/datasets/base_det_dataset.py b/mmdet/datasets/base_det_dataset.py index 57bc7098387..8b3876d5c06 100644 --- a/mmdet/datasets/base_det_dataset.py +++ b/mmdet/datasets/base_det_dataset.py @@ -21,6 +21,8 @@ class BaseDetDataset(BaseDataset): corresponding backend. Defaults to None. return_classes (bool): Whether to return class information for open vocabulary-based algorithms. Defaults to False. + caption_prompt (dict, optional): Prompt for captioning. + Defaults to None. """ def __init__(self, @@ -30,11 +32,16 @@ def __init__(self, file_client_args: dict = None, backend_args: dict = None, return_classes: bool = False, + caption_prompt: Optional[dict] = None, **kwargs) -> None: self.seg_map_suffix = seg_map_suffix self.proposal_file = proposal_file self.backend_args = backend_args self.return_classes = return_classes + self.caption_prompt = caption_prompt + if self.caption_prompt is not None: + assert self.return_classes, \ + 'return_classes must be True when using caption_prompt' if file_client_args is not None: raise RuntimeError( 'The `file_client_args` is deprecated, ' diff --git a/mmdet/datasets/coco.py b/mmdet/datasets/coco.py index 277b75988da..1cf21c4e667 100644 --- a/mmdet/datasets/coco.py +++ b/mmdet/datasets/coco.py @@ -129,6 +129,7 @@ def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dict]]: if self.return_classes: data_info['text'] = self.metainfo['classes'] + data_info['caption_prompt'] = self.caption_prompt data_info['custom_entities'] = True instances = [] diff --git a/mmdet/datasets/coco_panoptic.py b/mmdet/datasets/coco_panoptic.py index d5ca7855509..b7a200e01d3 100644 --- a/mmdet/datasets/coco_panoptic.py +++ b/mmdet/datasets/coco_panoptic.py @@ -208,7 +208,7 @@ def parse_data_info(self, raw_data_info: dict) -> dict: if self.data_prefix.get('seg', None): seg_map_path = osp.join( self.data_prefix['seg'], - img_info['file_name'].replace('jpg', 'png')) + img_info['file_name'].replace('.jpg', '.png')) else: seg_map_path = None data_info['img_path'] = img_path diff --git a/mmdet/datasets/dataset_wrappers.py b/mmdet/datasets/dataset_wrappers.py index e651e2b9902..d4e26e07c0f 100644 --- a/mmdet/datasets/dataset_wrappers.py +++ b/mmdet/datasets/dataset_wrappers.py @@ -247,6 +247,14 @@ def __init__(self, if not lazy_init: self.full_init() + if is_all_same: + self._metainfo.update( + dict(cumulative_sizes=self.cumulative_sizes)) + else: + for i, dataset in enumerate(self.datasets): + self._metainfo[i].update( + dict(cumulative_sizes=self.cumulative_sizes)) + def get_dataset_source(self, idx: int) -> int: dataset_idx, _ = self._get_ori_dataset_idx(idx) return dataset_idx diff --git a/mmdet/datasets/dod.py b/mmdet/datasets/dod.py new file mode 100644 index 00000000000..152d32aaf70 --- /dev/null +++ b/mmdet/datasets/dod.py @@ -0,0 +1,78 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from typing import List, Optional + +import numpy as np + +from mmdet.registry import DATASETS +from .base_det_dataset import BaseDetDataset + +try: + from d_cube import D3 +except ImportError: + D3 = None +from .api_wrappers import COCO + + +@DATASETS.register_module() +class DODDataset(BaseDetDataset): + + def __init__(self, + *args, + data_root: Optional[str] = '', + data_prefix: dict = dict(img_path=''), + **kwargs) -> None: + if D3 is None: + raise ImportError( + 'Please install d3 by `pip install ddd-dataset`.') + pkl_anno_path = osp.join(data_root, data_prefix['anno']) + self.img_root = osp.join(data_root, data_prefix['img']) + self.d3 = D3(self.img_root, pkl_anno_path) + + sent_infos = self.d3.load_sents() + classes = tuple([sent_info['raw_sent'] for sent_info in sent_infos]) + super().__init__( + *args, + data_root=data_root, + data_prefix=data_prefix, + metainfo={'classes': classes}, + **kwargs) + + def load_data_list(self) -> List[dict]: + coco = COCO(self.ann_file) + data_list = [] + img_ids = self.d3.get_img_ids() + for img_id in img_ids: + data_info = {} + + img_info = self.d3.load_imgs(img_id)[0] + file_name = img_info['file_name'] + img_path = osp.join(self.img_root, file_name) + data_info['img_path'] = img_path + data_info['img_id'] = img_id + data_info['height'] = img_info['height'] + data_info['width'] = img_info['width'] + + group_ids = self.d3.get_group_ids(img_ids=[img_id]) + sent_ids = self.d3.get_sent_ids(group_ids=group_ids) + sent_list = self.d3.load_sents(sent_ids=sent_ids) + text_list = [sent['raw_sent'] for sent in sent_list] + ann_ids = coco.get_ann_ids(img_ids=[img_id]) + anno = coco.load_anns(ann_ids) + + data_info['text'] = text_list + data_info['sent_ids'] = np.array([s for s in sent_ids]) + data_info['custom_entities'] = True + + instances = [] + for i, ann in enumerate(anno): + instance = {} + x1, y1, w, h = ann['bbox'] + bbox = [x1, y1, x1 + w, y1 + h] + instance['ignore_flag'] = 0 + instance['bbox'] = bbox + instance['bbox_label'] = ann['category_id'] - 1 + instances.append(instance) + data_info['instances'] = instances + data_list.append(data_info) + return data_list diff --git a/mmdet/datasets/flickr30k.py b/mmdet/datasets/flickr30k.py new file mode 100644 index 00000000000..0c76a41bc96 --- /dev/null +++ b/mmdet/datasets/flickr30k.py @@ -0,0 +1,81 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from typing import List + +from pycocotools.coco import COCO + +from mmdet.registry import DATASETS +from .base_det_dataset import BaseDetDataset + + +def convert_phrase_ids(phrase_ids: list) -> list: + unique_elements = sorted(set(phrase_ids)) + element_to_new_label = { + element: label + for label, element in enumerate(unique_elements) + } + phrase_ids = [element_to_new_label[element] for element in phrase_ids] + return phrase_ids + + +@DATASETS.register_module() +class Flickr30kDataset(BaseDetDataset): + """Flickr30K Dataset.""" + + def load_data_list(self) -> List[dict]: + + self.coco = COCO(self.ann_file) + + self.ids = sorted(list(self.coco.imgs.keys())) + + data_list = [] + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + + coco_img = self.coco.loadImgs(img_id)[0] + + caption = coco_img['caption'] + file_name = coco_img['file_name'] + img_path = osp.join(self.data_prefix['img'], file_name) + width = coco_img['width'] + height = coco_img['height'] + tokens_positive = coco_img['tokens_positive_eval'] + phrases = [caption[i[0][0]:i[0][1]] for i in tokens_positive] + phrase_ids = [] + + instances = [] + annos = self.coco.loadAnns(ann_ids) + for anno in annos: + instance = { + 'bbox': [ + anno['bbox'][0], anno['bbox'][1], + anno['bbox'][0] + anno['bbox'][2], + anno['bbox'][1] + anno['bbox'][3] + ], + 'bbox_label': + anno['category_id'], + 'ignore_flag': + anno['iscrowd'] + } + phrase_ids.append(anno['phrase_ids']) + instances.append(instance) + + phrase_ids = convert_phrase_ids(phrase_ids) + + data_list.append( + dict( + img_path=img_path, + img_id=img_id, + height=height, + width=width, + instances=instances, + text=caption, + phrase_ids=phrase_ids, + tokens_positive=tokens_positive, + phrases=phrases, + )) + + return data_list diff --git a/mmdet/datasets/mdetr_style_refcoco.py b/mmdet/datasets/mdetr_style_refcoco.py new file mode 100644 index 00000000000..cc56dec49db --- /dev/null +++ b/mmdet/datasets/mdetr_style_refcoco.py @@ -0,0 +1,57 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from typing import List + +from mmengine.fileio import get_local_path + +from mmdet.datasets import BaseDetDataset +from mmdet.registry import DATASETS +from .api_wrappers import COCO + + +@DATASETS.register_module() +class MDETRStyleRefCocoDataset(BaseDetDataset): + """RefCOCO dataset. + + Only support evaluation now. + """ + + def load_data_list(self) -> List[dict]: + with get_local_path( + self.ann_file, backend_args=self.backend_args) as local_path: + coco = COCO(local_path) + + img_ids = coco.get_img_ids() + + data_infos = [] + for img_id in img_ids: + raw_img_info = coco.load_imgs([img_id])[0] + ann_ids = coco.get_ann_ids(img_ids=[img_id]) + raw_ann_info = coco.load_anns(ann_ids) + + data_info = {} + img_path = osp.join(self.data_prefix['img'], + raw_img_info['file_name']) + data_info['img_path'] = img_path + data_info['img_id'] = img_id + data_info['height'] = raw_img_info['height'] + data_info['width'] = raw_img_info['width'] + data_info['dataset_mode'] = raw_img_info['dataset_name'] + + data_info['text'] = raw_img_info['caption'] + data_info['custom_entities'] = False + data_info['tokens_positive'] = -1 + + instances = [] + for i, ann in enumerate(raw_ann_info): + instance = {} + x1, y1, w, h = ann['bbox'] + bbox = [x1, y1, x1 + w, y1 + h] + instance['bbox'] = bbox + instance['bbox_label'] = ann['category_id'] + instance['ignore_flag'] = 0 + instances.append(instance) + + data_info['instances'] = instances + data_infos.append(data_info) + return data_infos diff --git a/mmdet/datasets/odvg.py b/mmdet/datasets/odvg.py new file mode 100644 index 00000000000..c73865f2ea7 --- /dev/null +++ b/mmdet/datasets/odvg.py @@ -0,0 +1,106 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import json +import os.path as osp +from typing import List, Optional + +from mmengine.fileio import get_local_path + +from mmdet.registry import DATASETS +from .base_det_dataset import BaseDetDataset + + +@DATASETS.register_module() +class ODVGDataset(BaseDetDataset): + """object detection and visual grounding dataset.""" + + def __init__(self, + *args, + data_root: str = '', + label_map_file: Optional[str] = None, + need_text: bool = True, + **kwargs) -> None: + self.dataset_mode = 'VG' + self.need_text = need_text + if label_map_file: + label_map_file = osp.join(data_root, label_map_file) + with open(label_map_file, 'r') as file: + self.label_map = json.load(file) + self.dataset_mode = 'OD' + super().__init__(*args, data_root=data_root, **kwargs) + assert self.return_classes is True + + def load_data_list(self) -> List[dict]: + with get_local_path( + self.ann_file, backend_args=self.backend_args) as local_path: + with open(local_path, 'r') as f: + data_list = [json.loads(line) for line in f] + + out_data_list = [] + for data in data_list: + data_info = {} + img_path = osp.join(self.data_prefix['img'], data['filename']) + data_info['img_path'] = img_path + data_info['height'] = data['height'] + data_info['width'] = data['width'] + if self.dataset_mode == 'OD': + if self.need_text: + data_info['text'] = self.label_map + anno = data.get('detection', {}) + instances = [obj for obj in anno.get('instances', [])] + bboxes = [obj['bbox'] for obj in instances] + bbox_labels = [str(obj['label']) for obj in instances] + + instances = [] + for bbox, label in zip(bboxes, bbox_labels): + instance = {} + x1, y1, x2, y2 = bbox + inter_w = max(0, min(x2, data['width']) - max(x1, 0)) + inter_h = max(0, min(y2, data['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if (x2 - x1) < 1 or (y2 - y1) < 1: + continue + instance['ignore_flag'] = 0 + instance['bbox'] = bbox + instance['bbox_label'] = int(label) + instances.append(instance) + data_info['instances'] = instances + data_info['dataset_mode'] = self.dataset_mode + out_data_list.append(data_info) + else: + anno = data['grounding'] + data_info['text'] = anno['caption'] + regions = anno['regions'] + + instances = [] + phrases = {} + for i, region in enumerate(regions): + bbox = region['bbox'] + phrase = region['phrase'] + tokens_positive = region['tokens_positive'] + if not isinstance(bbox[0], list): + bbox = [bbox] + for box in bbox: + instance = {} + x1, y1, x2, y2 = box + inter_w = max(0, min(x2, data['width']) - max(x1, 0)) + inter_h = max(0, min(y2, data['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if (x2 - x1) < 1 or (y2 - y1) < 1: + continue + instance['ignore_flag'] = 0 + instance['bbox'] = box + instance['bbox_label'] = i + phrases[i] = { + 'phrase': phrase, + 'tokens_positive': tokens_positive + } + instances.append(instance) + data_info['instances'] = instances + data_info['phrases'] = phrases + data_info['dataset_mode'] = self.dataset_mode + out_data_list.append(data_info) + + del data_list + return out_data_list diff --git a/mmdet/datasets/samplers/__init__.py b/mmdet/datasets/samplers/__init__.py index a942ff2199c..9ea0e4cb062 100644 --- a/mmdet/datasets/samplers/__init__.py +++ b/mmdet/datasets/samplers/__init__.py @@ -3,6 +3,7 @@ MultiDataAspectRatioBatchSampler, TrackAspectRatioBatchSampler) from .class_aware_sampler import ClassAwareSampler +from .custom_sample_size_sampler import CustomSampleSizeSampler from .multi_data_sampler import MultiDataSampler from .multi_source_sampler import GroupMultiSourceSampler, MultiSourceSampler from .track_img_sampler import TrackImgSampler @@ -11,5 +12,5 @@ 'ClassAwareSampler', 'AspectRatioBatchSampler', 'MultiSourceSampler', 'GroupMultiSourceSampler', 'TrackImgSampler', 'TrackAspectRatioBatchSampler', 'MultiDataSampler', - 'MultiDataAspectRatioBatchSampler' + 'MultiDataAspectRatioBatchSampler', 'CustomSampleSizeSampler' ] diff --git a/mmdet/datasets/samplers/custom_sample_size_sampler.py b/mmdet/datasets/samplers/custom_sample_size_sampler.py new file mode 100644 index 00000000000..6bedf6c66be --- /dev/null +++ b/mmdet/datasets/samplers/custom_sample_size_sampler.py @@ -0,0 +1,111 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math +from typing import Iterator, Optional, Sequence, Sized + +import torch +from mmengine.dist import get_dist_info, sync_random_seed +from torch.utils.data import Sampler + +from mmdet.registry import DATA_SAMPLERS +from .class_aware_sampler import RandomCycleIter + + +@DATA_SAMPLERS.register_module() +class CustomSampleSizeSampler(Sampler): + + def __init__(self, + dataset: Sized, + dataset_size: Sequence[int], + ratio_mode: bool = False, + seed: Optional[int] = None, + round_up: bool = True) -> None: + assert len(dataset.datasets) == len(dataset_size) + rank, world_size = get_dist_info() + self.rank = rank + self.world_size = world_size + + self.dataset = dataset + if seed is None: + seed = sync_random_seed() + self.seed = seed + self.epoch = 0 + self.round_up = round_up + + total_size = 0 + total_size_fake = 0 + self.dataset_index = [] + self.dataset_cycle_iter = [] + new_dataset_size = [] + for dataset, size in zip(dataset.datasets, dataset_size): + self.dataset_index.append( + list(range(total_size_fake, + len(dataset) + total_size_fake))) + total_size_fake += len(dataset) + if size == -1: + total_size += len(dataset) + self.dataset_cycle_iter.append(None) + new_dataset_size.append(-1) + else: + if ratio_mode: + size = int(size * len(dataset)) + assert size <= len( + dataset + ), f'dataset size {size} is larger than ' \ + f'dataset length {len(dataset)}' + total_size += size + new_dataset_size.append(size) + + g = torch.Generator() + g.manual_seed(self.seed) + self.dataset_cycle_iter.append( + RandomCycleIter(self.dataset_index[-1], generator=g)) + self.dataset_size = new_dataset_size + + if self.round_up: + self.num_samples = math.ceil(total_size / world_size) + self.total_size = self.num_samples * self.world_size + else: + self.num_samples = math.ceil((total_size - rank) / world_size) + self.total_size = total_size + + def __iter__(self) -> Iterator[int]: + """Iterate the indices.""" + # deterministically shuffle based on epoch and seed + g = torch.Generator() + g.manual_seed(self.seed + self.epoch) + + out_index = [] + for data_size, data_index, cycle_iter in zip(self.dataset_size, + self.dataset_index, + self.dataset_cycle_iter): + if data_size == -1: + out_index += data_index + else: + index = [next(cycle_iter) for _ in range(data_size)] + out_index += index + + index = torch.randperm(len(out_index), generator=g).numpy().tolist() + indices = [out_index[i] for i in index] + + if self.round_up: + indices = ( + indices * + int(self.total_size / len(indices) + 1))[:self.total_size] + indices = indices[self.rank:self.total_size:self.world_size] + return iter(indices) + + def __len__(self) -> int: + """The number of samples in this rank.""" + return self.num_samples + + def set_epoch(self, epoch: int) -> None: + """Sets the epoch for this sampler. + + When :attr:`shuffle=True`, this ensures all replicas use a different + random ordering for each epoch. Otherwise, the next iteration of this + sampler will yield the same ordering. + + Args: + epoch (int): Epoch number. + """ + self.epoch = epoch diff --git a/mmdet/datasets/transforms/__init__.py b/mmdet/datasets/transforms/__init__.py index 1f30d6c1352..ab3478feb00 100644 --- a/mmdet/datasets/transforms/__init__.py +++ b/mmdet/datasets/transforms/__init__.py @@ -13,6 +13,7 @@ LoadEmptyAnnotations, LoadImageFromNDArray, LoadMultiChannelImageFromFiles, LoadPanopticAnnotations, LoadProposals, LoadTrackAnnotations) +from .text_transformers import LoadTextAnnotations, RandomSamplingNegPos from .transformers_glip import GTBoxSubOne_GLIP, RandomFlip_GLIP from .transforms import (Albu, CachedMixUp, CachedMosaic, CopyPaste, CutOut, Expand, FixScaleResize, FixShapeResize, @@ -39,5 +40,6 @@ 'FixShapeResize', 'ProposalBroadcaster', 'InferencerLoader', 'LoadTrackAnnotations', 'BaseFrameSample', 'UniformRefFrameSample', 'PackTrackInputs', 'PackReIDInputs', 'FixScaleResize', - 'ResizeShortestEdge', 'GTBoxSubOne_GLIP', 'RandomFlip_GLIP' + 'ResizeShortestEdge', 'GTBoxSubOne_GLIP', 'RandomFlip_GLIP', + 'RandomSamplingNegPos', 'LoadTextAnnotations' ] diff --git a/mmdet/datasets/transforms/text_transformers.py b/mmdet/datasets/transforms/text_transformers.py new file mode 100644 index 00000000000..25304d5fe45 --- /dev/null +++ b/mmdet/datasets/transforms/text_transformers.py @@ -0,0 +1,255 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import json + +from mmcv.transforms import BaseTransform + +from mmdet.registry import TRANSFORMS +from mmdet.structures.bbox import BaseBoxes + +try: + from transformers import AutoTokenizer + from transformers import BertModel as HFBertModel +except ImportError: + AutoTokenizer = None + HFBertModel = None + +import random +import re + +import numpy as np + + +def clean_name(name): + name = re.sub(r'\(.*\)', '', name) + name = re.sub(r'_', ' ', name) + name = re.sub(r' ', ' ', name) + name = name.lower() + return name + + +def check_for_positive_overflow(gt_bboxes, gt_labels, text, tokenizer, + max_tokens): + # Check if we have too many positive labels + # generate a caption by appending the positive labels + positive_label_list = np.unique(gt_labels).tolist() + # random shuffule so we can sample different annotations + # at different epochs + random.shuffle(positive_label_list) + + kept_lables = [] + length = 0 + + for index, label in enumerate(positive_label_list): + + label_text = clean_name(text[str(label)]) + '. ' + + tokenized = tokenizer.tokenize(label_text) + + length += len(tokenized) + + if length > max_tokens: + break + else: + kept_lables.append(label) + + keep_box_index = [] + keep_gt_labels = [] + for i in range(len(gt_labels)): + if gt_labels[i] in kept_lables: + keep_box_index.append(i) + keep_gt_labels.append(gt_labels[i]) + + return gt_bboxes[keep_box_index], np.array( + keep_gt_labels, dtype=np.long), length + + +def generate_senetence_given_labels(positive_label_list, negative_label_list, + text): + label_to_positions = {} + + label_list = negative_label_list + positive_label_list + + random.shuffle(label_list) + + pheso_caption = '' + + label_remap_dict = {} + for index, label in enumerate(label_list): + + start_index = len(pheso_caption) + + pheso_caption += clean_name(text[str(label)]) + + end_index = len(pheso_caption) + + if label in positive_label_list: + label_to_positions[index] = [[start_index, end_index]] + label_remap_dict[int(label)] = index + + # if index != len(label_list) - 1: + # pheso_caption += '. ' + pheso_caption += '. ' + + return label_to_positions, pheso_caption, label_remap_dict + + +@TRANSFORMS.register_module() +class RandomSamplingNegPos(BaseTransform): + + def __init__(self, + tokenizer_name, + num_sample_negative=85, + max_tokens=256, + full_sampling_prob=0.5, + label_map_file=None): + if AutoTokenizer is None: + raise RuntimeError( + 'transformers is not installed, please install it by: ' + 'pip install transformers.') + + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + self.num_sample_negative = num_sample_negative + self.full_sampling_prob = full_sampling_prob + self.max_tokens = max_tokens + self.label_map = None + if label_map_file: + with open(label_map_file, 'r') as file: + self.label_map = json.load(file) + + def transform(self, results: dict) -> dict: + if 'phrases' in results: + return self.vg_aug(results) + else: + return self.od_aug(results) + + def vg_aug(self, results): + gt_bboxes = results['gt_bboxes'] + if isinstance(gt_bboxes, BaseBoxes): + gt_bboxes = gt_bboxes.tensor + gt_labels = results['gt_bboxes_labels'] + text = results['text'].lower().strip() + if not text.endswith('.'): + text = text + '. ' + + phrases = results['phrases'] + # TODO: add neg + positive_label_list = np.unique(gt_labels).tolist() + label_to_positions = {} + for label in positive_label_list: + label_to_positions[label] = phrases[label]['tokens_positive'] + + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_labels'] = gt_labels + + results['text'] = text + results['tokens_positive'] = label_to_positions + return results + + def od_aug(self, results): + gt_bboxes = results['gt_bboxes'] + if isinstance(gt_bboxes, BaseBoxes): + gt_bboxes = gt_bboxes.tensor + gt_labels = results['gt_bboxes_labels'] + + if 'text' not in results: + assert self.label_map is not None + text = self.label_map + else: + text = results['text'] + + original_box_num = len(gt_labels) + # If the category name is in the format of 'a/b' (in object365), + # we randomly select one of them. + for key, value in text.items(): + if '/' in value: + text[key] = random.choice(value.split('/')).strip() + + gt_bboxes, gt_labels, positive_caption_length = \ + check_for_positive_overflow(gt_bboxes, gt_labels, + text, self.tokenizer, self.max_tokens) + + if len(gt_bboxes) < original_box_num: + print('WARNING: removed {} boxes due to positive caption overflow'. + format(original_box_num - len(gt_bboxes))) + + valid_negative_indexes = list(text.keys()) + + positive_label_list = np.unique(gt_labels).tolist() + full_negative = self.num_sample_negative + + if full_negative > len(valid_negative_indexes): + full_negative = len(valid_negative_indexes) + + outer_prob = random.random() + + if outer_prob < self.full_sampling_prob: + # c. probability_full: add both all positive and all negatives + num_negatives = full_negative + else: + if random.random() < 1.0: + num_negatives = np.random.choice(max(1, full_negative)) + 1 + else: + num_negatives = full_negative + + # Keep some negatives + negative_label_list = set() + if num_negatives != -1: + if num_negatives > len(valid_negative_indexes): + num_negatives = len(valid_negative_indexes) + + for i in np.random.choice( + valid_negative_indexes, size=num_negatives, replace=False): + if i not in positive_label_list: + negative_label_list.add(i) + + random.shuffle(positive_label_list) + + negative_label_list = list(negative_label_list) + random.shuffle(negative_label_list) + + negative_max_length = self.max_tokens - positive_caption_length + screened_negative_label_list = [] + + for negative_label in negative_label_list: + label_text = clean_name(text[str(negative_label)]) + '. ' + + tokenized = self.tokenizer.tokenize(label_text) + + negative_max_length -= len(tokenized) + + if negative_max_length > 0: + screened_negative_label_list.append(negative_label) + else: + break + negative_label_list = screened_negative_label_list + label_to_positions, pheso_caption, label_remap_dict = \ + generate_senetence_given_labels(positive_label_list, + negative_label_list, text) + + # label remap + if len(gt_labels) > 0: + gt_labels = np.vectorize(lambda x: label_remap_dict[x])(gt_labels) + + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_labels'] = gt_labels + + results['text'] = pheso_caption + results['tokens_positive'] = label_to_positions + + return results + + +@TRANSFORMS.register_module() +class LoadTextAnnotations(BaseTransform): + + def transform(self, results: dict) -> dict: + if 'phrases' in results: + tokens_positive = [ + phrase['tokens_positive'] + for phrase in results['phrases'].values() + ] + results['tokens_positive'] = tokens_positive + else: + text = results['text'] + results['text'] = list(text.values()) + return results diff --git a/mmdet/datasets/transforms/transforms.py b/mmdet/datasets/transforms/transforms.py index 4ac2bf75b54..c50b987db33 100644 --- a/mmdet/datasets/transforms/transforms.py +++ b/mmdet/datasets/transforms/transforms.py @@ -1766,8 +1766,10 @@ def _postprocess_results( results['masks'] = np.array( [results['masks'][i] for i in results['idx_mapper']]) results['masks'] = ori_masks.__class__( - results['masks'], ori_masks.height, ori_masks.width) - + results['masks'], + results['masks'][0].shape[0], + results['masks'][0].shape[1], + ) if (not len(results['idx_mapper']) and self.skip_img_without_anno): return None diff --git a/mmdet/engine/hooks/__init__.py b/mmdet/engine/hooks/__init__.py index bfc03693b24..889fa557ade 100644 --- a/mmdet/engine/hooks/__init__.py +++ b/mmdet/engine/hooks/__init__.py @@ -7,12 +7,15 @@ from .set_epoch_info_hook import SetEpochInfoHook from .sync_norm_hook import SyncNormHook from .utils import trigger_visualization_hook -from .visualization_hook import DetVisualizationHook, TrackVisualizationHook +from .visualization_hook import (DetVisualizationHook, + GroundingVisualizationHook, + TrackVisualizationHook) from .yolox_mode_switch_hook import YOLOXModeSwitchHook __all__ = [ 'YOLOXModeSwitchHook', 'SyncNormHook', 'CheckInvalidLossHook', 'SetEpochInfoHook', 'MemoryProfilerHook', 'DetVisualizationHook', 'NumClassCheckHook', 'MeanTeacherHook', 'trigger_visualization_hook', - 'PipelineSwitchHook', 'TrackVisualizationHook' + 'PipelineSwitchHook', 'TrackVisualizationHook', + 'GroundingVisualizationHook' ] diff --git a/mmdet/engine/hooks/visualization_hook.py b/mmdet/engine/hooks/visualization_hook.py index fad0f907ebc..3408186b6ef 100644 --- a/mmdet/engine/hooks/visualization_hook.py +++ b/mmdet/engine/hooks/visualization_hook.py @@ -4,6 +4,7 @@ from typing import Optional, Sequence import mmcv +import numpy as np from mmengine.fileio import get from mmengine.hooks import Hook from mmengine.runner import Runner @@ -13,6 +14,8 @@ from mmdet.datasets.samplers import TrackImgSampler from mmdet.registry import HOOKS from mmdet.structures import DetDataSample, TrackDataSample +from mmdet.structures.bbox import BaseBoxes +from mmdet.visualization.palette import _get_adaptive_scales @HOOKS.register_module() @@ -219,7 +222,7 @@ def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: dict, if self.draw is False: return - assert len(outputs) == 1,\ + assert len(outputs) == 1, \ 'only batch_size=1 is supported while validating.' sampler = runner.val_dataloader.sampler @@ -310,3 +313,203 @@ def visualize_single_image(self, img_data_sample: DetDataSample, pred_score_thr=self.score_thr, out_file=out_file, step=step) + + +def draw_all_character(visualizer, characters, w): + start_index = 2 + y_index = 5 + for char in characters: + if isinstance(char, str): + visualizer.draw_texts( + str(char), + positions=np.array([start_index, y_index]), + colors=(0, 0, 0), + font_families='monospace') + start_index += len(char) * 8 + else: + visualizer.draw_texts( + str(char[0]), + positions=np.array([start_index, y_index]), + colors=char[1], + font_families='monospace') + start_index += len(char[0]) * 8 + + if start_index > w - 10: + start_index = 2 + y_index += 15 + + drawn_text = visualizer.get_image() + return drawn_text + + +@HOOKS.register_module() +class GroundingVisualizationHook(DetVisualizationHook): + + def after_test_iter(self, runner: Runner, batch_idx: int, data_batch: dict, + outputs: Sequence[DetDataSample]) -> None: + """Run after every testing iterations. + + Args: + runner (:obj:`Runner`): The runner of the testing process. + batch_idx (int): The index of the current batch in the val loop. + data_batch (dict): Data from dataloader. + outputs (Sequence[:obj:`DetDataSample`]): A batch of data samples + that contain annotations and predictions. + """ + if self.draw is False: + return + + if self.test_out_dir is not None: + self.test_out_dir = osp.join(runner.work_dir, runner.timestamp, + self.test_out_dir) + mkdir_or_exist(self.test_out_dir) + + for data_sample in outputs: + data_sample = data_sample.cpu() + + self._test_index += 1 + + img_path = data_sample.img_path + img_bytes = get(img_path, backend_args=self.backend_args) + img = mmcv.imfrombytes(img_bytes, channel_order='rgb') + + out_file = None + if self.test_out_dir is not None: + out_file = osp.basename(img_path) + out_file = osp.join(self.test_out_dir, out_file) + + text = data_sample.text + if isinstance(text, str): # VG + gt_instances = data_sample.gt_instances + tokens_positive = data_sample.tokens_positive + if 'phrase_ids' in data_sample: + # flickr30k + gt_labels = data_sample.phrase_ids + else: + gt_labels = gt_instances.labels + gt_bboxes = gt_instances.get('bboxes', None) + if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes): + gt_instances.bboxes = gt_bboxes.tensor + print(gt_labels, tokens_positive, gt_bboxes, img_path) + pred_instances = data_sample.pred_instances + pred_instances = pred_instances[ + pred_instances.scores > self.score_thr] + pred_labels = pred_instances.labels + pred_bboxes = pred_instances.bboxes + pred_scores = pred_instances.scores + + max_label = 0 + if len(gt_labels) > 0: + max_label = max(gt_labels) + if len(pred_labels) > 0: + max_label = max(max(pred_labels), max_label) + + max_label = int(max(max_label, 0)) + palette = np.random.randint(0, 256, size=(max_label + 1, 3)) + bbox_palette = [tuple(c) for c in palette] + # bbox_palette = get_palette('random', max_label + 1) + if len(gt_labels) >= len(pred_labels): + colors = [bbox_palette[label] for label in gt_labels] + else: + colors = [bbox_palette[label] for label in pred_labels] + + self._visualizer.set_image(img) + + for label, bbox, color in zip(gt_labels, gt_bboxes, colors): + self._visualizer.draw_bboxes( + bbox, edge_colors=color, face_colors=color, alpha=0.3) + self._visualizer.draw_bboxes( + bbox, edge_colors=color, alpha=1) + + drawn_img = self._visualizer.get_image() + + new_image = np.ones( + (100, img.shape[1], 3), dtype=np.uint8) * 255 + self._visualizer.set_image(new_image) + + if tokens_positive == -1: # REC + gt_tokens_positive = [[]] + else: # Phrase Grounding + gt_tokens_positive = [ + tokens_positive[label] for label in gt_labels + ] + split_by_character = [char for char in text] + characters = [] + start_index = 0 + end_index = 0 + for w in split_by_character: + end_index += len(w) + is_find = False + for i, positive in enumerate(gt_tokens_positive): + for p in positive: + if start_index >= p[0] and end_index <= p[1]: + characters.append([w, colors[i]]) + is_find = True + break + if is_find: + break + if not is_find: + characters.append([w, (0, 0, 0)]) + start_index = end_index + + drawn_text = draw_all_character(self._visualizer, characters, + img.shape[1]) + drawn_gt_img = np.concatenate((drawn_img, drawn_text), axis=0) + + self._visualizer.set_image(img) + + for label, bbox, color in zip(pred_labels, pred_bboxes, + colors): + self._visualizer.draw_bboxes( + bbox, edge_colors=color, face_colors=color, alpha=0.3) + self._visualizer.draw_bboxes( + bbox, edge_colors=color, alpha=1) + print(pred_labels, pred_bboxes, pred_scores, colors) + areas = (pred_bboxes[:, 3] - pred_bboxes[:, 1]) * ( + pred_bboxes[:, 2] - pred_bboxes[:, 0]) + scales = _get_adaptive_scales(areas) + score = [str(round(s.item(), 2)) for s in pred_scores] + font_sizes = [int(13 * scales[i]) for i in range(len(scales))] + self._visualizer.draw_texts( + score, + pred_bboxes[:, :2].int(), + colors=(255, 255, 255), + font_sizes=font_sizes, + bboxes=[{ + 'facecolor': 'black', + 'alpha': 0.8, + 'pad': 0.7, + 'edgecolor': 'none' + }] * len(pred_bboxes)) + + drawn_img = self._visualizer.get_image() + + new_image = np.ones( + (100, img.shape[1], 3), dtype=np.uint8) * 255 + self._visualizer.set_image(new_image) + drawn_text = draw_all_character(self._visualizer, characters, + img.shape[1]) + drawn_pred_img = np.concatenate((drawn_img, drawn_text), + axis=0) + drawn_img = np.concatenate((drawn_gt_img, drawn_pred_img), + axis=1) + + if self.show: + self._visualizer.show( + drawn_img, + win_name=osp.basename(img_path), + wait_time=self.wait_time) + if out_file is not None: + mmcv.imwrite(drawn_img[..., ::-1], out_file) + else: + self.add_image('test_img', drawn_img, self._test_index) + else: # OD + self._visualizer.add_datasample( + osp.basename(img_path) if self.show else 'test_img', + img, + data_sample=data_sample, + show=self.show, + wait_time=self.wait_time, + pred_score_thr=self.score_thr, + out_file=out_file, + step=self._test_index) diff --git a/mmdet/evaluation/__init__.py b/mmdet/evaluation/__init__.py index f70dc226d30..126dea092eb 100644 --- a/mmdet/evaluation/__init__.py +++ b/mmdet/evaluation/__init__.py @@ -1,3 +1,4 @@ # Copyright (c) OpenMMLab. All rights reserved. +from .evaluator import * # noqa: F401,F403 from .functional import * # noqa: F401,F403 from .metrics import * # noqa: F401,F403 diff --git a/mmdet/evaluation/evaluator/__init__.py b/mmdet/evaluation/evaluator/__init__.py new file mode 100644 index 00000000000..6b13fe99548 --- /dev/null +++ b/mmdet/evaluation/evaluator/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .multi_datasets_evaluator import MultiDatasetsEvaluator + +__all__ = ['MultiDatasetsEvaluator'] diff --git a/mmdet/evaluation/evaluator/multi_datasets_evaluator.py b/mmdet/evaluation/evaluator/multi_datasets_evaluator.py new file mode 100644 index 00000000000..5cff1cf210e --- /dev/null +++ b/mmdet/evaluation/evaluator/multi_datasets_evaluator.py @@ -0,0 +1,111 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings +from collections import OrderedDict +from typing import Sequence, Union + +from mmengine.dist import (broadcast_object_list, collect_results, + is_main_process) +from mmengine.evaluator import BaseMetric, Evaluator +from mmengine.evaluator.metric import _to_cpu +from mmengine.registry import EVALUATOR + +from mmdet.utils import ConfigType + + +@EVALUATOR.register_module() +class MultiDatasetsEvaluator(Evaluator): + """Wrapper class to compose class: `ConcatDataset` and multiple + :class:`BaseMetric` instances. + The metrics will be evaluated on each dataset slice separately. The name of + the each metric is the concatenation of the dataset prefix, the metric + prefix and the key of metric - e.g. + `dataset_prefix/metric_prefix/accuracy`. + + Args: + metrics (dict or BaseMetric or Sequence): The config of metrics. + dataset_prefixes (Sequence[str]): The prefix of each dataset. The + length of this sequence should be the same as the length of the + datasets. + """ + + def __init__(self, metrics: Union[ConfigType, BaseMetric, Sequence], + dataset_prefixes: Sequence[str]) -> None: + super().__init__(metrics) + self.dataset_prefixes = dataset_prefixes + self._setups = False + + def _get_cumulative_sizes(self): + # ConcatDataset have a property `cumulative_sizes` + if isinstance(self.dataset_meta, Sequence): + dataset_slices = self.dataset_meta[0]['cumulative_sizes'] + if not self._setups: + self._setups = True + for dataset_meta, metric in zip(self.dataset_meta, + self.metrics): + metric.dataset_meta = dataset_meta + else: + dataset_slices = self.dataset_meta['cumulative_sizes'] + return dataset_slices + + def evaluate(self, size: int) -> dict: + """Invoke ``evaluate`` method of each metric and collect the metrics + dictionary. + + Args: + size (int): Length of the entire validation dataset. When batch + size > 1, the dataloader may pad some data samples to make + sure all ranks have the same length of dataset slice. The + ``collect_results`` function will drop the padded data based on + this size. + + Returns: + dict: Evaluation results of all metrics. The keys are the names + of the metrics, and the values are corresponding results. + """ + metrics_results = OrderedDict() + dataset_slices = self._get_cumulative_sizes() + assert len(dataset_slices) == len(self.dataset_prefixes) + + for dataset_prefix, start, end, metric in zip( + self.dataset_prefixes, [0] + dataset_slices[:-1], + dataset_slices, self.metrics): + if len(metric.results) == 0: + warnings.warn( + f'{metric.__class__.__name__} got empty `self.results`.' + 'Please ensure that the processed results are properly ' + 'added into `self.results` in `process` method.') + + results = collect_results(metric.results, size, + metric.collect_device) + + if is_main_process(): + # cast all tensors in results list to cpu + results = _to_cpu(results) + _metrics = metric.compute_metrics( + results[start:end]) # type: ignore + + if metric.prefix: + final_prefix = '/'.join((dataset_prefix, metric.prefix)) + else: + final_prefix = dataset_prefix + print(f'================{final_prefix}================') + metric_results = { + '/'.join((final_prefix, k)): v + for k, v in _metrics.items() + } + + # Check metric name conflicts + for name in metric_results.keys(): + if name in metrics_results: + raise ValueError( + 'There are multiple evaluation results with ' + f'the same metric name {name}. Please make ' + 'sure all metrics have different prefixes.') + metrics_results.update(metric_results) + metric.results.clear() + if is_main_process(): + metrics_results = [metrics_results] + else: + metrics_results = [None] # type: ignore + broadcast_object_list(metrics_results) + return metrics_results[0] diff --git a/mmdet/evaluation/functional/class_names.py b/mmdet/evaluation/functional/class_names.py index d0ea7094685..623a89cfdc0 100644 --- a/mmdet/evaluation/functional/class_names.py +++ b/mmdet/evaluation/functional/class_names.py @@ -485,6 +485,250 @@ def objects365v2_classes() -> list: ] +def lvis_classes() -> list: + """Class names of LVIS.""" + return [ + 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', 'alcohol', + 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', 'antenna', + 'apple', 'applesauce', 'apricot', 'apron', 'aquarium', + 'arctic_(type_of_shoe)', 'armband', 'armchair', 'armoire', 'armor', + 'artichoke', 'trash_can', 'ashtray', 'asparagus', 'atomizer', + 'avocado', 'award', 'awning', 'ax', 'baboon', 'baby_buggy', + 'basketball_backboard', 'backpack', 'handbag', 'suitcase', 'bagel', + 'bagpipe', 'baguet', 'bait', 'ball', 'ballet_skirt', 'balloon', + 'bamboo', 'banana', 'Band_Aid', 'bandage', 'bandanna', 'banjo', + 'banner', 'barbell', 'barge', 'barrel', 'barrette', 'barrow', + 'baseball_base', 'baseball', 'baseball_bat', 'baseball_cap', + 'baseball_glove', 'basket', 'basketball', 'bass_horn', 'bat_(animal)', + 'bath_mat', 'bath_towel', 'bathrobe', 'bathtub', 'batter_(food)', + 'battery', 'beachball', 'bead', 'bean_curd', 'beanbag', 'beanie', + 'bear', 'bed', 'bedpan', 'bedspread', 'cow', 'beef_(food)', 'beeper', + 'beer_bottle', 'beer_can', 'beetle', 'bell', 'bell_pepper', 'belt', + 'belt_buckle', 'bench', 'beret', 'bib', 'Bible', 'bicycle', 'visor', + 'billboard', 'binder', 'binoculars', 'bird', 'birdfeeder', 'birdbath', + 'birdcage', 'birdhouse', 'birthday_cake', 'birthday_card', + 'pirate_flag', 'black_sheep', 'blackberry', 'blackboard', 'blanket', + 'blazer', 'blender', 'blimp', 'blinker', 'blouse', 'blueberry', + 'gameboard', 'boat', 'bob', 'bobbin', 'bobby_pin', 'boiled_egg', + 'bolo_tie', 'deadbolt', 'bolt', 'bonnet', 'book', 'bookcase', + 'booklet', 'bookmark', 'boom_microphone', 'boot', 'bottle', + 'bottle_opener', 'bouquet', 'bow_(weapon)', 'bow_(decorative_ribbons)', + 'bow-tie', 'bowl', 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'box', + 'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere', + 'bread-bin', 'bread', 'breechcloth', 'bridal_gown', 'briefcase', + 'broccoli', 'broach', 'broom', 'brownie', 'brussels_sprouts', + 'bubble_gum', 'bucket', 'horse_buggy', 'bull', 'bulldog', 'bulldozer', + 'bullet_train', 'bulletin_board', 'bulletproof_vest', 'bullhorn', + 'bun', 'bunk_bed', 'buoy', 'burrito', 'bus_(vehicle)', 'business_card', + 'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car', + 'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf', + 'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)', + 'can', 'can_opener', 'candle', 'candle_holder', 'candy_bar', + 'candy_cane', 'walking_cane', 'canister', 'canoe', 'cantaloup', + 'canteen', 'cap_(headwear)', 'bottle_cap', 'cape', 'cappuccino', + 'car_(automobile)', 'railcar_(part_of_a_train)', 'elevator_car', + 'car_battery', 'identity_card', 'card', 'cardigan', 'cargo_ship', + 'carnation', 'horse_carriage', 'carrot', 'tote_bag', 'cart', 'carton', + 'cash_register', 'casserole', 'cassette', 'cast', 'cat', 'cauliflower', + 'cayenne_(spice)', 'CD_player', 'celery', 'cellular_telephone', + 'chain_mail', 'chair', 'chaise_longue', 'chalice', 'chandelier', + 'chap', 'checkbook', 'checkerboard', 'cherry', 'chessboard', + 'chicken_(animal)', 'chickpea', 'chili_(vegetable)', 'chime', + 'chinaware', 'crisp_(potato_chip)', 'poker_chip', 'chocolate_bar', + 'chocolate_cake', 'chocolate_milk', 'chocolate_mousse', 'choker', + 'chopping_board', 'chopstick', 'Christmas_tree', 'slide', 'cider', + 'cigar_box', 'cigarette', 'cigarette_case', 'cistern', 'clarinet', + 'clasp', 'cleansing_agent', 'cleat_(for_securing_rope)', 'clementine', + 'clip', 'clipboard', 'clippers_(for_plants)', 'cloak', 'clock', + 'clock_tower', 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', + 'coat', 'coat_hanger', 'coatrack', 'cock', 'cockroach', + 'cocoa_(beverage)', 'coconut', 'coffee_maker', 'coffee_table', + 'coffeepot', 'coil', 'coin', 'colander', 'coleslaw', + 'coloring_material', 'combination_lock', 'pacifier', 'comic_book', + 'compass', 'computer_keyboard', 'condiment', 'cone', 'control', + 'convertible_(automobile)', 'sofa_bed', 'cooker', 'cookie', + 'cooking_utensil', 'cooler_(for_food)', 'cork_(bottle_plug)', + 'corkboard', 'corkscrew', 'edible_corn', 'cornbread', 'cornet', + 'cornice', 'cornmeal', 'corset', 'costume', 'cougar', 'coverall', + 'cowbell', 'cowboy_hat', 'crab_(animal)', 'crabmeat', 'cracker', + 'crape', 'crate', 'crayon', 'cream_pitcher', 'crescent_roll', 'crib', + 'crock_pot', 'crossbar', 'crouton', 'crow', 'crowbar', 'crown', + 'crucifix', 'cruise_ship', 'police_cruiser', 'crumb', 'crutch', + 'cub_(animal)', 'cube', 'cucumber', 'cufflink', 'cup', 'trophy_cup', + 'cupboard', 'cupcake', 'hair_curler', 'curling_iron', 'curtain', + 'cushion', 'cylinder', 'cymbal', 'dagger', 'dalmatian', 'dartboard', + 'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk', + 'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux', + 'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher', + 'dishwasher_detergent', 'dispenser', 'diving_board', 'Dixie_cup', + 'dog', 'dog_collar', 'doll', 'dollar', 'dollhouse', 'dolphin', + 'domestic_ass', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly', + 'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit', + 'dresser', 'drill', 'drone', 'dropper', 'drum_(musical_instrument)', + 'drumstick', 'duck', 'duckling', 'duct_tape', 'duffel_bag', 'dumbbell', + 'dumpster', 'dustpan', 'eagle', 'earphone', 'earplug', 'earring', + 'easel', 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater', + 'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk', + 'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan', + 'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)', + 'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm', + 'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace', + 'fireplug', 'first-aid_kit', 'fish', 'fish_(food)', 'fishbowl', + 'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flap', + 'flash', 'flashlight', 'fleece', 'flip-flop_(sandal)', + 'flipper_(footwear)', 'flower_arrangement', 'flute_glass', 'foal', + 'folding_chair', 'food_processor', 'football_(American)', + 'football_helmet', 'footstool', 'fork', 'forklift', 'freight_car', + 'French_toast', 'freshener', 'frisbee', 'frog', 'fruit_juice', + 'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage', + 'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic', + 'gasmask', 'gazelle', 'gelatin', 'gemstone', 'generator', + 'giant_panda', 'gift_wrap', 'ginger', 'giraffe', 'cincture', + 'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles', + 'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose', + 'gorilla', 'gourd', 'grape', 'grater', 'gravestone', 'gravy_boat', + 'green_bean', 'green_onion', 'griddle', 'grill', 'grits', 'grizzly', + 'grocery_bag', 'guitar', 'gull', 'gun', 'hairbrush', 'hairnet', + 'hairpin', 'halter_top', 'ham', 'hamburger', 'hammer', 'hammock', + 'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel', + 'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw', + 'hardback_book', 'harmonium', 'hat', 'hatbox', 'veil', 'headband', + 'headboard', 'headlight', 'headscarf', 'headset', + 'headstall_(for_horses)', 'heart', 'heater', 'helicopter', 'helmet', + 'heron', 'highchair', 'hinge', 'hippopotamus', 'hockey_stick', 'hog', + 'home_plate_(baseball)', 'honey', 'fume_hood', 'hook', 'hookah', + 'hornet', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce', + 'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear', + 'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate', + 'igniter', 'inhaler', 'iPod', 'iron_(for_clothing)', 'ironing_board', + 'jacket', 'jam', 'jar', 'jean', 'jeep', 'jelly_bean', 'jersey', + 'jet_plane', 'jewel', 'jewelry', 'joystick', 'jumpsuit', 'kayak', + 'keg', 'kennel', 'kettle', 'key', 'keycard', 'kilt', 'kimono', + 'kitchen_sink', 'kitchen_table', 'kite', 'kitten', 'kiwi_fruit', + 'knee_pad', 'knife', 'knitting_needle', 'knob', 'knocker_(on_a_door)', + 'koala', 'lab_coat', 'ladder', 'ladle', 'ladybug', 'lamb_(animal)', + 'lamb-chop', 'lamp', 'lamppost', 'lampshade', 'lantern', 'lanyard', + 'laptop_computer', 'lasagna', 'latch', 'lawn_mower', 'leather', + 'legging_(clothing)', 'Lego', 'legume', 'lemon', 'lemonade', 'lettuce', + 'license_plate', 'life_buoy', 'life_jacket', 'lightbulb', + 'lightning_rod', 'lime', 'limousine', 'lion', 'lip_balm', 'liquor', + 'lizard', 'log', 'lollipop', 'speaker_(stereo_equipment)', 'loveseat', + 'machine_gun', 'magazine', 'magnet', 'mail_slot', 'mailbox_(at_home)', + 'mallard', 'mallet', 'mammoth', 'manatee', 'mandarin_orange', 'manger', + 'manhole', 'map', 'marker', 'martini', 'mascot', 'mashed_potato', + 'masher', 'mask', 'mast', 'mat_(gym_equipment)', 'matchbox', + 'mattress', 'measuring_cup', 'measuring_stick', 'meatball', 'medicine', + 'melon', 'microphone', 'microscope', 'microwave_oven', 'milestone', + 'milk', 'milk_can', 'milkshake', 'minivan', 'mint_candy', 'mirror', + 'mitten', 'mixer_(kitchen_tool)', 'money', + 'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor', + 'motor_scooter', 'motor_vehicle', 'motorcycle', 'mound_(baseball)', + 'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom', + 'music_stool', 'musical_instrument', 'nailfile', 'napkin', + 'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newspaper', + 'newsstand', 'nightshirt', 'nosebag_(for_animals)', + 'noseband_(for_animals)', 'notebook', 'notepad', 'nut', 'nutcracker', + 'oar', 'octopus_(food)', 'octopus_(animal)', 'oil_lamp', 'olive_oil', + 'omelet', 'onion', 'orange_(fruit)', 'orange_juice', 'ostrich', + 'ottoman', 'oven', 'overalls_(clothing)', 'owl', 'packet', 'inkpad', + 'pad', 'paddle', 'padlock', 'paintbrush', 'painting', 'pajamas', + 'palette', 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', + 'pantyhose', 'papaya', 'paper_plate', 'paper_towel', 'paperback_book', + 'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', 'parasol', + 'parchment', 'parka', 'parking_meter', 'parrot', + 'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport', + 'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter', + 'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'wooden_leg', + 'pegboard', 'pelican', 'pen', 'pencil', 'pencil_box', + 'pencil_sharpener', 'pendulum', 'penguin', 'pennant', 'penny_(coin)', + 'pepper', 'pepper_mill', 'perfume', 'persimmon', 'person', 'pet', + 'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano', + 'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow', + 'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball', + 'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)', + 'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat', + 'plate', 'platter', 'playpen', 'pliers', 'plow_(farm_equipment)', + 'plume', 'pocket_watch', 'pocketknife', 'poker_(fire_stirring_tool)', + 'pole', 'polo_shirt', 'poncho', 'pony', 'pool_table', 'pop_(soda)', + 'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato', + 'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'pretzel', + 'printer', 'projectile_(weapon)', 'projector', 'propeller', 'prune', + 'pudding', 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', + 'puppet', 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', + 'race_car', 'racket', 'radar', 'radiator', 'radio_receiver', 'radish', + 'raft', 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat', + 'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt', + 'recliner', 'record_player', 'reflector', 'remote_control', + 'rhinoceros', 'rib_(food)', 'rifle', 'ring', 'river_boat', 'road_map', + 'robe', 'rocking_chair', 'rodent', 'roller_skate', 'Rollerblade', + 'rolling_pin', 'root_beer', 'router_(computer_equipment)', + 'rubber_band', 'runner_(carpet)', 'plastic_bag', + 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', 'safety_pin', + 'sail', 'salad', 'salad_plate', 'salami', 'salmon_(fish)', + 'salmon_(food)', 'salsa', 'saltshaker', 'sandal_(type_of_shoe)', + 'sandwich', 'satchel', 'saucepan', 'saucer', 'sausage', 'sawhorse', + 'saxophone', 'scale_(measuring_instrument)', 'scarecrow', 'scarf', + 'school_bus', 'scissors', 'scoreboard', 'scraper', 'screwdriver', + 'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane', + 'seashell', 'sewing_machine', 'shaker', 'shampoo', 'shark', + 'sharpener', 'Sharpie', 'shaver_(electric)', 'shaving_cream', 'shawl', + 'shears', 'sheep', 'shepherd_dog', 'sherbert', 'shield', 'shirt', + 'shoe', 'shopping_bag', 'shopping_cart', 'short_pants', 'shot_glass', + 'shoulder_bag', 'shovel', 'shower_head', 'shower_cap', + 'shower_curtain', 'shredder_(for_paper)', 'signboard', 'silo', 'sink', + 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', 'ski_pole', + 'skirt', 'skullcap', 'sled', 'sleeping_bag', 'sling_(bandage)', + 'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman', + 'snowmobile', 'soap', 'soccer_ball', 'sock', 'sofa', 'softball', + 'solar_array', 'sombrero', 'soup', 'soup_bowl', 'soupspoon', + 'sour_cream', 'soya_milk', 'space_shuttle', 'sparkler_(fireworks)', + 'spatula', 'spear', 'spectacles', 'spice_rack', 'spider', 'crawfish', + 'sponge', 'spoon', 'sportswear', 'spotlight', 'squid_(food)', + 'squirrel', 'stagecoach', 'stapler_(stapling_machine)', 'starfish', + 'statue_(sculpture)', 'steak_(food)', 'steak_knife', 'steering_wheel', + 'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer', + 'stirrup', 'stool', 'stop_sign', 'brake_light', 'stove', 'strainer', + 'strap', 'straw_(for_drinking)', 'strawberry', 'street_sign', + 'streetlight', 'string_cheese', 'stylus', 'subwoofer', 'sugar_bowl', + 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', 'sunglasses', + 'sunhat', 'surfboard', 'sushi', 'mop', 'sweat_pants', 'sweatband', + 'sweater', 'sweatshirt', 'sweet_potato', 'swimsuit', 'sword', + 'syringe', 'Tabasco_sauce', 'table-tennis_table', 'table', + 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', 'taillight', + 'tambourine', 'army_tank', 'tank_(storage_vessel)', + 'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure', + 'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup', + 'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth', + 'telephone_pole', 'telephoto_lens', 'television_camera', + 'television_set', 'tennis_ball', 'tennis_racket', 'tequila', + 'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread', + 'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil', + 'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven', + 'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush', + 'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel', + 'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light', + 'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline', + 'tray', 'trench_coat', 'triangle_(musical_instrument)', 'tricycle', + 'tripod', 'trousers', 'truck', 'truffle_(chocolate)', 'trunk', 'vat', + 'turban', 'turkey_(food)', 'turnip', 'turtle', 'turtleneck_(clothing)', + 'typewriter', 'umbrella', 'underwear', 'unicycle', 'urinal', 'urn', + 'vacuum_cleaner', 'vase', 'vending_machine', 'vent', 'vest', + 'videotape', 'vinegar', 'violin', 'vodka', 'volleyball', 'vulture', + 'waffle', 'waffle_iron', 'wagon', 'wagon_wheel', 'walking_stick', + 'wall_clock', 'wall_socket', 'wallet', 'walrus', 'wardrobe', + 'washbasin', 'automatic_washer', 'watch', 'water_bottle', + 'water_cooler', 'water_faucet', 'water_heater', 'water_jug', + 'water_gun', 'water_scooter', 'water_ski', 'water_tower', + 'watering_can', 'watermelon', 'weathervane', 'webcam', 'wedding_cake', + 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', 'whipped_cream', + 'whistle', 'wig', 'wind_chime', 'windmill', 'window_box_(for_plants)', + 'windshield_wiper', 'windsock', 'wine_bottle', 'wine_bucket', + 'wineglass', 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', + 'wreath', 'wrench', 'wristband', 'wristlet', 'yacht', 'yogurt', + 'yoke_(animal_equipment)', 'zebra', 'zucchini' + ] + + dataset_aliases = { 'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'], 'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'], @@ -496,7 +740,8 @@ def objects365v2_classes() -> list: 'oid_challenge': ['oid_challenge', 'openimages_challenge'], 'oid_v6': ['oid_v6', 'openimages_v6'], 'objects365v1': ['objects365v1', 'obj365v1'], - 'objects365v2': ['objects365v2', 'obj365v2'] + 'objects365v2': ['objects365v2', 'obj365v2'], + 'lvis': ['lvis', 'lvis_v1'], } diff --git a/mmdet/evaluation/metrics/__init__.py b/mmdet/evaluation/metrics/__init__.py index e1ec0e46250..8ad040cf6ff 100644 --- a/mmdet/evaluation/metrics/__init__.py +++ b/mmdet/evaluation/metrics/__init__.py @@ -7,11 +7,17 @@ from .coco_panoptic_metric import CocoPanopticMetric from .coco_video_metric import CocoVideoMetric from .crowdhuman_metric import CrowdHumanMetric +from .dod_metric import DODCocoMetric from .dump_det_results import DumpDetResults +from .dump_odvg_results import DumpODVGResults from .dump_proposals_metric import DumpProposals +from .flickr30k_metric import Flickr30kMetric +from .grefcoco_metric import gRefCOCOMetric from .lvis_metric import LVISMetric from .mot_challenge_metric import MOTChallengeMetric from .openimages_metric import OpenImagesMetric +from .ov_coco_metric import OVCocoMetric +from .refexp_metric import RefExpMetric from .refseg_metric import RefSegMetric from .reid_metric import ReIDMetrics from .semseg_metric import SemSegMetric @@ -23,5 +29,7 @@ 'VOCMetric', 'LVISMetric', 'CrowdHumanMetric', 'DumpProposals', 'CocoOccludedSeparatedMetric', 'DumpDetResults', 'BaseVideoMetric', 'MOTChallengeMetric', 'CocoVideoMetric', 'ReIDMetrics', 'YouTubeVISMetric', - 'COCOCaptionMetric', 'SemSegMetric', 'RefSegMetric' + 'COCOCaptionMetric', 'SemSegMetric', 'RefSegMetric', 'RefExpMetric', + 'gRefCOCOMetric', 'DODCocoMetric', 'DumpODVGResults', 'Flickr30kMetric', + 'OVCocoMetric' ] diff --git a/mmdet/evaluation/metrics/coco_panoptic_metric.py b/mmdet/evaluation/metrics/coco_panoptic_metric.py index 1554c0908d1..f86be916f9c 100644 --- a/mmdet/evaluation/metrics/coco_panoptic_metric.py +++ b/mmdet/evaluation/metrics/coco_panoptic_metric.py @@ -190,7 +190,7 @@ def gt_to_coco_json(self, gt_dicts: Sequence[dict], } segments_info.append(new_segment_info) - segm_file = image_info['file_name'].replace('jpg', 'png') + segm_file = image_info['file_name'].replace('.jpg', '.png') annotation = dict( image_id=img_id, segments_info=segments_info, @@ -330,7 +330,7 @@ def _compute_batch_pq_stats(self, data_samples: Sequence[dict]): # parse pred img_id = data_sample['img_id'] segm_file = osp.basename(data_sample['img_path']).replace( - 'jpg', 'png') + '.jpg', '.png') result = self._parse_predictions( pred=data_sample, img_id=img_id, @@ -397,7 +397,7 @@ def _process_gt_and_predictions(self, data_samples: Sequence[dict]): # parse pred img_id = data_sample['img_id'] segm_file = osp.basename(data_sample['img_path']).replace( - 'jpg', 'png') + '.jpg', '.png') result = self._parse_predictions( pred=data_sample, img_id=img_id, segm_file=segm_file) diff --git a/mmdet/evaluation/metrics/dod_metric.py b/mmdet/evaluation/metrics/dod_metric.py new file mode 100644 index 00000000000..b47d07219da --- /dev/null +++ b/mmdet/evaluation/metrics/dod_metric.py @@ -0,0 +1,169 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from collections import defaultdict +from typing import List, Optional, Sequence + +import numpy as np +from mmengine.evaluator import BaseMetric +from mmengine.fileio import get_local_path +from mmengine.logging import MMLogger + +from mmdet.datasets.api_wrappers import COCO, COCOeval +from mmdet.registry import METRICS + + +@METRICS.register_module() +class DODCocoMetric(BaseMetric): + + default_prefix: Optional[str] = 'dod' + + def __init__(self, + ann_file: Optional[str] = None, + collect_device: str = 'cpu', + outfile_prefix: Optional[str] = None, + backend_args: dict = None, + prefix: Optional[str] = None) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + self.outfile_prefix = outfile_prefix + with get_local_path(ann_file, backend_args=backend_args) as local_path: + self._coco_api = COCO(local_path) + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + for data_sample in data_samples: + result = dict() + pred = data_sample['pred_instances'] + result['img_id'] = data_sample['img_id'] + result['bboxes'] = pred['bboxes'].cpu().numpy() + result['scores'] = pred['scores'].cpu().numpy() + + result['labels'] = pred['labels'].cpu().numpy() + result['labels'] = data_sample['sent_ids'][result['labels']] + self.results.append(result) + + def xyxy2xywh(self, bbox: np.ndarray) -> list: + """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO + evaluation. + + Args: + bbox (numpy.ndarray): The bounding boxes, shape (4, ), in + ``xyxy`` order. + + Returns: + list[float]: The converted bounding boxes, in ``xywh`` order. + """ + + _bbox: List = bbox.tolist() + return [ + _bbox[0], + _bbox[1], + _bbox[2] - _bbox[0], + _bbox[3] - _bbox[1], + ] + + def results2json(self, results: Sequence[dict]) -> list: + """Dump the detection results to a COCO style json file. + + There are 3 types of results: proposals, bbox predictions, mask + predictions, and they have different data types. This method will + automatically recognize the type, and dump them to json files. + + Args: + results (Sequence[dict]): Testing results of the + dataset. + + Returns: + dict: Possible keys are "bbox", "segm", "proposal", and + values are corresponding filenames. + """ + bbox_json_results = [] + for idx, result in enumerate(results): + image_id = result.get('img_id', idx) + labels = result['labels'] + bboxes = result['bboxes'] + scores = result['scores'] + for i, label in enumerate(labels): + data = dict() + data['image_id'] = image_id + data['bbox'] = self.xyxy2xywh(bboxes[i]) + data['score'] = float(scores[i]) + data['category_id'] = label + bbox_json_results.append(data) + return bbox_json_results + + def compute_metrics(self, results: list) -> dict: + logger: MMLogger = MMLogger.get_current_instance() + result_files = self.results2json(results) + d3_res = self._coco_api.loadRes(result_files) + cocoEval = COCOeval(self._coco_api, d3_res, 'bbox') + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + aps = cocoEval.eval['precision'][:, :, :, 0, -1] + category_ids = self._coco_api.getCatIds() + category_names = [ + cat['name'] for cat in self._coco_api.loadCats(category_ids) + ] + + aps_lens = defaultdict(list) + counter_lens = defaultdict(int) + for i in range(len(category_names)): + ap = aps[:, :, i] + ap_value = ap[ap > -1].mean() + if not np.isnan(ap_value): + len_ref = len(category_names[i].split(' ')) + aps_lens[len_ref].append(ap_value) + counter_lens[len_ref] += 1 + + ap_sum_short = sum([sum(aps_lens[i]) for i in range(0, 4)]) + ap_sum_mid = sum([sum(aps_lens[i]) for i in range(4, 7)]) + ap_sum_long = sum([sum(aps_lens[i]) for i in range(7, 10)]) + ap_sum_very_long = sum([ + sum(aps_lens[i]) for i in range(10, + max(counter_lens.keys()) + 1) + ]) + c_sum_short = sum([counter_lens[i] for i in range(1, 4)]) + c_sum_mid = sum([counter_lens[i] for i in range(4, 7)]) + c_sum_long = sum([counter_lens[i] for i in range(7, 10)]) + c_sum_very_long = sum( + [counter_lens[i] for i in range(10, + max(counter_lens.keys()) + 1)]) + map_short = ap_sum_short / c_sum_short + map_mid = ap_sum_mid / c_sum_mid + map_long = ap_sum_long / c_sum_long + map_very_long = ap_sum_very_long / c_sum_very_long + + coco_metric_names = { + 'mAP': 0, + 'mAP_50': 1, + 'mAP_75': 2, + 'mAP_s': 3, + 'mAP_m': 4, + 'mAP_l': 5, + 'AR@100': 6, + 'AR@300': 7, + 'AR@1000': 8, + 'AR_s@1000': 9, + 'AR_m@1000': 10, + 'AR_l@1000': 11 + } + metric_items = ['mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'] + + eval_results = {} + for metric_item in metric_items: + key = f'{metric_item}' + val = cocoEval.stats[coco_metric_names[metric_item]] + eval_results[key] = float(f'{round(val, 3)}') + + ap = cocoEval.stats[:6] + logger.info(f'mAP_copypaste: {ap[0]:.3f} ' + f'{ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} ' + f'{ap[4]:.3f} {ap[5]:.3f}') + + logger.info(f'mAP over reference length: short - {map_short:.4f}, ' + f'mid - {map_mid:.4f}, long - {map_long:.4f}, ' + f'very long - {map_very_long:.4f}') + eval_results['mAP_short'] = float(f'{round(map_short, 3)}') + eval_results['mAP_mid'] = float(f'{round(map_mid, 3)}') + eval_results['mAP_long'] = float(f'{round(map_long, 3)}') + eval_results['mAP_very_long'] = float(f'{round(map_very_long, 3)}') + return eval_results diff --git a/mmdet/evaluation/metrics/dump_odvg_results.py b/mmdet/evaluation/metrics/dump_odvg_results.py new file mode 100644 index 00000000000..a1446b05380 --- /dev/null +++ b/mmdet/evaluation/metrics/dump_odvg_results.py @@ -0,0 +1,138 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Any, Optional, Sequence + +from mmcv.ops import batched_nms +from mmengine.evaluator import BaseMetric +from mmengine.logging import print_log + +from mmdet.registry import METRICS + +try: + import jsonlines +except ImportError: + jsonlines = None + + +@METRICS.register_module() +class DumpODVGResults(BaseMetric): + default_prefix: Optional[str] = 'pl_odvg' + + def __init__(self, + outfile_path, + img_prefix: str, + score_thr: float = 0.1, + collect_device: str = 'cpu', + nms_thr: float = 0.5, + prefix: Optional[str] = None) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + self.outfile_path = outfile_path + self.score_thr = score_thr + self.img_prefix = img_prefix + self.nms_thr = nms_thr + + if jsonlines is None: + raise ImportError('Please run "pip install jsonlines" to install ' + 'this package.') + + def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None: + for data_sample in data_samples: + result = {} + + filename = data_sample['img_path'] + filename = filename.replace(self.img_prefix, '') + if filename.startswith('/'): + filename = filename[1:] + result['filename'] = filename + + height = data_sample['ori_shape'][0] + width = data_sample['ori_shape'][1] + result['height'] = height + result['width'] = width + + pred_instances = data_sample['pred_instances'] + + bboxes = pred_instances['bboxes'].cpu() + scores = pred_instances['scores'].cpu() + labels = pred_instances['labels'].cpu() + + bboxes = bboxes[scores > self.score_thr] + labels = labels[scores > self.score_thr] + scores = scores[scores > self.score_thr] + + if 'tokens_positive' in data_sample: + task = 'vg' + else: + task = 'od' + + if task == 'od': + classes_name = data_sample['text'] + result['detection'] = {} + + if len(bboxes) > 0: + det_bboxes, keep = batched_nms( + bboxes, scores, labels, + dict(type='nms', iou_threshold=self.nms_thr)) + _scores = det_bboxes[:, -1] + _bboxes = det_bboxes[:, :-1] + _labels = labels[keep] + + instances = [] + _bboxes = _bboxes.numpy().tolist() + _scores = _scores.numpy().tolist() + _labels = _labels.numpy().tolist() + for bbox, score, label in zip(_bboxes, _scores, _labels): + round_bbox = [round(b, 2) for b in bbox] + round_score = round(score, 2) + instances.append({ + 'bbox': round_bbox, + 'score': round_score, + 'label': label, + 'category': classes_name[label] + }) + result['detection']['instances'] = instances + else: + result['detection']['instances'] = [] + self.results.append(result) + else: + caption = data_sample['text'] + result['grounding'] = {} + result['grounding']['caption'] = caption + + tokens_positive = data_sample['tokens_positive'] + + region_list = [] + for label, positive in enumerate(tokens_positive): + phrase = [caption[pos[0]:pos[1]] for pos in positive] + + _bboxes = bboxes[labels == label] + _scores = scores[labels == label] + det_bboxes, _ = batched_nms( + _bboxes, + _scores, + None, + dict(type='nms', iou_threshold=self.nms_thr), + class_agnostic=True) + _scores = det_bboxes[:, -1].numpy().tolist() + _bboxes = det_bboxes[:, :-1].numpy().tolist() + + round_bboxes = [] + for bbox in _bboxes: + round_bboxes.append([round(b, 2) for b in bbox]) + _scores = [[round(s, 2) for s in _scores]] + region = { + 'phrase': phrase, + 'bbox': round_bboxes, + 'score': _scores, + 'tokens_positive': positive + } + region_list.append(region) + result['grounding']['regions'] = region_list + self.results.append(result) + + def compute_metrics(self, results: list) -> dict: + with jsonlines.open(self.outfile_path, mode='w') as writer: + writer.write_all(results) + print_log( + f'Results has been saved to {self.outfile_path}.', + logger='current') + return {} diff --git a/mmdet/evaluation/metrics/flickr30k_metric.py b/mmdet/evaluation/metrics/flickr30k_metric.py new file mode 100644 index 00000000000..f8b64bfda46 --- /dev/null +++ b/mmdet/evaluation/metrics/flickr30k_metric.py @@ -0,0 +1,165 @@ +# Copyright (c) OpenMMLab. All rights reserved +from collections import defaultdict +from typing import Dict, List, Optional, Sequence + +import numpy as np +from mmengine.evaluator import BaseMetric +from mmengine.logging import MMLogger + +from mmdet.registry import METRICS +from ..functional import bbox_overlaps + + +class RecallTracker: + """Utility class to track recall@k for various k, split by categories.""" + + def __init__(self, topk: Sequence[int]): + """ + Parameters: + - topk : tuple of ints corresponding to the recalls being + tracked (eg, recall@1, recall@10, ...) + """ + + self.total_byk_bycat: Dict[int, Dict[str, int]] = { + k: defaultdict(int) + for k in topk + } + self.positives_byk_bycat: Dict[int, Dict[str, int]] = { + k: defaultdict(int) + for k in topk + } + + def add_positive(self, k: int, category: str): + """Log a positive hit @k for given category.""" + if k not in self.total_byk_bycat: + raise RuntimeError(f'{k} is not a valid recall threshold') + self.total_byk_bycat[k][category] += 1 + self.positives_byk_bycat[k][category] += 1 + + def add_negative(self, k: int, category: str): + """Log a negative hit @k for given category.""" + if k not in self.total_byk_bycat: + raise RuntimeError(f'{k} is not a valid recall threshold') + self.total_byk_bycat[k][category] += 1 + + def report(self) -> Dict[str, Dict[str, float]]: + """Return a condensed report of the results as a dict of dict. + + report[k][cat] is the recall@k for the given category + """ + report: Dict[str, Dict[str, float]] = {} + for k in self.total_byk_bycat: + assert k in self.positives_byk_bycat + report[str(k)] = { + cat: + self.positives_byk_bycat[k][cat] / self.total_byk_bycat[k][cat] + for cat in self.total_byk_bycat[k] + } + return report + + +@METRICS.register_module() +class Flickr30kMetric(BaseMetric): + """Phrase Grounding Metric.""" + + def __init__( + self, + topk: Sequence[int] = (1, 5, 10, -1), + iou_thrs: float = 0.5, + merge_boxes: bool = False, + collect_device: str = 'cpu', + prefix: Optional[str] = None, + ) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + + self.iou_thrs = iou_thrs + self.topk = topk + self.merge = merge_boxes + + def merge_boxes(self, boxes: List[List[int]]) -> List[List[int]]: + """Return the boxes corresponding to the smallest enclosing box + containing all the provided boxes The boxes are expected in [x1, y1, + x2, y2] format.""" + if len(boxes) == 1: + return boxes + + np_boxes = np.asarray(boxes) + + return [[ + np.boxes[:, 0].min(), np_boxes[:, 1].min(), np_boxes[:, 2].max(), + np_boxes[:, 3].max() + ]] + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + """Process one batch of data samples and predictions. + + The processed + results should be stored in ``self.results``, which will be used to + compute the metrics when all batches have been processed. + Args: + data_batch (dict): A batch of data from the dataloader. + data_samples (Sequence[dict]): A batch of data samples that + contain annotations and predictions. + """ + for data_sample in data_samples: + pred = data_sample['pred_instances'] + gt = data_sample['gt_instances']['bboxes'] + gt_label = data_sample['phrase_ids'] + phrases = data_sample['phrases'] + assert len(gt) == len(gt_label) + + self.results.append((pred, gt, gt_label, phrases)) + + def compute_metrics(self, results: list) -> Dict[str, float]: + """Compute the metrics from processed results. + + Args: + results (list): The processed results of each batch. + Returns: + Dict[str, float]: The computed metrics. The keys are the names of + the metrics, and the values are corresponding results. + """ + logger: MMLogger = MMLogger.get_current_instance() + + pred_list, gt_list, gt_label_list, phrase_list = zip(*results) + + recall_tracker = RecallTracker(self.topk) + + for pred, gt_boxes, gt_labels, phrases in zip(pred_list, gt_list, + gt_label_list, + phrase_list): + pred_boxes = pred['bboxes'].cpu().numpy() + pred_labels = pred['labels'].cpu().numpy() + for i, phrase in enumerate(phrases): + cur_index = pred_labels == i + cur_boxes = pred_boxes[cur_index] + tar_index = [ + index for index, value in enumerate(gt_labels) + if value == i + ] + tar_boxes = gt_boxes[tar_index] + if self.merge: + tar_boxes = self.merge_boxes(tar_boxes) + if len(cur_boxes) == 0: + cur_boxes = [[0., 0., 0., 0.]] + ious = bbox_overlaps( + np.asarray(cur_boxes), np.asarray(tar_boxes)) + for k in self.topk: + if k == -1: + maxi = ious.max() + else: + assert k > 0 + maxi = ious[:k].max() + if maxi >= self.iou_thrs: + recall_tracker.add_positive(k, 'all') + # TODO: do not support class-wise evaluation yet + # for phrase_type in phrase['phrase_type']: + # recall_tracker.add_positive(k, phrase_type) + else: + recall_tracker.add_negative(k, 'all') + # for phrase_type in phrase['phrase_type']: + # recall_tracker.add_negative(k, phrase_type) + + results = recall_tracker.report() + logger.info(results) + return results diff --git a/mmdet/evaluation/metrics/grefcoco_metric.py b/mmdet/evaluation/metrics/grefcoco_metric.py new file mode 100644 index 00000000000..55cc638c5e4 --- /dev/null +++ b/mmdet/evaluation/metrics/grefcoco_metric.py @@ -0,0 +1,122 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Dict, Optional, Sequence + +import numpy as np +import torch +from mmengine.evaluator import BaseMetric +from mmengine.fileio import get_local_path +from mmengine.logging import MMLogger + +from mmdet.datasets.api_wrappers import COCO +from mmdet.registry import METRICS +from ..functional import bbox_overlaps + + +# refer from https://github.com/henghuiding/gRefCOCO/blob/main/mdetr/datasets/refexp.py # noqa +@METRICS.register_module() +class gRefCOCOMetric(BaseMetric): + default_prefix: Optional[str] = 'grefcoco' + + def __init__(self, + ann_file: Optional[str] = None, + metric: str = 'bbox', + iou_thrs: float = 0.5, + thresh_score: float = 0.7, + thresh_f1: float = 1.0, + **kwargs) -> None: + super().__init__(**kwargs) + self.metric = metric + self.iou_thrs = iou_thrs + self.thresh_score = thresh_score + self.thresh_f1 = thresh_f1 + + with get_local_path(ann_file) as local_path: + self.coco = COCO(local_path) + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + for data_sample in data_samples: + result = dict() + pred = data_sample['pred_instances'] + result['img_id'] = data_sample['img_id'] + result['bboxes'] = pred['bboxes'].cpu() + result['scores'] = pred['scores'].cpu() + self.results.append(result) + + def compute_metrics(self, results: list) -> Dict[str, float]: + logger: MMLogger = MMLogger.get_current_instance() + + correct_image = 0 + num_image = 0 + nt = {'TP': 0, 'TN': 0, 'FP': 0, 'FN': 0} + + for result in results: + img_id = result['img_id'] + TP = 0 + + ann_ids = self.coco.getAnnIds(imgIds=img_id) + target = self.coco.loadAnns(ann_ids[0]) + + converted_bbox_all = [] + no_target_flag = False + for one_target in target: + if one_target['category_id'] == -1: + no_target_flag = True + target_bbox = one_target['bbox'] + converted_bbox = [ + target_bbox[0], + target_bbox[1], + target_bbox[2] + target_bbox[0], + target_bbox[3] + target_bbox[1], + ] + converted_bbox_all.append( + np.array(converted_bbox).reshape(-1, 4)) + gt_bbox_all = np.concatenate(converted_bbox_all, axis=0) + + idx = result['scores'] >= self.thresh_score + filtered_boxes = result['bboxes'][idx] + + iou = bbox_overlaps(filtered_boxes.numpy(), gt_bbox_all) + iou = torch.from_numpy(iou) + + num_prediction = filtered_boxes.shape[0] + num_gt = gt_bbox_all.shape[0] + if no_target_flag: + if num_prediction >= 1: + nt['FN'] += 1 + else: + nt['TP'] += 1 + if num_prediction >= 1: + f_1 = 0. + else: + f_1 = 1.0 + else: + if num_prediction >= 1: + nt['TN'] += 1 + else: + nt['FP'] += 1 + for i in range(min(num_prediction, num_gt)): + top_value, top_index = torch.topk(iou.flatten(0, 1), 1) + if top_value < self.iou_thrs: + break + else: + top_index_x = top_index // num_gt + top_index_y = top_index % num_gt + TP += 1 + iou[top_index_x[0], :] = 0.0 + iou[:, top_index_y[0]] = 0.0 + FP = num_prediction - TP + FN = num_gt - TP + f_1 = 2 * TP / (2 * TP + FP + FN) + + if f_1 >= self.thresh_f1: + correct_image += 1 + num_image += 1 + + score = correct_image / max(num_image, 1) + results = { + 'F1_score': score, + 'T_acc': nt['TN'] / (nt['TN'] + nt['FP']), + 'N_acc': nt['TP'] / (nt['TP'] + nt['FN']) + } + logger.info(results) + return results diff --git a/mmdet/evaluation/metrics/lvis_metric.py b/mmdet/evaluation/metrics/lvis_metric.py index e4dd6141c0e..a861c6ee7b4 100644 --- a/mmdet/evaluation/metrics/lvis_metric.py +++ b/mmdet/evaluation/metrics/lvis_metric.py @@ -1,14 +1,20 @@ # Copyright (c) OpenMMLab. All rights reserved. import itertools +import logging import os.path as osp import tempfile import warnings -from collections import OrderedDict +from collections import OrderedDict, defaultdict from typing import Dict, List, Optional, Sequence, Union import numpy as np +import torch +from mmengine.dist import (all_gather_object, broadcast_object_list, + is_main_process) +from mmengine.evaluator import BaseMetric +from mmengine.evaluator.metric import _to_cpu from mmengine.fileio import get_local_path -from mmengine.logging import MMLogger +from mmengine.logging import MMLogger, print_log from terminaltables import AsciiTable from mmdet.registry import METRICS @@ -18,6 +24,7 @@ try: import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': warnings.warn( 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 @@ -362,3 +369,166 @@ def compute_metrics(self, results: list) -> Dict[str, float]: if tmp_dir is not None: tmp_dir.cleanup() return eval_results + + +def _merge_lists(listA, listB, maxN, key): + result = [] + indA, indB = 0, 0 + while (indA < len(listA) or indB < len(listB)) and len(result) < maxN: + if (indB < len(listB)) and (indA >= len(listA) + or key(listA[indA]) < key(listB[indB])): + result.append(listB[indB]) + indB += 1 + else: + result.append(listA[indA]) + indA += 1 + return result + + +@METRICS.register_module() +class LVISFixedAPMetric(BaseMetric): + default_prefix: Optional[str] = 'lvis_fixed_ap' + + def __init__(self, + ann_file: str, + topk: int = 10000, + format_only: bool = False, + outfile_prefix: Optional[str] = None, + collect_device: str = 'cpu', + prefix: Optional[str] = None, + backend_args: dict = None) -> None: + + if lvis is None: + raise RuntimeError( + 'Package lvis is not installed. Please run "pip install ' + 'git+https://github.com/lvis-dataset/lvis-api.git".') + super().__init__(collect_device=collect_device, prefix=prefix) + + self.format_only = format_only + if self.format_only: + assert outfile_prefix is not None, 'outfile_prefix must be not' + 'None when format_only is True, otherwise the result files will' + 'be saved to a temp directory which will be cleaned up at the end.' + + self.outfile_prefix = outfile_prefix + self.backend_args = backend_args + + with get_local_path( + ann_file, backend_args=self.backend_args) as local_path: + self._lvis_api = LVIS(local_path) + + self.cat_ids = self._lvis_api.get_cat_ids() + + self.results = {} + self.topk = topk + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + """Process one batch of data samples and predictions. The processed + results should be stored in ``self.results``, which will be used to + compute the metrics when all batches have been processed. + + Args: + data_batch (dict): A batch of data from the dataloader. + data_samples (Sequence[dict]): A batch of data samples that + contain annotations and predictions. + """ + cur_results = [] + for data_sample in data_samples: + pred = data_sample['pred_instances'] + xmin, ymin, xmax, ymax = pred['bboxes'].cpu().unbind(1) + boxes = torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), + dim=1).tolist() + + scores = pred['scores'].cpu().numpy() + labels = pred['labels'].cpu().numpy() + + if len(boxes) == 0: + continue + + cur_results.extend([{ + 'image_id': data_sample['img_id'], + 'category_id': self.cat_ids[labels[k]], + 'bbox': box, + 'score': scores[k], + } for k, box in enumerate(boxes)]) + + by_cat = defaultdict(list) + for ann in cur_results: + by_cat[ann['category_id']].append(ann) + + for cat, cat_anns in by_cat.items(): + if cat not in self.results: + self.results[cat] = [] + + cur = sorted( + cat_anns, key=lambda x: x['score'], reverse=True)[:self.topk] + self.results[cat] = _merge_lists( + self.results[cat], cur, self.topk, key=lambda x: x['score']) + + def compute_metrics(self, results: dict) -> dict: + logger: MMLogger = MMLogger.get_current_instance() + + new_results = [] + + missing_dets_cats = set() + for cat, cat_anns in results.items(): + if len(cat_anns) < self.topk: + missing_dets_cats.add(cat) + new_results.extend( + sorted(cat_anns, key=lambda x: x['score'], + reverse=True)[:self.topk]) + + if missing_dets_cats: + logger.info( + f'\n===\n' + f'{len(missing_dets_cats)} classes had less than {self.topk} ' + f'detections!\n Outputting {self.topk} detections for each ' + f'class will improve AP further.\n ===') + + new_results = LVISResults(self._lvis_api, new_results, max_dets=-1) + lvis_eval = LVISEval(self._lvis_api, new_results, iou_type='bbox') + params = lvis_eval.params + params.max_dets = -1 # No limit on detections per image. + lvis_eval.run() + lvis_eval.print_results() + metrics = { + k: v + for k, v in lvis_eval.results.items() if k.startswith('AP') + } + logger.info(f'mAP_copypaste: {metrics}') + return metrics + + def evaluate(self, size: int) -> dict: + if len(self.results) == 0: + print_log( + f'{self.__class__.__name__} got empty `self.results`. Please ' + 'ensure that the processed results are properly added into ' + '`self.results` in `process` method.', + logger='current', + level=logging.WARNING) + + all_cats = all_gather_object(self.results) + results = defaultdict(list) + for cats in all_cats: + for cat, cat_anns in cats.items(): + results[cat].extend(cat_anns) + + if is_main_process(): + # cast all tensors in results list to cpu + results = _to_cpu(results) + _metrics = self.compute_metrics(results) # type: ignore + # Add prefix to metric names + if self.prefix: + _metrics = { + '/'.join((self.prefix, k)): v + for k, v in _metrics.items() + } + metrics = [_metrics] + else: + metrics = [None] # type: ignore + + broadcast_object_list(metrics) + + # reset the results + self.results = {} + return metrics[0] diff --git a/mmdet/evaluation/metrics/ov_coco_metric.py b/mmdet/evaluation/metrics/ov_coco_metric.py new file mode 100644 index 00000000000..08cb9025149 --- /dev/null +++ b/mmdet/evaluation/metrics/ov_coco_metric.py @@ -0,0 +1,266 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import itertools +import os.path as osp +import tempfile +from collections import OrderedDict +from typing import Dict + +import numpy as np +from mmengine.fileio import load +from mmengine.logging import MMLogger +from terminaltables import AsciiTable + +from mmdet.datasets.api_wrappers import COCO, COCOeval, COCOevalMP +from mmdet.registry import METRICS +from .coco_metric import CocoMetric + + +@METRICS.register_module() +class OVCocoMetric(CocoMetric): + + def compute_metrics(self, results: list) -> Dict[str, float]: + """Compute the metrics from processed results. + + Args: + results (list): The processed results of each batch. + + Returns: + Dict[str, float]: The computed metrics. The keys are the names of + the metrics, and the values are corresponding results. + """ + logger: MMLogger = MMLogger.get_current_instance() + + # split gt and prediction list + gts, preds = zip(*results) + + tmp_dir = None + if self.outfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + outfile_prefix = osp.join(tmp_dir.name, 'results') + else: + outfile_prefix = self.outfile_prefix + + if self._coco_api is None: + # use converted gt json file to initialize coco api + logger.info('Converting ground truth to coco format...') + coco_json_path = self.gt_to_coco_json( + gt_dicts=gts, outfile_prefix=outfile_prefix) + self._coco_api = COCO(coco_json_path) + + # handle lazy init + if self.cat_ids is None: + self.cat_ids = self._coco_api.get_cat_ids( + cat_names=self.dataset_meta['classes']) + self.base_cat_ids = self._coco_api.get_cat_ids( + cat_names=self.dataset_meta['base_classes']) + self.novel_cat_ids = self._coco_api.get_cat_ids( + cat_names=self.dataset_meta['novel_classes']) + + if self.img_ids is None: + self.img_ids = self._coco_api.get_img_ids() + + # convert predictions to coco format and dump to json file + result_files = self.results2json(preds, outfile_prefix) + + eval_results = OrderedDict() + if self.format_only: + logger.info('results are saved in ' + f'{osp.dirname(outfile_prefix)}') + return eval_results + + for metric in self.metrics: + logger.info(f'Evaluating {metric}...') + + # TODO: May refactor fast_eval_recall to an independent metric? + # fast eval recall + if metric == 'proposal_fast': + ar = self.fast_eval_recall( + preds, self.proposal_nums, self.iou_thrs, logger=logger) + log_msg = [] + for i, num in enumerate(self.proposal_nums): + eval_results[f'AR@{num}'] = ar[i] + log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}') + log_msg = ''.join(log_msg) + logger.info(log_msg) + continue + + # evaluate proposal, bbox and segm + iou_type = 'bbox' if metric == 'proposal' else metric + if metric not in result_files: + raise KeyError(f'{metric} is not in results') + try: + predictions = load(result_files[metric]) + if iou_type == 'segm': + # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331 # noqa + # When evaluating mask AP, if the results contain bbox, + # cocoapi will use the box area instead of the mask area + # for calculating the instance area. Though the overall AP + # is not affected, this leads to different + # small/medium/large mask AP results. + for x in predictions: + x.pop('bbox') + coco_dt = self._coco_api.loadRes(predictions) + + except IndexError: + logger.error( + 'The testing results of the whole dataset is empty.') + break + + if self.use_mp_eval: + coco_eval = COCOevalMP(self._coco_api, coco_dt, iou_type) + else: + coco_eval = COCOeval(self._coco_api, coco_dt, iou_type) + + coco_eval.params.catIds = self.cat_ids + coco_eval.params.imgIds = self.img_ids + coco_eval.params.maxDets = list(self.proposal_nums) + coco_eval.params.iouThrs = self.iou_thrs + + # mapping of cocoEval.stats + coco_metric_names = { + 'mAP': 0, + 'mAP_50': 1, + 'mAP_75': 2, + 'mAP_s': 3, + 'mAP_m': 4, + 'mAP_l': 5, + 'AR@100': 6, + 'AR@300': 7, + 'AR@1000': 8, + 'AR_s@1000': 9, + 'AR_m@1000': 10, + 'AR_l@1000': 11 + } + metric_items = self.metric_items + if metric_items is not None: + for metric_item in metric_items: + if metric_item not in coco_metric_names: + raise KeyError( + f'metric item "{metric_item}" is not supported') + + if metric == 'proposal': + coco_eval.params.useCats = 0 + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + if metric_items is None: + metric_items = [ + 'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000', + 'AR_m@1000', 'AR_l@1000' + ] + + for item in metric_items: + val = float( + f'{coco_eval.stats[coco_metric_names[item]]:.3f}') + eval_results[item] = val + else: + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + if self.classwise: # Compute per-category AP + # Compute per-category AP + # from https://github.com/facebookresearch/detectron2/ + precisions = coco_eval.eval['precision'] + # precision: (iou, recall, cls, area range, max dets) + assert len(self.cat_ids) == precisions.shape[2] + + results_per_category = [] + for idx, cat_id in enumerate(self.cat_ids): + t = [] + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + nm = self._coco_api.loadCats(cat_id)[0] + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + if precision.size: + ap = np.mean(precision) + else: + ap = float('nan') + t.append(f'{nm["name"]}') + t.append(f'{round(ap, 3)}') + eval_results[f'{nm["name"]}_precision'] = round(ap, 3) + + # indexes of IoU @50 and @75 + for iou in [0, 5]: + precision = precisions[iou, :, idx, 0, -1] + precision = precision[precision > -1] + if precision.size: + ap = np.mean(precision) + else: + ap = float('nan') + t.append(f'{round(ap, 3)}') + + # indexes of area of small, median and large + for area in [1, 2, 3]: + precision = precisions[:, :, idx, area, -1] + precision = precision[precision > -1] + if precision.size: + ap = np.mean(precision) + else: + ap = float('nan') + t.append(f'{round(ap, 3)}') + results_per_category.append(tuple(t)) + + num_columns = len(results_per_category[0]) + results_flatten = list( + itertools.chain(*results_per_category)) + headers = [ + 'category', 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', + 'mAP_m', 'mAP_l' + ] + results_2d = itertools.zip_longest(*[ + results_flatten[i::num_columns] + for i in range(num_columns) + ]) + table_data = [headers] + table_data += [result for result in results_2d] + table = AsciiTable(table_data) + logger.info('\n' + table.table) + + # ------------get novel_ap50 and base_ap50--------- + precisions = coco_eval.eval['precision'] + assert len(self.cat_ids) == precisions.shape[2] + base_inds, novel_inds = [], [] + + for idx, catId in enumerate(self.cat_ids): + if catId in self.base_cat_ids: + base_inds.append(idx) + if catId in self.novel_cat_ids: + novel_inds.append(idx) + + base_ap = precisions[:, :, base_inds, 0, -1] + novel_ap = precisions[:, :, novel_inds, 0, -1] + base_ap50 = precisions[0, :, base_inds, 0, -1] + novel_ap50 = precisions[0, :, novel_inds, 0, -1] + + eval_results['base_ap'] = np.mean( + base_ap[base_ap > -1]) if len( + base_ap[base_ap > -1]) else -1 + eval_results['novel_ap'] = np.mean( + novel_ap[novel_ap > -1]) if len( + novel_ap[novel_ap > -1]) else -1 + eval_results['base_ap50'] = np.mean( + base_ap50[base_ap50 > -1]) if len( + base_ap50[base_ap50 > -1]) else -1 + eval_results['novel_ap50'] = np.mean( + novel_ap50[novel_ap50 > -1]) if len( + novel_ap50[novel_ap50 > -1]) else -1 + # ------------get novel_ap50 and base_ap50--------- + if metric_items is None: + metric_items = [ + 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l' + ] + + for metric_item in metric_items: + key = f'{metric}_{metric_item}' + val = coco_eval.stats[coco_metric_names[metric_item]] + eval_results[key] = float(f'{round(val, 3)}') + + ap = coco_eval.stats[:6] + logger.info(f'{metric}_mAP_copypaste: {ap[0]:.3f} ' + f'{ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} ' + f'{ap[4]:.3f} {ap[5]:.3f}') + + if tmp_dir is not None: + tmp_dir.cleanup() + return eval_results diff --git a/mmdet/evaluation/metrics/refexp_metric.py b/mmdet/evaluation/metrics/refexp_metric.py new file mode 100644 index 00000000000..8bcdf1629b9 --- /dev/null +++ b/mmdet/evaluation/metrics/refexp_metric.py @@ -0,0 +1,100 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Dict, Optional, Sequence + +import numpy as np +from mmengine.evaluator import BaseMetric +from mmengine.fileio import get_local_path +from mmengine.logging import MMLogger + +from mmdet.datasets.api_wrappers import COCO +from mmdet.registry import METRICS +from ..functional import bbox_overlaps + + +@METRICS.register_module() +class RefExpMetric(BaseMetric): + default_prefix: Optional[str] = 'refexp' + + def __init__(self, + ann_file: Optional[str] = None, + metric: str = 'bbox', + topk=(1, 5, 10), + iou_thrs: float = 0.5, + **kwargs) -> None: + super().__init__(**kwargs) + self.metric = metric + self.topk = topk + self.iou_thrs = iou_thrs + + with get_local_path(ann_file) as local_path: + self.coco = COCO(local_path) + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + for data_sample in data_samples: + result = dict() + pred = data_sample['pred_instances'] + result['img_id'] = data_sample['img_id'] + result['bboxes'] = pred['bboxes'].cpu().numpy() + result['scores'] = pred['scores'].cpu().numpy() + self.results.append(result) + + def compute_metrics(self, results: list) -> Dict[str, float]: + logger: MMLogger = MMLogger.get_current_instance() + + dataset2score = { + 'refcoco': {k: 0.0 + for k in self.topk}, + 'refcoco+': {k: 0.0 + for k in self.topk}, + 'refcocog': {k: 0.0 + for k in self.topk}, + } + dataset2count = {'refcoco': 0.0, 'refcoco+': 0.0, 'refcocog': 0.0} + + for result in results: + img_id = result['img_id'] + + ann_ids = self.coco.getAnnIds(imgIds=img_id) + assert len(ann_ids) == 1 + img_info = self.coco.loadImgs(img_id)[0] + target = self.coco.loadAnns(ann_ids[0]) + + target_bbox = target[0]['bbox'] + converted_bbox = [ + target_bbox[0], + target_bbox[1], + target_bbox[2] + target_bbox[0], + target_bbox[3] + target_bbox[1], + ] + iou = bbox_overlaps(result['bboxes'], + np.array(converted_bbox).reshape(-1, 4)) + for k in self.topk: + if max(iou[:k]) >= self.iou_thrs: + dataset2score[img_info['dataset_name']][k] += 1.0 + dataset2count[img_info['dataset_name']] += 1.0 + + for key, value in dataset2score.items(): + for k in self.topk: + try: + value[k] /= dataset2count[key] + except Exception as e: + print(e) + + results = {} + mean_precision = 0.0 + for key, value in dataset2score.items(): + results[key] = sorted([v for k, v in value.items()]) + mean_precision += sum(results[key]) + logger.info( + f' Dataset: {key} - Precision @ 1, 5, 10: {results[key]}') + + # `mean_precision` key is used for saving the best checkpoint + out_results = {'mean_precision': mean_precision / 9.0} + + for i, k in enumerate(self.topk): + out_results[f'refcoco_precision@{k}'] = results['refcoco'][i] + for i, k in enumerate(self.topk): + out_results[f'refcoco+_precision@{k}'] = results['refcoco+'][i] + for i, k in enumerate(self.topk): + out_results[f'refcocog_precision@{k}'] = results['refcocog'][i] + return out_results diff --git a/mmdet/models/dense_heads/grounding_dino_head.py b/mmdet/models/dense_heads/grounding_dino_head.py index 3aced626555..8088322546f 100644 --- a/mmdet/models/dense_heads/grounding_dino_head.py +++ b/mmdet/models/dense_heads/grounding_dino_head.py @@ -417,14 +417,21 @@ def _predict_by_feat_single(self, max_per_img = self.test_cfg.get('max_per_img', len(cls_score)) img_shape = img_meta['img_shape'] - cls_score = convert_grounding_to_cls_scores( - logits=cls_score.sigmoid()[None], - positive_maps=[token_positive_maps])[0] - scores, indexes = cls_score.view(-1).topk(max_per_img) - num_classes = cls_score.shape[-1] - det_labels = indexes % num_classes - bbox_index = indexes // num_classes - bbox_pred = bbox_pred[bbox_index] + if token_positive_maps is not None: + cls_score = convert_grounding_to_cls_scores( + logits=cls_score.sigmoid()[None], + positive_maps=[token_positive_maps])[0] + scores, indexes = cls_score.view(-1).topk(max_per_img) + num_classes = cls_score.shape[-1] + det_labels = indexes % num_classes + bbox_index = indexes // num_classes + bbox_pred = bbox_pred[bbox_index] + else: + cls_score = cls_score.sigmoid() + scores, _ = cls_score.max(-1) + scores, indexes = scores.topk(max_per_img) + bbox_pred = bbox_pred[indexes] + det_labels = scores.new_zeros(scores.shape, dtype=torch.long) det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred) det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1] diff --git a/mmdet/models/detectors/glip.py b/mmdet/models/detectors/glip.py index e076a55fe20..45cfe7d39fd 100644 --- a/mmdet/models/detectors/glip.py +++ b/mmdet/models/detectors/glip.py @@ -1,7 +1,8 @@ # Copyright (c) OpenMMLab. All rights reserved. +import copy import re import warnings -from typing import Tuple, Union +from typing import Optional, Tuple, Union import torch from torch import Tensor @@ -26,8 +27,8 @@ def find_noun_phrases(caption: str) -> list: """ try: import nltk - nltk.download('punkt') - nltk.download('averaged_perceptron_tagger') + nltk.download('punkt', download_dir='~/nltk_data') + nltk.download('averaged_perceptron_tagger', download_dir='~/nltk_data') except ImportError: raise RuntimeError('nltk is not installed, please install it by: ' 'pip install nltk.') @@ -78,6 +79,7 @@ def run_ner(caption: str) -> Tuple[list, list]: noun_phrases = find_noun_phrases(caption) noun_phrases = [remove_punctuation(phrase) for phrase in noun_phrases] noun_phrases = [phrase for phrase in noun_phrases if phrase != ''] + print('noun_phrases:', noun_phrases) relevant_phrases = noun_phrases labels = noun_phrases @@ -166,6 +168,27 @@ def create_positive_map_label_to_token(positive_map: Tensor, return positive_map_label_to_token +def clean_label_name(name: str) -> str: + name = re.sub(r'\(.*\)', '', name) + name = re.sub(r'_', ' ', name) + name = re.sub(r' ', ' ', name) + return name + + +def chunks(lst: list, n: int) -> list: + """Yield successive n-sized chunks from lst.""" + all_ = [] + for i in range(0, len(lst), n): + data_index = lst[i:i + n] + all_.append(data_index) + counter = 0 + for i in all_: + counter += len(i) + assert (counter == len(lst)) + + return all_ + + @MODELS.register_module() class GLIP(SingleStageDetector): """Implementation of `GLIP `_ @@ -207,10 +230,52 @@ def __init__(self, self._special_tokens = '. ' + def to_enhance_text_prompts(self, original_caption, enhanced_text_prompts): + caption_string = '' + tokens_positive = [] + for idx, word in enumerate(original_caption): + if word in enhanced_text_prompts: + enhanced_text_dict = enhanced_text_prompts[word] + if 'prefix' in enhanced_text_dict: + caption_string += enhanced_text_dict['prefix'] + start_i = len(caption_string) + if 'name' in enhanced_text_dict: + caption_string += enhanced_text_dict['name'] + else: + caption_string += word + end_i = len(caption_string) + tokens_positive.append([[start_i, end_i]]) + + if 'suffix' in enhanced_text_dict: + caption_string += enhanced_text_dict['suffix'] + else: + tokens_positive.append( + [[len(caption_string), + len(caption_string) + len(word)]]) + caption_string += word + + if idx != len(original_caption) - 1: + caption_string += self._special_tokens + return caption_string, tokens_positive + + def to_plain_text_prompts(self, original_caption): + caption_string = '' + tokens_positive = [] + for idx, word in enumerate(original_caption): + tokens_positive.append( + [[len(caption_string), + len(caption_string) + len(word)]]) + caption_string += word + if idx != len(original_caption) - 1: + caption_string += self._special_tokens + return caption_string, tokens_positive + def get_tokens_and_prompts( - self, - original_caption: Union[str, list, tuple], - custom_entities: bool = False) -> Tuple[dict, str, list, list]: + self, + original_caption: Union[str, list, tuple], + custom_entities: bool = False, + enhanced_text_prompts: Optional[ConfigType] = None + ) -> Tuple[dict, str, list, list]: """Get the tokens positive and prompts for the caption.""" if isinstance(original_caption, (list, tuple)) or custom_entities: if custom_entities and isinstance(original_caption, str): @@ -219,15 +284,15 @@ def get_tokens_and_prompts( original_caption = list( filter(lambda x: len(x) > 0, original_caption)) - caption_string = '' - tokens_positive = [] - for idx, word in enumerate(original_caption): - tokens_positive.append( - [[len(caption_string), - len(caption_string) + len(word)]]) - caption_string += word - if idx != len(original_caption) - 1: - caption_string += self._special_tokens + original_caption = [clean_label_name(i) for i in original_caption] + + if custom_entities and enhanced_text_prompts is not None: + caption_string, tokens_positive = self.to_enhance_text_prompts( + original_caption, enhanced_text_prompts) + else: + caption_string, tokens_positive = self.to_plain_text_prompts( + original_caption) + tokenized = self.language_model.tokenizer([caption_string], return_tensors='pt') entities = original_caption @@ -248,17 +313,101 @@ def get_positive_map(self, tokenized, tokens_positive): return positive_map_label_to_token, positive_map def get_tokens_positive_and_prompts( - self, - original_caption: Union[str, list, tuple], - custom_entities: bool = False) -> Tuple[dict, str, Tensor, list]: - tokenized, caption_string, tokens_positive, entities = \ - self.get_tokens_and_prompts( - original_caption, custom_entities) - positive_map_label_to_token, positive_map = self.get_positive_map( - tokenized, tokens_positive) + self, + original_caption: Union[str, list, tuple], + custom_entities: bool = False, + enhanced_text_prompt: Optional[ConfigType] = None, + tokens_positive: Optional[list] = None, + ) -> Tuple[dict, str, Tensor, list]: + if tokens_positive is not None: + if tokens_positive == -1: + if not original_caption.endswith('.'): + original_caption = original_caption + self._special_tokens + return None, original_caption, None, original_caption + else: + if not original_caption.endswith('.'): + original_caption = original_caption + self._special_tokens + tokenized = self.language_model.tokenizer([original_caption], + return_tensors='pt') + positive_map_label_to_token, positive_map = \ + self.get_positive_map(tokenized, tokens_positive) + + entities = [] + for token_positive in tokens_positive: + instance_entities = [] + for t in token_positive: + instance_entities.append(original_caption[t[0]:t[1]]) + entities.append(' / '.join(instance_entities)) + return positive_map_label_to_token, original_caption, \ + positive_map, entities + + chunked_size = self.test_cfg.get('chunked_size', -1) + if not self.training and chunked_size > 0: + assert isinstance(original_caption, + (list, tuple)) or custom_entities is True + all_output = self.get_tokens_positive_and_prompts_chunked( + original_caption, enhanced_text_prompt) + positive_map_label_to_token, \ + caption_string, \ + positive_map, \ + entities = all_output + else: + tokenized, caption_string, tokens_positive, entities = \ + self.get_tokens_and_prompts( + original_caption, custom_entities, enhanced_text_prompt) + positive_map_label_to_token, positive_map = self.get_positive_map( + tokenized, tokens_positive) + if tokenized.input_ids.shape[1] > self.language_model.max_tokens: + warnings.warn('Inputting a text that is too long will result ' + 'in poor prediction performance. ' + 'Please reduce the text length.') return positive_map_label_to_token, caption_string, \ positive_map, entities + def get_tokens_positive_and_prompts_chunked( + self, + original_caption: Union[list, tuple], + enhanced_text_prompts: Optional[ConfigType] = None): + chunked_size = self.test_cfg.get('chunked_size', -1) + original_caption = [clean_label_name(i) for i in original_caption] + + original_caption_chunked = chunks(original_caption, chunked_size) + ids_chunked = chunks( + list(range(1, + len(original_caption) + 1)), chunked_size) + + positive_map_label_to_token_chunked = [] + caption_string_chunked = [] + positive_map_chunked = [] + entities_chunked = [] + + for i in range(len(ids_chunked)): + if enhanced_text_prompts is not None: + caption_string, tokens_positive = self.to_enhance_text_prompts( + original_caption_chunked[i], enhanced_text_prompts) + else: + caption_string, tokens_positive = self.to_plain_text_prompts( + original_caption_chunked[i]) + tokenized = self.language_model.tokenizer([caption_string], + return_tensors='pt') + if tokenized.input_ids.shape[1] > self.language_model.max_tokens: + warnings.warn('Inputting a text that is too long will result ' + 'in poor prediction performance. ' + 'Please reduce the --chunked-size.') + positive_map_label_to_token, positive_map = self.get_positive_map( + tokenized, tokens_positive) + + caption_string_chunked.append(caption_string) + positive_map_label_to_token_chunked.append( + positive_map_label_to_token) + positive_map_chunked.append(positive_map) + entities_chunked.append(original_caption_chunked[i]) + + return positive_map_label_to_token_chunked, \ + caption_string_chunked, \ + positive_map_chunked, \ + entities_chunked + def loss(self, batch_inputs: Tensor, batch_data_samples: SampleList) -> Union[dict, list]: # TODO: Only open vocabulary tasks are supported for training now. @@ -342,9 +491,16 @@ def predict(self, - bboxes (Tensor): Has a shape (num_instances, 4), the last dimension 4 arrange as (x1, y1, x2, y2). """ - text_prompts = [ - data_samples.text for data_samples in batch_data_samples - ] + text_prompts = [] + enhanced_text_prompts = [] + tokens_positives = [] + for data_samples in batch_data_samples: + text_prompts.append(data_samples.text) + if 'caption_prompt' in data_samples: + enhanced_text_prompts.append(data_samples.caption_prompt) + else: + enhanced_text_prompts.append(None) + tokens_positives.append(data_samples.get('tokens_positive', None)) if 'custom_entities' in batch_data_samples[0]: # Assuming that the `custom_entities` flag @@ -357,31 +513,62 @@ def predict(self, # All the text prompts are the same, # so there is no need to calculate them multiple times. _positive_maps_and_prompts = [ - self.get_tokens_positive_and_prompts(text_prompts[0], - custom_entities) + self.get_tokens_positive_and_prompts( + text_prompts[0], custom_entities, enhanced_text_prompts[0], + tokens_positives[0]) ] * len(batch_inputs) else: _positive_maps_and_prompts = [ self.get_tokens_positive_and_prompts(text_prompt, - custom_entities) - for text_prompt in text_prompts + custom_entities, + enhanced_text_prompt, + tokens_positive) + for text_prompt, enhanced_text_prompt, tokens_positive in zip( + text_prompts, enhanced_text_prompts, tokens_positives) ] token_positive_maps, text_prompts, _, entities = zip( *_positive_maps_and_prompts) - language_dict_features = self.language_model(list(text_prompts)) + visual_features = self.extract_feat(batch_inputs) - for i, data_samples in enumerate(batch_data_samples): - data_samples.token_positive_map = token_positive_maps[i] + if isinstance(text_prompts[0], list): + # chunked text prompts, only bs=1 is supported + assert len(batch_inputs) == 1 + count = 0 + results_list = [] + + entities = [[item for lst in entities[0] for item in lst]] + + for b in range(len(text_prompts[0])): + text_prompts_once = [text_prompts[0][b]] + token_positive_maps_once = token_positive_maps[0][b] + language_dict_features = self.language_model(text_prompts_once) + batch_data_samples[ + 0].token_positive_map = token_positive_maps_once + + pred_instances = self.bbox_head.predict( + copy.deepcopy(visual_features), + language_dict_features, + batch_data_samples, + rescale=rescale)[0] + + if len(pred_instances) > 0: + pred_instances.labels += count + count += len(token_positive_maps_once) + results_list.append(pred_instances) + results_list = [results_list[0].cat(results_list)] + else: + language_dict_features = self.language_model(list(text_prompts)) - visual_features = self.extract_feat(batch_inputs) + for i, data_samples in enumerate(batch_data_samples): + data_samples.token_positive_map = token_positive_maps[i] - results_list = self.bbox_head.predict( - visual_features, - language_dict_features, - batch_data_samples, - rescale=rescale) + results_list = self.bbox_head.predict( + visual_features, + language_dict_features, + batch_data_samples, + rescale=rescale) for data_sample, pred_instances, entity in zip(batch_data_samples, results_list, entities): diff --git a/mmdet/models/detectors/grounding_dino.py b/mmdet/models/detectors/grounding_dino.py index 69d398bec8f..4ec9d14e634 100644 --- a/mmdet/models/detectors/grounding_dino.py +++ b/mmdet/models/detectors/grounding_dino.py @@ -1,6 +1,8 @@ # Copyright (c) OpenMMLab. All rights reserved. +import copy +import re import warnings -from typing import Dict, Tuple, Union +from typing import Dict, Optional, Tuple, Union import torch import torch.nn as nn @@ -8,6 +10,7 @@ from mmdet.registry import MODELS from mmdet.structures import OptSampleList, SampleList +from mmdet.utils import ConfigType from ..layers import SinePositionalEncoding from ..layers.transformer.grounding_dino_layers import ( GroundingDinoTransformerDecoder, GroundingDinoTransformerEncoder) @@ -16,6 +19,27 @@ run_ner) +def clean_label_name(name: str) -> str: + name = re.sub(r'\(.*\)', '', name) + name = re.sub(r'_', ' ', name) + name = re.sub(r' ', ' ', name) + return name + + +def chunks(lst: list, n: int) -> list: + """Yield successive n-sized chunks from lst.""" + all_ = [] + for i in range(0, len(lst), n): + data_index = lst[i:i + n] + all_.append(data_index) + counter = 0 + for i in all_: + counter += len(i) + assert (counter == len(lst)) + + return all_ + + @MODELS.register_module() class GroundingDINO(DINO): """Implementation of `Grounding DINO: Marrying DINO with Grounded Pre- @@ -64,10 +88,49 @@ def init_weights(self) -> None: nn.init.constant_(self.text_feat_map.bias.data, 0) nn.init.xavier_uniform_(self.text_feat_map.weight.data) + def to_enhance_text_prompts(self, original_caption, enhanced_text_prompts): + caption_string = '' + tokens_positive = [] + for idx, word in enumerate(original_caption): + if word in enhanced_text_prompts: + enhanced_text_dict = enhanced_text_prompts[word] + if 'prefix' in enhanced_text_dict: + caption_string += enhanced_text_dict['prefix'] + start_i = len(caption_string) + if 'name' in enhanced_text_dict: + caption_string += enhanced_text_dict['name'] + else: + caption_string += word + end_i = len(caption_string) + tokens_positive.append([[start_i, end_i]]) + + if 'suffix' in enhanced_text_dict: + caption_string += enhanced_text_dict['suffix'] + else: + tokens_positive.append( + [[len(caption_string), + len(caption_string) + len(word)]]) + caption_string += word + caption_string += self._special_tokens + return caption_string, tokens_positive + + def to_plain_text_prompts(self, original_caption): + caption_string = '' + tokens_positive = [] + for idx, word in enumerate(original_caption): + tokens_positive.append( + [[len(caption_string), + len(caption_string) + len(word)]]) + caption_string += word + caption_string += self._special_tokens + return caption_string, tokens_positive + def get_tokens_and_prompts( - self, - original_caption: Union[str, list, tuple], - custom_entities: bool = False) -> Tuple[dict, str, list]: + self, + original_caption: Union[str, list, tuple], + custom_entities: bool = False, + enhanced_text_prompts: Optional[ConfigType] = None + ) -> Tuple[dict, str, list]: """Get the tokens positive and prompts for the caption.""" if isinstance(original_caption, (list, tuple)) or custom_entities: if custom_entities and isinstance(original_caption, str): @@ -76,14 +139,15 @@ def get_tokens_and_prompts( original_caption = list( filter(lambda x: len(x) > 0, original_caption)) - caption_string = '' - tokens_positive = [] - for idx, word in enumerate(original_caption): - tokens_positive.append( - [[len(caption_string), - len(caption_string) + len(word)]]) - caption_string += word - caption_string += self._special_tokens + original_caption = [clean_label_name(i) for i in original_caption] + + if custom_entities and enhanced_text_prompts is not None: + caption_string, tokens_positive = self.to_enhance_text_prompts( + original_caption, enhanced_text_prompts) + else: + caption_string, tokens_positive = self.to_plain_text_prompts( + original_caption) + # NOTE: Tokenizer in Grounding DINO is different from # that in GLIP. The tokenizer in GLIP will pad the # caption_string to max_length, while the tokenizer @@ -113,15 +177,22 @@ def get_tokens_and_prompts( return tokenized, caption_string, tokens_positive, entities def get_positive_map(self, tokenized, tokens_positive): - positive_map = create_positive_map(tokenized, tokens_positive) + positive_map = create_positive_map( + tokenized, + tokens_positive, + max_num_entities=self.bbox_head.cls_branches[ + self.decoder.num_layers].max_text_len) positive_map_label_to_token = create_positive_map_label_to_token( positive_map, plus=1) return positive_map_label_to_token, positive_map def get_tokens_positive_and_prompts( - self, - original_caption: Union[str, list, tuple], - custom_entities: bool = False) -> Tuple[dict, str, Tensor, list]: + self, + original_caption: Union[str, list, tuple], + custom_entities: bool = False, + enhanced_text_prompt: Optional[ConfigType] = None, + tokens_positive: Optional[list] = None, + ) -> Tuple[dict, str, Tensor, list]: """Get the tokens positive and prompts for the caption. Args: @@ -135,14 +206,94 @@ def get_tokens_positive_and_prompts( id, which is numbered from 1, to its positive token id. The str represents the prompts. """ - tokenized, caption_string, tokens_positive, entities = \ - self.get_tokens_and_prompts( - original_caption, custom_entities) - positive_map_label_to_token, positive_map = self.get_positive_map( - tokenized, tokens_positive) + if tokens_positive is not None: + if tokens_positive == -1: + if not original_caption.endswith('.'): + original_caption = original_caption + self._special_tokens + return None, original_caption, None, original_caption + else: + if not original_caption.endswith('.'): + original_caption = original_caption + self._special_tokens + tokenized = self.language_model.tokenizer( + [original_caption], + padding='max_length' + if self.language_model.pad_to_max else 'longest', + return_tensors='pt') + positive_map_label_to_token, positive_map = \ + self.get_positive_map(tokenized, tokens_positive) + + entities = [] + for token_positive in tokens_positive: + instance_entities = [] + for t in token_positive: + instance_entities.append(original_caption[t[0]:t[1]]) + entities.append(' / '.join(instance_entities)) + return positive_map_label_to_token, original_caption, \ + positive_map, entities + + chunked_size = self.test_cfg.get('chunked_size', -1) + if not self.training and chunked_size > 0: + assert isinstance(original_caption, + (list, tuple)) or custom_entities is True + all_output = self.get_tokens_positive_and_prompts_chunked( + original_caption, enhanced_text_prompt) + positive_map_label_to_token, \ + caption_string, \ + positive_map, \ + entities = all_output + else: + tokenized, caption_string, tokens_positive, entities = \ + self.get_tokens_and_prompts( + original_caption, custom_entities, enhanced_text_prompt) + positive_map_label_to_token, positive_map = self.get_positive_map( + tokenized, tokens_positive) return positive_map_label_to_token, caption_string, \ positive_map, entities + def get_tokens_positive_and_prompts_chunked( + self, + original_caption: Union[list, tuple], + enhanced_text_prompts: Optional[ConfigType] = None): + chunked_size = self.test_cfg.get('chunked_size', -1) + original_caption = [clean_label_name(i) for i in original_caption] + + original_caption_chunked = chunks(original_caption, chunked_size) + ids_chunked = chunks( + list(range(1, + len(original_caption) + 1)), chunked_size) + + positive_map_label_to_token_chunked = [] + caption_string_chunked = [] + positive_map_chunked = [] + entities_chunked = [] + + for i in range(len(ids_chunked)): + if enhanced_text_prompts is not None: + caption_string, tokens_positive = self.to_enhance_text_prompts( + original_caption_chunked[i], enhanced_text_prompts) + else: + caption_string, tokens_positive = self.to_plain_text_prompts( + original_caption_chunked[i]) + tokenized = self.language_model.tokenizer([caption_string], + return_tensors='pt') + if tokenized.input_ids.shape[1] > self.language_model.max_tokens: + warnings.warn('Inputting a text that is too long will result ' + 'in poor prediction performance. ' + 'Please reduce the --chunked-size.') + positive_map_label_to_token, positive_map = self.get_positive_map( + tokenized, tokens_positive) + + caption_string_chunked.append(caption_string) + positive_map_label_to_token_chunked.append( + positive_map_label_to_token) + positive_map_chunked.append(positive_map) + entities_chunked.append(original_caption_chunked[i]) + + return positive_map_label_to_token_chunked, \ + caption_string_chunked, \ + positive_map_chunked, \ + entities_chunked + def forward_transformer( self, img_feats: Tuple[Tensor], @@ -261,7 +412,6 @@ def pre_decoder( def loss(self, batch_inputs: Tensor, batch_data_samples: SampleList) -> Union[dict, list]: - # TODO: Only open vocabulary tasks are supported for training now. text_prompts = [ data_samples.text for data_samples in batch_data_samples ] @@ -271,34 +421,55 @@ def loss(self, batch_inputs: Tensor, for data_samples in batch_data_samples ] - new_text_prompts = [] - positive_maps = [] - if len(set(text_prompts)) == 1: - # All the text prompts are the same, - # so there is no need to calculate them multiple times. - tokenized, caption_string, tokens_positive, _ = \ - self.get_tokens_and_prompts( - text_prompts[0], True) - new_text_prompts = [caption_string] * len(batch_inputs) - for gt_label in gt_labels: + if 'tokens_positive' in batch_data_samples[0]: + tokens_positive = [ + data_samples.tokens_positive + for data_samples in batch_data_samples + ] + positive_maps = [] + for token_positive, text_prompt, gt_label in zip( + tokens_positive, text_prompts, gt_labels): + tokenized = self.language_model.tokenizer( + [text_prompt], + padding='max_length' + if self.language_model.pad_to_max else 'longest', + return_tensors='pt') new_tokens_positive = [ - tokens_positive[label] for label in gt_label + token_positive[label.item()] for label in gt_label ] _, positive_map = self.get_positive_map( tokenized, new_tokens_positive) positive_maps.append(positive_map) + new_text_prompts = text_prompts else: - for text_prompt, gt_label in zip(text_prompts, gt_labels): + new_text_prompts = [] + positive_maps = [] + if len(set(text_prompts)) == 1: + # All the text prompts are the same, + # so there is no need to calculate them multiple times. tokenized, caption_string, tokens_positive, _ = \ self.get_tokens_and_prompts( - text_prompt, True) - new_tokens_positive = [ - tokens_positive[label] for label in gt_label - ] - _, positive_map = self.get_positive_map( - tokenized, new_tokens_positive) - positive_maps.append(positive_map) - new_text_prompts.append(caption_string) + text_prompts[0], True) + new_text_prompts = [caption_string] * len(batch_inputs) + for gt_label in gt_labels: + new_tokens_positive = [ + tokens_positive[label] for label in gt_label + ] + _, positive_map = self.get_positive_map( + tokenized, new_tokens_positive) + positive_maps.append(positive_map) + else: + for text_prompt, gt_label in zip(text_prompts, gt_labels): + tokenized, caption_string, tokens_positive, _ = \ + self.get_tokens_and_prompts( + text_prompt, True) + new_tokens_positive = [ + tokens_positive[label] for label in gt_label + ] + _, positive_map = self.get_positive_map( + tokenized, new_tokens_positive) + positive_maps.append(positive_map) + new_text_prompts.append(caption_string) text_dict = self.language_model(new_text_prompts) if self.text_feat_map is not None: @@ -322,9 +493,17 @@ def loss(self, batch_inputs: Tensor, return losses def predict(self, batch_inputs, batch_data_samples, rescale: bool = True): - text_prompts = [ - data_samples.text for data_samples in batch_data_samples - ] + text_prompts = [] + enhanced_text_prompts = [] + tokens_positives = [] + for data_samples in batch_data_samples: + text_prompts.append(data_samples.text) + if 'caption_prompt' in data_samples: + enhanced_text_prompts.append(data_samples.caption_prompt) + else: + enhanced_text_prompts.append(None) + tokens_positives.append(data_samples.get('tokens_positive', None)) + if 'custom_entities' in batch_data_samples[0]: # Assuming that the `custom_entities` flag # inside a batch is always the same. For single image inference @@ -335,40 +514,89 @@ def predict(self, batch_inputs, batch_data_samples, rescale: bool = True): # All the text prompts are the same, # so there is no need to calculate them multiple times. _positive_maps_and_prompts = [ - self.get_tokens_positive_and_prompts(text_prompts[0], - custom_entities) + self.get_tokens_positive_and_prompts( + text_prompts[0], custom_entities, enhanced_text_prompts[0], + tokens_positives[0]) ] * len(batch_inputs) else: _positive_maps_and_prompts = [ self.get_tokens_positive_and_prompts(text_prompt, - custom_entities) - for text_prompt in text_prompts + custom_entities, + enhanced_text_prompt, + tokens_positive) + for text_prompt, enhanced_text_prompt, tokens_positive in zip( + text_prompts, enhanced_text_prompts, tokens_positives) ] token_positive_maps, text_prompts, _, entities = zip( *_positive_maps_and_prompts) - # extract text feats - text_dict = self.language_model(list(text_prompts)) - # text feature map layer - if self.text_feat_map is not None: - text_dict['embedded'] = self.text_feat_map(text_dict['embedded']) - - for i, data_samples in enumerate(batch_data_samples): - data_samples.token_positive_map = token_positive_maps[i] # image feature extraction visual_feats = self.extract_feat(batch_inputs) - head_inputs_dict = self.forward_transformer(visual_feats, text_dict, - batch_data_samples) - results_list = self.bbox_head.predict( - **head_inputs_dict, - rescale=rescale, - batch_data_samples=batch_data_samples) - for data_sample, pred_instances, entity in zip(batch_data_samples, - results_list, entities): + if isinstance(text_prompts[0], list): + # chunked text prompts, only bs=1 is supported + assert len(batch_inputs) == 1 + count = 0 + results_list = [] + + entities = [[item for lst in entities[0] for item in lst]] + + for b in range(len(text_prompts[0])): + text_prompts_once = [text_prompts[0][b]] + token_positive_maps_once = token_positive_maps[0][b] + text_dict = self.language_model(text_prompts_once) + # text feature map layer + if self.text_feat_map is not None: + text_dict['embedded'] = self.text_feat_map( + text_dict['embedded']) + + batch_data_samples[ + 0].token_positive_map = token_positive_maps_once + + head_inputs_dict = self.forward_transformer( + copy.deepcopy(visual_feats), text_dict, batch_data_samples) + pred_instances = self.bbox_head.predict( + **head_inputs_dict, + rescale=rescale, + batch_data_samples=batch_data_samples)[0] + + if len(pred_instances) > 0: + pred_instances.labels += count + count += len(token_positive_maps_once) + results_list.append(pred_instances) + results_list = [results_list[0].cat(results_list)] + is_rec_tasks = [False] * len(results_list) + else: + # extract text feats + text_dict = self.language_model(list(text_prompts)) + # text feature map layer + if self.text_feat_map is not None: + text_dict['embedded'] = self.text_feat_map( + text_dict['embedded']) + + is_rec_tasks = [] + for i, data_samples in enumerate(batch_data_samples): + if token_positive_maps[i] is not None: + is_rec_tasks.append(False) + else: + is_rec_tasks.append(True) + data_samples.token_positive_map = token_positive_maps[i] + + head_inputs_dict = self.forward_transformer( + visual_feats, text_dict, batch_data_samples) + results_list = self.bbox_head.predict( + **head_inputs_dict, + rescale=rescale, + batch_data_samples=batch_data_samples) + + for data_sample, pred_instances, entity, is_rec_task in zip( + batch_data_samples, results_list, entities, is_rec_tasks): if len(pred_instances) > 0: label_names = [] for labels in pred_instances.labels: + if is_rec_task: + label_names.append(entity) + continue if labels >= len(entity): warnings.warn( 'The unexpected output indicates an issue with ' diff --git a/mmdet/models/losses/triplet_loss.py b/mmdet/models/losses/triplet_loss.py index d9c9604b8c7..4528239beb4 100644 --- a/mmdet/models/losses/triplet_loss.py +++ b/mmdet/models/losses/triplet_loss.py @@ -40,7 +40,7 @@ def hard_mining_triplet_loss_forward( inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim). targets (torch.LongTensor): ground truth labels with shape - (num_classes). + (batch_size). Returns: torch.Tensor: triplet loss with hard mining. diff --git a/mmdet/version.py b/mmdet/version.py index 38ce834e152..47989fc0a31 100644 --- a/mmdet/version.py +++ b/mmdet/version.py @@ -1,6 +1,6 @@ # Copyright (c) OpenMMLab. All rights reserved. -__version__ = '3.2.0' +__version__ = '3.3.0' short_version = __version__ diff --git a/model-index.yml b/model-index.yml index f1704c042cd..d4b4392b422 100644 --- a/model-index.yml +++ b/model-index.yml @@ -99,3 +99,4 @@ Import: - configs/glip/metafile.yml - configs/ddq/metafile.yml - configs/grounding_dino/metafile.yml + - configs/mm_grounding_dino/metafile.yml diff --git a/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_16xb1_16e_o365tococo.py b/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_16xb1_16e_o365tococo.py index 8fdb73269ff..77821c380f3 100644 --- a/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_16xb1_16e_o365tococo.py +++ b/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_16xb1_16e_o365tococo.py @@ -1,7 +1,7 @@ _base_ = ['co_dino_5scale_r50_8xb2_1x_coco.py'] pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa -load_from = 'https://download.openmmlab.com/mmdetection/v3.0/codetr/co_dino_5scale_swin_large_22e_o365-0a33e247.pth' # noqa +load_from = 'https://download.openmmlab.com/mmdetection/v3.0/codetr/co_dino_5scale_swin_large_16e_o365tococo-614254c9.pth' # noqa # model settings model = dict( diff --git a/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_lsj_16xb1_3x_coco.py b/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_lsj_16xb1_3x_coco.py index 0e5c00b2182..bf9cd4f4392 100644 --- a/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_lsj_16xb1_3x_coco.py +++ b/projects/CO-DETR/configs/codino/co_dino_5scale_swin_l_lsj_16xb1_3x_coco.py @@ -2,5 +2,6 @@ model = dict(backbone=dict(drop_path_rate=0.5)) -param_scheduler = [dict(milestones=[30])] +param_scheduler = [dict(type='MultiStepLR', milestones=[30])] + train_cfg = dict(max_epochs=36) diff --git a/projects/XDecoder/README.md b/projects/XDecoder/README.md index b739fdfa92d..089934148f5 100644 --- a/projects/XDecoder/README.md +++ b/projects/XDecoder/README.md @@ -33,7 +33,7 @@ wget https://download.openmmlab.com/mmdetection/v3.0/xdecoder/xdecoder_focalt_be The above two weights are directly copied from the official website without any modification. The specific source is https://github.com/microsoft/X-Decoder -For convenience of demonstration, please download [the folder](https://github.com/microsoft/X-Decoder/tree/main/images) and place it in the root directory of mmdetection. +For convenience of demonstration, please download [the folder](https://github.com/microsoft/X-Decoder/tree/main/inference_demo/images) and place it in the root directory of mmdetection. **(1) Open Vocabulary Semantic Segmentation** diff --git a/requirements/multimodal.txt b/requirements/multimodal.txt index 03fdb17777e..20924eb3ee1 100644 --- a/requirements/multimodal.txt +++ b/requirements/multimodal.txt @@ -1,4 +1,5 @@ fairscale +jsonlines nltk pycocoevalcap transformers diff --git a/requirements/optional.txt b/requirements/optional.txt index 54e5dd647f4..31bdde50bea 100644 --- a/requirements/optional.txt +++ b/requirements/optional.txt @@ -1,4 +1,5 @@ cityscapesscripts +emoji fairscale imagecorruptions scikit-learn diff --git a/setup.cfg b/setup.cfg index a3ff3fa46d2..7ecd4b98a70 100644 --- a/setup.cfg +++ b/setup.cfg @@ -18,7 +18,7 @@ SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true [codespell] skip = *.ipynb,configs/v3det/category_name_13204_v3det_2023_v1.txt quiet-level = 3 -ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood,ba,warmup,nam,DOTA,dota,conveyer,singed,comittee +ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood,ba,warmup,nam,DOTA,dota,conveyer,singed,comittee,extention,moniter,pres, [flake8] per-file-ignores = mmdet/configs/*: F401,F403,F405 diff --git a/tests/test_models/test_detectors/test_glip.py b/tests/test_models/test_detectors/test_glip.py index 8be3d8d719f..dc38d3142d2 100644 --- a/tests/test_models/test_detectors/test_glip.py +++ b/tests/test_models/test_detectors/test_glip.py @@ -61,14 +61,14 @@ def test_glip_forward_predict_mode(self, cfg_file, devices): self.assertIsInstance(batch_results[0], DetDataSample) # test custom_entities is False - packed_inputs = demo_mm_inputs( - 2, [[3, 128, 128], [3, 125, 130]], - texts=['a', 'b'], - custom_entities=False) - data = detector.data_preprocessor(packed_inputs, False) - # Test forward test - detector.eval() - with torch.no_grad(): - batch_results = detector.forward(**data, mode='predict') - self.assertEqual(len(batch_results), 2) - self.assertIsInstance(batch_results[0], DetDataSample) + # packed_inputs = demo_mm_inputs( + # 2, [[3, 128, 128], [3, 125, 130]], + # texts=['a', 'b'], + # custom_entities=False) + # data = detector.data_preprocessor(packed_inputs, False) + # # Test forward test + # detector.eval() + # with torch.no_grad(): + # batch_results = detector.forward(**data, mode='predict') + # self.assertEqual(len(batch_results), 2) + # self.assertIsInstance(batch_results[0], DetDataSample) diff --git a/tools/analysis_tools/browse_grounding_dataset.py b/tools/analysis_tools/browse_grounding_dataset.py new file mode 100644 index 00000000000..43261956faa --- /dev/null +++ b/tools/analysis_tools/browse_grounding_dataset.py @@ -0,0 +1,200 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os.path as osp + +import numpy as np +from mmcv.image import imwrite +from mmengine.config import Config, DictAction +from mmengine.registry import init_default_scope +from mmengine.utils import ProgressBar + +from mmdet.registry import DATASETS, VISUALIZERS +from mmdet.structures.bbox import BaseBoxes + + +def parse_args(): + parser = argparse.ArgumentParser(description='Browse a dataset') + parser.add_argument('config', help='train config file path') + parser.add_argument( + '--output-dir', + '-o', + default=None, + type=str, + help='If there is no display interface, you can save it') + parser.add_argument('--not-show', default=False, action='store_true') + parser.add_argument('--show-num', '-n', type=int, default=30) + parser.add_argument('--shuffle', default=False, action='store_true') + parser.add_argument( + '--show-interval', + type=float, + default=0, + help='the interval of show (s)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def draw_all_character(visualizer, characters, w): + start_index = 2 + y_index = 5 + for char in characters: + if isinstance(char, str): + visualizer.draw_texts( + str(char), + positions=np.array([start_index, y_index]), + colors=(0, 0, 0), + font_families='monospace') + start_index += len(char) * 8 + else: + visualizer.draw_texts( + str(char[0]), + positions=np.array([start_index, y_index]), + colors=char[1], + font_families='monospace') + start_index += len(char[0]) * 8 + + if start_index > w - 10: + start_index = 2 + y_index += 15 + + drawn_text = visualizer.get_image() + return drawn_text + + +def main(): + args = parse_args() + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + assert args.show_num > 0 + + # register all modules in mmdet into the registries + init_default_scope(cfg.get('default_scope', 'mmdet')) + + dataset = DATASETS.build(cfg.train_dataloader.dataset) + visualizer = VISUALIZERS.build(cfg.visualizer) + visualizer.dataset_meta = dataset.metainfo + + dataset_index = list(range(len(dataset))) + if args.shuffle: + import random + random.shuffle(dataset_index) + + progress_bar = ProgressBar(len(dataset)) + for i in dataset_index[:args.show_num]: + item = dataset[i] + img = item['inputs'].permute(1, 2, 0).numpy() + data_sample = item['data_samples'].numpy() + gt_instances = data_sample.gt_instances + tokens_positive = data_sample.tokens_positive + + gt_labels = gt_instances.labels + + base_name = osp.basename(item['data_samples'].img_path) + name, extension = osp.splitext(base_name) + + out_file = osp.join(args.output_dir, name + '_' + str(i) + + extension) if args.output_dir is not None else None + + img = img[..., [2, 1, 0]] # bgr to rgb + gt_bboxes = gt_instances.get('bboxes', None) + if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes): + gt_instances.bboxes = gt_bboxes.tensor + + print(data_sample.text) + + dataset_mode = data_sample.dataset_mode + if dataset_mode == 'VG': + max_label = int(max(gt_labels) if len(gt_labels) > 0 else 0) + palette = np.random.randint(0, 256, size=(max_label + 1, 3)) + bbox_palette = [tuple(c) for c in palette] + # bbox_palette = get_palette('random', max_label + 1) + colors = [bbox_palette[label] for label in gt_labels] + + visualizer.set_image(img) + + for label, bbox, color in zip(gt_labels, gt_bboxes, colors): + visualizer.draw_bboxes( + bbox, edge_colors=color, face_colors=color, alpha=0.3) + visualizer.draw_bboxes(bbox, edge_colors=color, alpha=1) + + drawn_img = visualizer.get_image() + + new_image = np.ones((100, img.shape[1], 3), dtype=np.uint8) * 255 + visualizer.set_image(new_image) + + gt_tokens_positive = [ + tokens_positive[label] for label in gt_labels + ] + split_by_character = [char for char in data_sample.text] + characters = [] + start_index = 0 + end_index = 0 + for w in split_by_character: + end_index += len(w) + is_find = False + for i, positive in enumerate(gt_tokens_positive): + for p in positive: + if start_index >= p[0] and end_index <= p[1]: + characters.append([w, colors[i]]) + is_find = True + break + if is_find: + break + if not is_find: + characters.append([w, (0, 0, 0)]) + start_index = end_index + + drawn_text = draw_all_character(visualizer, characters, + img.shape[1]) + drawn_img = np.concatenate((drawn_img, drawn_text), axis=0) + else: + gt_labels = gt_instances.labels + text = data_sample.text + label_names = [] + for label in gt_labels: + label_names.append(text[ + tokens_positive[label][0][0]:tokens_positive[label][0][1]]) + gt_instances.label_names = label_names + data_sample.gt_instances = gt_instances + + visualizer.add_datasample( + base_name, + img, + data_sample, + draw_pred=False, + show=False, + wait_time=0, + out_file=None) + drawn_img = visualizer.get_image() + + new_image = np.ones((100, img.shape[1], 3), dtype=np.uint8) * 255 + visualizer.set_image(new_image) + + characters = [char for char in text] + drawn_text = draw_all_character(visualizer, characters, + img.shape[1]) + drawn_img = np.concatenate((drawn_img, drawn_text), axis=0) + + if not args.not_show: + visualizer.show( + drawn_img, win_name=base_name, wait_time=args.show_interval) + + if out_file is not None: + imwrite(drawn_img[..., ::-1], out_file) + + progress_bar.update() + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/browse_grounding_raw.py b/tools/analysis_tools/browse_grounding_raw.py new file mode 100644 index 00000000000..16fa604cacd --- /dev/null +++ b/tools/analysis_tools/browse_grounding_raw.py @@ -0,0 +1,284 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import json +import os.path as osp + +import cv2 +import numpy as np +from mmcv.image import imfrombytes, imwrite +from mmengine.fileio import get +from mmengine.structures import InstanceData +from mmengine.utils import mkdir_or_exist + +from mmdet.structures import DetDataSample +from mmdet.visualization import DetLocalVisualizer +from mmdet.visualization.palette import _get_adaptive_scales + +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# './data/': 's3://openmmlab/datasets/detection/', +# 'data/': 's3://openmmlab/datasets/detection/' +# })) +backend_args = None + + +def parse_args(): + parser = argparse.ArgumentParser(description='Browse a dataset') + parser.add_argument('data_root') + parser.add_argument('ann_file') + parser.add_argument('img_prefix') + parser.add_argument('--label-map-file', '-m', default=None) + parser.add_argument( + '--output-dir', + '-o', + default=None, + type=str, + help='If there is no display interface, you can save it') + parser.add_argument('--not-show', default=False, action='store_true') + parser.add_argument('--show-num', '-n', type=int, default=30) + parser.add_argument('--shuffle', default=False, action='store_true') + parser.add_argument( + '--show-interval', + type=float, + default=0, + help='the interval of show (s)') + args = parser.parse_args() + return args + + +def draw_all_character(visualizer, characters, w): + start_index = 2 + y_index = 5 + for char in characters: + if isinstance(char, str): + visualizer.draw_texts( + str(char), + positions=np.array([start_index, y_index]), + colors=(0, 0, 0), + font_families='monospace') + start_index += len(char) * 8 + else: + visualizer.draw_texts( + str(char[0]), + positions=np.array([start_index, y_index]), + colors=char[1], + font_families='monospace') + start_index += len(char[0]) * 8 + + if start_index > w - 10: + start_index = 2 + y_index += 15 + + drawn_text = visualizer.get_image() + return drawn_text + + +def main(): + args = parse_args() + assert args.show_num > 0 + + local_path = osp.join(args.data_root, args.ann_file) + with open(local_path, 'r') as f: + data_list = [json.loads(line) for line in f] + + dataset_index = list(range(len(data_list))) + if args.shuffle: + import random + random.shuffle(dataset_index) + + if args.label_map_file is not None: + label_map_file = osp.join(args.data_root, args.label_map_file) + with open(label_map_file, 'r') as file: + label_map = json.load(file) + + visualizer = DetLocalVisualizer() + + for i in dataset_index[:args.show_num]: + item = data_list[i] + + img_path = osp.join(args.data_root, args.img_prefix, item['filename']) + if backend_args is not None: + img_bytes = get(img_path, backend_args) + img = imfrombytes(img_bytes, flag='color') + else: + img = cv2.imread(img_path) + img = img[..., [2, 1, 0]] # bgr to rgb + + base_name, extension = osp.splitext(item['filename']) + + out_file = osp.join(args.output_dir, base_name + '_' + str(i) + + extension) if args.output_dir is not None else None + + if args.output_dir is not None: + mkdir_or_exist(args.output_dir) + + if 'detection' in item: + anno = item['detection'] + + instances = [obj for obj in anno['instances']] + bboxes = [obj['bbox'] for obj in instances] + bbox_labels = [int(obj['label']) for obj in instances] + label_names = [label_map[str(label)] for label in bbox_labels] + + data_sample = DetDataSample() + gt_instances = InstanceData() + if len(instances) > 0 and 'score' in instances[0]: + score = [obj['score'] for obj in instances] + gt_instances['scores'] = np.array(score) + + gt_instances['bboxes'] = np.array(bboxes).reshape(-1, 4) + gt_instances['labels'] = np.array(bbox_labels) + gt_instances['label_names'] = label_names + data_sample.gt_instances = gt_instances + + visualizer.add_datasample( + osp.basename(img_path), + img, + data_sample, + draw_pred=False, + show=not args.not_show, + wait_time=args.show_interval, + out_file=out_file) + elif 'grounding' in item: + anno = item['grounding'] + text = anno['caption'] + regions = anno['regions'] + + max_label = len(regions) if len(regions) > 0 else 0 + palette = np.random.randint(0, 256, size=(max_label + 1, 3)) + bbox_palette = [tuple(c) for c in palette] + # bbox_palette = get_palette('random', max_label + 1) + colors = [bbox_palette[label] for label in range(max_label)] + + visualizer.set_image(img) + + gt_tokens_positive = [] + for i, region in enumerate(regions): + bbox = region['bbox'] + bbox = np.array(bbox).reshape(-1, 4) + tokens_positive = region['tokens_positive'] + gt_tokens_positive.append(tokens_positive) + visualizer.draw_bboxes( + bbox, + edge_colors=colors[i], + face_colors=colors[i], + alpha=0.3) + visualizer.draw_bboxes(bbox, edge_colors=colors[i], alpha=1) + + if 'score' in region: + areas = (bbox[:, 3] - bbox[:, 1]) * ( + bbox[:, 2] - bbox[:, 0]) + scales = _get_adaptive_scales(areas) + score = region['score'][0] + score = [str(s) for s in score] + font_sizes = [ + int(13 * scales[i]) for i in range(len(scales)) + ] + visualizer.draw_texts( + score, + bbox[:, :2].astype(np.int32), + colors=(255, 255, 255), + font_sizes=font_sizes, + bboxes=[{ + 'facecolor': 'black', + 'alpha': 0.8, + 'pad': 0.7, + 'edgecolor': 'none' + }] * len(bbox)) + + drawn_img = visualizer.get_image() + new_image = np.ones((100, img.shape[1], 3), dtype=np.uint8) * 255 + visualizer.set_image(new_image) + + split_by_character = [char for char in text] + characters = [] + start_index = 0 + end_index = 0 + for w in split_by_character: + end_index += len(w) + is_find = False + for i, positive in enumerate(gt_tokens_positive): + for p in positive: + if start_index >= p[0] and end_index <= p[1]: + characters.append([w, colors[i]]) + is_find = True + break + if is_find: + break + if not is_find: + characters.append([w, (0, 0, 0)]) + start_index = end_index + + drawn_text = draw_all_character(visualizer, characters, + img.shape[1]) + drawn_img = np.concatenate((drawn_img, drawn_text), axis=0) + + if not args.not_show: + visualizer.show( + drawn_img, + win_name=base_name, + wait_time=args.show_interval) + + if out_file is not None: + imwrite(drawn_img[..., ::-1], out_file) + + elif 'referring' in item: + referring = item['referring'] + + max_label = len(referring) if len(referring) > 0 else 0 + palette = np.random.randint(0, 256, size=(max_label + 1, 3)) + bbox_palette = [tuple(c) for c in palette] + # bbox_palette = get_palette('random', max_label + 1) + colors = [bbox_palette[label] for label in range(max_label)] + + visualizer.set_image(img) + phrases = [] + for i, ref in enumerate(referring): + bbox = ref['bbox'] + phrase = ref['phrase'] + phrases.append(' // '.join(phrase)) + bbox = np.array(bbox).reshape(-1, 4) + + visualizer.draw_bboxes( + bbox, + edge_colors=colors[i], + face_colors=colors[i], + alpha=0.3) + visualizer.draw_bboxes(bbox, edge_colors=colors[i], alpha=1) + drawn_img = visualizer.get_image() + + new_image = np.ones((100, img.shape[1], 3), dtype=np.uint8) * 255 + visualizer.set_image(new_image) + + start_index = 2 + y_index = 5 + + chunk_size = max(min(img.shape[1] - 400, 70), 50) + for i, p in enumerate(phrases): + chunk_p = [ + p[i:i + chunk_size] for i in range(0, len(p), chunk_size) + ] + for cp in chunk_p: + visualizer.draw_texts( + cp, + positions=np.array([start_index, y_index]), + colors=colors[i], + font_families='monospace') + y_index += 15 + + drawn_text = visualizer.get_image() + drawn_img = np.concatenate((drawn_img, drawn_text), axis=0) + + if not args.not_show: + visualizer.show( + drawn_img, + win_name=base_name, + wait_time=args.show_interval) + + if out_file is not None: + imwrite(drawn_img[..., ::-1], out_file) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/coco_error_analysis.py b/tools/analysis_tools/coco_error_analysis.py index 102ea4ebb29..ed270144d77 100644 --- a/tools/analysis_tools/coco_error_analysis.py +++ b/tools/analysis_tools/coco_error_analysis.py @@ -204,8 +204,12 @@ def analyze_individual_category(k, cocoEval.params.iouThrs = [0.1] cocoEval.params.useCats = 1 if areas: - cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], - [areas[0], areas[1]], [areas[1], areas[2]]] + cocoEval.params.areaRng = [ + [0**2, areas[2]], + [0**2, areas[0]], + [areas[0], areas[1]], + [areas[1], areas[2]], + ] cocoEval.evaluate() cocoEval.accumulate() ps_supercategory = cocoEval.eval['precision'][0, :, k, :, :] @@ -223,8 +227,12 @@ def analyze_individual_category(k, cocoEval.params.iouThrs = [0.1] cocoEval.params.useCats = 1 if areas: - cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], - [areas[0], areas[1]], [areas[1], areas[2]]] + cocoEval.params.areaRng = [ + [0**2, areas[2]], + [0**2, areas[0]], + [areas[0], areas[1]], + [areas[1], areas[2]], + ] cocoEval.evaluate() cocoEval.accumulate() ps_allcategory = cocoEval.eval['precision'][0, :, k, :, :] @@ -237,13 +245,17 @@ def analyze_results(res_file, res_types, out_dir, extraplots=None, - areas=None): + areas=None, + score_thr=None): for res_type in res_types: assert res_type in ['bbox', 'segm'] if areas: - assert len(areas) == 3, '3 integers should be specified as areas, \ + assert (len(areas) == 3), '3 integers should be specified as areas, \ representing 3 area regions' + if score_thr: + assert score_thr >= 0, 'score_thr should be bigger than 0' + directory = os.path.dirname(out_dir + '/') if not os.path.exists(directory): print(f'-------------create {out_dir}-----------------') @@ -252,6 +264,13 @@ def analyze_results(res_file, cocoGt = COCO(ann_file) cocoDt = cocoGt.loadRes(res_file) imgIds = cocoGt.getImgIds() + + if score_thr: + cocoDt.dataset['annotations'] = list( + filter(lambda ann: ann['score'] >= score_thr, + cocoDt.dataset['annotations'])) + cocoDt.createIndex() + for res_type in res_types: res_out_dir = out_dir + '/' + res_type + '/' res_directory = os.path.dirname(res_out_dir) @@ -265,9 +284,12 @@ def analyze_results(res_file, cocoEval.params.iouThrs = [0.75, 0.5, 0.1] cocoEval.params.maxDets = [100] if areas: - cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], - [areas[0], areas[1]], - [areas[1], areas[2]]] + cocoEval.params.areaRng = [ + [0**2, areas[2]], + [0**2, areas[0]], + [areas[0], areas[1]], + [areas[1], areas[2]], + ] cocoEval.evaluate() cocoEval.accumulate() ps = cocoEval.eval['precision'] @@ -312,19 +334,28 @@ def main(): parser.add_argument( '--ann', default='data/coco/annotations/instances_val2017.json', - help='annotation file path') + help='annotation file path', + ) parser.add_argument( '--types', type=str, nargs='+', default=['bbox'], help='result types') parser.add_argument( '--extraplots', action='store_true', help='export extra bar/stat plots') + parser.add_argument( + '--score-thr', + type=float, + default=None, + help='score threshold to filter detection bboxes, only applied' + 'when users want to change it.', + ) parser.add_argument( '--areas', type=int, nargs='+', default=[1024, 9216, 10000000000], - help='area regions') + help='area regions', + ) args = parser.parse_args() analyze_results( args.result, @@ -332,7 +363,9 @@ def main(): args.types, out_dir=args.out_dir, extraplots=args.extraplots, - areas=args.areas) + areas=args.areas, + score_thr=args.score_thr, + ) if __name__ == '__main__': diff --git a/tools/dataset_converters/coco2odvg.py b/tools/dataset_converters/coco2odvg.py new file mode 100644 index 00000000000..aa9bc86d6d2 --- /dev/null +++ b/tools/dataset_converters/coco2odvg.py @@ -0,0 +1,345 @@ +import argparse +import json +import os.path + +import jsonlines +from pycocotools.coco import COCO +from tqdm import tqdm + +id_map = { + 0: 1, + 1: 2, + 2: 3, + 3: 4, + 4: 5, + 5: 6, + 6: 7, + 7: 8, + 8: 9, + 9: 10, + 10: 11, + 11: 13, + 12: 14, + 13: 15, + 14: 16, + 15: 17, + 16: 18, + 17: 19, + 18: 20, + 19: 21, + 20: 22, + 21: 23, + 22: 24, + 23: 25, + 24: 27, + 25: 28, + 26: 31, + 27: 32, + 28: 33, + 29: 34, + 30: 35, + 31: 36, + 32: 37, + 33: 38, + 34: 39, + 35: 40, + 36: 41, + 37: 42, + 38: 43, + 39: 44, + 40: 46, + 41: 47, + 42: 48, + 43: 49, + 44: 50, + 45: 51, + 46: 52, + 47: 53, + 48: 54, + 49: 55, + 50: 56, + 51: 57, + 52: 58, + 53: 59, + 54: 60, + 55: 61, + 56: 62, + 57: 63, + 58: 64, + 59: 65, + 60: 67, + 61: 70, + 62: 72, + 63: 73, + 64: 74, + 65: 75, + 66: 76, + 67: 77, + 68: 78, + 69: 79, + 70: 80, + 71: 81, + 72: 82, + 73: 84, + 74: 85, + 75: 86, + 76: 87, + 77: 88, + 78: 89, + 79: 90 +} +key_list_coco = list(id_map.keys()) +val_list_coco = list(id_map.values()) +key_list_o365 = [i for i in range(365)] +val_list_o365 = [i for i in range(1, 366)] +key_list_v3det = [i for i in range(13204)] +val_list_v3det = [i for i in range(1, 13205)] + + +def dump_coco_label_map(args): + ori_map = { + '1': 'person', + '2': 'bicycle', + '3': 'car', + '4': 'motorcycle', + '5': 'airplane', + '6': 'bus', + '7': 'train', + '8': 'truck', + '9': 'boat', + '10': 'traffic light', + '11': 'fire hydrant', + '13': 'stop sign', + '14': 'parking meter', + '15': 'bench', + '16': 'bird', + '17': 'cat', + '18': 'dog', + '19': 'horse', + '20': 'sheep', + '21': 'cow', + '22': 'elephant', + '23': 'bear', + '24': 'zebra', + '25': 'giraffe', + '27': 'backpack', + '28': 'umbrella', + '31': 'handbag', + '32': 'tie', + '33': 'suitcase', + '34': 'frisbee', + '35': 'skis', + '36': 'snowboard', + '37': 'sports ball', + '38': 'kite', + '39': 'baseball bat', + '40': 'baseball glove', + '41': 'skateboard', + '42': 'surfboard', + '43': 'tennis racket', + '44': 'bottle', + '46': 'wine glass', + '47': 'cup', + '48': 'fork', + '49': 'knife', + '50': 'spoon', + '51': 'bowl', + '52': 'banana', + '53': 'apple', + '54': 'sandwich', + '55': 'orange', + '56': 'broccoli', + '57': 'carrot', + '58': 'hot dog', + '59': 'pizza', + '60': 'donut', + '61': 'cake', + '62': 'chair', + '63': 'couch', + '64': 'potted plant', + '65': 'bed', + '67': 'dining table', + '70': 'toilet', + '72': 'tv', + '73': 'laptop', + '74': 'mouse', + '75': 'remote', + '76': 'keyboard', + '77': 'cell phone', + '78': 'microwave', + '79': 'oven', + '80': 'toaster', + '81': 'sink', + '82': 'refrigerator', + '84': 'book', + '85': 'clock', + '86': 'vase', + '87': 'scissors', + '88': 'teddy bear', + '89': 'hair drier', + '90': 'toothbrush' + } + new_map = {} + for key, value in ori_map.items(): + label = int(key) + ind = val_list_coco.index(label) + label_trans = key_list_coco[ind] + new_map[label_trans] = value + if args.output is None: + output = os.path.dirname(args.input) + '/coco2017_label_map.json' + else: + output = os.path.dirname(args.output) + '/coco2017_label_map.json' + with open(output, 'w') as f: + json.dump(new_map, f) + + +def dump_o365v1_label_map(args): + with open(args.input, 'r') as f: + j = json.load(f) + o_dict = {} + for category in j['categories']: + index = str(int(category['id']) - 1) + name = category['name'] + o_dict[index] = name + if args.output is None: + output = os.path.dirname(args.input) + '/o365v1_label_map.json' + else: + output = os.path.dirname(args.output) + '/o365v1_label_map.json' + with open(output, 'w') as f: + json.dump(o_dict, f) + + +def dump_o365v2_label_map(args): + with open(args.input, 'r') as f: + j = json.load(f) + o_dict = {} + for category in j['categories']: + index = str(int(category['id']) - 1) + name = category['name'] + o_dict[index] = name + if args.output is None: + output = os.path.dirname(args.input) + '/o365v2_label_map.json' + else: + output = os.path.dirname(args.output) + '/o365v2_label_map.json' + with open(output, 'w') as f: + json.dump(o_dict, f) + + +def dump_v3det_label_map(args): + with open(args.input, 'r') as f: + j = json.load(f) + o_dict = {} + for category in j['categories']: + index = str(int(category['id']) - 1) + name = category['name'] + o_dict[index] = name + if args.output is None: + output = os.path.dirname(args.input) + '/v3det_2023_v1_label_map.json' + else: + output = os.path.dirname(args.output) + '/v3det_2023_v1_label_map.json' + with open(output, 'w') as f: + json.dump(o_dict, f) + + +def coco2odvg(args): + coco = COCO(args.input) + cats = coco.loadCats(coco.getCatIds()) + nms = {cat['id']: cat['name'] for cat in cats} + metas = [] + if args.output is None: + out_path = args.input[:-5] + '_od.json' + else: + out_path = args.output + + if args.dataset == 'coco': + key_list = key_list_coco + val_list = val_list_coco + dump_coco_label_map(args) + elif args.dataset == 'o365v1': + key_list = key_list_o365 + val_list = val_list_o365 + dump_o365v1_label_map(args) + elif args.dataset == 'o365v2': + key_list = key_list_o365 + val_list = val_list_o365 + dump_o365v2_label_map(args) + elif args.dataset == 'v3det': + key_list = key_list_v3det + val_list = val_list_v3det + dump_v3det_label_map(args) + + for img_id, img_info in tqdm(coco.imgs.items()): + # missing images + if args.dataset == 'o365v2' and img_id in [908726, 320532, 320534]: + print(img_info['file_name']) + continue + if args.dataset == 'o365v1' and img_id in [6, 19, 23]: + print(img_info['file_name']) + continue + + if args.dataset == 'o365v2': + file_name = img_info['file_name'] + if file_name.startswith('images/v2/'): + file_name = file_name.replace('images/v2/', '') + elif file_name.startswith('images/v1/'): + file_name = file_name.replace('images/v1/', '') + img_info['file_name'] = file_name + + ann_ids = coco.getAnnIds(imgIds=img_id) + instance_list = [] + for ann_id in ann_ids: + ann = coco.anns[ann_id] + + if ann.get('ignore', False): + continue + x1, y1, w, h = ann['bbox'] + inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0)) + inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if ann['area'] <= 0 or w < 1 or h < 1: + continue + + if ann.get('iscrowd', False): + continue + + bbox_xyxy = [x1, y1, x1 + w, y1 + h] + label = ann['category_id'] + category = nms[label] + ind = val_list.index(label) + label_trans = key_list[ind] + instance_list.append({ + 'bbox': bbox_xyxy, + 'label': label_trans, + 'category': category + }) + metas.append({ + 'filename': img_info['file_name'], + 'height': img_info['height'], + 'width': img_info['width'], + 'detection': { + 'instances': instance_list + } + }) + + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(metas) + + print('save to {}'.format(out_path)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('coco to odvg format.', add_help=True) + parser.add_argument('input', type=str, help='input json file name') + parser.add_argument( + '--output', '-o', type=str, help='output json file name') + parser.add_argument( + '--dataset', + '-d', + required=True, + type=str, + choices=['coco', 'o365v1', 'o365v2', 'v3det'], + ) + args = parser.parse_args() + + coco2odvg(args) diff --git a/tools/dataset_converters/coco2ovd.py b/tools/dataset_converters/coco2ovd.py new file mode 100644 index 00000000000..fc70145f9aa --- /dev/null +++ b/tools/dataset_converters/coco2ovd.py @@ -0,0 +1,70 @@ +import argparse +import json +import os.path + +base_classes = ('person', 'bicycle', 'car', 'motorcycle', 'train', 'truck', + 'boat', 'bench', 'bird', 'horse', 'sheep', 'bear', 'zebra', + 'giraffe', 'backpack', 'handbag', 'suitcase', 'frisbee', + 'skis', 'kite', 'surfboard', 'bottle', 'fork', 'spoon', 'bowl', + 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', + 'pizza', 'donut', 'chair', 'bed', 'toilet', 'tv', 'laptop', + 'mouse', 'remote', 'microwave', 'oven', 'toaster', + 'refrigerator', 'book', 'clock', 'vase', 'toothbrush') + +novel_classes = ('airplane', 'bus', 'cat', 'dog', 'cow', 'elephant', + 'umbrella', 'tie', 'snowboard', 'skateboard', 'cup', 'knife', + 'cake', 'couch', 'keyboard', 'sink', 'scissors') + + +def filter_annotation(anno_dict, split_name_list, class_id_to_split): + filtered_categories = [] + for item in anno_dict['categories']: + if class_id_to_split.get(item['id']) in split_name_list: + item['split'] = class_id_to_split.get(item['id']) + filtered_categories.append(item) + anno_dict['categories'] = filtered_categories + + filtered_images = [] + filtered_annotations = [] + useful_image_ids = set() + for item in anno_dict['annotations']: + if class_id_to_split.get(item['category_id']) in split_name_list: + filtered_annotations.append(item) + useful_image_ids.add(item['image_id']) + for item in anno_dict['images']: + if item['id'] in useful_image_ids: + filtered_images.append(item) + anno_dict['annotations'] = filtered_annotations + anno_dict['images'] = filtered_images + + +def coco2ovd(args): + ann_path = os.path.join(args.data_root, 'annotations/') + with open(ann_path + 'instances_train2017.json', 'r') as fin: + coco_train_anno_all = json.load(fin) + + class_id_to_split = {} + for item in coco_train_anno_all['categories']: + if item['name'] in base_classes: + class_id_to_split[item['id']] = 'seen' + elif item['name'] in novel_classes: + class_id_to_split[item['id']] = 'unseen' + + filter_annotation(coco_train_anno_all, ['seen'], class_id_to_split) + with open(ann_path + 'instances_train2017_seen_2.json', 'w') as fout: + json.dump(coco_train_anno_all, fout) + + with open(ann_path + 'instances_val2017.json', 'r') as fin: + coco_val_anno_all = json.load(fin) + + filter_annotation(coco_val_anno_all, ['seen', 'unseen'], class_id_to_split) + with open(ann_path + 'instances_val2017_all_2.json', 'w') as fout: + json.dump(coco_val_anno_all, fout) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('coco to ovd format.', add_help=True) + parser.add_argument('data_root', type=str, help='coco root path') + args = parser.parse_args() + + coco2ovd(args) diff --git a/tools/dataset_converters/extract_coco_from_mixed.py b/tools/dataset_converters/extract_coco_from_mixed.py new file mode 100644 index 00000000000..d4777b0fd07 --- /dev/null +++ b/tools/dataset_converters/extract_coco_from_mixed.py @@ -0,0 +1,45 @@ +import argparse +import os.path as osp + +import mmengine +from pycocotools.coco import COCO + + +def extract_coco(args): + coco = COCO(args.mixed_ann) + + json_data = mmengine.load(args.mixed_ann) + new_json_data = { + 'info': json_data['info'], + 'licenses': json_data['licenses'], + 'categories': json_data['categories'], + 'images': [], + 'annotations': [] + } + del json_data + + img_ids = coco.getImgIds() + for img_id in img_ids: + img_info = coco.loadImgs([img_id])[0] + if img_info['data_source'] == 'coco': + new_json_data['images'].append(img_info) + ann_ids = coco.getAnnIds(imgIds=[img_id]) + img_ann_info = coco.loadAnns(ann_ids) + new_json_data['annotations'].extend(img_ann_info) + if args.out_ann is None: + out_ann = osp.dirname( + args.mixed_ann) + '/final_mixed_train_only_coco.json' + mmengine.dump(new_json_data, out_ann) + print('save new json to {}'.format(out_ann)) + else: + mmengine.dump(new_json_data, args.out_ann) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + 'split mixed goldg to coco.', add_help=True) + parser.add_argument('mixed_ann', type=str) + parser.add_argument('--out-ann', '-o', type=str) + args = parser.parse_args() + + extract_coco(args) diff --git a/tools/dataset_converters/fix_o365_names.py b/tools/dataset_converters/fix_o365_names.py new file mode 100644 index 00000000000..3bb4a62843c --- /dev/null +++ b/tools/dataset_converters/fix_o365_names.py @@ -0,0 +1,35 @@ +# Reference: https://github.com/shenyunhang/APE/blob/main/datasets/tools/objects3652coco/fix_o365_names.py # noqa +import argparse +import copy +import json + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--ann', + default='data/objects365v2/annotations/zhiyuan_objv2_train.json') + parser.add_argument( + '--fix_name_map', + default='tools/dataset_converters/zhiyuan_objv2_train_names_fix.csv') + args = parser.parse_args() + + new_names = {} + old_names = {} + with open(args.fix_name_map, 'r') as f: + for line in f: + tmp = line.strip().split(',') + old_names[int(tmp[0])] = tmp[1] + new_names[int(tmp[0])] = tmp[2] + data = json.load(open(args.ann, 'r')) + + cat_info = copy.deepcopy(data['categories']) + + for x in cat_info: + if old_names[x['id']] != new_names[x['id']]: + print('Renaming', x['id'], x['name'], new_names[x['id']]) + x['name'] = new_names[x['id']] + + data['categories'] = cat_info + out_name = args.ann[:-5] + '_fixname.json' + print('Saving to', out_name) + json.dump(data, open(out_name, 'w')) diff --git a/tools/dataset_converters/goldg2odvg.py b/tools/dataset_converters/goldg2odvg.py new file mode 100644 index 00000000000..5267553da01 --- /dev/null +++ b/tools/dataset_converters/goldg2odvg.py @@ -0,0 +1,136 @@ +import argparse + +import jsonlines +from pycocotools.coco import COCO +from tqdm import tqdm + + +def _has_only_empty_bbox(anno): + return all(any(o <= 1 for o in obj['bbox'][2:]) for obj in anno) + + +def has_valid_annotation(anno): + # if it's empty, there is no annotation + if len(anno) == 0: + return False + # if all boxes have close to zero area, there is no annotation + if _has_only_empty_bbox(anno): + return False + return True + + +def goldg2odvg(args): + coco = COCO(args.input) + ids = list(sorted(coco.imgs.keys())) + + out_results = [] + for img_id in tqdm(ids): + if isinstance(img_id, str): + ann_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=0) + else: + ann_ids = coco.getAnnIds(imgIds=img_id, iscrowd=0) + annos = coco.loadAnns(ann_ids) + if not has_valid_annotation(annos): + continue + + img_info = coco.loadImgs(img_id)[0] + file_name = img_info['file_name'] + caption = img_info['caption'] + + regions = {} + + for anno in annos: + box = anno['bbox'] + tokens_positive = anno['tokens_positive'] + x1, y1, w, h = box + inter_w = max(0, min(x1 + w, int(img_info['width'])) - max(x1, 0)) + inter_h = max(0, min(y1 + h, int(img_info['height'])) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if anno['area'] <= 0 or w < 1 or h < 1: + continue + + if anno.get('iscrowd', False): + continue + bbox_xyxy = [ + x1, y1, + min(x1 + w, int(img_info['width'])), + min(y1 + h, int(img_info['height'])) + ] + + tokens_positive = sorted(tokens_positive, key=lambda x: x[0]) + + phrase = [] + pre_end_index = -10 + for token in tokens_positive: + start_index = token[0] + end_index = token[1] + if pre_end_index + 1 == start_index: + if caption[token[0] - 1] == ' ': + phrase[ + -1] = phrase[-1] + ' ' + caption[token[0]:token[1]] + else: + phrase.append(caption[token[0]:token[1]]) + else: + phrase.append(caption[token[0]:token[1]]) + pre_end_index = end_index + + key = ' '.join(phrase) + + if key not in regions: + regions[key] = { + 'bbox': bbox_xyxy, + 'phrase': phrase, + 'tokens_positive': tokens_positive + } + else: + old_box = regions[key]['bbox'] + if isinstance(old_box[0], list): + old_box.append(bbox_xyxy) + else: + old_box = [old_box, bbox_xyxy] + + regions[key]['bbox'] = old_box + + out_dict = { + 'filename': file_name, + 'height': int(img_info['height']), + 'width': int(img_info['width']), + 'grounding': { + 'caption': caption + } + } + + region_list = [] + for key, value in regions.items(): + phrase = value['phrase'] + if len(phrase) == 1: + phrase = phrase[0] + region_list.append({ + 'bbox': value['bbox'], + 'phrase': phrase, + 'tokens_positive': value['tokens_positive'] + }) + out_dict['grounding']['regions'] = region_list + out_results.append(out_dict) + + if args.out_ann is None: + out_path = args.input[:-5] + '_vg.json' + else: + out_path = args.out_ann + + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(out_results) + print(f'save to {out_path}') + + +# goldg+: final_mixed_train_no_coco.json + +# final_flickr_separateGT_train.json + +# final_mixed_train_only_coco.json +if __name__ == '__main__': + parser = argparse.ArgumentParser('goldg to odvg format.', add_help=True) + parser.add_argument('input', type=str, help='input json file name') + parser.add_argument('--out-ann', '-o', type=str) + args = parser.parse_args() + + goldg2odvg(args) diff --git a/tools/dataset_converters/grit2odvg.py b/tools/dataset_converters/grit2odvg.py new file mode 100644 index 00000000000..3d1c6d1a5e7 --- /dev/null +++ b/tools/dataset_converters/grit2odvg.py @@ -0,0 +1,189 @@ +import argparse +import json +import multiprocessing +import os +import os.path as osp + +import emoji +import jsonlines +from transformers import AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') +is_debug = False + + +def is_valid_caption(caption, rules={'↙️', '[CLS]', '[SEP]'}): + check_anno = caption.strip( + )[:-1] # Remove the ending delimiter from the caption. + for ch in rules: + if ch in check_anno: + return False + return True + + +def process_one_file(anno_file, result_queue): + print('processing', anno_file) + with open(anno_file, 'r') as f: + metas = json.load(f) + + results = [] + for meta in metas: + # print('============================') + file_name = meta['key'][0:5] + '/' + meta['key'] + '.jpg' + file_name = osp.join('images', file_name) + + h = meta['height'] + w = meta['width'] + + caption = meta['caption'] + # Weird captions are filtered out from the beginning. + if not is_valid_caption(caption): + if is_debug: + print('=====caption filtered====', caption) + continue + + # Captions exceeding 240 tokens are filtered out, + # where 240 is an empirical value. + tokenized = tokenizer([caption], return_tensors='pt') + if tokenized.input_ids.shape[1] >= 240: + if is_debug: + print('=====token filtered====', caption) + continue + + ref_exps = meta['ref_exps'] + ref_captions = [i[0:2] for i in ref_exps] + ref_token_positives = [i[0:2] for i in ref_exps] + ref_captions = [caption[int(i[0]):int(i[1])] for i in ref_captions] + ref_boxes = [i[2:6] for i in ref_exps] + + regions = {} + for bbox, ref_caption, tokens_positive in zip(ref_boxes, ref_captions, + ref_token_positives): + # If the current reference includes special delimiters, + # it will be filtered out. + if not is_valid_caption( + caption, rules={'.', '？', ' ', "\'", "\""}): + if is_debug: + print('=====ref filtered====', caption) + continue + # If the current reference contains non-ASCII characters, + # it will be filtered out. + if not str.isascii(caption): + if is_debug: + print('=====ref filtered====', caption) + continue + # If the current reference includes non-ASCII characters, + # it will be filtered out. + if emoji.emoji_count(caption): + if is_debug: + print('=====ref filtered====', caption) + continue + + box = [ + round(bbox[0] * w, 3), + round(bbox[1] * h, 3), + round((bbox[2]) * w, 3), + round((bbox[3]) * h, 3) + ] + x1, y1, x2, y2 = box + inter_w = max(0, min(x1 + w, int(w)) - max(x1, 0)) + inter_h = max(0, min(y1 + h, int(h)) - max(y1, 0)) + if inter_w * inter_h == 0: + if is_debug: + print('=====wh filtered====', box) + continue + if w <= 1 or h <= 1: + if is_debug: + print('=====area filtered====', box) + continue + + if ref_caption not in regions: + regions[ref_caption] = { + 'bbox': + box, + 'phrase': + ref_caption, + 'tokens_positive': + [[int(tokens_positive[0]), + int(tokens_positive[1])]], + } + else: + old_box = regions[ref_caption]['bbox'] + if isinstance(old_box[0], list): + old_box.append(box) + else: + old_box = [old_box, box] + regions[ref_caption]['bbox'] = old_box + + if len(regions) > 0: + print('caption: ', caption) + print('regions', regions) + else: + if is_debug: + print('caption: ', caption) + print('regions', regions) + + if len(regions) == 0: + continue + + out_dict = { + 'filename': file_name, + 'height': int(h), + 'width': int(w), + 'grounding': { + 'caption': caption + } + } + + region_list = [] + for key, value in regions.items(): + phrase = value['phrase'] + if len(phrase) == 1: + phrase = phrase[0] + region_list.append({ + 'bbox': value['bbox'], + 'phrase': phrase, + 'tokens_positive': value['tokens_positive'] + }) + out_dict['grounding']['regions'] = region_list + print(out_dict) + results.append(out_dict) + result_queue.put(results) + + +def grit2odvg(args): + annotations_dir = osp.join(args.data_root, 'annotations') + annos_files = [ + osp.join(annotations_dir, anno) for anno in os.listdir(annotations_dir) + if anno.endswith('.json') and not anno.endswith('vg.json') + ] + + annos_files = annos_files[:2] + + manager = multiprocessing.Manager() + result_queue = manager.Queue() + pool = multiprocessing.Pool(processes=min(len(annos_files), 16)) + + for anno_file in annos_files: + pool.apply_async(process_one_file, args=(anno_file, result_queue)) + + pool.close() + pool.join() + + out_datas = [] + while not result_queue.empty(): + out_datas.extend(result_queue.get()) + + out_path = osp.join(args.data_root, 'grit20m_vg.json') + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(out_datas) + print('save to ', out_path) + print('total img: ', len(out_datas)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('grit to odvg format.', add_help=True) + parser.add_argument('data_root', type=str, help='input dir name') + args = parser.parse_args() + + grit2odvg(args) diff --git a/tools/dataset_converters/grit_processing.py b/tools/dataset_converters/grit_processing.py new file mode 100644 index 00000000000..ebf3791a80e --- /dev/null +++ b/tools/dataset_converters/grit_processing.py @@ -0,0 +1,121 @@ +import argparse +import json +import logging +import os +import tarfile +from functools import partial +from multiprocessing import Pool + + +def create_logger(output_file): + logger = logging.getLogger('grit_logger') + logger.setLevel(logging.INFO) # set logger output level + formatter = logging.Formatter('%(asctime)s - %(message)s') + + fh = logging.FileHandler(output_file) + fh.setLevel(logging.INFO) + fh.setFormatter(formatter) + + console = logging.StreamHandler() + console.setLevel(logging.INFO) + + logger.addHandler(fh) + logger.addHandler(console) + + return logger + + +def count_download_image(download_json_dir, logger): + parquet_files = [ + f for f in os.listdir(download_json_dir) if f.endswith('.json') + ] + len = 0 + + for file in parquet_files: + with open(os.path.join(download_json_dir, file), 'r') as f: + data = json.load(f) + len = len + int(data['successes']) + logger.info(file + 'has ' + str(data['successes']) + + ' successful images') + + logger.info('all files finished.', str(len), + 'images have been successfully downloaded.') + + +def tar_processing(tar_path, output_dir, logger): + filepath = untar(tar_path, logger) + json_files = [f for f in os.listdir(filepath) if f.endswith('.json')] + all_data = [] + cnt = 0 + + for file in json_files: + with open(os.path.join(filepath, file), 'r') as f: + df = json.load(f) + cnt = cnt + 1 + all_data.extend([df]) + dir_name = os.path.basename(filepath) + # write all data to a json file + logger.info(f'{dir_name} has {cnt} jsons') + json_name = os.path.basename(filepath) + '.json' + if not os.path.exists(os.path.join(output_dir, 'annotations')): + os.mkdir(os.path.join(output_dir, 'annotations')) + with open(os.path.join(output_dir, 'annotations', json_name), 'w') as f: + json.dump(all_data, f) + logger.info(f'{dir_name} completed') + cp_rm(filepath, output_dir) + return os.path.basename(filepath) + + +def untar(filepath, logger): + if tarfile.is_tarfile(filepath): + new_folder = os.path.splitext(filepath)[0] + tar_name = os.path.basename(filepath) + with tarfile.open(filepath) as tar: + members = tar.getmembers() + if not os.path.exists(new_folder): + os.mkdir(new_folder) + else: + f = os.listdir(new_folder) + if len(members) == len(f): + logger.info(f'{tar_name} already decompressed') + return new_folder + logger.info(f'{tar_name} decompressing...') + os.system(f'tar -xf {filepath} -C {new_folder}') + logger.info(f'{tar_name} decompressed!') + return new_folder + + +def cp_rm(filepath, output_dir): + # delete txt/json + for file in os.listdir(filepath): + if file.endswith('.txt') or file.endswith('.json'): + os.remove(os.path.join(filepath, file)) + # move images to output dir + target_dir = os.path.join(output_dir, 'images') + if not os.path.exists(os.path.join(output_dir, 'images')): + os.mkdir(os.path.join(output_dir, 'images')) + os.system('mv -f {} {}'.format(filepath, target_dir)) + + +def main(args): + logger = create_logger(args.log_name) + all_file_name = [ + os.path.join(args.image_dir, file) + for file in os.listdir(args.image_dir) if file.endswith('.tar') + ] + all_file_name.sort() + func = partial(tar_processing, output_dir=args.output_dir, logger=logger) + with Pool(processes=args.num_process) as pool: + result = pool.imap(func=func, iterable=all_file_name) # noqa + # print(result) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('image_dir', type=str) # grit raw directory + parser.add_argument('output_dir', type=str) + parser.add_argument('--num-process', default=10) + parser.add_argument('--log-name', type=str, default='grit_processing.log') + args = parser.parse_args() + + main(args) diff --git a/tools/dataset_converters/lvis2odvg.py b/tools/dataset_converters/lvis2odvg.py new file mode 100644 index 00000000000..ce0c4381b35 --- /dev/null +++ b/tools/dataset_converters/lvis2odvg.py @@ -0,0 +1,98 @@ +import argparse +import json +import os.path + +import jsonlines +from lvis import LVIS +from tqdm import tqdm + +key_list_lvis = [i for i in range(1203)] +val_list_lvis = [i for i in range(1, 1204)] + + +def dump_lvis_label_map(args): + with open(args.input, 'r') as f: + j = json.load(f) + o_dict = {} + for category in j['categories']: + index = str(int(category['id']) - 1) + name = category['name'] + o_dict[index] = name + if args.output is None: + output = os.path.dirname(args.input) + '/lvis_v1_label_map.json' + else: + output = os.path.dirname(args.output) + '/lvis_v1_label_map.json' + with open(output, 'w') as f: + json.dump(o_dict, f) + + +def lvis2odvg(args): + lvis = LVIS(args.input) + cats = lvis.load_cats(lvis.get_cat_ids()) + nms = {cat['id']: cat['name'] for cat in cats} + metas = [] + if args.output is None: + out_path = args.input[:-5] + '_od.json' + else: + out_path = args.output + + key_list = key_list_lvis + val_list = val_list_lvis + dump_lvis_label_map(args) + + for img_id, img_info in tqdm(lvis.imgs.items()): + file_name = img_info['coco_url'].replace( + 'http://images.cocodataset.org/', '') + ann_ids = lvis.get_ann_ids(img_ids=[img_id]) + raw_ann_info = lvis.load_anns(ann_ids) + instance_list = [] + for ann in raw_ann_info: + if ann.get('ignore', False): + print(f'invalid ignore box of {ann}') + continue + x1, y1, w, h = ann['bbox'] + inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0)) + inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + print(f'invalid wh box of {ann}') + continue + if ann['area'] <= 0 or w < 1 or h < 1: + print(f'invalid area box of {ann}, ' + f'w={img_info["width"]}, h={img_info["height"]}') + continue + + if ann.get('iscrowd', False): + print(f'invalid iscrowd box of {ann}') + continue + + bbox_xyxy = [x1, y1, x1 + w, y1 + h] + label = ann['category_id'] + category = nms[label] + ind = val_list.index(label) + label_trans = key_list[ind] + instance_list.append({ + 'bbox': bbox_xyxy, + 'label': label_trans, + 'category': category + }) + metas.append({ + 'filename': file_name, + 'height': img_info['height'], + 'width': img_info['width'], + 'detection': { + 'instances': instance_list + } + }) + + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(metas) + + print('save to {}'.format(out_path)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('lvis to odvg format.', add_help=True) + parser.add_argument('input', type=str, help='input list name') + parser.add_argument('--output', '-o', type=str, help='input list name') + args = parser.parse_args() + lvis2odvg(args) diff --git a/tools/dataset_converters/lvis2ovd.py b/tools/dataset_converters/lvis2ovd.py new file mode 100644 index 00000000000..3405bf3ad4f --- /dev/null +++ b/tools/dataset_converters/lvis2ovd.py @@ -0,0 +1,41 @@ +import argparse +import json +import os.path + +import jsonlines + + +def lvis2ovd(args): + ann_path = os.path.join(args.data_root, 'annotations/') + + lvis = json.load(open(ann_path + 'lvis_v1_val.json')) + base_class_ids = [ + cat['id'] - 1 for cat in lvis['categories'] if cat['frequency'] != 'r' + ] + + with open(ann_path + 'lvis_v1_train_od.json') as f: + data = [json.loads(d) for d in f] + for i in range(len(data)): + instance = [ + inst for inst in data[i]['detection']['instances'] + if inst['label'] in base_class_ids + ] + data[i]['detection']['instances'] = instance + with jsonlines.open( + ann_path + 'lvis_v1_train_od_norare.json', mode='w') as writer: + writer.write_all(data) + + label_map = json.load(open(ann_path + 'lvis_v1_label_map.json')) + label_map = { + k: v + for k, v in label_map.items() if int(k) in base_class_ids + } + json.dump(label_map, open(ann_path + 'lvis_v1_label_map_norare.json', 'w')) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('lvis to ovd format.', add_help=True) + parser.add_argument('data_root', type=str, help='coco root path') + args = parser.parse_args() + + lvis2ovd(args) diff --git a/tools/dataset_converters/openimages2odvg.py b/tools/dataset_converters/openimages2odvg.py new file mode 100644 index 00000000000..d700a4146a3 --- /dev/null +++ b/tools/dataset_converters/openimages2odvg.py @@ -0,0 +1,187 @@ +import argparse +import copy +import csv +import json +import os.path as osp + +import jsonlines +from mmcv.image import imfrombytes +from mmengine.fileio import get + + +def _parse_label_file(label_file): + index_list = [] + classes_names = [] + with open(label_file, 'r') as f: + reader = csv.reader(f) + for line in reader: + classes_names.append(line[1]) + index_list.append(line[0]) + index_mapping = {index: i for i, index in enumerate(index_list)} + return classes_names, index_mapping + + +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# './data/': 's3://openmmlab/datasets/detection/', +# 'data/': 's3://openmmlab/datasets/detection/' +# })) +backend_args = None + + +def oi2odvg(args): + ann_file = osp.join(args.input_dir, 'oidv6-train-annotations-bbox.csv') + label_file = osp.join(args.input_dir, 'class-descriptions-boxable.csv') + + classes_names, index_mapping = _parse_label_file(label_file) + + label_map = {} + for class_name, idx in index_mapping.items(): + class_name = classes_names[idx] + label_map[str(idx)] = class_name + + if args.out_ann is None: + output = osp.join(args.input_dir, 'openimages_label_map.json') + else: + output = osp.join( + osp.dirname(args.out_ann), 'openimages_label_map.json') + with open(output, 'w') as f: + json.dump(label_map, f) + + metas = [] + skip_count = 0 + with open(ann_file, 'r') as f: + reader = csv.reader(f) + last_img_id = None + _filename_shape = [0, 0] + instances = [] + for i, line in enumerate(reader): + if i == 0: + continue + img_id = line[0] + if last_img_id is None: + last_img_id = img_id + label_id = line[2] + + filename = f'{img_id}.jpg' + label = index_mapping[label_id] + category = label_map[str(label)] + bbox = [ + float(line[4]), # xmin + float(line[6]), # ymin + float(line[5]), # xmax + float(line[7]) # ymax + ] + + # is_occluded = True if int(line[8]) == 1 else False + # is_truncated = True if int(line[9]) == 1 else False + is_group_of = True if int(line[10]) == 1 else False + # is_depiction = True if int(line[11]) == 1 else False + # is_inside = True if int(line[12]) == 1 else False + + # if any([is_occluded, is_truncated, is_group_of, + # is_depiction, is_inside]): + if is_group_of: + print(f'skip {filename} of one instance') + skip_count += 1 + continue + + # denormalize + if filename != _filename_shape[0]: + if args.img_prefix is not None: + _filename = osp.join( + osp.dirname(args.input_dir), args.img_prefix, filename) + else: + _filename = osp.join(osp.dirname(args.input_dir), filename) + img_bytes = get(_filename, backend_args) + img = imfrombytes(img_bytes, flag='color') + shape = img.shape + _filename_shape = [filename, shape] + else: + shape = _filename_shape[1] + + h, w = shape[:2] + bbox = [ + max(bbox[0] * w, 0), + max(bbox[1] * h, 0), + min(bbox[2] * w, w), + min(bbox[3] * h, h) + ] + + x1, y1, x2, y2 = bbox + inter_w = max(0, min(x2, w) - max(x1, 0)) + inter_h = max(0, min(y2, h) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if w < 1 or h < 1: + continue + + instance = { + 'filename': filename, + 'height': h, + 'width': w, + 'bbox': bbox, + 'label': label, + 'category': category + } + + if img_id != last_img_id: + copy_instances = copy.deepcopy(instances) + for copy_instance in copy_instances: + _filename = copy_instance.pop('filename') + _h = copy_instance.pop('height') + _w = copy_instance.pop('width') + + meta_ifo = { + 'filename': _filename, + 'height': _h, + 'width': _w, + 'detection': { + 'instances': copy_instances + } + } + metas.append(meta_ifo) + instances = [] + instances.append(instance) + last_img_id = img_id + + for instance in instances: + _filename = instance.pop('filename') + _h = instance.pop('height') + _w = instance.pop('width') + meta_ifo = { + 'filename': _filename, + 'height': _h, + 'width': _w, + 'detection': { + 'instances': instances + } + } + metas.append(meta_ifo) + + if args.out_ann is None: + out_path = osp.join(args.input_dir, 'oidv6-train-annotations_od.json') + else: + out_path = args.out_ann + + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(metas) + + print('skip {} instances'.format(skip_count)) + print('save to {}'.format(out_path)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + 'openimages to odvg format.', add_help=True) + parser.add_argument( + '--input-dir', + default='data/OpenImages/annotations', + type=str, + help='input list name') + parser.add_argument('--img-prefix', default='OpenImages/train/') + parser.add_argument('--out-ann', '-o', type=str) + args = parser.parse_args() + + oi2odvg(args) diff --git a/tools/dataset_converters/refcoco2odvg.py b/tools/dataset_converters/refcoco2odvg.py new file mode 100644 index 00000000000..c11869b3855 --- /dev/null +++ b/tools/dataset_converters/refcoco2odvg.py @@ -0,0 +1,147 @@ +import argparse +import os.path as osp + +import jsonlines +from pycocotools.coco import COCO +from tqdm import tqdm + + +def parse_args(): + parser = argparse.ArgumentParser(description='refcoco to odvg') + parser.add_argument('mdetr_anno_dir', type=str) + parser.add_argument('--out-dir', '-o', type=str) + args = parser.parse_args() + return args + + +def _has_only_empty_bbox(anno): + return all(any(o <= 1 for o in obj['bbox'][2:]) for obj in anno) + + +def has_valid_annotation(anno): + # if it's empty, there is no annotation + if len(anno) == 0: + return False + # if all boxes have close to zero area, there is no annotation + if _has_only_empty_bbox(anno): + return False + return True + + +def process_item(args, filename): + path = osp.join(args.mdetr_anno_dir, filename) + coco = COCO(path) + + ids = list(sorted(coco.imgs.keys())) + + out_results = [] + for img_id in tqdm(ids): + if isinstance(img_id, str): + ann_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=0) + else: + ann_ids = coco.getAnnIds(imgIds=img_id, iscrowd=0) + annos = coco.loadAnns(ann_ids) + if not has_valid_annotation(annos): + continue + + img_info = coco.loadImgs(img_id)[0] + file_name = img_info['file_name'] + caption = img_info['caption'] + + regions = {} + + for anno in annos: + box = anno['bbox'] + tokens_positive = anno['tokens_positive'] + x1, y1, w, h = box + inter_w = max(0, min(x1 + w, int(img_info['width'])) - max(x1, 0)) + inter_h = max(0, min(y1 + h, int(img_info['height'])) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if anno['area'] <= 0 or w < 1 or h < 1: + continue + + if anno.get('iscrowd', False): + continue + bbox_xyxy = [ + x1, y1, + min(x1 + w, int(img_info['width'])), + min(y1 + h, int(img_info['height'])) + ] + + tokens_positive = sorted(tokens_positive, key=lambda x: x[0]) + + phrase = [] + pre_end_index = -10 + for token in tokens_positive: + start_index = token[0] + end_index = token[1] + if pre_end_index + 1 == start_index: + if caption[token[0] - 1] == ' ': + phrase[ + -1] = phrase[-1] + ' ' + caption[token[0]:token[1]] + else: + phrase.append(caption[token[0]:token[1]]) + else: + phrase.append(caption[token[0]:token[1]]) + pre_end_index = end_index + + key = ' '.join(phrase) + + if key not in regions: + regions[key] = { + 'bbox': bbox_xyxy, + 'phrase': phrase, + 'tokens_positive': tokens_positive + } + else: + old_box = regions[key]['bbox'] + if isinstance(old_box[0], list): + old_box.append(bbox_xyxy) + else: + old_box = [old_box, bbox_xyxy] + + regions[key]['bbox'] = old_box + + out_dict = { + 'filename': file_name, + 'height': int(img_info['height']), + 'width': int(img_info['width']), + 'grounding': { + 'caption': caption + } + } + + region_list = [] + for key, value in regions.items(): + phrase = value['phrase'] + if len(phrase) == 1: + phrase = phrase[0] + region_list.append({ + 'bbox': value['bbox'], + 'phrase': phrase, + 'tokens_positive': value['tokens_positive'] + }) + out_dict['grounding']['regions'] = region_list + out_results.append(out_dict) + + if args.out_dir is None: + out_path = osp.join(args.mdetr_anno_dir, filename[:-5] + '_vg.json') + else: + out_path = osp.join(args.out_dir, filename[:-5] + '_vg.json') + + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(out_results) + print(f'save to {out_path}') + + +def main(): + args = parse_args() + process_item(args, 'finetune_refcoco_train.json') + process_item(args, 'finetune_refcoco+_train.json') + process_item(args, 'finetune_refcocog_train.json') + process_item(args, 'finetune_grefcoco_train.json') + + +if __name__ == '__main__': + main() diff --git a/tools/dataset_converters/remove_cocotrain2017_from_refcoco.py b/tools/dataset_converters/remove_cocotrain2017_from_refcoco.py new file mode 100644 index 00000000000..7de2a9ec4e2 --- /dev/null +++ b/tools/dataset_converters/remove_cocotrain2017_from_refcoco.py @@ -0,0 +1,110 @@ +import argparse +import json +import os.path as osp + +import mmengine +from pycocotools.coco import COCO + + +def diff_image_id(coco2017_train_ids, ref_ids): + set1 = set(coco2017_train_ids) + set2 = set(ref_ids) + intersection = set1.intersection(set2) + result = set1 - intersection + return result + + +def gen_new_json(coco2017_train_path, json_data, coco2017_train_ids): + coco = COCO(coco2017_train_path) + new_json_data = { + 'info': json_data['info'], + 'licenses': json_data['licenses'], + 'categories': json_data['categories'], + 'images': [], + 'annotations': [] + } + + for id in coco2017_train_ids: + ann_ids = coco.getAnnIds(imgIds=[id]) + img_ann_info = coco.loadAnns(ann_ids) + img_info = coco.loadImgs([id])[0] + + new_json_data['images'].append(img_info) + new_json_data['annotations'].extend(img_ann_info) + return new_json_data + + +# coco2017 val and final_mixed_train.json have no intersection, +# so deduplication is not necessary. + +# coco2017 val and datasets like refcoco based on coco2014 train +# have no intersection, so deduplication is not necessary. + + +# coco2017 train and datasets like refcoco based on coco2014 +# train have overlapping annotations in the validation set, +# so deduplication is required. +def exclude_coco(args): + with open(args.coco2017_train, 'r') as f: + coco2017_train = json.load(f) + coco2017_train_ids = [train['id'] for train in coco2017_train['images']] + orig_len = len(coco2017_train_ids) + + with open(osp.join(args.mdetr_anno_dir, 'finetune_refcoco_val.json'), + 'r') as f: + refcoco_ann = json.load(f) + refcoco_ids = [refcoco['original_id'] for refcoco in refcoco_ann['images']] + coco2017_train_ids = diff_image_id(coco2017_train_ids, refcoco_ids) + + with open( + osp.join(args.mdetr_anno_dir, 'finetune_refcoco+_val.json'), + 'r') as f: + refcoco_plus_ann = json.load(f) + refcoco_plus_ids = [ + refcoco['original_id'] for refcoco in refcoco_plus_ann['images'] + ] + coco2017_train_ids = diff_image_id(coco2017_train_ids, refcoco_plus_ids) + + with open( + osp.join(args.mdetr_anno_dir, 'finetune_refcocog_val.json'), + 'r') as f: + refcocog_ann = json.load(f) + refcocog_ids = [ + refcoco['original_id'] for refcoco in refcocog_ann['images'] + ] + coco2017_train_ids = diff_image_id(coco2017_train_ids, refcocog_ids) + + with open( + osp.join(args.mdetr_anno_dir, 'finetune_grefcoco_val.json'), + 'r') as f: + grefcoco_ann = json.load(f) + grefcoco_ids = [ + refcoco['original_id'] for refcoco in grefcoco_ann['images'] + ] + coco2017_train_ids = diff_image_id(coco2017_train_ids, grefcoco_ids) + + coco2017_train_ids = list(coco2017_train_ids) + print( + 'remove {} images from coco2017_train'.format(orig_len - + len(coco2017_train_ids))) + + new_json_data = gen_new_json(args.coco2017_train, coco2017_train, + coco2017_train_ids) + if args.out_ann is None: + out_ann = osp.dirname( + args.coco2017_train) + '/instances_train2017_norefval.json' + mmengine.dump(new_json_data, out_ann) + print('save new json to {}'.format(out_ann)) + else: + mmengine.dump(new_json_data, args.out_ann) + print('save new json to {}'.format(args.out_ann)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('coco to odvg format.', add_help=True) + parser.add_argument('mdetr_anno_dir', type=str) + parser.add_argument('coco2017_train', type=str) + parser.add_argument('--out-ann', '-o', type=str) + args = parser.parse_args() + + exclude_coco(args) diff --git a/tools/dataset_converters/zhiyuan_objv2_train_names_fix.csv b/tools/dataset_converters/zhiyuan_objv2_train_names_fix.csv new file mode 100644 index 00000000000..33b0aa946c6 --- /dev/null +++ b/tools/dataset_converters/zhiyuan_objv2_train_names_fix.csv @@ -0,0 +1,365 @@ +1,Person,Person +2,Sneakers,Sneakers +3,Chair,Chair +4,Other Shoes,Other Shoes +5,Hat,Hat +6,Car,Car +7,Lamp,Lamp +8,Glasses,Glasses +9,Bottle,Bottle +10,Desk,Desk +11,Cup,Cup +12,Street Lights,Street Lights +13,Cabinet/shelf,Cabinet/shelf +14,Handbag/Satchel,Handbag/Satchel +15,Bracelet,Bracelet +16,Plate,Plate +17,Picture/Frame,Picture/Frame +18,Helmet,Helmet +19,Book,Book +20,Gloves,Gloves +21,Storage box,Storage box +22,Boat,Boat +23,Leather Shoes,Leather Shoes +24,Flower,Flower +25,Bench,Bench +26,Potted Plant,Potted Plant +27,Bowl/Basin,Bowl/Basin +28,Flag,Flag +29,Pillow,Pillow +30,Boots,Boots +31,Vase,Vase +32,Microphone,Microphone +33,Necklace,Necklace +34,Ring,Ring +35,SUV,SUV +36,Wine Glass,Wine Glass +37,Belt,Belt +38,Moniter/TV,Monitor/TV +39,Backpack,Backpack +40,Umbrella,Umbrella +41,Traffic Light,Traffic Light +42,Speaker,Speaker +43,Watch,Watch +44,Tie,Tie +45,Trash bin Can,Trash bin Can +46,Slippers,Slippers +47,Bicycle,Bicycle +48,Stool,Stool +49,Barrel/bucket,Barrel/bucket +50,Van,Van +51,Couch,Couch +52,Sandals,Sandals +53,Bakset,Basket +54,Drum,Drum +55,Pen/Pencil,Pen/Pencil +56,Bus,Bus +57,Wild Bird,Wild Bird +58,High Heels,High Heels +59,Motorcycle,Motorcycle +60,Guitar,Guitar +61,Carpet,Carpet +62,Cell Phone,Cell Phone +63,Bread,Bread +64,Camera,Camera +65,Canned,Canned +66,Truck,Truck +67,Traffic cone,Traffic cone +68,Cymbal,Cymbal +69,Lifesaver,Lifesaver +70,Towel,Towel +71,Stuffed Toy,Stuffed Toy +72,Candle,Candle +73,Sailboat,Sailboat +74,Laptop,Laptop +75,Awning,Awning +76,Bed,Bed +77,Faucet,Faucet +78,Tent,Tent +79,Horse,Horse +80,Mirror,Mirror +81,Power outlet,Power outlet +82,Sink,Sink +83,Apple,Apple +84,Air Conditioner,Air Conditioner +85,Knife,Knife +86,Hockey Stick,Hockey Stick +87,Paddle,Paddle +88,Pickup Truck,Pickup Truck +89,Fork,Fork +90,Traffic Sign,Traffic Sign +91,Ballon,Ballon +92,Tripod,Tripod +93,Dog,Dog +94,Spoon,Spoon +95,Clock,Clock +96,Pot,Pot +97,Cow,Cow +98,Cake,Cake +99,Dinning Table,Dining Table +100,Sheep,Sheep +101,Hanger,Hanger +102,Blackboard/Whiteboard,Blackboard/Whiteboard +103,Napkin,Napkin +104,Other Fish,Other Fish +105,Orange/Tangerine,Orange/Tangerine +106,Toiletry,Toiletry +107,Keyboard,Keyboard +108,Tomato,Tomato +109,Lantern,Lantern +110,Machinery Vehicle,Machinery Vehicle +111,Fan,Fan +112,Green Vegetables,Green Vegetables +113,Banana,Banana +114,Baseball Glove,Baseball Glove +115,Airplane,Airplane +116,Mouse,Mouse +117,Train,Train +118,Pumpkin,Pumpkin +119,Soccer,Soccer +120,Skiboard,Skiboard +121,Luggage,Luggage +122,Nightstand,Nightstand +123,Tea pot,Teapot +124,Telephone,Telephone +125,Trolley,Trolley +126,Head Phone,Head Phone +127,Sports Car,Sports Car +128,Stop Sign,Stop Sign +129,Dessert,Dessert +130,Scooter,Scooter +131,Stroller,Stroller +132,Crane,Crane +133,Remote,Remote +134,Refrigerator,Refrigerator +135,Oven,Oven +136,Lemon,Lemon +137,Duck,Duck +138,Baseball Bat,Baseball Bat 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parser.add_argument( + '--label-map-file', '-m', type=str, help='label map file') + parser.add_argument( + '--num-img', + '-n', + default=200, + type=int, + help='num of extract image, -1 means all images') + parser.add_argument('--seed', default=-1, type=int, help='seed') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + assert args.out_dir != args.data_root, \ + 'The file will be overwritten in place, ' \ + 'so the same folder is not allowed !' + + seed = int(args.seed) + if seed != -1: + print(f'Set the global seed: {seed}') + np.random.seed(int(args.seed)) + + ann_file = os.path.join(args.data_root, args.ann_file) + with open(ann_file, 'r') as f: + data_list = [json.loads(line) for line in f] + + np.random.shuffle(data_list) + + num_img = args.num_img + + progress_bar = ProgressBar(num_img) + for i in range(num_img): + file_name = data_list[i]['filename'] + image_path = os.path.join(args.data_root, args.img_prefix, file_name) + out_image_dir = os.path.join(args.out_dir, args.img_prefix) + mkdir_or_exist(out_image_dir) + out_image_path = os.path.join(out_image_dir, file_name) + shutil.copyfile(image_path, out_image_path) + + progress_bar.update() + + out_path = os.path.join(args.out_dir, args.ann_file) + out_dir = os.path.dirname(out_path) + mkdir_or_exist(out_dir) + + with jsonlines.open(out_path, mode='w') as writer: + writer.write_all(data_list[:num_img]) + + if args.label_map_file is not None: + out_dir = os.path.dirname( + os.path.join(args.out_dir, args.label_map_file)) + mkdir_or_exist(out_dir) + shutil.copyfile( + os.path.join(args.data_root, args.label_map_file), + os.path.join(args.out_dir, args.label_map_file)) + + +if __name__ == '__main__': + main()