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update bignas cfg #412

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Jan 6, 2023
Merged

update bignas cfg #412

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94f6a9f
check attentivenas training
Dec 28, 2022
827f629
update ckpt link
Dec 29, 2022
fb8620c
update supernet log
Jan 3, 2023
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16 changes: 8 additions & 8 deletions configs/nas/mmcls/bignas/README.md
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Expand Up @@ -37,23 +37,23 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh \

## Results and models

| Dataset | Supernet | Subnet | Params(M) | Flops(G) | Top-1 | Config | Download | Remarks |
| :------: | :------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------: | :------------------: | :---------------------: | :---------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------: |
| ImageNet | AttentiveMobileNetV3 | [mutable](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml) | 8.9(min) / 23.3(max) | 203(min) / 1939(max) | 77.25(min) / 81.72(max) | [config](./detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py) | [pretrain](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_shufflenetv2_8xb128_in1k_acc-74.08_20211223-92e9b66a.pth) \|[model](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.log.json) | MMRazor searched |
| ImageNet | AttentiveMobileNetV3 | [AttentiveNAS-A0\*](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml) | 11.559 | 414 | 77.252 | [config](./detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py) | [pretrain](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_shufflenetv2_8xb128_in1k_acc-74.08_20211223-92e9b66a.pth) \|[model](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.log.json) | Converted from the repo |
| ImageNet | AttentiveMobileNetV3 | [AttentiveNAS-A6\*](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml) | 16.476 | 1163 | 80.790 | [config](./detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py) | [pretrain](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_shufflenetv2_8xb128_in1k_acc-74.08_20211223-92e9b66a.pth) \|[model](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.log.json) | Converted from the repo |
| Dataset | Supernet | Subnet | Params(M) | Flops(G) | Top-1 | Config | Download | Remarks |
| :------: | :------------------: | :--------------------------------------------------------------------------------------------: | :--------------------: | :------------------: | :---------------------: | :----------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
| ImageNet | AttentiveMobileNetV3 | [search space](../../../../configs/_base_/nas_backbones/attentive_mobilenetv3_supernet.py) | 11.56(min) / 23.3(max) | 414(min) / 1944(max) | 76.88(min) / 81.42(max) | [config](../../../../configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py) | [model\*](https://download.openmmlab.com/mmrazor/v0.1/nas/bignas/attentive_mobilenet_subnet_8xb256_in1k/attentive_mobilenet_supernet_32xb64_in1k_flops-2G_acc-81.72_20221229_200440-954772a3.pth) | [log](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_supernet_32xb64_in1k_20221227_175800-bcf94eaa.json) (`sandwich rule`) |
| ImageNet | AttentiveMobileNetV3 | [AttentiveNAS-A0\*](https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A0.yaml) | 11.559 | 414 | 77.19 | [config](../../../../configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.21G_acc-77.19_20221229_200440-282a1f70.pth) | Converted from the repo |
| ImageNet | AttentiveMobileNetV3 | [AttentiveNAS-A6\*](https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A6.yaml) | 16.476 | 1163 | 80.81 | [config](../../../../configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.93G_acc-80.81_20221229_200440-73d92cc6.pth) | Converted from the repo |

*Models with * are converted from the [official repo](https://github.com/facebookresearch/AttentiveNAS). The config files of these models
are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
are only for inference. We support training the supernet by `sandwich rule`, which is different from `rejection sampling` in [official repo](https://github.com/facebookresearch/AttentiveNAS), and welcome you to contribute your reproduction results.*

**Note**: In the official `AttentiveNAS` code, the `AutoAugmentation` in Calib-BN subnet recommended to use large batchsize to evaluation like `256`, which leads to higher performance. Compared with the original configuration file, this configuration has been modified as follows:

- modified the settings related to `batchsize` in `train_pipeline` and `test_pipeline`, e.g. setting `train_dataloader.batch_size=256`、 `val_dataloader.batch_size=256`、`test_cfg.calibrate_sample_num=16384` and `collate_fn=dict(type='default_collate')` in train_dataloader.
- setting `dict(type='mmrazor.AutoAugment', policies='original')` instead of `dict(type='mmrazor.AutoAugmentV2', policies=policies)` in train_pipeline.

1. Used search_space in AttentiveNAS, which is different from BigNAS paper.
2. The Top-1 Acc is unstable and may fluctuate by about 0.1, convert the official weight according to the [converter script](../../../../tools/model_converters/convert_attentivenas_nas_ckpt.py). A Calib-BN model will be released later.
3. We have observed that the searchable model has been officially released. Although the subnet accuracy has decreased, it is more efficient. We will also provide the supernet training configuration in the future.
2. The Top-1 Acc is unstable and may fluctuate by about 0.1, convert [the official weight](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_supernet_32xb64_in1k_flops-2G_acc-81.72_20221229_200440-954772a3.pth) according to the [converter script](../../../../tools/model_converters/convert_attentivenas_nas_ckpt.py). A Calib-BN model will be released later.
3. We have observed that the searchable model has been officially released. We will also provide the completed version of supernet training configuration in the future.

## Citation

Expand Down
3 changes: 3 additions & 0 deletions configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py
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Expand Up @@ -61,6 +61,9 @@
broadcast_buffers=False,
find_unused_parameters=True)

optim_wrapper_cfg = dict(
type='OptimWrapper', clip_grad=dict(type='value', clip_value=0.2))

default_hooks = dict(
checkpoint=dict(
type='CheckpointHook', interval=1, max_keep_ckpts=1, save_best='auto'))
12 changes: 8 additions & 4 deletions mmrazor/models/architectures/utils/set_dropout.py
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Expand Up @@ -16,16 +16,20 @@ def set_dropout(layers, module, dropout_stages: List[int],
"""
assert hasattr(module, 'drop_path_rate')
visited_block_nums = 0
total_block_nums = sum(len(layer) for layer in layers) + 1

total_block_nums = len([
block for layer in layers for block in layer
if isinstance(block, module)
])
for idx, layer in enumerate(layers, start=1):
assert isinstance(layer, DynamicSequential)
visited_block_nums += len(layer)
mblayer_nums = len(
[block for block in layer if isinstance(block, module)])
visited_block_nums += mblayer_nums
if idx not in dropout_stages:
continue

for block_idx, block in enumerate(layer):
if isinstance(block, module) and hasattr(block, 'drop_path_rate'):
ratio = (visited_block_nums - len(layer) +
ratio = (visited_block_nums - mblayer_nums +
block_idx) / total_block_nums
block.drop_path_rate = drop_path_rate * ratio