From 94f6a9fb0aed6429cbf30cd6a7e80403de9ec8d6 Mon Sep 17 00:00:00 2001 From: aptsunny Date: Wed, 28 Dec 2022 23:31:46 +0800 Subject: [PATCH 1/3] check attentivenas training --- configs/nas/mmcls/bignas/README.md | 14 +++++++------- .../attentive_mobilenet_supernet_32xb64_in1k.py | 3 +++ mmrazor/models/architectures/utils/set_dropout.py | 12 ++++++++---- 3 files changed, 18 insertions(+), 11 deletions(-) diff --git a/configs/nas/mmcls/bignas/README.md b/configs/nas/mmcls/bignas/README.md index 0d84d2599..9cdf2ce99 100644 --- a/configs/nas/mmcls/bignas/README.md +++ b/configs/nas/mmcls/bignas/README.md @@ -37,14 +37,14 @@ 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 | [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) | 11.56(min) / 23.3(max) | 414(min) / 1944(max) | 76.57(min) / 81.35(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 | *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: @@ -53,7 +53,7 @@ are only for inference. We don't ensure these config files' training accuracy an 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. +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 diff --git a/configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py b/configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py index a4cecf209..15a11ed99 100644 --- a/configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py +++ b/configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py @@ -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')) diff --git a/mmrazor/models/architectures/utils/set_dropout.py b/mmrazor/models/architectures/utils/set_dropout.py index 254764c2c..f45f03a03 100644 --- a/mmrazor/models/architectures/utils/set_dropout.py +++ b/mmrazor/models/architectures/utils/set_dropout.py @@ -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 From 827f6294ed95217e9ad8cfa5afe093179fbf570b Mon Sep 17 00:00:00 2001 From: aptsunny Date: Thu, 29 Dec 2022 22:20:24 +0800 Subject: [PATCH 2/3] update ckpt link --- configs/nas/mmcls/bignas/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/configs/nas/mmcls/bignas/README.md b/configs/nas/mmcls/bignas/README.md index 9cdf2ce99..cbed160a4 100644 --- a/configs/nas/mmcls/bignas/README.md +++ b/configs/nas/mmcls/bignas/README.md @@ -39,9 +39,9 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh \ | 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) | 11.56(min) / 23.3(max) | 414(min) / 1944(max) | 76.57(min) / 81.35(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 | +| ImageNet | AttentiveMobileNetV3 | [search space](../../../../configs/_base_/nas_backbones/attentive_mobilenetv3_supernet.py) | 11.56(min) / 23.3(max) | 414(min) / 1944(max) | 76.57(min) / 81.35(max) | [config](../../../../configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py) | model | MMRazor searched | +| 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 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.* @@ -52,7 +52,7 @@ are only for inference. We support training the supernet by `sandwich rule`, whi - 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. +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 From fb8620c2906b496f501ceab3ebc51d6507f93557 Mon Sep 17 00:00:00 2001 From: aptsunny Date: Tue, 3 Jan 2023 18:35:19 +0800 Subject: [PATCH 3/3] update supernet log --- configs/nas/mmcls/bignas/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/configs/nas/mmcls/bignas/README.md b/configs/nas/mmcls/bignas/README.md index cbed160a4..4d9f749ca 100644 --- a/configs/nas/mmcls/bignas/README.md +++ b/configs/nas/mmcls/bignas/README.md @@ -37,11 +37,11 @@ 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 | [search space](../../../../configs/_base_/nas_backbones/attentive_mobilenetv3_supernet.py) | 11.56(min) / 23.3(max) | 414(min) / 1944(max) | 76.57(min) / 81.35(max) | [config](../../../../configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py) | model | MMRazor searched | -| 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 | +| 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 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.*