diff --git a/configs/nas/mmcls/autoslim/README.md b/configs/nas/mmcls/autoslim/README.md index e7292fae3..37a651ddb 100644 --- a/configs/nas/mmcls/autoslim/README.md +++ b/configs/nas/mmcls/autoslim/README.md @@ -60,11 +60,11 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh \ ### Subnet retrain -| Supernet | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | Subnet | Remark | -| :----------------- | :-------: | -------: | :-------: | :-------: | :---------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------: | -| MobileNet v2(x1.5) | 6.5 | 0.53 | 74.23 | 91.74 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd_channel_cfg.yaml) | official channel cfg | -| MobileNet v2(x1.5) | 5.77 | 0.32 | 72.73 | 90.83 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c_channel_cfg.yaml) | official channel cfg | -| MobileNet v2(x1.5) | 4.13 | 0.22 | 71.39 | 90.08 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b_channel_cfg.yaml) | official channel cfg | +| Supernet | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | Subnet | Remark | +| :----------------- | :-------: | -------: | :-------: | :-------: | :---------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------: | +| MobileNet v2(x1.5) | 6.5 | 0.53 | 74.23 | 91.74 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v1/autoslim/autoslim_mbv2_subnet_8xb256_in1k_flops-530M_acc-74.23_20220715-aa8754fe.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd_channel_cfg.yaml) | official channel cfg | +| MobileNet v2(x1.5) | 5.77 | 0.32 | 72.73 | 90.83 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v1/autoslim/autoslim_mbv2_subnet_8xb256_in1k_flops-320M_acc-72.73_20220715-9aa8f8ae.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c_channel_cfg.yaml) | official channel cfg | +| MobileNet v2(x1.5) | 4.13 | 0.22 | 71.39 | 90.08 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v1/autoslim/autoslim_mbv2_subnet_8xb256_in1k_flops-220M_acc-71.4_20220715-9c288f3b.pth) \| [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b_channel_cfg.yaml) | official channel cfg | Note that we ran the official code and the Top-1 Acc of the models with official channel cfg are 73.8%, 72.5% and 71.1%. And there are 3 differences between our diff --git a/configs/pruning/mmcls/dmcp/README.md b/configs/pruning/mmcls/dmcp/README.md index d2c7aa7a4..60dbd9849 100644 --- a/configs/pruning/mmcls/dmcp/README.md +++ b/configs/pruning/mmcls/dmcp/README.md @@ -30,14 +30,17 @@ GPUS=32 sh tools/slurm_train.sh $PARTITION $JOB_NAME \ ### 1.Classification -| Dataset | Supernet | Flops(M) | Top-1 (%) | Top-5 (%) | config | Download | Remark | -| :------: | :---------: | :-------------: | :-------: | :-------: | :----------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | -| ImageNet | ResNet50 | 4.09G(Supernet) | 77.46 | 93.55 | - | - | - | +| Dataset | Supernet | Flops(M) | Top-1 (%) | Top-5 (%) | config | Download | Remark | +| :------: | :---------: | :------------: | :-------: | :-------: | :------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | +| ImageNet | MobilenetV2 | 319M(Supernet) | 72.30 | 90.42 | - | - | - | +| ImageNet | MobilenetV2 | 209M(Subnet) | 71.94 | 90.05 | [config](./dmcp_mbv2_subnet_32xb64.py) | [model](https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/mobilenetv2/200M/DMCP_MBV2_200M.pth) / [log](https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/mobilenetv2/200M/dmcp_mobilenetv2_supernet_32xb64_target_flops_200m_20230129_184919.log) | [arch](https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/mobilenetv2/200M/DMCP_MBV2_200M.json) | +| ImageNet | MobilenetV2 | 102M(Subnet) | 67.22 | 88.61 | [config](./dmcp_mbv2_subnet_32xb64.py) | [model](https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/mobilenetv2/100M/DMCP_MBV2_100M.pth) / [log](https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/mobilenetv2/100M/dmcp_mobilenetv2_supernet_32xb64_target_flops_100m_20230129_184919.log) | [arch\*](./DMCP_MBV2_100M.json) | + + + + **Note** diff --git a/configs/pruning/mmcls/dmcp/metafile.yml b/configs/pruning/mmcls/dmcp/metafile.yml index 4c1268093..f20e2488f 100644 --- a/configs/pruning/mmcls/dmcp/metafile.yml +++ b/configs/pruning/mmcls/dmcp/metafile.yml @@ -1,13 +1,13 @@ Models: - - Name: dmcp_resnet50_subnet_32xb64 - In Collection: DMCP - Config: configs/pruning/mmcls/dmcp/dmcp_resnet50_subnet_32xb64.py - Weights: https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/resnet50/2G/DMCP_R50_2G.pth - Results: - - Task: Image Classification - Dataset: ImageNet-1k - Metrics: - Top 1 Accuracy: 76.11 + # - Name: dmcp_resnet50_subnet_32xb64 + # In Collection: DMCP + # Config: configs/pruning/mmcls/dmcp/dmcp_resnet50_subnet_32xb64.py + # Weights: https://download.openmmlab.com/mmrazor/v1/pruning/dmcp/resnet50/2G/DMCP_R50_2G.pth + # Results: + # - Task: Image Classification + # Dataset: ImageNet-1k + # Metrics: + # Top 1 Accuracy: 76.11 - Name: dmcp_mbv2_subnet_32xb64 In Collection: DMCP Config: configs/pruning/mmcls/dmcp/dmcp_mbv2_subnet_32xb64.py diff --git a/tests/data/MBV2_slimmable_config.json b/tests/data/MBV2_slimmable_config.json index 5b9a5573a..9010b83e2 100644 --- a/tests/data/MBV2_slimmable_config.json +++ b/tests/data/MBV2_slimmable_config.json @@ -1,396 +1,377 @@ { - "type":"OneShotChannelMutator", - "channel_unit_cfg":{ - "type":"OneShotMutableChannelUnit", - "default_args":{ - "choice_mode":"number" + "backbone.conv1.conv_(0, 48)_48": { + "init_args": { + "num_channels": 48, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 8, + 8, + 32 + ], + "choice_mode": "number" }, - "units":{ - "backbone.conv1.conv_(0, 48)_48": { - "init_args": { - "num_channels": 48, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 8, - 8, - 32 - ], - "choice_mode": "number" - }, - "choice": 32 - }, - "backbone.layer1.0.conv.1.conv_(0, 24)_24": { - "init_args": { - "num_channels": 24, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 8, - 8, - 16 - ], - "choice_mode": "number" - }, - "choice": 16 - }, - "backbone.layer2.0.conv.0.conv_(0, 144)_144": { - "init_args": { - "num_channels": 144, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 96, - 96, - 144 - ], - "choice_mode": "number" - }, - "choice": 144 - }, - "backbone.layer2.0.conv.2.conv_(0, 40)_40": { - "init_args": { - "num_channels": 40, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 16, - 16, - 24 - ], - "choice_mode": "number" - }, - "choice": 24 - }, - "backbone.layer2.1.conv.0.conv_(0, 240)_240": { - "init_args": { - "num_channels": 240, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 96, - 96, - 176 - ], - "choice_mode": "number" - }, - "choice": 176 - }, - "backbone.layer3.0.conv.0.conv_(0, 240)_240": { - "init_args": { - "num_channels": 240, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 96, - 96, - 192 - ], - "choice_mode": "number" - }, - "choice": 192 - }, - "backbone.layer3.0.conv.2.conv_(0, 48)_48": { - "init_args": { - "num_channels": 48, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 24, - 24, - 48 - ], - "choice_mode": "number" - }, - "choice": 48 - }, - "backbone.layer3.1.conv.0.conv_(0, 288)_288": { - "init_args": { - "num_channels": 288, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 144, - 144, - 240 - ], - 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- }, - "choice": 288 - }, - "backbone.layer4.2.conv.0.conv_(0, 576)_576": { - "init_args": { - "num_channels": 576, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 288, - 288, - 336 - ], - "choice_mode": "number" - }, - "choice": 336 - }, - "backbone.layer4.3.conv.0.conv_(0, 576)_576": { - "init_args": { - "num_channels": 576, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 288, - 288, - 432 - ], - "choice_mode": "number" - }, - "choice": 432 - }, - "backbone.layer5.0.conv.0.conv_(0, 576)_576": { - "init_args": { - "num_channels": 576, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 288, - 288, - 576 - ], - "choice_mode": "number" - }, - "choice": 576 - }, - "backbone.layer5.0.conv.2.conv_(0, 144)_144": { - "init_args": { - "num_channels": 144, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 64, - 96, - 144 - ], - "choice_mode": "number" - }, - "choice": 144 - }, - "backbone.layer5.1.conv.0.conv_(0, 864)_864": { - "init_args": { - "num_channels": 864, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 432, - 432, - 576 - ], - "choice_mode": "number" - }, - "choice": 576 - }, - "backbone.layer5.2.conv.0.conv_(0, 864)_864": { - "init_args": { - "num_channels": 864, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 432, - 432, - 648 - ], - "choice_mode": "number" - }, - "choice": 648 - }, - "backbone.layer6.0.conv.0.conv_(0, 864)_864": { - "init_args": { - "num_channels": 864, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 648, - 864, - 864 - ], - "choice_mode": "number" - }, - "choice": 864 - }, - "backbone.layer6.0.conv.2.conv_(0, 240)_240": { - "init_args": { - "num_channels": 240, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 176, - 240, - 240 - ], - "choice_mode": "number" - }, - "choice": 240 - }, - 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- "backbone.conv2.conv_(0, 1920)_1920": { - "init_args": { - "num_channels": 1920, - "divisor": 1, - "min_value": 1, - "min_ratio": 0.9, - "candidate_choices": [ - 1920, - 1920, - 1920 - ], - "choice_mode": "number" - }, - "choice": 1920 - } - } + "choice": 32 }, - "parse_cfg":{ - "type":"ChannelAnalyzer", - "demo_input":[ - 1, - 3, - 224, - 224 - ], - "tracer_type":"BackwardTracer" + "backbone.layer1.0.conv.1.conv_(0, 24)_24": { + "init_args": { + "num_channels": 24, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 8, + 8, + 16 + ], + "choice_mode": "number" + }, + "choice": 16 + }, + "backbone.layer2.0.conv.0.conv_(0, 144)_144": { + "init_args": { + "num_channels": 144, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 96, + 96, + 144 + ], + "choice_mode": "number" + }, + "choice": 144 + }, + "backbone.layer2.0.conv.2.conv_(0, 40)_40": { + "init_args": { + "num_channels": 40, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 16, + 16, + 24 + ], + "choice_mode": "number" + }, + "choice": 24 + }, + "backbone.layer2.1.conv.0.conv_(0, 240)_240": { + "init_args": { + "num_channels": 240, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 96, + 96, + 176 + ], + "choice_mode": "number" + }, + "choice": 176 + }, + "backbone.layer3.0.conv.0.conv_(0, 240)_240": { + "init_args": { + "num_channels": 240, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 96, + 96, + 192 + ], + "choice_mode": "number" + }, + "choice": 192 + }, + "backbone.layer3.0.conv.2.conv_(0, 48)_48": { + "init_args": { + "num_channels": 48, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + "candidate_choices": [ + 24, + 24, + 48 + ], + "choice_mode": "number" + }, + "choice": 48 + }, + "backbone.layer3.1.conv.0.conv_(0, 288)_288": { + "init_args": { + "num_channels": 288, + "divisor": 1, + "min_value": 1, + "min_ratio": 0.9, + 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