From 90496b4687fee3bdeee9b51e0a8fa0389e902d7a Mon Sep 17 00:00:00 2001 From: zhangrui_wolf Date: Wed, 29 Sep 2021 11:06:23 +0800 Subject: [PATCH] [Feature] Add RepVGG backbone and checkpoints. (#414) * Add RepVGG code. * Add se_module as plugin. * Add the repvggA0 primitive config * Change repvggA0.py to fit mmcls * Add RepVGG configs * Add repvgg_to_mmcls * Add tools/deployment/convert_repvggblock_param_to_deploy.py * Change configs/repvgg/README.md * Streamlining the number of configuration files. * Fix lints * Delete plugins * Delete code about plugin. * Modify the code for using se module. * Modify config to fit repvgg with se. * Change se_cfg to allow loading of pre-training parameters. * Reduce the complexity of the configuration file. * Finsh unitest for repvgg. * Fix bug about se in repvgg_to_mmcls. * Rename convert_repvggblock_param_to_deploy.py to reparameterize_repvgg.py, and delete setting about device. * test commit * test commit * test commit command * Modify repvgg.py to make the code more readable. * Add value=0 in F.pad() * Add se_cfg to arch_settings. * Fix bug. * modeify some attr name and Update unit tests * rename stage_0 to stem and branch_identity to branch_norm * update unit tests * add m.eval in unit tests * [Enhance] Enhence SE layer to support custom squeeze channels. (#417) * add enhenced SE * Update * rm basechannel * fix docstring * Update se_layer.py fix docstring * [Docs] Add algorithm readme and update meta yml (#418) * Add README.md for models without checkpoints. * Update model-index.yml * Update metafile.yml of seresnet * [Enhance] Add `hparams` argument in `AutoAugment` and `RandAugment` and some other improvement. (#398) * Add hparams argument in `AutoAugment` and `RandAugment`. And `pad_val` supports sequence instead of tuple only. * Add unit tests for `AutoAugment` and `hparams` in `RandAugment`. * Use smaller test image to speed up uni tests. * Use hparams to simplify RandAugment config in swin-transformer. * Rename augment config name from `pipeline` to `pipelines`. * Add some commnet ad docstring. * [Feature] Support classwise weight in losses (#388) * Add classwise weight in losses:CE,BCE,softBCE * Update unit test * rm some extra code * rm some extra code * fix broadcast * fix broadcast * update unit tests * use new_tensor * fix lint * [Enhance] Better result visualization (#419) * Imporve result visualization to support wait time and change the backend to matplotlib. * Add unit test for visualization * Add adaptive dpi function * Rename `imshow_cls_result` to `imshow_infos`. * Support str in `imshow_infos` * Improve docstring. * Bump version to v0.15.0 (#426) * [CI] Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. (#422) * Add PyTorch 1.9 build workflow, and remove some CI. * Add Python 3.9 CI * Show Python 3.9 support. * [Enhance] Rename the option `--options` in some tools to `--cfg-options`. (#425) * [Docs] Fix sphinx version (#429) * [Docs] Add `CITATION.cff` (#428) * Add CITATION.cff * Fix typo in setup.py * Change author in setup.py * modeify some attr name and Update unit tests * rename stage_0 to stem and branch_identity to branch_norm * update unit tests * add m.eval in unit tests * Update unit tests * refactor * refactor * Alignment inference accuracy * Update configs, readme and metafile * Update readme * return tuple and fix metafile * fix unit test * rm regnet and classifiers changes * update auto_aug * update metafile & readme * use delattr * rename cfgs * Update checkpoint url * Update readme * Rename config files. * Update readme and metafile * add comment * Update mmcls/models/backbones/repvgg.py Co-authored-by: Ma Zerun * Update docstring * Improve docstring. * Update unittest_testblock Co-authored-by: Ezra-Yu <1105212286@qq.com> Co-authored-by: Ma Zerun --- .../_base_/datasets/imagenet_bs64_autoaug.py | 43 ++ configs/_base_/datasets/pipelines/auto_aug.py | 96 ++++ configs/_base_/models/repvgg-A0_in1k.py | 15 + .../_base_/models/repvgg-B3_lbs-mixup_in1k.py | 23 + .../imagenet_bs256_200e_coslr_warmup.py | 11 + configs/repvgg/README.md | 48 ++ .../repvgg-A0_deploy_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-A1_deploy_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-A2_deploy_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-B0_deploy_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-B1_deploy_4xb64-coslr-120e_in1k.py | 3 + ...epvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py | 3 + ...epvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-B2_deploy_4xb64-coslr-120e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + configs/repvgg/metafile.yml | 205 +++++++ .../repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py | 8 + .../repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py | 3 + .../repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py | 3 + .../repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py | 3 + .../repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-B1g2_4xb64-coslr-120e_in1k.py | 3 + .../repvgg-B1g4_4xb64-coslr-120e_in1k.py | 3 + .../repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 6 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + ...4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py | 3 + mmcls/models/backbones/__init__.py | 3 +- mmcls/models/backbones/repvgg.py | 537 ++++++++++++++++++ model-index.yml | 1 + .../test_models/test_backbones/test_repvgg.py | 293 ++++++++++ tools/convert_models/reparameterize_repvgg.py | 46 ++ tools/convert_models/repvgg_to_mmcls.py | 59 ++ 37 files changed, 1459 insertions(+), 1 deletion(-) create mode 100644 configs/_base_/datasets/imagenet_bs64_autoaug.py create mode 100644 configs/_base_/datasets/pipelines/auto_aug.py create mode 100644 configs/_base_/models/repvgg-A0_in1k.py create mode 100644 configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py create mode 100644 configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py create mode 100644 configs/repvgg/README.md create mode 100644 configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/metafile.yml create mode 100644 configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py create mode 100644 configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py create mode 100644 mmcls/models/backbones/repvgg.py create mode 100644 tests/test_models/test_backbones/test_repvgg.py create mode 100644 tools/convert_models/reparameterize_repvgg.py create mode 100644 tools/convert_models/repvgg_to_mmcls.py diff --git a/configs/_base_/datasets/imagenet_bs64_autoaug.py b/configs/_base_/datasets/imagenet_bs64_autoaug.py new file mode 100644 index 00000000000..a1092a31246 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_autoaug.py @@ -0,0 +1,43 @@ +_base_ = ['./pipelines/auto_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies={{_base_.auto_increasing_policies}}), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/pipelines/auto_aug.py b/configs/_base_/datasets/pipelines/auto_aug.py new file mode 100644 index 00000000000..5a10f7eec61 --- /dev/null +++ b/configs/_base_/datasets/pipelines/auto_aug.py @@ -0,0 +1,96 @@ +# Policy for ImageNet, refers to +# https://github.com/DeepVoltaire/AutoAugment/blame/master/autoaugment.py +policy_imagenet = [ + [ + dict(type='Posterize', bits=4, prob=0.4), + dict(type='Rotate', angle=30., prob=0.6) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], + [ + dict(type='Posterize', bits=5, prob=0.6), + dict(type='Posterize', bits=5, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8) + ], + [ + dict(type='Solarize', thr=256 / 9 * 6, prob=0.6), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Posterize', bits=6, prob=0.8), + dict(type='Equalize', prob=1.)], + [ + dict(type='Rotate', angle=10., prob=0.2), + dict(type='Solarize', thr=256 / 9, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.6), + dict(type='Posterize', bits=5, prob=0.4) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0., prob=0.4) + ], + [ + dict(type='Rotate', angle=30., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Equalize', prob=0.0), + dict(type='Equalize', prob=0.8)], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0.2, prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0.8, prob=0.8), + dict(type='Solarize', thr=256 / 9 * 2, prob=0.8) + ], + [ + dict(type='Sharpness', magnitude=0.7, prob=0.4), + dict(type='Invert', prob=0.6) + ], + [ + dict( + type='Shear', + magnitude=0.3 / 9 * 5, + prob=0.6, + direction='horizontal'), + dict(type='Equalize', prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], +] diff --git a/configs/_base_/models/repvgg-A0_in1k.py b/configs/_base_/models/repvgg-A0_in1k.py new file mode 100644 index 00000000000..093ffb7eea9 --- /dev/null +++ b/configs/_base_/models/repvgg-A0_in1k.py @@ -0,0 +1,15 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='RepVGG', + arch='A0', + out_indices=(3, ), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py b/configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py new file mode 100644 index 00000000000..5bb07db54de --- /dev/null +++ b/configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py @@ -0,0 +1,23 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='RepVGG', + arch='B3', + out_indices=(3, ), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2560, + loss=dict( + type='LabelSmoothLoss', + loss_weight=1.0, + label_smooth_val=0.1, + mode='classy_vision', + num_classes=1000), + topk=(1, 5), + ), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py b/configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py new file mode 100644 index 00000000000..49456b2cd00 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=25025, + warmup_ratio=0.25) +runner = dict(type='EpochBasedRunner', max_epochs=200) diff --git a/configs/repvgg/README.md b/configs/repvgg/README.md new file mode 100644 index 00000000000..de1749f16a7 --- /dev/null +++ b/configs/repvgg/README.md @@ -0,0 +1,48 @@ +# Repvgg: Making vgg-style convnets great again + +## Introduction + + + +```latex +@inproceedings{ding2021repvgg, + title={Repvgg: Making vgg-style convnets great again}, + author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={13733--13742}, + year={2021} +} +``` + +## Pretrain model + +| Model | Epochs | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------: | :----: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :----------------------------------------------------------: | +| RepVGG-A0 | 120 | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) | +| RepVGG-A1 | 120 | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) | +| RepVGG-A2 | 120 | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/masterconfigs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | +| RepVGG-B0 | 120 | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) | +| RepVGG-B1 | 120 | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | +| RepVGG-B1g2 | 120 | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) | +| RepVGG-B1g4 | 120 | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) | +| RepVGG-B2 | 120 | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) | +| RepVGG-B2g4 | 200 | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) | +| RepVGG-B3 | 200 | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) | +| RepVGG-B3g4 | 200 | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) | +| RepVGG-D2se | 200 | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | + +## Reparameterize RepVGG + +The checkpoints provided are all in `train` form. Use the reparameterize tool to switch them to more efficient `deploy` form, which not only has fewer parameters but also less calculations. + +```bash +python ./tools/convert_models/reparameterize_repvgg.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH} +``` + +`${CFG_PATH}` is the config file, `${SRC_CKPT_PATH}` is the source chenpoint file, `${TARGET_CKPT_PATH}` is the target deploy weight file path. + +To use reparameterized repvgg weight, the config file must switch to [the deploy config files](./configs/repvgg/deploy) as below: + +```bash +python ./tools/test.py ${RapVGG_Deploy_CFG} ${CHECK_POINT} +``` diff --git a/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..20787f286da --- /dev/null +++ b/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..eea0da9c58c --- /dev/null +++ b/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-A1_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..7b0cea7b7d5 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-A2_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..23a2898ac56 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..24355edac7f --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B1_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..579fcc47b9c --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B1g2_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..eab5d440374 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B1g4_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..0681f14dc36 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B2_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..8f1840145f7 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..e60b0678a9e --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..46f187789a3 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..66dff3b6d44 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/metafile.yml b/configs/repvgg/metafile.yml new file mode 100644 index 00000000000..97d6ab5522c --- /dev/null +++ b/configs/repvgg/metafile.yml @@ -0,0 +1,205 @@ +Collections: + - Name: RepVGG + Metadata: + Training Data: ImageNet-1k + Architecture: + - re-parameterization Convolution + - VGG-style Neural Network + Paper: + URL: https://arxiv.org/abs/2101.03697 + Title: 'RepVGG: Making VGG-style ConvNets Great Again' + README: configs/repvgg/README.md + +Models: + - Name: repvgg-A0_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 1520000000 + Parameters: 9110000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 72.41 + Top 5 Accuracy: 90.50 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L196 + - Name: repvgg-A1_4xb64-coslr-120e_in1k + In Collection: Repvgg + Config: configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 2640000000 + Parameters: 14090000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 74.47 + Top 5 Accuracy: 91.85 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L200 + - Name: repvgg-A2_4xb64-coslr-120e_in1k + In Collection: Repvgg + Config: configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 28210000000 + Parameters: 5700000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 76.48 + Top 5 Accuracy: 93.01 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L204 + - Name: repvgg-B0_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 15820000000 + Parameters: 3420000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 75.14 + Top 5 Accuracy: 92.42 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L208 + - Name: repvgg-B1_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 57420000000 + Parameters: 13160000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 78.37 + Top 5 Accuracy: 94.11 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L212 + - Name: repvgg-B1g2_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 45780000000 + Parameters: 9820000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 77.79 + Top 5 Accuracy: 93.88 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L216 + - Name: repvgg-B1g4_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 39970000000 + Parameters: 8150000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 77.58 + Top 5 Accuracy: 93.84 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L220 + - Name: repvgg-B2_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 89020000000 + Parameters: 20420000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 78.78 + Top 5 Accuracy: 94.42 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L225 + - Name: repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 61760000000 + Parameters: 12630000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 79.38 + Top 5 Accuracy: 94.68 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L229 + - Name: repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 123090000000 + Parameters: 29170000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 80.52 + Top 5 Accuracy: 95.26 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L238 + - Name: repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 83830000000 + Parameters: 17900000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 80.22 + Top 5 Accuracy: 95.10 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L238 + - Name: repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 133330000000 + Parameters: 36560000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 81.81 + Top 5 Accuracy: 95.94 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L250 diff --git a/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..a7fd3bbe910 --- /dev/null +++ b/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/repvgg-A0_in1k.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] + +runner = dict(max_epochs=120) diff --git a/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..649020f2c6f --- /dev/null +++ b/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='A1')) diff --git a/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..eedaf2d29b7 --- /dev/null +++ b/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='A2'), head=dict(in_channels=1408)) diff --git a/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..b3ce7ea27d2 --- /dev/null +++ b/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B0'), head=dict(in_channels=1280)) diff --git a/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..30adea3dc8e --- /dev/null +++ b/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B1'), head=dict(in_channels=2048)) diff --git a/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..2749db8d955 --- /dev/null +++ b/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B1g2'), head=dict(in_channels=2048)) diff --git a/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..2647690975d --- /dev/null +++ b/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048)) diff --git a/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py new file mode 100644 index 00000000000..4d215567f4d --- /dev/null +++ b/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560)) diff --git a/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..11331cf02f2 --- /dev/null +++ b/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(arch='B2g4')) diff --git a/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..7b6dc5065df --- /dev/null +++ b/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/repvgg-B3_lbs-mixup_in1k.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs256_200e_coslr_warmup.py', + '../_base_/default_runtime.py' +] diff --git a/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..67e3688c5ae --- /dev/null +++ b/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(arch='B3g4')) diff --git a/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 00000000000..d235610f07d --- /dev/null +++ b/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(arch='D2se')) diff --git a/mmcls/models/backbones/__init__.py b/mmcls/models/backbones/__init__.py index ee255a0b0c9..866dae5b25f 100644 --- a/mmcls/models/backbones/__init__.py +++ b/mmcls/models/backbones/__init__.py @@ -4,6 +4,7 @@ from .mobilenet_v2 import MobileNetV2 from .mobilenet_v3 import MobileNetV3 from .regnet import RegNet +from .repvgg import RepVGG from .resnest import ResNeSt from .resnet import ResNet, ResNetV1d from .resnet_cifar import ResNet_CIFAR @@ -22,5 +23,5 @@ 'LeNet5', 'AlexNet', 'VGG', 'RegNet', 'ResNet', 'ResNeXt', 'ResNetV1d', 'ResNeSt', 'ResNet_CIFAR', 'SEResNet', 'SEResNeXt', 'ShuffleNetV1', 'ShuffleNetV2', 'MobileNetV2', 'MobileNetV3', 'VisionTransformer', - 'SwinTransformer', 'TNT', 'TIMMBackbone' + 'SwinTransformer', 'TNT', 'RepVGG', 'TIMMBackbone' ] diff --git a/mmcls/models/backbones/repvgg.py b/mmcls/models/backbones/repvgg.py new file mode 100644 index 00000000000..074360803c6 --- /dev/null +++ b/mmcls/models/backbones/repvgg.py @@ -0,0 +1,537 @@ +import torch +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import build_activation_layer, build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule, Sequential +from mmcv.utils.parrots_wrapper import _BatchNorm + +from ..builder import BACKBONES +from ..utils.se_layer import SELayer +from .base_backbone import BaseBackbone + + +class RepVGGBlock(BaseModule): + """RepVGG block for RepVGG backbone. + + Args: + in_channels (int): The input channels of the block. + out_channels (int): The output channels of the block. + stride (int): Stride of the 3x3 and 1x1 convolution layer. Default: 1. + padding (int): Padding of the 3x3 convolution layer. + dilation (int): Dilation of the 3x3 convolution layer. + groups (int): Groups of the 3x3 and 1x1 convolution layer. Default: 1. + padding_mode (str): Padding mode of the 3x3 convolution layer. + Default: 'zeros'. + se_cfg (None or dict): The configuration of the se module. + Default: None. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + conv_cfg (dict, optional): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU'). + deploy (bool): Whether to switch the model structure to + deployment mode. Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + stride=1, + padding=1, + dilation=1, + groups=1, + padding_mode='zeros', + se_cfg=None, + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + deploy=False, + init_cfg=None): + super(RepVGGBlock, self).__init__(init_cfg) + + assert se_cfg is None or isinstance(se_cfg, dict) + + self.in_channels = in_channels + self.out_channels = out_channels + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.se_cfg = se_cfg + self.with_cp = with_cp + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.deploy = deploy + + if deploy: + self.branch_reparam = build_conv_layer( + conv_cfg, + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=True, + padding_mode=padding_mode) + else: + # judge if input shape and output shape are the same. + # If true, add a normalized identity shortcut. + if out_channels == in_channels and stride == 1 and \ + padding == dilation: + self.branch_norm = build_norm_layer(norm_cfg, in_channels)[1] + else: + self.branch_norm = None + + self.branch_3x3 = self.create_conv_bn( + kernel_size=3, + dilation=dilation, + padding=padding, + ) + self.branch_1x1 = self.create_conv_bn(kernel_size=1) + + if se_cfg is not None: + self.se_layer = SELayer(channels=out_channels, **se_cfg) + else: + self.se_layer = None + + self.act = build_activation_layer(act_cfg) + + def create_conv_bn(self, kernel_size, dilation=1, padding=0): + conv_bn = Sequential() + conv_bn.add_module( + 'conv', + build_conv_layer( + self.conv_cfg, + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=kernel_size, + stride=self.stride, + dilation=dilation, + padding=padding, + groups=self.groups, + bias=False)) + conv_bn.add_module( + 'norm', + build_norm_layer(self.norm_cfg, num_features=self.out_channels)[1]) + + return conv_bn + + def forward(self, x): + + def _inner_forward(inputs): + if self.deploy: + return self.branch_reparam(inputs) + + if self.branch_norm is None: + branch_norm_out = 0 + else: + branch_norm_out = self.branch_norm(inputs) + + inner_out = self.branch_3x3(inputs) + self.branch_1x1( + inputs) + branch_norm_out + + if self.se_cfg is not None: + inner_out = self.se_layer(inner_out) + + return inner_out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.act(out) + + return out + + def switch_to_deploy(self): + """Switch the model structure from training mode to deployment mode.""" + if self.deploy: + return + assert self.norm_cfg['type'] == 'BN', \ + "Switch is not allowed when norm_cfg['type'] != 'BN'." + + reparam_weight, reparam_bias = self.reparameterize() + self.branch_reparam = build_conv_layer( + self.conv_cfg, + self.in_channels, + self.out_channels, + kernel_size=3, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups, + bias=True) + self.branch_reparam.weight.data = reparam_weight + self.branch_reparam.bias.data = reparam_bias + + for param in self.parameters(): + param.detach_() + delattr(self, 'branch_3x3') + delattr(self, 'branch_1x1') + delattr(self, 'branch_norm') + + self.deploy = True + + def reparameterize(self): + """Fuse all the parameters of all branchs. + + Returns: + tuple[torch.Tensor, torch.Tensor]: Parameters after fusion of all + branches. the first element is the weights and the second is + the bias. + """ + weight_3x3, bias_3x3 = self._fuse_conv_bn(self.branch_3x3) + weight_1x1, bias_1x1 = self._fuse_conv_bn(self.branch_1x1) + # pad a conv1x1 weight to a conv3x3 weight + weight_1x1 = F.pad(weight_1x1, [1, 1, 1, 1], value=0) + + weight_norm, bias_norm = 0, 0 + if self.branch_norm: + tmp_conv_bn = self._norm_to_conv3x3(self.branch_norm) + weight_norm, bias_norm = self._fuse_conv_bn(tmp_conv_bn) + + return (weight_3x3 + weight_1x1 + weight_norm, + bias_3x3 + bias_1x1 + bias_norm) + + def _fuse_conv_bn(self, branch): + """Fuse the parameters in a branch with a conv and bn. + + Args: + branch (mmcv.runner.Sequential): A branch with conv and bn. + + Returns: + tuple[torch.Tensor, torch.Tensor]: The parameters obtained after + fusing the parameters of conv and bn in one branch. + The first element is the weight and the second is the bias. + """ + if branch is None: + return 0, 0 + conv_weight = branch.conv.weight + running_mean = branch.norm.running_mean + running_var = branch.norm.running_var + gamma = branch.norm.weight + beta = branch.norm.bias + eps = branch.norm.eps + + std = (running_var + eps).sqrt() + fused_weight = (gamma / std).reshape(-1, 1, 1, 1) * conv_weight + fused_bias = -running_mean * gamma / std + beta + + return fused_weight, fused_bias + + def _norm_to_conv3x3(self, branch_nrom): + """Convert a norm layer to a conv3x3-bn sequence. + + Args: + branch (nn.BatchNorm2d): A branch only with bn in the block. + + Returns: + tmp_conv3x3 (mmcv.runner.Sequential): a sequential with conv3x3 and + bn. + """ + input_dim = self.in_channels // self.groups + conv_weight = torch.zeros((self.in_channels, input_dim, 3, 3), + dtype=branch_nrom.weight.dtype) + + for i in range(self.in_channels): + conv_weight[i, i % input_dim, 1, 1] = 1 + conv_weight = conv_weight.to(branch_nrom.weight.device) + + tmp_conv3x3 = self.create_conv_bn(kernel_size=3) + tmp_conv3x3.conv.weight.data = conv_weight + tmp_conv3x3.norm = branch_nrom + return tmp_conv3x3 + + +@BACKBONES.register_module() +class RepVGG(BaseBackbone): + """RepVGG backbone. + + A PyTorch impl of : `RepVGG: Making VGG-style ConvNets Great Again + `_ + + Args: + arch (str | dict): The parameter of RepVGG. + If it's a dict, it should contain the following keys: + + - num_blocks (Sequence[int]): Number of blocks in each stage. + - width_factor (Sequence[float]): Width deflator in each stage. + - group_layer_map (dict | None): RepVGG Block that declares + the need to apply group convolution. + - se_cfg (dict | None): Se Layer config + in_channels (int): Number of input image channels. Default: 3. + base_channels (int): Base channels of RepVGG backbone, work + with width_factor together. Default: 64. + out_indices (Sequence[int]): Output from which stages. Default: (3, ). + strides (Sequence[int]): Strides of the first block of each stage. + Default: (2, 2, 2, 2). + dilations (Sequence[int]): Dilation of each stage. + Default: (1, 1, 1, 1). + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. Default: -1. + conv_cfg (dict | None): The config dict for conv layers. Default: None. + norm_cfg (dict): The config dict for norm layers. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU'). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + deploy (bool): Whether to switch the model structure to deployment + mode. Default: False. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26] + g2_layer_map = {layer: 2 for layer in groupwise_layers} + g4_layer_map = {layer: 4 for layer in groupwise_layers} + + arch_settings = { + 'A0': + dict( + num_blocks=[2, 4, 14, 1], + width_factor=[0.75, 0.75, 0.75, 2.5], + group_layer_map=None, + se_cfg=None), + 'A1': + dict( + num_blocks=[2, 4, 14, 1], + width_factor=[1, 1, 1, 2.5], + group_layer_map=None, + se_cfg=None), + 'A2': + dict( + num_blocks=[2, 4, 14, 1], + width_factor=[1.5, 1.5, 1.5, 2.75], + group_layer_map=None, + se_cfg=None), + 'B0': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[1, 1, 1, 2.5], + group_layer_map=None, + se_cfg=None), + 'B1': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[2, 2, 2, 4], + group_layer_map=None, + se_cfg=None), + 'B1g2': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[2, 2, 2, 4], + group_layer_map=g2_layer_map, + se_cfg=None), + 'B1g4': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[2, 2, 2, 4], + group_layer_map=g4_layer_map, + se_cfg=None), + 'B2': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[2.5, 2.5, 2.5, 5], + group_layer_map=None, + se_cfg=None), + 'B2g2': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[2.5, 2.5, 2.5, 5], + group_layer_map=g2_layer_map, + se_cfg=None), + 'B2g4': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[2.5, 2.5, 2.5, 5], + group_layer_map=g4_layer_map, + se_cfg=None), + 'B3': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[3, 3, 3, 5], + group_layer_map=None, + se_cfg=None), + 'B3g2': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[3, 3, 3, 5], + group_layer_map=g2_layer_map, + se_cfg=None), + 'B3g4': + dict( + num_blocks=[4, 6, 16, 1], + width_factor=[3, 3, 3, 5], + group_layer_map=g4_layer_map, + se_cfg=None), + 'D2se': + dict( + num_blocks=[8, 14, 24, 1], + width_factor=[2.5, 2.5, 2.5, 5], + group_layer_map=None, + se_cfg=dict(ratio=16, divisor=1)) + } + + def __init__(self, + arch, + in_channels=3, + base_channels=64, + out_indices=(3, ), + strides=(2, 2, 2, 2), + dilations=(1, 1, 1, 1), + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + with_cp=False, + deploy=False, + norm_eval=False, + init_cfg=[ + dict(type='Kaiming', layer=['Conv2d']), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ]): + super(RepVGG, self).__init__(init_cfg) + + if isinstance(arch, str): + assert arch in self.arch_settings, \ + f'"arch": "{arch}" is not one of the arch_settings' + arch = self.arch_settings[arch] + elif not isinstance(arch, dict): + raise TypeError('Expect "arch" to be either a string ' + f'or a dict, got {type(arch)}') + + assert len(arch['num_blocks']) == len( + arch['width_factor']) == len(strides) == len(dilations) + assert max(out_indices) < len(arch['num_blocks']) + if arch['group_layer_map'] is not None: + assert max(arch['group_layer_map'].keys()) <= sum( + arch['num_blocks']) + + if arch['se_cfg'] is not None: + assert isinstance(arch['se_cfg'], dict) + + self.arch = arch + self.in_channels = in_channels + self.base_channels = base_channels + self.out_indices = out_indices + self.strides = strides + self.dilations = dilations + self.deploy = deploy + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.with_cp = with_cp + self.norm_eval = norm_eval + + channels = min(64, int(base_channels * self.arch['width_factor'][0])) + self.stem = RepVGGBlock( + self.in_channels, + channels, + stride=2, + se_cfg=arch['se_cfg'], + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + deploy=deploy) + + next_create_block_idx = 1 + self.stages = [] + for i in range(len(arch['num_blocks'])): + num_blocks = self.arch['num_blocks'][i] + stride = self.strides[i] + dilation = self.dilations[i] + out_channels = int(base_channels * 2**i * + self.arch['width_factor'][i]) + + stage, next_create_block_idx = self._make_stage( + channels, out_channels, num_blocks, stride, dilation, + next_create_block_idx, init_cfg) + stage_name = f'stage_{i + 1}' + self.add_module(stage_name, stage) + self.stages.append(stage_name) + + channels = out_channels + + def _make_stage(self, in_channels, out_channels, num_blocks, stride, + dilation, next_create_block_idx, init_cfg): + strides = [stride] + [1] * (num_blocks - 1) + dilations = [dilation] * num_blocks + + blocks = [] + for i in range(num_blocks): + groups = self.arch['group_layer_map'].get( + next_create_block_idx, + 1) if self.arch['group_layer_map'] is not None else 1 + blocks.append( + RepVGGBlock( + in_channels, + out_channels, + stride=strides[i], + padding=dilations[i], + dilation=dilations[i], + groups=groups, + se_cfg=self.arch['se_cfg'], + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + deploy=self.deploy, + init_cfg=init_cfg)) + in_channels = out_channels + next_create_block_idx += 1 + + return Sequential(*blocks), next_create_block_idx + + def forward(self, x): + x = self.stem(x) + outs = [] + for i, stage_name in enumerate(self.stages): + stage = getattr(self, stage_name) + x = stage(x) + if i in self.out_indices: + outs.append(x) + + return tuple(outs) + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.stem.eval() + for param in self.stem.parameters(): + param.requires_grad = False + for i in range(self.frozen_stages): + stage = getattr(self, f'stage_{i+1}') + stage.eval() + for param in stage.parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(RepVGG, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() + + def switch_to_deploy(self): + for m in self.modules(): + if isinstance(m, RepVGGBlock): + m.switch_to_deploy() + self.deploy = True diff --git a/model-index.yml b/model-index.yml index 4d873f94966..3411f0f0512 100644 --- a/model-index.yml +++ b/model-index.yml @@ -8,4 +8,5 @@ Import: - configs/shufflenet_v2/metafile.yml - configs/swin_transformer/metafile.yml - configs/vgg/metafile.yml + - configs/repvgg/metafile.yml - configs/tnt/metafile.yml diff --git a/tests/test_models/test_backbones/test_repvgg.py b/tests/test_models/test_backbones/test_repvgg.py new file mode 100644 index 00000000000..f8a4fdc8a39 --- /dev/null +++ b/tests/test_models/test_backbones/test_repvgg.py @@ -0,0 +1,293 @@ +import os +import tempfile + +import pytest +import torch +from mmcv.runner import load_checkpoint, save_checkpoint +from torch import nn +from torch.nn.modules import GroupNorm +from torch.nn.modules.batchnorm import _BatchNorm + +from mmcls.models.backbones import RepVGG +from mmcls.models.backbones.repvgg import RepVGGBlock +from mmcls.models.utils import SELayer + + +def check_norm_state(modules, train_state): + """Check if norm layer is in correct train state.""" + for mod in modules: + if isinstance(mod, _BatchNorm): + if mod.training != train_state: + return False + return True + + +def is_norm(modules): + """Check if is one of the norms.""" + if isinstance(modules, (GroupNorm, _BatchNorm)): + return True + return False + + +def is_repvgg_block(modules): + if isinstance(modules, RepVGGBlock): + return True + return False + + +def test_repvgg_repvggblock(): + # Test RepVGGBlock with in_channels != out_channels, stride = 1 + block = RepVGGBlock(5, 10, stride=1) + block.eval() + x = torch.randn(1, 5, 16, 16) + x_out_not_deploy = block(x) + assert block.branch_norm is None + assert not hasattr(block, 'branch_reparam') + assert hasattr(block, 'branch_1x1') + assert hasattr(block, 'branch_3x3') + assert hasattr(block, 'branch_norm') + assert block.se_cfg is None + assert x_out_not_deploy.shape == torch.Size((1, 10, 16, 16)) + block.switch_to_deploy() + assert block.deploy is True + x_out_deploy = block(x) + assert x_out_deploy.shape == torch.Size((1, 10, 16, 16)) + assert torch.allclose(x_out_not_deploy, x_out_deploy, atol=1e-5, rtol=1e-4) + + # Test RepVGGBlock with in_channels == out_channels, stride = 1 + block = RepVGGBlock(12, 12, stride=1) + block.eval() + x = torch.randn(1, 12, 8, 8) + x_out_not_deploy = block(x) + assert isinstance(block.branch_norm, nn.BatchNorm2d) + assert not hasattr(block, 'branch_reparam') + assert x_out_not_deploy.shape == torch.Size((1, 12, 8, 8)) + block.switch_to_deploy() + assert block.deploy is True + x_out_deploy = block(x) + assert x_out_deploy.shape == torch.Size((1, 12, 8, 8)) + assert torch.allclose(x_out_not_deploy, x_out_deploy, atol=1e-5, rtol=1e-4) + + # Test RepVGGBlock with in_channels == out_channels, stride = 2 + block = RepVGGBlock(16, 16, stride=2) + block.eval() + x = torch.randn(1, 16, 8, 8) + x_out_not_deploy = block(x) + assert block.branch_norm is None + assert x_out_not_deploy.shape == torch.Size((1, 16, 4, 4)) + block.switch_to_deploy() + assert block.deploy is True + x_out_deploy = block(x) + assert x_out_deploy.shape == torch.Size((1, 16, 4, 4)) + assert torch.allclose(x_out_not_deploy, x_out_deploy, atol=1e-5, rtol=1e-4) + + # Test RepVGGBlock with padding == dilation == 2 + block = RepVGGBlock(14, 14, stride=1, padding=2, dilation=2) + block.eval() + x = torch.randn(1, 14, 16, 16) + x_out_not_deploy = block(x) + assert isinstance(block.branch_norm, nn.BatchNorm2d) + assert x_out_not_deploy.shape == torch.Size((1, 14, 16, 16)) + block.switch_to_deploy() + assert block.deploy is True + x_out_deploy = block(x) + assert x_out_deploy.shape == torch.Size((1, 14, 16, 16)) + assert torch.allclose(x_out_not_deploy, x_out_deploy, atol=1e-5, rtol=1e-4) + + # Test RepVGGBlock with groups = 2 + block = RepVGGBlock(4, 4, stride=1, groups=2) + block.eval() + x = torch.randn(1, 4, 5, 6) + x_out_not_deploy = block(x) + assert x_out_not_deploy.shape == torch.Size((1, 4, 5, 6)) + block.switch_to_deploy() + assert block.deploy is True + x_out_deploy = block(x) + assert x_out_deploy.shape == torch.Size((1, 4, 5, 6)) + assert torch.allclose(x_out_not_deploy, x_out_deploy, atol=1e-5, rtol=1e-4) + + # Test RepVGGBlock with se + se_cfg = dict(ratio=4, divisor=1) + block = RepVGGBlock(18, 18, stride=1, se_cfg=se_cfg) + block.train() + x = torch.randn(1, 18, 5, 5) + x_out_not_deploy = block(x) + assert isinstance(block.se_layer, SELayer) + assert x_out_not_deploy.shape == torch.Size((1, 18, 5, 5)) + + # Test RepVGGBlock with checkpoint forward + block = RepVGGBlock(24, 24, stride=1, with_cp=True) + assert block.with_cp + x = torch.randn(1, 24, 7, 7) + x_out = block(x) + assert x_out.shape == torch.Size((1, 24, 7, 7)) + + # Test RepVGGBlock with deploy == True + block = RepVGGBlock(8, 8, stride=1, deploy=True) + assert isinstance(block.branch_reparam, nn.Conv2d) + assert not hasattr(block, 'branch_3x3') + assert not hasattr(block, 'branch_1x1') + assert not hasattr(block, 'branch_norm') + x = torch.randn(1, 8, 16, 16) + x_out = block(x) + assert x_out.shape == torch.Size((1, 8, 16, 16)) + + +def test_repvgg_backbone(): + with pytest.raises(TypeError): + # arch must be str or dict + RepVGG(arch=[4, 6, 16, 1]) + + with pytest.raises(AssertionError): + # arch must in arch_settings + RepVGG(arch='A3') + + with pytest.raises(KeyError): + # arch must have num_blocks and width_factor + arch = dict(num_blocks=[2, 4, 14, 1]) + RepVGG(arch=arch) + + # len(arch['num_blocks']) == len(arch['width_factor']) + # == len(strides) == len(dilations) + with pytest.raises(AssertionError): + arch = dict(num_blocks=[2, 4, 14, 1], width_factor=[0.75, 0.75, 0.75]) + RepVGG(arch=arch) + + # len(strides) must equal to 4 + with pytest.raises(AssertionError): + RepVGG('A0', strides=(1, 1, 1)) + + # len(dilations) must equal to 4 + with pytest.raises(AssertionError): + RepVGG('A0', strides=(1, 1, 1, 1), dilations=(1, 1, 2)) + + # max(out_indices) < len(arch['num_blocks']) + with pytest.raises(AssertionError): + RepVGG('A0', out_indices=(5, )) + + # max(arch['group_idx'].keys()) <= sum(arch['num_blocks']) + with pytest.raises(AssertionError): + arch = dict( + num_blocks=[2, 4, 14, 1], + width_factor=[0.75, 0.75, 0.75], + group_idx={22: 2}) + RepVGG(arch=arch) + + # Test RepVGG norm state + model = RepVGG('A0') + model.train() + assert check_norm_state(model.modules(), True) + + # Test RepVGG with first stage frozen + frozen_stages = 1 + model = RepVGG('A0', frozen_stages=frozen_stages) + model.train() + for param in model.stem.parameters(): + assert param.requires_grad is False + for i in range(0, frozen_stages): + stage_name = model.stages[i] + stage = model.__getattr__(stage_name) + for mod in stage: + if isinstance(mod, _BatchNorm): + assert mod.training is False + for param in stage.parameters(): + assert param.requires_grad is False + + # Test RepVGG with norm_eval + model = RepVGG('A0', norm_eval=True) + model.train() + assert check_norm_state(model.modules(), False) + + # Test RepVGG forward with layer 3 forward + model = RepVGG('A0', out_indices=(3, )) + model.init_weights() + model.train() + + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert isinstance(feat, tuple) + assert len(feat) == 1 + assert isinstance(feat[0], torch.Tensor) + assert feat[0].shape == torch.Size((1, 1280, 7, 7)) + + # Test RepVGG forward + model_test_settings = [ + dict(model_name='A0', out_sizes=(48, 96, 192, 1280)), + dict(model_name='A1', out_sizes=(64, 128, 256, 1280)), + dict(model_name='A2', out_sizes=(96, 192, 384, 1408)), + dict(model_name='B0', out_sizes=(64, 128, 256, 1280)), + dict(model_name='B1', out_sizes=(128, 256, 512, 2048)), + dict(model_name='B1g2', out_sizes=(128, 256, 512, 2048)), + dict(model_name='B1g4', out_sizes=(128, 256, 512, 2048)), + dict(model_name='B2', out_sizes=(160, 320, 640, 2560)), + dict(model_name='B2g2', out_sizes=(160, 320, 640, 2560)), + dict(model_name='B2g4', out_sizes=(160, 320, 640, 2560)), + dict(model_name='B3', out_sizes=(192, 384, 768, 2560)), + dict(model_name='B3g2', out_sizes=(192, 384, 768, 2560)), + dict(model_name='B3g4', out_sizes=(192, 384, 768, 2560)), + dict(model_name='D2se', out_sizes=(160, 320, 640, 2560)) + ] + + choose_models = ['A0', 'B1', 'B1g2', 'D2se'] + # Test RepVGG model forward + for model_test_setting in model_test_settings: + if model_test_setting['model_name'] not in choose_models: + continue + model = RepVGG( + model_test_setting['model_name'], out_indices=(0, 1, 2, 3)) + model.init_weights() + + # Test Norm + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + + model.train() + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert feat[0].shape == torch.Size( + (1, model_test_setting['out_sizes'][0], 56, 56)) + assert feat[1].shape == torch.Size( + (1, model_test_setting['out_sizes'][1], 28, 28)) + assert feat[2].shape == torch.Size( + (1, model_test_setting['out_sizes'][2], 14, 14)) + assert feat[3].shape == torch.Size( + (1, model_test_setting['out_sizes'][3], 7, 7)) + + # Test eval of "train" mode and "deploy" mode + gap = nn.AdaptiveAvgPool2d(output_size=(1)) + fc = nn.Linear(model_test_setting['out_sizes'][3], 10) + model.eval() + feat = model(imgs) + pred = fc(gap(feat[3]).flatten(1)) + model.switch_to_deploy() + for m in model.modules(): + if isinstance(m, RepVGGBlock): + assert m.deploy is True + feat_deploy = model(imgs) + pred_deploy = fc(gap(feat_deploy[3]).flatten(1)) + for i in range(4): + torch.allclose(feat[i], feat_deploy[i]) + torch.allclose(pred, pred_deploy) + + +def test_repvgg_load(): + # Test ouput before and load from deploy checkpoint + model = RepVGG('A1', out_indices=(0, 1, 2, 3)) + inputs = torch.randn((1, 3, 224, 224)) + ckpt_path = os.path.join(tempfile.gettempdir(), 'ckpt.pth') + model.switch_to_deploy() + model.eval() + outputs = model(inputs) + + model_deploy = RepVGG('A1', out_indices=(0, 1, 2, 3), deploy=True) + save_checkpoint(model, ckpt_path) + load_checkpoint(model_deploy, ckpt_path, strict=True) + + outputs_load = model_deploy(inputs) + for feat, feat_load in zip(outputs, outputs_load): + assert torch.allclose(feat, feat_load) diff --git a/tools/convert_models/reparameterize_repvgg.py b/tools/convert_models/reparameterize_repvgg.py new file mode 100644 index 00000000000..0eb7b203b42 --- /dev/null +++ b/tools/convert_models/reparameterize_repvgg.py @@ -0,0 +1,46 @@ +import argparse +from pathlib import Path + +import torch + +from mmcls.apis import init_model + + +def convert_repvggblock_param(config_path, checkpoint_path, save_path): + model = init_model(config_path, checkpoint=checkpoint_path) + print('Converting...') + + model.backbone.switch_to_deploy() + torch.save(model.state_dict(), save_path) + + print('Done! Save at path "{}"'.format(save_path)) + + +def main(): + parser = argparse.ArgumentParser( + description='Convert the parameters of the repvgg block ' + 'from training mode to deployment mode.') + parser.add_argument( + 'config_path', + help='The path to the configuration file of the network ' + 'containing the repvgg block.') + parser.add_argument( + 'checkpoint_path', + help='The path to the checkpoint file corresponding to the model.') + parser.add_argument( + 'save_path', + help='The path where the converted checkpoint file is stored.') + args = parser.parse_args() + + save_path = Path(args.save_path) + if save_path.suffix != '.pth': + print('The path should contain the name of the pth format file.') + exit() + save_path.parent.mkdir(parents=True, exist_ok=True) + + convert_repvggblock_param(args.config_path, args.checkpoint_path, + args.save_path) + + +if __name__ == '__main__': + main() diff --git a/tools/convert_models/repvgg_to_mmcls.py b/tools/convert_models/repvgg_to_mmcls.py new file mode 100644 index 00000000000..fb281010839 --- /dev/null +++ b/tools/convert_models/repvgg_to_mmcls.py @@ -0,0 +1,59 @@ +import argparse +from collections import OrderedDict +from pathlib import Path + +import torch + + +def convert(src, dst): + print('Converting...') + blobs = torch.load(src, map_location='cpu') + converted_state_dict = OrderedDict() + + for key in blobs: + splited_key = key.split('.') + splited_key = ['norm' if i == 'bn' else i for i in splited_key] + splited_key = [ + 'branch_norm' if i == 'rbr_identity' else i for i in splited_key + ] + splited_key = [ + 'branch_1x1' if i == 'rbr_1x1' else i for i in splited_key + ] + splited_key = [ + 'branch_3x3' if i == 'rbr_dense' else i for i in splited_key + ] + splited_key = [ + 'backbone.stem' if i[:6] == 'stage0' else i for i in splited_key + ] + splited_key = [ + 'backbone.stage_' + i[5] if i[:5] == 'stage' else i + for i in splited_key + ] + splited_key = ['se_layer' if i == 'se' else i for i in splited_key] + splited_key = ['conv1.conv' if i == 'down' else i for i in splited_key] + splited_key = ['conv2.conv' if i == 'up' else i for i in splited_key] + splited_key = ['head.fc' if i == 'linear' else i for i in splited_key] + new_key = '.'.join(splited_key) + converted_state_dict[new_key] = blobs[key] + + torch.save(converted_state_dict, dst) + print('Done!') + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('src', help='src detectron model path') + parser.add_argument('dst', help='save path') + args = parser.parse_args() + + dst = Path(args.dst) + if dst.suffix != '.pth': + print('The path should contain the name of the pth format file.') + exit() + dst.parent.mkdir(parents=True, exist_ok=True) + + convert(args.src, args.dst) + + +if __name__ == '__main__': + main()