[Feature] Add RepVGG backbone and checkpoints

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')

configs/_base_/datasets/pipelines/auto_aug.py

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+# 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)],
+]

configs/_base_/models/repvgg-A0_in1k.py

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+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),
+    ))

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.)))

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)

configs/repvgg/README.md

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+# Repvgg: Making vgg-style convnets great again
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```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}
+```

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))

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))

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))

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))

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))

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))

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))

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))

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))

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))

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))

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))