add part code

configs/regnet/regnetx-800mf_8xb128_in1k.py

+_base_ = ['./regnetx-400mf_8xb128_in1k.py']
+
+# model settings
+model = dict(
+    backbone=dict(type='RegNet', arch='regnetx_800mf'),
+    head=dict(in_channels=672, ))

configs/replknet/README.md

+# RepLKNet
+
+> [Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs](https://arxiv.org/abs/2203.06717)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested five guidelines, e.g., applying re-parameterized large depth-wise convolutions, to design efficient highperformance large-kernel CNNs. Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31×31, in contrast to commonly used 3×3. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/48375204/197546040-cdf078c3-7fbd-400f-8b27-01668c8dfebf.png" width="60%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model, get_model
+
+model = get_model('replknet-31B_3rdparty_in1k', pretrained=True)
+model.backbone.switch_to_deploy()
+predict = inference_model(model, 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('replknet-31B_3rdparty_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Test:
+
+```shell
+python tools/test.py configs/replknet/replknet-31B_32xb64_in1k.py https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k_20221118-fd08e268.pth
+```
+
+**Reparameterization**
+
+The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations.
+
+```bash
+python tools/convert_models/reparameterize_model.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 weights, the config file must switch to the deploy config files.
+
+```bash
+python tools/test.py ${deploy_cfg} ${deploy_checkpoint} --metrics accuracy
+```
+
+You can also use `backbone.switch_to_deploy()` to switch to the deploy mode in Python code. For example:
+
+```python
+from mmpretrain.models import RepLKNet
+
+backbone = RepLKNet(arch='31B')
+backbone.switch_to_deploy()
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                                          |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                   Config                    |                            Download                            |
+| :--------------------------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :-----------------------------------------: | :------------------------------------------------------------: |
+| `replknet-31B_3rdparty_in1k`\*                 | From scratch |   79.86    |   15.64   |   83.48   |   96.57   |    [config](replknet-31B_32xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k_20221118-fd08e268.pth) |
+| `replknet-31B_3rdparty_in1k-384px`\*           | From scratch |   79.86    |   45.95   |   84.84   |   97.34   | [config](replknet-31B_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k-384px_20221118-03a170ce.pth) |
+| `replknet-31B_in21k-pre_3rdparty_in1k`\*       | ImageNet-21k |   79.86    |   15.64   |   85.20   |   97.56   |    [config](replknet-31B_32xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k_20221118-54ed5c46.pth) |
+| `replknet-31B_in21k-pre_3rdparty_in1k-384px`\* | ImageNet-21k |   79.86    |   45.95   |   85.99   |   97.75   | [config](replknet-31B_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k-384px_20221118-76c92b24.pth) |
+| `replknet-31L_in21k-pre_3rdparty_in1k-384px`\* | ImageNet-21k |   172.67   |   97.24   |   86.63   |   98.00   | [config](replknet-31L_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31L_in21k-pre_3rdparty_in1k-384px_20221118-dc3fc07c.pth) |
+| `replknet-XL_meg73m-pre_3rdparty_in1k-320px`\* |    MEG73M    |   335.44   |  129.57   |   87.57   |   98.39   | [config](replknet-XL_32xb64_in1k-320px.py)  | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-XL_meg73m-pre_3rdparty_in1k-320px_20221118-88259b1d.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+## Citation
+
+```bibtex
+@inproceedings{ding2022scaling,
+  title={Scaling up your kernels to 31x31: Revisiting large kernel design in cnns},
+  author={Ding, Xiaohan and Zhang, Xiangyu and Han, Jungong and Ding, Guiguang},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={11963--11975},
+  year={2022}
+}
+```

configs/replknet/deploy/replknet-31B-deploy_32xb64_in1k-384px.py

+_base_ = '../replknet-31B_32xb64_in1k-384px.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))

configs/replknet/deploy/replknet-31B-deploy_32xb64_in1k.py

+_base_ = '../replknet-31B_32xb64_in1k.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))

configs/replknet/deploy/replknet-31L-deploy_32xb64_in1k-384px.py

+_base_ = '../replknet-31L_32xb64_in1k-384px.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))

configs/replknet/deploy/replknet-XL-deploy_32xb64_in1k-320px.py

+_base_ = '../replknet-XL_32xb64_in1k-320px.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))

configs/replknet/metafile.yml

+Collections:
+  - Name: RepLKNet
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Large-Kernel Convolution
+        - VGG-style Neural Network
+    Paper:
+      URL: https://arxiv.org/abs/2203.06717
+      Title: 'Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs'
+    README: configs/replknet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmpretrain/blob/v1.0.0rc3/mmcls/models/backbones/replknet.py
+      Version: v1.0.0rc3
+
+Models:
+  - Name: replknet-31B_3rdparty_in1k
+    In Collection: RepLKNet
+    Config: configs/replknet/replknet-31B_32xb64_in1k.py
+    Metadata:
+      FLOPs: 15636547584
+      Parameters: 79864168
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 83.48
+        Top 5 Accuracy: 96.57
+    Weights: https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k_20221118-fd08e268.pth
+    Converted From:
+      Weights: https://drive.google.com/u/0/uc?id=1azQUiCxK9feYVkkrPqwVPBtNsTzDrX7S&export=download
+      Code: https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py
+
+  - Name: replknet-31B_3rdparty_in1k-384px
+    In Collection: RepLKNet
+    Config: configs/replknet/replknet-31B_32xb64_in1k-384px.py
+    Metadata:
+      FLOPs: 45952303104
+      Parameters: 79864168
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 84.84
+        Top 5 Accuracy: 97.34
+    Weights: https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k-384px_20221118-03a170ce.pth
+    Converted From:
+      Weights: https://drive.google.com/u/0/uc?id=1vo-P3XB6mRLUeDzmgv90dOu73uCeLfZN&export=download
+      Code: https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py
+
+  - Name: replknet-31B_in21k-pre_3rdparty_in1k
+    In Collection: RepLKNet
+    Config: configs/replknet/replknet-31B_32xb64_in1k.py
+    Metadata:
+      Training Data:
+        - ImageNet-21k
+        - ImageNet-1k
+      FLOPs: 15636547584
+      Parameters: 79864168
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 85.20
+        Top 5 Accuracy: 97.56
+    Weights: https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k_20221118-54ed5c46.pth
+    Converted From:
+      Weights: https://drive.google.com/u/0/uc?id=1DslZ2voXZQR1QoFY9KnbsHAeF84hzS0s&export=download
+      Code: https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py
+
+  - Name: replknet-31B_in21k-pre_3rdparty_in1k-384px
+    In Collection: RepLKNet
+    Config: configs/replknet/replknet-31B_32xb64_in1k-384px.py
+    Metadata:
+      Training Data:
+        - ImageNet-21k
+        - ImageNet-1k
+      FLOPs: 45952303104
+      Parameters: 79864168
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 85.99
+        Top 5 Accuracy: 97.75
+    Weights: https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k-384px_20221118-76c92b24.pth
+    Converted From:
+      Weights: https://drive.google.com/u/0/uc?id=1Sc46BWdXXm2fVP-K_hKKU_W8vAB-0duX&export=download
+      Code: https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py
+
+  - Name: replknet-31L_in21k-pre_3rdparty_in1k-384px
+    In Collection: RepLKNet
+    Config: configs/replknet/replknet-31L_32xb64_in1k-384px.py
+    Metadata:
+      Training Data:
+        - ImageNet-21k
+        - ImageNet-1k
+      FLOPs: 97240006656
+      Parameters: 172671016
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 86.63
+        Top 5 Accuracy: 98.00
+    Weights: https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31L_in21k-pre_3rdparty_in1k-384px_20221118-dc3fc07c.pth
+    Converted From:
+      Weights: https://drive.google.com/u/0/uc?id=1JYXoNHuRvC33QV1pmpzMTKEni1hpWfBl&export=download
+      Code: https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py
+
+  - Name: replknet-XL_meg73m-pre_3rdparty_in1k-320px
+    In Collection: RepLKNet
+    Config: configs/replknet/replknet-XL_32xb64_in1k-320px.py
+    Metadata:
+      Training Data:
+        - MegData-73M
+        - ImageNet-1k
+      FLOPs: 129570201600
+      Parameters: 335435752
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 87.57
+        Top 5 Accuracy: 98.39
+    Weights: https://download.openmmlab.com/mmclassification/v0/replknet/replknet-XL_meg73m-pre_3rdparty_in1k-320px_20221118-88259b1d.pth
+    Converted From:
+      Weights: https://drive.google.com/u/0/uc?id=1tPC60El34GntXByIRHb-z-Apm4Y5LX1T&export=download
+      Code: https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py

configs/replknet/replknet-31B_32xb64_in1k-384px.py

+_base_ = [
+    '../_base_/models/replknet-31B_in1k.py',
+    '../_base_/datasets/imagenet_bs16_pil_bicubic_384.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]
+
+# schedule settings
+param_scheduler = dict(
+    type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
+
+train_cfg = dict(by_epoch=True, max_epochs=300)

configs/replknet/replknet-31B_32xb64_in1k.py

+_base_ = [
+    '../_base_/models/replknet-31B_in1k.py',
+    '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]
+
+# schedule settings
+param_scheduler = dict(
+    type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
+
+train_cfg = dict(by_epoch=True, max_epochs=300)

configs/replknet/replknet-31L_32xb64_in1k-384px.py

+_base_ = [
+    '../_base_/models/replknet-31L_in1k.py',
+    '../_base_/datasets/imagenet_bs16_pil_bicubic_384.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]
+
+# schedule settings
+param_scheduler = dict(
+    type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
+
+train_cfg = dict(by_epoch=True, max_epochs=300)

configs/replknet/replknet-XL_32xb64_in1k-320px.py

+_base_ = [
+    '../_base_/models/replknet-XL_in1k.py',
+    '../_base_/datasets/imagenet_bs8_pil_bicubic_320.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]
+
+# schedule settings
+param_scheduler = dict(
+    type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
+
+train_cfg = dict(by_epoch=True, max_epochs=300)

configs/repmlp/README.md

+# RepMLP
+
+> [RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition](https://arxiv.org/abs/2105.01883)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers. Compared to convolutional layers, FC layers are more efficient, better at modeling the long-range dependencies and positional patterns, but worse at capturing the local structures, hence usually less favored for image recognition. We propose a structural re-parameterization technique that adds local prior into an FC to make it powerful for image recognition. Specifically, we construct convolutional layers inside a RepMLP during training and merge them into the FC for inference. On CIFAR, a simple pure-MLP model shows performance very close to CNN. By inserting RepMLP in traditional CNN, we improve ResNets by 1.8% accuracy on ImageNet, 2.9% for face recognition, and 2.3% mIoU on Cityscapes with lower FLOPs. Our intriguing findings highlight that combining the global representational capacity and positional perception of FC with the local prior of convolution can improve the performance of neural network with faster speed on both the tasks with translation invariance (e.g., semantic segmentation) and those with aligned images and positional patterns (e.g., face recognition).
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/18586273/155455288-a17a5c48-11af-4b74-995a-cf7183f0e2d2.png" width="80%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model, get_model
+
+model = get_model('repmlp-base_3rdparty_8xb64_in1k', pretrained=True)
+model.backbone.switch_to_deploy()
+predict = inference_model(model, 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('repmlp-base_3rdparty_8xb64_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Test:
+
+```shell
+python tools/test.py configs/repmlp/repmlp-base_8xb64_in1k.py https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth
+```
+
+**Reparameterization**
+
+The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations.
+
+```bash
+python tools/convert_models/reparameterize_model.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 weights, the config file must switch to the deploy config files.
+
+```bash
+python tools/test.py ${deploy_cfg} ${deploy_checkpoint} --metrics accuracy
+```
+
+You can also use `backbone.switch_to_deploy()` to switch to the deploy mode in Python code. For example:
+
+```python
+from mmpretrain.models import RepMLPNet
+
+backbone = RepMLPNet(arch='B', img_size=224, reparam_conv_kernels=(1, 3))
+backbone.switch_to_deploy()
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                                     |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                  Config                   |                               Download                                |
+| :---------------------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :---------------------------------------: | :-------------------------------------------------------------------: |
+| `repmlp-base_3rdparty_8xb64_in1k`\*       | From scratch |   68.24    |   6.71    |   80.41   |   95.14   |    [config](repmlp-base_8xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth) |
+| `repmlp-base_3rdparty_8xb64_in1k-256px`\* | From scratch |   96.45    |   9.69    |   81.11   |   95.50   | [config](repmlp-base_8xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k-256px_20220330-7c5a91ce.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepMLP/blob/072d8516beba83d75dfe6ebb12f625abad4b53d5/repmlpnet.py#L278). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+## Citation
+
+```bibtex
+@article{ding2021repmlp,
+  title={Repmlp: Re-parameterizing convolutions into fully-connected layers for image recognition},
+  author={Ding, Xiaohan and Xia, Chunlong and Zhang, Xiangyu and Chu, Xiaojie and Han, Jungong and Ding, Guiguang},
+  journal={arXiv preprint arXiv:2105.01883},
+  year={2021}
+}
+```

configs/repmlp/metafile.yml

+Collections:
+  - Name: RepMLP
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Multi-layer Perceptron
+        - Re-parameterization Convolution
+    Paper:
+      URL: https://arxiv.org/abs/2105.01883
+      Title: 'RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition'
+    README: configs/repmlp/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmpretrain/blob/v0.21.0/mmcls/models/backbones/repmlp.py
+      Version: v0.21.0
+
+Models:
+  - Name: repmlp-base_3rdparty_8xb64_in1k
+    In Collection: RepMLP
+    Config: configs/repmlp/repmlp-base_8xb64_in1k.py
+    Metadata:
+      FLOPs: 6710000000  # 6.71 G
+      Parameters: 68240000  # 68.24 M
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.41
+          Top 5 Accuracy: 95.14
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth
+    Converted From:
+      Weights: https://github.com/DingXiaoH/RepMLP
+      Code: https://github.com/DingXiaoH/RepMLP/blob/072d8516beba83d75dfe6ebb12f625abad4b53d5/repmlpnet.py#L274
+  - Name: repmlp-base_3rdparty_8xb64_in1k-256px
+    In Collection: RepMLP
+    Config: configs/repmlp/repmlp-base_8xb64_in1k-256px.py
+    Metadata:
+      FLOPs: 9690000000  # 9.69 G
+      Parameters: 96450000  # 96.45M
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.11
+          Top 5 Accuracy: 95.50
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k-256px_20220330-7c5a91ce.pth
+    Converted From:
+      Weights: https://github.com/DingXiaoH/RepMLP
+      Code: https://github.com/DingXiaoH/RepMLP/blob/072d8516beba83d75dfe6ebb12f625abad4b53d5/repmlpnet.py#L278

configs/repmlp/repmlp-base_8xb64_in1k-256px.py

+_base_ = [
+    '../_base_/models/repmlp-base_224.py',
+    '../_base_/datasets/imagenet_bs64_pil_resize.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(backbone=dict(img_size=256))
+
+# dataset settings
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', scale=256),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='ResizeEdge', scale=292, edge='short', backend='pillow'),
+    dict(type='CenterCrop', crop_size=256),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (8 GPUs) x (64 samples per GPU)
+auto_scale_lr = dict(base_batch_size=512)

configs/repmlp/repmlp-base_8xb64_in1k.py

+_base_ = [
+    '../_base_/models/repmlp-base_224.py',
+    '../_base_/datasets/imagenet_bs64_pil_resize.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset settings
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    # resizing to (256, 256) here, different from resizing shorter edge to 256
+    dict(type='Resize', scale=(256, 256), backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='PackInputs'),
+]
+
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (8 GPUs) x (64 samples per GPU)
+auto_scale_lr = dict(base_batch_size=512)

configs/repmlp/repmlp-base_delopy_8xb64_in1k.py

+_base_ = ['./repmlp-base_8xb64_in1k.py']
+
+model = dict(backbone=dict(deploy=True))

configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py

+_base_ = ['./repmlp-base_8xb64_in1k-256px.py']
+
+model = dict(backbone=dict(deploy=True))

configs/repvgg/README.md

+# RepVGG
+
+> [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697)
+
+<!-- [ALGORITHM] -->
+
+## Introduction
+
+RepVGG is a VGG-style convolutional architecture. It has the following advantages:
+
+1. The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes the output of its only preceding layer as input and feeds the output into its only following layer.
+2. The model’s body uses only 3 × 3 conv and ReLU.
+3. The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic search, manual refinement, compound scaling, nor other heavy designs.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/142573223-f7f14d32-ea08-43a1-81ad-5a6a83ee0122.png" width="60%"/>
+</div>
+
+## Abstract
+
+<details>
+
+<summary>Show the paper's abstract</summary>
+
+<br>
+We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet.
+</br>
+
+</details>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model, get_model
+
+model = get_model('repvgg-A0_8xb32_in1k', pretrained=True)
+model.backbone.switch_to_deploy()
+predict = inference_model(model, 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('repvgg-A0_8xb32_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Train/Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Train:
+
+```shell
+python tools/train.py configs/repvgg/repvgg-A0_8xb32_in1k.py
+```
+
+Test:
+
+```shell
+python tools/test.py configs/repvgg/repvgg-A0_8xb32_in1k.py https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth
+```
+
+Test with reparameterized model:
+
+```shell
+python tools/test.py configs/repvgg/repvgg-A0_8xb32_in1k.py repvgg_A0_deploy.pth --cfg-options model.backbone.deploy=True
+```
+
+**Reparameterization**
+
+The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations.
+
+```bash
+python tools/convert_models/reparameterize_model.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 weights, the config file must switch to the deploy config files.
+
+```bash
+python tools/test.py ${deploy_cfg} ${deploy_checkpoint} --metrics accuracy
+```
+
+You can also use `backbone.switch_to_deploy()` to switch to the deploy mode in Python code. For example:
+
+```python
+from mmpretrain.models import RepVGG
+
+backbone = RepVGG(arch='A0')
+backbone.switch_to_deploy()
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                         |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |               Config                |                                        Download                                         |
+| :---------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :---------------------------------: | :-------------------------------------------------------------------------------------: |
+| `repvgg-A0_8xb32_in1k`        | From scratch |    8.31    |   1.36    |   72.37   |   90.56   |  [config](repvgg-A0_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.log) |
+| `repvgg-A1_8xb32_in1k`        | From scratch |   12.79    |   2.36    |   74.23   |   91.80   |  [config](repvgg-A1_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_8xb32_in1k_20221213-f81bf3df.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_8xb32_in1k_20221213-f81bf3df.log) |
+| `repvgg-A2_8xb32_in1k`        | From scratch |   25.50    |   5.12    |   76.49   |   93.09   |  [config](repvgg-A2_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_8xb32_in1k_20221213-a8767caf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_8xb32_in1k_20221213-a8767caf.log) |
+| `repvgg-B0_8xb32_in1k`        | From scratch |    3.42    |   15.82   |   75.27   |   92.21   |  [config](repvgg-B0_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_8xb32_in1k_20221213-5091ecc7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_8xb32_in1k_20221213-5091ecc7.log) |
+| `repvgg-B1_8xb32_in1k`        | From scratch |   51.83    |   11.81   |   78.19   |   94.04   |  [config](repvgg-B1_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_8xb32_in1k_20221213-d17c45e7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_8xb32_in1k_20221213-d17c45e7.log) |
+| `repvgg-B1g2_8xb32_in1k`      | From scratch |   41.36    |   8.81    |   77.87   |   93.99   | [config](repvgg-B1g2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_8xb32_in1k_20221213-ae6428fd.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_8xb32_in1k_20221213-ae6428fd.log) |
+| `repvgg-B1g4_8xb32_in1k`      | From scratch |   36.13    |   7.30    |   77.81   |   93.77   | [config](repvgg-B1g4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_8xb32_in1k_20221213-a7a4aaea.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_8xb32_in1k_20221213-a7a4aaea.log) |
+| `repvgg-B2_8xb32_in1k`        | From scratch |   80.32    |   18.37   |   78.58   |   94.23   |  [config](repvgg-B2_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_8xb32_in1k_20221213-d8b420ef.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_8xb32_in1k_20221213-d8b420ef.log) |
+| `repvgg-B2g4_8xb32_in1k`      | From scratch |   55.78    |   11.33   |   79.44   |   94.72   | [config](repvgg-B2g4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_8xb32_in1k_20221213-0c1990eb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_8xb32_in1k_20221213-0c1990eb.log) |
+| `repvgg-B3_8xb32_in1k`        | From scratch |   110.96   |   26.21   |   80.58   |   95.33   |  [config](repvgg-B3_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_8xb32_in1k_20221213-927a329a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_8xb32_in1k_20221213-927a329a.log) |
+| `repvgg-B3g4_8xb32_in1k`      | From scratch |   75.63    |   16.06   |   80.26   |   95.15   | [config](repvgg-B3g4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_8xb32_in1k_20221213-e01cb280.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_8xb32_in1k_20221213-e01cb280.log) |
+| `repvgg-D2se_3rdparty_in1k`\* | From scratch |   120.39   |   32.84   |   81.81   |   95.94   | [config](repvgg-D2se_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L250). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+## Citation
+
+```bibtex
+@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}
+}
+```

configs/repvgg/metafile.yml

+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
+    Code:
+      URL: https://github.com/open-mmlab/mmpretrain/blob/v0.16.0/mmcls/models/backbones/repvgg.py#L257
+      Version: v0.16.0
+
+Models:
+  - Name: repvgg-A0_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-A0_8xb32_in1k.py
+    Metadata:
+      FLOPs: 1360233728
+      Parameters: 8309384
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 72.37
+        Top 5 Accuracy: 90.56
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth
+  - Name: repvgg-A1_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-A1_8xb32_in1k.py
+    Metadata:
+      FLOPs: 2362750208
+      Parameters: 12789864
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 74.23
+        Top 5 Accuracy: 91.80
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_8xb32_in1k_20221213-f81bf3df.pth
+  - Name: repvgg-A2_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-A2_8xb32_in1k.py
+    Metadata:
+      FLOPs: 5115612544
+      Parameters: 25499944
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 76.49
+        Top 5 Accuracy: 93.09
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_8xb32_in1k_20221213-a8767caf.pth
+  - Name: repvgg-B0_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B0_8xb32_in1k.py
+    Metadata:
+      FLOPs: 15820000000
+      Parameters: 3420000
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 75.27
+        Top 5 Accuracy: 92.21
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_8xb32_in1k_20221213-5091ecc7.pth
+  - Name: repvgg-B1_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B1_8xb32_in1k.py
+    Metadata:
+      FLOPs: 11813537792
+      Parameters: 51829480
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 78.19
+        Top 5 Accuracy: 94.04
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_8xb32_in1k_20221213-d17c45e7.pth
+  - Name: repvgg-B1g2_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B1g2_8xb32_in1k.py
+    Metadata:
+      FLOPs: 8807794688
+      Parameters: 41360104
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 77.87
+        Top 5 Accuracy: 93.99
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_8xb32_in1k_20221213-ae6428fd.pth
+  - Name: repvgg-B1g4_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B1g4_8xb32_in1k.py
+    Metadata:
+      FLOPs: 7304923136
+      Parameters: 36125416
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 77.81
+        Top 5 Accuracy: 93.77
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_8xb32_in1k_20221213-a7a4aaea.pth
+  - Name: repvgg-B2_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B2_8xb32_in1k.py
+    Metadata:
+      FLOPs: 18374175232
+      Parameters: 80315112
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 78.58
+        Top 5 Accuracy: 94.23
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_8xb32_in1k_20221213-d8b420ef.pth
+  - Name: repvgg-B2g4_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B2g4_8xb32_in1k.py
+    Metadata:
+      FLOPs: 11329464832
+      Parameters: 55777512
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 79.44
+        Top 5 Accuracy: 94.72
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_8xb32_in1k_20221213-0c1990eb.pth
+  - Name: repvgg-B3_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B3_8xb32_in1k.py
+    Metadata:
+      FLOPs: 26206448128
+      Parameters: 110960872
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 80.58
+        Top 5 Accuracy: 95.33
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_8xb32_in1k_20221213-927a329a.pth
+  - Name: repvgg-B3g4_8xb32_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-B3g4_8xb32_in1k.py
+    Metadata:
+      FLOPs: 16062065152
+      Parameters: 75626728
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 80.26
+        Top 5 Accuracy: 95.15
+    Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_8xb32_in1k_20221213-e01cb280.pth
+  - Name: repvgg-D2se_3rdparty_in1k
+    In Collection: RepVGG
+    Config: configs/repvgg/repvgg-D2se_8xb32_in1k.py
+    Metadata:
+      FLOPs: 32838581760
+      Parameters: 120387572
+    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

configs/repvgg/repvgg-A0_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/repvgg-A0_in1k.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]
+
+val_dataloader = dict(batch_size=256)
+test_dataloader = dict(batch_size=256)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        bias_decay_mult=0.0,
+        custom_keys={
+            'branch_3x3.norm': dict(decay_mult=0.0),
+            'branch_1x1.norm': dict(decay_mult=0.0),
+            'branch_norm.bias': dict(decay_mult=0.0),
+        }))
+
+# schedule settings
+param_scheduler = dict(
+    type='CosineAnnealingLR',
+    T_max=120,
+    by_epoch=True,
+    begin=0,
+    end=120,
+    convert_to_iter_based=True)
+
+train_cfg = dict(by_epoch=True, max_epochs=120)
+
+default_hooks = dict(
+    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))