[Feature] Support RepLKnet backbone.

README.md

@@ -151,6 +151,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea
 - [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
 - [x] [MobileViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilevit)
 - [x] [DaViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/davit)
+- [x] [RepLKNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/replknet)
 
 </details>
 

README_zh-CN.md

@@ -150,6 +150,7 @@ mim install -e .
 - [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
 - [x] [MobileViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilevit)
 - [x] [DaViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/davit)
+- [x] [RepLKNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/replknet)
 
 </details>
 

configs/_base_/datasets/imagenet_bs16_pil_bicubic_384.py

@@ -0,0 +1,57 @@
+# dataset settings
+dataset_type = 'ImageNet'
+data_preprocessor = dict(
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackClsInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=384, backend='pillow', interpolation='bicubic'),
+    dict(type='PackClsInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        ann_file='meta/val.txt',
+        data_prefix='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/imagenet_bs8_pil_bicubic_320.py

@@ -0,0 +1,63 @@
+# dataset settings
+dataset_type = 'ImageNet'
+data_preprocessor = dict(
+    # RGB format normalization parameters
+    mean=[122.5, 122.5, 122.5],
+    std=[122.5, 122.5, 122.5],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=320,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackClsInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='ResizeEdge',
+        scale=int(320 / 224 * 256),
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=320),
+    dict(type='PackClsInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        ann_file='meta/val.txt',
+        data_prefix='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/models/replknet-31B_in1k.py

@@ -0,0 +1,25 @@
+from mmcls.models import build_classifier
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='31B',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))
+
+if __name__ == '__main__':
+    # model.pop('type')
+    model = build_classifier(model)
+    model.eval()
+    print('------------------- training-time model -------------')
+    for i in model.state_dict().keys():
+        print(i)

configs/_base_/models/replknet-31L_in1k.py

@@ -0,0 +1,15 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='31L',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/replknet-XL_in1k.py

@@ -0,0 +1,15 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='XL',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/replknet/README.md

@@ -0,0 +1,95 @@
+# 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>
+
+## Results and models
+
+### ImageNet-1k
+
+|     Model      | Resolution | Pretrained Dataset |            Params(M)            |            Flops(G)             | Top-1 (%) | Top-5 (%) |                 Config                 |                 Download                 |
+| :------------: | :--------: | :----------------: | :-----------------------------: | :-----------------------------: | :-------: | :-------: | :------------------------------------: | :--------------------------------------: |
+| RepLKNet-31B\* |  224x224   |    From Scratch    |  79.9（train) \| 79.5 (deploy)  |  15.6 (train) \| 15.4 (deploy)  |   83.48   |   96.57   | [config (train)](./replknet-31B_32xb64_in1k.py) \| [config (deploy)](./deploy/replknet-31B-deploy_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k_20221118-fd08e268.pth) |
+| RepLKNet-31B\* |  384x384   |    From Scratch    |  79.9（train) \| 79.5 (deploy)  |  46.0 (train) \| 45.3 (deploy)  |   84.84   |   97.34   | [config (train)](./replknet-31B_32xb64_in1k-384px.py) \| [config (deploy)](./deploy/replknet-31B-deploy_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k-384px_20221118-03a170ce.pth) |
+| RepLKNet-31B\* |  224x224   |    ImageNet-21K    |  79.9（train) \| 79.5 (deploy)  |  15.6 (train) \| 15.4 (deploy)  |   85.20   |   97.56   | [config (train)](./replknet-31B_32xb64_in1k.py) \| [config (deploy)](./deploy/replknet-31B-deploy_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k_20221118-54ed5c46.pth) |
+| RepLKNet-31B\* |  384x384   |    ImageNet-21K    |  79.9（train) \| 79.5 (deploy)  |  46.0 (train) \| 45.3 (deploy)  |   85.99   |   97.75   | [config (train)](./replknet-31B_32xb64_in1k-384px.py) \| [config (deploy)](./deploy/replknet-31B-deploy_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k-384px_20221118-76c92b24.pth) |
+| RepLKNet-31L\* |  384x384   |    ImageNet-21K    | 172.7（train) \| 172.0 (deploy) |  97.2 (train) \| 97.0 (deploy)  |   86.63   |   98.00   | [config (train)](./replknet-31L_32xb64_in1k-384px.py) \| [config (deploy)](./deploy/replknet-31L-deploy_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31L_in21k-pre_3rdparty_in1k-384px_20221118-dc3fc07c.pth) |
+| RepLKNet-XL\*  |  320x320   |    MegData-73M     | 335.4（train) \| 335.0 (deploy) | 129.6 (train) \| 129.0 (deploy) |   87.57   |   98.39   | [config (train)](./replknet-XL_32xb64_in1k-320px.py) \| [config (deploy)](./deploy/replknet-XL-deploy_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/RepVGG). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
+
+## How to use
+
+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.
+
+### Use tool
+
+Use provided tool to reparameterize the given model and save the checkpoint:
+
+```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
+```
+
+### In the code
+
+Use `backbone.switch_to_deploy()` or `classificer.backbone.switch_to_deploy()` to switch to the deploy mode. For example:
+
+```python
+from mmcls.models import build_backbone
+
+backbone_cfg=dict(type='RepLKNet',arch='31B'),
+backbone = build_backbone(backbone_cfg)
+backbone.switch_to_deploy()
+```
+
+or
+
+```python
+from mmcls.models import build_classifier
+
+cfg = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='31B'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))
+
+classifier = build_classifier(cfg)
+classifier.backbone.switch_to_deploy()
+```
+
+## Citation
+
+```
+@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

@@ -0,0 +1,3 @@
+_base_ = '../replknet-31B_32xb64_in1k-384px.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))

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

@@ -0,0 +1,3 @@
+_base_ = '../replknet-31B_32xb64_in1k.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))

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

@@ -0,0 +1,3 @@
+_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

@@ -0,0 +1,3 @@
+_base_ = '../replknet-XL_32xb64_in1k-320px.py'
+
+model = dict(backbone=dict(small_kernel_merged=True))