[Feature] Support VAN

.gitignore

@@ -120,6 +120,7 @@ venv.bak/
 *.log.json
 /work_dirs
 /mmcls/.mim
+.DS_Store
 
 # Pytorch
 *.pth

README.md

@@ -126,6 +126,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea
 - [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)
 - [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)
 - [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)
+- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)
 
 </details>
 

README_zh-CN.md

@@ -124,6 +124,7 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O
 - [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)
 - [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)
 - [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)
+- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)
 
 </details>
 

configs/_base_/models/van/van_base.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='base', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_large.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='large', drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_small.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='small', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/van/van_tiny.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='tiny', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=256,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/van/README.md

@@ -0,0 +1,39 @@
+# Visual Attention Network
+
+> [Visual Attention Network](https://arxiv.org/pdf/2202.09741v2.pdf)
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24734142/157409484-f26fcc1f-a856-48c2-a7a7-d157c38877ac.png" width="90%"/>
+</div>
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24734142/157409411-2f622ba7-553c-4702-91be-eba03f9ea04f.png" width="90%"/>
+</div>
+
+
+## Results and models
+
+### ImageNet-1k
+
+|   Model   |   Pretrain   | resolution  | Params(M) |  Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------:|:------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:------:|:--------:|
+|  VAN-T   | From scratch |   224x224   |   4.1   |    0.9   |   75.4   |      |  | |
+|  VAN-S   | From scratch |   224x224   |   13.9   |    2.5   |   81.1   |      |  | |
+|  VAN-B   | From scratch |   224x224   |   26.6   |   5.0   |   82.8   |      |  | |
+|  VAN-L | From scratch |   224x224   |   44.8   |    9.0   |   83.9   |      |  | |
+
+## Citation
+
+```
+@article{guo2022visual,
+  title={Visual Attention Network},
+  author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
+  journal={arXiv preprint arXiv:2202.09741},
+  year={2022}
+}
+```

configs/van/metafile.yml

@@ -0,0 +1,73 @@
+Collections:
+  - Name: Visual-Attention-Network
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Training Resources:
+      Epochs: 300
+      Batch Size: 1024
+      Architecture:
+        - Visual Attention Network
+    Paper:
+      URL: https://arxiv.org/pdf/2202.09741v2.pdf
+      Title: "Visual Attention Network"
+    README: configs/van/README.md
+    Code:
+      URL:
+      Version:
+
+Models:
+  - Name: van-tiny_8xb128_in1k
+    Metadata:
+      FLOPs:
+      Parameters:
+    In Collection: Visual-Attention-Network
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 75.4
+          Top 5 Accuracy:
+        Task: Image Classification
+    Weights:
+    Config: configs/van/van-tiny_8xb128_in1k.py
+  - Name: van-small_8xb128_in1k
+    Metadata:
+      FLOPs:
+      Parameters:
+    In Collection: Visual-Attention-Network
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 81.1
+            Top 5 Accuracy:
+          Task: Image Classification
+    Weights:
+    Config: configs/van/van-small_8xb128_in1k.py
+  - Name: van-base_8xb128_in1k
+    Metadata:
+      FLOPs:
+      Parameters:
+    In Collection: Visual-Attention-Network
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 82.8
+            Top 5 Accuracy:
+          Task: Image Classification
+    Weights:
+    Config: configs/van/van-base_8xb128_in1k.py
+  - Name: van-large_8xb128_in1k
+    Metadata:
+      FLOPs:
+      Parameters:
+    In Collection: Visual-Attention-Network
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 83.9
+            Top 5 Accuracy:
+          Task: Image Classification
+    Weights:
+    Config: configs/van/van-large_8xb128_in1k.py

configs/van/van-base-8xb128_in1k.py

@@ -0,0 +1,56 @@
+_base_ = [
+    '../_base_/models/van/van_base.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+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,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    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=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    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=128)

configs/van/van-large-8xb128_in1k.py

@@ -0,0 +1,56 @@
+_base_ = [
+    '../_base_/models/van/van_large.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+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,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    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=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    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=128)

configs/van/van-small-8xb128_in1k.py

@@ -0,0 +1,56 @@
+_base_ = [
+    '../_base_/models/van/van_small.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+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,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    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=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    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=128)