mirror of
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[Project] Added a supported for Visual Attention Network (VAN) (#2987)
## Motivation The original version of Visual Attention Network (VAN) can be found from https://github.com/Visual-Attention-Network/VAN-Segmentation 添加Visual Attention Network (VAN)的支持。 ## Modification added a floder mmsegmentation/projects/van/ added 13 configs totally and aligned performance basically. 只增加了一个文件夹，共增加13个配置文件，基本对齐性能（没有全部跑）。 ## Use cases (Optional) Before running, you may need to download the pretrain model from https://cloud.tsinghua.edu.cn/d/0100f0cea37d41ba8d08/ and then move them to the folder mmsegmentation/pretrained/, i.e. "mmsegmentation/pretrained/van_b2.pth". After that, run the following command: cd mmsegmentation bash tools/dist_train.sh projects/van/configs/van/van-b2_pre1k_upernet_4xb2-160k_ade20k-512x512.py 4 --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
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projects/van/README.md
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projects/van/README.md
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# Visual Attention Network (VAN) for Segmentation
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This repo is a PyTorch implementation of applying **VAN** (**Visual Attention Network**) to semantic segmentation.
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The code is an integration from [VAN-Segmentation](https://github.com/Visual-Attention-Network/VAN-Segmentation/blob/main/README.md?plain=1)
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More details can be found in [**Visual Attention Network**](https://arxiv.org/abs/2202.09741).
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## Citation
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```bib
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@article{guo2022visual,
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title={Visual Attention Network},
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author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
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journal={arXiv preprint arXiv:2202.09741},
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year={2022}
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}
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```
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## Results
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**Notes**: Pre-trained models can be found in [TsingHua Cloud](https://cloud.tsinghua.edu.cn/d/0100f0cea37d41ba8d08/).
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Results can be found in [VAN-Segmentation](https://github.com/Visual-Attention-Network/VAN-Segmentation/blob/main/README.md?plain=1)
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We provide evaluation results of the converted weights.
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| Method | Backbone | mIoU | Download |
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| :-----: | :----------: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------: |
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| UPerNet | VAN-B2 | 49.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b2-in1kpre_upernet_3rdparty_512x512-ade20k_20230522-19c58aee.pth) |
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| UPerNet | VAN-B3 | 49.71 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b3-in1kpre_upernet_3rdparty_512x512-ade20k_20230522-653bd6b7.pth) |
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| UPerNet | VAN-B4 | 51.56 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b4-in1kpre_upernet_3rdparty_512x512-ade20k_20230522-653bd6b7.pth) |
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| UPerNet | VAN-B4-in22k | 52.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b4-in22kpre_upernet_3rdparty_512x512-ade20k_20230522-4a4d744a.pth) |
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| UPerNet | VAN-B5-in22k | 53.11 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b5-in22kpre_upernet_3rdparty_512x512-ade20k_20230522-5bb6f2b4.pth) |
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| UPerNet | VAN-B6-in22k | 54.25 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b6-in22kpre_upernet_3rdparty_512x512-ade20k_20230522-e226b363.pth) |
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| FPN | VAN-B0 | 38.65 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b0-in1kpre_fpn_3rdparty_512x512-ade20k_20230522-75a76298.pth) |
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| FPN | VAN-B1 | 43.22 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b1-in1kpre_fpn_3rdparty_512x512-ade20k_20230522-104499ff.pth) |
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| FPN | VAN-B2 | 46.84 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b2-in1kpre_fpn_3rdparty_512x512-ade20k_20230522-7074e6f8.pth) |
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| FPN | VAN-B3 | 48.32 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b3-in1kpre_fpn_3rdparty_512x512-ade20k_20230522-2c3b7f5e.pth) |
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## Preparation
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Install MMSegmentation and download ADE20K according to the guidelines in MMSegmentation.
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## Requirement
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**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
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```shell
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pip install -U openmim
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mim install mmengine
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mim install "mmcv>=2.0.0"
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```
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**Step 1.** Install MMSegmentation.
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Case a: If you develop and run mmseg directly, install it from source:
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```shell
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git clone -b main https://github.com/open-mmlab/mmsegmentation.git
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cd mmsegmentation
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pip install -v -e .
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```
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Case b: If you use mmsegmentation as a dependency or third-party package, install it with pip:
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```shell
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pip install "mmsegmentation>=1.0.0"
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```
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## Training
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If you use 4 GPUs for training by default. Run:
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```bash
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bash tools/dist_train.sh projects/van/configs/van/van-b2_pre1k_upernet_4xb2-160k_ade20k-512x512.py 4
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```
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## Evaluation
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To evaluate the model, an example is:
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```bash
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bash tools/dist_train.sh projects/van/configs/van/van-b2_pre1k_upernet_4xb2-160k_ade20k-512x512.py work_dirs/van-b2_pre1k_upernet_4xb2-160k_ade20k-512x512/iter_160000.pth 4 --eval mIoU
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```
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## FLOPs
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To calculate FLOPs for a model, run:
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```bash
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bash tools/analysis_tools/get_flops.py projects/van/configs/van/van-b2_pre1k_upernet_4xb2-160k_ade20k-512x512.py --shape 512 512
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```
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## Acknowledgment
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Our implementation is mainly based on [mmsegmentation](https://github.com/open-mmlab/mmsegmentation/tree/v0.12.0), [Swin-Transformer](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation), [PoolFormer](https://github.com/sail-sg/poolformer), [Enjoy-Hamburger](https://github.com/Gsunshine/Enjoy-Hamburger) and [VAN-Segmentation](https://github.com/Visual-Attention-Network/VAN-Segmentation/blob/main/README.md?plain=1). Thanks for their authors.
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## LICENSE
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This repo is under the Apache-2.0 license. For commercial use, please contact the authors.
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4
projects/van/backbones/__init__.py
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projects/van/backbones/__init__.py
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# Copyright (c) OpenMMLab. All rights reserved.
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from .van import VAN
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__all__ = ['VAN']
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projects/van/backbones/van.py
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projects/van/backbones/van.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import torch
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import torch.nn as nn
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from mmengine.model import BaseModule
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from mmseg.models.backbones.mscan import (MSCAN, MSCABlock,
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MSCASpatialAttention,
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OverlapPatchEmbed)
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from mmseg.registry import MODELS
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class VANAttentionModule(BaseModule):
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def __init__(self, in_channels):
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super().__init__()
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self.conv0 = nn.Conv2d(
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in_channels, in_channels, 5, padding=2, groups=in_channels)
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self.conv_spatial = nn.Conv2d(
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in_channels,
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in_channels,
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7,
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stride=1,
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padding=9,
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groups=in_channels,
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dilation=3)
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self.conv1 = nn.Conv2d(in_channels, in_channels, 1)
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def forward(self, x):
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u = x.clone()
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attn = self.conv0(x)
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attn = self.conv_spatial(attn)
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attn = self.conv1(attn)
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return u * attn
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class VANSpatialAttention(MSCASpatialAttention):
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def __init__(self, in_channels, act_cfg=dict(type='GELU')):
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super().__init__(in_channels, act_cfg=act_cfg)
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self.spatial_gating_unit = VANAttentionModule(in_channels)
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class VANBlock(MSCABlock):
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def __init__(self,
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channels,
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mlp_ratio=4.,
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drop=0.,
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drop_path=0.,
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act_cfg=dict(type='GELU'),
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norm_cfg=dict(type='SyncBN', requires_grad=True)):
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super().__init__(
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channels,
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mlp_ratio=mlp_ratio,
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drop=drop,
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drop_path=drop_path,
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act_cfg=act_cfg,
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norm_cfg=norm_cfg)
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self.attn = VANSpatialAttention(channels)
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@MODELS.register_module()
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class VAN(MSCAN):
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def __init__(self,
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in_channels=3,
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embed_dims=[64, 128, 256, 512],
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mlp_ratios=[8, 8, 4, 4],
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drop_rate=0.,
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drop_path_rate=0.,
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depths=[3, 4, 6, 3],
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num_stages=4,
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act_cfg=dict(type='GELU'),
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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pretrained=None,
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init_cfg=None):
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super(MSCAN, self).__init__(init_cfg=init_cfg)
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assert not (init_cfg and pretrained), \
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'init_cfg and pretrained cannot be set at the same time'
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if isinstance(pretrained, str):
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warnings.warn('DeprecationWarning: pretrained is deprecated, '
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'please use "init_cfg" instead')
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self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
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elif pretrained is not None:
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raise TypeError('pretrained must be a str or None')
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self.depths = depths
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self.num_stages = num_stages
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# stochastic depth decay rule
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
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]
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cur = 0
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for i in range(num_stages):
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patch_embed = OverlapPatchEmbed(
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patch_size=7 if i == 0 else 3,
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stride=4 if i == 0 else 2,
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in_channels=in_channels if i == 0 else embed_dims[i - 1],
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embed_dim=embed_dims[i],
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norm_cfg=norm_cfg)
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block = nn.ModuleList([
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VANBlock(
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channels=embed_dims[i],
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mlp_ratio=mlp_ratios[i],
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drop=drop_rate,
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drop_path=dpr[cur + j],
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act_cfg=act_cfg,
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norm_cfg=norm_cfg) for j in range(depths[i])
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])
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norm = nn.LayerNorm(embed_dims[i])
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cur += depths[i]
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setattr(self, f'patch_embed{i + 1}', patch_embed)
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setattr(self, f'block{i + 1}', block)
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setattr(self, f'norm{i + 1}', norm)
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def init_weights(self):
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return super().init_weights()
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14
projects/van/configs/_base_/datasets/ade20k.py
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projects/van/configs/_base_/datasets/ade20k.py
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# dataset settings
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_base_ = '../../../../../configs/_base_/datasets/ade20k.py'
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', scale=(2048, 512), keep_ratio=True),
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dict(type='ResizeToMultiple', size_divisor=32),
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='PackSegInputs')
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]
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = val_dataloader
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projects/van/configs/_base_/models/van_fpn.py
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projects/van/configs/_base_/models/van_fpn.py
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# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_val=0,
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seg_pad_val=255,
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size=(512, 512))
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model = dict(
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type='EncoderDecoder',
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='VAN',
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embed_dims=[32, 64, 160, 256],
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drop_rate=0.0,
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drop_path_rate=0.1,
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depths=[3, 3, 5, 2],
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act_cfg=dict(type='GELU'),
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norm_cfg=norm_cfg,
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init_cfg=dict()),
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neck=dict(
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type='FPN',
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in_channels=[32, 64, 160, 256],
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out_channels=256,
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num_outs=4),
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decode_head=dict(
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type='FPNHead',
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in_channels=[256, 256, 256, 256],
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in_index=[0, 1, 2, 3],
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feature_strides=[4, 8, 16, 32],
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channels=128,
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dropout_ratio=0.1,
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num_classes=150,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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51
projects/van/configs/_base_/models/van_upernet.py
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projects/van/configs/_base_/models/van_upernet.py
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# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_val=0,
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seg_pad_val=255,
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size=(512, 512))
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model = dict(
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type='EncoderDecoder',
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='VAN',
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embed_dims=[32, 64, 160, 256],
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drop_rate=0.0,
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drop_path_rate=0.1,
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depths=[3, 3, 5, 2],
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act_cfg=dict(type='GELU'),
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norm_cfg=norm_cfg,
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init_cfg=dict()),
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decode_head=dict(
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type='UPerHead',
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in_channels=[32, 64, 160, 256],
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in_index=[0, 1, 2, 3],
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pool_scales=(1, 2, 3, 6),
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channels=512,
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dropout_ratio=0.1,
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num_classes=150,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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auxiliary_head=dict(
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type='FCNHead',
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in_channels=160,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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num_classes=150,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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_base_ = './van-b2_fpn_8xb4-40k_ade20k-512x512.py'
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ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b0_3rdparty_20230522-956f5e0d.pth' # noqa
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model = dict(
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backbone=dict(
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embed_dims=[32, 64, 160, 256],
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depths=[3, 3, 5, 2],
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init_cfg=dict(type='Pretrained', checkpoint=ckpt_path)),
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neck=dict(in_channels=[32, 64, 160, 256]))
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_base_ = './van-b2_fpn_8xb4-40k_ade20k-512x512.py'
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ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b1_3rdparty_20230522-3adb117f.pth' # noqa
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model = dict(
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backbone=dict(
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depths=[2, 2, 4, 2],
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init_cfg=dict(type='Pretrained', checkpoint=ckpt_path)))
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_base_ = [
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'../_base_/models/van_fpn.py',
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'../_base_/datasets/ade20k.py',
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'../../../../configs/_base_/default_runtime.py',
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]
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custom_imports = dict(imports=['projects.van.backbones'])
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ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b2_3rdparty_20230522-636fac93.pth' # noqa
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model = dict(
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type='EncoderDecoder',
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backbone=dict(
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embed_dims=[64, 128, 320, 512],
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depths=[3, 3, 12, 3],
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init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
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||||
drop_path_rate=0.2),
|
||||
neck=dict(in_channels=[64, 128, 320, 512]),
|
||||
decode_head=dict(num_classes=150))
|
||||
|
||||
train_dataloader = dict(batch_size=4)
|
||||
|
||||
# we use 8 gpu instead of 4 in mmsegmentation, so lr*2 and max_iters/2
|
||||
gpu_multiples = 2
|
||||
max_iters = 80000 // gpu_multiples
|
||||
interval = 8000 // gpu_multiples
|
||||
optim_wrapper = dict(
|
||||
type='OptimWrapper',
|
||||
optimizer=dict(
|
||||
type='AdamW',
|
||||
lr=0.0001 * gpu_multiples,
|
||||
# betas=(0.9, 0.999),
|
||||
weight_decay=0.0001),
|
||||
clip_grad=None)
|
||||
# learning policy
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type='PolyLR',
|
||||
power=0.9,
|
||||
eta_min=0.0,
|
||||
begin=0,
|
||||
end=max_iters,
|
||||
by_epoch=False,
|
||||
)
|
||||
]
|
||||
train_cfg = dict(
|
||||
type='IterBasedTrainLoop', max_iters=max_iters, val_interval=interval)
|
||||
val_cfg = dict(type='ValLoop')
|
||||
test_cfg = dict(type='TestLoop')
|
||||
default_hooks = dict(
|
||||
timer=dict(type='IterTimerHook'),
|
||||
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
|
||||
param_scheduler=dict(type='ParamSchedulerHook'),
|
||||
checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=interval),
|
||||
sampler_seed=dict(type='DistSamplerSeedHook'),
|
||||
visualization=dict(type='SegVisualizationHook'))
|
||||
@ -0,0 +1,46 @@
|
||||
_base_ = [
|
||||
'../_base_/models/van_upernet.py', '../_base_/datasets/ade20k.py',
|
||||
'../../../../configs/_base_/default_runtime.py',
|
||||
'../../../../configs/_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
custom_imports = dict(imports=['projects.van.backbones'])
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b2_3rdparty_20230522-636fac93.pth' # noqa
|
||||
model = dict(
|
||||
type='EncoderDecoder',
|
||||
backbone=dict(
|
||||
embed_dims=[64, 128, 320, 512],
|
||||
depths=[3, 3, 12, 3],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path)),
|
||||
decode_head=dict(in_channels=[64, 128, 320, 512], num_classes=150),
|
||||
auxiliary_head=dict(in_channels=320, num_classes=150))
|
||||
|
||||
# AdamW optimizer
|
||||
# no weight decay for position embedding & layer norm in backbone
|
||||
optim_wrapper = dict(
|
||||
_delete_=True,
|
||||
type='OptimWrapper',
|
||||
optimizer=dict(
|
||||
type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
|
||||
clip_grad=None,
|
||||
paramwise_cfg=dict(
|
||||
custom_keys={
|
||||
'absolute_pos_embed': dict(decay_mult=0.),
|
||||
'relative_position_bias_table': dict(decay_mult=0.),
|
||||
'norm': dict(decay_mult=0.)
|
||||
}))
|
||||
# learning policy
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
|
||||
dict(
|
||||
type='PolyLR',
|
||||
power=1.0,
|
||||
begin=1500,
|
||||
end=_base_.train_cfg.max_iters,
|
||||
eta_min=0.0,
|
||||
by_epoch=False,
|
||||
)
|
||||
]
|
||||
|
||||
# By default, models are trained on 8 GPUs with 2 images per GPU
|
||||
train_dataloader = dict(batch_size=2)
|
||||
@ -0,0 +1,11 @@
|
||||
_base_ = './van-b2_fpn_8xb4-40k_ade20k-512x512.py'
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b3_3rdparty_20230522-a184e051.pth' # noqa
|
||||
model = dict(
|
||||
type='EncoderDecoder',
|
||||
backbone=dict(
|
||||
embed_dims=[64, 128, 320, 512],
|
||||
depths=[3, 5, 27, 3],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
|
||||
drop_path_rate=0.3),
|
||||
neck=dict(in_channels=[64, 128, 320, 512]))
|
||||
train_dataloader = dict(batch_size=4)
|
||||
@ -0,0 +1,8 @@
|
||||
_base_ = './van-b2_upernet_4xb2-160k_ade20k-512x512.py'
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b3_3rdparty_20230522-a184e051.pth' # noqa
|
||||
model = dict(
|
||||
type='EncoderDecoder',
|
||||
backbone=dict(
|
||||
depths=[3, 5, 27, 3],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
|
||||
drop_path_rate=0.3))
|
||||
@ -0,0 +1,10 @@
|
||||
_base_ = './van-b2_upernet_4xb2-160k_ade20k-512x512.py'
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b4-in22k_3rdparty_20230522-5e31cafb.pth' # noqa
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
depths=[3, 6, 40, 3],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
|
||||
drop_path_rate=0.4))
|
||||
|
||||
# By default, models are trained on 8 GPUs with 2 images per GPU
|
||||
train_dataloader = dict(batch_size=4)
|
||||
@ -0,0 +1,10 @@
|
||||
_base_ = './van-b2_upernet_4xb2-160k_ade20k-512x512.py'
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b4_3rdparty_20230522-1d71c077.pth' # noqa
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
depths=[3, 6, 40, 3],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
|
||||
drop_path_rate=0.4))
|
||||
|
||||
# By default, models are trained on 4 GPUs with 4 images per GPU
|
||||
train_dataloader = dict(batch_size=4)
|
||||
@ -0,0 +1,10 @@
|
||||
_base_ = './van-b2_upernet_4xb2-160k_ade20k-512x512.py'
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b5-in22k_3rdparty_20230522-b26134d7.pth' # noqa
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
embed_dims=[96, 192, 480, 768],
|
||||
depths=[3, 3, 24, 3],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
|
||||
drop_path_rate=0.4),
|
||||
decode_head=dict(in_channels=[96, 192, 480, 768], num_classes=150),
|
||||
auxiliary_head=dict(in_channels=480, num_classes=150))
|
||||
@ -0,0 +1,10 @@
|
||||
_base_ = './van-b2_upernet_4xb2-160k_ade20k-512x512.py'
|
||||
ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b6-in22k_3rdparty_20230522-5e5172a3.pth' # noqa
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
embed_dims=[96, 192, 384, 768],
|
||||
depths=[6, 6, 90, 6],
|
||||
init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
|
||||
drop_path_rate=0.5),
|
||||
decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=150),
|
||||
auxiliary_head=dict(in_channels=384, num_classes=150))
|
||||
Loading…
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Reference in New Issue
Block a user