mirror of
https://github.com/open-mmlab/mmsegmentation.git
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[Feature] Support FastFCN (#885)
* FastFCN first commit * FastFCN first commit * Fixing lint error * Fixing lint error * use for loop on JPU * Use For Loop * Refactor FastFCN * FastFCN * FastFCN * temp * Uploading models & logs (4x4) * Fixing typos * fix typos * rename config * change README.md * use _delete_=True * change configs * change start_level to 0 * change start_level to 0 * jpu * add unittest for start_level!=0
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MengzhangLI
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@ -90,6 +90,7 @@ Supported methods:
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- [x] [DMNet (ICCV'2019)](configs/dmnet)
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- [x] [DMNet (ICCV'2019)](configs/dmnet)
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- [x] [ANN (ICCV'2019)](configs/ann)
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- [x] [ANN (ICCV'2019)](configs/ann)
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- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
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- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
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- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
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- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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@ -89,6 +89,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
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- [x] [DMNet (ICCV'2019)](configs/dmnet)
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- [x] [DMNet (ICCV'2019)](configs/dmnet)
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- [x] [ANN (ICCV'2019)](configs/ann)
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- [x] [ANN (ICCV'2019)](configs/ann)
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- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
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- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
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- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
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- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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53
configs/_base_/models/fastfcn_r50-d32_jpu_psp.py
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configs/_base_/models/fastfcn_r50-d32_jpu_psp.py
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# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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pretrained='open-mmlab://resnet50_v1c',
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backbone=dict(
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type='ResNetV1c',
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depth=50,
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num_stages=4,
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dilations=(1, 1, 2, 4),
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strides=(1, 2, 2, 2),
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out_indices=(1, 2, 3),
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norm_cfg=norm_cfg,
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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neck=dict(
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type='JPU',
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in_channels=(512, 1024, 2048),
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mid_channels=512,
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start_level=0,
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end_level=-1,
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dilations=(1, 2, 4, 8),
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align_corners=False,
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norm_cfg=norm_cfg),
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decode_head=dict(
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type='PSPHead',
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in_channels=2048,
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in_index=2,
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channels=512,
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pool_scales=(1, 2, 3, 6),
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dropout_ratio=0.1,
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num_classes=19,
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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=1024,
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in_index=1,
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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=19,
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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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41
configs/fastfcn/README.md
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configs/fastfcn/README.md
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# FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation
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## Introduction
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<!-- [ALGORITHM] -->
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<a href="https://github.com/wuhuikai/FastFCN">Official Repo</a>
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<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12">Code Snippet</a>
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<details>
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<summary align="right"><a href="https://arxiv.org/abs/1903.11816">FastFCN (ArXiv'2019) </a></summary>
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```latex
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@article{wu2019fastfcn,
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title={Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation},
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author={Wu, Huikai and Zhang, Junge and Huang, Kaiqi and Liang, Kongming and Yu, Yizhou},
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journal={arXiv preprint arXiv:1903.11816},
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year={2019}
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}
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```
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</details>
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## Results and models
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### Cityscapes
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| DeepLabV3 + JPU | R-50-D32 | 512x1024 | 80000 | 5.67 | 2.64 | 79.12 | 80.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json) |
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| DeepLabV3 + JPU (4x4) | R-50-D32 | 512x1024 | 80000 | 9.79 | - | 79.52 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
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| PSPNet + JPU | R-50-D32 | 512x1024 | 80000 | 5.67 | 4.40 | 79.26 | 80.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json) |
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| PSPNet + JPU (4x4) | R-50-D32 | 512x1024 | 80000 | 9.94 | - | 78.76 | 80.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json) |
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| EncNet + JPU | R-50-D32 | 512x1024 | 80000 | 8.15 | 4.77 | 77.97 |79.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json) |
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| EncNet + JPU (4x4)| R-50-D32 | 512x1024 | 80000 | 15.45 | - | 78.6 | 80.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json) |
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Note:
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- `4x4` means 4 GPUs with 4 samples per GPU in training, default setting is 4 GPUs with 2 samples per GPU in training.
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- Results of [DeepLabV3 (mIoU: 79.32)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3), [PSPNet (mIoU: 78.55)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet) and [ENCNet (mIoU: 77.94)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet) can be found in each original repository.
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126
configs/fastfcn/fastfcn.yml
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126
configs/fastfcn/fastfcn.yml
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Collections:
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- Name: fastfcn
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Metadata:
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Training Data:
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- Cityscapes
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Paper:
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URL: https://arxiv.org/abs/1903.11816
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
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README: configs/fastfcn/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
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Version: v0.18.0
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Converted From:
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Code: https://github.com/wuhuikai/FastFCN
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Models:
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- Name: fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes
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In Collection: fastfcn
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Metadata:
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backbone: R-50-D32
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 378.79
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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memory (GB): 5.67
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.12
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mIoU(ms+flip): 80.58
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
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- Name: fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes
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In Collection: fastfcn
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Metadata:
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backbone: R-50-D32
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crop size: (512,1024)
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lr schd: 80000
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memory (GB): 9.79
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.52
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mIoU(ms+flip): 80.91
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
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- Name: fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes
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In Collection: fastfcn
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Metadata:
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backbone: R-50-D32
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 227.27
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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memory (GB): 5.67
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.26
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mIoU(ms+flip): 80.86
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
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- Name: fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes
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In Collection: fastfcn
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Metadata:
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backbone: R-50-D32
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crop size: (512,1024)
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lr schd: 80000
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memory (GB): 9.94
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.76
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mIoU(ms+flip): 80.03
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
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- Name: fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes
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In Collection: fastfcn
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Metadata:
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backbone: R-50-D32
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 209.64
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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memory (GB): 8.15
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.97
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mIoU(ms+flip): 79.92
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
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- Name: fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes
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In Collection: fastfcn
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Metadata:
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|
backbone: R-50-D32
|
||||||
|
crop size: (512,1024)
|
||||||
|
lr schd: 80000
|
||||||
|
memory (GB): 15.45
|
||||||
|
Results:
|
||||||
|
- Task: Semantic Segmentation
|
||||||
|
Dataset: Cityscapes
|
||||||
|
Metrics:
|
||||||
|
mIoU: 78.6
|
||||||
|
mIoU(ms+flip): 80.25
|
||||||
|
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py
|
||||||
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
|
||||||
@ -0,0 +1,6 @@
|
|||||||
|
# model settings
|
||||||
|
_base_ = './fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py'
|
||||||
|
data = dict(
|
||||||
|
samples_per_gpu=4,
|
||||||
|
workers_per_gpu=4,
|
||||||
|
)
|
||||||
@ -0,0 +1,20 @@
|
|||||||
|
# model settings
|
||||||
|
_base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py'
|
||||||
|
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
||||||
|
model = dict(
|
||||||
|
decode_head=dict(
|
||||||
|
_delete_=True,
|
||||||
|
type='ASPPHead',
|
||||||
|
in_channels=2048,
|
||||||
|
in_index=2,
|
||||||
|
channels=512,
|
||||||
|
dilations=(1, 12, 24, 36),
|
||||||
|
dropout_ratio=0.1,
|
||||||
|
num_classes=19,
|
||||||
|
norm_cfg=norm_cfg,
|
||||||
|
align_corners=False,
|
||||||
|
loss_decode=dict(
|
||||||
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
||||||
|
# model training and testing settings
|
||||||
|
train_cfg=dict(),
|
||||||
|
test_cfg=dict(mode='whole'))
|
||||||
@ -0,0 +1,6 @@
|
|||||||
|
# model settings
|
||||||
|
_base_ = './fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py'
|
||||||
|
data = dict(
|
||||||
|
samples_per_gpu=4,
|
||||||
|
workers_per_gpu=4,
|
||||||
|
)
|
||||||
@ -0,0 +1,24 @@
|
|||||||
|
# model settings
|
||||||
|
_base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py'
|
||||||
|
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
||||||
|
model = dict(
|
||||||
|
decode_head=dict(
|
||||||
|
_delete_=True,
|
||||||
|
type='EncHead',
|
||||||
|
in_channels=[512, 1024, 2048],
|
||||||
|
in_index=(0, 1, 2),
|
||||||
|
channels=512,
|
||||||
|
num_codes=32,
|
||||||
|
use_se_loss=True,
|
||||||
|
add_lateral=False,
|
||||||
|
dropout_ratio=0.1,
|
||||||
|
num_classes=19,
|
||||||
|
norm_cfg=norm_cfg,
|
||||||
|
align_corners=False,
|
||||||
|
loss_decode=dict(
|
||||||
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
||||||
|
loss_se_decode=dict(
|
||||||
|
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)),
|
||||||
|
# model training and testing settings
|
||||||
|
train_cfg=dict(),
|
||||||
|
test_cfg=dict(mode='whole'))
|
||||||
@ -0,0 +1,9 @@
|
|||||||
|
_base_ = [
|
||||||
|
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
|
||||||
|
'../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
|
||||||
|
'../_base_/schedules/schedule_80k.py'
|
||||||
|
]
|
||||||
|
data = dict(
|
||||||
|
samples_per_gpu=4,
|
||||||
|
workers_per_gpu=4,
|
||||||
|
)
|
||||||
@ -0,0 +1,5 @@
|
|||||||
|
_base_ = [
|
||||||
|
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
|
||||||
|
'../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
|
||||||
|
'../_base_/schedules/schedule_80k.py'
|
||||||
|
]
|
||||||
@ -1,7 +1,8 @@
|
|||||||
# Copyright (c) OpenMMLab. All rights reserved.
|
# Copyright (c) OpenMMLab. All rights reserved.
|
||||||
from .fpn import FPN
|
from .fpn import FPN
|
||||||
from .ic_neck import ICNeck
|
from .ic_neck import ICNeck
|
||||||
|
from .jpu import JPU
|
||||||
from .mla_neck import MLANeck
|
from .mla_neck import MLANeck
|
||||||
from .multilevel_neck import MultiLevelNeck
|
from .multilevel_neck import MultiLevelNeck
|
||||||
|
|
||||||
__all__ = ['FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck']
|
__all__ = ['FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck', 'JPU']
|
||||||
|
|||||||
131
mmseg/models/necks/jpu.py
Normal file
131
mmseg/models/necks/jpu.py
Normal file
@ -0,0 +1,131 @@
|
|||||||
|
# Copyright (c) OpenMMLab. All rights reserved.
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
|
||||||
|
from mmcv.runner import BaseModule
|
||||||
|
|
||||||
|
from mmseg.ops import resize
|
||||||
|
from ..builder import NECKS
|
||||||
|
|
||||||
|
|
||||||
|
@NECKS.register_module()
|
||||||
|
class JPU(BaseModule):
|
||||||
|
"""FastFCN: Rethinking Dilated Convolution in the Backbone
|
||||||
|
for Semantic Segmentation.
|
||||||
|
|
||||||
|
This Joint Pyramid Upsampling (JPU) neck is the implementation of
|
||||||
|
`FastFCN <https://arxiv.org/abs/1903.11816>`_.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
in_channels (Tuple[int], optional): The number of input channels
|
||||||
|
for each convolution operations before upsampling.
|
||||||
|
Default: (512, 1024, 2048).
|
||||||
|
mid_channels (int): The number of output channels of JPU.
|
||||||
|
Default: 512.
|
||||||
|
start_level (int): Index of the start input backbone level used to
|
||||||
|
build the feature pyramid. Default: 0.
|
||||||
|
end_level (int): Index of the end input backbone level (exclusive) to
|
||||||
|
build the feature pyramid. Default: -1, which means the last level.
|
||||||
|
dilations (tuple[int]): Dilation rate of each Depthwise
|
||||||
|
Separable ConvModule. Default: (1, 2, 4, 8).
|
||||||
|
align_corners (bool, optional): The align_corners argument of
|
||||||
|
resize operation. Default: False.
|
||||||
|
conv_cfg (dict | None): Config of conv layers.
|
||||||
|
Default: None.
|
||||||
|
norm_cfg (dict | None): Config of norm layers.
|
||||||
|
Default: dict(type='BN').
|
||||||
|
act_cfg (dict): Config of activation layers.
|
||||||
|
Default: dict(type='ReLU').
|
||||||
|
init_cfg (dict or list[dict], optional): Initialization config dict.
|
||||||
|
Default: None.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self,
|
||||||
|
in_channels=(512, 1024, 2048),
|
||||||
|
mid_channels=512,
|
||||||
|
start_level=0,
|
||||||
|
end_level=-1,
|
||||||
|
dilations=(1, 2, 4, 8),
|
||||||
|
align_corners=False,
|
||||||
|
conv_cfg=None,
|
||||||
|
norm_cfg=dict(type='BN'),
|
||||||
|
act_cfg=dict(type='ReLU'),
|
||||||
|
init_cfg=None):
|
||||||
|
super(JPU, self).__init__(init_cfg=init_cfg)
|
||||||
|
assert isinstance(in_channels, tuple)
|
||||||
|
assert isinstance(dilations, tuple)
|
||||||
|
self.in_channels = in_channels
|
||||||
|
self.mid_channels = mid_channels
|
||||||
|
self.start_level = start_level
|
||||||
|
self.num_ins = len(in_channels)
|
||||||
|
if end_level == -1:
|
||||||
|
self.backbone_end_level = self.num_ins
|
||||||
|
else:
|
||||||
|
self.backbone_end_level = end_level
|
||||||
|
assert end_level <= len(in_channels)
|
||||||
|
|
||||||
|
self.dilations = dilations
|
||||||
|
self.align_corners = align_corners
|
||||||
|
|
||||||
|
self.conv_layers = nn.ModuleList()
|
||||||
|
self.dilation_layers = nn.ModuleList()
|
||||||
|
for i in range(self.start_level, self.backbone_end_level):
|
||||||
|
conv_layer = nn.Sequential(
|
||||||
|
ConvModule(
|
||||||
|
self.in_channels[i],
|
||||||
|
self.mid_channels,
|
||||||
|
kernel_size=3,
|
||||||
|
padding=1,
|
||||||
|
conv_cfg=conv_cfg,
|
||||||
|
norm_cfg=norm_cfg,
|
||||||
|
act_cfg=act_cfg))
|
||||||
|
self.conv_layers.append(conv_layer)
|
||||||
|
for i in range(len(dilations)):
|
||||||
|
dilation_layer = nn.Sequential(
|
||||||
|
DepthwiseSeparableConvModule(
|
||||||
|
in_channels=(self.backbone_end_level - self.start_level) *
|
||||||
|
self.mid_channels,
|
||||||
|
out_channels=self.mid_channels,
|
||||||
|
kernel_size=3,
|
||||||
|
stride=1,
|
||||||
|
padding=dilations[i],
|
||||||
|
dilation=dilations[i],
|
||||||
|
dw_norm_cfg=norm_cfg,
|
||||||
|
dw_act_cfg=None,
|
||||||
|
pw_norm_cfg=norm_cfg,
|
||||||
|
pw_act_cfg=act_cfg))
|
||||||
|
self.dilation_layers.append(dilation_layer)
|
||||||
|
|
||||||
|
def forward(self, inputs):
|
||||||
|
"""Forward function."""
|
||||||
|
assert len(inputs) == len(self.in_channels), 'Length of inputs must \
|
||||||
|
be the same with self.in_channels!'
|
||||||
|
|
||||||
|
feats = [
|
||||||
|
self.conv_layers[i - self.start_level](inputs[i])
|
||||||
|
for i in range(self.start_level, self.backbone_end_level)
|
||||||
|
]
|
||||||
|
|
||||||
|
h, w = feats[0].shape[2:]
|
||||||
|
for i in range(1, len(feats)):
|
||||||
|
feats[i] = resize(
|
||||||
|
feats[i],
|
||||||
|
size=(h, w),
|
||||||
|
mode='bilinear',
|
||||||
|
align_corners=self.align_corners)
|
||||||
|
|
||||||
|
feat = torch.cat(feats, dim=1)
|
||||||
|
concat_feat = torch.cat([
|
||||||
|
self.dilation_layers[i](feat) for i in range(len(self.dilations))
|
||||||
|
],
|
||||||
|
dim=1)
|
||||||
|
|
||||||
|
outs = []
|
||||||
|
|
||||||
|
# Default: outs[2] is the output of JPU for decoder head, outs[1] is
|
||||||
|
# the feature map from backbone for auxiliary head. Additionally,
|
||||||
|
# outs[0] can also be used for auxiliary head.
|
||||||
|
for i in range(self.start_level, self.backbone_end_level - 1):
|
||||||
|
outs.append(inputs[i])
|
||||||
|
outs.append(concat_feat)
|
||||||
|
return tuple(outs)
|
||||||
@ -13,6 +13,7 @@ Import:
|
|||||||
- configs/dpt/dpt.yml
|
- configs/dpt/dpt.yml
|
||||||
- configs/emanet/emanet.yml
|
- configs/emanet/emanet.yml
|
||||||
- configs/encnet/encnet.yml
|
- configs/encnet/encnet.yml
|
||||||
|
- configs/fastfcn/fastfcn.yml
|
||||||
- configs/fastscnn/fastscnn.yml
|
- configs/fastscnn/fastscnn.yml
|
||||||
- configs/fcn/fcn.yml
|
- configs/fcn/fcn.yml
|
||||||
- configs/fp16/fp16.yml
|
- configs/fp16/fp16.yml
|
||||||
|
|||||||
40
tests/test_models/test_necks/test_jpu.py
Normal file
40
tests/test_models/test_necks/test_jpu.py
Normal file
@ -0,0 +1,40 @@
|
|||||||
|
# Copyright (c) OpenMMLab. All rights reserved.
|
||||||
|
import pytest
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from mmseg.models.necks import JPU
|
||||||
|
|
||||||
|
|
||||||
|
def test_fastfcn_neck():
|
||||||
|
# Test FastFCN Standard Forward
|
||||||
|
model = JPU()
|
||||||
|
model.init_weights()
|
||||||
|
model.train()
|
||||||
|
batch_size = 1
|
||||||
|
input = [
|
||||||
|
torch.randn(batch_size, 512, 64, 128),
|
||||||
|
torch.randn(batch_size, 1024, 32, 64),
|
||||||
|
torch.randn(batch_size, 2048, 16, 32)
|
||||||
|
]
|
||||||
|
feat = model(input)
|
||||||
|
|
||||||
|
assert len(feat) == 3
|
||||||
|
assert feat[0].shape == torch.Size([batch_size, 512, 64, 128])
|
||||||
|
assert feat[1].shape == torch.Size([batch_size, 1024, 32, 64])
|
||||||
|
assert feat[2].shape == torch.Size([batch_size, 2048, 64, 128])
|
||||||
|
|
||||||
|
with pytest.raises(AssertionError):
|
||||||
|
# FastFCN input and in_channels constraints.
|
||||||
|
JPU(in_channels=(256, 512, 1024), start_level=0, end_level=5)
|
||||||
|
|
||||||
|
# Test not default start_level
|
||||||
|
model = JPU(in_channels=(512, 1024, 2048), start_level=1, end_level=-1)
|
||||||
|
input = [
|
||||||
|
torch.randn(batch_size, 512, 64, 128),
|
||||||
|
torch.randn(batch_size, 1024, 32, 64),
|
||||||
|
torch.randn(batch_size, 2048, 16, 32)
|
||||||
|
]
|
||||||
|
feat = model(input)
|
||||||
|
assert len(feat) == 2
|
||||||
|
assert feat[0].shape == torch.Size([batch_size, 1024, 32, 64])
|
||||||
|
assert feat[1].shape == torch.Size([batch_size, 2048, 32, 64])
|
||||||
Loading…
x
Reference in New Issue
Block a user