Support APCNet (#299)
* Support APCNet * code optimization * add apcnet configs * add benchmark * add readme and model zoo * fix docpull/313/head
Junjun2016
GitHub
parent
5c6e65759f
commit
e3f6f655d6
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@ -73,6 +73,7 @@ Supported methods:
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- [x] [EncNet](configs/encnet)
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- [x] [CCNet](configs/ccnet)
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- [x] [DANet](configs/danet)
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- [x] [APCNet](configs/apcnet)
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- [x] [GCNet](configs/gcnet)
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- [x] [ANN](configs/ann)
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- [x] [OCRNet](configs/ocrnet)
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@ -0,0 +1,44 @@
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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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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 2, 4),
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strides=(1, 2, 1, 1),
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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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decode_head=dict(
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type='APCHead',
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in_channels=2048,
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in_index=3,
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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=dict(type='SyncBN', requires_grad=True),
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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=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=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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@ -0,0 +1,37 @@
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# Adaptive Pyramid Context Network for Semantic Segmentation
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## Introduction
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```latex
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@InProceedings{He_2019_CVPR,
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author = {He, Junjun and Deng, Zhongying and Zhou, Lei and Wang, Yali and Qiao, Yu},
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title = {Adaptive Pyramid Context Network for Semantic Segmentation},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {June},
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year = {2019}
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}
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```
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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) | download |
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|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| APCNet | R-50-D8 | 512x1024 | 40000 | 7.7 | 3.57 | 78.02 | 79.26 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) |
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| APCNet | R-101-D8 | 512x1024 | 40000 | 11.2 | 2.15 | 79.08 | 80.34 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) |
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| APCNet | R-50-D8 | 769x769 | 40000 | 8.7 | 1.52 | 77.89 | 79.75 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) |
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| APCNet | R-101-D8 | 769x769 | 40000 | 12.7 | 1.03 | 77.96 | 79.24 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) |
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| APCNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.96 | 79.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) |
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| APCNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) |
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| APCNet | R-50-D8 | 769x769 | 80000 | - | - | 78.79 | 80.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) |
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| APCNet | R-101-D8 | 769x769 | 80000 | - | - | 78.45 | 79.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) |
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
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|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| APCNet | R-50-D8 | 512x512 | 80000 | 10.1 | 19.61 | 42.20 | 43.30 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) |
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| APCNet | R-101-D8 | 512x512 | 80000 | 13.6 | 13.10 | 45.54 | 46.65 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) |
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| APCNet | R-50-D8 | 512x512 | 160000 | - | - | 43.40 | 43.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) |
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| APCNet | R-101-D8 | 512x512 | 160000 | - | - | 45.41 | 46.63 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) |
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@ -0,0 +1,2 @@
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_base_ = './apcnet_r50-d8_512x1024_40k_cityscapes.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './apcnet_r50-d8_512x1024_80k_cityscapes.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './apcnet_r50-d8_512x512_160k_ade20k.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './apcnet_r50-d8_512x512_80k_ade20k.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './apcnet_r50-d8_769x769_40k_cityscapes.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = './apcnet_r50-d8_769x769_80k_cityscapes.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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_base_ = [
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'../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
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]
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_base_ = [
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'../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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_base_ = [
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'../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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model = dict(
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decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
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test_cfg = dict(mode='whole')
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_base_ = [
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'../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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model = dict(
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decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
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test_cfg = dict(mode='whole')
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_base_ = [
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'../_base_/models/apcnet_r50-d8.py',
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'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_40k.py'
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]
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model = dict(
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decode_head=dict(align_corners=True),
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auxiliary_head=dict(align_corners=True))
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test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
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_base_ = [
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'../_base_/models/apcnet_r50-d8.py',
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'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_80k.py'
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]
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model = dict(
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decode_head=dict(align_corners=True),
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auxiliary_head=dict(align_corners=True))
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test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
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@ -67,6 +67,10 @@ Please refer to [CCNet](https://github.com/open-mmlab/mmsegmentation/blob/master
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Please refer to [DANet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet) for details.
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### APCNet
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Please refer to [APCNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet) for details.
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### HRNet
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Please refer to [HRNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet) for details.
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@ -1,4 +1,5 @@
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from .ann_head import ANNHead
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from .apc_head import APCHead
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from .aspp_head import ASPPHead
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from .cc_head import CCHead
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from .da_head import DAHead
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@ -21,5 +22,5 @@ __all__ = [
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'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
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'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
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'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
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'PointHead'
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'PointHead', 'APCHead'
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]
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from mmcv.cnn import ConvModule
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from mmseg.ops import resize
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from ..builder import HEADS
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from .decode_head import BaseDecodeHead
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class ACM(nn.Module):
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"""Adaptive Context Module used in APCNet.
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Args:
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pool_scale (int): Pooling scale used in Adaptive Context
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Module to extract region fetures.
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fusion (bool): Add one conv to fuse residual feature.
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in_channels (int): Input channels.
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channels (int): Channels after modules, before conv_seg.
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conv_cfg (dict | None): Config of conv layers.
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norm_cfg (dict | None): Config of norm layers.
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act_cfg (dict): Config of activation layers.
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"""
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def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
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norm_cfg, act_cfg):
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super(ACM, self).__init__()
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self.pool_scale = pool_scale
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self.fusion = fusion
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self.in_channels = in_channels
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self.channels = channels
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.act_cfg = act_cfg
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self.pooled_redu_conv = ConvModule(
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self.in_channels,
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self.channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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self.input_redu_conv = ConvModule(
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self.in_channels,
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self.channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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self.global_info = ConvModule(
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self.channels,
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self.channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0)
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self.residual_conv = ConvModule(
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self.channels,
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self.channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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if self.fusion:
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self.fusion_conv = ConvModule(
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self.channels,
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self.channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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def forward(self, x):
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"""Forward function."""
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pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale)
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# [batch_size, channels, h, w]
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x = self.input_redu_conv(x)
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# [batch_size, channels, pool_scale, pool_scale]
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pooled_x = self.pooled_redu_conv(pooled_x)
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batch_size = x.size(0)
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# [batch_size, pool_scale * pool_scale, channels]
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pooled_x = pooled_x.view(batch_size, self.channels,
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-1).permute(0, 2, 1).contiguous()
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# [batch_size, h * w, pool_scale * pool_scale]
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affinity_matrix = self.gla(x + resize(
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self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:])
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).permute(0, 2, 3, 1).reshape(
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batch_size, -1, self.pool_scale**2)
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affinity_matrix = F.sigmoid(affinity_matrix)
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# [batch_size, h * w, channels]
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z_out = torch.matmul(affinity_matrix, pooled_x)
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# [batch_size, channels, h * w]
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z_out = z_out.permute(0, 2, 1).contiguous()
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# [batch_size, channels, h, w]
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z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3))
|
||||
z_out = self.residual_conv(z_out)
|
||||
z_out = F.relu(z_out + x)
|
||||
if self.fusion:
|
||||
z_out = self.fusion_conv(z_out)
|
||||
|
||||
return z_out
|
||||
|
||||
|
||||
@HEADS.register_module()
|
||||
class APCHead(BaseDecodeHead):
|
||||
"""Adaptive Pyramid Context Network for Semantic Segmentation.
|
||||
|
||||
This head is the implementation of
|
||||
`APCNet <https://openaccess.thecvf.com/content_CVPR_2019/papers/\
|
||||
He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_\
|
||||
CVPR_2019_paper.pdf>`_.
|
||||
|
||||
Args:
|
||||
pool_scales (tuple[int]): Pooling scales used in Adaptive Context
|
||||
Module. Default: (1, 2, 3, 6).
|
||||
fusion (bool): Add one conv to fuse residual feature.
|
||||
"""
|
||||
|
||||
def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
|
||||
super(APCHead, self).__init__(**kwargs)
|
||||
assert isinstance(pool_scales, (list, tuple))
|
||||
self.pool_scales = pool_scales
|
||||
self.fusion = fusion
|
||||
acm_modules = []
|
||||
for pool_scale in self.pool_scales:
|
||||
acm_modules.append(
|
||||
ACM(pool_scale,
|
||||
self.fusion,
|
||||
self.in_channels,
|
||||
self.channels,
|
||||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
act_cfg=self.act_cfg))
|
||||
self.acm_modules = nn.ModuleList(acm_modules)
|
||||
self.bottleneck = ConvModule(
|
||||
self.in_channels + len(pool_scales) * self.channels,
|
||||
self.channels,
|
||||
3,
|
||||
padding=1,
|
||||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
act_cfg=self.act_cfg)
|
||||
|
||||
def forward(self, inputs):
|
||||
"""Forward function."""
|
||||
x = self._transform_inputs(inputs)
|
||||
acm_outs = [x]
|
||||
for acm_module in self.acm_modules:
|
||||
acm_outs.append(acm_module(x))
|
||||
acm_outs = torch.cat(acm_outs, dim=1)
|
||||
output = self.bottleneck(acm_outs)
|
||||
output = self.cls_seg(output)
|
||||
return output
|
||||
|
|
@ -6,8 +6,8 @@ from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
|
|||
from mmcv.utils import ConfigDict
|
||||
from mmcv.utils.parrots_wrapper import SyncBatchNorm
|
||||
|
||||
from mmseg.models.decode_heads import (ANNHead, ASPPHead, CCHead, DAHead,
|
||||
DepthwiseSeparableASPPHead,
|
||||
from mmseg.models.decode_heads import (ANNHead, APCHead, ASPPHead, CCHead,
|
||||
DAHead, DepthwiseSeparableASPPHead,
|
||||
DepthwiseSeparableFCNHead, DNLHead,
|
||||
EMAHead, EncHead, FCNHead, GCHead,
|
||||
NLHead, OCRHead, PointHead, PSAHead,
|
||||
|
|
@ -223,6 +223,59 @@ def test_psp_head():
|
|||
assert outputs.shape == (1, head.num_classes, 45, 45)
|
||||
|
||||
|
||||
def test_apc_head():
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
# pool_scales must be list|tuple
|
||||
APCHead(in_channels=32, channels=16, num_classes=19, pool_scales=1)
|
||||
|
||||
# test no norm_cfg
|
||||
head = APCHead(in_channels=32, channels=16, num_classes=19)
|
||||
assert not _conv_has_norm(head, sync_bn=False)
|
||||
|
||||
# test with norm_cfg
|
||||
head = APCHead(
|
||||
in_channels=32,
|
||||
channels=16,
|
||||
num_classes=19,
|
||||
norm_cfg=dict(type='SyncBN'))
|
||||
assert _conv_has_norm(head, sync_bn=True)
|
||||
|
||||
# fusion=True
|
||||
inputs = [torch.randn(1, 32, 45, 45)]
|
||||
head = APCHead(
|
||||
in_channels=32,
|
||||
channels=16,
|
||||
num_classes=19,
|
||||
pool_scales=(1, 2, 3),
|
||||
fusion=True)
|
||||
if torch.cuda.is_available():
|
||||
head, inputs = to_cuda(head, inputs)
|
||||
assert head.fusion is True
|
||||
assert head.acm_modules[0].pool_scale == 1
|
||||
assert head.acm_modules[1].pool_scale == 2
|
||||
assert head.acm_modules[2].pool_scale == 3
|
||||
outputs = head(inputs)
|
||||
assert outputs.shape == (1, head.num_classes, 45, 45)
|
||||
|
||||
# fusion=False
|
||||
inputs = [torch.randn(1, 32, 45, 45)]
|
||||
head = APCHead(
|
||||
in_channels=32,
|
||||
channels=16,
|
||||
num_classes=19,
|
||||
pool_scales=(1, 2, 3),
|
||||
fusion=False)
|
||||
if torch.cuda.is_available():
|
||||
head, inputs = to_cuda(head, inputs)
|
||||
assert head.fusion is False
|
||||
assert head.acm_modules[0].pool_scale == 1
|
||||
assert head.acm_modules[1].pool_scale == 2
|
||||
assert head.acm_modules[2].pool_scale == 3
|
||||
outputs = head(inputs)
|
||||
assert outputs.shape == (1, head.num_classes, 45, 45)
|
||||
|
||||
|
||||
def test_aspp_head():
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
|
|
|
|||
Loading…
Reference in New Issue