Support DMNet (#313)

* Support DMNet

* fix doc and delete norm_name

README.md

@@ -75,6 +75,7 @@ Supported methods:
 - [x] [DANet](configs/danet)
 - [x] [APCNet](configs/apcnet)
 - [x] [GCNet](configs/gcnet)
+- [x] [DMNet](configs/dmnet)
 - [x] [ANN](configs/ann)
 - [x] [OCRNet](configs/ocrnet)
 - [x] [Fast-SCNN](configs/fastscnn)

configs/_base_/models/dmnet_r50-d8.py

@@ -0,0 +1,44 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+ type='EncoderDecoder',
+ pretrained='open-mmlab://resnet50_v1c',
+ backbone=dict(
+ type='ResNetV1c',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ dilations=(1, 1, 2, 4),
+ strides=(1, 2, 1, 1),
+ norm_cfg=norm_cfg,
+ norm_eval=False,
+ style='pytorch',
+ contract_dilation=True),
+ decode_head=dict(
+ type='DMHead',
+ in_channels=2048,
+ in_index=3,
+ channels=512,
+ filter_sizes=(1, 3, 5, 7),
+ dropout_ratio=0.1,
+ num_classes=19,
+ norm_cfg=dict(type='SyncBN', requires_grad=True),
+ align_corners=False,
+ loss_decode=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+ auxiliary_head=dict(
+ type='FCNHead',
+ in_channels=1024,
+ in_index=2,
+ channels=256,
+ num_convs=1,
+ concat_input=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=0.4)))
+# model training and testing settings
+train_cfg = dict()
+test_cfg = dict(mode='whole')

configs/dmnet/README.md

@@ -0,0 +1,37 @@
+# Dynamic Multi-scale Filters for Semantic Segmentation
+
+## Introduction
+
+```latex
+@InProceedings{He_2019_ICCV,
+author = {He, Junjun and Deng, Zhongying and Qiao, Yu},
+title = {Dynamic Multi-Scale Filters for Semantic Segmentation},
+booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+month = {October},
+year = {2019}
+}
+```
+
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
+|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| DMNet | R-50-D8 | 512x1024 | 40000 | 7.0 | 3.66 | 77.78 | 79.14 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) |
+| DMNet | R-101-D8 | 512x1024 | 40000 | 10.6 | 2.54 | 78.37 | 79.72 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) |
+| DMNet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.57 | 78.49 | 80.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) |
+| DMNet | R-101-D8 | 769x769 | 40000 | 12.0 | 1.01 | 77.62 | 78.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) |
+| DMNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.07 | 80.22 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) |
+| DMNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.67 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) |
+| DMNet | R-50-D8 | 769x769 | 80000 | - | - | 79.22 | 80.55 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) |
+| DMNet | R-101-D8 | 769x769 | 80000 | - | - | 79.19 | 80.65 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
+|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| DMNet | R-50-D8 | 512x512 | 80000 | 9.4 | 20.95 | 42.37 | 43.62 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) |
+| DMNet | R-101-D8 | 512x512 | 80000 | 13.0 | 13.88 | 45.34 | 46.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) |
+| DMNet | R-50-D8 | 512x512 | 160000 | - | - | 43.15 | 44.17 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) |
+| DMNet | R-101-D8 | 512x512 | 160000 | - | - | 45.42 | 46.76 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) |

configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './dmnet_r50-d8_512x1024_40k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './dmnet_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './dmnet_r50-d8_512x512_160k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './dmnet_r50-d8_512x512_80k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './dmnet_r50-d8_769x769_40k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './dmnet_r50-d8_769x769_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py

@@ -0,0 +1,4 @@
+_base_ = [
+ '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+ '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]

configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,4 @@
+_base_ = [
+ '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+ '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]

configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py

@@ -0,0 +1,7 @@
+_base_ = [
+ '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
+ '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+model = dict(
+ decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
+test_cfg = dict(mode='whole')

configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py

@@ -0,0 +1,7 @@
+_base_ = [
+ '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
+ '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+ decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
+test_cfg = dict(mode='whole')

configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py

@@ -0,0 +1,9 @@
+_base_ = [
+ '../_base_/models/dmnet_r50-d8.py',
+ '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
+ '../_base_/schedules/schedule_40k.py'
+]
+model = dict(
+ decode_head=dict(align_corners=True),
+ auxiliary_head=dict(align_corners=True))
+test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))

configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py

@@ -0,0 +1,9 @@
+_base_ = [
+ '../_base_/models/dmnet_r50-d8.py',
+ '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
+ '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+ decode_head=dict(align_corners=True),
+ auxiliary_head=dict(align_corners=True))
+test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))

docs/model_zoo.md

@@ -79,6 +79,10 @@ Please refer to [HRNet](https://github.com/open-mmlab/mmsegmentation/blob/master
 
 Please refer to [GCNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet) for details.
 
+### DMNet
+
+Please refer to [DMNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet) for details.
+
 ### ANN
 
 Please refer to [ANN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann) for details.

mmseg/models/decode_heads/__init__.py

@@ -3,6 +3,7 @@
 from .aspp_head import ASPPHead
 from .cc_head import CCHead
 from .da_head import DAHead
+from .dm_head import DMHead
 from .dnl_head import DNLHead
 from .ema_head import EMAHead
 from .enc_head import EncHead
@@ -22,5 +23,5 @@
  'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
  'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
  'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
- 'PointHead', 'APCHead'
+ 'PointHead', 'APCHead', 'DMHead'
 ]

mmseg/models/decode_heads/dm_head.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
+
+from ..builder import HEADS
+from .decode_head import BaseDecodeHead
+
+
+class DCM(nn.Module):
+ """Dynamic Convolutional Module used in DMNet.
+
+ Args:
+ filter_size (int): The filter size of generated convolution kernel
+ used in Dynamic Convolutional Module.
+ fusion (bool): Add one conv to fuse DCM output feature.
+ in_channels (int): Input channels.
+ channels (int): Channels after modules, before conv_seg.
+ conv_cfg (dict | None): Config of conv layers.
+ norm_cfg (dict | None): Config of norm layers.
+ act_cfg (dict): Config of activation layers.
+ """
+
+ def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
+ norm_cfg, act_cfg):
+ super(DCM, self).__init__()
+ self.filter_size = filter_size
+ self.fusion = fusion
+ self.in_channels = in_channels
+ self.channels = channels
+ self.conv_cfg = conv_cfg
+ self.norm_cfg = norm_cfg
+ self.act_cfg = act_cfg
+ self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1,
+ 0)
+
+ self.input_redu_conv = ConvModule(
+ self.in_channels,
+ self.channels,
+ 1,
+ conv_cfg=self.conv_cfg,
+ norm_cfg=self.norm_cfg,
+ act_cfg=self.act_cfg)
+
+ if self.norm_cfg is not None:
+ self.norm = build_norm_layer(self.norm_cfg, self.channels)[1]
+ else:
+ self.norm = None
+ self.activate = build_activation_layer(self.act_cfg)
+
+ if self.fusion:
+ self.fusion_conv = ConvModule(
+ self.channels,
+ self.channels,
+ 1,
+ conv_cfg=self.conv_cfg,
+ norm_cfg=self.norm_cfg,
+ act_cfg=self.act_cfg)
+
+ def forward(self, x):
+ """Forward function."""
+ generted_filter = self.filter_gen_conv(
+ F.adaptive_avg_pool2d(x, self.filter_size))
+ x = self.input_redu_conv(x)
+ b, c, h, w = x.shape
+ # [1, b * c, h, w], c = self.channels
+ x = x.view(1, b * c, h, w)
+ # [b * c, 1, filter_size, filter_size]
+ generted_filter = generted_filter.view(b * c, 1, self.filter_size,
+ self.filter_size)
+ pad = (self.filter_size - 1) // 2
+ if (self.filter_size - 1) % 2 == 0:
+ p2d = (pad, pad, pad, pad)
+ else:
+ p2d = (pad + 1, pad, pad + 1, pad)
+ x = F.pad(input=x, pad=p2d, mode='constant', value=0)
+ # [1, b * c, h, w]
+ output = F.conv2d(input=x, weight=generted_filter, groups=b * c)
+ # [b, c, h, w]
+ output = output.view(b, c, h, w)
+ if self.norm is not None:
+ output = self.norm(output)
+ output = self.activate(output)
+
+ if self.fusion:
+ output = self.fusion_conv(output)
+
+ return output
+
+
+@HEADS.register_module()
+class DMHead(BaseDecodeHead):
+ """Dynamic Multi-scale Filters for Semantic Segmentation.
+
+ This head is the implementation of
+ `DMNet <https://openaccess.thecvf.com/content_ICCV_2019/papers/\
+ He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_\
+ ICCV_2019_paper.pdf>`_.
+
+ Args:
+ filter_sizes (tuple[int]): The size of generated convolutional filters
+ used in Dynamic Convolutional Module. Default: (1, 3, 5, 7).
+ fusion (bool): Add one conv to fuse DCM output feature.
+ """
+
+ def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
+ super(DMHead, self).__init__(**kwargs)
+ assert isinstance(filter_sizes, (list, tuple))
+ self.filter_sizes = filter_sizes
+ self.fusion = fusion
+ dcm_modules = []
+ for filter_size in self.filter_sizes:
+ dcm_modules.append(
+ DCM(filter_size,
+ self.fusion,
+ self.in_channels,
+ self.channels,
+ conv_cfg=self.conv_cfg,
+ norm_cfg=self.norm_cfg,
+ act_cfg=self.act_cfg))
+ self.dcm_modules = nn.ModuleList(dcm_modules)
+ self.bottleneck = ConvModule(
+ self.in_channels + len(filter_sizes) * 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)
+ dcm_outs = [x]
+ for dcm_module in self.dcm_modules:
+ dcm_outs.append(dcm_module(x))
+ dcm_outs = torch.cat(dcm_outs, dim=1)
+ output = self.bottleneck(dcm_outs)
+ output = self.cls_seg(output)
+ return output