Support DMNet (#313)

* Support DMNet * fix doc and delete norm_name
2025-06-03 22:03:48 +08:00 · 2020-12-24 14:16:34 +08:00 · 2020-12-24 14:16:34 +08:00 · 94e12e8d21
commit 94e12e8d21
parent feefc6a9de
19 changed files with 337 additions and 5 deletions
--- a/README.md
+++ b/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)
--- a/configs/_base_/models/dmnet_r50-d8.py
+++ b/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')
--- a/configs/dmnet/README.md
+++ b/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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) |
--- a/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
+++ b/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))
--- a/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
+++ b/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))
--- a/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
+++ b/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))
--- a/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
+++ b/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))
--- a/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
+++ b/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))
--- a/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
+++ b/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))
--- a/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
+++ b/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'
+]
--- a/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
+++ b/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'
+]
--- a/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
+++ b/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')
--- a/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
+++ b/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')
--- a/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
+++ b/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))
--- a/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
+++ b/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))
--- a/docs/model_zoo.md
+++ b/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.
--- a/mmseg/models/decode_heads/init.py
+++ b/mmseg/models/decode_heads/init.py
@ -3,6 +3,7 @@ from .apc_head import APCHead
 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 @@ __all__ = [
    'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
    'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
    'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
-    'PointHead', 'APCHead'
+    'PointHead', 'APCHead', 'DMHead'
 ]
--- a/mmseg/models/decode_heads/dm_head.py
+++ b/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
--- a/tests/test_models/test_heads.py
+++ b/tests/test_models/test_heads.py
@ -8,10 +8,10 @@ from mmcv.utils.parrots_wrapper import SyncBatchNorm

 from mmseg.models.decode_heads import (ANNHead, APCHead, ASPPHead, CCHead,
                                       DAHead, DepthwiseSeparableASPPHead,
-                                       DepthwiseSeparableFCNHead, DNLHead,
-                                       EMAHead, EncHead, FCNHead, GCHead,
-                                       NLHead, OCRHead, PointHead, PSAHead,
-                                       PSPHead, UPerHead)
+                                       DepthwiseSeparableFCNHead, DMHead,
+                                       DNLHead, EMAHead, EncHead, FCNHead,
+                                       GCHead, NLHead, OCRHead, PointHead,
+                                       PSAHead, PSPHead, UPerHead)
 from mmseg.models.decode_heads.decode_head import BaseDecodeHead


@ -276,6 +276,59 @@ def test_apc_head():
    assert outputs.shape == (1, head.num_classes, 45, 45)


+def test_dm_head():
+
+    with pytest.raises(AssertionError):
+        # filter_sizes must be list|tuple
+        DMHead(in_channels=32, channels=16, num_classes=19, filter_sizes=1)
+
+    # test no norm_cfg
+    head = DMHead(in_channels=32, channels=16, num_classes=19)
+    assert not _conv_has_norm(head, sync_bn=False)
+
+    # test with norm_cfg
+    head = DMHead(
+        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 = DMHead(
+        in_channels=32,
+        channels=16,
+        num_classes=19,
+        filter_sizes=(1, 3, 5),
+        fusion=True)
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+    assert head.fusion is True
+    assert head.dcm_modules[0].filter_size == 1
+    assert head.dcm_modules[1].filter_size == 3
+    assert head.dcm_modules[2].filter_size == 5
+    outputs = head(inputs)
+    assert outputs.shape == (1, head.num_classes, 45, 45)
+
+    # fusion=False
+    inputs = [torch.randn(1, 32, 45, 45)]
+    head = DMHead(
+        in_channels=32,
+        channels=16,
+        num_classes=19,
+        filter_sizes=(1, 3, 5),
+        fusion=False)
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(head, inputs)
+    assert head.fusion is False
+    assert head.dcm_modules[0].filter_size == 1
+    assert head.dcm_modules[1].filter_size == 3
+    assert head.dcm_modules[2].filter_size == 5
+    outputs = head(inputs)
+    assert outputs.shape == (1, head.num_classes, 45, 45)
+
+
 def test_aspp_head():

    with pytest.raises(AssertionError):