├── images └── dcm.png ├── dm_head.py ├── README.md └── LICENSE /images/dcm.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junjun2016/DMNet/HEAD/images/dcm.png -------------------------------------------------------------------------------- /dm_head.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer 5 | 6 | from ..builder import HEADS 7 | from .decode_head import BaseDecodeHead 8 | 9 | 10 | class DCM(nn.Module): 11 | """Dynamic Convolutional Module used in DMNet. 12 | 13 | Args: 14 | filter_size (int): The filter size of generated convolution kernel 15 | used in Dynamic Convolutional Module. 16 | fusion (bool): Add one conv to fuse DCM output feature. 17 | in_channels (int): Input channels. 18 | channels (int): Channels after modules, before conv_seg. 19 | conv_cfg (dict | None): Config of conv layers. 20 | norm_cfg (dict | None): Config of norm layers. 21 | act_cfg (dict): Config of activation layers. 22 | """ 23 | 24 | def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg, 25 | norm_cfg, act_cfg): 26 | super(DCM, self).__init__() 27 | self.filter_size = filter_size 28 | self.fusion = fusion 29 | self.in_channels = in_channels 30 | self.channels = channels 31 | self.conv_cfg = conv_cfg 32 | self.norm_cfg = norm_cfg 33 | self.act_cfg = act_cfg 34 | self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1, 35 | 0) 36 | 37 | self.input_redu_conv = ConvModule( 38 | self.in_channels, 39 | self.channels, 40 | 1, 41 | conv_cfg=self.conv_cfg, 42 | norm_cfg=self.norm_cfg, 43 | act_cfg=self.act_cfg) 44 | 45 | if self.norm_cfg is not None: 46 | self.norm = build_norm_layer(self.norm_cfg, self.channels)[1] 47 | else: 48 | self.norm = None 49 | self.activate = build_activation_layer(self.act_cfg) 50 | 51 | if self.fusion: 52 | self.fusion_conv = ConvModule( 53 | self.channels, 54 | self.channels, 55 | 1, 56 | conv_cfg=self.conv_cfg, 57 | norm_cfg=self.norm_cfg, 58 | act_cfg=self.act_cfg) 59 | 60 | def forward(self, x): 61 | """Forward function.""" 62 | generated_filter = self.filter_gen_conv( 63 | F.adaptive_avg_pool2d(x, self.filter_size)) 64 | x = self.input_redu_conv(x) 65 | b, c, h, w = x.shape 66 | # [1, b * c, h, w], c = self.channels 67 | x = x.view(1, b * c, h, w) 68 | # [b * c, 1, filter_size, filter_size] 69 | generated_filter = generated_filter.view(b * c, 1, self.filter_size, 70 | self.filter_size) 71 | pad = (self.filter_size - 1) // 2 72 | if (self.filter_size - 1) % 2 == 0: 73 | p2d = (pad, pad, pad, pad) 74 | else: 75 | p2d = (pad + 1, pad, pad + 1, pad) 76 | x = F.pad(input=x, pad=p2d, mode='constant', value=0) 77 | # [1, b * c, h, w] 78 | output = F.conv2d(input=x, weight=generated_filter, groups=b * c) 79 | # [b, c, h, w] 80 | output = output.view(b, c, h, w) 81 | if self.norm is not None: 82 | output = self.norm(output) 83 | output = self.activate(output) 84 | 85 | if self.fusion: 86 | output = self.fusion_conv(output) 87 | 88 | return output 89 | 90 | 91 | @HEADS.register_module() 92 | class DMHead(BaseDecodeHead): 93 | """Dynamic Multi-scale Filters for Semantic Segmentation. 94 | 95 | This head is the implementation of 96 | `DMNet `_. 99 | 100 | Args: 101 | filter_sizes (tuple[int]): The size of generated convolutional filters 102 | used in Dynamic Convolutional Module. Default: (1, 3, 5, 7). 103 | fusion (bool): Add one conv to fuse DCM output feature. 104 | """ 105 | 106 | def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs): 107 | super(DMHead, self).__init__(**kwargs) 108 | assert isinstance(filter_sizes, (list, tuple)) 109 | self.filter_sizes = filter_sizes 110 | self.fusion = fusion 111 | dcm_modules = [] 112 | for filter_size in self.filter_sizes: 113 | dcm_modules.append( 114 | DCM(filter_size, 115 | self.fusion, 116 | self.in_channels, 117 | self.channels, 118 | conv_cfg=self.conv_cfg, 119 | norm_cfg=self.norm_cfg, 120 | act_cfg=self.act_cfg)) 121 | self.dcm_modules = nn.ModuleList(dcm_modules) 122 | self.bottleneck = ConvModule( 123 | self.in_channels + len(filter_sizes) * self.channels, 124 | self.channels, 125 | 3, 126 | padding=1, 127 | conv_cfg=self.conv_cfg, 128 | norm_cfg=self.norm_cfg, 129 | act_cfg=self.act_cfg) 130 | 131 | def forward(self, inputs): 132 | """Forward function.""" 133 | x = self._transform_inputs(inputs) 134 | dcm_outs = [x] 135 | for dcm_module in self.dcm_modules: 136 | dcm_outs.append(dcm_module(x)) 137 | dcm_outs = torch.cat(dcm_outs, dim=1) 138 | output = self.bottleneck(dcm_outs) 139 | output = self.cls_seg(output) 140 | return output 141 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019) 2 | 3 | ## Introduction 4 | 5 | Official implementation of **Dynamic Multi-scale Filters for Semantic Segmentation** ([Paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf)). 6 | 🔥🔥 DMNet is on [MMsegmentation](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/dmnet). 🔥🔥 7 | 8 | 9 | 10 | ```latex 11 | @InProceedings{He_2019_ICCV, 12 | author = {He, Junjun and Deng, Zhongying and Qiao, Yu}, 13 | title = {Dynamic Multi-Scale Filters for Semantic Segmentation}, 14 | booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, 15 | month = {October}, 16 | year = {2019} 17 | } 18 | ``` 19 | 20 | ## Overview 21 | 22 | ### Framework 23 | 24 | ![image](images/dcm.png) 25 | 26 | ## Results and models 27 | 28 | ### Cityscapes 29 | 30 | | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | 31 | | ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | 32 | | DMNet | R-50-D8 | 512x1024 | 40000 | 7.0 | 3.66 | 77.78 | 79.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201215_042326.log.json) | 33 | | DMNet | R-101-D8 | 512x1024 | 40000 | 10.6 | 2.54 | 78.37 | 79.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201215_043100.log.json) | 34 | | DMNet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.57 | 78.49 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201215_093706.log.json) | 35 | | DMNet | R-101-D8 | 769x769 | 40000 | 12.0 | 1.01 | 77.62 | 78.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201215_081348.log.json) | 36 | | DMNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.07 | 80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201215_053728.log.json) | 37 | | DMNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201215_031718.log.json) | 38 | | DMNet | R-50-D8 | 769x769 | 80000 | - | - | 79.22 | 80.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201215_034006.log.json) | 39 | | DMNet | R-101-D8 | 769x769 | 80000 | - | - | 79.19 | 80.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201215_082810.log.json) | 40 | 41 | ### ADE20K 42 | 43 | | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | 44 | | ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 45 | | DMNet | R-50-D8 | 512x512 | 80000 | 9.4 | 20.95 | 42.37 | 43.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201215_144744.log.json) | 46 | | DMNet | R-101-D8 | 512x512 | 80000 | 13.0 | 13.88 | 45.34 | 46.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201215_104812.log.json) | 47 | | DMNet | R-50-D8 | 512x512 | 160000 | - | - | 43.15 | 44.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201215_115313.log.json) | 48 | | DMNet | R-101-D8 | 512x512 | 160000 | - | - | 45.42 | 46.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201215_111145.log.json) | 49 | -------------------------------------------------------------------------------- /LICENSE: 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