diff --git a/README.md b/README.md index ba9184c90a..7b16a636b8 100644 --- 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) diff --git a/configs/_base_/models/dmnet_r50-d8.py b/configs/_base_/models/dmnet_r50-d8.py new file mode 100644 index 0000000000..329c4fe8c2 --- /dev/null +++ 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') diff --git a/configs/dmnet/README.md b/configs/dmnet/README.md new file mode 100644 index 0000000000..64c4e65722 --- /dev/null +++ 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) | [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) | diff --git a/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000..fd6897691d --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000..116cbdcede --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py b/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000..d78d46c040 --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py b/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000..9713b731a4 --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000..6b222e7300 --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000..f36d490e9c --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000..1f9a917fa4 --- /dev/null +++ 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' +] diff --git a/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000..1b38f90dc4 --- /dev/null +++ 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' +] diff --git a/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py b/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000..69f4165c7c --- /dev/null +++ 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') diff --git a/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py b/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000..513f58cbe4 --- /dev/null +++ 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') diff --git a/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000..49db4da110 --- /dev/null +++ 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)) diff --git a/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000..1cf136e110 --- /dev/null +++ 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)) diff --git a/docs/model_zoo.md b/docs/model_zoo.md index c130baf6a1..fe132f39b2 100644 --- 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. diff --git a/mmseg/models/decode_heads/__init__.py b/mmseg/models/decode_heads/__init__.py index 1ac8c1ae31..44ff80feab 100644 --- a/mmseg/models/decode_heads/__init__.py +++ b/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' ] diff --git a/mmseg/models/decode_heads/dm_head.py b/mmseg/models/decode_heads/dm_head.py new file mode 100644 index 0000000000..1c918fc35d --- /dev/null +++ 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 `_. + + 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 diff --git a/tests/test_models/test_heads.py b/tests/test_models/test_heads.py index 5a8ab74637..612da300f7 100644 --- a/tests/test_models/test_heads.py +++ b/tests/test_models/test_heads.py @@ -8,10 +8,10 @@ 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):