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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from annotator.uniformer.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.""" | |
| generated_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] | |
| generated_filter = generated_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=generated_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 | |
| 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 | |