| import torch |
| import torch.nn as nn |
| from mmcv.cnn import ConvModule |
|
|
| from mmseg.ops import resize |
| from ..builder import HEADS |
| from .decode_head import BaseDecodeHead |
|
|
|
|
| class ASPPModule(nn.ModuleList): |
| """Atrous Spatial Pyramid Pooling (ASPP) Module. |
| |
| Args: |
| dilations (tuple[int]): Dilation rate of each layer. |
| 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, dilations, in_channels, channels, conv_cfg, norm_cfg, |
| act_cfg): |
| super(ASPPModule, self).__init__() |
| self.dilations = dilations |
| self.in_channels = in_channels |
| self.channels = channels |
| self.conv_cfg = conv_cfg |
| self.norm_cfg = norm_cfg |
| self.act_cfg = act_cfg |
| for dilation in dilations: |
| self.append( |
| ConvModule( |
| self.in_channels, |
| self.channels, |
| 1 if dilation == 1 else 3, |
| dilation=dilation, |
| padding=0 if dilation == 1 else dilation, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg)) |
|
|
| def forward(self, x): |
| """Forward function.""" |
| aspp_outs = [] |
| for aspp_module in self: |
| aspp_outs.append(aspp_module(x)) |
|
|
| return aspp_outs |
|
|
|
|
| @HEADS.register_module() |
| class ASPPHead(BaseDecodeHead): |
| """Rethinking Atrous Convolution for Semantic Image Segmentation. |
| |
| This head is the implementation of `DeepLabV3 |
| <https://arxiv.org/abs/1706.05587>`_. |
| |
| Args: |
| dilations (tuple[int]): Dilation rates for ASPP module. |
| Default: (1, 6, 12, 18). |
| """ |
|
|
| def __init__(self, dilations=(1, 6, 12, 18), **kwargs): |
| super(ASPPHead, self).__init__(**kwargs) |
| assert isinstance(dilations, (list, tuple)) |
| self.dilations = dilations |
| self.image_pool = nn.Sequential( |
| nn.AdaptiveAvgPool2d(1), |
| ConvModule( |
| self.in_channels, |
| self.channels, |
| 1, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg)) |
| self.aspp_modules = ASPPModule( |
| dilations, |
| self.in_channels, |
| self.channels, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg) |
| self.bottleneck = ConvModule( |
| (len(dilations) + 1) * 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) |
| aspp_outs = [ |
| resize( |
| self.image_pool(x), |
| size=x.size()[2:], |
| mode='bilinear', |
| align_corners=self.align_corners) |
| ] |
| aspp_outs.extend(self.aspp_modules(x)) |
| aspp_outs = torch.cat(aspp_outs, dim=1) |
| output = self.bottleneck(aspp_outs) |
| output = self.cls_seg(output) |
| return output |
|
|
