import torch import torch.nn as nn from annotator.mmpkg.mmcv.cnn import ConvModule, build_upsample_layer class UpConvBlock(nn.Module): """Upsample convolution block in decoder for UNet. This upsample convolution block consists of one upsample module followed by one convolution block. The upsample module expands the high-level low-resolution feature map and the convolution block fuses the upsampled high-level low-resolution feature map and the low-level high-resolution feature map from encoder. Args: conv_block (nn.Sequential): Sequential of convolutional layers. in_channels (int): Number of input channels of the high-level skip_channels (int): Number of input channels of the low-level high-resolution feature map from encoder. out_channels (int): Number of output channels. num_convs (int): Number of convolutional layers in the conv_block. Default: 2. stride (int): Stride of convolutional layer in conv_block. Default: 1. dilation (int): Dilation rate of convolutional layer in conv_block. Default: 1. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. conv_cfg (dict | None): Config dict for convolution layer. Default: None. norm_cfg (dict | None): Config dict for normalization layer. Default: dict(type='BN'). act_cfg (dict | None): Config dict for activation layer in ConvModule. Default: dict(type='ReLU'). upsample_cfg (dict): The upsample config of the upsample module in decoder. Default: dict(type='InterpConv'). If the size of high-level feature map is the same as that of skip feature map (low-level feature map from encoder), it does not need upsample the high-level feature map and the upsample_cfg is None. dcn (bool): Use deformable convolution in convolutional layer or not. Default: None. plugins (dict): plugins for convolutional layers. Default: None. """ def __init__(self, conv_block, in_channels, skip_channels, out_channels, num_convs=2, stride=1, dilation=1, with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), upsample_cfg=dict(type='InterpConv'), dcn=None, plugins=None): super(UpConvBlock, self).__init__() assert dcn is None, 'Not implemented yet.' assert plugins is None, 'Not implemented yet.' self.conv_block = conv_block( in_channels=2 * skip_channels, out_channels=out_channels, num_convs=num_convs, stride=stride, dilation=dilation, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg, dcn=None, plugins=None) if upsample_cfg is not None: self.upsample = build_upsample_layer( cfg=upsample_cfg, in_channels=in_channels, out_channels=skip_channels, with_cp=with_cp, norm_cfg=norm_cfg, act_cfg=act_cfg) else: self.upsample = ConvModule( in_channels, skip_channels, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) def forward(self, skip, x): """Forward function.""" x = self.upsample(x) out = torch.cat([skip, x], dim=1) out = self.conv_block(out) return out