import torch import torch.nn as nn import torch.nn.functional as F from models.modules.deform_conv import DeformableConv2d from config import Config config = Config() class ASPPComplex(nn.Module): def __init__(self, in_channels=64, out_channels=None, output_stride=16): super(ASPPComplex, self).__init__() self.down_scale = 1 if out_channels is None: out_channels = in_channels self.in_channelster = 256 // self.down_scale if output_stride == 16: dilations = [1, 6, 12, 18] elif output_stride == 8: dilations = [1, 12, 24, 36] else: raise NotImplementedError self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0]) self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1]) self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2]) self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3]) self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False), nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout(0.5) def forward(self, x): x1 = self.aspp1(x) x2 = self.aspp2(x) x3 = self.aspp3(x) x4 = self.aspp4(x) x5 = self.global_avg_pool(x) x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True) x = torch.cat((x1, x2, x3, x4, x5), dim=1) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) return self.dropout(x) class _ASPPModule(nn.Module): def __init__(self, in_channels, planes, kernel_size, padding, dilation): super(_ASPPModule, self).__init__() self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size, stride=1, padding=padding, dilation=dilation, bias=False) self.bn = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.atrous_conv(x) x = self.bn(x) return self.relu(x) class ASPP(nn.Module): def __init__(self, in_channels=64, out_channels=None, output_stride=16): super(ASPP, self).__init__() self.down_scale = 1 if out_channels is None: out_channels = in_channels self.in_channelster = 256 // self.down_scale if output_stride == 16: dilations = [1, 6, 12, 18] elif output_stride == 8: dilations = [1, 12, 24, 36] else: raise NotImplementedError self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0]) self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1]) self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2]) self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3]) self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False), nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout(0.5) def forward(self, x): x1 = self.aspp1(x) x2 = self.aspp2(x) x3 = self.aspp3(x) x4 = self.aspp4(x) x5 = self.global_avg_pool(x) x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True) x = torch.cat((x1, x2, x3, x4, x5), dim=1) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) return self.dropout(x) ##################### Deformable class _ASPPModuleDeformable(nn.Module): def __init__(self, in_channels, planes, kernel_size, padding): super(_ASPPModuleDeformable, self).__init__() self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size, stride=1, padding=padding, bias=False) self.bn = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.atrous_conv(x) x = self.bn(x) return self.relu(x) class ASPPDeformable(nn.Module): def __init__(self, in_channels, out_channels=None, num_parallel_block=1): super(ASPPDeformable, self).__init__() self.down_scale = 1 if out_channels is None: out_channels = in_channels self.in_channelster = 256 // self.down_scale self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0) self.aspp_deforms = nn.ModuleList([ _ASPPModuleDeformable(in_channels, self.in_channelster, 3, padding=1) for _ in range(num_parallel_block) ]) self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False), nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout(0.5) def forward(self, x): x1 = self.aspp1(x) x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms] x5 = self.global_avg_pool(x) x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True) x = torch.cat((x1, *x_aspp_deforms, x5), dim=1) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) return self.dropout(x)