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Running
on
Zero
| 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) | |