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