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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class DoubleConv(nn.Module): | |
| """(convolution => [BN] => ReLU) * 2""" | |
| def __init__(self, in_channels, out_channels, mid_channels=None): | |
| super().__init__() | |
| if not mid_channels: | |
| mid_channels = out_channels | |
| self.double_conv = nn.Sequential( | |
| nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False), | |
| nn.InstanceNorm2d(mid_channels), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False), | |
| nn.InstanceNorm2d(out_channels), | |
| nn.ReLU(inplace=True) | |
| ) | |
| def forward(self, x): | |
| return self.double_conv(x) | |
| class Down(nn.Module): | |
| """Downscaling with maxpool then double conv""" | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| self.maxpool_conv = nn.Sequential( | |
| nn.MaxPool2d(2), | |
| DoubleConv(in_channels, out_channels) | |
| ) | |
| def forward(self, x): | |
| return self.maxpool_conv(x) | |
| class Up(nn.Module): | |
| """Upscaling then double conv""" | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) | |
| def forward(self, x1, x2=None): | |
| x1 = self.up(x1) | |
| if x2 is not None: | |
| diffY = x2.size()[2] - x1.size()[2] | |
| diffX = x2.size()[3] - x1.size()[3] | |
| x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, | |
| diffY // 2, diffY - diffY // 2]) | |
| x = torch.cat([x2, x1], dim=1) | |
| else: | |
| x = x1 | |
| return self.conv(x) | |
| class OutConv(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(OutConv, self).__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) | |
| self.act_fn = nn.Sigmoid() | |
| def forward(self, x): | |
| y = self.act_fn(self.conv(x)) | |
| return y | |
| class Upsampler(nn.Module): | |
| def __init__(self, input_dim=32, output_dim=3, network_capacity=128): | |
| super(Upsampler, self).__init__() | |
| self.inc = DoubleConv(input_dim, network_capacity * 4) | |
| self.up1 = Up(network_capacity * 4, network_capacity * 2) | |
| self.up2 = Up(network_capacity * 2, network_capacity) | |
| self.outc = OutConv(network_capacity, output_dim) | |
| def forward(self, x): | |
| x = self.inc(x) | |
| x = self.up1(x) | |
| x = self.up2(x) | |
| x = self.outc(x) | |
| return x |