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import torch |
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import torch.nn as nn |
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import torch.nn.init as init |
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import torchvision.models as models |
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from torchvision.models import ResNet34_Weights |
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class ResNetEncoder(nn.Module): |
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def __init__(self, freeze=True): |
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super().__init__() |
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resnet = models.resnet34(weights=ResNet34_Weights.IMAGENET1K_V1) |
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self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) |
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with torch.no_grad(): |
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self.conv1.weight[:] = resnet.conv1.weight.mean(dim=1, keepdim=True) |
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self.bn1 = resnet.bn1 |
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self.relu = resnet.relu |
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self.maxpool = resnet.maxpool |
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self.layer1 = resnet.layer1 |
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self.layer2 = resnet.layer2 |
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self.layer3 = resnet.layer3 |
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self.layer4 = resnet.layer4 |
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if freeze: |
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for param in self.parameters(): |
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param.requires_grad = False |
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def forward(self, x): |
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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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x1 = self.maxpool(x) |
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x2 = self.layer1(x1) |
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del x1 |
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x3 = self.layer2(x2) |
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x4 = self.layer3(x3) |
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x5 = self.layer4(x4) |
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return x, x2, x3, x4, x5 |
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def icnr(tensor, scale=2, init_func=init.kaiming_normal_): |
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ni, nf, h, w = tensor.shape |
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ni2 = int(ni / (scale ** 2)) |
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k = init_func(torch.zeros([ni2, nf, h, w])) |
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k = k.repeat_interleave(scale ** 2, 0) |
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with torch.no_grad(): |
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tensor.copy_(k) |
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class PixelShuffleICNR(nn.Module): |
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def __init__(self, in_channels, out_channels, scale=2): |
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super().__init__() |
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self.conv = nn.Conv2d(in_channels, out_channels * (scale ** 2), kernel_size=3, padding=1) |
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icnr(self.conv.weight, scale=scale) |
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self.pixel_shuffle = nn.PixelShuffle(scale) |
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self.bn = nn.BatchNorm2d(out_channels) |
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self.relu = nn.ReLU(inplace=True) |
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def forward(self, x): |
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x = self.conv(x) |
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x = self.pixel_shuffle(x) |
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x = self.bn(x) |
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x = self.relu(x) |
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return x |
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class DecoderBlock(nn.Module): |
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def __init__(self, in_channels, skip_channels, out_channels): |
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super().__init__() |
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self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) |
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self.conv = nn.Sequential( |
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nn.Conv2d(in_channels + skip_channels, out_channels, kernel_size=3, padding=1), |
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nn.BatchNorm2d(out_channels), |
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nn.ReLU(inplace=True), |
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nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), |
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nn.BatchNorm2d(out_channels), |
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nn.ReLU(inplace=True), |
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) |
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def forward(self, x, skip): |
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x = self.upsample(x) |
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if skip is not None: |
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x = torch.cat([x, skip], dim=1) |
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return self.conv(x) |
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class Decoder(nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.dec4 = DecoderBlock(512, 256, 256) |
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self.dec3 = DecoderBlock(256, 128, 128) |
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self.dec2 = DecoderBlock(128, 64, 64) |
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self.dec1 = DecoderBlock(64, 64, 64) |
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self.pixel_shuffle = PixelShuffleICNR(64, 16, scale=2) |
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self.final = nn.Conv2d(16, 2, kernel_size=3, padding=1) |
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def forward(self, x5, x4, x3, x2, x1): |
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d4 = self.dec4(x5, x4) |
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d3 = self.dec3(d4, x3) |
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del d4, x4, x3 |
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d2 = self.dec2(d3, x2) |
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del d3, x2 |
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d1 = self.dec1(d2, x1) |
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del d2, x1 |
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out = self.pixel_shuffle(d1) |
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del d1 |
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out = self.final(out) |
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return torch.tanh(out) |
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class UNet(nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.encoder = ResNetEncoder() |
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self.decoder = Decoder() |
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def forward(self, x): |
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x, x2, x3, x4, x5 = self.encoder(x) |
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return self.decoder(x5, x4, x3, x2, x) |
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