Create model.py

model.py

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+import torch
+import torch.nn as nn
+import torch.nn.init as init
+import torchvision.models as models
+from torchvision.models import ResNet34_Weights
+
+
+class ResNetEncoder(nn.Module):
+    def __init__(self, freeze=True):
+        super().__init__()
+        resnet = models.resnet34(weights=ResNet34_Weights.IMAGENET1K_V1)
+        
+        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        with torch.no_grad():
+            self.conv1.weight[:] = resnet.conv1.weight.mean(dim=1, keepdim=True)
+
+        self.bn1 = resnet.bn1
+        self.relu = resnet.relu
+        self.maxpool = resnet.maxpool
+        self.layer1 = resnet.layer1
+        self.layer2 = resnet.layer2
+        self.layer3 = resnet.layer3
+        self.layer4 = resnet.layer4
+
+        if freeze:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, x):
+        # x = (x - 0.449) / 0.226
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x1 = self.maxpool(x)
+        x2 = self.layer1(x1)
+        del x1
+
+        x3 = self.layer2(x2)
+        x4 = self.layer3(x3)
+        x5 = self.layer4(x4)
+
+        return x, x2, x3, x4, x5
+
+
+def icnr(tensor, scale=2, init_func=init.kaiming_normal_):
+    ni, nf, h, w = tensor.shape
+    ni2 = int(ni / (scale ** 2))
+    k = init_func(torch.zeros([ni2, nf, h, w]))
+    k = k.repeat_interleave(scale ** 2, 0)
+    with torch.no_grad():
+        tensor.copy_(k)
+
+
+class PixelShuffleICNR(nn.Module):
+    def __init__(self, in_channels, out_channels, scale=2):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels * (scale ** 2), kernel_size=3, padding=1)
+        icnr(self.conv.weight, scale=scale)
+        self.pixel_shuffle = nn.PixelShuffle(scale)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.pixel_shuffle(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class DecoderBlock(nn.Module):
+    def __init__(self, in_channels, skip_channels, out_channels):
+        super().__init__()
+        
+        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels + skip_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+        )
+
+    def forward(self, x, skip):
+        x = self.upsample(x)
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+        return self.conv(x)
+
+
+class Decoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.dec4 = DecoderBlock(512, 256, 256)
+        self.dec3 = DecoderBlock(256, 128, 128)
+        self.dec2 = DecoderBlock(128, 64, 64)
+        self.dec1 = DecoderBlock(64, 64, 64)
+        self.pixel_shuffle = PixelShuffleICNR(64, 16, scale=2)
+        self.final = nn.Conv2d(16, 2, kernel_size=3, padding=1)
+
+    def forward(self, x5, x4, x3, x2, x1):
+        d4 = self.dec4(x5, x4)
+        d3 = self.dec3(d4, x3)
+        del d4, x4, x3
+        d2 = self.dec2(d3, x2)
+        del d3, x2
+        d1 = self.dec1(d2, x1)
+        del d2, x1
+        out = self.pixel_shuffle(d1)
+        del d1
+        out = self.final(out)
+        return torch.tanh(out)
+
+
+class UNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.encoder = ResNetEncoder()
+        self.decoder = Decoder()
+
+    def forward(self, x):
+        x, x2, x3, x4, x5 = self.encoder(x)
+        return self.decoder(x5, x4, x3, x2, x)