Delete unet.py

unet.py

@@ -1,102 +0,0 @@
-import torch
-import torch.nn as nn
-
-class DownSampling(nn.Module):
-    
-    def __init__(self, in_channels, out_channels, max_pool):
-        """
-        DownSampling block in the U-Net architecture.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            max_pool (bool): Whether to use max pooling.
-        """
-        super(DownSampling, self).__init__()
-        self.max_pool = max_pool
-        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
-        self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
-        self.batchnorm2d = nn.BatchNorm2d(out_channels)
-        self.relu = nn.ReLU()
-        self.maxpool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-
-        x = self.relu(self.batchnorm2d(x))
-        skip_connection = x
-        
-        if self.max_pool:
-            next_layer = self.maxpool2d(x)
-        else:
-            return x
-        return next_layer, skip_connection
-
-class UpSampling(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        """
-        UpSampling block in the U-Net architecture.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-        """
-        super(UpSampling, self).__init__()
-        self.up = nn.ConvTranspose2d(in_channels, out_channels=out_channels, kernel_size=2, stride=2)
-        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
-        self.relu = nn.ReLU()
-        self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
-        self.batchnorm = nn.BatchNorm2d(out_channels)
-
-    def forward(self, x, prev_skip):
-        x = self.up(x)
-        x = torch.cat((x, prev_skip), dim=1)
-        x = self.conv1(x)
-        x = self.conv2(x)
-        next_layer = self.relu(self.batchnorm(x))
-        return next_layer
-
-class UNet(nn.Module):
-    
-    """
-        U-Net architecture.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            features (list): List of feature sizes for downsampling and upsampling.
-    """
-    def __init__(self, in_channels, out_channels, features):
-        super(UNet, self).__init__()
-        self.ups = nn.ModuleList()
-        self.downs = nn.ModuleList()
-
-        for feature in features:
-            self.downs.append(DownSampling(in_channels, feature, True))
-            in_channels = feature
-
-        for feature in reversed(features):
-            self.ups.append(UpSampling(2 * feature, feature))
-
-        self.bottleneck = DownSampling(features[-1], 2 * features[-1], False)
-        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
-
-    def forward(self, x):
-        skip_connections = []
-        for down in self.downs:
-            x, skip_connection = down(x)
-            skip_connections.append(skip_connection)
-        skip_connections = skip_connections[::-1]
-        x = self.bottleneck(x)
-        for i, up in enumerate(self.ups):
-            x = up(x, skip_connections[i])
-
-        return self.final_conv(x)
-    
-if __name__ == "__main__":
-    #Example Usage
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    features = [64, 128, 256, 512]
-    model = UNet(1, 1, features=features).to(device)
-    print(model(torch.rand(1, 1, 512, 512)).shape)