--- a
+++ b/UNET.py
@@ -0,0 +1,61 @@
+import torch
+import torch.nn as nn
+import torchvision.transforms.functional as TF
+
+class DoubleConvolution(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(DoubleConvolution, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
+            # Although in the original paper, these convolutions are unpadded but we didn't do it here.
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+class UNET(nn.Module):
+    def __init__(self, in_channels=3, out_channels=1, features = [64, 128, 256, 512]):
+        super(UNET, self).__init__()
+        self.downs = nn.ModuleList()
+        self.ups = nn.ModuleList()
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+
+        for feature in features:
+            self.downs.append(DoubleConvolution(in_channels, feature))
+            in_channels = feature
+
+        for feature in reversed(features):
+            self.ups.append(
+                nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2)
+            )
+            self.ups.append(DoubleConvolution(feature*2, feature))
+
+        self.bottleneck = DoubleConvolution(features[-1], features[-1]*2)
+        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
+
+    def forward(self, x):
+        skip_connections = []
+        for down in self.downs:
+            x = down(x)
+            skip_connections.append(x)
+            x = self.pool(x)
+        x = self.bottleneck(x)
+        skip_connections = skip_connections[::-1]
+
+        for idx in range(0, len(self.ups), 2):
+            x = self.ups[idx](x)
+            skip_connection = skip_connections[idx//2]
+
+            if x.shape != skip_connection.shape:
+                x = TF.resize(x, size=skip_connection.shape[2:])
+
+            concat_skip = torch.cat((skip_connection, x), dim=1)
+            x = self.ups[idx+1](concat_skip)
+
+        return self.final_conv(x)
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