Update model.py

model.py

@@ -5,35 +5,32 @@ class ModelColorization(nn.Module, PyTorchModelHubMixin):
     def __init__(self):
         super(ModelColorization, self).__init__()
         self.encoder = nn.Sequential(
-            nn.Conv2d(1, 256, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
             nn.MaxPool2d(kernel_size=2, stride=2),
             nn.ReLU(),
-            nn.BatchNorm2d(256),
-            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64),
+            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
             nn.MaxPool2d(kernel_size=2, stride=2),
             nn.ReLU(),
-            nn.BatchNorm2d(128),
-            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(32),
+            nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
             nn.MaxPool2d(kernel_size=2, stride=2),
             nn.ReLU(),
-            nn.BatchNorm2d(64),
+            nn.BatchNorm2d(16),
             nn.Flatten(),
-            nn.Linear(64*16*16, 3000),
+            nn.Linear(16*45*45, 4000),
         )
         self.decoder = nn.Sequential(
-            nn.Linear(3000, 64 * 16 * 16),
+            nn.Linear(4000, 16 * 45 * 45),
             nn.ReLU(),
-
-            nn.Unflatten(1, (64, 16, 16)),
-            nn.ConvTranspose2d(64, 128, kernel_size=2, stride=2),
+            nn.Unflatten(1, (16, 45, 45)),
+            nn.ConvTranspose2d(16, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
             nn.ReLU(),
-            nn.BatchNorm2d(128),
-
-            nn.ConvTranspose2d(128, 256, kernel_size=2, stride=2),
+            nn.BatchNorm2d(32),
+            nn.ConvTranspose2d(32, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
             nn.ReLU(),
-            nn.BatchNorm2d(256),
-
-            nn.ConvTranspose2d(256, 3, kernel_size=2, stride=2),
+            nn.BatchNorm2d(64),
+            nn.ConvTranspose2d(64, 3, kernel_size=3, stride=2, padding=1, output_padding=1),
             nn.Sigmoid()
         )