Isolated STNet from test pipeline

model/stn.py

@@ -8,9 +8,9 @@ from torchvision import datasets, transforms
 import matplotlib.pyplot as plt
 import numpy as np
 
-class StnNet(nn.Module):
+class STNet(nn.Module):
     def __init__(self, input_size: torch.Size):
-        super(StnNet, self).__init__()
+        super(STNet, self).__init__()
 
         # Sanity check
         assert 5 > len(input_size) > 2  # single or batch ([N, ]C, H, W)
@@ -19,13 +19,7 @@ class StnNet(nn.Module):
         else:
             channels, height, width = input_size[1:]
 
-        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        # (3.1) Spatial transformer localization-network
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.conv2_drop = nn.Dropout2d()
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, 10)
-
-        # Spatial transformer localization-network
         self.localization_net = nn.Sequential(      # (N, C, H, W)
             nn.Conv2d(channels, 8, kernel_size=7),  # (N, 8, H-6, W-6)
             nn.MaxPool2d(2, stride=2),              # (N, 8, (H-6)/2, (W-6)/2)
@@ -40,8 +34,9 @@ class StnNet(nn.Module):
             (((width - 6) // 2) - 4) // 2
         ) # TODO: PLEASE let me know if there are better ways of doing this...
 
-        # Regressor for the 3 * 2 affine matrix
+        # (3.2) Regressor for the 3 * 2 affine matrix
         self.fc_loc = nn.Sequential(
+            # XXX: Should
             nn.Linear(np.prod(self._loc_net_out_shape), 32),
             nn.ReLU(True),
             nn.Linear(32, 3 * 2)
@@ -57,6 +52,7 @@ class StnNet(nn.Module):
             )
         )
 
+
     # Spatial transformer network forward function
     def stn(self, x):
         xs = self.localization_net(x)
@@ -69,11 +65,28 @@ class StnNet(nn.Module):
 
         return x
 
+
     def forward(self, x):
-        # transform the input
+        # transform the input, do nothing else
+        return self.stn(x)
+
+
+if __name__ == "__main__":
+    class STNetDebug(STNet):
+        def __init__(self, input_size: torch.Size):
+            super(STNetDebug, self).__init__(input_size)
+
+            self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+            self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+            self.conv2_drop = nn.Dropout2d()
+            self.fc1 = nn.Linear(320, 50)
+            self.fc2 = nn.Linear(50, 10)
+
+        def forward(self, x):
+            # Transform the input
             x = self.stn(x)
 
-        # Perform the usual forward pass
+            # Perform usual forward pass for MNIST
             x = F.relu(F.max_pool2d(self.conv1(x), 2))
             x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
             x = x.view(-1, 320)
@@ -82,7 +95,7 @@ class StnNet(nn.Module):
             x = self.fc2(x)
             return F.log_softmax(x, dim=1)
 
-if __name__ == "__main__":
+
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     train_loader = torch.utils.data.DataLoader(
         dataset=datasets.MNIST(
@@ -112,7 +125,7 @@ if __name__ == "__main__":
         num_workers=4,
     )
     shape_of_input = next(iter(train_loader))[0].shape
-    model = StnNet(shape_of_input).to(device)
+    model = STNetDebug(shape_of_input).to(device)
     optimizer = optim.SGD(model.parameters(), lr=.01)
     def train(epoch):
         model.train()