feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging

test_bug.py

@@ -0,0 +1,33 @@
+import torch
+import numpy as np
+
+device = "cpu"
+input_array = np.zeros((4, 16, 16), dtype=np.float32)
+input_tensor = torch.from_numpy(input_array).unsqueeze(0).to(device)
+
+print("Before pad:", input_tensor.shape)
+
+from train_cloud_removal import UNet
+model = UNet(in_channels=6, out_channels=4).to(device)
+
+if hasattr(model, 'inc') and hasattr(model.inc.double_conv[0], 'in_channels'):
+    expected_channels = model.inc.double_conv[0].in_channels
+elif hasattr(model, 'conv1') and hasattr(model.conv1, 'in_channels'):
+    expected_channels = model.conv1.in_channels
+else:
+    expected_channels = list(model.parameters())[0].shape[1]
+
+print("Expected channels:", expected_channels)
+
+if expected_channels > input_tensor.shape[1]:
+    pad_channels = expected_channels - input_tensor.shape[1]
+    padding = torch.zeros(1, pad_channels, *input_tensor.shape[2:]).to(device)
+    input_tensor = torch.cat([input_tensor, padding], dim=1)
+
+print("After pad:", input_tensor.shape)
+
+try:
+    model(input_tensor)
+    print("Success!")
+except Exception as e:
+    print("Error:", e)