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

cloud_removal.py

@@ -9,6 +9,7 @@ from typing import Tuple, Optional, Dict
 from sklearn.neighbors import KNeighborsRegressor
 from sklearn.ensemble import RandomForestRegressor
 import warnings
+from pathlib import Path
 warnings.filterwarnings('ignore')
 
 
@@ -375,6 +376,22 @@ class DeepInpaintingStrategy(CloudRemovalStrategy):
                     # Convert to tensor and add batch dimension
                     input_tensor = torch.from_numpy(input_array).unsqueeze(0).to(self.device)
                     
+                    if hasattr(self.model, 'encoder'):
+                        # Custom UNet from train_cloud_removal.py
+                        expected_channels = self.model.encoder[0].double_conv[0].in_channels
+                    elif hasattr(self.model, 'inc') and hasattr(self.model.inc.double_conv[0], 'in_channels'):
+                        expected_channels = self.model.inc.double_conv[0].in_channels
+                    elif hasattr(self.model, 'conv1') and hasattr(self.model.conv1, 'in_channels'):
+                        expected_channels = self.model.conv1.in_channels
+                    else:
+                        expected_channels = 6
+
+                        
+                    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(self.device)
+                        input_tensor = torch.cat([input_tensor, padding], dim=1)
+                    
                     # Run through U-Net
                     with torch.no_grad():
                         output_tensor = self.model(input_tensor)