Update app.py

app.py

@@ -227,88 +227,28 @@ try:
         Repo.clone_from(URI, clone_dir)
         print(f"✅ Repository cloned to {clone_dir}")
     
-    # ===== MONKEY PATCH FOR ZNORMALIZATION ERROR =====
-    # This patch fixes the 'ZNormalization' object has no attribute 'masking_method' error
-    from fastMONAI.vision_augmentation import ZNormalization
-    from fastMONAI.vision_core import MedImage
+    # ===== MONKEY PATCH FOR PADORCROP ERROR =====
+    # This patch fixes the 'PadOrCrop' object has no attribute 'size' error
+    from fastMONAI.vision_augmentation import PadOrCrop
+    
+    def patched_pad_or_crop_encodes(self, o):
+        """Patched version of PadOrCrop.encodes to handle attribute mismatch."""
+        # Access the attributes through self.pad_or_crop instead of directly
+        return do_pad_or_crop(
+            o,
+            target_shape=self.pad_or_crop.target_shape,
+            padding_mode=self.pad_or_crop.padding_mode,
+            mask_name=self.pad_or_crop.mask_name,
+            dtype=type(o)
+        )
     
-    # Create a patched version of the encodes method
-    def patched_znormalization_encodes(self, o):
-        """Patched version of ZNormalization.encodes to handle attribute and dimension issues."""
-        # Handle the attribute mismatch - ensure we have what we need
-        if not hasattr(self, 'z_normalization'):
-            # Get masking_method with fallback
-            masking_method = getattr(self, 'masking_method', None)
-            self.z_normalization = tio.ZNormalization(masking_method=masking_method)
-        
-        # Ensure channel_wise exists
-        if not hasattr(self, 'channel_wise'):
-            self.channel_wise = True
-        
-        # Handle dimension issues properly
-        try:
-            if self.channel_wise:
-                if o.dim() == 5:  # (batch, channels, x, y, z)
-                    # Process each item in the batch
-                    results = []
-                    for batch_item in o:
-                        channel_results = []
-                        for channel in batch_item:
-                            # channel should be 3D (x, y, z)
-                            normalized = self.z_normalization(channel[None])[0]
-                            channel_results.append(normalized)
-                        results.append(torch.stack(channel_results))
-                    o = torch.stack(results)
-                elif o.dim() == 4:  # (channels, x, y, z)
-                    # Process each channel
-                    channel_results = []
-                    for channel in o:
-                        # channel should be 3D (x, y, z)
-                        normalized = self.z_normalization(channel[None])[0]
-                        channel_results.append(normalized)
-                    o = torch.stack(channel_results)
-                else:  # (x, y, z) - single channel image
-                    o = self.z_normalization(o[None])[0]
-            else:
-                # For non-channel-wise normalization, handle batched tensors correctly
-                if o.dim() == 5:  # (batch, channels, x, y, z)
-                    # Process each item in the batch separately
-                    results = []
-                    for batch_item in o:
-                        # batch_item is 4D: (channels, x, y, z)
-                        normalized = self.z_normalization(batch_item)
-                        results.append(normalized)
-                    o = torch.stack(results)
-                else:
-                    # For non-batched tensors (4D: channels, x, y, z)
-                    o = self.z_normalization(o)
-                    
-        except RuntimeError as e:
-            if "Standard deviation is 0" in str(e):
-                mean = float(o.mean())
-                error_msg = (
-                    f"Standard deviation is 0 for image (mean={mean:.3f}).\n"
-                    f"This indicates uniform pixel values.\n\n"
-                    f"Possible causes:\n"
-                    f"• Corrupted or blank image\n"
-                    f"• Oversaturated regions\n"
-                    f"• Background-only regions\n"
-                    f"• All-zero mask being processed as image\n\n"
-                    f"Suggested solutions:\n"
-                    f"• Check image quality and acquisition\n"
-                    f"• Verify image vs mask data loading"
-                )
-                raise RuntimeError(error_msg) from e
-
-        return MedImage.create(o)
-
     # Replace the problematic method
-    ZNormalization.encodes = patched_znormalization_encodes
-    print("✅ Applied ZNormalization patch to fix attribute and dimension errors")
+    PadOrCrop.encodes = patched_pad_or_crop_encodes
+    print("✅ Applied PadOrCrop patch to fix attribute errors")
     # ===== END OF MONKEY PATCH =====
     
-    # Remove the PadOrCrop patch entirely - it's not needed
-    # The issue was with ZNormalization, not PadOrCrop
+    # Note: We don't need the ZNormalization patch anymore
+    # The error is specifically with PadOrCrop
     
     models_path = clone_dir
     save_dir = Path.cwd() / 'hs_pred'