init

torchvision_fix.py

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+# Torchvision compatibility fix for functional_tensor module
+# This file helps resolve compatibility issues between different torchvision versions
+
+import sys
+import torchvision
+
+def fix_torchvision_functional_tensor():
+    """
+    Fix torchvision.transforms.functional_tensor import issue
+    """
+    try:
+        # Check if the module exists in the expected location
+        import torchvision.transforms.functional_tensor
+        print("torchvision.transforms.functional_tensor is available")
+        return True
+    except ImportError:
+        print("torchvision.transforms.functional_tensor not found, applying compatibility fix...")
+        
+        try:
+            # Create a mock functional_tensor module with the required functions
+            import torch
+            import torchvision.transforms.functional as F
+            
+            class FunctionalTensorMock:
+                """Mock module to replace functional_tensor"""
+                
+                @staticmethod
+                def rgb_to_grayscale(img, num_output_channels=1):
+                    """Convert RGB image to grayscale"""
+                    if hasattr(F, 'rgb_to_grayscale'):
+                        return F.rgb_to_grayscale(img, num_output_channels)
+                    else:
+                        # Fallback implementation
+                        if len(img.shape) == 4:  # Batch of images
+                            weights = torch.tensor([0.299, 0.587, 0.114], 
+                                                   device=img.device, dtype=img.dtype).view(1, 3, 1, 1)
+                        else:  # Single image
+                            weights = torch.tensor([0.299, 0.587, 0.114], 
+                                                   device=img.device, dtype=img.dtype).view(3, 1, 1)
+                        
+                        grayscale = torch.sum(img * weights, dim=-3, keepdim=True)
+                        
+                        if num_output_channels == 3:
+                            if len(img.shape) == 4:
+                                grayscale = grayscale.repeat(1, 3, 1, 1) 
+                            else:
+                                grayscale = grayscale.repeat(3, 1, 1)
+                        
+                        return grayscale
+                
+                @staticmethod
+                def resize(img, size, interpolation=2, antialias=None):
+                    """Resize function wrapper"""
+                    try:
+                        from torchvision.transforms.v2.functional import resize as v2_resize
+                        return v2_resize(img, size, 
+                                         interpolation=interpolation, 
+                                         antialias=antialias)
+                    except ImportError:
+                        if hasattr(F, 'resize'):
+                            return F.resize(img, size, interpolation=interpolation)
+                        else:
+                            import torch.nn.functional as torch_F
+                            if isinstance(size, int):
+                                size = (size, size)
+                            return torch_F.interpolate(
+                                img.unsqueeze(0) if len(img.shape) == 3 else img,
+                                size=size, mode='bilinear', align_corners=False)
+                
+                def __getattr__(self, name):
+                    """Fallback to regular functional module"""
+                    if hasattr(F, name):
+                        return getattr(F, name)
+                    else:
+                        # Try v2.functional
+                        try:
+                            import torchvision.transforms.v2.functional as v2_F
+                            if hasattr(v2_F, name):
+                                return getattr(v2_F, name)
+                        except ImportError:
+                            pass
+                        
+                        raise AttributeError(f"'{name}' not found in functional_tensor mock")
+            
+            # Create the mock module instance
+            functional_tensor_mock = FunctionalTensorMock()
+            
+            # Monkey patch the old location
+            sys.modules['torchvision.transforms.functional_tensor'] = functional_tensor_mock
+            print("Applied compatibility fix: created functional_tensor mock module")
+            return True
+            
+        except Exception as e:
+            print(f"Failed to create functional_tensor mock: {e}")
+            return False
+
+def apply_fix():
+    """Apply the torchvision compatibility fix"""
+    print(f"Torchvision version: {torchvision.__version__}")
+    return fix_torchvision_functional_tensor()
+
+if __name__ == "__main__":
+    apply_fix() 
+