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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()
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