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# Torchvision compatibility fix for functional_tensor module
# This file helps resolve compatibility issues between different torchvision versions
import sys
import torch
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 torchvision.transforms.functional as F
class FunctionalTensorMock:
"""Mock module to replace functional_tensor"""
@staticmethod
def _get_grayscale_weights(img):
"""Helper to create grayscale weights based on image dimensions"""
weights = torch.tensor([0.299, 0.587, 0.114], device=img.device, dtype=img.dtype)
return weights.view(1, 3, 1, 1) if len(img.shape) == 4 else weights.view(3, 1, 1)
@staticmethod
def _try_import_fallback(module_names, attr_name):
"""Helper to try importing from multiple modules"""
for module_name in module_names:
try:
module = __import__(module_name, fromlist=[attr_name])
if hasattr(module, attr_name):
return getattr(module, attr_name)
except ImportError:
continue
return None
@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)
# Fallback implementation
weights = FunctionalTensorMock._get_grayscale_weights(img)
grayscale = torch.sum(img * weights, dim=-3, keepdim=True)
if num_output_channels == 3:
repeat_dims = (1, 3, 1, 1) if len(img.shape) == 4 else (3, 1, 1)
grayscale = grayscale.repeat(*repeat_dims)
return grayscale
@staticmethod
def resize(img, size, interpolation=2, antialias=None):
"""Resize function wrapper"""
# Try v2.functional first, then regular functional, then torch.nn.functional
resize_func = FunctionalTensorMock._try_import_fallback([
'torchvision.transforms.v2.functional',
'torchvision.transforms.functional'
], 'resize')
if resize_func:
try:
return resize_func(img, size, interpolation=interpolation, antialias=antialias)
except TypeError:
# Fallback for older versions without antialias parameter
return resize_func(img, size, interpolation=interpolation)
# Final fallback using torch.nn.functional
import torch.nn.functional as torch_F
size = (size, size) if isinstance(size, int) else size
img_input = img.unsqueeze(0) if len(img.shape) == 3 else img
return torch_F.interpolate(img_input, size=size, mode='bilinear', align_corners=False)
def __getattr__(self, name):
"""Fallback to regular functional module"""
func = self._try_import_fallback([
'torchvision.transforms.functional',
'torchvision.transforms.v2.functional'
], name)
if func:
return func
raise AttributeError(f"'{name}' not found in functional_tensor mock")
# Create the mock module instance and monkey patch
sys.modules['torchvision.transforms.functional_tensor'] = FunctionalTensorMock()
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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