RuntimeError: Integer division of tensors using div or/is no longer supported, and in a future release div will perform true division as in Python 3. Use true_divide or floor_divide (// in Python) instead.
from torchvision import transforms
import numpy as np
data = np.random.randint(0, 255, size=12)
img = data.reshape(2, 2, 3)
print(img.shape)
img_tensor = transforms.ToTensor()(img) # Convert to tensor
print(img_tensor)
print(img_tensor.shape)
print("*" * 20)
norm_img = transforms.Normalize((10, 10, 10), (1, 1, 1))(img_tensor) # Perform normative processing
print(norm_img)
Operation effect:
reason:
Pytorch1.5.0 is OK, but when upgrading to 1.6.0, it is found that division between tenor and int cannot be directly performed with ‘/’.
Solution:
Standardize the data processing
Example code:
from torchvision import transforms
import numpy as np
data = np.random.randint(0, 255, size=12)
img = data.reshape(2, 2, 3)
print(img.shape)
img_tensor = transforms.ToTensor()(img) # convert to tensor
print(img_tensor)
print(img_tensor.shape)
print("*" * 20)
img_tensor = img_tensor.float() # Add this line
norm_img = transforms.Normalize((10, 10, 10), (1, 1, 1))(img_tensor) # Perform normalization
print(norm_img)
Results of operation:
. I found a solution on the Internet, which may be useful to you. But I tried it, but it didn’t work. So when I looked at it, I found that it was caused by my carelessness. Ah, as shown in the figure below, there is a comma missing in front of longitude, as shown in the figure below: 
, so this problem is solved. OK, I will continue my course design.
