ToTensor transform function, ndarray dimensions are not rearranged about vision HOT 5 CLOSED

To change a numpy HxWxC array to CxHxW, and get the same behavior as if you called ToPILImage() and then ToTensor(), do

npimg = np.transpose(npimg,(2,0,1))

Doing a npimg.reshape() will not produce the same results

from vision.

I ran into this issue today too, I think a normalize parameter would be nice as sometimes the image loading function used before this would already put the data into [0,1].

@ClementPinard As a quick work-around you can call ToPILImage first in a Compose i.e.

transform = Compose([ToPILImage(), ToTensor()])

from vision.

@alykhantejani +1 on the normalize parameter.. even better would be for toTensor not to normalize at all.. isn't that what the Normalize transform is for? I got burned doing a Normalize transform with a ToTensor transform

from vision.

as @alykhantejani said ,another way to get the same effect is using numpy to convert,
assume your image is opencv format,
img_tensor = cv_img[:,:,::-1].transpose((2,0,1)).copy() # chw, RGB order,[0,255]
img_tensor = torch.from_numpy(img_tensor).float().div(255) # chw , FloatTensor type,[0,1]
img_tensor = img_tensor.unsqueeze(0) # nch*w

from vision.

fixed in master

from vision.