I’m sorry for the delay i had problems
and I’m sorry for my English
my model is:
number of classes=2 or 3 or 10
And the output dimension of the model is [No x Co x Ho x Wo]
where,
No -> is the batch size (same as Ni)
Co -> is the number of classes that the dataset have!
Ho -> the height of the image (which is the same as Hi in almost all cases)
Wo -> the width of the image (which is the same as Wi in almost all cases)


number of channels=1 or 3
the target dimension is [Ni x Ci x Hi x Wi]
where,
Ni -> the batch size
Ci -> the number of channels (which is 3 or 1)
Hi -> the height of the image
Wi -> the width of the image 


and thanks for your reply
I apply train transformation into image and mask:
train_transform = et.ExtCompose([
#et.ExtResize(size=opts.crop_size),
et.ExtRandomScale((0.5, 2.0)),
et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True),
et.ExtRandomHorizontalFlip()
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
mask  is rgb or gray with values ​​of 0 background and 1class1 2class2