AttributeError: module 'transforms' has no attribute 'Normalize'

Hi. I’m using torchvision 0.13.0.

I had this problem:

TypeError: Expected input images to be of floating type (in range [0, 1]), but found type torch.uint8 instead

When I was attempting to do this:

import transforms as T

def get_transform(train):
    transform = []
    # converts the image, a PIL image, into a PyTorch Tensor
    transform.append([T.PILToTensor(), T.Normalize()])
    if train:
        # during training, randomly flip the training images
        # and ground-truth for data augmentation
    return T.Compose(transform)

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=True)
dataset = four_chs(root = '/home/jn/Downloads/', transforms = get_transform(train=True))   

data_loader =
 dataset, batch_size=1, shuffle=True, num_workers=2,

# For Training
images,targets = next(iter(data_loader))
images = list(image for image in images)
targets = [{k: v for k, v in t.items()} for t in targets]
output = model(images,targets)   # Returns losses and detections

I searched some and found something that suggested using a Normalize transform. I’m not sure how to string together multiple transforms, so I tried the code above. That returns this error:

AttributeError: module ‘transforms’ has no attribute ‘Normalize’

The Normalize transform seems to be defined, so I’m not sure why this error happens. How do I address this?

It seems you are importing a custom transforms module and not torchvision.transforms, which doesn’t seem to have the Normalize transformation.
Could you check where this module is defined and also check why it’s used?

Hi ptrblck. I used this section earlier in the code. Sorry I didn’t include it in first post.

if os.path.isdir("vision") == True:
    print("vision present")
    Repo.clone_from("", "vision")

shutil.copy('vision/references/detection/', '')
shutil.copy('vision/references/detection/', '')
shutil.copy('vision/references/detection/', '')
shutil.copy('vision/references/detection/', '')
shutil.copy('vision/references/detection/', '')

I used that because it’s mostly the same as what’s provided here:

That Colab tutorial relies on torchvision 0.12.0, I think, and I’m trying to use 0.13.0 at this point, so I’m using most of the same code.

I see “vision/torchvision/transforms at main · pytorch/vision · GitHub”. I should use that instead? It seems to have the transforms module, but not engine. I suppose I should copy both repos and import the requisite modules accordingly.

Thanks for the explanation.
Import the torchvision.transforms and your local transforms as different modules and it should work.

from transforms as det_transforms
import torchvision.transforms as transforms

and use these two names to separate the transformations.

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