I’m trying to resize my input images. They are pretty big (5312x2988) and I’m trying to shrink them.
This is my code. It’s based on the code in a tutorial 1:
import transforms as T

def get_transform(train):
    transforms = []
    # converts the image, a PIL image, into a PyTorch Tensor
    transforms.append(T.ToTensor())
    transforms.append(T.Resize((400*5312/2988,400)))  # *<-- this is where I added T.Resize()*
    if train:
        # during training, randomly flip the training images
        # and ground-truth for data augmentation
        transforms.append(T.RandomHorizontalFlip(0.5))
    return T.Compose(transforms)

This is the error message:

AttributeError                            Traceback (most recent call last)
 in ()
1 model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
----> 2 dataset = four_chs(root = ‘/content/drive/MyDrive/four_chambers’, transforms = get_transform(train=True))
3 data_loader = torch.utils.data.DataLoader(
4  dataset, batch_size=1, shuffle=True, num_workers=2,
5  collate_fn=utils.collate_fn)
 in get_transform(train)
8     # converts the image, a PIL image, into a PyTorch Tensor
9     transforms.append(T.ToTensor())
—> 10     transforms.append(T.Resize((360,360)))
11     if train:
12         # during training, randomly flip the training images
AttributeError: module ‘transforms’ has no attribute ‘Resize’

I suppose that I’ve coded transforms.Resize incorrectly if it’s being read as an attribute? I wrote that line the way I did because it seems to be the same format as T.ToTensor(). I don’t see the difference between applying ToTensor and Resize. Please tell me how should I code the transforms chunk to apply Resize to the tensors?