transform = transforms.Compose([transforms.Resize(224),
                                    transforms.ToTensor(),
                                    transforms.Normalize(mean = [0.5, 0.5, 0.5],
                                                         std = [0.5, 0.5, 0.5])])


    train_dataset = torchvision.datasets.ImageFolder(root=DATASET_PATH + '/train/train_data', transform=transform)

    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=2)



    print("Found {} number of mini-batches".format(len(train_loader)))

    t0 = time.time()
    total_step = len(train_loader)
    for epoch in range(nb_epoch):
        
        avg_loss = 0.0
        
        for i, (images, lables) in enumerate(train_loader):
            images = images.to(device)
            labels = lables.to(device)

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 224 and 475 in dimension 2 at /pytorch/aten/src/TH/generic/THTensorMath.cpp:3616
I tried to apply transform Resize and loaded during training and this error came up. It says that image sizes are different. How is this happening even though I resized all the images in the same size??
Any advice would be welcome!