Batch equivalent of PyTorch Transforms.
Batch equivalent of PyTorch Transforms.
transform_batch = transforms.Compose([
ToTensor(),
Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
for images in data_iterator:
images = transform_batch(images)
output = model(images)
Applies the equivalent of torchvision.transforms.Normalize
to a batch of images.
Note: This transform acts out of place by default, i.e., it does not mutate the input tensor.
__init__(mean, std, inplace=False, dtype=torch.float, device='cpu')
__call__(tensor)
Applies the equivalent of torchvision.transforms.RandomCrop
to a batch of images. Images are independently transformed.
__init__(size, padding=None, device='cpu')
__call__(tensor)
Applies the equivalent of torchvision.transforms.RandomHorizontalFlip
to a batch of images. Images are independently transformed.
Note: This transform acts out of place by default, i.e., it does not mutate the input tensor.
__init__(p=0.5, inplace=False)
__call__(tensor)
Applies the equivalent of torchvision.transforms.ToTensor
to a batch of images.
__init__()
__call__(tensor)