• torchvision.transforms()

1. Transforms are common image transformations.
2. They can be chained together using Compose.
3. Functional transforms give fine-grained control over the transformations.

• Chained with multiple transform. Thus, transforms is composed of transform.

    transforms.Compose([
    transforms.CenterCrop(10),
    transforms.Normalize((0.5, 0.4, 0.5), (0.5, 0.5, 0.5)),
    transforms.RandomHorizontalFlip(p=0.5),
    transforms.RandomRotation(),
    transforms.RandomApply(random_transforms, p=0.3),
    transforms.ToTensor()
 ])

• class torchvision.transforms.Scale(size, interpolation=2)

# No contents yet

• class torchvision.transforms.RandomCrop(size, padding=0)
  Crop the given PIL image at a random location.

• class torchvision.transforms.RandomHorizontalFlip(p=0.5)
  Horizontally flip the given image randomly with a given probability.

• class torchvision.transforms.RandomRotation

• class torchvision.transforms.Resize(size)
  Resize the input PIL image to the given size, usually used with training_testing set

• class torchvision.transforms.RandomApply

• class torchvision.transforms.ToTensor

• class torchvision.transforms.Normalize(mean, std)
  Only use Normalize with Tensor, not PIL.image

• class torchvision.transforms.ToTensor // HWC > CHW
  Convert PIL.image to Tensor

Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].

C: Channels, H: Heights, W: Widths

• class torchvision.transforms.ToPILImage(mode=None) // CHW > HWC
  Convert Tensor to PIL.image