Illustration of transforms

This example illustrates the various transforms available in the torchvision.transforms module.

from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np

import torch
import torchvision.transforms as T


plt.rcParams["savefig.bbox"] = 'tight'
orig_img = Image.open(Path('assets') / 'astronaut.jpg')
# if you change the seed, make sure that the randomly-applied transforms
# properly show that the image can be both transformed and *not* transformed!
torch.manual_seed(0)


def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
    if not isinstance(imgs[0], list):
        # Make a 2d grid even if there's just 1 row
        imgs = [imgs]

    num_rows = len(imgs)
    num_cols = len(imgs[0]) + with_orig
    fig, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)
    for row_idx, row in enumerate(imgs):
        row = [orig_img] + row if with_orig else row
        for col_idx, img in enumerate(row):
            ax = axs[row_idx, col_idx]
            ax.imshow(np.asarray(img), **imshow_kwargs)
            ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])

    if with_orig:
        axs[0, 0].set(title='Original image')
        axs[0, 0].title.set_size(8)
    if row_title is not None:
        for row_idx in range(num_rows):
            axs[row_idx, 0].set(ylabel=row_title[row_idx])

    plt.tight_layout()

Pad

The Pad transform (see also pad()) fills image borders with some pixel values.

padded_imgs = [T.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
plot(padded_imgs)

Resize

The Resize transform (see also resize()) resizes an image.

resized_imgs = [T.Resize(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot(resized_imgs)

CenterCrop

The CenterCrop transform (see also center_crop()) crops the given image at the center.

center_crops = [T.CenterCrop(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot(center_crops)

FiveCrop

The FiveCrop transform (see also five_crop()) crops the given image into four corners and the central crop.

(top_left, top_right, bottom_left, bottom_right, center) = T.FiveCrop(size=(100, 100))(orig_img)
plot([top_left, top_right, bottom_left, bottom_right, center])

Grayscale

The Grayscale transform (see also to_grayscale()) converts an image to grayscale

gray_img = T.Grayscale()(orig_img)
plot([gray_img], cmap='gray')

Random transforms

The following transforms are random, which means that the same transfomer instance will produce different result each time it transforms a given image.

ColorJitter

The ColorJitter transform randomly changes the brightness, saturation, and other properties of an image.

jitter = T.ColorJitter(brightness=.5, hue=.3)
jitted_imgs = [jitter(orig_img) for _ in range(4)]
plot(jitted_imgs)

GaussianBlur

The GaussianBlur transform (see also gaussian_blur()) performs gaussian blur transform on an image.

blurrer = T.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5))
blurred_imgs = [blurrer(orig_img) for _ in range(4)]
plot(blurred_imgs)

RandomPerspective

The RandomPerspective transform (see also perspective()) performs random perspective transform on an image.

perspective_transformer = T.RandomPerspective(distortion_scale=0.6, p=1.0)
perspective_imgs = [perspective_transformer(orig_img) for _ in range(4)]
plot(perspective_imgs)

Out:

/home/matti/miniconda3/envs/pytorch-test/lib/python3.8/site-packages/torchvision/transforms/functional.py:594: UserWarning: torch.lstsq is deprecated in favor of torch.linalg.lstsq and will be removed in a future PyTorch release.
torch.linalg.lstsq has reversed arguments and does not return the QR decomposition in the returned tuple (although it returns other information about the problem).
To get the qr decomposition consider using torch.linalg.qr.
The returned solution in torch.lstsq stored the residuals of the solution in the last m - n columns of the returned value whenever m > n. In torch.linalg.lstsq, the residuals in the field 'residuals' of the returned named tuple.
The unpacking of the solution, as in
X, _ = torch.lstsq(B, A).solution[:A.size(1)]
should be replaced with
X = torch.linalg.lstsq(A, B).solution (Triggered internally at  /opt/conda/conda-bld/pytorch_1623448216815/work/aten/src/ATen/LegacyTHFunctionsCPU.cpp:389.)
  res = torch.lstsq(b_matrix, a_matrix)[0]

RandomRotation

The RandomRotation transform (see also rotate()) rotates an image with random angle.

rotater = T.RandomRotation(degrees=(0, 180))
rotated_imgs = [rotater(orig_img) for _ in range(4)]
plot(rotated_imgs)

RandomAffine

The RandomAffine transform (see also affine()) performs random affine transform on an image.

affine_transfomer = T.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), scale=(0.5, 0.75))
affine_imgs = [affine_transfomer(orig_img) for _ in range(4)]
plot(affine_imgs)

RandomCrop

The RandomCrop transform (see also crop()) crops an image at a random location.

cropper = T.RandomCrop(size=(128, 128))
crops = [cropper(orig_img) for _ in range(4)]
plot(crops)

RandomResizedCrop

The RandomResizedCrop transform (see also resized_crop()) crops an image at a random location, and then resizes the crop to a given size.

resize_cropper = T.RandomResizedCrop(size=(32, 32))
resized_crops = [resize_cropper(orig_img) for _ in range(4)]
plot(resized_crops)

RandomInvert

The RandomInvert transform (see also invert()) randomly inverts the colors of the given image.

inverter = T.RandomInvert()
invertered_imgs = [inverter(orig_img) for _ in range(4)]
plot(invertered_imgs)

RandomPosterize

The RandomPosterize transform (see also posterize()) randomly posterizes the image by reducing the number of bits of each color channel.

posterizer = T.RandomPosterize(bits=2)
posterized_imgs = [posterizer(orig_img) for _ in range(4)]
plot(posterized_imgs)

RandomSolarize

The RandomSolarize transform (see also solarize()) randomly solarizes the image by inverting all pixel values above the threshold.

solarizer = T.RandomSolarize(threshold=192.0)
solarized_imgs = [solarizer(orig_img) for _ in range(4)]
plot(solarized_imgs)

RandomAdjustSharpness

The RandomAdjustSharpness transform (see also adjust_sharpness()) randomly adjusts the sharpness of the given image.

sharpness_adjuster = T.RandomAdjustSharpness(sharpness_factor=2)
sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
plot(sharpened_imgs)

RandomAutocontrast

The RandomAutocontrast transform (see also autocontrast()) randomly applies autocontrast to the given image.

autocontraster = T.RandomAutocontrast()
autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
plot(autocontrasted_imgs)

RandomEqualize

The RandomEqualize transform (see also equalize()) randomly equalizes the histogram of the given image.

equalizer = T.RandomEqualize()
equalized_imgs = [equalizer(orig_img) for _ in range(4)]
plot(equalized_imgs)

AutoAugment

The AutoAugment transform automatically augments data based on a given auto-augmentation policy. See AutoAugmentPolicy for the available policies.

policies = [T.AutoAugmentPolicy.CIFAR10, T.AutoAugmentPolicy.IMAGENET, T.AutoAugmentPolicy.SVHN]
augmenters = [T.AutoAugment(policy) for policy in policies]
imgs = [
    [augmenter(orig_img) for _ in range(4)]
    for augmenter in augmenters
]
row_title = [str(policy).split('.')[-1] for policy in policies]
plot(imgs, row_title=row_title)

Randomly-applied transforms

Some transforms are randomly-applied given a probability p. That is, the transformed image may actually be the same as the original one, even when called with the same transformer instance!

RandomHorizontalFlip

The RandomHorizontalFlip transform (see also hflip()) performs horizontal flip of an image, with a given probability.

hflipper = T.RandomHorizontalFlip(p=0.5)
transformed_imgs = [hflipper(orig_img) for _ in range(4)]
plot(transformed_imgs)

RandomVerticalFlip

The RandomVerticalFlip transform (see also vflip()) performs vertical flip of an image, with a given probability.

vflipper = T.RandomVerticalFlip(p=0.5)
transformed_imgs = [vflipper(orig_img) for _ in range(4)]
plot(transformed_imgs)

RandomApply

The RandomApply transform randomly applies a list of transforms, with a given probability.

applier = T.RandomApply(transforms=[T.RandomCrop(size=(64, 64))], p=0.5)
transformed_imgs = [applier(orig_img) for _ in range(4)]
plot(transformed_imgs)