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Data augmentation

The goal

What is available as data augmentation methods in torchvision?

Questions to David Rotermund

Initial Image:

Initial Image Photo by Udo Ernst

Loading an example image (with opencv2)

Load it via cv2.imread( filename[, flags]) -> retval

import cv2
import matplotlib.pyplot as plt

filename: str = "data_augmentation_test_image.jpg"

original_image = cv2.imread(filename)

plt.imshow(original_image)
plt.show()

image0

As you can see (not very well I might add) is that the color channels are wrong. But may be we want no color anyway ( options can be found here ):

original_image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)

plt.imshow(original_image, cmap="gray")
plt.show()

image1

import numpy as np

original_image = cv2.imread(filename, cv2.IMREAD_COLOR)

# "Convert" from BlueGreenRed (BGR) to RGB (RedGreenBlue)
# This is a flip in the third dimension.
original_image = np.flip(original_image, axis=2)
plt.imshow(original_image)
plt.show()

Torchvision: A selection of transformations

Into PyTorch

First we need to convert the np.ndarray into a suitable torch tensor

image2

import torch

torch_image = torch.tensor(
    np.moveaxis(original_image.astype(dtype=np.float32) / 255.0, 2, 0)
)
print(torch_image.shape) # -> torch.Size([3, 1200, 1600])

Note: For the following random opertions, we can control the random seed of torch via torch.manual_seed(seed).

Some example transformations from torchvision:

torchvision.transforms.Pad(padding, fill=0, padding_mode=’constant’) 

import torchvision as tv

pad_transform = tv.transforms.Pad(padding=(50, 100), fill=0.5)
new_image = pad_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image3

torchvision.transforms.RandomHorizontalFlip(p=0.5)

Horizontally flip the given image randomly with a given probability.

torchvision.transforms.RandomVerticalFlip(p=0.5)

Vertically flip the given image randomly with a given probability.

torchvision.transforms.Resize(size, interpolation=<InterpolationMode.BILINEAR: ‘bilinear’>, max_size=None, antialias=None)

The Resize transform resizes an image.

resize_transform = tv.transforms.Resize(size=(50, 100))
new_image = resize_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image4

torchvision.transforms.CenterCrop(size)

The CenterCrop transform crops the given image at the center.

center_crop_transform = tv.transforms.CenterCrop(size=(250, 200))
new_image = center_crop_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image5

torchvision.transforms.FiveCrop(size)

The FiveCrop transform crops the given image into four corners and the central crop.

position = (1, 3, 7, 9, 5)
five_crop_transform = tv.transforms.FiveCrop(size=(250, 200))
new_image = five_crop_transform(torch_image)

for i, p in enumerate(position):
    plt.subplot(3, 3, p)
    plt.imshow(np.moveaxis(new_image[i].detach().numpy(), 0, 2))

plt.show()

image6

torchvision.transforms.TenCrop(size, vertical_flip=False)

Crop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default).

torchvision.transforms.Grayscale(num_output_channels=1)

The Grayscale transform converts an image to grayscale.

gray_transform = tv.transforms.Grayscale()
new_image = gray_transform(torch_image)
plt.imshow(new_image.squeeze().detach().numpy(), cmap="gray")
plt.show()

image7

torchvision.transforms.RandomGrayscale(p=0.1)

Randomly convert image to grayscale with a probability of p (default 0.1).

torchvision.transforms.RandomInvert(p=0.5)

Inverts the colors of the given image randomly with a given probability.

random_invert_transform = tv.transforms.RandomInvert(p=0.5)
for i in range(1, 3):
    new_image = random_invert_transform(torch_image)
    plt.subplot(2, 1, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image14

torchvision.transforms.Normalize(mean, std, inplace=False)

Normalize a tensor image with mean and standard deviation.

torchvision.transforms.RandomEqualize(p=0.5)

Equalize the histogram of the given image randomly with a given probability.

torchvision.transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)

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

color_jitter_transform = tv.transforms.ColorJitter(brightness=0.75, hue=0.5)
for i in range(1, 10):
    new_image = color_jitter_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image8

torchvision.transforms.GaussianBlur(kernel_size, sigma=(0.1, 2.0))

The GaussianBlur transform performs gaussian blur transform on an image.

Note: Big kernel sizes are slow. (51,51) is rather big. Kernel size needs to be odd and positive.

gauss_transform = tv.transforms.GaussianBlur(kernel_size=(101, 101), sigma=(0.1, 10))
new_image = gauss_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image9

torchvision.transforms.RandomPerspective(distortion_scale=0.5, p=0.5, interpolation=<InterpolationMode.BILINEAR: ‘bilinear’>, fill=0)

The RandomPerspective transform performs random perspective transform on an image.

random_perspective_transform = tv.transforms.RandomPerspective(
    distortion_scale=0.6, p=1.0
)
for i in range(1, 10):
    new_image = random_perspective_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image10

torchvision.transforms.RandomRotation(degrees, interpolation=<InterpolationMode.NEAREST: ‘nearest’>, expand=False, center=None, fill=0, resample=None)

The RandomRotation transform rotates an image with random angle.

random_rotation_transform = tv.transforms.RandomRotation(degrees=(0, 180))
for i in range(1, 10):
    new_image = random_rotation_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image11

torchvision.transforms.RandomAffine(degrees, translate=None, scale=None, shear=None, interpolation=<InterpolationMode.NEAREST: ‘nearest’>, fill=0, fillcolor=None, resample=None)

The RandomAffine transform performs random affine transform on an image.

random_affine_transform = tv.transforms.RandomAffine(degrees=(0, 180))
for i in range(1, 10):
    new_image = random_affine_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image12

torchvision.transforms.RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode=’constant’)

The RandomCrop transform crops an image at a random location.

random_crop_transform = tv.transforms.RandomCrop(size=(250, 200))
for i in range(1, 10):
    new_image = random_crop_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image13

torchvision.transforms.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=<InterpolationMode.BILINEAR: ‘bilinear’>)

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

torchvision.transforms.RandomPosterize(bits, p=0.5)

Posterize the image randomly with a given probability by reducing the number of bits for each color channel.

for i in range(1, 5):
    random_posterize_transform = tv.transforms.RandomPosterize(bits=i, p=1.0)
    new_image = random_posterize_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.subplot(2, 2, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image15

torchvision.transforms.RandomSolarize(threshold, p=0.5)

Solarize the image randomly with a given probability by inverting all pixel values above a threshold.

random_solarize_transform = tv.transforms.RandomSolarize(threshold=0.5)
new_image = random_solarize_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image16

torchvision.transforms.RandomAdjustSharpness(sharpness_factor, p=0.5)

Adjust the sharpness of the image randomly with a given probability.

random_sharpness_transform = tv.transforms.RandomAdjustSharpness(
    sharpness_factor=50, p=1.0
)
new_image = random_sharpness_transform(torch_image)
plt.subplot(1, 2, 1)
plt.imshow(np.moveaxis(torch_image.detach().numpy(), 0, 2))
plt.subplot(1, 2, 2)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image17

torchvision.transforms.RandomAutocontrast(p=0.5)

Autocontrast the pixels of the given image randomly with a given probability.

I don’t see any effect.

random_autocontrast_transform = tv.transforms.RandomAutocontrast(p=1.0)

new_image = random_autocontrast_transform(torch_image)
plt.subplot(1, 2, 1)
plt.imshow(np.moveaxis(torch_image.detach().numpy(), 0, 2))
plt.subplot(1, 2, 2)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image18

torchvision.transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False)

Randomly selects a rectangle region in an torch Tensor image and erases its pixels.

random_erasing_transform = tv.transforms.RandomErasing(p=1.0)

new_image = random_erasing_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image19

Predefined processing chains

torchvision.transforms.AutoAugment(policy: torchvision.transforms.autoaugment.AutoAugmentPolicy = <AutoAugmentPolicy.IMAGENET: ‘imagenet’>, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: ‘nearest’>, fill: Optional[List[float]] = None)

AutoAugment data augmentation method based on “AutoAugment: Learning Augmentation Strategies from Data”.

torchvision.transforms.AutoAugmentPolicy(value)

AutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN.

CIFAR10

random_auto1_transform = tv.transforms.AutoAugment(
    tv.transforms.AutoAugmentPolicy.CIFAR10
)
for i in range(1, 10):
    new_image = random_auto1_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image20

IMAGENET

random_auto2_transform = tv.transforms.AutoAugment(
    tv.transforms.AutoAugmentPolicy.IMAGENET
)
for i in range(1, 10):
    new_image = random_auto2_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image21

SVHN

random_auto3_transform = tv.transforms.AutoAugment(tv.transforms.AutoAugmentPolicy.SVHN)
for i in range(1, 10):
    new_image = random_auto3_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image22

Building custom processing chains

torch.nn.Sequential(*args)

A sequential container. Modules will be added to it in the order they are passed in the constructor.

sequential_transform = torch.nn.Sequential(
    tv.transforms.RandomSolarize(threshold=0.5, p=1.0),
    tv.transforms.RandomErasing(p=1.0),
)
new_image = sequential_transform((torch_image * 255).type(dtype=torch.uint8))
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Depending on the transformation used, I can be possible to just-in-time (jit) compile it.

sequential_transform_jit = torch.jit.script(sequential_transform)

image23

torchvision.transforms.Compose(transforms)

Composes several transforms together. This transform does not support torchscript.

compose_transform = tv.transforms.Compose(
    [
        tv.transforms.RandomSolarize(threshold=0.5, p=1.0),
        tv.transforms.RandomErasing(p=1.0),
    ]
)
new_image = compose_transform((torch_image * 255).type(dtype=torch.uint8))
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image24

torchvision.transforms.RandomApply(transforms, p=0.5)

Apply randomly a list of transformations with a given probability.

Note: It randomly applies the whole list of transformation or none.

randomapply_transform = tv.transforms.RandomApply(
    [
        tv.transforms.RandomSolarize(threshold=0.5, p=1.0),
        tv.transforms.RandomErasing(p=1.0),
    ],
    p=0.5,
)
for i in range(1, 3):
    plt.subplot(2, 1, i)
    new_image = randomapply_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

image25

Building your own filter

In the case you need a special filter then you just can write it very easily on your own. Here is an example.

import torch


class OnOffFilter(torch.nn.Module):
    def __init__(self, p: float = 0.5) -> None:
        super(OnOffFilter, self).__init__()

        self.p: float = p

    def forward(self, tensor: torch.Tensor) -> torch.Tensor:

        assert tensor.shape[1] == 1

        tensor -= self.p
        temp_0: torch.Tensor = torch.where(
            tensor < 0.0, -tensor, tensor.new_zeros(tensor.shape, dtype=tensor.dtype)
        )
        temp_1: torch.Tensor = torch.where(
            tensor >= 0.0, tensor, tensor.new_zeros(tensor.shape, dtype=tensor.dtype)
        )

        new_tensor: torch.Tensor = torch.cat((temp_0, temp_1), dim=1)

        return new_tensor

    def __repr__(self):
        return self.__class__.__name__ + "(p={0})".format(self.p)


if __name__ == "__main__":
    pass

The source code is Open Source and can be found on GitHub.