Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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# Data augmentation
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## The goal
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What is available as data augmentation methods in torchvision?
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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Initial Image:
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Photo by Udo Ernst
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## Loading an example image (with opencv2)
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Load it via [cv2.imread( filename[, flags]) -> retval](https://docs.opencv.org/4.5.3/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56)
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```python
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import cv2
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@ -18,6 +35,7 @@ plt.show()
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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](https://docs.opencv.org/4.5.3/d8/d6a/group__imgcodecs__flags.html#ga61d9b0126a3e57d9277ac48327799c80) ):
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```python
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original_image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
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@ -41,8 +59,12 @@ plt.imshow(original_image)
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plt.show()
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```
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## Torchvision: A selection of transformations
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### Into PyTorch
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First we need to convert the np.ndarray into a suitable torch tensor
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```python
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@ -54,7 +76,12 @@ torch_image = torch.tensor(
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print(torch_image.shape) # -> torch.Size([3, 1200, 1600])
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```
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### Pad
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Note: For the following random opertions, we can control the random seed of torch via [torch.manual_seed(seed)](https://pytorch.org/docs/stable/generated/torch.manual_seed.html).
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Some example transformations from [torchvision](https://pytorch.org/vision/stable/transforms.html):
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### [torchvision.transforms.Pad(padding, fill=0, padding_mode='constant') ](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Pad)
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```python
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import torchvision as tv
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@ -68,7 +95,17 @@ plt.show()
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### Resize
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### [torchvision.transforms.RandomHorizontalFlip(p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomHorizontalFlip)
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Horizontally flip the given image randomly with a given probability.
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### [torchvision.transforms.RandomVerticalFlip(p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomVerticalFlip)
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Vertically flip the given image randomly with a given probability.
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### [torchvision.transforms.Resize(size, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, max_size=None, antialias=None)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Pad)
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The Resize transform resizes an image.
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```python
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resize_transform = tv.transforms.Resize(size=(50, 100))
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@ -79,7 +116,9 @@ plt.show()
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### CenterCrop
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### [torchvision.transforms.CenterCrop(size)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.CenterCrop)
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The CenterCrop transform crops the given image at the center.
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```python
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center_crop_transform = tv.transforms.CenterCrop(size=(250, 200))
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@ -90,7 +129,9 @@ plt.show()
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### FiveCrop
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### [torchvision.transforms.FiveCrop(size)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.FiveCrop)
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The FiveCrop transform crops the given image into four corners and the central crop.
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```python
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position = (1, 3, 7, 9, 5)
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### [torchvision.transforms.TenCrop(size, vertical_flip=False)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Scale)
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### Grayscale
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Crop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default).
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### [torchvision.transforms.Grayscale(num_output_channels=1)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Grayscale)
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The Grayscale transform converts an image to grayscale.
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```python
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gray_transform = tv.transforms.Grayscale()
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@ -119,7 +164,36 @@ plt.show()
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### ColorJitter
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### [torchvision.transforms.RandomGrayscale(p=0.1)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomGrayscale)
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Randomly convert image to grayscale with a probability of p (default 0.1).
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### [torchvision.transforms.RandomInvert(p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomInvert)
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Inverts the colors of the given image randomly with a given probability.
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```python
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random_invert_transform = tv.transforms.RandomInvert(p=0.5)
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for i in range(1, 3):
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new_image = random_invert_transform(torch_image)
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plt.subplot(2, 1, i)
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plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
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plt.show()
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```
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### [torchvision.transforms.Normalize(mean, std, inplace=False)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Normalize)
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Normalize a tensor image with mean and standard deviation.
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### [torchvision.transforms.RandomEqualize(p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomEqualize)
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Equalize the histogram of the given image randomly with a given probability.
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### [torchvision.transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ColorJitter)
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The ColorJitter transform randomly changes the brightness, saturation, and other properties of an image.
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```python
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color_jitter_transform = tv.transforms.ColorJitter(brightness=0.75, hue=0.5)
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@ -133,7 +207,11 @@ plt.show()
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### Gaussian Blur
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### [torchvision.transforms.GaussianBlur(kernel_size, sigma=(0.1, 2.0))](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.GaussianBlur)
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The GaussianBlur transform performs gaussian blur transform on an image.
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Note: Big kernel sizes are slow. (51,51) is rather big. Kernel size needs to be odd and positive.
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```python
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gauss_transform = tv.transforms.GaussianBlur(kernel_size=(101, 101), sigma=(0.1, 10))
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### Random Perspective
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### [torchvision.transforms.RandomPerspective(distortion_scale=0.5, p=0.5, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, fill=0)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomPerspective)
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The RandomPerspective transform performs random perspective transform on an image.
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```python
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random_perspective_transform = tv.transforms.RandomPerspective(
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### Random Rotation
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### [torchvision.transforms.RandomRotation(degrees, interpolation=<InterpolationMode.NEAREST: 'nearest'>, expand=False, center=None, fill=0, resample=None)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomRotation)
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The RandomRotation transform rotates an image with random angle.
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```python
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random_rotation_transform = tv.transforms.RandomRotation(degrees=(0, 180))
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for i in range(1, 10):
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### Random Affine
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### [torchvision.transforms.RandomAffine(degrees, translate=None, scale=None, shear=None, interpolation=<InterpolationMode.NEAREST: 'nearest'>, fill=0, fillcolor=None, resample=None)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomAffine)
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The RandomAffine transform performs random affine transform on an image.
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```python
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random_affine_transform = tv.transforms.RandomAffine(degrees=(0, 180))
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### Random Crop
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### [torchvision.transforms.RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant')](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomCrop)
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The RandomCrop transform crops an image at a random location.
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```python
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random_crop_transform = tv.transforms.RandomCrop(size=(250, 200))
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### Random Invert
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```python
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random_invert_transform = tv.transforms.RandomInvert(p=0.5)
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for i in range(1, 3):
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new_image = random_invert_transform(torch_image)
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plt.subplot(2, 1, i)
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plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
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plt.show()
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```
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### [torchvision.transforms.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=<InterpolationMode.BILINEAR: 'bilinear'>)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomResizedCrop)
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The RandomResizedCrop transform crops an image at a random location, and then resizes the crop to a given size.
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### Random Posterize
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### [torchvision.transforms.RandomPosterize(bits, p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomPosterize)
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Posterize the image randomly with a given probability by reducing the number of bits for each color channel.
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```python
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for i in range(1, 5):
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### Random Solarize
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### [torchvision.transforms.RandomSolarize(threshold, p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomSolarize)
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Solarize the image randomly with a given probability by inverting all pixel values above a threshold.
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```python
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random_solarize_transform = tv.transforms.RandomSolarize(threshold=0.5)
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### Random Adjust Sharpness
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### [torchvision.transforms.RandomAdjustSharpness(sharpness_factor, p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomAdjustSharpness)
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Adjust the sharpness of the image randomly with a given probability.
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```python
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random_sharpness_transform = tv.transforms.RandomAdjustSharpness(
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### Random Autocontrast
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### [torchvision.transforms.RandomAutocontrast(p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomAutocontrast)
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Autocontrast the pixels of the given image randomly with a given probability.
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I don't see any effect.
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```python
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random_autocontrast_transform = tv.transforms.RandomAutocontrast(p=1.0)
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### Random Erasing
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### [torchvision.transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomErasing)
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Randomly selects a rectangle region in an torch Tensor image and erases its pixels.
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```python
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random_erasing_transform = tv.transforms.RandomErasing(p=1.0)
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### Auto Augment
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## Predefined processing chains
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[torchvision.transforms.AutoAugment(policy: torchvision.transforms.autoaugment.AutoAugmentPolicy = <AutoAugmentPolicy.IMAGENET: 'imagenet'>, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.AutoAugment)
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AutoAugment data augmentation method based on [“AutoAugment: Learning Augmentation Strategies from Data”](https://arxiv.org/pdf/1805.09501.pdf).
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[torchvision.transforms.AutoAugmentPolicy(value)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.AutoAugmentPolicy)
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AutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN.
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#### CIFAR10
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## Sequential
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## Building custom processing chains
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### [torch.nn.Sequential(*args)](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential)
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A sequential container. Modules will be added to it in the order they are passed in the constructor.
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```python
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sequential_transform = torch.nn.Sequential(
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plt.show()
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```
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Depending on the transformation used, I can be possible to just-in-time (jit) compile it.
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```python
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sequential_transform_jit = torch.jit.script(sequential_transform)
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```
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## Compose
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### [torchvision.transforms.Compose(transforms)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Compose)
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Composes several transforms together. **This transform does not support torchscript.**
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```python
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compose_transform = tv.transforms.Compose(
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# Random Apply
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### [torchvision.transforms.RandomApply(transforms, p=0.5)](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomApply)
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Apply randomly a list of transformations with a given probability.
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**Note: It randomly applies the whole list of transformation or none. **
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```python
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randomapply_transform = tv.transforms.RandomApply(
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