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How to use AutoAlbument

How to use AutoAlbument

  1. You need to create a configuration file with AutoAlbument parameters and a Python file that implements a custom PyTorch Dataset for your data. Next, you need to pass those files to AutoAlbument.
  2. AutoAlbument will use Generative Adversarial Network to discover augmentation policies and then create a file containing those policies.
  3. Finally, you can use Albumentations to load augmentation policies from the file and utilize them in your computer vision pipeline.

Step 1. Create a configuration file and a custom PyTorch Dataset for your data.

a. Create a directory with configuration files.

Run autoalbument-create --config-dir </path/to/directory> --task <deep_learning_task> --num-classes <num_classes>, e.g. autoalbument-create --config-dir ~/experiments/autoalbument-search-cifar10 --task classification --num-classes 10. - A value for the --config-dir option should contain a path to the directory. AutoAlbument will create this directory and put two files into it: and search.yaml (more on them later). - A value for the --task option should contain the name of a deep learning task. Supported values are classification and semantic_segmentation. - A value for the --num-classes option should contain the number of distinct classes in the classification or segmentation dataset.

By default, AutoAlbument creates a search.yaml file that contains only most important configuration parameters. To explore all avaiable parameters you can create a config file that contans them all by providing the --generate-full-config argument, e.g. autoalbument-create --config-dir ~/experiments/autoalbument-search-cifar10 --task classification --num-classes 10 --generate-full-config

b. Add implementation for __len__ and __getitem__ methods in

The file created at step 1 by autoalbument-create contains stubs for implementing a PyTorch dataset (you can read more about creating custom PyTorch datasets here). You need to add implementation for __len__ and __getitem__ methods (and optionally add the initialization logic if required).

A dataset for a classification task should return an image and a class label. A dataset for a segmentation task should return an image and an associated mask.

c. [Optional] Adjust search parameters in search.yaml.

You may want to change the parameters that AutoAlbument will use to search for augmentation policies. To do this, you need to edit the search.yaml file created by autoalbument-create at step 1. Each configuration parameter contains a comment that describes the meaning of the setting. Please refer to the "Tuning the search parameters" section that includes a description of the most critical parameters.

search.yaml is a Hydra config file. You can use all Hydra features inside it.

Step 2. Use AutoAlbument to search for augmentation policies.

To search for augmentation policies, run autoalbument-search --config-dir </path/to/directory>, e.g. autoalbument-search --config-dir ~/experiments/autoalbument-search-cifar10. The value of --config-dir should be the same value that was passed to autoalbument-create at step 1.

autoalbument-search will create a directory with output files (by default the path of the directory will be <config_dir>/outputs/<current_date>/<current_time>, but you can customize it in search.yaml). The policy subdirectory will contain JSON files with policies found at each search phase's epoch.

autoalbument-search is a command wrapped with the @hydra.main decorator from Hydra. You can use all Hydra features when calling this command.

AutoAlbument uses PyTorch to search for augmentation policies. You can speed up the search by using a CUDA-capable GPU.

Step 3. Use Albumentations to load augmentation policies and utilize them in your training pipeline.

AutoAlbument produces a JSON file that contains a configuration for an augmentation pipeline. You can load that JSON file with Albumentations:

import albumentations as A
transform = A.load("/path/to/policy.json")

Then you can use the created augmentation pipeline to augment the input data.

For example, to augment an image for a classification task:

transformed = transform(image=image)
transformed_image = transformed["image"]

To augment an image and a mask for a semantic segmentation task:

transformed = transform(image=image, mask=mask)
transformed_image = transformed["image"]
transformed_mask = transformed["mask"]

Additional resources