InstaAug: Learning Instance-Specific Data Augmentations

This is the official codebase for the paper 'Learning Instance-Specific Data Augmentations'.

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InstaAug is a method for automatically learning input-specific augmentations from data. This is achieved by introducing an augmentation module that maps an input to a distribution over transformations. As a plug-in module, InstaAug can effeciently work with supervised and unsupervised models to learn and apply input-specific augmentations.

Requirements

• Pytorch
• Numpy

Usage

The main package is InstaAug_module and we show how to apply InstaAug to various tasks in examples. Please use the following commands to reproduce the results in the paper.

Rotation on Mario-Iggy

Change to the mixmo folder:

cd examples/mixmo-pytorch

Train the model:

python -m scripts.train --config_path config/marioiggy/exp_tinyimagenet_res18_1net_standard_bar1_test.yaml --dataplace ../../data/ --saveplace model_marioiggy/ --gpu 0 --info test --from_scratch --Li_config_path ../../InstaAug_module/configs/config_rotation_supervised.yaml

Cropping on Tiny-Imagenet

Change to the mixmo folder:

cd examples/mixmo-pytorch

First per-train the model with no augmentation:

python -m scripts.train --config_path config/tiny/exp_tinyimagenet_res18_1net_standard_bar1_test_pretrain.yaml --dataplace ../../data/ --saveplace model_tiny/ --gpu 5 --info memory --from_scratch --max_tolerance 10

Then train with InstaAug:

python -m scripts.train --config_path config/tiny/exp_tinyimagenet_res18_1net_standard_bar1_test.yaml --dataplace ../../data --saveplace model_tiny/ --gpu 0 --info test --from_scratch --Li_config_path ../../InstaAug_module/configs/config_crop_supervised.yaml --max_tolerance 10 -checkpoint model_tiny/exp_tinyimagenet_res18_1net_testmemory/checkpoint_epoch_010.ckpt

Color-jittering on RawFooT

Change to the mixmo folder:

cd examples/mixmo-pytorch

Train the model:

python -m scripts.train --config_path config/rawfoot/exp_tinyimagenet_res18_1net_standard_bar1_test_pretrain.yaml --dataplace ../../data/ --saveplace model_rawfoot/ --gpu 0 --info test --from_scratch --Li_config_path ../../InstaAug_module/configs/config_color_jittering_supervised.yaml

Contrastive learning on Tiny-Imagenet

Change to the self-supervised folder:

cd examples/self-supervised

Train the model:

python -m train --dataset tiny_in --epoch 500 --lr 2e-3 --emb 128 --method contrastive--model_folder model/test --Li_config_path ../../InstaAug_module/configs/config_crop_contrastive.yaml--eval_every 50 --crop_s0 1.0 --crop_s1 1.0 --crop_r0 1.0 --crop_r1 1.0 --wandb_name test --entropy_weights 0.003 --num_workers 4 --target_entropy 3.7