ReMixMatch

Code for the paper: "ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring" by David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel.

This is not an officially supported Google product.

Setup

Important: ML_DATA is a shell environment variable that should point to the location where the datasets are installed. See the Install datasets section for more details.

Install dependencies

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3 --system-site-packages env3
. env3/bin/activate
pip install -r requirements.txt

Conda

conda create --name <env_name>  --file requirements.txt -y
conda activate <env_name>
conda install -c conda-forge easydict -y
conda install -c anaconda pillow -y
conda install -c bioconda perl-xml-libxml -y

Install datasets

export ML_DATA="path to where you want the datasets saved"
# Download datasets
CUDA_VISIBLE_DEVICES= ./scripts/create_datasets.py
cp $ML_DATA/svhn-test.tfrecord $ML_DATA/svhn_noextra-test.tfrecord

# Create unlabeled datasets
CUDA_VISIBLE_DEVICES= scripts/create_unlabeled.py $ML_DATA/SSL2/svhn $ML_DATA/svhn-train.tfrecord $ML_DATA/svhn-extra.tfrecord &
CUDA_VISIBLE_DEVICES= scripts/create_unlabeled.py $ML_DATA/SSL2/svhn_noextra $ML_DATA/svhn-train.tfrecord &
CUDA_VISIBLE_DEVICES= scripts/create_unlabeled.py $ML_DATA/SSL2/cifar10 $ML_DATA/cifar10-train.tfrecord &
CUDA_VISIBLE_DEVICES= scripts/create_unlabeled.py $ML_DATA/SSL2/cifar100 $ML_DATA/cifar100-train.tfrecord &
CUDA_VISIBLE_DEVICES= scripts/create_unlabeled.py $ML_DATA/SSL2/stl10 $ML_DATA/stl10-train.tfrecord $ML_DATA/stl10-unlabeled.tfrecord &
wait

# Create semi-supervised subsets
for seed in 0 1 2 3 4 5; do
    for size in 40 250 1000 4000; do
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL2/svhn $ML_DATA/svhn-train.tfrecord $ML_DATA/svhn-extra.tfrecord &
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL2/svhn_noextra $ML_DATA/svhn-train.tfrecord &
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL2/cifar10 $ML_DATA/cifar10-train.tfrecord &
    done
    CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=10000 $ML_DATA/SSL2/cifar100 $ML_DATA/cifar100-train.tfrecord &
    CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=2500 $ML_DATA/SSL2/cifar100 $ML_DATA/cifar100-train.tfrecord &
    CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=1000 $ML_DATA/SSL2/stl10 $ML_DATA/stl10-train.tfrecord $ML_DATA/stl10-unlabeled.tfrecord &
    wait
done
CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=1 --size=5000 $ML_DATA/SSL2/stl10 $ML_DATA/stl10-train.tfrecord $ML_DATA/stl10-unlabeled.tfrecord

Running

Setup

All commands must be ran from the project root. The following environment variables must be defined:

export ML_DATA="path to where you want the datasets saved"
export PYTHONPATH=$PYTHONPATH:.

Example

For example, training a remixmatch with 32 filters and 4 augmentations on cifar10 shuffled with seed=3, 250 labeled samples and 5000 validation samples:

CUDA_VISIBLE_DEVICES=0 python cta/cta_remixmatch.py --filters=32 --K=4 --dataset=cifar10.3@250-5000 --w_match=1.5 --beta=0.75 --train_dir ./experiments/remixmatch

Available labelled sizes are 40, 100, 250, 1000, 4000. For validation, available sizes are 1, 5000. Possible shuffling seeds are 1, 2, 3, 4, 5 and 0 for no shuffling (0 is not used in practiced since data requires to be shuffled for gradient descent to work properly).

Multi-GPU training

Just pass more GPUs and remixmatch automatically scales to them, here we assign GPUs 4-7 to the program:

CUDA_VISIBLE_DEVICES=4,5,6,7 python cta/cta_remixmatch.py --filters=32 --K=4 --dataset=cifar10.3@250-5000 --w_match=1.5 --beta=0.75 --train_dir ./experiments/remixmatch

Valid dataset names

for dataset in cifar10 svhn svhn_noextra; do
for seed in 0 1 2 3 4 5; do
for valid in 1 5000; do
for size in 40 250 1000 4000; do
    echo "${dataset}.${seed}@${size}-${valid}"
done; done; done; done

for seed in 0 1 2 3 4 5; do
for valid in 1 5000; do
    echo "cifar100.${seed}@10000-${valid}"
done; done

for seed in 1 2 3 4 5; do
for valid in 1 5000; do
    echo "stl10.${seed}@1000-${valid}"
done; done
echo "stl10.1@5000-1"

Monitoring training progress

You can point tensorboard to the training folder (by default it is --train_dir=./experiments) to monitor the training process:

tensorboard.sh --port 6007 --logdir experiments

Checkpoint accuracy

We compute the median accuracy of the last 20 checkpoints in the paper, this is done through this code:

# Following the previous example in which we trained cifar10.3@250-5000, extracting accuracy:
./scripts/extract_accuracy.py experiments/cifar10.d.d.d.3\@250-5000/CTAugment_depth2_th0.80_decay0.990/CTAReMixMatch_K4_archresnet_batch64_beta0.75_filters32_lr0.002_nclass10_redux1st_repeat4_scales3_use_dmTrue_use_xeTrue_w_kl0.5_w_match1.5_w_rot0.5_warmup_kimg1024_wd0.02/
# The command above will create a stats/accuracy.json file in the model folder.
# The format is JSON so you can either see its content as a text file or process it to your liking.

Reproducing tables from the paper

Check the contents of the runs/*.sh files, these will give you the commands (and the hyper-parameters) to reproduce the results from the paper.

Citing this work

@article{berthelot2019remixmatch,
    title={ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring},
    author={David Berthelot and Nicholas Carlini and Ekin D. Cubuk and Alex Kurakin and Kihyuk Sohn and Han Zhang and Colin Raffel},
    journal={arXiv preprint arXiv:1911.09785},
    year={2019},
}