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FixMatch

Code for the paper: "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence" by Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel.

This is not an officially supported Google product.

FixMatch diagram

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

Install datasets

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

# 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 10 20 30 40 100 250 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 $ML_DATA/cifar10-train.tfrecord &
    done
    for size in 400 1000 2500 10000; do
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL2/cifar100 $ML_DATA/cifar100-train.tfrecord &
    done
    CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$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

ImageNet

Codebase for ImageNet experiments located in the imagenet subdirectory.

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 FixMatch with 32 filters on cifar10 shuffled with seed=3, 40 labeled samples and 1 validation sample:

CUDA_VISIBLE_DEVICES=0 python fixmatch.py --filters=32 --dataset=cifar10.3@40-1 --train_dir ./experiments/fixmatch

Available labelled sizes are 10, 20, 30, 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 fixmatch automatically scales to them, here we assign GPUs 4-7 to the program:

CUDA_VISIBLE_DEVICES=4,5,6,7 python fixmatch.py --filters=32 --dataset=cifar10.3@40-1 --train_dir ./experiments/fixmatch

Flags

python fixmatch.py --help
# The following option might be too slow to be really practical.
# python fixmatch.py --helpfull
# So instead I use this hack to find the flags:
fgrep -R flags.DEFINE libml fixmatch.py

The --augment flag can use a little more explanation. It is composed of 3 values, for example d.d.d (d=default augmentation, for example shift/mirror, x=identity, e.g. no augmentation, ra=rand-augment, rac=rand-augment + cutout):

  • the first d refers to data augmentation to apply to the labeled example.
  • the second d refers to data augmentation to apply to the weakly augmented unlabeled example.
  • the third d refers to data augmentation to apply to the strongly augmented unlabeled example. For the strong augmentation, d is followed by CTAugment for fixmatch.py and code inside cta/ folder.

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 10 20 30 40 100 250 1000 4000; do
    echo "${dataset}.${seed}@${size}-${valid}"
done; done; done; done

for seed in 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/fixmatch/cifar10.d.d.d.3@40-1/CTAugment_depth2_th0.80_decay0.990/FixMatch_archresnet_batch64_confidence0.95_filters32_lr0.03_nclass10_repeat4_scales3_uratio7_wd0.0005_wu1.0/

# 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.

Adding datasets

You can add custom datasets into the codebase by taking the following steps:

  1. Add a function to acquire the dataset to scripts/create_datasets.py similar to the present ones, e.g. _load_cifar10. You need to call _encode_png to create encoded strings from the original images. The created function should return a dictionary of the format {'train' : {'images': <encoded 4D NHWC>, 'labels': <1D int array>}, 'test' : {'images': <encoded 4D NHWC>, 'labels': <1D int array>}} .
  2. Add the dataset to the variable CONFIGS in scripts/create_datasets.py with the previous function as loader. You can now run the create_datasets script to obtain a tf record for it.
  3. Use the create_unlabeled and create_split script to create unlabeled and differently split tf records as above in the Install Datasets section.
  4. In libml/data.py add your dataset in the create_datasets function. The specified "label" for the dataset has to match the created splits for your dataset. You will need to specify the corresponding variables if your dataset has a different # of classes than 10 and different resolution and # of channels than 32x32x3
  5. In libml/augment.py add your dataset to the DEFAULT_AUGMENT variable. Primitives "s", "m", "ms" represent mirror, shift and mirror+shift.

Citing this work

@article{sohn2020fixmatch,
    title={FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence},
    author={Kihyuk Sohn and David Berthelot and Chun-Liang Li and Zizhao Zhang and Nicholas Carlini and Ekin D. Cubuk and Alex Kurakin and Han Zhang and Colin Raffel},
    journal={arXiv preprint arXiv:2001.07685},
    year={2020},
}