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byol

README.md

Bootstrap Your Own Latent

This is the implementation of the pre-training and linear evaluation pipeline of BYOL - https://arxiv.org/abs/2006.07733.

Using this implementation should achieve a top-1 accuracy on Imagenet between 74.0% and 74.5% after about 8h of training using 512 Cloud TPU v3.

The main pretraining module is byol_experiment.py. By default it uses BYOL to pretrain a Resnet-50 on Imagenet. In parallel, we train a classifier on top of the representation to assess its performance during training. This classifier does not back-propagate any gradient to the ResNet-50.

The evaluation module is eval_experiment.py. It evaluates the performance of the representation learnt by BYOL (using a given checkpoint).

Setup

To set up a Python virtual environment with the required dependencies, run:

python3 -m venv byol_env
source byol_env/bin/activate
pip install --upgrade pip
pip install -r byol/requirements.txt

The code uses tensorflow_datasets to load the Imagenet dataset. Manual download may be required; see https://www.tensorflow.org/datasets/catalog/imagenet2012 for details.

Running a short pre-training locally

To run a short (40 epochs) pre-training experiment on a local machine, use:

mkdir /tmp/byol_checkpoints
python -m byol.main_loop \
  --experiment_mode='pretrain' \
  --worker_mode='train' \
  --checkpoint_root='/tmp/byol_checkpoints' \
  --pretrain_epochs=40

Full pipeline and presets

The various parts of the pipeline can be run using:

python -m byol.main_loop \
  --experiment_mode=<'pretrain' or 'linear-eval'> \
  --worker_mode=<'train' or 'eval'> \
  --checkpoint_root=</path/to/the/checkpointing/folder> \
  --pretrain_epochs=<40, 100, 300 or 1000>

Pretraining

Setting --experiment_mode=pretrain will configure the main loop for pretraining; we provide presets for 40, 100, 300 and 1000 epochs.

Use --worker_mode=train for a training job, which will regularly save checkpoints under <checkpoint_root>/pretrain.pkl. To monitor the progress of the pretraining, you can run a second worker (using --worker_mode=eval) with the same checkpoint_root setting. This worker will regularly load the checkpoint and evaluate the performance of a linear classifier (trained by the pretraining train worker) on the TEST set.

Note that the default settings are set for large-scale training on Cloud TPUs, with a total batch size of 4096. To avoid the need to re-run the full experiment, we provide the following pre-trained checkpoints:

  • ResNet-50 1x (570 MB): should evaluate to ~74.4% top-1 accuracy.
  • ResNet-200 2x (4.6GB): should evaluate to ~79.6% top-1 accuracy.

Linear evaluation

Setting --experiment_mode=linear-eval will configure the main loop for linear evaluation; we provide presets for 80 epochs.

Use --worker_mode=train for a training job, which will load the encoder weights from an existing checkpoint (form a pretrain experiment) located at <checkpoint_root>/pretrain.pkl, and train a linear classifier on top of this encoder. The weights from the linear classifier trained in the pretraining phase will be discarded.

The training job will regularly save checkpoints under <checkpoint_root>/linear-eval.pkl. You can run a second worker (using --worker_mode=eval) with the same checkpoint_root setting to regularly load the checkpoint and evaluate the performance of the classifier (trained by the linear-eval train worker) on the test set.

Note that the above will run a simplified version of the linear evaluation pipeline described in the paper, with a single value of the base learning rate and without using a validation set. To fully reproduce the results from the paper, one should run 5 instances of the linear-eval train worker (using only the TRAIN subset, and each using a different checkpoint_root), run the eval worker (using only the VALID subset) for each checkpoint, then run a final eval worker on the TEST set.

Note on batch normalization

We found that using Goyal et al.'s initialization for the batch-normalization (i.e., initializing the scaling coefficient gamma to 0 in the last batchnorm of each residual block) led to more stable training, but slightly harms BYOL's performance for very large networks (e.g., ResNet-50 (3x), ResNet-200 (2x)). We didn't observe any change in performance for smaller networks (ResNet-50 (1x) and (2x)).

Results in the paper were obtained without this modified initialization, i.e. using Haiku's default of $\gamma = 1$. To fully reproduce, please remove the scale_init argument in Haiku's ResNet BlockV1.

Running on GCP

Notice: we currently do not recommend running the full experiment on public Cloud TPUs. We provide an alternative small-scale GPU setup in the next section.

The experiments from the paper were run on Google's internal infrastructure. Unfortunately, training JAX models on publicly available Cloud TPUs is currently in its early stages; in particular, we found the learning to be heavily bottlenecked by the data loading pipeline, resulting in a significantly slower training. We will update the code and instructions below once the limitation is addressed.

To train the model on TPU using Google Compute Engine, please follow the instructions at https://cloud.google.com/tpu/docs/imagenet-setup to first download and preprocess the ImageNet dataset and upload it to a Cloud Storage Bucket. From a GCE VM, you can check that tensorflow_datasets can correctly load the dataset by running:

import tensorflow_datasets as tfds
tfds.load('imagenet2012', data_dir='gs://<your-bucket-name>')

To learn how to use JAX with Cloud TPUs, please follow the instructions here: https://github.com/google/jax/tree/master/cloud_tpu_colabs.

Setup for fast iteration

In order to make reproduction easier, it is possible to change the training setup to use the smaller imagenette dataset (9469 training images with 10 classes). The following setup and hyperparameters can be used on a machine with a single V100 GPU:

  • in utils/dataset.py:
    • update Split.num_examples with the figures from tfds (with Split.VALID: 0)
    • use imagenette/160px-v2 in the call to tfds.load
    • use 128x128 px images (i.e., replace all instances of 224 by 128)
    • it doesn't seem necessary to change the color normalization (make sure to not replace the value 0.224 by mistake in the previous step).
  • in configs/byol.py, use:
    • num_classes: 10
    • network_config.encoder: ResNet18
    • optimizer_config.weight_decay: 1e-6
    • lr_schedule_config.base_learning_rate: 2.0
    • evaluation_config.batch_size: 25
    • other parameters unchanged.

You can then run:

mkdir /tmp/byol_checkpoints
python -m byol.main_loop \
  --experiment_mode='pretrain' \
  --worker_mode='train' \
  --checkpoint_root='/tmp/byol_checkpoints' \
  --batch_size=256 \
  --pretrain_epochs=1000

With these settings, BYOL should achieve ~92.3% top-1 accuracy (for the online classifier) in roughly 4 hours. Note that the above parameters were not finely tuned and may not be optimal.

Additional checkpoints

Alongside with the pretrained ResNet-50 and ResNet-200 2x, we provide the following checkpoints from our ablation study. They all correspond to a ResNet-50 1x pre-trained over 300 epochs and were randomly selected within the three seeds; file size is roughly 640MB each.

License

While the code is licensed under the Apache 2.0 License, the checkpoints weights are made available for non-commercial use only under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode.