This repository hosts the open source JAX implementation of XMC-GAN.
Set up virtualenv, and install required libraries:
virtualenv venv
source venv/bin/activate
Add the XMC-GAN library to PYTHONPATH:
export PYTHONPATH=$PYTHONPATH:/home/path/to/xmcgan/root/
Note: Please follow the official JAX instructions for installing a GPU compatible version of JAX.
After installing JAX, install the remaining dependencies with:
pip install -r requirements.txt
To create the training and eval data, first start a directory. By default, the training scripts expect to save results in data/ in the base directory.
mkdir data/
The TFRecords required for training and validation on COCO-2014 can be created by running a preprocessing script over the TFDS coco_captions dataset:
python preprocess_data.py
This may take a while to complete, as it runs a pretrained BERT model over the captions and stores the embeddings. With a GPU, it runs in about 2.5 hours for train, and 1 hour for validation. Once it is done, the train and validation tfrecords files will be saved in the data/ directory. The train files require around 58G of disk space, and the validation requires 29G.
Note: If you run into an error related to TensorFlow gfile, one workaround is to edit site-packages/bert/tokenization.py and change tf.gfile.GFile to tf.io.gfile.GFile. For more details, refer to the following link.
If you run into a tensorflow.python.framework.errors_impl.ResourceExhaustedError about having too many open files, you may have to increase the machine's open file limits. To do so, open the limit configuration file for editing:
vi /etc/security/limits.conf
and append the following lines to the end of the file:
* hard nofile 500000
* soft nofile 500000
root hard nofile 500000
root soft nofile 500000
You may have to adjust the limit values depending on your machine. You will need to logout and login to your machine for these values to take effect.
To train XMC-GAN, we need a network pretrained on ImageNet to extract features. For our purposes, we train a ResNet-50 network for this. To download the weights, run:
gsutil cp gs://gresearch/xmcgan/resnet_pretrained.npy data/
If you would like to pretrain your own network on ImageNet, please refer to the official Flax ImageNet example.
Start a training run, by first editing train.sh to specify an appropriate work directory. By default, the script assumes that 8 GPUs are available, and runs training on the first 7 GPUs, while test.sh assumes testing will run on the last GPU.
After configuring the training job, start an experiment by running it on bash:
mkdir exp
bash train.sh exp_name &> train.txt
Checkpoints and Tensorboard logs will be saved in /path/to/exp/exp_name. By default, the configs/coco_xmc.py config is used, which runs an experiment for 128px images. This is able to accommodate a batch size of 8 on each GPU, and achieves an FID of around 10.5 - 11.0 with the EMA weights. To reproduce the full results on 256px images in our paper, the full model needs to be run using a 32-core Pod slice of Google Cloud TPU v3 devices.
To run an evaluation job, update test.sh with the correct settings used in the training script. Then, execute
bash test.sh exp_name &> eval.txt
to start an evaluation job. All checkpoints in workdir will be evaluated for FID and Inception Score. If you can spare the GPUs, you can also run train.sh and test.sh in parallel, which will continuously evaluate new checkpoints saved into the work directory. Scores will be written to Tensorboard and output to eval.txt.
To start a Tensorboard for monitoring training progress, run:
tensorboard --logdir /path/to/exp/exp_name
If you find this work useful, please consider citing:
@inproceedings{zhang2021cross,
title={Cross-Modal Contrastive Learning for Text-to-Image Generation},
author={Zhang, Han and Koh, Jing Yu and Baldridge, Jason and Lee, Honglak and Yang, Yinfei},
journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}
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