Code for the paper: "Creating High Resolution Images with a Latent Adversarial Generator" by David Berthelot, Peyman Milanfar, and Ian Goodfellow.
This is not an officially supported Google product.
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.
sudo apt install python3-dev python3-virtualenv python3-tk imagemagick virtualenv
virtualenv -p python3 --system-site-packages ~/env3
~/env3/bin/pip install -r requirements.txtTo use virtualenv, when starting a new shell, always run the following lines first:
. ~/env3/bin/activate
export PYTHONPATH=$PYTHONPATH:.# You can define ML_DATA to whatever path you like.
export ML_DATA="path to where you want the datasets saved"
mkdir $ML_DATA $ML_DATA/Models
Download vgg19.npy from https://github.com/machrisaa/tensorflow-vgg
to $ML_DATA/Models.
# Important ML_DATA and PYTONPATH must be defined (see previous sections).
scripts/create_datasets.pyAlternatively you can download just some selected datasets to save space and time, for example:
scripts/create_datasets.py celeba svhnThe supported datasets are (%s represents the image size, when present it can be replaced by 32, 64, 128 or 256):
celeba%s, cifar10, mnist, svhn,
lsun_bedroom%s, lsun_bridge%s, lsun_church_outdoor%s, lsun_classroom%s,
lsun_conference_room%s, lsun_dining_room%s, lsun_kitchen%s, lsun_living_room%s,
lsun_restaurant%s, lsun_tower%s
All commands must be ran from the project root. The following environment variables must be defined:
export ML_DATA="path to where you want the datasets saved"
export PYTHONPATH=$PYTHONPATH:.For example, training lag for a 32x zoom using CelebA128:
CUDA_VISIBLE_DEVICES=0 python lag.py --dataset=celeba128 --scale=32Just pass more GPUs and the code automatically scales to them, here we assign GPUs 4-7 to the program:
CUDA_VISIBLE_DEVICES=4,5,6,7 python lag.py --dataset=celeba128 --scale=32python lag.py --help
# The following option might be too slow to be really practical.
# python lag.py --helpfull
# So instead I use this hack to find the flags:
fgrep -R flags.DEFINE libml lag.pyYou can point tensorboard to the training folder (by default it is --train_dir=./experiments) to monitor the training
process:
tensorboard --logdir experiments
# And point your browser the link printed when tensorboard starts.Following the previous example, for a LAG model trained on CelebA128, the ckpt is the folder where the model was
trained:
python scripts/lag_generate_candidates.py\
--dataset=celeba128\
--samples 0,3,4,6,8,9,12,13,15\
--ckpt experiments/celeba128/average32X/LAG_batch16_blocks8_filters256_filters_min64_lod_min1_lr0.001_mse_weight10.0_noise_dim64_training_kimg2048_transition_kimg2048_ttur4_wass_target1.0_weight_avg0.999/@article{berthelot2020creating,
title={Creating High Resolution Images with a Latent Adversarial Generator},
author={Berthelot, David and Milanfar, Peyman and Goodfellow, Ian},
journal={arXiv preprint arXiv:2003.02365},
year={2020}
}
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