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real_nvp

README.md

Real NVP in TensorFlow

A Tensorflow implementation of the training procedure of Density estimation using Real NVP, by Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio, for Imagenet (32x32 and 64x64), CelebA and LSUN Including the scripts to put the datasets in .tfrecords format.

We are happy to open source the code for Real NVP, a novel approach to density estimation using deep neural networks that enables tractable density estimation and efficient one-pass inference and sampling. This model successfully decomposes images into hierarchical features ranging from high-level concepts to low-resolution details. Visualizations are available here.

Installation

  • python 2.7:
    • python 3 support is not available yet
  • pip (python package manager)
    • apt-get install python-pip on Ubuntu
    • brew installs pip along with python on OSX
  • Install the dependencies for LSUN
    • Install OpenCV
    • pip install numpy lmdb
  • Install the python dependencies
    • pip install scipy scikit-image Pillow
  • Install the latest Tensorflow Pip package for Python 2.7

Getting Started

Once you have successfully installed the dependencies, you can start by downloading the repository:

git clone --recursive https://github.com/tensorflow/models.git

Afterward, you can use the utilities in this folder prepare the datasets.

Preparing datasets

CelebA

For CelebA, download img_align_celeba.zip from the Dropbox link on this page under the link Align&Cropped Images in the Img directory and list_eval_partition.txt under the link Train/Val/Test Partitions in the Eval directory. Then do:

mkdir celeba
cd celeba
unzip img_align_celeba.zip

We'll format the training subset:

python2.7 ../models/real_nvp/celeba_formatting.py \
    --partition_fn list_eval_partition.txt \
    --file_out celeba_train \
    --fn_root img_align_celeba \
    --set 0

Then the validation subset:

python2.7 ../models/real_nvp/celeba_formatting.py \
    --partition_fn list_eval_partition.txt \
    --file_out celeba_valid \
    --fn_root img_align_celeba \
    --set 1

And finally the test subset:

python2.7 ../models/real_nvp/celeba_formatting.py \
    --partition_fn list_eval_partition.txt \
    --file_out celeba_test \
    --fn_root img_align_celeba \
    --set 2

Afterward:

cd ..

Small Imagenet

Downloading the small Imagenet dataset is more straightforward and can be done entirely in Shell:

mkdir small_imnet
cd small_imnet
for FILENAME in train_32x32.tar valid_32x32.tar train_64x64.tar valid_64x64.tar
do
    curl -O http://image-net.org/small/$FILENAME
    tar -xvf $FILENAME
done

Then, you can format the datasets as follow:

for DIRNAME in train_32x32 valid_32x32 train_64x64 valid_64x64
do
    python2.7 ../models/real_nvp/imnet_formatting.py \
        --file_out $DIRNAME \
        --fn_root $DIRNAME
done
cd ..

LSUN

To prepare the LSUN dataset, we will need to use the code associated:

git clone https://github.com/fyu/lsun.git
cd lsun

Then we'll download the db files:

for CATEGORY in bedroom church_outdoor tower
do
    python2.7 download.py -c $CATEGORY
    unzip "$CATEGORY"_train_lmdb.zip
    unzip "$CATEGORY"_val_lmdb.zip
    python2.7 data.py export "$CATEGORY"_train_lmdb \
        --out_dir "$CATEGORY"_train --flat
    python2.7 data.py export "$CATEGORY"_val_lmdb \
        --out_dir "$CATEGORY"_val --flat
done

Finally, we then format the dataset into .tfrecords:

for CATEGORY in bedroom church_outdoor tower
do
    python2.7 ../models/real_nvp/lsun_formatting.py \
        --file_out "$CATEGORY"_train \
        --fn_root "$CATEGORY"_train
    python2.7 ../models/real_nvp/lsun_formatting.py \
        --file_out "$CATEGORY"_val \
        --fn_root "$CATEGORY"_val
done
cd ..

Training

We'll give an example on how to train a model on the small Imagenet dataset (32x32):

cd models/real_nvp/
python2.7 real_nvp_multiscale_dataset.py \
--image_size 32 \
--hpconfig=n_scale=4,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset imnet \
--traindir /tmp/real_nvp_imnet32/train \
--logdir /tmp/real_nvp_imnet32/train \
--data_path ../../small_imnet/train_32x32_?????.tfrecords

In parallel, you can run the script to generate visualization from the model:

python2.7 real_nvp_multiscale_dataset.py \
--image_size 32 \
--hpconfig=n_scale=4,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset imnet \
--traindir /tmp/real_nvp_imnet32/train \
--logdir /tmp/real_nvp_imnet32/sample \
--data_path ../../small_imnet/valid_32x32_?????.tfrecords \
--mode sample

Additionally, you can also run in the script to evaluate the model on the validation set:

python2.7 real_nvp_multiscale_dataset.py \
--image_size 32 \
--hpconfig=n_scale=4,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset imnet \
--traindir /tmp/real_nvp_imnet32/train \
--logdir /tmp/real_nvp_imnet32/eval \
--data_path ../../small_imnet/valid_32x32_?????.tfrecords \
--eval_set_size 50000
--mode eval

The visualizations and validation set evaluation can be seen through Tensorboard.

Another example would be how to run the model on LSUN (bedroom category):

# train the model
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset lsun \
--traindir /tmp/real_nvp_church_outdoor/train \
--logdir /tmp/real_nvp_church_outdoor/train \
--data_path ../../lsun/church_outdoor_train_?????.tfrecords
# sample from the model
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset lsun \
--traindir /tmp/real_nvp_church_outdoor/train \
--logdir /tmp/real_nvp_church_outdoor/sample \
--data_path ../../lsun/church_outdoor_val_?????.tfrecords \
--mode sample
# evaluate the model
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset lsun \
--traindir /tmp/real_nvp_church_outdoor/train \
--logdir /tmp/real_nvp_church_outdoor/eval \
--data_path ../../lsun/church_outdoor_val_?????.tfrecords \
--eval_set_size 300
--mode eval

Finally, we'll give the commands to run the model on the CelebA dataset:

# train the model
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset lsun \
--traindir /tmp/real_nvp_celeba/train \
--logdir /tmp/real_nvp_celeba/train \
--data_path ../../celeba/celeba_train.tfrecords
# sample from the model
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset celeba \
--traindir /tmp/real_nvp_celeba/train \
--logdir /tmp/real_nvp_celeba/sample \
--data_path ../../celeba/celeba_valid.tfrecords \
--mode sample
# evaluate the model on validation set
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset celeba \
--traindir /tmp/real_nvp_celeba/train \
--logdir /tmp/real_nvp_celeba/eval_valid \
--data_path ../../celeba/celeba_valid.tfrecords \
--eval_set_size 19867
--mode eval

# evaluate the model on test set
python2.7 real_nvp_multiscale_dataset.py \
--image_size 64 \
--hpconfig=n_scale=5,base_dim=32,clip_gradient=100,residual_blocks=4 \
--dataset celeba \
--traindir /tmp/real_nvp_celeba/train \
--logdir /tmp/real_nvp_celeba/eval_test \
--data_path ../../celeba/celeba_test.tfrecords \
--eval_set_size 19962
--mode eval

Credits

This code was written by Laurent Dinh (@laurent-dinh) with the help of Jascha Sohl-Dickstein (@Sohl-Dickstein and [email protected]), Samy Bengio, Jon Shlens, Sherry Moore and David Andersen.