RealNvp
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.
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
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.
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 celebacd 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 ..
Downloading the small Imagenet dataset is more straightforward and can be done entirely in Shell:
mkdir small_imnetcd small_imnetfor FILENAME in train_32x32.tar valid_32x32.tar train_64x64.tar valid_64x64.tardo curl -O http://image-net.org/small/$FILENAME tar -xvf $FILENAMEdone
Then, you can format the datasets as follow:
for DIRNAME in train_32x32 valid_32x32 train_64x64 valid_64x64do python2.7 ../models/real_nvp/imnet_formatting.py --file_out $DIRNAME --fn_root $DIRNAMEdonecd ..
To prepare the LSUN dataset, we will need to use the code associated:
git clone https://github.com/fyu/lsun.gitcd lsun
Then we'll download the db files:
for CATEGORY in bedroom church_outdoor towerdo 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 "$CATEGORY"_val --flatdone
Finally, we then format the dataset into .tfrecords
:
for CATEGORY in bedroom church_outdoor towerdo python2.7 ../models/real_nvp/lsun_formatting.py --file_out "$CATEGORY"_train --fn_root "$CATEGORY"_train python2.7 ../models/real_nvp/lsun_formatting.py --file_out "$CATEGORY"_val --fn_root "$CATEGORY"_valdonecd ..
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 modelpython2.7 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 modelpython2.7 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 modelpython2.7 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 modelpython2.7 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 modelpython2.7 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 setpython2.7 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 setpython2.7 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
This code was written by Laurent Dinh (@laurent-dinh) with the help of Jascha Sohl-Dickstein (@Sohl-Dickstein and jaschasd@google.com), Samy Bengio, Jon Shlens, Sherry Moore and David Andersen.
链接:https://github.com/tensorflow/models/tree/master/research/real_nvp
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