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Repository: barisgecer/TBGAN
Branch: master
Commit: b2776c0ed923
Files: 26
Total size: 33.6 MB
Directory structure:
gitextract__mj9yhwx/
├── .gitignore
├── 512_UV_dict.pkl
├── LICENSE.txt
├── LICENSE_NVIDIA.txt
├── README.md
├── UV_manipulation_2.py
├── __init__.py
├── config.py
├── config_test.py
├── dataset.py
├── dataset_tool.py
├── legacy.py
├── loss.py
├── metrics/
│ ├── __init__.py
│ ├── frechet_inception_distance.py
│ ├── inception_score.py
│ ├── ms_ssim.py
│ └── sliced_wasserstein.py
├── misc.py
├── myutil.py
├── networks.py
├── requirements-pip.txt
├── test.py
├── tfutil.py
├── train.py
└── util_scripts.py
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FILE: LICENSE.txt
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================================================
FILE: README.md
================================================
# The model is now available [HERE](https://ibug.doc.ic.ac.uk/resources/tbgan/)
(Requires to sign End User License Agreement)
# [Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks](https://barisgecer.github.io/files/gecer_tbgan_arxiv.pdf)
[ArXiv](https://arxiv.org/pdf/1909.02215.pdf), [Supplementary Video](https://www.youtube.com/watch?v=wehBCetIb7E)
[Baris Gecer](http://barisgecer.github.io)<sup> 1,2</sup>, [Alexander Lattas](https://alexanderlattas.com/)<sup> 1,2</sup>, [Stylianos Ploumpis](https://ibug.doc.ic.ac.uk/people/sploumpis)<sup> 1,2</sup>, [Jiankang Deng](https://jiankangdeng.github.io/)<sup> 1,2</sup>, [Athanasios Papaioannou](https://ibug.doc.ic.ac.uk/people/apapaioannou)<sup> 1,2</sup>, [Stylianos Moschoglou](https://ibug.doc.ic.ac.uk/people/smoschoglou)<sup> 1,2</sup>, & [Stefanos Zafeiriou](https://wp.doc.ic.ac.uk/szafeiri/)<sup> 1,2</sup>
<br/>
<sup>1 </sup>Imperial College London
<br/>
<sup>2 </sup>FaceSoft.io
#### This repo provides Tensorflow implementation of above paper for training
## Abstract
<p align="center"><img width="100%" src="representative.png" /></p>
Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture or the normals of the face, which are very crucial for photo-realistic face synthesis. Recently, it was demonstrated that Generative Adversarial Networks (GANs) can be used for generating high-quality textures of faces. Nevertheless, the generation process either omits the geometry and normals, or independent processes are used to produce 3D shape information. In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. The qualitative results shown in this paper are compressed due to size limitations, full-resolution results and the accompanying video can be found in the supplementary documents.
<br/>
## Supplementary Video
[<p align="center"><img width="100%" alt="Watch the video" title="Click to Watch on YouTube" src="https://img.youtube.com/vi/wehBCetIb7E/sddefault.jpg" /></p>](https://www.youtube.com/watch?v=wehBCetIb7E)
## Testing the Model
- Download the model after signing the agreement and place it under '/results' directory
- Install menpo3d by
> pip install menpo3d
- And then Run the test script:
> python test.py
## Preparing datasets for training
The TBGAN code repository contains a command-line tool for recreating bit-exact replicas of the datasets that we used in the paper. The tool also provides various utilities for operating on the datasets:
```
usage: dataset_tool.py [-h] <command> ...
display Display images in dataset.
extract Extract images from dataset.
compare Compare two datasets.
create_from_pkl_img_norm Create dataset from a directory full of texture, normals and shape.
Type "dataset_tool.py <command> -h" for more information.
Please ignore other functions. The main function to prepare tf_records is 'create_from_pkl_img_norm'
```
The datasets are represented by directories containing the same image data in several resolutions to enable efficient streaming. There is a separate `*.tfrecords` file for each resolution, and if the dataset contains labels, they are stored in a separate file as well:
```
> python dataset_tool.py create_from_pkl_img_norm datasets/tf_records datasets/texture(/*.png) dataset/shape(/*.pkl) dataset/normals(/*.pkl)
```
The ```create_*``` commands take the standard version of a given dataset as input and produce the corresponding `*.tfrecords` files as output.
## Training networks
```
Please see how to start training with a PROGAN
Additionally, you will need to add
> "dynamic_range=[-1,1],dtype = 'float32'"
arguments to 'dataset' EasyDict() in config.py
```
Once the necessary datasets are set up, you can proceed to train your own networks. The general procedure is as follows:
1. Edit `config.py` to specify the dataset and training configuration by uncommenting/editing specific lines.
2. Run the training script with `python train.py`.
3. The results are written into a newly created subdirectory under `config.result_dir`
4. Wait several days (or weeks) for the training to converge, and analyze the results.
By default, `config.py` is configured to train a 1024x1024 network for CelebA-HQ using a single-GPU. This is expected to take about two weeks even on the highest-end NVIDIA GPUs. The key to enabling faster training is to employ multiple GPUs and/or go for a lower-resolution dataset. To this end, `config.py` contains several examples for commonly used datasets, as well as a set of "configuration presets" for multi-GPU training. All of the presets are expected to yield roughly the same image quality for CelebA-HQ, but their total training time can vary considerably:
* `preset-v1-1gpu`: Original config that was used to produce the CelebA-HQ and LSUN results shown in the paper. Expected to take about 1 month on NVIDIA Tesla V100.
* `preset-v2-1gpu`: Optimized config that converges considerably faster than the original one. Expected to take about 2 weeks on 1xV100.
* `preset-v2-2gpus`: Optimized config for 2 GPUs. Takes about 1 week on 2xV100.
* `preset-v2-4gpus`: Optimized config for 4 GPUs. Takes about 3 days on 4xV100.
* `preset-v2-8gpus`: Optimized config for 8 GPUs. Takes about 2 days on 8xV100.
For reference, the expected output of each configuration preset for CelebA-HQ can be found in [`networks/tensorflow-version/example_training_runs`](https://drive.google.com/open?id=1A9SKoQ7Xu2fqK22GHdMw8LZTh6qLvR7H)
Other noteworthy config options:
* `fp16`: Enable [FP16 mixed-precision training](http://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) to reduce the training times even further. The actual speedup is heavily dependent on GPU architecture and cuDNN version, and it can be expected to increase considerably in the future.
* `BENCHMARK`: Quickly iterate through the resolutions to measure the raw training performance.
* `BENCHMARK0`: Same as `BENCHMARK`, but only use the highest resolution.
* `syn1024rgb`: Synthetic 1024x1024 dataset consisting of just black images. Useful for benchmarking.
* `VERBOSE`: Save image and network snapshots very frequently to facilitate debugging.
* `GRAPH` and `HIST`: Include additional data in the TensorBoard report.
## Analyzing results
Training results can be analyzed in several ways:
* **Manual inspection**: The training script saves a snapshot of randomly generated images at regular intervals in `fakes*.png` and reports the overall progress in `log.txt`.
* **TensorBoard**: The training script also exports various running statistics in a `*.tfevents` file that can be visualized in TensorBoard with `tensorboard --logdir <result_subdir>`.
* **Generating images and videos**: At the end of `config.py`, there are several pre-defined configs to launch utility scripts (`generate_*`). For example:
* Suppose you have an ongoing training run titled `010-pgan-celebahq-preset-v1-1gpu-fp32`, and you want to generate a video of random interpolations for the latest snapshot.
* Uncomment the `generate_interpolation_video` line in `config.py`, replace `run_id=10`, and run `python train.py`
* The script will automatically locate the latest network snapshot and create a new result directory containing a single MP4 file.
* **Quality metrics**: Similar to the previous example, `config.py` also contains pre-defined configs to compute various quality metrics (Sliced Wasserstein distance, Fréchet inception distance, etc.) for an existing training run. The metrics are computed for each network snapshot in succession and stored in `metric-*.txt` in the original result directory.
## Acknowledgement
Baris Gecer is supported by the Turkish Ministry of National Education, Stylianos Ploumpis by the EPSRC Project EP/N007743/1 (FACER2VM), and Stefanos Zafeiriou by EPSRC Fellowship DEFORM (EP/S010203/1).
Code borrows heavily from NVIDIA's [PRO-GAN implementation](https://github.com/tkarras/progressive_growing_of_gans), please check and comply with its [License](https://github.com/tkarras/progressive_growing_of_gans/blob/master/LICENSE.txt). and cite their paper:
```
@inproceedings{karras2018progressive,
title={Progressive Growing of GANs for Improved Quality, Stability, and Variation},
author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},
booktitle={International Conference on Learning Representations},
year={2018}
}
```
## Citation
If you find this work is useful for your research, please cite our [paper](https://arxiv.org/abs/1909.02215):
```
@inproceedings{gecer2020tbgan,
title={Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks},
author={{Gecer}, Baris and {Lattas}, Alexander and {Ploumpis}, Stylianos and
{Deng}, Jiankang and {Papaioannou}, Athanasios and
{Moschoglou}, Stylianos and {Zafeiriou}, Stefanos},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
year={2020},
organization={Springer}
doi = {10.1007/978-3-030-58526-6_25}
}
```
================================================
FILE: UV_manipulation_2.py
================================================
import os
import menpo3d.io as m3io
from menpo3d import io
import menpo.io as mio
import numpy as np
from menpo.shape import TriMesh, ColouredTriMesh, PointCloud, TexturedTriMesh
from menpo.image import Image
from scipy.interpolate import NearestNDInterpolator
from menpo.image import MaskedImage
from functools import lru_cache
from menpo.transform import AlignmentSimilarity
#-------------------------------------------------------------------#
#Full face load dictionaries
@lru_cache()
def load_512_ifo_dict():
return mio.import_pickle('512_UV_dict.pkl')
#-------------------------------------------------------------------#
@lru_cache()
def load_mean():
return mio.import_pickle('./pkls/all_all_all_mean.pkl'),mio.import_pickle('./pkls/all_all_all_lands_ids.pkl')
#-------------------------------------------------------------------#
def alignment(mesh):
if mesh.n_points==53215:
template, idxs= load_mean()
alignment = AlignmentSimilarity(PointCloud(mesh.points[idxs]), PointCloud(template.points[idxs]))
aligned_mesh = alignment.apply(mesh)
return aligned_mesh
def import_uv_info(instance, res, uv_layout='oval', topology='full'):
if np.logical_or(type(instance).__name__=='TriMesh',type(instance).__name__=='TexturedTriMesh'):
if instance.n_points == 53215:
topology = 'full'
else:
raise ValueError('Unknown topology')
if topology == 'full':
if res==512:
if uv_layout=='oval':
info_dict = load_512_ifo_dict()
elif uv_layout=='stretch':
info_dict = load_512_ifo_dict_strech()
else:
raise ValueError('Wrong resolution')
elif type(instance).__name__=='Image':
if topology == 'full':
if res==512:
if uv_layout=='oval':
info_dict = load_512_ifo_dict()
elif uv_layout=='stretch':
info_dict = load_512_ifo_dict_strech()
else:
raise ValueError('Wrong resolution')
return info_dict
def from_UV_2_3D(uv, uv_layout='oval', topology='full', plot=False):
res = uv.shape[0]
info_dict = import_uv_info(uv,res,uv_layout=uv_layout,topology=topology)
tmask = info_dict['tmask']
tc_ps = info_dict['tcoords_pixel_scaled']
tmask_im = info_dict['tmask_image']
trilist = info_dict['trilist']
#uv = interpolaton_of_uv_xyz(uv,tmask).as_unmasked()
x = uv.pixels[0][(tc_ps.points.astype(int).T[0,:], tc_ps.points.astype(int).T[1,:])]
y = uv.pixels[1][(tc_ps.points.astype(int).T[0,:], tc_ps.points.astype(int).T[1,:])]
z = uv.pixels[2][(tc_ps.points.astype(int).T[0,:], tc_ps.points.astype(int).T[1,:])]
points = np.hstack((x.T[:,None],y.T[:,None],z.T[:,None]))
if plot is True:
TriMesh(points,trilist).view()
return TriMesh(points,trilist)
================================================
FILE: __init__.py
================================================
__all__ = ["config", "dataset", "dataset_tool","legacy","loss","misc","myutil","networks","tfutil","util_scripts","train"]
import tfutil
import util_scripts
================================================
FILE: config.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
#----------------------------------------------------------------------------
# Convenience class that behaves exactly like dict(), but allows accessing
# the keys and values using the attribute syntax, i.e., "mydict.key = value".
class EasyDict(dict):
def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
def __getattr__(self, name): return self[name]
def __setattr__(self, name, value): self[name] = value
def __delattr__(self, name): del self[name]
#----------------------------------------------------------------------------
# Paths.
data_dir = './'
result_dir = './results'
#----------------------------------------------------------------------------
# TensorFlow options.
tf_config = EasyDict() # TensorFlow session config, set by tfutil.init_tf().
env = EasyDict() # Environment variables, set by the main program in train.py.
tf_config['graph_options.place_pruned_graph'] = False # False (default) = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
tf_config['gpu_options.allow_growth'] = False # False (default) = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
#env.CUDA_VISIBLE_DEVICES = '0' # Unspecified (default) = Use all available GPUs. List of ints = CUDA device numbers to use.
env.TF_CPP_MIN_LOG_LEVEL = '1' # 0 (default) = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
#----------------------------------------------------------------------------
# Official training configs, targeted mainly for CelebA-HQ.
# To run, comment/uncomment the lines as appropriate and launch train.py.
desc = 'pgan' # Description string included in result subdir name.
random_seed = 1000 # Global random seed.
dataset = EasyDict() # Options for dataset.load_dataset().
train = EasyDict(func='train.train_progressive_gan') # Options for main training func.
G = EasyDict(func='networks.G_paper') # Options for generator network.
D = EasyDict(func='networks.D_paper') # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
G_loss = EasyDict(func='loss.G_wgan_acgan') # Options for generator loss.
D_loss = EasyDict(func='loss.D_wgangp_acgan') # Options for discriminator loss.
sched = EasyDict() # Options for train.TrainingSchedule.
grid = EasyDict(size='1080p', layout='random') # Options for train.setup_snapshot_image_grid().
# desc += '-mein3d_texture_uv_tf_512'; dataset = EasyDict(tfrecord_dir='mein3d_texture_uv_tf_512');
# desc += '-mein3d_shape_uv_tf_512_bary'; dataset = EasyDict(tfrecord_dir='mein3d_shape_uv_tf_512_bary',dynamic_range=[-1,1],dtype = 'float32');
desc += '-3dmd_all_newuv_crop_tf'; dataset = EasyDict(tfrecord_dir='3dmd_all_newuv_crop_tf',dynamic_range=[-1,1],dtype = 'float32');
G.lod_sep = 7
D.lod_sep = 7
dataset.max_label_size = 'full'
grid.layout = 'row_per_class'
grid.size = '4k'
# Continue
#train.resume_run_id = 30
#train.resume_kimg = 12000
#train.resume_time = 7*24*60*60 + 5*60*60 + 0*60
# Conditioning & snapshot options.
#desc += '-cond'; dataset.max_label_size = 'full' # conditioned on full label
#desc += '-cond1'; dataset.max_label_size = 1 # conditioned on first component of the label
#desc += '-g4k'; grid.size = '4k'
#desc += '-grpc'; grid.layout = 'row_per_class'
# Config presets (choose one).
#desc += '-preset-v1-1gpu'; num_gpus = 1; D.mbstd_group_size = 16; sched.minibatch_base = 16; sched.minibatch_dict = {256: 14, 512: 6, 1024: 3}; sched.lod_training_kimg = 800; sched.lod_transition_kimg = 800; train.total_kimg = 19000
# desc += '-preset-v2-1gpu'; num_gpus = 1; sched.minibatch_base = 4; sched.minibatch_dict = {4: 128, 8: 128, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}; sched.G_lrate_dict = {1024: 0.0015}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
# desc += '-preset-v2-2gpus'; num_gpus = 2; sched.minibatch_base = 8; sched.minibatch_dict = {4: 256, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}; sched.G_lrate_dict = {512: 0.0015, 1024: 0.002}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
desc += '-preset-v2-4gpus'; num_gpus = 4; sched.minibatch_base = 16; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}; sched.G_lrate_dict = {256: 0.0015, 512: 0.002, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 16000
#desc += '-preset-v2-8gpus'; num_gpus = 8; sched.minibatch_base = 32; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32}; sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
# Numerical precision (choose one).
# desc += '-fp32'; sched.max_minibatch_per_gpu = {256: 16, 512: 8, 1024: 4}
#desc += '-fp16'; G.dtype = 'float16'; D.dtype = 'float16'; G.pixelnorm_epsilon=1e-4; G_opt.use_loss_scaling = True; D_opt.use_loss_scaling = True; sched.max_minibatch_per_gpu = {512: 16, 1024: 8}
# Disable individual features.
#desc += '-nogrowing'; sched.lod_initial_resolution = 1024; sched.lod_training_kimg = 0; sched.lod_transition_kimg = 0; train.total_kimg = 10000
#desc += '-nopixelnorm'; G.use_pixelnorm = False
#desc += '-nowscale'; G.use_wscale = False; D.use_wscale = False
#desc += '-noleakyrelu'; G.use_leakyrelu = False
#desc += '-nosmoothing'; train.G_smoothing = 0.0
#desc += '-norepeat'; train.minibatch_repeats = 1
#desc += '-noreset'; train.reset_opt_for_new_lod = False
# Special modes.
#desc += '-BENCHMARK'; sched.lod_initial_resolution = 4; sched.lod_training_kimg = 3; sched.lod_transition_kimg = 3; train.total_kimg = (8*2+1)*3; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
#desc += '-BENCHMARK0'; sched.lod_initial_resolution = 1024; train.total_kimg = 10; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
desc += '-VERBOSE'; sched.tick_kimg_base = 100; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 2; train.network_snapshot_ticks = 2
#desc += '-GRAPH'; train.save_tf_graph = True
#desc += '-HIST'; train.save_weight_histograms = True
#----------------------------------------------------------------------------
# Utility scripts.
# To run, uncomment the appropriate line and launch train.py.
# train = EasyDict(func='util_scripts.fit_real_images', run_id=0, png_prefix='', num_pngs=1000); num_gpus = 1; desc = 'real-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images_glob', run_id=0, num_pngs=1000); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images', run_id=0, png_prefix='', num_pngs=100000); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images', run_id=23, grid_size=[15,8], num_pngs=10, image_shrink=4); num_gpus = 1; desc = 'fake-grids-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_video', run_id=0, grid_size=[1,1], duration_sec=600.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_images', run_id=30, grid_size=[1,1], duration_sec=60.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_video_bydim', run_id=0, grid_size=[1,1], duration_sec=10.0, mp4_fps=30, smoothing_sec=1.0,dim=3); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id) + '_dim'+str(train.dim)
#train = EasyDict(func='util_scripts.generate_training_video', run_id=0, duration_sec=20.0); num_gpus = 1; desc = 'training-video-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-swd-16k.txt', metrics=['swd'], num_images=16384, real_passes=2); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-10k.txt', metrics=['fid'], num_images=10000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-50k.txt', metrics=['fid'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-is-50k.txt', metrics=['is'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-msssim-20k.txt', metrics=['msssim'], num_images=20000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
================================================
FILE: config_test.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
#----------------------------------------------------------------------------
# Convenience class that behaves exactly like dict(), but allows accessing
# the keys and values using the attribute syntax, i.e., "mydict.key = value".
class EasyDict(dict):
def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
def __getattr__(self, name): return self[name]
def __setattr__(self, name, value): self[name] = value
def __delattr__(self, name): del self[name]
#----------------------------------------------------------------------------
# Paths.
data_dir = './'
result_dir = './results'
#----------------------------------------------------------------------------
# TensorFlow options.
tf_config = EasyDict() # TensorFlow session config, set by tfutil.init_tf().
env = EasyDict() # Environment variables, set by the main program in train.py.
tf_config['graph_options.place_pruned_graph'] = False # False (default) = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
tf_config['gpu_options.allow_growth'] = False # False (default) = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
#env.CUDA_VISIBLE_DEVICES = '0' # Unspecified (default) = Use all available GPUs. List of ints = CUDA device numbers to use.
env.TF_CPP_MIN_LOG_LEVEL = '1' # 0 (default) = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
#----------------------------------------------------------------------------
# Official training configs, targeted mainly for CelebA-HQ.
# To run, comment/uncomment the lines as appropriate and launch train.py.
desc = 'pgan' # Description string included in result subdir name.
random_seed = 1000 # Global random seed.
dataset = EasyDict() # Options for dataset.load_dataset().
train = EasyDict(func='train.train_progressive_gan') # Options for main training func.
G = EasyDict(func='networks.G_paper') # Options for generator network.
D = EasyDict(func='networks.D_paper') # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
G_loss = EasyDict(func='loss.G_wgan_acgan') # Options for generator loss.
D_loss = EasyDict(func='loss.D_wgangp_acgan') # Options for discriminator loss.
sched = EasyDict() # Options for train.TrainingSchedule.
grid = EasyDict(size='1080p', layout='random') # Options for train.setup_snapshot_image_grid().
# desc += '-mein3d_texture_uv_tf_512'; dataset = EasyDict(tfrecord_dir='mein3d_texture_uv_tf_512');
# desc += '-mein3d_shape_uv_tf_512_bary'; dataset = EasyDict(tfrecord_dir='mein3d_shape_uv_tf_512_bary',dynamic_range=[-1,1],dtype = 'float32');
desc += '-3dmd_all_newuv_crop_tf'; dataset = EasyDict(tfrecord_dir='3dmd_all_newuv_crop_tf',dynamic_range=[-1,1],dtype = 'float32');
G.lod_sep = 7
D.lod_sep = 7
dataset.max_label_size = 'full'
grid.layout = 'row_per_class'
grid.size = '4k'
# Continue
#train.resume_run_id = 30
#train.resume_kimg = 12000
#train.resume_time = 7*24*60*60 + 5*60*60 + 0*60
# Conditioning & snapshot options.
#desc += '-cond'; dataset.max_label_size = 'full' # conditioned on full label
#desc += '-cond1'; dataset.max_label_size = 1 # conditioned on first component of the label
#desc += '-g4k'; grid.size = '4k'
#desc += '-grpc'; grid.layout = 'row_per_class'
# Config presets (choose one).
desc += '-preset-v1-1gpu'; num_gpus = 1; D.mbstd_group_size = 16; sched.minibatch_base = 16; sched.minibatch_dict = {256: 14, 512: 6, 1024: 3}; sched.lod_training_kimg = 800; sched.lod_transition_kimg = 800; train.total_kimg = 19000
# desc += '-preset-v2-1gpu'; num_gpus = 1; sched.minibatch_base = 4; sched.minibatch_dict = {4: 128, 8: 128, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}; sched.G_lrate_dict = {1024: 0.0015}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
# desc += '-preset-v2-2gpus'; num_gpus = 2; sched.minibatch_base = 8; sched.minibatch_dict = {4: 256, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}; sched.G_lrate_dict = {512: 0.0015, 1024: 0.002}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-4gpus'; num_gpus = 4; sched.minibatch_base = 16; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}; sched.G_lrate_dict = {256: 0.0015, 512: 0.002, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 16000
#desc += '-preset-v2-8gpus'; num_gpus = 8; sched.minibatch_base = 32; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32}; sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
# Numerical precision (choose one).
# desc += '-fp32'; sched.max_minibatch_per_gpu = {256: 16, 512: 8, 1024: 4}
#desc += '-fp16'; G.dtype = 'float16'; D.dtype = 'float16'; G.pixelnorm_epsilon=1e-4; G_opt.use_loss_scaling = True; D_opt.use_loss_scaling = True; sched.max_minibatch_per_gpu = {512: 16, 1024: 8}
# Disable individual features.
#desc += '-nogrowing'; sched.lod_initial_resolution = 1024; sched.lod_training_kimg = 0; sched.lod_transition_kimg = 0; train.total_kimg = 10000
#desc += '-nopixelnorm'; G.use_pixelnorm = False
#desc += '-nowscale'; G.use_wscale = False; D.use_wscale = False
#desc += '-noleakyrelu'; G.use_leakyrelu = False
#desc += '-nosmoothing'; train.G_smoothing = 0.0
#desc += '-norepeat'; train.minibatch_repeats = 1
#desc += '-noreset'; train.reset_opt_for_new_lod = False
# Special modes.
#desc += '-BENCHMARK'; sched.lod_initial_resolution = 4; sched.lod_training_kimg = 3; sched.lod_transition_kimg = 3; train.total_kimg = (8*2+1)*3; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
#desc += '-BENCHMARK0'; sched.lod_initial_resolution = 1024; train.total_kimg = 10; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
desc += '-VERBOSE'; sched.tick_kimg_base = 100; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 2; train.network_snapshot_ticks = 2
#desc += '-GRAPH'; train.save_tf_graph = True
#desc += '-HIST'; train.save_weight_histograms = True
#----------------------------------------------------------------------------
# Utility scripts.
# To run, uncomment the appropriate line and launch train.py.
# train = EasyDict(func='util_scripts.fit_real_images', run_id=0, png_prefix='', num_pngs=1000); num_gpus = 1; desc = 'real-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images_glob', run_id=0, num_pngs=1000); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
# train = EasyDict(func='util_scripts.generate_fake_images', run_id=32, png_prefix='', num_pngs=100); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images', run_id=23, grid_size=[15,8], num_pngs=10, image_shrink=4); num_gpus = 1; desc = 'fake-grids-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_video', run_id=0, grid_size=[1,1], duration_sec=600.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id)
train = EasyDict(func='util_scripts.generate_interpolation_images', run_id=32, grid_size=[1,1], duration_sec=5.0, smoothing_sec=0.1); num_gpus = 1; desc = 'interpolation-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_video_bydim', run_id=0, grid_size=[1,1], duration_sec=10.0, mp4_fps=30, smoothing_sec=1.0,dim=3); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id) + '_dim'+str(train.dim)
#train = EasyDict(func='util_scripts.generate_training_video', run_id=0, duration_sec=20.0); num_gpus = 1; desc = 'training-video-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-swd-16k.txt', metrics=['swd'], num_images=16384, real_passes=2); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-10k.txt', metrics=['fid'], num_images=10000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-50k.txt', metrics=['fid'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-is-50k.txt', metrics=['is'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-msssim-20k.txt', metrics=['msssim'], num_images=20000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
================================================
FILE: dataset.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import os
import glob
import numpy as np
import tensorflow as tf
import tfutil
#----------------------------------------------------------------------------
# Parse individual image from a tfrecords file.
#----------------------------------------------------------------------------
# Dataset class that loads data from tfrecords files.
class TFRecordDataset:
def __init__(self,
tfrecord_dir, # Directory containing a collection of tfrecords files.
resolution = None, # Dataset resolution, None = autodetect.
label_file = None, # Relative path of the labels file, None = autodetect.
max_label_size = 0, # 0 = no labels, 'full' = full labels, <int> = N first label components.
repeat = True, # Repeat dataset indefinitely.
shuffle_mb = 4096, # Shuffle data within specified window (megabytes), 0 = disable shuffling.
prefetch_mb = 2048, # Amount of data to prefetch (megabytes), 0 = disable prefetching.
buffer_mb = 256, # Read buffer size (megabytes).
num_threads = 2, # Number of concurrent threads.
dtype ='uint8',
dynamic_range =[0, 255]):
self.tfrecord_dir = tfrecord_dir
self.resolution = None
self.resolution_log2 = None
self.shape = [] # [channel, height, width]
self.dtype = dtype
self.dynamic_range = dynamic_range
self.label_file = label_file
self.label_size = None # [component]
self.label_dtype = None
self._np_labels = None
self._tf_minibatch_in = None
self._tf_labels_var = None
self._tf_labels_dataset = None
self._tf_datasets = dict()
self._tf_iterator = None
self._tf_init_ops = dict()
self._tf_minibatch_np = None
self._cur_minibatch = -1
self._cur_lod = -1
# List tfrecords files and inspect their shapes.
assert os.path.isdir(self.tfrecord_dir)
tfr_files = sorted(glob.glob(os.path.join(self.tfrecord_dir, '*.tfrecords')))
assert len(tfr_files) >= 1
tfr_shapes = []
for tfr_file in tfr_files:
tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
for record in tf.python_io.tf_record_iterator(tfr_file, tfr_opt):
tfr_shapes.append(self.parse_tfrecord_np(record).shape)
break
# Autodetect label filename.
if self.label_file is None:
guess = sorted(glob.glob(os.path.join(self.tfrecord_dir, '*.labels')))
if len(guess):
self.label_file = guess[0]
elif not os.path.isfile(self.label_file):
guess = os.path.join(self.tfrecord_dir, self.label_file)
if os.path.isfile(guess):
self.label_file = guess
# Determine shape and resolution.
max_shape = max(tfr_shapes, key=lambda shape: np.prod(shape))
self.resolution = resolution if resolution is not None else max_shape[1]
self.resolution_log2 = int(np.log2(self.resolution))
self.shape = [max_shape[0], self.resolution, self.resolution]
tfr_lods = [self.resolution_log2 - int(np.log2(shape[1])) for shape in tfr_shapes]
assert all(shape[0] == max_shape[0] for shape in tfr_shapes)
assert all(shape[1] == shape[2] for shape in tfr_shapes)
assert all(shape[1] == self.resolution // (2**lod) for shape, lod in zip(tfr_shapes, tfr_lods))
assert all(lod in tfr_lods for lod in range(self.resolution_log2 - 1))
# Load labels.
assert max_label_size == 'full' or max_label_size >= 0
self._np_labels = np.zeros([1<<20, 0], dtype=np.float32)
if self.label_file is not None and max_label_size != 0:
self._np_labels = np.load(self.label_file)
assert self._np_labels.ndim == 2
if max_label_size != 'full' and self._np_labels.shape[1] > max_label_size:
self._np_labels = self._np_labels[:, :max_label_size]
self.label_size = self._np_labels.shape[1]
self.label_dtype = self._np_labels.dtype.name
# Build TF expressions.
with tf.name_scope('Dataset'), tf.device('/cpu:0'):
self._tf_minibatch_in = tf.placeholder(tf.int64, name='minibatch_in', shape=[])
tf_labels_init = tf.zeros(self._np_labels.shape, self._np_labels.dtype)
self._tf_labels_var = tf.Variable(tf_labels_init, name='labels_var')
tfutil.set_vars({self._tf_labels_var: self._np_labels})
self._tf_labels_dataset = tf.data.Dataset.from_tensor_slices(self._tf_labels_var)
for tfr_file, tfr_shape, tfr_lod in zip(tfr_files, tfr_shapes, tfr_lods):
if tfr_lod < 0:
continue
dset = tf.data.TFRecordDataset(tfr_file, compression_type='', buffer_size=buffer_mb<<20)
dset = dset.map(self.parse_tfrecord_tf, num_parallel_calls=num_threads)
dset = tf.data.Dataset.zip((dset, self._tf_labels_dataset))
bytes_per_item = np.prod(tfr_shape) * np.dtype(self.dtype).itemsize
if shuffle_mb > 0:
dset = dset.shuffle(((shuffle_mb << 20) - 1) // bytes_per_item + 1)
if repeat:
dset = dset.repeat()
if prefetch_mb > 0:
dset = dset.prefetch(((prefetch_mb << 20) - 1) // bytes_per_item + 1)
dset = dset.batch(self._tf_minibatch_in)
self._tf_datasets[tfr_lod] = dset
self._tf_iterator = tf.data.Iterator.from_structure(self._tf_datasets[0].output_types, self._tf_datasets[0].output_shapes)
self._tf_init_ops = {lod: self._tf_iterator.make_initializer(dset) for lod, dset in self._tf_datasets.items()}
# Use the given minibatch size and level-of-detail for the data returned by get_minibatch_tf().
def configure(self, minibatch_size, lod=0):
lod = int(np.floor(lod))
assert minibatch_size >= 1 and lod in self._tf_datasets
if self._cur_minibatch != minibatch_size or self._cur_lod != lod:
self._tf_init_ops[lod].run({self._tf_minibatch_in: minibatch_size})
self._cur_minibatch = minibatch_size
self._cur_lod = lod
# Get next minibatch as TensorFlow expressions.
def get_minibatch_tf(self): # => images, labels
return self._tf_iterator.get_next()
# Get next minibatch as NumPy arrays.
def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
self.configure(minibatch_size, lod)
if self._tf_minibatch_np is None:
self._tf_minibatch_np = self.get_minibatch_tf()
return tfutil.run(self._tf_minibatch_np)
# Get random labels as TensorFlow expression.
def get_random_labels_tf(self, minibatch_size): # => labels
if self.label_size > 0:
return tf.gather(self._tf_labels_var, tf.random_uniform([minibatch_size], 0, self._np_labels.shape[0], dtype=tf.int32))
else:
return tf.zeros([minibatch_size, 0], self.label_dtype)
# Get random labels as NumPy array.
def get_random_labels_np(self, minibatch_size): # => labels
if self.label_size > 0:
return self._np_labels[np.random.randint(self._np_labels.shape[0], size=[minibatch_size])]
else:
return np.zeros([minibatch_size, 0], self.label_dtype)
def parse_tfrecord_tf(self, record):
features = tf.parse_single_example(record, features={
'shape': tf.FixedLenFeature([3], tf.int64),
'data': tf.FixedLenFeature([], tf.string)})
data = tf.decode_raw(features['data'], tf.as_dtype(self.dtype))
return tf.reshape(data, features['shape'])
def parse_tfrecord_np(self, record):
ex = tf.train.Example()
ex.ParseFromString(record)
shape = ex.features.feature['shape'].int64_list.value
data = ex.features.feature['data'].bytes_list.value[0]
return np.fromstring(data, np.dtype(self.dtype).type).reshape(shape)
#----------------------------------------------------------------------------
# Base class for datasets that are generated on the fly.
class SyntheticDataset:
def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float32'):
self.resolution = resolution
self.resolution_log2 = int(np.log2(resolution))
self.shape = [num_channels, resolution, resolution]
self.dtype = dtype
self.dynamic_range = dynamic_range
self.label_size = label_size
self.label_dtype = label_dtype
self._tf_minibatch_var = None
self._tf_lod_var = None
self._tf_minibatch_np = None
self._tf_labels_np = None
assert self.resolution == 2 ** self.resolution_log2
with tf.name_scope('Dataset'):
self._tf_minibatch_var = tf.Variable(np.int32(0), name='minibatch_var')
self._tf_lod_var = tf.Variable(np.int32(0), name='lod_var')
def configure(self, minibatch_size, lod=0):
lod = int(np.floor(lod))
assert minibatch_size >= 1 and lod >= 0 and lod <= self.resolution_log2
tfutil.set_vars({self._tf_minibatch_var: minibatch_size, self._tf_lod_var: lod})
def get_minibatch_tf(self): # => images, labels
with tf.name_scope('SyntheticDataset'):
shrink = tf.cast(2.0 ** tf.cast(self._tf_lod_var, tf.float32), tf.int32)
shape = [self.shape[0], self.shape[1] // shrink, self.shape[2] // shrink]
images = self._generate_images(self._tf_minibatch_var, self._tf_lod_var, shape)
labels = self._generate_labels(self._tf_minibatch_var)
return images, labels
def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
self.configure(minibatch_size, lod)
if self._tf_minibatch_np is None:
self._tf_minibatch_np = self.get_minibatch_tf()
return tfutil.run(self._tf_minibatch_np)
def get_random_labels_tf(self, minibatch_size): # => labels
with tf.name_scope('SyntheticDataset'):
return self._generate_labels(minibatch_size)
def get_random_labels_np(self, minibatch_size): # => labels
self.configure(minibatch_size)
if self._tf_labels_np is None:
self._tf_labels_np = self.get_random_labels_tf()
return tfutil.run(self._tf_labels_np)
def _generate_images(self, minibatch, lod, shape): # to be overridden by subclasses
return tf.zeros([minibatch] + shape, self.dtype)
def _generate_labels(self, minibatch): # to be overridden by subclasses
return tf.zeros([minibatch, self.label_size], self.label_dtype)
#----------------------------------------------------------------------------
# Helper func for constructing a dataset object using the given options.
def load_dataset(class_name='dataset.TFRecordDataset', data_dir=None, verbose=False, **kwargs):
adjusted_kwargs = dict(kwargs)
if 'tfrecord_dir' in adjusted_kwargs and data_dir is not None:
adjusted_kwargs['tfrecord_dir'] = os.path.join(data_dir, adjusted_kwargs['tfrecord_dir'])
if verbose:
print('Streaming data using %s...' % class_name)
dataset = tfutil.import_obj(class_name)(**adjusted_kwargs)
if verbose:
print('Dataset shape =', np.int32(dataset.shape).tolist())
print('Dynamic range =', dataset.dynamic_range)
print('Label size =', dataset.label_size)
return dataset
#----------------------------------------------------------------------------
================================================
FILE: dataset_tool.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import os
import sys
import glob
import argparse
import threading
import six.moves.queue as Queue
import traceback
import numpy as np
import tensorflow as tf
import PIL.Image
import tfutil
import dataset
import menpo.io as mio
import scipy
#----------------------------------------------------------------------------
def error(msg):
print('Error: ' + msg)
exit(1)
#----------------------------------------------------------------------------
class TFRecordExporter:
def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10):
self.tfrecord_dir = tfrecord_dir
self.tfr_prefix = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir))
self.expected_images = expected_images
self.cur_images = 0
self.shape = None
self.resolution_log2 = None
self.tfr_writers = []
self.print_progress = print_progress
self.progress_interval = progress_interval
if self.print_progress:
print('Creating dataset "%s"' % tfrecord_dir)
if not os.path.isdir(self.tfrecord_dir):
os.makedirs(self.tfrecord_dir)
assert(os.path.isdir(self.tfrecord_dir))
def close(self):
if self.print_progress:
print('%-40s\r' % 'Flushing data...', end='', flush=True)
for tfr_writer in self.tfr_writers:
tfr_writer.close()
self.tfr_writers = []
if self.print_progress:
print('%-40s\r' % '', end='', flush=True)
print('Added %d images.' % self.cur_images)
def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order.
order = np.arange(self.expected_images)
np.random.RandomState(123).shuffle(order)
return order
def add_image(self, img):
if self.print_progress and self.cur_images % self.progress_interval == 0:
print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
if self.shape is None:
self.shape = img.shape
self.resolution_log2 = int(np.log2(self.shape[1]))
assert self.shape[0] in [1, 3]
assert self.shape[1] == self.shape[2]
assert self.shape[1] == 2**self.resolution_log2
tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
for lod in range(self.resolution_log2 - 1):
tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
assert img.shape == self.shape
for lod, tfr_writer in enumerate(self.tfr_writers):
if lod:
img = img.astype(np.float32)
img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
quant = np.rint(img).clip(0, 255).astype(np.uint8)
ex = tf.train.Example(features=tf.train.Features(feature={
'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
tfr_writer.write(ex.SerializeToString())
self.cur_images += 1
def add_shape(self, img):
if self.print_progress and self.cur_images % self.progress_interval == 0:
print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
if self.shape is None:
self.shape = img.shape
self.resolution_log2 = int(np.log2(self.shape[1]))
assert self.shape[0] in [1, 3]
assert self.shape[1] == self.shape[2]
assert self.shape[1] == 2**self.resolution_log2
tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
for lod in range(self.resolution_log2 - 1):
tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
assert img.shape == self.shape
for lod, tfr_writer in enumerate(self.tfr_writers):
if lod:
img = img.astype(np.float32)
img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
ex = tf.train.Example(features=tf.train.Features(feature={
'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=img.shape)),
'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img.tostring()]))}))
tfr_writer.write(ex.SerializeToString())
self.cur_images += 1
def add_both(self, img):
if self.print_progress and self.cur_images % self.progress_interval == 0:
print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
if self.shape is None:
self.shape = img.shape
self.resolution_log2 = int(np.log2(self.shape[1]))
assert self.shape[0] in [1, 6, 9]
assert self.shape[1] == self.shape[2]
assert self.shape[1] == 2**self.resolution_log2
tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
for lod in range(self.resolution_log2 - 1):
tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
assert img.shape == self.shape
for lod, tfr_writer in enumerate(self.tfr_writers):
if lod:
img = img.astype(np.float32)
img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
ex = tf.train.Example(features=tf.train.Features(feature={
'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=img.shape)),
'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img.tostring()]))}))
tfr_writer.write(ex.SerializeToString())
self.cur_images += 1
def add_labels(self, labels):
if self.print_progress:
print('%-40s\r' % 'Saving labels...', end='', flush=True)
assert labels.shape[0] == self.cur_images
with open(self.tfr_prefix + '-rxx.labels', 'wb') as f:
np.save(f, labels.astype(np.float32))
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
#----------------------------------------------------------------------------
class ExceptionInfo(object):
def __init__(self):
self.value = sys.exc_info()[1]
self.traceback = traceback.format_exc()
#----------------------------------------------------------------------------
class WorkerThread(threading.Thread):
def __init__(self, task_queue):
threading.Thread.__init__(self)
self.task_queue = task_queue
def run(self):
while True:
func, args, result_queue = self.task_queue.get()
if func is None:
break
try:
result = func(*args)
except:
result = ExceptionInfo()
result_queue.put((result, args))
#----------------------------------------------------------------------------
class ThreadPool(object):
def __init__(self, num_threads):
assert num_threads >= 1
self.task_queue = Queue.Queue()
self.result_queues = dict()
self.num_threads = num_threads
for idx in range(self.num_threads):
thread = WorkerThread(self.task_queue)
thread.daemon = True
thread.start()
def add_task(self, func, args=()):
assert hasattr(func, '__call__') # must be a function
if func not in self.result_queues:
self.result_queues[func] = Queue.Queue()
self.task_queue.put((func, args, self.result_queues[func]))
def get_result(self, func): # returns (result, args)
result, args = self.result_queues[func].get()
if isinstance(result, ExceptionInfo):
print('\n\nWorker thread caught an exception:\n' + result.traceback)
raise result.value
return result, args
def finish(self):
for idx in range(self.num_threads):
self.task_queue.put((None, (), None))
def __enter__(self): # for 'with' statement
return self
def __exit__(self, *excinfo):
self.finish()
def process_items_concurrently(self, item_iterator, process_func=lambda x: x, pre_func=lambda x: x, post_func=lambda x: x, max_items_in_flight=None):
if max_items_in_flight is None: max_items_in_flight = self.num_threads * 4
assert max_items_in_flight >= 1
results = []
retire_idx = [0]
def task_func(prepared, idx):
return process_func(prepared)
def retire_result():
processed, (prepared, idx) = self.get_result(task_func)
results[idx] = processed
while retire_idx[0] < len(results) and results[retire_idx[0]] is not None:
yield post_func(results[retire_idx[0]])
results[retire_idx[0]] = None
retire_idx[0] += 1
for idx, item in enumerate(item_iterator):
prepared = pre_func(item)
results.append(None)
self.add_task(func=task_func, args=(prepared, idx))
while retire_idx[0] < idx - max_items_in_flight + 2:
for res in retire_result(): yield res
while retire_idx[0] < len(results):
for res in retire_result(): yield res
#----------------------------------------------------------------------------
def display(tfrecord_dir):
print('Loading dataset "%s"' % tfrecord_dir)
tfutil.init_tf({'gpu_options.allow_growth': True})
dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size='full', repeat=False, shuffle_mb=0)
tfutil.init_uninited_vars()
idx = 0
while True:
try:
images, labels = dset.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
break
if idx == 0:
print('Displaying images')
import cv2 # pip install opencv-python
cv2.namedWindow('dataset_tool')
print('Press SPACE or ENTER to advance, ESC to exit')
print('\nidx = %-8d\nlabel = %s' % (idx, labels[0].tolist()))
cv2.imshow('dataset_tool', images[0].transpose(1, 2, 0)[:, :, ::-1]) # CHW => HWC, RGB => BGR
idx += 1
if cv2.waitKey() == 27:
break
print('\nDisplayed %d images.' % idx)
#----------------------------------------------------------------------------
def extract(tfrecord_dir, output_dir):
print('Loading dataset "%s"' % tfrecord_dir)
tfutil.init_tf({'gpu_options.allow_growth': True})
dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size=0, repeat=False, shuffle_mb=0)
tfutil.init_uninited_vars()
print('Extracting images to "%s"' % output_dir)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
idx = 0
while True:
if idx % 10 == 0:
print('%d\r' % idx, end='', flush=True)
try:
images, labels = dset.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
break
if images.shape[1] == 1:
img = PIL.Image.fromarray(images[0][0], 'L')
else:
img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB')
img.save(os.path.join(output_dir, 'img%08d.png' % idx))
idx += 1
print('Extracted %d images.' % idx)
#----------------------------------------------------------------------------
def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
max_label_size = 0 if ignore_labels else 'full'
print('Loading dataset "%s"' % tfrecord_dir_a)
tfutil.init_tf({'gpu_options.allow_growth': True})
dset_a = dataset.TFRecordDataset(tfrecord_dir_a, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
print('Loading dataset "%s"' % tfrecord_dir_b)
dset_b = dataset.TFRecordDataset(tfrecord_dir_b, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
tfutil.init_uninited_vars()
print('Comparing datasets')
idx = 0
identical_images = 0
identical_labels = 0
while True:
if idx % 100 == 0:
print('%d\r' % idx, end='', flush=True)
try:
images_a, labels_a = dset_a.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
images_a, labels_a = None, None
try:
images_b, labels_b = dset_b.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
images_b, labels_b = None, None
if images_a is None or images_b is None:
if images_a is not None or images_b is not None:
print('Datasets contain different number of images')
break
if images_a.shape == images_b.shape and np.all(images_a == images_b):
identical_images += 1
else:
print('Image %d is different' % idx)
if labels_a.shape == labels_b.shape and np.all(labels_a == labels_b):
identical_labels += 1
else:
print('Label %d is different' % idx)
idx += 1
print('Identical images: %d / %d' % (identical_images, idx))
if not ignore_labels:
print('Identical labels: %d / %d' % (identical_labels, idx))
#----------------------------------------------------------------------------
def create_mnist(tfrecord_dir, mnist_dir):
print('Loading MNIST from "%s"' % mnist_dir)
import gzip
with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
images = np.frombuffer(file.read(), np.uint8, offset=16)
with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
labels = np.frombuffer(file.read(), np.uint8, offset=8)
images = images.reshape(-1, 1, 28, 28)
images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
assert labels.shape == (60000,) and labels.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
print('Loading MNIST from "%s"' % mnist_dir)
import gzip
with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
images = np.frombuffer(file.read(), np.uint8, offset=16)
images = images.reshape(-1, 28, 28)
images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
with TFRecordExporter(tfrecord_dir, num_images) as tfr:
rnd = np.random.RandomState(random_seed)
for idx in range(num_images):
tfr.add_image(images[rnd.randint(images.shape[0], size=3)])
#----------------------------------------------------------------------------
def create_cifar10(tfrecord_dir, cifar10_dir):
print('Loading CIFAR-10 from "%s"' % cifar10_dir)
import pickle
images = []
labels = []
for batch in range(1, 6):
with open(os.path.join(cifar10_dir, 'data_batch_%d' % batch), 'rb') as file:
data = pickle.load(file, encoding='latin1')
images.append(data['data'].reshape(-1, 3, 32, 32))
labels.append(data['labels'])
images = np.concatenate(images)
labels = np.concatenate(labels)
assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
assert labels.shape == (50000,) and labels.dtype == np.int32
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_cifar100(tfrecord_dir, cifar100_dir):
print('Loading CIFAR-100 from "%s"' % cifar100_dir)
import pickle
with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
data = pickle.load(file, encoding='latin1')
images = data['data'].reshape(-1, 3, 32, 32)
labels = np.array(data['fine_labels'])
assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
assert labels.shape == (50000,) and labels.dtype == np.int32
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 99
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_svhn(tfrecord_dir, svhn_dir):
print('Loading SVHN from "%s"' % svhn_dir)
import pickle
images = []
labels = []
for batch in range(1, 4):
with open(os.path.join(svhn_dir, 'train_%d.pkl' % batch), 'rb') as file:
data = pickle.load(file, encoding='latin1')
images.append(data[0])
labels.append(data[1])
images = np.concatenate(images)
labels = np.concatenate(labels)
assert images.shape == (73257, 3, 32, 32) and images.dtype == np.uint8
assert labels.shape == (73257,) and labels.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
print('Loading LSUN dataset from "%s"' % lmdb_dir)
import lmdb # pip install lmdb
import cv2 # pip install opencv-python
import io
with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
total_images = txn.stat()['entries']
if max_images is None:
max_images = total_images
with TFRecordExporter(tfrecord_dir, max_images) as tfr:
for idx, (key, value) in enumerate(txn.cursor()):
try:
try:
img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
if img is None:
raise IOError('cv2.imdecode failed')
img = img[:, :, ::-1] # BGR => RGB
except IOError:
img = np.asarray(PIL.Image.open(io.BytesIO(value)))
crop = np.min(img.shape[:2])
img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
img = PIL.Image.fromarray(img, 'RGB')
img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS)
img = np.asarray(img)
img = img.transpose(2, 0, 1) # HWC => CHW
tfr.add_image(img)
except:
print(sys.exc_info()[1])
if tfr.cur_images == max_images:
break
#----------------------------------------------------------------------------
def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
print('Loading CelebA from "%s"' % celeba_dir)
glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
image_filenames = sorted(glob.glob(glob_pattern))
expected_images = 202599
if len(image_filenames) != expected_images:
error('Expected to find %d images' % expected_images)
with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
assert img.shape == (218, 178, 3)
img = img[cy - 64 : cy + 64, cx - 64 : cx + 64]
img = img.transpose(2, 0, 1) # HWC => CHW
tfr.add_image(img)
#----------------------------------------------------------------------------
def create_celebahq(tfrecord_dir, celeba_dir, delta_dir, num_threads=4, num_tasks=100):
print('Loading CelebA from "%s"' % celeba_dir)
expected_images = 202599
if len(glob.glob(os.path.join(celeba_dir, 'img_celeba', '*.jpg'))) != expected_images:
error('Expected to find %d images' % expected_images)
with open(os.path.join(celeba_dir, 'Anno', 'list_landmarks_celeba.txt'), 'rt') as file:
landmarks = [[float(value) for value in line.split()[1:]] for line in file.readlines()[2:]]
landmarks = np.float32(landmarks).reshape(-1, 5, 2)
print('Loading CelebA-HQ deltas from "%s"' % delta_dir)
import scipy.ndimage
import hashlib
import bz2
import zipfile
import base64
import cryptography.hazmat.primitives.hashes
import cryptography.hazmat.backends
import cryptography.hazmat.primitives.kdf.pbkdf2
import cryptography.fernet
expected_zips = 30
if len(glob.glob(os.path.join(delta_dir, 'delta*.zip'))) != expected_zips:
error('Expected to find %d zips' % expected_zips)
with open(os.path.join(delta_dir, 'image_list.txt'), 'rt') as file:
lines = [line.split() for line in file]
fields = dict()
for idx, field in enumerate(lines[0]):
type = int if field.endswith('idx') else str
fields[field] = [type(line[idx]) for line in lines[1:]]
indices = np.array(fields['idx'])
# Must use pillow version 3.1.1 for everything to work correctly.
if getattr(PIL, 'PILLOW_VERSION', '') != '3.1.1':
error('create_celebahq requires pillow version 3.1.1') # conda install pillow=3.1.1
# Must use libjpeg version 8d for everything to work correctly.
img = np.array(PIL.Image.open(os.path.join(celeba_dir, 'img_celeba', '000001.jpg')))
md5 = hashlib.md5()
md5.update(img.tobytes())
if md5.hexdigest() != '9cad8178d6cb0196b36f7b34bc5eb6d3':
error('create_celebahq requires libjpeg version 8d') # conda install jpeg=8d
def rot90(v):
return np.array([-v[1], v[0]])
def process_func(idx):
# Load original image.
orig_idx = fields['orig_idx'][idx]
orig_file = fields['orig_file'][idx]
orig_path = os.path.join(celeba_dir, 'img_celeba', orig_file)
img = PIL.Image.open(orig_path)
# Choose oriented crop rectangle.
lm = landmarks[orig_idx]
eye_avg = (lm[0] + lm[1]) * 0.5 + 0.5
mouth_avg = (lm[3] + lm[4]) * 0.5 + 0.5
eye_to_eye = lm[1] - lm[0]
eye_to_mouth = mouth_avg - eye_avg
x = eye_to_eye - rot90(eye_to_mouth)
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = rot90(x)
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
zoom = 1024 / (np.hypot(*x) * 2)
# Shrink.
shrink = int(np.floor(0.5 / zoom))
if shrink > 1:
size = (int(np.round(float(img.size[0]) / shrink)), int(np.round(float(img.size[1]) / shrink)))
img = img.resize(size, PIL.Image.ANTIALIAS)
quad /= shrink
zoom *= shrink
# Crop.
border = max(int(np.round(1024 * 0.1 / zoom)), 3)
crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Simulate super-resolution.
superres = int(np.exp2(np.ceil(np.log2(zoom))))
if superres > 1:
img = img.resize((img.size[0] * superres, img.size[1] * superres), PIL.Image.ANTIALIAS)
quad *= superres
zoom /= superres
# Pad.
pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
if max(pad) > border - 4:
pad = np.maximum(pad, int(np.round(1024 * 0.3 / zoom)))
img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
h, w, _ = img.shape
y, x, _ = np.mgrid[:h, :w, :1]
mask = 1.0 - np.minimum(np.minimum(np.float32(x) / pad[0], np.float32(y) / pad[1]), np.minimum(np.float32(w-1-x) / pad[2], np.float32(h-1-y) / pad[3]))
blur = 1024 * 0.02 / zoom
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.round(img), 0, 255)), 'RGB')
quad += pad[0:2]
# Transform.
img = img.transform((4096, 4096), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
img = img.resize((1024, 1024), PIL.Image.ANTIALIAS)
img = np.asarray(img).transpose(2, 0, 1)
# Verify MD5.
md5 = hashlib.md5()
md5.update(img.tobytes())
assert md5.hexdigest() == fields['proc_md5'][idx]
# Load delta image and original JPG.
with zipfile.ZipFile(os.path.join(delta_dir, 'deltas%05d.zip' % (idx - idx % 1000)), 'r') as zip:
delta_bytes = zip.read('delta%05d.dat' % idx)
with open(orig_path, 'rb') as file:
orig_bytes = file.read()
# Decrypt delta image, using original JPG data as decryption key.
algorithm = cryptography.hazmat.primitives.hashes.SHA256()
backend = cryptography.hazmat.backends.default_backend()
salt = bytes(orig_file, 'ascii')
kdf = cryptography.hazmat.primitives.kdf.pbkdf2.PBKDF2HMAC(algorithm=algorithm, length=32, salt=salt, iterations=100000, backend=backend)
key = base64.urlsafe_b64encode(kdf.derive(orig_bytes))
delta = np.frombuffer(bz2.decompress(cryptography.fernet.Fernet(key).decrypt(delta_bytes)), dtype=np.uint8).reshape(3, 1024, 1024)
# Apply delta image.
img = img + delta
# Verify MD5.
md5 = hashlib.md5()
md5.update(img.tobytes())
assert md5.hexdigest() == fields['final_md5'][idx]
return img
with TFRecordExporter(tfrecord_dir, indices.size) as tfr:
order = tfr.choose_shuffled_order()
with ThreadPool(num_threads) as pool:
for img in pool.process_items_concurrently(indices[order].tolist(), process_func=process_func, max_items_in_flight=num_tasks):
tfr.add_image(img)
#----------------------------------------------------------------------------
def create_from_images(tfrecord_dir, image_dir, shuffle):
print('Loading images from "%s"' % image_dir)
image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
if len(image_filenames) == 0:
error('No input images found')
good_ids = mio.import_pickle('/vol/construct3dmm/visualizations/nicp/mein3d/good_ids.pkl')
img = np.asarray(PIL.Image.open(image_filenames[0]))
resolution = img.shape[0]
channels = img.shape[2] if img.ndim == 3 else 1
# if img.shape[1] != resolution:
# error('Input images must have the same width and height')
# if resolution != 2 ** int(np.floor(np.log2(resolution))):
# error('Input image resolution must be a power-of-two')
if channels not in [1, 3]:
error('Input images must be stored as RGB or grayscale')
with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
for idx in range(order.size):
img = PIL.Image.open(image_filenames[order[idx]])
# img.crop((41, 0, img.size[0]-42, resolution))
# new_im = PIL.Image.new("RGB", (512, 512),(0, 0, 0))
# new_im.paste(img, ((512 - img.size[0]) // 2,(512 - img.size[1]) // 2))
img = np.asarray(img)
if channels == 1:
img = img[np.newaxis, :, :] # HW => CHW
else:
img = img.transpose(2, 0, 1) # HWC => CHW
tfr.add_image(img)
#----------------------------------------------------------------------------
def create_from_pkl(tfrecord_dir, image_dir, shuffle):
print('Loading images from "%s"' % image_dir)
image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
if len(image_filenames) == 0:
error('No input images found')
# good_ids = mio.import_pickle('/vol/construct3dmm/visualizations/nicp/mein3d/good_ids.pkl')
img = mio.import_pickle(image_filenames[0])
resolution = img.shape[2]
channels = img.shape[0] if img.ndim == 3 else 1
if img.shape[1] != resolution:
error('Input images must have the same width and height')
if resolution != 2 ** int(np.floor(np.log2(resolution))):
error('Input image resolution must be a power-of-two')
if channels not in [1, 3]:
error('Input images must be stored as RGB or grayscale')
with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
for idx in range(order.size):
img = mio.import_pickle(image_filenames[order[idx]]).astype(np.float32)
# img[0, :, :] = scipy.ndimage.gaussian_filter(img[0, :, :], 2)
# img[1, :, :] = scipy.ndimage.gaussian_filter(img[1, :, :], 2)
# img[2, :, :] = scipy.ndimage.gaussian_filter(img[2, :, :], 2)
# img_resized = np.stack((cv2.resize(img[0],dsize=(256,256)),cv2.resize(img[1],dsize=(256,256)),cv2.resize(img[2],dsize=(256,256))))
tfr.add_shape(img)
#----------------------------------------------------------------------------
def create_from_pkl_img(tfrecord_dir, image_dir, pickle_dir, shuffle):
print('Loading images from "%s"' % image_dir)
image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
pickle_filenames = sorted(glob.glob(os.path.join(pickle_dir, '*')))
if len(image_filenames) == 0:
error('No input images found')
# good_ids = mio.import_pickle('/vol/construct3dmm/visualizations/nicp/mein3d/good_ids.pkl')
img = mio.import_pickle(pickle_filenames[0])
resolution = img.shape[2]
channels = img.shape[0] if img.ndim == 3 else 1
if img.shape[1] != resolution:
error('Input images must have the same width and height')
if resolution != 2 ** int(np.floor(np.log2(resolution))):
error('Input image resolution must be a power-of-two')
if channels not in [1, 3]:
error('Input images must be stored as RGB or grayscale')
with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
for idx in range(order.size):
img = mio.import_image(image_filenames[order[idx]]).pixels.astype(np.float32)*2-1
pkl = mio.import_pickle(pickle_filenames[order[idx]]).astype(np.float32)
# pkl[0, :, :] = scipy.ndimage.gaussian_filter(pkl[0, :, :], 2)
# pkl[1, :, :] = scipy.ndimage.gaussian_filter(pkl[1, :, :], 2)
# pkl[2, :, :] = scipy.ndimage.gaussian_filter(pkl[2, :, :], 2)
# img_resized = np.stack((cv2.resize(img[0],dsize=(256,256)),cv2.resize(img[1],dsize=(256,256)),cv2.resize(img[2],dsize=(256,256))))
tfr.add_both(np.concatenate([img,pkl]))
#----------------------------------------------------------------------------
def create_from_pkl_img_norm(tfrecord_dir, mein3d_image_dir, mein3d_pickle_dir, mein3d_normal_dir, shuffle):
print('Loading images from "%s"' % mein3d_image_dir)
_3dmd_image_dir = '/raid/baris/data/3dmd_crop/texture/'
_3dmd_pickle_dir = '/raid/baris/data/3dmd_crop/shape/'
_3dmd_normal_dir = '/raid/baris/data/3dmd_crop/normals/'
labels_mein3d = mio.import_pickle('../Prepare_dataset/results_mein3d.pkl')
paths_mein3d = mio.import_pickle('../Prepare_dataset/paths_mein3d.pkl')
paths_mein3d_tex = [os.path.join(mein3d_image_dir, im + '.png') for im in paths_mein3d]
actual_paths_mein3d_tex = glob.glob(mein3d_image_dir + '/*.png')
actual_paths_mein3d_shp = glob.glob(mein3d_pickle_dir + '/*.pkl')
actual_paths_mein3d_nor = glob.glob(mein3d_normal_dir + '/*.pkl')
idx =[]
for path in actual_paths_mein3d_tex:
idx.append(paths_mein3d_tex.index(path))
actual_labels_mein3d = labels_mein3d[idx]
assert len(actual_paths_mein3d_tex) == len(actual_paths_mein3d_shp) == len(actual_paths_mein3d_nor) == len(actual_labels_mein3d)
labels_3dmd = mio.import_pickle('../Prepare_dataset/results_3dmd.pkl')
paths_3dmd = mio.import_pickle('../Prepare_dataset/paths_3dmd.pkl')
paths_3dmd_shp = [os.path.join(_3dmd_pickle_dir,str.split(im,'.')[0], im + '.pkl') for im in paths_3dmd]
actual_paths_3dmd_tex = glob.glob(_3dmd_image_dir + '/*/*.*.png')
actual_paths_3dmd_shp = glob.glob(_3dmd_pickle_dir + '/*/*.*.pkl')
actual_paths_3dmd_nor = glob.glob(_3dmd_normal_dir + '/*/*.*.pkl')
idx =[]
for path in actual_paths_3dmd_shp:
if path in paths_3dmd_shp and path.replace('shape','texture').replace('.pkl','.png') in actual_paths_3dmd_tex:
idx.append(paths_3dmd_shp.index(path))
actual_labels_3dmd = labels_3dmd[idx]
intersection_paths_tex = [os.path.join(_3dmd_image_dir, str.split(im, '.')[0], im + '.png') for im in [paths_3dmd[i] for i in idx]]
intersection_paths_shp = [os.path.join(_3dmd_pickle_dir,str.split(im,'.')[0], im + '.pkl') for im in [paths_3dmd[i] for i in idx]]
intersection_paths_nor = [os.path.join(_3dmd_normal_dir, str.split(im, '.')[0], im + '.pkl') for im in [paths_3dmd[i] for i in idx]]
assert len(intersection_paths_tex) == len(intersection_paths_shp) == len(intersection_paths_nor) == len(actual_labels_3dmd)
image_filenames = actual_paths_mein3d_tex + intersection_paths_tex
pickle_filenames = actual_paths_mein3d_shp + intersection_paths_shp
normal_filenames = actual_paths_mein3d_nor + intersection_paths_nor
# image_filenames = paths_mein3d[0:100]
if len(image_filenames) == 0:
error('No input images found')
labels = np.append(actual_labels_mein3d,actual_labels_3dmd,0)
# labels = labels[0:100]
if len(image_filenames) == 0:
error('No input images found')
# good_ids = mio.import_pickle('/vol/construct3dmm/visualizations/nicp/mein3d/good_ids.pkl')
img = mio.import_pickle(pickle_filenames[0])
resolution = img.shape[2]
channels = img.shape[0] if img.ndim == 3 else 1
if img.shape[1] != resolution:
error('Input images must have the same width and height')
if resolution != 2 ** int(np.floor(np.log2(resolution))):
error('Input image resolution must be a power-of-two')
if channels not in [1, 3]:
error('Input images must be stored as RGB or grayscale')
with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
for idx in range(order.size):
img = mio.import_image(image_filenames[order[idx]]).pixels.astype(np.float32)*2-1
pkl = mio.import_pickle(pickle_filenames[order[idx]]).astype(np.float32)
normal = mio.import_pickle(normal_filenames[order[idx]]).astype(np.float32)
# pkl[0, :, :] = scipy.ndimage.gaussian_filter(pkl[0, :, :], 2)
# pkl[1, :, :] = scipy.ndimage.gaussian_filter(pkl[1, :, :], 2)
# pkl[2, :, :] = scipy.ndimage.gaussian_filter(pkl[2, :, :], 2)
# img_resized = np.stack((cv2.resize(img[0],dsize=(256,256)),cv2.resize(img[1],dsize=(256,256)),cv2.resize(img[2],dsize=(256,256))))
tfr.add_both(np.concatenate([img,pkl,normal]))
tfr.add_labels(labels[order,:])
#----------------------------------------------------------------------------
def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
print('Loading HDF5 archive from "%s"' % hdf5_filename)
import h5py # conda install h5py
with h5py.File(hdf5_filename, 'r') as hdf5_file:
hdf5_data = max([value for key, value in hdf5_file.items() if key.startswith('data')], key=lambda lod: lod.shape[3])
with TFRecordExporter(tfrecord_dir, hdf5_data.shape[0]) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(hdf5_data.shape[0])
for idx in range(order.size):
tfr.add_image(hdf5_data[order[idx]])
npy_filename = os.path.splitext(hdf5_filename)[0] + '-labels.npy'
if os.path.isfile(npy_filename):
tfr.add_labels(np.load(npy_filename)[order])
#----------------------------------------------------------------------------
def execute_cmdline(argv):
prog = argv[0]
parser = argparse.ArgumentParser(
prog = prog,
description = 'Tool for creating, extracting, and visualizing Progressive GAN datasets.',
epilog = 'Type "%s <command> -h" for more information.' % prog)
subparsers = parser.add_subparsers(dest='command')
subparsers.required = True
def add_command(cmd, desc, example=None):
epilog = 'Example: %s %s' % (prog, example) if example is not None else None
return subparsers.add_parser(cmd, description=desc, help=desc, epilog=epilog)
p = add_command( 'display', 'Display images in dataset.',
'display datasets/mnist')
p.add_argument( 'tfrecord_dir', help='Directory containing dataset')
p = add_command( 'extract', 'Extract images from dataset.',
'extract datasets/mnist mnist-images')
p.add_argument( 'tfrecord_dir', help='Directory containing dataset')
p.add_argument( 'output_dir', help='Directory to extract the images into')
p = add_command( 'compare', 'Compare two datasets.',
'compare datasets/mydataset datasets/mnist')
p.add_argument( 'tfrecord_dir_a', help='Directory containing first dataset')
p.add_argument( 'tfrecord_dir_b', help='Directory containing second dataset')
p.add_argument( '--ignore_labels', help='Ignore labels (default: 0)', type=int, default=0)
p = add_command( 'create_mnist', 'Create dataset for MNIST.',
'create_mnist datasets/mnist ~/downloads/mnist')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'mnist_dir', help='Directory containing MNIST')
p = add_command( 'create_mnistrgb', 'Create dataset for MNIST-RGB.',
'create_mnistrgb datasets/mnistrgb ~/downloads/mnist')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'mnist_dir', help='Directory containing MNIST')
p.add_argument( '--num_images', help='Number of composite images to create (default: 1000000)', type=int, default=1000000)
p.add_argument( '--random_seed', help='Random seed (default: 123)', type=int, default=123)
p = add_command( 'create_cifar10', 'Create dataset for CIFAR-10.',
'create_cifar10 datasets/cifar10 ~/downloads/cifar10')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'cifar10_dir', help='Directory containing CIFAR-10')
p = add_command( 'create_cifar100', 'Create dataset for CIFAR-100.',
'create_cifar100 datasets/cifar100 ~/downloads/cifar100')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'cifar100_dir', help='Directory containing CIFAR-100')
p = add_command( 'create_svhn', 'Create dataset for SVHN.',
'create_svhn datasets/svhn ~/downloads/svhn')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'svhn_dir', help='Directory containing SVHN')
p = add_command( 'create_lsun', 'Create dataset for single LSUN category.',
'create_lsun datasets/lsun-car-100k ~/downloads/lsun/car_lmdb --resolution 256 --max_images 100000')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'lmdb_dir', help='Directory containing LMDB database')
p.add_argument( '--resolution', help='Output resolution (default: 256)', type=int, default=256)
p.add_argument( '--max_images', help='Maximum number of images (default: none)', type=int, default=None)
p = add_command( 'create_celeba', 'Create dataset for CelebA.',
'create_celeba datasets/celeba ~/downloads/celeba')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'celeba_dir', help='Directory containing CelebA')
p.add_argument( '--cx', help='Center X coordinate (default: 89)', type=int, default=89)
p.add_argument( '--cy', help='Center Y coordinate (default: 121)', type=int, default=121)
p = add_command( 'create_celebahq', 'Create dataset for CelebA-HQ.',
'create_celebahq datasets/celebahq ~/downloads/celeba ~/downloads/celeba-hq-deltas')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'celeba_dir', help='Directory containing CelebA')
p.add_argument( 'delta_dir', help='Directory containing CelebA-HQ deltas')
p.add_argument( '--num_threads', help='Number of concurrent threads (default: 4)', type=int, default=4)
p.add_argument( '--num_tasks', help='Number of concurrent processing tasks (default: 100)', type=int, default=100)
p = add_command( 'create_from_images', 'Create dataset from a directory full of images.',
'create_from_images datasets/mydataset myimagedir')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'image_dir', help='Directory containing the images')
p.add_argument( '--shuffle', help='Randomize image order (default: 1)', type=int, default=1)
p = add_command( 'create_from_pkl', 'Create dataset from a directory full of images.',
'create_from_pkl datasets/mydataset myimagedir')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'image_dir', help='Directory containing the images')
p.add_argument( '--shuffle', help='Randomize image order (default: 1)', type=int, default=1)
p = add_command( 'create_from_pkl_img', 'Create dataset from a directory full of images.',
'create_from_pkl_img datasets/mydataset myimagedir myshapedir')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'image_dir', help='Directory containing the images')
p.add_argument( 'pickle_dir', help='Directory containing the shape pickles')
p.add_argument( '--shuffle', help='Randomize image order (default: 1)', type=int, default=1)
p = add_command( 'create_from_pkl_img_norm', 'Create dataset from a directory full of images.',
'create_from_pkl_img_norm datasets/mydataset myimagedir myshapedir')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'mein3d_image_dir', help='Directory containing the images')
p.add_argument( 'mein3d_pickle_dir', help='Directory containing the shape pickles')
p.add_argument( 'mein3d_normal_dir', help='Directory containing the normal pickles')
p.add_argument( '--shuffle', help='Randomize image order (default: 1)', type=int, default=1)
p = add_command( 'create_from_hdf5', 'Create dataset from legacy HDF5 archive.',
'create_from_hdf5 datasets/celebahq ~/downloads/celeba-hq-1024x1024.h5')
p.add_argument( 'tfrecord_dir', help='New dataset directory to be created')
p.add_argument( 'hdf5_filename', help='HDF5 archive containing the images')
p.add_argument( '--shuffle', help='Randomize image order (default: 1)', type=int, default=1)
args = parser.parse_args(argv[1:] if len(argv) > 1 else ['-h'])
func = globals()[args.command]
del args.command
func(**vars(args))
#----------------------------------------------------------------------------
if __name__ == "__main__":
execute_cmdline(sys.argv)
#----------------------------------------------------------------------------
================================================
FILE: legacy.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import pickle
import inspect
import numpy as np
import tfutil
import networks
#----------------------------------------------------------------------------
# Custom unpickler that is able to load network pickles produced by
# the old Theano implementation.
class LegacyUnpickler(pickle.Unpickler):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def find_class(self, module, name):
module = module.replace('uv_gan.','')
if module == 'network' and name == 'Network':
return tfutil.Network
return super().find_class(module, name)
#----------------------------------------------------------------------------
# Import handler for tfutil.Network that silently converts networks produced
# by the old Theano implementation to a suitable format.
theano_gan_remap = {
'G_paper': 'G_paper',
'G_progressive_8': 'G_paper',
'D_paper': 'D_paper',
'D_progressive_8': 'D_paper'}
def patch_theano_gan(state):
if 'version' in state or state['build_func_spec']['func'] not in theano_gan_remap:
return state
spec = dict(state['build_func_spec'])
func = spec.pop('func')
resolution = spec.get('resolution', 32)
resolution_log2 = int(np.log2(resolution))
use_wscale = spec.get('use_wscale', True)
assert spec.pop('label_size', 0) == 0
assert spec.pop('use_batchnorm', False) == False
assert spec.pop('tanh_at_end', None) is None
assert spec.pop('mbstat_func', 'Tstdeps') == 'Tstdeps'
assert spec.pop('mbstat_avg', 'all') == 'all'
assert spec.pop('mbdisc_kernels', None) is None
spec.pop( 'use_gdrop', True) # doesn't make a difference
assert spec.pop('use_layernorm', False) == False
spec[ 'fused_scale'] = False
spec[ 'mbstd_group_size'] = 16
vars = []
param_iter = iter(state['param_values'])
relu = np.sqrt(2); linear = 1.0
def flatten2(w): return w.reshape(w.shape[0], -1)
def he_std(gain, w): return gain / np.sqrt(np.prod(w.shape[:-1]))
def wscale(gain, w): return w * next(param_iter) / he_std(gain, w) if use_wscale else w
def layer(name, gain, w): return [(name + '/weight', wscale(gain, w)), (name + '/bias', next(param_iter))]
if func.startswith('G'):
vars += layer('4x4/Dense', relu/4, flatten2(next(param_iter).transpose(1,0,2,3)))
vars += layer('4x4/Conv', relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
for res in range(3, resolution_log2 + 1):
vars += layer('%dx%d/Conv0' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
vars += layer('%dx%d/Conv1' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
for lod in range(0, resolution_log2 - 1):
vars += layer('ToRGB_lod%d' % lod, linear, next(param_iter)[np.newaxis, np.newaxis])
if func.startswith('D'):
vars += layer('FromRGB_lod0', relu, next(param_iter)[np.newaxis, np.newaxis])
for res in range(resolution_log2, 2, -1):
vars += layer('%dx%d/Conv0' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
vars += layer('%dx%d/Conv1' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
vars += layer('FromRGB_lod%d' % (resolution_log2 - (res - 1)), relu, next(param_iter)[np.newaxis, np.newaxis])
vars += layer('4x4/Conv', relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
vars += layer('4x4/Dense0', relu, flatten2(next(param_iter)[:,:,::-1,::-1]).transpose())
vars += layer('4x4/Dense1', linear, next(param_iter))
vars += [('lod', state['toplevel_params']['cur_lod'])]
return {
'version': 2,
'name': func,
'build_module_src': inspect.getsource(networks),
'build_func_name': theano_gan_remap[func],
'static_kwargs': spec,
'variables': vars}
tfutil.network_import_handlers.append(patch_theano_gan)
#----------------------------------------------------------------------------
# Import handler for tfutil.Network that ignores unsupported/deprecated
# networks produced by older versions of the code.
def ignore_unknown_theano_network(state):
if 'version' in state:
return state
print('Ignoring unknown Theano network:', state['build_func_spec']['func'])
return {
'version': 2,
'name': 'Dummy',
'build_module_src': 'def dummy(input, **kwargs): input.set_shape([None, 1]); return input',
'build_func_name': 'dummy',
'static_kwargs': {},
'variables': []}
tfutil.network_import_handlers.append(ignore_unknown_theano_network)
#----------------------------------------------------------------------------
================================================
FILE: loss.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import tensorflow as tf
import tfutil
#----------------------------------------------------------------------------
# Convenience func that casts all of its arguments to tf.float32.
def fp32(*values):
if len(values) == 1 and isinstance(values[0], tuple):
values = values[0]
values = tuple(tf.cast(v, tf.float32) for v in values)
return values if len(values) >= 2 else values[0]
#----------------------------------------------------------------------------
# Generator loss function used in the paper (WGAN + AC-GAN).
def G_wgan_acgan(G, D, opt, training_set, minibatch_size,
cond_weight = 1.0): # Weight of the conditioning term.
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
labels = training_set.get_random_labels_tf(minibatch_size)
labels = tf.nn.softmax(labels)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True))
loss = -fake_scores_out
if D.output_shapes[1][1] > 0:
with tf.name_scope('LabelPenalty'):
label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out)
loss += label_penalty_fakes * cond_weight
return loss
#----------------------------------------------------------------------------
# Discriminator loss function used in the paper (WGAN-GP + AC-GAN).
def D_wgangp_acgan(G, D, opt, training_set, minibatch_size, reals, labels,
wgan_lambda = 10.0, # Weight for the gradient penalty term.
wgan_epsilon = 0.001, # Weight for the epsilon term, \epsilon_{drift}.
wgan_target = 1.0, # Target value for gradient magnitudes.
cond_weight = 1.0): # Weight of the conditioning terms.
labels = tf.nn.softmax(labels)
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out, real_labels_out = fp32(D.get_output_for(reals, is_training=True))
fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True))
real_scores_out = tfutil.autosummary('Loss/real_scores', real_scores_out)
fake_scores_out = tfutil.autosummary('Loss/fake_scores', fake_scores_out)
loss = fake_scores_out - real_scores_out
with tf.name_scope('GradientPenalty'):
mixing_factors = tf.random_uniform([minibatch_size, 1, 1, 1], 0.0, 1.0, dtype=fake_images_out.dtype)
mixed_images_out = tfutil.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
mixed_scores_out, mixed_labels_out = fp32(D.get_output_for(mixed_images_out, is_training=True))
mixed_scores_out = tfutil.autosummary('Loss/mixed_scores', mixed_scores_out)
mixed_loss = opt.apply_loss_scaling(tf.reduce_sum(mixed_scores_out))
mixed_grads = opt.undo_loss_scaling(fp32(tf.gradients(mixed_loss, [mixed_images_out])[0]))
mixed_norms = tf.sqrt(tf.reduce_sum(tf.square(mixed_grads), axis=[1,2,3]))
mixed_norms = tfutil.autosummary('Loss/mixed_norms', mixed_norms)
gradient_penalty = tf.square(mixed_norms - wgan_target)
loss += gradient_penalty * (wgan_lambda / (wgan_target**2))
with tf.name_scope('EpsilonPenalty'):
epsilon_penalty = tfutil.autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
loss += epsilon_penalty * wgan_epsilon
if D.output_shapes[1][1] > 0:
with tf.name_scope('LabelPenalty'):
label_penalty_reals = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=real_labels_out)
label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out)
label_penalty_reals = tfutil.autosummary('Loss/label_penalty_reals', label_penalty_reals)
label_penalty_fakes = tfutil.autosummary('Loss/label_penalty_fakes', label_penalty_fakes)
loss += (label_penalty_reals + label_penalty_fakes) * cond_weight
return loss
#----------------------------------------------------------------------------
================================================
FILE: metrics/__init__.py
================================================
# empty
================================================
FILE: metrics/frechet_inception_distance.py
================================================
#!/usr/bin/env python3
#
# Copyright 2017 Martin Heusel
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Adapted from the original implementation by Martin Heusel.
# Source https://github.com/bioinf-jku/TTUR/blob/master/fid.py
''' Calculates the Frechet Inception Distance (FID) to evalulate GANs.
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run as a stand-alone program, it compares the distribution of
images that are stored as PNG/JPEG at a specified location with a
distribution given by summary statistics (in pickle format).
The FID is calculated by assuming that X_1 and X_2 are the activations of
the pool_3 layer of the inception net for generated samples and real world
samples respectivly.
See --help to see further details.
'''
from __future__ import absolute_import, division, print_function
import numpy as np
import scipy as sp
import os
import gzip, pickle
import tensorflow as tf
from scipy.misc import imread
import pathlib
import urllib
class InvalidFIDException(Exception):
pass
def create_inception_graph(pth):
"""Creates a graph from saved GraphDef file."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile( pth, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString( f.read())
_ = tf.import_graph_def( graph_def, name='FID_Inception_Net')
#-------------------------------------------------------------------------------
# code for handling inception net derived from
# https://github.com/openai/improved-gan/blob/master/inception_score/model.py
def _get_inception_layer(sess):
"""Prepares inception net for batched usage and returns pool_3 layer. """
layername = 'FID_Inception_Net/pool_3:0'
pool3 = sess.graph.get_tensor_by_name(layername)
ops = pool3.graph.get_operations()
for op_idx, op in enumerate(ops):
for o in op.outputs:
shape = o.get_shape()
if shape._dims is not None:
shape = [s.value for s in shape]
new_shape = []
for j, s in enumerate(shape):
if s == 1 and j == 0:
new_shape.append(None)
else:
new_shape.append(s)
try:
o._shape = tf.TensorShape(new_shape)
except ValueError:
o._shape_val = tf.TensorShape(new_shape) # EDIT: added for compatibility with tensorflow 1.6.0
return pool3
#-------------------------------------------------------------------------------
def get_activations(images, sess, batch_size=50, verbose=False):
"""Calculates the activations of the pool_3 layer for all images.
Params:
-- images : Numpy array of dimension (n_images, hi, wi, 3). The values
must lie between 0 and 256.
-- sess : current session
-- batch_size : the images numpy array is split into batches with batch size
batch_size. A reasonable batch size depends on the disposable hardware.
-- verbose : If set to True and parameter out_step is given, the number of calculated
batches is reported.
Returns:
-- A numpy array of dimension (num images, 2048) that contains the
activations of the given tensor when feeding inception with the query tensor.
"""
inception_layer = _get_inception_layer(sess)
d0 = images.shape[0]
if batch_size > d0:
print("warning: batch size is bigger than the data size. setting batch size to data size")
batch_size = d0
n_batches = d0//batch_size
n_used_imgs = n_batches*batch_size
pred_arr = np.empty((n_used_imgs,2048))
for i in range(n_batches):
if verbose:
print("\rPropagating batch %d/%d" % (i+1, n_batches), end="", flush=True)
start = i*batch_size
end = start + batch_size
batch = images[start:end]
pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
pred_arr[start:end] = pred.reshape(batch_size,-1)
if verbose:
print(" done")
return pred_arr
#-------------------------------------------------------------------------------
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2):
"""Numpy implementation of the Frechet Distance.
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Params:
-- mu1 : Numpy array containing the activations of the pool_3 layer of the
inception net ( like returned by the function 'get_predictions')
-- mu2 : The sample mean over activations of the pool_3 layer, precalcualted
on an representive data set.
-- sigma2: The covariance matrix over activations of the pool_3 layer,
precalcualted on an representive data set.
Returns:
-- dist : The Frechet Distance.
Raises:
-- InvalidFIDException if nan occures.
"""
m = np.square(mu1 - mu2).sum()
#s = sp.linalg.sqrtm(np.dot(sigma1, sigma2)) # EDIT: commented out
s, _ = sp.linalg.sqrtm(np.dot(sigma1, sigma2), disp=False) # EDIT: added
dist = m + np.trace(sigma1+sigma2 - 2*s)
#if np.isnan(dist): # EDIT: commented out
# raise InvalidFIDException("nan occured in distance calculation.") # EDIT: commented out
#return dist # EDIT: commented out
return np.real(dist) # EDIT: added
#-------------------------------------------------------------------------------
def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
"""Calculation of the statistics used by the FID.
Params:
-- images : Numpy array of dimension (n_images, hi, wi, 3). The values
must lie between 0 and 255.
-- sess : current session
-- batch_size : the images numpy array is split into batches with batch size
batch_size. A reasonable batch size depends on the available hardware.
-- verbose : If set to True and parameter out_step is given, the number of calculated
batches is reported.
Returns:
-- mu : The mean over samples of the activations of the pool_3 layer of
the incption model.
-- sigma : The covariance matrix of the activations of the pool_3 layer of
the incption model.
"""
act = get_activations(images, sess, batch_size, verbose)
mu = np.mean(act, axis=0)
sigma = np.cov(act, rowvar=False)
return mu, sigma
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# The following functions aren't needed for calculating the FID
# they're just here to make this module work as a stand-alone script
# for calculating FID scores
#-------------------------------------------------------------------------------
def check_or_download_inception(inception_path):
''' Checks if the path to the inception file is valid, or downloads
the file if it is not present. '''
INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
if inception_path is None:
inception_path = '/tmp'
inception_path = pathlib.Path(inception_path)
model_file = inception_path / 'classify_image_graph_def.pb'
if not model_file.exists():
print("Downloading Inception model")
from urllib import request
import tarfile
fn, _ = request.urlretrieve(INCEPTION_URL)
with tarfile.open(fn, mode='r') as f:
f.extract('classify_image_graph_def.pb', str(model_file.parent))
return str(model_file)
def _handle_path(path, sess):
if path.endswith('.npz'):
f = np.load(path)
m, s = f['mu'][:], f['sigma'][:]
f.close()
else:
path = pathlib.Path(path)
files = list(path.glob('*.jpg')) + list(path.glob('*.png'))
x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
m, s = calculate_activation_statistics(x, sess)
return m, s
def calculate_fid_given_paths(paths, inception_path):
''' Calculates the FID of two paths. '''
inception_path = check_or_download_inception(inception_path)
for p in paths:
if not os.path.exists(p):
raise RuntimeError("Invalid path: %s" % p)
create_inception_graph(str(inception_path))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
m1, s1 = _handle_path(paths[0], sess)
m2, s2 = _handle_path(paths[1], sess)
fid_value = calculate_frechet_distance(m1, s1, m2, s2)
return fid_value
if __name__ == "__main__":
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument("path", type=str, nargs=2,
help='Path to the generated images or to .npz statistic files')
parser.add_argument("-i", "--inception", type=str, default=None,
help='Path to Inception model (will be downloaded if not provided)')
parser.add_argument("--gpu", default="", type=str,
help='GPU to use (leave blank for CPU only)')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
fid_value = calculate_fid_given_paths(args.path, args.inception)
print("FID: ", fid_value)
#----------------------------------------------------------------------------
# EDIT: added
class API:
def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
import config
self.network_dir = os.path.join(config.result_dir, '_inception_fid')
self.network_file = check_or_download_inception(self.network_dir)
self.sess = tf.get_default_session()
create_inception_graph(self.network_file)
def get_metric_names(self):
return ['FID']
def get_metric_formatting(self):
return ['%-10.4f']
def begin(self, mode):
assert mode in ['warmup', 'reals', 'fakes']
self.activations = []
def feed(self, mode, minibatch):
act = get_activations(minibatch.transpose(0,2,3,1), self.sess, batch_size=minibatch.shape[0])
self.activations.append(act)
def end(self, mode):
act = np.concatenate(self.activations)
mu = np.mean(act, axis=0)
sigma = np.cov(act, rowvar=False)
if mode in ['warmup', 'reals']:
self.mu_real = mu
self.sigma_real = sigma
fid = calculate_frechet_distance(mu, sigma, self.mu_real, self.sigma_real)
return [fid]
#----------------------------------------------------------------------------
================================================
FILE: metrics/inception_score.py
================================================
# Copyright 2016 Wojciech Zaremba
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Adapted from the original implementation by Wojciech Zaremba.
# Source: https://github.com/openai/improved-gan/blob/master/inception_score/model.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import sys
import tarfile
import numpy as np
from six.moves import urllib
import tensorflow as tf
import glob
import scipy.misc
import math
import sys
MODEL_DIR = '/tmp/imagenet'
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
softmax = None
# Call this function with list of images. Each of elements should be a
# numpy array with values ranging from 0 to 255.
def get_inception_score(images, splits=10):
assert(type(images) == list)
assert(type(images[0]) == np.ndarray)
assert(len(images[0].shape) == 3)
#assert(np.max(images[0]) > 10) # EDIT: commented out
#assert(np.min(images[0]) >= 0.0)
inps = []
for img in images:
img = img.astype(np.float32)
inps.append(np.expand_dims(img, 0))
bs = 100
with tf.Session() as sess:
preds = []
n_batches = int(math.ceil(float(len(inps)) / float(bs)))
for i in range(n_batches):
#sys.stdout.write(".") # EDIT: commented out
#sys.stdout.flush()
inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
inp = np.concatenate(inp, 0)
pred = sess.run(softmax, {'ExpandDims:0': inp})
preds.append(pred)
preds = np.concatenate(preds, 0)
scores = []
for i in range(splits):
part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
kl = np.mean(np.sum(kl, 1))
scores.append(np.exp(kl))
return np.mean(scores), np.std(scores)
# This function is called automatically.
def _init_inception():
global softmax
if not os.path.exists(MODEL_DIR):
os.makedirs(MODEL_DIR)
filename = DATA_URL.split('/')[-1]
filepath = os.path.join(MODEL_DIR, filename)
if not os.path.exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %s %.1f%%' % (
filename, float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR) # EDIT: increased indent
with tf.gfile.FastGFile(os.path.join(
MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
# Works with an arbitrary minibatch size.
with tf.Session() as sess:
pool3 = sess.graph.get_tensor_by_name('pool_3:0')
ops = pool3.graph.get_operations()
for op_idx, op in enumerate(ops):
for o in op.outputs:
shape = o.get_shape()
shape = [s.value for s in shape]
new_shape = []
for j, s in enumerate(shape):
if s == 1 and j == 0:
new_shape.append(None)
else:
new_shape.append(s)
try:
o._shape = tf.TensorShape(new_shape)
except ValueError:
o._shape_val = tf.TensorShape(new_shape) # EDIT: added for compatibility with tensorflow 1.6.0
w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
logits = tf.matmul(tf.squeeze(pool3), w)
softmax = tf.nn.softmax(logits)
#if softmax is None: # EDIT: commented out
# _init_inception() # EDIT: commented out
#----------------------------------------------------------------------------
# EDIT: added
class API:
def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
import config
globals()['MODEL_DIR'] = os.path.join(config.result_dir, '_inception')
self.sess = tf.get_default_session()
_init_inception()
def get_metric_names(self):
return ['IS_mean', 'IS_std']
def get_metric_formatting(self):
return ['%-10.4f', '%-10.4f']
def begin(self, mode):
assert mode in ['warmup', 'reals', 'fakes']
self.images = []
def feed(self, mode, minibatch):
self.images.append(minibatch.transpose(0, 2, 3, 1))
def end(self, mode):
images = list(np.concatenate(self.images))
with self.sess.as_default():
mean, std = get_inception_score(images)
return [mean, std]
#----------------------------------------------------------------------------
================================================
FILE: metrics/ms_ssim.py
================================================
#!/usr/bin/python
#
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Adapted from the original implementation by The TensorFlow Authors.
# Source: https://github.com/tensorflow/models/blob/master/research/compression/image_encoder/msssim.py
import numpy as np
from scipy import signal
from scipy.ndimage.filters import convolve
def _FSpecialGauss(size, sigma):
"""Function to mimic the 'fspecial' gaussian MATLAB function."""
radius = size // 2
offset = 0.0
start, stop = -radius, radius + 1
if size % 2 == 0:
offset = 0.5
stop -= 1
x, y = np.mgrid[offset + start:stop, offset + start:stop]
assert len(x) == size
g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))
return g / g.sum()
def _SSIMForMultiScale(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
"""Return the Structural Similarity Map between `img1` and `img2`.
This function attempts to match the functionality of ssim_index_new.m by
Zhou Wang: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
Arguments:
img1: Numpy array holding the first RGB image batch.
img2: Numpy array holding the second RGB image batch.
max_val: the dynamic range of the images (i.e., the difference between the
maximum the and minimum allowed values).
filter_size: Size of blur kernel to use (will be reduced for small images).
filter_sigma: Standard deviation for Gaussian blur kernel (will be reduced
for small images).
k1: Constant used to maintain stability in the SSIM calculation (0.01 in
the original paper).
k2: Constant used to maintain stability in the SSIM calculation (0.03 in
the original paper).
Returns:
Pair containing the mean SSIM and contrast sensitivity between `img1` and
`img2`.
Raises:
RuntimeError: If input images don't have the same shape or don't have four
dimensions: [batch_size, height, width, depth].
"""
if img1.shape != img2.shape:
raise RuntimeError('Input images must have the same shape (%s vs. %s).' % (img1.shape, img2.shape))
if img1.ndim != 4:
raise RuntimeError('Input images must have four dimensions, not %d' % img1.ndim)
img1 = img1.astype(np.float32)
img2 = img2.astype(np.float32)
_, height, width, _ = img1.shape
# Filter size can't be larger than height or width of images.
size = min(filter_size, height, width)
# Scale down sigma if a smaller filter size is used.
sigma = size * filter_sigma / filter_size if filter_size else 0
if filter_size:
window = np.reshape(_FSpecialGauss(size, sigma), (1, size, size, 1))
mu1 = signal.fftconvolve(img1, window, mode='valid')
mu2 = signal.fftconvolve(img2, window, mode='valid')
sigma11 = signal.fftconvolve(img1 * img1, window, mode='valid')
sigma22 = signal.fftconvolve(img2 * img2, window, mode='valid')
sigma12 = signal.fftconvolve(img1 * img2, window, mode='valid')
else:
# Empty blur kernel so no need to convolve.
mu1, mu2 = img1, img2
sigma11 = img1 * img1
sigma22 = img2 * img2
sigma12 = img1 * img2
mu11 = mu1 * mu1
mu22 = mu2 * mu2
mu12 = mu1 * mu2
sigma11 -= mu11
sigma22 -= mu22
sigma12 -= mu12
# Calculate intermediate values used by both ssim and cs_map.
c1 = (k1 * max_val) ** 2
c2 = (k2 * max_val) ** 2
v1 = 2.0 * sigma12 + c2
v2 = sigma11 + sigma22 + c2
ssim = np.mean((((2.0 * mu12 + c1) * v1) / ((mu11 + mu22 + c1) * v2)), axis=(1, 2, 3)) # Return for each image individually.
cs = np.mean(v1 / v2, axis=(1, 2, 3))
return ssim, cs
def _HoxDownsample(img):
return (img[:, 0::2, 0::2, :] + img[:, 1::2, 0::2, :] + img[:, 0::2, 1::2, :] + img[:, 1::2, 1::2, :]) * 0.25
def msssim(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03, weights=None):
"""Return the MS-SSIM score between `img1` and `img2`.
This function implements Multi-Scale Structural Similarity (MS-SSIM) Image
Quality Assessment according to Zhou Wang's paper, "Multi-scale structural
similarity for image quality assessment" (2003).
Link: https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf
Author's MATLAB implementation:
http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
Arguments:
img1: Numpy array holding the first RGB image batch.
img2: Numpy array holding the second RGB image batch.
max_val: the dynamic range of the images (i.e., the difference between the
maximum the and minimum allowed values).
filter_size: Size of blur kernel to use (will be reduced for small images).
filter_sigma: Standard deviation for Gaussian blur kernel (will be reduced
for small images).
k1: Constant used to maintain stability in the SSIM calculation (0.01 in
the original paper).
k2: Constant used to maintain stability in the SSIM calculation (0.03 in
the original paper).
weights: List of weights for each level; if none, use five levels and the
weights from the original paper.
Returns:
MS-SSIM score between `img1` and `img2`.
Raises:
RuntimeError: If input images don't have the same shape or don't have four
dimensions: [batch_size, height, width, depth].
"""
if img1.shape != img2.shape:
raise RuntimeError('Input images must have the same shape (%s vs. %s).' % (img1.shape, img2.shape))
if img1.ndim != 4:
raise RuntimeError('Input images must have four dimensions, not %d' % img1.ndim)
# Note: default weights don't sum to 1.0 but do match the paper / matlab code.
weights = np.array(weights if weights else [0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
levels = weights.size
downsample_filter = np.ones((1, 2, 2, 1)) / 4.0
im1, im2 = [x.astype(np.float32) for x in [img1, img2]]
mssim = []
mcs = []
for _ in range(levels):
ssim, cs = _SSIMForMultiScale(
im1, im2, max_val=max_val, filter_size=filter_size,
filter_sigma=filter_sigma, k1=k1, k2=k2)
mssim.append(ssim)
mcs.append(cs)
im1, im2 = [_HoxDownsample(x) for x in [im1, im2]]
# Clip to zero. Otherwise we get NaNs.
mssim = np.clip(np.asarray(mssim), 0.0, np.inf)
mcs = np.clip(np.asarray(mcs), 0.0, np.inf)
# Average over images only at the end.
return np.mean(np.prod(mcs[:-1, :] ** weights[:-1, np.newaxis], axis=0) * (mssim[-1, :] ** weights[-1]))
#----------------------------------------------------------------------------
# EDIT: added
class API:
def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
assert num_images % 2 == 0 and minibatch_size % 2 == 0
self.num_pairs = num_images // 2
def get_metric_names(self):
return ['MS-SSIM']
def get_metric_formatting(self):
return ['%-10.4f']
def begin(self, mode):
assert mode in ['warmup', 'reals', 'fakes']
self.sum = 0.0
def feed(self, mode, minibatch):
images = minibatch.transpose(0, 2, 3, 1)
score = msssim(images[0::2], images[1::2])
self.sum += score * (images.shape[0] // 2)
def end(self, mode):
avg = self.sum / self.num_pairs
return [avg]
#----------------------------------------------------------------------------
================================================
FILE: metrics/sliced_wasserstein.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import numpy as np
import scipy.ndimage
#----------------------------------------------------------------------------
def get_descriptors_for_minibatch(minibatch, nhood_size, nhoods_per_image):
S = minibatch.shape # (minibatch, channel, height, width)
assert len(S) == 4 and S[1] == 3
N = nhoods_per_image * S[0]
H = nhood_size // 2
nhood, chan, x, y = np.ogrid[0:N, 0:3, -H:H+1, -H:H+1]
img = nhood // nhoods_per_image
x = x + np.random.randint(H, S[3] - H, size=(N, 1, 1, 1))
y = y + np.random.randint(H, S[2] - H, size=(N, 1, 1, 1))
idx = ((img * S[1] + chan) * S[2] + y) * S[3] + x
return minibatch.flat[idx]
#----------------------------------------------------------------------------
def finalize_descriptors(desc):
if isinstance(desc, list):
desc = np.concatenate(desc, axis=0)
assert desc.ndim == 4 # (neighborhood, channel, height, width)
desc -= np.mean(desc, axis=(0, 2, 3), keepdims=True)
desc /= np.std(desc, axis=(0, 2, 3), keepdims=True)
desc = desc.reshape(desc.shape[0], -1)
return desc
#----------------------------------------------------------------------------
def sliced_wasserstein(A, B, dir_repeats, dirs_per_repeat):
assert A.ndim == 2 and A.shape == B.shape # (neighborhood, descriptor_component)
results = []
for repeat in range(dir_repeats):
dirs = np.random.randn(A.shape[1], dirs_per_repeat) # (descriptor_component, direction)
dirs /= np.sqrt(np.sum(np.square(dirs), axis=0, keepdims=True)) # normalize descriptor components for each direction
dirs = dirs.astype(np.float32)
projA = np.matmul(A, dirs) # (neighborhood, direction)
projB = np.matmul(B, dirs)
projA = np.sort(projA, axis=0) # sort neighborhood projections for each direction
projB = np.sort(projB, axis=0)
dists = np.abs(projA - projB) # pointwise wasserstein distances
results.append(np.mean(dists)) # average over neighborhoods and directions
return np.mean(results) # average over repeats
#----------------------------------------------------------------------------
def downscale_minibatch(minibatch, lod):
if lod == 0:
return minibatch
t = minibatch.astype(np.float32)
for i in range(lod):
t = (t[:, :, 0::2, 0::2] + t[:, :, 0::2, 1::2] + t[:, :, 1::2, 0::2] + t[:, :, 1::2, 1::2]) * 0.25
return np.round(t).clip(0, 255).astype(np.uint8)
#----------------------------------------------------------------------------
gaussian_filter = np.float32([
[1, 4, 6, 4, 1],
[4, 16, 24, 16, 4],
[6, 24, 36, 24, 6],
[4, 16, 24, 16, 4],
[1, 4, 6, 4, 1]]) / 256.0
def pyr_down(minibatch): # matches cv2.pyrDown()
assert minibatch.ndim == 4
return scipy.ndimage.convolve(minibatch, gaussian_filter[np.newaxis, np.newaxis, :, :], mode='mirror')[:, :, ::2, ::2]
def pyr_up(minibatch): # matches cv2.pyrUp()
assert minibatch.ndim == 4
S = minibatch.shape
res = np.zeros((S[0], S[1], S[2] * 2, S[3] * 2), minibatch.dtype)
res[:, :, ::2, ::2] = minibatch
return scipy.ndimage.convolve(res, gaussian_filter[np.newaxis, np.newaxis, :, :] * 4.0, mode='mirror')
def generate_laplacian_pyramid(minibatch, num_levels):
pyramid = [np.float32(minibatch)]
for i in range(1, num_levels):
pyramid.append(pyr_down(pyramid[-1]))
pyramid[-2] -= pyr_up(pyramid[-1])
return pyramid
def reconstruct_laplacian_pyramid(pyramid):
minibatch = pyramid[-1]
for level in pyramid[-2::-1]:
minibatch = pyr_up(minibatch) + level
return minibatch
#----------------------------------------------------------------------------
class API:
def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
self.nhood_size = 7
self.nhoods_per_image = 128
self.dir_repeats = 4
self.dirs_per_repeat = 128
self.resolutions = []
res = image_shape[1]
while res >= 16:
self.resolutions.append(res)
res //= 2
def get_metric_names(self):
return ['SWDx1e3_%d' % res for res in self.resolutions] + ['SWDx1e3_avg']
def get_metric_formatting(self):
return ['%-13.4f'] * len(self.get_metric_names())
def begin(self, mode):
assert mode in ['warmup', 'reals', 'fakes']
self.descriptors = [[] for res in self.resolutions]
def feed(self, mode, minibatch):
for lod, level in enumerate(generate_laplacian_pyramid(minibatch, len(self.resolutions))):
desc = get_descriptors_for_minibatch(level, self.nhood_size, self.nhoods_per_image)
self.descriptors[lod].append(desc)
def end(self, mode):
desc = [finalize_descriptors(d) for d in self.descriptors]
del self.descriptors
if mode in ['warmup', 'reals']:
self.desc_real = desc
dist = [sliced_wasserstein(dreal, dfake, self.dir_repeats, self.dirs_per_repeat) for dreal, dfake in zip(self.desc_real, desc)]
del desc
dist = [d * 1e3 for d in dist] # multiply by 10^3
return dist + [np.mean(dist)]
#----------------------------------------------------------------------------
================================================
FILE: misc.py
================================================
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import os
import sys
import glob
import datetime
import pickle
import re
import numpy as np
from collections import OrderedDict
import scipy.ndimage
import PIL.Image
import config
import dataset
import legacy
#----------------------------------------------------------------------------
# Convenience wrappers for pickle that are able to load data produced by
# older versions of the code.
def load_pkl(filename):
with open(filename, 'rb') as file:
return legacy.LegacyUnpickler(file, encoding='latin1').load()
def save_pkl(obj, filename):
with open(filename, 'wb') as file:
pickle.dump(obj, file, protocol=pickle.HIGHEST_PROTOCOL)
#----------------------------------------------------------------------------
# Image utils.
def adjust_dynamic_range(data, drange_in, drange_out):
if drange_in != drange_out:
scale = (np.float32(drange_out[1]) - np.float32(drange_out[0])) / (np.float32(drange_in[1]) - np.float32(drange_in[0]))
bias = (np.float32(drange_out[0]) - np.float32(drange_in[0]) * scale)
data = data * scale + bias
return data
def create_image_grid(images, grid_size=None):
assert images.ndim == 3 or images.ndim == 4
num, img_w, img_h = images.shape[0], images.shape[-1], images.shape[-2]
if grid_size is not None:
grid_w, grid_h = tuple(grid_size)
else:
grid_w = max(int(np.ceil(np.sqrt(num))), 1)
grid_h = max((num - 1) // grid_w + 1, 1)
grid = np.zeros(list(images.shape[1:-2]) + [grid_h * img_h, grid_w * img_w], dtype=images.dtype)
for idx in range(num):
x = (idx % grid_w) * img_w
y = (idx // grid_w) * img_h
grid[..., y : y + img_h, x : x + img_w] = images[idx]
return grid
def convert_to_pil_image(image, drange=[0,1]):
assert image.ndim == 2 or image.ndim == 3
if image.ndim == 3:
if image.shape[0] == 1:
image = image[0] # grayscale CHW => HW
else:
image = image.transpose(1, 2, 0) # CHW -> HWC
image = adjust_dynamic_range(image, drange, [0,255])
image = np.rint(image).clip(0, 255).astype(np.uint8)
format = 'RGB' if image.ndim == 3 else 'L'
return PIL.Image.fromarray(image, format)
def save_image(image, filename, drange=[0,1], quality=95):
img = convert_to_pil_image(image, drange)
if '.jpg' in filename:
img.save(filename,"JPEG", quality=quality, optimize=True)
else:
img.save(filename)
def save_image_grid(images, filename, drange=[0,1], grid_size=None):
if images.shape[-3] <=3:
convert_to_pil_image(create_image_grid(images, grid_size), drange).save(filename)
elif images.shape[-3]==6:
convert_to_pil_image(create_image_grid(images[:,0:3,:,:], grid_size), drange).save(filename)
convert_to_pil_image(create_image_grid(images[:, 3:6, :, :], grid_size), drange).save(os.path.splitext(filename)[0]+'_shp'+os.path.splitext(filename)[1])
elif images.shape[-3] == 9:
convert_to_pil_image(create_image_grid(images[:, 0:3, :, :], grid_size), drange).save(filename)
convert_to_pil_image(create_image_grid(images[:, 3:6, :, :], grid_size), drange).save(
os.path.splitext(filename)[0] + '_shp' + os.path.splitext(filename)[1])
convert_to_pil_image(create_image_grid(images[:, 6:9, :, :], grid_size), drange).save(
os.path.splitext(filename)[0] + '_nor' + os.path.splitext(filename)[1])
#----------------------------------------------------------------------------
# Logging of stdout and stderr to a file.
class OutputLogger(object):
def __init__(self):
self.file = None
self.buffer = ''
def set_log_file(self, filename, mode='wt'):
assert self.file is None
self.file = open(filename, mode)
if self.buffer is not None:
self.file.write(self.buffer)
self.buffer = None
def write(self, data):
if self.file is not None:
self.file.write(data)
if self.buffer is not None:
self.buffer += data
def flush(self):
if self.file is not None:
self.file.flush()
class TeeOutputStream(object):
def __init__(self, child_streams, autoflush=False):
self.child_streams = child_streams
self.autoflush = autoflush
def write(self, data):
for stream in self.child_streams:
stream.write(data)
if self.autoflush:
self.flush()
def flush(self):
for stream in self.child_streams:
stream.flush()
output_logger = None
def init_output_logging():
global output_logger
if output_logger is None:
output_logger = OutputLogger()
sys.stdout = TeeOutputStream([sys.stdout, output_logger], autoflush=True)
sys.stderr = TeeOutputStream([sys.stderr, output_logger], autoflush=True)
def set_output_log_file(filename, mode='wt'):
if output_logger is not None:
output_logger.set_log_file(filename, mode)
#----------------------------------------------------------------------------
# Reporting results.
def create_result_subdir(result_dir, desc):
# Select run ID and create subdir.
while True:
run_id = 0
for fname in glob.glob(os.path.join(result_dir, '*')):
try:
fbase = os.path.basename(fname)
ford = int(fbase[:fbase.find('-')])
run_id = max(run_id, ford + 1)
except ValueError:
pass
result_subdir = os.path.join(result_dir, '%03d-%s' % (run_id, desc))
try:
os.makedirs(result_subdir)
break
except OSError:
if os.path.isdir(result_subdir):
continue
raise
print("Saving results to", result_subdir)
set_output_log_file(os.path.join(result_subdir, 'log.txt'))
# Export config.
try:
with open(os.path.join(result_subdir, 'config.txt'), 'wt') as fout:
for k, v in sorted(config.__dict__.items()):
if not k.startswith('_'):
fout.write("%s = %s\n" % (k, str(v)))
except:
pass
return result_subdir
def format_time(seconds):
s = int(np.rint(seconds))
if s < 60: return '%ds' % (s)
elif s < 60*60: return '%dm %02ds' % (s // 60, s % 60)
elif s < 24*60*60: return '%dh %02dm %02ds' % (s // (60*60), (s // 60) % 60, s % 60)
else: return '%dd %02dh %02dm' % (s // (24*60*60), (s // (60*60)) % 24, (s // 60) % 60)
#----------------------------------------------------------------------------
# Locating results.
def locate_result_subdir(run_id_or_result_subdir):
if isinstance(run_id_or_result_subdir, str) and os.path.isdir(run_id_or_result_subdir):
return run_id_or_result_subdir
searchdirs = []
searchdirs += ['']
searchdirs += ['results']
searchdirs += ['networks']
for searchdir in searchdirs:
dir = config.result_dir if searchdir == '' else os.path.join(config.result_dir, searchdir)
dir = os.path.join(dir, str(run_id_or_result_subdir))
if os.path.isdir(dir):
return dir
prefix = '%03d' % run_id_or_result_subdir if isinstance(run_id_or_result_subdir, int) else str(run_id_or_result_subdir)
dirs = sorted(glob.glob(os.path.join(config.result_dir, searchdir, prefix + '-*')))
dirs = [dir for dir in dirs if os.path.isdir(dir)]
if len(dirs) == 1:
return dirs[0]
raise IOError('Cannot locate result subdir for run', run_id_or_result_subdir)
def list_network_pkls(run_id_or_result_subdir, include_final=True):
result_subdir = locate_result_subdir(run_id_or_result_subdir)
pkls = sorted(glob.glob(os.path.join(result_subdir, 'network-*.pkl')))
if len(pkls) >= 1 and os.path.basename(pkls[0]) == 'network-final.pkl':
if include_final:
pkls.append(pkls[0])
del pkls[0]
return pkls
def locate_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot=None):
if isinstance(run_id_or_result_subdir_or_network_pkl, str) and os.path.isfile(run_id_or_result_subdir_or_network_pkl):
return run_id_or_result_subdir_or_network_pkl
pkls = list_network_pkls(run_id_or_result_subdir_or_network_pkl)
if len(pkls) >= 1 and snapshot is None:
return pkls[-1]
for pkl in pkls:
try:
name = os.path.splitext(os.path.basename(pkl))[0]
number = int(name.split('-')[-1])
if number == snapshot:
return pkl
except ValueError: pass
except IndexError: pass
raise IOError('Cannot locate network pkl for snapshot', snapshot)
def get_id_string_for_network_pkl(network_pkl):
p = network_pkl.replace('.pkl', '').replace('\\', '/').split('/')
return '-'.join(p[max(len(p) - 2, 0):])
#----------------------------------------------------------------------------
# Loading and using trained networks.
def load_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot=None):
return load_pkl(locate_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot))
def random_latents(num_latents, G, random_state=None):
if random_state is not None:
return random_state.randn(num_latents, *G.input_shape[1:]).astype(np.float32)
else:
return np.random.randn(num_latents, *G.input_shape[1:]).astype(np.float32)
def load_dataset_for_previous_run(run_id, **kwargs): # => dataset_obj, mirror_augment
result_subdir = locate_result_subdir(run_id)
# Parse config.txt.
parsed_cfg = dict()
with open(os.path.join
gitextract__mj9yhwx/ ├── .gitignore ├── 512_UV_dict.pkl ├── LICENSE.txt ├── LICENSE_NVIDIA.txt ├── README.md ├── UV_manipulation_2.py ├── __init__.py ├── config.py ├── config_test.py ├── dataset.py ├── dataset_tool.py ├── legacy.py ├── loss.py ├── metrics/ │ ├── __init__.py │ ├── frechet_inception_distance.py │ ├── inception_score.py │ ├── ms_ssim.py │ └── sliced_wasserstein.py ├── misc.py ├── myutil.py ├── networks.py ├── requirements-pip.txt ├── test.py ├── tfutil.py ├── train.py └── util_scripts.py
SYMBOL INDEX (260 symbols across 18 files)
FILE: UV_manipulation_2.py
function load_512_ifo_dict (line 16) | def load_512_ifo_dict():
function load_mean (line 21) | def load_mean():
function alignment (line 25) | def alignment(mesh):
function import_uv_info (line 35) | def import_uv_info(instance, res, uv_layout='oval', topology='full'):
function from_UV_2_3D (line 61) | def from_UV_2_3D(uv, uv_layout='oval', topology='full', plot=False):
FILE: config.py
class EasyDict (line 13) | class EasyDict(dict):
method __init__ (line 14) | def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
method __getattr__ (line 15) | def __getattr__(self, name): return self[name]
method __setattr__ (line 16) | def __setattr__(self, name, value): self[name] = value
method __delattr__ (line 17) | def __delattr__(self, name): del self[name]
FILE: config_test.py
class EasyDict (line 13) | class EasyDict(dict):
method __init__ (line 14) | def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
method __getattr__ (line 15) | def __getattr__(self, name): return self[name]
method __setattr__ (line 16) | def __setattr__(self, name, value): self[name] = value
method __delattr__ (line 17) | def __delattr__(self, name): del self[name]
FILE: dataset.py
class TFRecordDataset (line 21) | class TFRecordDataset:
method __init__ (line 22) | def __init__(self,
method configure (line 124) | def configure(self, minibatch_size, lod=0):
method get_minibatch_tf (line 133) | def get_minibatch_tf(self): # => images, labels
method get_minibatch_np (line 137) | def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
method get_random_labels_tf (line 144) | def get_random_labels_tf(self, minibatch_size): # => labels
method get_random_labels_np (line 151) | def get_random_labels_np(self, minibatch_size): # => labels
method parse_tfrecord_tf (line 157) | def parse_tfrecord_tf(self, record):
method parse_tfrecord_np (line 164) | def parse_tfrecord_np(self, record):
class SyntheticDataset (line 173) | class SyntheticDataset:
method __init__ (line 174) | def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dyn...
method configure (line 192) | def configure(self, minibatch_size, lod=0):
method get_minibatch_tf (line 197) | def get_minibatch_tf(self): # => images, labels
method get_minibatch_np (line 205) | def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
method get_random_labels_tf (line 211) | def get_random_labels_tf(self, minibatch_size): # => labels
method get_random_labels_np (line 215) | def get_random_labels_np(self, minibatch_size): # => labels
method _generate_images (line 221) | def _generate_images(self, minibatch, lod, shape): # to be overridden ...
method _generate_labels (line 224) | def _generate_labels(self, minibatch): # to be overridden by subclasses
function load_dataset (line 230) | def load_dataset(class_name='dataset.TFRecordDataset', data_dir=None, ve...
FILE: dataset_tool.py
function error (line 26) | def error(msg):
class TFRecordExporter (line 32) | class TFRecordExporter:
method __init__ (line 33) | def __init__(self, tfrecord_dir, expected_images, print_progress=True,...
method close (line 49) | def close(self):
method choose_shuffled_order (line 59) | def choose_shuffled_order(self): # Note: Images and labels must be add...
method add_image (line 64) | def add_image(self, img):
method add_shape (line 89) | def add_shape(self, img):
method add_both (line 113) | def add_both(self, img):
method add_labels (line 137) | def add_labels(self, labels):
method __enter__ (line 144) | def __enter__(self):
method __exit__ (line 147) | def __exit__(self, *args):
class ExceptionInfo (line 152) | class ExceptionInfo(object):
method __init__ (line 153) | def __init__(self):
class WorkerThread (line 159) | class WorkerThread(threading.Thread):
method __init__ (line 160) | def __init__(self, task_queue):
method run (line 164) | def run(self):
class ThreadPool (line 177) | class ThreadPool(object):
method __init__ (line 178) | def __init__(self, num_threads):
method add_task (line 188) | def add_task(self, func, args=()):
method get_result (line 194) | def get_result(self, func): # returns (result, args)
method finish (line 201) | def finish(self):
method __enter__ (line 205) | def __enter__(self): # for 'with' statement
method __exit__ (line 208) | def __exit__(self, *excinfo):
method process_items_concurrently (line 211) | def process_items_concurrently(self, item_iterator, process_func=lambd...
function display (line 239) | def display(tfrecord_dir):
function extract (line 265) | def extract(tfrecord_dir, output_dir):
function compare (line 292) | def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
function create_mnist (line 335) | def create_mnist(tfrecord_dir, mnist_dir):
function create_mnistrgb (line 359) | def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_...
function create_cifar10 (line 376) | def create_cifar10(tfrecord_dir, cifar10_dir):
function create_cifar100 (line 403) | def create_cifar100(tfrecord_dir, cifar100_dir):
function create_svhn (line 425) | def create_svhn(tfrecord_dir, svhn_dir):
function create_lsun (line 452) | def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
function create_celeba (line 485) | def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
function create_celebahq (line 504) | def create_celebahq(tfrecord_dir, celeba_dir, delta_dir, num_threads=4, ...
function create_from_images (line 648) | def create_from_images(tfrecord_dir, image_dir, shuffle):
function create_from_pkl (line 682) | def create_from_pkl(tfrecord_dir, image_dir, shuffle):
function create_from_pkl_img (line 713) | def create_from_pkl_img(tfrecord_dir, image_dir, pickle_dir, shuffle):
function create_from_pkl_img_norm (line 746) | def create_from_pkl_img_norm(tfrecord_dir, mein3d_image_dir, mein3d_pick...
function create_from_hdf5 (line 829) | def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
function execute_cmdline (line 844) | def execute_cmdline(argv):
FILE: legacy.py
class LegacyUnpickler (line 19) | class LegacyUnpickler(pickle.Unpickler):
method __init__ (line 20) | def __init__(self, *args, **kwargs):
method find_class (line 23) | def find_class(self, module, name):
function patch_theano_gan (line 39) | def patch_theano_gan(state):
function ignore_unknown_theano_network (line 103) | def ignore_unknown_theano_network(state):
FILE: loss.py
function fp32 (line 15) | def fp32(*values):
function G_wgan_acgan (line 24) | def G_wgan_acgan(G, D, opt, training_set, minibatch_size,
function D_wgangp_acgan (line 43) | def D_wgangp_acgan(G, D, opt, training_set, minibatch_size, reals, labels,
FILE: metrics/frechet_inception_distance.py
class InvalidFIDException (line 48) | class InvalidFIDException(Exception):
function create_inception_graph (line 52) | def create_inception_graph(pth):
function _get_inception_layer (line 64) | def _get_inception_layer(sess):
function get_activations (line 88) | def get_activations(images, sess, batch_size=50, verbose=False):
function calculate_frechet_distance (line 125) | def calculate_frechet_distance(mu1, sigma1, mu2, sigma2):
function calculate_activation_statistics (line 156) | def calculate_activation_statistics(images, sess, batch_size=50, verbose...
function check_or_download_inception (line 184) | def check_or_download_inception(inception_path):
function _handle_path (line 202) | def _handle_path(path, sess):
function calculate_fid_given_paths (line 215) | def calculate_fid_given_paths(paths, inception_path):
class API (line 249) | class API:
method __init__ (line 250) | def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
method get_metric_names (line 257) | def get_metric_names(self):
method get_metric_formatting (line 260) | def get_metric_formatting(self):
method begin (line 263) | def begin(self, mode):
method feed (line 267) | def feed(self, mode, minibatch):
method end (line 271) | def end(self, mode):
FILE: metrics/inception_score.py
function get_inception_score (line 41) | def get_inception_score(images, splits=10):
function _init_inception (line 72) | def _init_inception():
class API (line 121) | class API:
method __init__ (line 122) | def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
method get_metric_names (line 128) | def get_metric_names(self):
method get_metric_formatting (line 131) | def get_metric_formatting(self):
method begin (line 134) | def begin(self, mode):
method feed (line 138) | def feed(self, mode, minibatch):
method end (line 141) | def end(self, mode):
FILE: metrics/ms_ssim.py
function _FSpecialGauss (line 25) | def _FSpecialGauss(size, sigma):
function _SSIMForMultiScale (line 38) | def _SSIMForMultiScale(img1, img2, max_val=255, filter_size=11, filter_s...
function _HoxDownsample (line 110) | def _HoxDownsample(img):
function msssim (line 113) | def msssim(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5, k1...
class API (line 176) | class API:
method __init__ (line 177) | def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
method get_metric_names (line 181) | def get_metric_names(self):
method get_metric_formatting (line 184) | def get_metric_formatting(self):
method begin (line 187) | def begin(self, mode):
method feed (line 191) | def feed(self, mode, minibatch):
method end (line 196) | def end(self, mode):
FILE: metrics/sliced_wasserstein.py
function get_descriptors_for_minibatch (line 13) | def get_descriptors_for_minibatch(minibatch, nhood_size, nhoods_per_image):
function finalize_descriptors (line 27) | def finalize_descriptors(desc):
function sliced_wasserstein (line 38) | def sliced_wasserstein(A, B, dir_repeats, dirs_per_repeat):
function downscale_minibatch (line 55) | def downscale_minibatch(minibatch, lod):
function pyr_down (line 72) | def pyr_down(minibatch): # matches cv2.pyrDown()
function pyr_up (line 76) | def pyr_up(minibatch): # matches cv2.pyrUp()
function generate_laplacian_pyramid (line 83) | def generate_laplacian_pyramid(minibatch, num_levels):
function reconstruct_laplacian_pyramid (line 90) | def reconstruct_laplacian_pyramid(pyramid):
class API (line 98) | class API:
method __init__ (line 99) | def __init__(self, num_images, image_shape, image_dtype, minibatch_size):
method get_metric_names (line 110) | def get_metric_names(self):
method get_metric_formatting (line 113) | def get_metric_formatting(self):
method begin (line 116) | def begin(self, mode):
method feed (line 120) | def feed(self, mode, minibatch):
method end (line 125) | def end(self, mode):
FILE: misc.py
function load_pkl (line 27) | def load_pkl(filename):
function save_pkl (line 31) | def save_pkl(obj, filename):
function adjust_dynamic_range (line 38) | def adjust_dynamic_range(data, drange_in, drange_out):
function create_image_grid (line 45) | def create_image_grid(images, grid_size=None):
function convert_to_pil_image (line 62) | def convert_to_pil_image(image, drange=[0,1]):
function save_image (line 75) | def save_image(image, filename, drange=[0,1], quality=95):
function save_image_grid (line 82) | def save_image_grid(images, filename, drange=[0,1], grid_size=None):
class OutputLogger (line 98) | class OutputLogger(object):
method __init__ (line 99) | def __init__(self):
method set_log_file (line 103) | def set_log_file(self, filename, mode='wt'):
method write (line 110) | def write(self, data):
method flush (line 116) | def flush(self):
class TeeOutputStream (line 120) | class TeeOutputStream(object):
method __init__ (line 121) | def __init__(self, child_streams, autoflush=False):
method write (line 125) | def write(self, data):
method flush (line 131) | def flush(self):
function init_output_logging (line 137) | def init_output_logging():
function set_output_log_file (line 144) | def set_output_log_file(filename, mode='wt'):
function create_result_subdir (line 151) | def create_result_subdir(result_dir, desc):
function format_time (line 187) | def format_time(seconds):
function locate_result_subdir (line 197) | def locate_result_subdir(run_id_or_result_subdir):
function list_network_pkls (line 218) | def list_network_pkls(run_id_or_result_subdir, include_final=True):
function locate_network_pkl (line 227) | def locate_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot=...
function get_id_string_for_network_pkl (line 244) | def get_id_string_for_network_pkl(network_pkl):
function load_network_pkl (line 251) | def load_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot=No...
function random_latents (line 254) | def random_latents(num_latents, G, random_state=None):
function load_dataset_for_previous_run (line 260) | def load_dataset_for_previous_run(run_id, **kwargs): # => dataset_obj, m...
function apply_mirror_augment (line 305) | def apply_mirror_augment(minibatch):
function draw_text_label (line 316) | def draw_text_label(img, text, x, y, alignx=0.5, aligny=0.5, color=255, ...
function setup_text_label (line 329) | def setup_text_label(text, font='Calibri', fontsize=32, padding=6, glow_...
FILE: myutil.py
function crop_im (line 6) | def crop_im(img):
function crop_im_512 (line 12) | def crop_im_512(img_377):
function crop_im_377 (line 26) | def crop_im_377(img_512):
function concat_image (line 42) | def concat_image(im1, im2):
function rgb2tf (line 53) | def rgb2tf(img):
function tf2rgb (line 56) | def tf2rgb(img):
function files_gen (line 59) | def files_gen(path):
function files (line 64) | def files(path):
FILE: networks.py
function lerp (line 15) | def lerp(a, b, t): return a + (b - a) * t
function lerp_clip (line 16) | def lerp_clip(a, b, t): return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
function cset (line 17) | def cset(cur_lambda, new_cond, new_lambda): return lambda: tf.cond(new_c...
function get_weight (line 22) | def get_weight(shape, gain=np.sqrt(2), use_wscale=False, fan_in=None):
function dense (line 34) | def dense(x, fmaps, gain=np.sqrt(2), use_wscale=False):
function conv2d (line 44) | def conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
function apply_bias (line 53) | def apply_bias(x):
function leaky_relu (line 64) | def leaky_relu(x, alpha=0.2):
function upscale2d (line 72) | def upscale2d(x, factor=2):
function upscale2d_conv2d (line 86) | def upscale2d_conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
function downscale2d (line 98) | def downscale2d(x, factor=2):
function conv2d_downscale2d (line 109) | def conv2d_downscale2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=Fal...
function pixel_norm (line 120) | def pixel_norm(x, epsilon=1e-8):
function minibatch_stddev_layer (line 127) | def minibatch_stddev_layer(x, group_size=4):
function G_paper (line 144) | def G_paper(
function D_paper (line 278) | def D_paper(
FILE: test.py
function setup_snapshot_image_grid (line 22) | def setup_snapshot_image_grid(G, training_set,
function process_reals (line 56) | def process_reals(x, lod, mirror_augment, drange_data, drange_net):
class TrainingSchedule (line 86) | class TrainingSchedule:
method __init__ (line 87) | def __init__(
function train_progressive_gan (line 134) | def train_progressive_gan(
FILE: tfutil.py
function run (line 18) | def run(*args, **kwargs): # Run the specified ops in the default session.
function is_tf_expression (line 21) | def is_tf_expression(x):
function shape_to_list (line 24) | def shape_to_list(shape):
function flatten (line 27) | def flatten(x):
function log2 (line 31) | def log2(x):
function exp2 (line 35) | def exp2(x):
function lerp (line 39) | def lerp(a, b, t):
function lerp_clip (line 43) | def lerp_clip(a, b, t):
function absolute_name_scope (line 47) | def absolute_name_scope(scope): # Forcefully enter the specified name sc...
function init_tf (line 53) | def init_tf(config_dict=dict()):
function create_session (line 62) | def create_session(config_dict=dict(), force_as_default=False):
function init_uninited_vars (line 82) | def init_uninited_vars(vars=None):
function set_vars (line 103) | def set_vars(var_to_value_dict):
function autosummary (line 137) | def autosummary(name, value):
function finalize_autosummaries (line 156) | def finalize_autosummaries():
function _create_autosummary_var (line 174) | def _create_autosummary_var(name, value_expr):
function save_summaries (line 199) | def save_summaries(filewriter, global_step=None):
function import_module (line 210) | def import_module(module_or_obj_name):
function find_obj_in_module (line 222) | def find_obj_in_module(module, relative_obj_name):
function import_obj (line 228) | def import_obj(obj_name):
function call_func_by_name (line 232) | def call_func_by_name(*args, func=None, **kwargs):
class Optimizer (line 244) | class Optimizer:
method __init__ (line 245) | def __init__(
method register_gradients (line 275) | def register_gradients(self, loss, vars):
method apply_updates (line 302) | def apply_updates(self):
method reset_optimizer_state (line 373) | def reset_optimizer_state(self):
method get_loss_scaling_var (line 377) | def get_loss_scaling_var(self, device):
method apply_loss_scaling (line 386) | def apply_loss_scaling(self, value):
method undo_loss_scaling (line 393) | def undo_loss_scaling(self, value):
class Network (line 414) | class Network:
method __init__ (line 415) | def __init__(self,
method _init_fields (line 433) | def _init_fields(self):
method _init_graph (line 454) | def _init_graph(self):
method reset_vars (line 492) | def reset_vars(self):
method reset_trainables (line 496) | def reset_trainables(self):
method get_output_for (line 500) | def get_output_for(self, *in_expr, return_as_list=False, **dynamic_kwa...
method get_var_localname (line 514) | def get_var_localname(self, var_or_globalname):
method find_var (line 523) | def find_var(self, var_or_localname):
method get_var (line 529) | def get_var(self, var_or_localname):
method set_var (line 534) | def set_var(self, var_or_localname, new_value):
method __getstate__ (line 538) | def __getstate__(self):
method __setstate__ (line 548) | def __setstate__(self, state):
method clone (line 574) | def clone(self, name=None):
method copy_vars_from (line 587) | def copy_vars_from(self, src_net):
method copy_trainables_from (line 593) | def copy_trainables_from(self, src_net):
method convert (line 599) | def convert(self, name=None, func=None, **static_kwargs):
method setup_as_moving_average_of (line 606) | def setup_as_moving_average_of(self, src_net, beta=0.99, beta_nontrain...
method fit (line 619) | def fit(self, *in_arrays,
method run (line 663) | def run(self, *in_arrays,
method list_layers (line 723) | def list_layers(self):
method print_layers (line 755) | def print_layers(self, title=None, hide_layers_with_no_params=False):
method setup_weight_histograms (line 780) | def setup_weight_histograms(self, title=None):
FILE: train.py
function setup_snapshot_image_grid (line 22) | def setup_snapshot_image_grid(G, training_set,
function process_reals (line 56) | def process_reals(x, lod, mirror_augment, drange_data, drange_net):
class TrainingSchedule (line 86) | class TrainingSchedule:
method __init__ (line 87) | def __init__(
function train_progressive_gan (line 134) | def train_progressive_gan(
FILE: util_scripts.py
function get_generator (line 35) | def get_generator(run_id, snapshot=None, image_shrink=1, minibatch_size=8):
function fit_real_images (line 49) | def fit_real_images(run_id, snapshot=None, num_pngs=1, image_shrink=1, p...
function generate_fake_images_glob (line 91) | def generate_fake_images_glob(run_id, snapshot=None, grid_size=[1,1], nu...
function generate_fake_images (line 111) | def generate_fake_images(run_id, snapshot=None, grid_size=[1,1],batch_si...
function generate_interpolation_video (line 149) | def generate_interpolation_video(run_id, snapshot=None, grid_size=[1,1],...
function generate_interpolation_images (line 188) | def generate_interpolation_images(run_id, snapshot=None, grid_size=[1,1]...
function generate_interpolation_video_bydim (line 239) | def generate_interpolation_video_bydim(run_id, snapshot=None, grid_size=...
function generate_training_video (line 280) | def generate_training_video(run_id, duration_sec=20.0, time_warp=1.5, mp...
function evaluate_metrics (line 331) | def evaluate_metrics(run_id, log, metrics, num_images, real_passes, mini...
function fix_obj (line 431) | def fix_obj(fp):
Condensed preview — 26 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (331K chars).
[
{
"path": ".gitignore",
"chars": 18,
"preview": "/results/\n/.idea/\n"
},
{
"path": "LICENSE.txt",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "LICENSE_NVIDIA.txt",
"chars": 19406,
"preview": "Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n\n\nAttribution-NonCommercial 4.0 International\n\n============"
},
{
"path": "README.md",
"chars": 9672,
"preview": "# The model is now available [HERE](https://ibug.doc.ic.ac.uk/resources/tbgan/) \n(Requires to sign End User License Agre"
},
{
"path": "UV_manipulation_2.py",
"chars": 2939,
"preview": "import os\n\nimport menpo3d.io as m3io\nfrom menpo3d import io\nimport menpo.io as mio\nimport numpy as np\nfrom menpo.shape i"
},
{
"path": "__init__.py",
"chars": 156,
"preview": "__all__ = [\"config\", \"dataset\", \"dataset_tool\",\"legacy\",\"loss\",\"misc\",\"myutil\",\"networks\",\"tfutil\",\"util_scripts\",\"train"
},
{
"path": "config.py",
"chars": 9824,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "config_test.py",
"chars": 9821,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "dataset.py",
"chars": 12286,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "dataset_tool.py",
"chars": 47885,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "legacy.py",
"chars": 5295,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "loss.py",
"chars": 4498,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "metrics/__init__.py",
"chars": 8,
"preview": "# empty\n"
},
{
"path": "metrics/frechet_inception_distance.py",
"chars": 11441,
"preview": "#!/usr/bin/env python3\n#\n# Copyright 2017 Martin Heusel\n#\n# Licensed under the Apache License, Version 2.0 (the \"License"
},
{
"path": "metrics/inception_score.py",
"chars": 5305,
"preview": "# Copyright 2016 Wojciech Zaremba\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use "
},
{
"path": "metrics/ms_ssim.py",
"chars": 8160,
"preview": "#!/usr/bin/python\n#\n# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License,"
},
{
"path": "metrics/sliced_wasserstein.py",
"chars": 5788,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "misc.py",
"chars": 13884,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "myutil.py",
"chars": 2091,
"preview": "import numpy as np\nimport os\nimport PIL.Image\nfrom menpo.image import Image\n\ndef crop_im(img):\n img = img.crop((41, 0"
},
{
"path": "networks.py",
"chars": 23128,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "requirements-pip.txt",
"chars": 159,
"preview": "numpy>=1.13.3\nscipy>=1.0.0\ntensorflow-gpu>=1.6.0\nmoviepy>=0.2.3.2\nPillow>=3.1.1\nlmdb>=0.93\nopencv-python>=3.4.0.12\ncrypt"
},
{
"path": "test.py",
"chars": 16028,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "tfutil.py",
"chars": 39764,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
},
{
"path": "train.py",
"chars": 16178,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\r\n#\r\n# This work is licensed under the Creative Commons At"
},
{
"path": "util_scripts.py",
"chars": 24301,
"preview": "# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attr"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the barisgecer/TBGAN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 26 files (33.6 MB), approximately 80.1k tokens, and a symbol index with 260 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.