Repository: Justin-Tan/generative-compression
Branch: master
Commit: 38d34c853836
Files: 24
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Directory structure:
gitextract_gpypn45i/
├── .gitignore
├── LICENSE
├── README.md
├── cGAN/
│ ├── config.py
│ ├── data.py
│ ├── model.py
│ ├── network.py
│ ├── train.py
│ └── utils.py
├── checkpoints/
│ └── .gitignore
├── compress.py
├── config.py
├── data/
│ ├── .gitignore
│ ├── cityscapes_paths_test.h5
│ ├── cityscapes_paths_train.h5
│ ├── cityscapes_paths_val.h5
│ └── resize_cityscapes.sh
├── data.py
├── model.py
├── network.py
├── samples/
│ └── .gitignore
├── tensorboard/
│ └── .gitignore
├── train.py
└── utils.py
================================================
FILE CONTENTS
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================================================
FILE: .gitignore
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================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2018 JTan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# generative-compression
TensorFlow Implementation for learned compression of images using Generative Adversarial Networks. The method was developed by Agustsson et. al. in [Generative Adversarial Networks for Extreme Learned Image Compression](https://arxiv.org/abs/1804.02958). The proposed idea is very interesting and their approach is well-described.
-----------------------------
## Usage
The code depends on [Tensorflow 1.8](https://github.com/tensorflow/tensorflow)
```bash
# Clone
$ git clone https://github.com/Justin-Tan/generative-compression.git
$ cd generative-compression
# To train, check command line arguments
$ python3 train.py -h
# Run
$ python3 train.py -opt momentum --name my_network
```
Training is conducted with batch size 1 and reconstructed samples / tensorboard summaries will be periodically written every certain number of steps (default is 128). Checkpoints are saved every 10 epochs.
To compress a single image:
```bash
# Compress
$ python3 compress.py -r /path/to/model/checkpoint -i /path/to/image -o path/to/output/image
```
The compressed image will be saved as a side-by-side comparison with the original image under the path specified in `directories.samples` in `config.py`. If you are using the provided pretrained model with noise sampling, retain the hyperparameters under `config_test` in `config.py`, otherwise the parameters during test time should match the parameters set during training.
*Note:* If you're willing to pay higher bitrates in exchange for much higher perceptual quality, you may want to check out this implementation of ["High-Fidelity Generative Image Compression"](https://github.com/Justin-Tan/high-fidelity-generative-compression), which is in the same vein but operates in higher bitrate regimes. Furthermore, it is capable of working with images of arbitrary size and resolution.
## Results
These globally compressed images are from the test split of the Cityscapes `leftImg8bit` dataset. The decoder seems to hallunicate greenery in buildings, and vice-versa.
#### Global conditional compression: Multiscale discriminator + feature-matching losses, C=8 channels - (compression to 0.072 bbp)
**Epoch 38**
**Epoch 44**
**Epoch 47**
**Epoch 48**
```
Show quantized C=4,8,16 channels image comparison
```
| Generator Loss | Discriminator Loss |
|-------|-------|
| |  |
## Pretrained Model
You can find the pretrained model for global compression with a channel bottleneck of `C = 8` (corresponding to a 0.072 bpp representation) below. The model was subject to the multiscale discriminator and feature matching losses. Noise is sampled from a 128-dim normal distribution, passed through a DCGAN-like generator and concatenated to the quantized image representation. The model was trained for 55 epochs on the train split of the [Cityscapes](https://www.cityscapes-dataset.com/) `leftImg8bit` dataset for the images and used the `gtFine` dataset for the corresponding semantic maps. This should work with the default settings under `config_test` in `config.py`.
A pretrained model for global conditional compression with a `C=8` bottleneck is also included. This model was, trained for 50 epochs with the same losses as above. Reconstruction is conditioned on semantic label maps (see the `cGAN/` folder and 'Conditional GAN usage').
* [Noise sampling model](https://drive.google.com/open?id=1gy6NJqlxflLDI1g9Rsileva-8G1ifsEC)
* [Conditional GAN model](https://drive.google.com/open?id=1L3G4l8IQukNrsf3hjHv5xRhpNE77TD2k)
** Warning: Tensorflow 1.3 was used to train the models, but it appears to load without problems on Tensorflow 1.8. Please raise an issue if you have any problems.
## Details / extensions
The network architectures are based on the description provided in the appendix of the original paper, which is in turn based on the paper [Perceptual Losses for Real-Time Style Transfer
and Super-Resolution](https://cs.stanford.edu/people/jcjohns/eccv16/) The multiscale discriminator loss used was originally proposed in the project [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/), consult `network.py` for the implementation. If you would like to add an extension you can create a new method under the `Network` class, e.g.
```python
@staticmethod
def my_generator(z, **kwargs):
"""
Inputs:
z: sampled noise
Returns:
upsampled image
"""
return tf.random_normal([z.get_shape()[0], height, width, channels], seed=42)
```
To change hyperparameters/toggle features use the knobs in `config.py`. (Bad form maybe. but I find it easier than a 20-line `argparse` specification).
### Data / Setup
Training was done using the [ADE 20k dataset](http://groups.csail.mit.edu/vision/datasets/ADE20K/) and the [Cityscapes leftImg8bit dataset](https://www.cityscapes-dataset.com/). In the former case images are rescaled to width `512` px, and in the latter images are [resampled to `[512 x 1024]` prior to training](https://www.imagemagick.org/script/command-line-options.php#resample). An example script for resampling using `Imagemagick` is provided under `data/`. In each case, you will need to create a Pandas dataframe containing a single column: `path`, which holds the absolute/relative path to the images. This should be saved as a `HDF5` file, and you should provide the path to this under the `directories` class in `config.py`. Examples for the Cityscapes dataset are provided in the `data` directory.
### Conditional GAN usage
The conditional GAN implementation for global compression is in the `cGAN` directory. The cGAN implementation appears to yield images with the highest image quality, but this implementation remains experimental. In this implementation generation is conditioned on the information in the semantic label map of the selected image. You will need to download the `gtFine` dataset of annotation maps and append a separate column `semantic_map_paths` to the Pandas dataframe pointing to the corresponding images from the `gtFine` dataset.
### Dependencies
* Python 3.6
* [Pandas](https://pandas.pydata.org/)
* [TensorFlow 1.8](https://github.com/tensorflow/tensorflow)
### Todo:
* Incorporate GAN noise sampling into the reconstructed image. The authors state that this step is optional and that the sampled noise is combined with the quantized representation but don't provide further details. Currently the model samples from a normal distribution and upsamples this using a DCGAN-like generator (see `network.py`) to be concatenated with the quantized image representation `w_hat`, but this appears to substantially increase the 'hallunication factor' in the reconstructed images.
* Integrate VGG loss.
* Experiment with WGAN-GP.
* Experiment with spectral normalization/
* Experiment with different generator architectures with noise sampling.
* Extend to selective compression using semantic maps (contributions welcome).
### Resources
* [Generative Adversarial Networks for Extreme Learned Image Compression](https://data.vision.ee.ethz.ch/aeirikur/extremecompression/#publication)
* [CycleGAN](https://arxiv.org/pdf/1703.10593.pdf)
* [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/)
## More Results
#### Global compression: Noise sampling, multiscale discriminator + feature-matching losses, C=8 channels - Compression to 0.072 bbp
================================================
FILE: cGAN/config.py
================================================
#!/usr/bin/env python3
class config_train(object):
mode = 'gan-train'
num_epochs = 512
batch_size = 1
ema_decay = 0.999
G_learning_rate = 2e-4
D_learning_rate = 2e-4
lr_decay_rate = 2e-5
momentum = 0.9
weight_decay = 5e-4
noise_dim = 128
optimizer = 'adam'
kernel_size = 3
diagnostic_steps = 256
# WGAN
gradient_penalty = True
lambda_gp = 10
weight_clipping = False
max_c = 1e-2
n_critic_iterations = 20
# Compression
lambda_X = 12
channel_bottleneck = 16
sample_noise = False
use_vanilla_GAN = False
use_feature_matching_loss = True
upsample_dim = 256
multiscale = True
feature_matching_weight = 10
use_conditional_GAN = False
class config_test(object):
mode = 'gan-test'
num_epochs = 512
batch_size = 1
ema_decay = 0.999
G_learning_rate = 2e-4
D_learning_rate = 2e-4
lr_decay_rate = 2e-5
momentum = 0.9
weight_decay = 5e-4
noise_dim = 128
optimizer = 'adam'
kernel_size = 3
diagnostic_steps = 256
# WGAN
gradient_penalty = True
lambda_gp = 10
weight_clipping = False
max_c = 1e-2
n_critic_iterations = 5
# Compression
lambda_X = 12
channel_bottleneck = 8
sample_noise = True
use_vanilla_GAN = False
use_feature_matching_loss = True
upsample_dim = 256
multiscale = True
feature_matching_weight = 10
use_conditional_GAN = False
class directories(object):
# train = 'data/ADE20K_paths_train.h5'
# test = 'data/ADE20K_paths_test.h5'
train = 'data/sm_cityscapes_paths_train.h5'
test = 'data/cityscapes_paths_test.h5'
val = 'data/cityscapes_paths_val.h5'
tensorboard = 'tensorboard'
checkpoints = 'checkpoints'
checkpoints_best = 'checkpoints/best'
samples = 'samples/cityscapes'
================================================
FILE: cGAN/data.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import pandas as pd
from config import directories
class Data(object):
@staticmethod
def load_dataframe(filename, load_semantic_maps=False):
df = pd.read_hdf(filename, key='df').sample(frac=1).reset_index(drop=True)
if load_semantic_maps:
return df['path'].values, df['semantic_map_path'].values
else:
return df['path'].values
@staticmethod
def load_dataset(image_paths, batch_size, test=False, augment=False, downsample=False,
training_dataset='cityscapes', use_conditional_GAN=False, **kwargs):
def _augment(image):
# On-the-fly data augmentation
image = tf.image.random_brightness(image, max_delta=0.1)
image = tf.image.random_contrast(image, 0.5, 1.5)
image = tf.image.random_flip_left_right(image)
return image
def _parser(image_path, semantic_map_path=None):
def _aspect_preserving_width_resize(image, width=512):
height_i = tf.shape(image)[0]
# width_i = tf.shape(image)[1]
# ratio = tf.to_float(width_i) / tf.to_float(height_i)
# new_height = tf.to_int32(tf.to_float(height_i) / ratio)
new_height = height_i - tf.floormod(height_i, 16)
return tf.image.resize_image_with_crop_or_pad(image, new_height, width)
def _image_decoder(path):
im = tf.image.decode_png(tf.read_file(path), channels=3)
im = tf.image.convert_image_dtype(im, dtype=tf.float32)
return 2 * im - 1 # [0,1] -> [-1,1] (tanh range)
image = _image_decoder(image_path)
# Explicitly set the shape if you want a sanity check
# or if you are using your own custom dataset, otherwise
# the model is shape-agnostic as it is fully convolutional
# im.set_shape([512,1024,3]) # downscaled cityscapes
if use_conditional_GAN:
# Semantic map only enabled for cityscapes
semantic_map = _image_decoder(semantic_map_path)
if training_dataset == 'ADE20k':
image = _aspect_preserving_width_resize(image)
# im.set_shape([None,512,3])
if use_conditional_GAN:
if training_dataset == 'ADE20k':
raise NotImplementedError('Conditional generation not implemented for ADE20k dataset.')
return image, semantic_map
else:
return image
print('Training on', training_dataset)
if use_conditional_GAN:
dataset = tf.data.Dataset.from_tensor_slices((image_paths, kwargs['semantic_map_paths']))
else:
dataset = tf.data.Dataset.from_tensor_slices(image_paths)
dataset = dataset.map(_parser)
dataset = dataset.shuffle(buffer_size=8)
dataset = dataset.batch(batch_size)
if test:
dataset = dataset.repeat()
return dataset
@staticmethod
def load_cGAN_dataset(image_paths, semantic_map_paths, batch_size, test=False, augment=False, downsample=False,
training_dataset='cityscapes'):
"""
Load image dataset with semantic label maps for conditional GAN
"""
def _parser(image_path, semantic_map_path):
def _aspect_preserving_width_resize(image, width=512):
# If training on ADE20k
height_i = tf.shape(image)[0]
new_height = height_i - tf.floormod(height_i, 16)
return tf.image.resize_image_with_crop_or_pad(image, new_height, width)
def _image_decoder(path):
im = tf.image.decode_png(tf.read_file(image_path), channels=3)
im = tf.image.convert_image_dtype(im, dtype=tf.float32)
return 2 * im - 1 # [0,1] -> [-1,1] (tanh range)
image, semantic_map = _image_decoder(image_path), _image_decoder(semantic_map_path)
print('Training on', training_dataset)
if training_dataset is 'ADE20k':
image = _aspect_preserving_width_resize(image)
semantic_map = _aspect_preserving_width_resize(semantic_map)
# im.set_shape([512,1024,3]) # downscaled cityscapes
return image, semantic_map
dataset = tf.data.Dataset.from_tensor_slices(image_paths, semantic_map_paths)
dataset = dataset.map(_parser)
dataset = dataset.shuffle(buffer_size=8)
dataset = dataset.batch(batch_size)
if test:
dataset = dataset.repeat()
return dataset
@staticmethod
def load_inference(filenames, labels, batch_size, resize=(32,32)):
# Single image estimation over multiple stochastic forward passes
def _preprocess_inference(image_path, label, resize=(32,32)):
# Preprocess individual images during inference
image_path = tf.squeeze(image_path)
image = tf.image.decode_png(tf.read_file(image_path))
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.image.resize_images(image, size=resize)
return image, label
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.map(_preprocess_inference)
dataset = dataset.batch(batch_size)
return dataset
================================================
FILE: cGAN/model.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import glob, time, os
from network import Network
from data import Data
from config import directories
from utils import Utils
class Model():
def __init__(self, config, paths, dataset, name='gan_compression', evaluate=False):
# Build the computational graph
print('Building computational graph ...')
self.G_global_step = tf.Variable(0, trainable=False)
self.D_global_step = tf.Variable(0, trainable=False)
self.handle = tf.placeholder(tf.string, shape=[])
self.training_phase = tf.placeholder(tf.bool)
# >>> Data handling
self.path_placeholder = tf.placeholder(paths.dtype, paths.shape)
self.test_path_placeholder = tf.placeholder(paths.dtype)
self.semantic_map_path_placeholder = tf.placeholder(paths.dtype, paths.shape)
self.test_semantic_map_path_placeholder = tf.placeholder(paths.dtype)
train_dataset = Data.load_dataset(self.path_placeholder,
config.batch_size,
augment=False,
training_dataset=dataset,
use_conditional_GAN=config.use_conditional_GAN,
semantic_map_paths=self.semantic_map_path_placeholder)
test_dataset = Data.load_dataset(self.test_path_placeholder,
config.batch_size,
augment=False,
training_dataset=dataset,
use_conditional_GAN=config.use_conditional_GAN,
semantic_map_paths=self.test_semantic_map_path_placeholder,
test=True)
self.iterator = tf.data.Iterator.from_string_handle(self.handle,
train_dataset.output_types,
train_dataset.output_shapes)
self.train_iterator = train_dataset.make_initializable_iterator()
self.test_iterator = test_dataset.make_initializable_iterator()
if config.use_conditional_GAN:
self.example, self.semantic_map = self.iterator.get_next()
else:
self.example = self.iterator.get_next()
# Global generator: Encode -> quantize -> reconstruct
# =======================================================================================================>>>
with tf.variable_scope('generator'):
self.feature_map = Network.encoder(self.example, config, self.training_phase, config.channel_bottleneck)
self.w_hat = Network.quantizer(self.feature_map, config)
if config.use_conditional_GAN:
self.semantic_feature_map = Network.encoder(self.semantic_map, config, self.training_phase,
config.channel_bottleneck, scope='semantic_map')
self.w_hat_semantic = Network.quantizer(self.semantic_feature_map, config, scope='semantic_map')
self.w_hat = tf.concat([self.w_hat, self.w_hat_semantic], axis=-1)
if config.sample_noise is True:
print('Sampling noise...')
# noise_prior = tf.contrib.distributions.Uniform(-1., 1.)
# self.noise_sample = noise_prior.sample([tf.shape(self.example)[0], config.noise_dim])
noise_prior = tf.contrib.distributions.MultivariateNormalDiag(loc=tf.zeros([config.noise_dim]), scale_diag=tf.ones([config.noise_dim]))
v = noise_prior.sample(tf.shape(self.example)[0])
Gv = Network.dcgan_generator(v, config, self.training_phase, C=config.channel_bottleneck, upsample_dim=config.upsample_dim)
self.z = tf.concat([self.w_hat, Gv], axis=-1)
else:
self.z = self.w_hat
self.reconstruction = Network.decoder(self.z, config, self.training_phase, C=config.channel_bottleneck)
print('Real image shape:', self.example.get_shape().as_list())
print('Reconstruction shape:', self.reconstruction.get_shape().as_list())
# Pass generated, real images to discriminator
# =======================================================================================================>>>
if config.use_conditional_GAN:
# Model conditional distribution
self.example = tf.concat([self.example, self.semantic_map], axis=-1)
self.reconstruction = tf.concat([self.reconstruction, self.semantic_map], axis=-1)
if config.multiscale:
D_x, D_x2, D_x4, *Dk_x = Network.multiscale_discriminator(self.example, config, self.training_phase,
use_sigmoid=config.use_vanilla_GAN, mode='real')
D_Gz, D_Gz2, D_Gz4, *Dk_Gz = Network.multiscale_discriminator(self.reconstruction, config, self.training_phase,
use_sigmoid=config.use_vanilla_GAN, mode='reconstructed', reuse=True)
else:
D_x = Network.discriminator(self.example, config, self.training_phase, use_sigmoid=config.use_vanilla_GAN)
D_Gz = Network.discriminator(self.reconstruction, config, self.training_phase, use_sigmoid=config.use_vanilla_GAN, reuse=True)
# Loss terms
# =======================================================================================================>>>
if config.use_vanilla_GAN is True:
# Minimize JS divergence
D_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_x,
labels=tf.ones_like(D_x)))
D_loss_gen = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_Gz,
labels=tf.zeros_like(D_Gz)))
self.D_loss = D_loss_real + D_loss_gen
# G_loss = max log D(G(z))
self.G_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_Gz,
labels=tf.ones_like(D_Gz)))
else:
# Minimize $\chi^2$ divergence
self.D_loss = tf.reduce_mean(tf.square(D_x - 1.)) + tf.reduce_mean(tf.square(D_Gz))
self.G_loss = tf.reduce_mean(tf.square(D_Gz - 1.))
if config.multiscale:
self.D_loss += tf.reduce_mean(tf.square(D_x2 - 1.)) + tf.reduce_mean(tf.square(D_x4 - 1.))
self.D_loss += tf.reduce_mean(tf.square(D_Gz2)) + tf.reduce_mean(tf.square(D_Gz4))
distortion_penalty = config.lambda_X * tf.losses.mean_squared_error(self.example, self.reconstruction)
self.G_loss += distortion_penalty
if config.use_feature_matching_loss: # feature extractor for generator
D_x_layers, D_Gz_layers = [j for i in Dk_x for j in i], [j for i in Dk_Gz for j in i]
feature_matching_loss = tf.reduce_sum([tf.reduce_mean(tf.abs(Dkx-Dkz)) for Dkx, Dkz in zip(D_x_layers, D_Gz_layers)])
self.G_loss += config.feature_matching_weight * feature_matching_loss
# Optimization
# =======================================================================================================>>>
G_opt = tf.train.AdamOptimizer(learning_rate=config.G_learning_rate, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=config.D_learning_rate, beta1=0.5)
theta_G = Utils.scope_variables('generator')
theta_D = Utils.scope_variables('discriminator')
print('Generator parameters:', theta_G)
print('Discriminator parameters:', theta_D)
G_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')
D_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='discriminator')
# Execute the update_ops before performing the train_step
with tf.control_dependencies(G_update_ops):
self.G_opt_op = G_opt.minimize(self.G_loss, name='G_opt', global_step=self.G_global_step, var_list=theta_G)
with tf.control_dependencies(D_update_ops):
self.D_opt_op = D_opt.minimize(self.D_loss, name='D_opt', global_step=self.D_global_step, var_list=theta_D)
G_ema = tf.train.ExponentialMovingAverage(decay=config.ema_decay, num_updates=self.G_global_step)
G_maintain_averages_op = G_ema.apply(theta_G)
D_ema = tf.train.ExponentialMovingAverage(decay=config.ema_decay, num_updates=self.D_global_step)
D_maintain_averages_op = D_ema.apply(theta_D)
with tf.control_dependencies(G_update_ops+[self.G_opt_op]):
self.G_train_op = tf.group(G_maintain_averages_op)
with tf.control_dependencies(D_update_ops+[self.D_opt_op]):
self.D_train_op = tf.group(D_maintain_averages_op)
# >>> Monitoring
# tf.summary.scalar('learning_rate', learning_rate)
tf.summary.scalar('generator_loss', self.G_loss)
tf.summary.scalar('discriminator_loss', self.D_loss)
tf.summary.scalar('distortion_penalty', distortion_penalty)
if config.use_feature_matching_loss:
tf.summary.scalar('feature_matching_loss', feature_matching_loss)
tf.summary.scalar('G_global_step', self.G_global_step)
tf.summary.scalar('D_global_step', self.D_global_step)
tf.summary.image('real_images', self.example[:,:,:,:3], max_outputs=4)
tf.summary.image('compressed_images', self.reconstruction[:,:,:,:3], max_outputs=4)
if config.use_conditional_GAN:
tf.summary.image('semantic_map', self.semantic_map, max_outputs=4)
self.merge_op = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(
os.path.join(directories.tensorboard, '{}_train_{}'.format(name, time.strftime('%d-%m_%I:%M'))), graph=tf.get_default_graph())
self.test_writer = tf.summary.FileWriter(
os.path.join(directories.tensorboard, '{}_test_{}'.format(name, time.strftime('%d-%m_%I:%M'))))
================================================
FILE: cGAN/network.py
================================================
""" Modular components of computational graph
JTan 2018
"""
import tensorflow as tf
from utils import Utils
class Network(object):
@staticmethod
def encoder(x, config, training, C, reuse=False, actv=tf.nn.relu, scope='image'):
"""
Process image x ([512,1024]) into a feature map of size W/16 x H/16 x C
+ C: Bottleneck depth, controls bpp
+ Output: Projection onto C channels, C = {2,4,8,16}
"""
init = tf.contrib.layers.xavier_initializer()
print('<------------ Building global {} generator architecture ------------>'.format(scope))
def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=actv, init=init):
bn_kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
# x = tf.layers.batch_normalization(x, **bn_kwargs)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
with tf.variable_scope('encoder_{}'.format(scope), reuse=reuse):
# Run convolutions
f = [60, 120, 240, 480, 960]
x = tf.pad(x, [[0, 0], [3, 3], [3, 3], [0, 0]], 'REFLECT')
out = conv_block(x, filters=f[0], kernel_size=7, strides=1, padding='VALID', actv=actv)
out = conv_block(out, filters=f[1], kernel_size=3, strides=2, actv=actv)
out = conv_block(out, filters=f[2], kernel_size=3, strides=2, actv=actv)
out = conv_block(out, filters=f[3], kernel_size=3, strides=2, actv=actv)
out = conv_block(out, filters=f[4], kernel_size=3, strides=2, actv=actv)
# Project channels onto space w/ dimension C
# Feature maps have dimension W/16 x H/16 x C
out = tf.pad(out, [[0, 0], [1, 1], [1, 1], [0, 0]], 'REFLECT')
feature_map = conv_block(out, filters=C, kernel_size=3, strides=1, padding='VALID', actv=actv)
return feature_map
@staticmethod
def quantizer(w, config, reuse=False, temperature=1, L=5, scope='image'):
"""
Quantize feature map over L centers to obtain discrete $\hat{w}$
+ Centers: {-2,-1,0,1,2}
+ TODO: Toggle learnable centers?
"""
with tf.variable_scope('quantizer_{}'.format(scope, reuse=reuse)):
centers = tf.cast(tf.range(-2,3), tf.float32)
# Partition W into the Voronoi tesellation over the centers
w_stack = tf.stack([w for _ in range(L)], axis=-1)
w_hard = tf.cast(tf.argmin(tf.abs(w_stack - centers), axis=-1), tf.float32) + tf.reduce_min(centers)
smx = tf.nn.softmax(-1.0/temperature * tf.abs(w_stack - centers), dim=-1)
# Contract last dimension
w_soft = tf.einsum('ijklm,m->ijkl', smx, centers) # w_soft = tf.tensordot(smx, centers, axes=((-1),(0)))
# Treat quantization as differentiable for optimization
w_bar = tf.round(tf.stop_gradient(w_hard - w_soft) + w_soft)
return w_bar
@staticmethod
def decoder(w_bar, config, training, C, reuse=False, actv=tf.nn.relu, channel_upsample=960):
"""
Attempt to reconstruct the image from the quantized representation w_bar.
Generated image should be consistent with the true image distribution while
recovering the specific encoded image
+ C: Bottleneck depth, controls bpp - last dimension of encoder output
+ TODO: Concatenate quantized w_bar with noise sampled from prior
"""
init = tf.contrib.layers.xavier_initializer()
def residual_block(x, n_filters, kernel_size=3, strides=1, actv=actv):
init = tf.contrib.layers.xavier_initializer()
# kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
strides = [1,1]
identity_map = x
p = int((kernel_size-1)/2)
res = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = actv(tf.contrib.layers.instance_norm(res))
res = tf.pad(res, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = tf.contrib.layers.instance_norm(res)
assert res.get_shape().as_list() == identity_map.get_shape().as_list(), 'Mismatched shapes between input/output!'
out = tf.add(res, identity_map)
return out
def upsample_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=actv, batch_norm=False):
bn_kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d_transpose(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
if batch_norm is True:
x = tf.layers.batch_normalization(x, **bn_kwargs)
else:
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
# Project channel dimension of w_bar to higher dimension
# W_pc = tf.get_variable('W_pc_{}'.format(C), shape=[C, channel_upsample], initializer=init)
# upsampled = tf.einsum('ijkl,lm->ijkm', w_bar, W_pc)
with tf.variable_scope('decoder', reuse=reuse):
w_bar = tf.pad(w_bar, [[0, 0], [1, 1], [1, 1], [0, 0]], 'REFLECT')
upsampled = Utils.conv_block(w_bar, filters=960, kernel_size=3, strides=1, padding='VALID', actv=actv)
# Process upsampled feature map with residual blocks
res = residual_block(upsampled, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
# Upsample to original dimensions - mirror decoder
f = [480, 240, 120, 60]
ups = upsample_block(res, f[0], 3, strides=[2,2], padding='same')
ups = upsample_block(ups, f[1], 3, strides=[2,2], padding='same')
ups = upsample_block(ups, f[2], 3, strides=[2,2], padding='same')
ups = upsample_block(ups, f[3], 3, strides=[2,2], padding='same')
ups = tf.pad(ups, [[0, 0], [3, 3], [3, 3], [0, 0]], 'REFLECT')
ups = tf.layers.conv2d(ups, 3, kernel_size=7, strides=1, padding='VALID')
out = tf.nn.tanh(ups)
return out
@staticmethod
def discriminator(x, config, training, reuse=False, actv=tf.nn.leaky_relu, use_sigmoid=False, ksize=4):
# x is either generator output G(z) or drawn from the real data distribution
# Patch-GAN discriminator based on arXiv 1711.11585
# bn_kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
in_kwargs = {'center':True, 'scale':True, 'activation_fn':actv}
print('Shape of x:', x.get_shape().as_list())
with tf.variable_scope('discriminator', reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=ksize, strides=2, padding='same', activation=actv)
c2 = tf.layers.conv2d(c1, 128, kernel_size=ksize, strides=2, padding='same')
c2 = actv(tf.contrib.layers.instance_norm(c2, **in_kwargs))
c3 = tf.layers.conv2d(c2, 256, kernel_size=ksize, strides=2, padding='same')
c3 = actv(tf.contrib.layers.instance_norm(c3, **in_kwargs))
c4 = tf.layers.conv2d(c3, 512, kernel_size=ksize, strides=2, padding='same')
c4 = actv(tf.contrib.layers.instance_norm(c4, **in_kwargs))
out = tf.layers.conv2d(c4, 1, kernel_size=ksize, strides=1, padding='same')
if use_sigmoid is True: # Otherwise use LS-GAN
out = tf.nn.sigmoid(out)
return out
@staticmethod
def multiscale_discriminator(x, config, training, actv=tf.nn.leaky_relu, use_sigmoid=False,
ksize=4, mode='real', reuse=False):
# x is either generator output G(z) or drawn from the real data distribution
# Multiscale + Patch-GAN discriminator architecture based on arXiv 1711.11585
print('<------------ Building multiscale discriminator architecture ------------>')
if mode == 'real':
print('Building discriminator D(x)')
elif mode == 'reconstructed':
print('Building discriminator D(G(z))')
else:
raise NotImplementedError('Invalid discriminator mode specified.')
# Downsample input
x2 = tf.layers.average_pooling2d(x, pool_size=3, strides=2, padding='same')
x4 = tf.layers.average_pooling2d(x2, pool_size=3, strides=2, padding='same')
print('Shape of x:', x.get_shape().as_list())
print('Shape of x downsampled by factor 2:', x2.get_shape().as_list())
print('Shape of x downsampled by factor 4:', x4.get_shape().as_list())
def discriminator(x, scope, actv=actv, use_sigmoid=use_sigmoid, ksize=ksize, reuse=reuse):
# Returns patch-GAN output + intermediate layers
with tf.variable_scope('discriminator_{}'.format(scope), reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=ksize, strides=2, padding='same', activation=actv)
c2 = Utils.conv_block(c1, filters=128, kernel_size=ksize, strides=2, padding='same', actv=actv)
c3 = Utils.conv_block(c2, filters=256, kernel_size=ksize, strides=2, padding='same', actv=actv)
c4 = Utils.conv_block(c3, filters=512, kernel_size=ksize, strides=2, padding='same', actv=actv)
out = tf.layers.conv2d(c4, 1, kernel_size=ksize, strides=1, padding='same')
if use_sigmoid is True: # Otherwise use LS-GAN
out = tf.nn.sigmoid(out)
return out, c1, c2, c3, c4
with tf.variable_scope('discriminator', reuse=reuse):
disc, *Dk = discriminator(x, 'original')
disc_downsampled_2, *Dk_2 = discriminator(x2, 'downsampled_2')
disc_downsampled_4, *Dk_4 = discriminator(x4, 'downsampled_4')
return disc, disc_downsampled_2, disc_downsampled_4, Dk, Dk_2, Dk_4
@staticmethod
def dcgan_generator(z, config, training, C, reuse=False, actv=tf.nn.relu, kernel_size=5, upsample_dim=256):
"""
Upsample noise to concatenate with quantized representation w_bar.
+ z: Drawn from latent distribution - [batch_size, noise_dim]
+ C: Bottleneck depth, controls bpp - last dimension of encoder output
"""
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
with tf.variable_scope('noise_generator', reuse=reuse):
# [batch_size, 4, 8, dim]
with tf.variable_scope('fc1', reuse=reuse):
h2 = tf.layers.dense(z, units=4 * 8 * upsample_dim, activation=actv, kernel_initializer=init) # cifar-10
h2 = tf.layers.batch_normalization(h2, **kwargs)
h2 = tf.reshape(h2, shape=[-1, 4, 8, upsample_dim])
# [batch_size, 8, 16, dim/2]
with tf.variable_scope('upsample1', reuse=reuse):
up1 = tf.layers.conv2d_transpose(h2, upsample_dim//2, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
up1 = tf.layers.batch_normalization(up1, **kwargs)
# [batch_size, 16, 32, dim/4]
with tf.variable_scope('upsample2', reuse=reuse):
up2 = tf.layers.conv2d_transpose(up1, upsample_dim//4, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
up2 = tf.layers.batch_normalization(up2, **kwargs)
# [batch_size, 32, 64, dim/8]
with tf.variable_scope('upsample3', reuse=reuse):
up3 = tf.layers.conv2d_transpose(up2, upsample_dim//8, kernel_size=kernel_size, strides=2, padding='same', activation=actv) # cifar-10
up3 = tf.layers.batch_normalization(up3, **kwargs)
with tf.variable_scope('conv_out', reuse=reuse):
out = tf.pad(up3, [[0, 0], [3, 3], [3, 3], [0, 0]], 'REFLECT')
out = tf.layers.conv2d(out, C, kernel_size=7, strides=1, padding='VALID')
return out
@staticmethod
def dcgan_discriminator(x, config, training, reuse=False, actv=tf.nn.relu):
# x is either generator output G(z) or drawn from the real data distribution
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
print('Shape of x:', x.get_shape().as_list())
x = tf.reshape(x, shape=[-1, 32, 32, 3])
# x = tf.reshape(x, shape=[-1, 28, 28, 1])
with tf.variable_scope('discriminator', reuse=reuse):
with tf.variable_scope('conv1', reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=5, strides=2, padding='same', activation=actv)
c1 = tf.layers.batch_normalization(c1, **kwargs)
with tf.variable_scope('conv2', reuse=reuse):
c2 = tf.layers.conv2d(c1, 128, kernel_size=5, strides=2, padding='same', activation=actv)
c2 = tf.layers.batch_normalization(c2, **kwargs)
with tf.variable_scope('fc1', reuse=reuse):
fc1 = tf.contrib.layers.flatten(c2)
# fc1 = tf.reshape(c2, shape=[-1, 8 * 8 * 128])
fc1 = tf.layers.dense(fc1, units=1024, activation=actv, kernel_initializer=init)
fc1 = tf.layers.batch_normalization(fc1, **kwargs)
with tf.variable_scope('out', reuse=reuse):
out = tf.layers.dense(fc1, units=2, activation=None, kernel_initializer=init)
return out
@staticmethod
def critic_grande(x, config, training, reuse=False, actv=tf.nn.relu, kernel_size=5, gradient_penalty=True):
# x is either generator output G(z) or drawn from the real data distribution
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
print('Shape of x:', x.get_shape().as_list())
x = tf.reshape(x, shape=[-1, 32, 32, 3])
# x = tf.reshape(x, shape=[-1, 28, 28, 1])
with tf.variable_scope('critic', reuse=reuse):
with tf.variable_scope('conv1', reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
if gradient_penalty is False:
c1 = tf.layers.batch_normalization(c1, **kwargs)
with tf.variable_scope('conv2', reuse=reuse):
c2 = tf.layers.conv2d(c1, 128, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
if gradient_penalty is False:
c2 = tf.layers.batch_normalization(c2, **kwargs)
with tf.variable_scope('conv3', reuse=reuse):
c3 = tf.layers.conv2d(c2, 256, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
if gradient_penalty is False:
c3 = tf.layers.batch_normalization(c3, **kwargs)
with tf.variable_scope('fc1', reuse=reuse):
fc1 = tf.contrib.layers.flatten(c3)
# fc1 = tf.reshape(c2, shape=[-1, 8 * 8 * 128])
fc1 = tf.layers.dense(fc1, units=1024, activation=actv, kernel_initializer=init)
#fc1 = tf.layers.batch_normalization(fc1, **kwargs)
with tf.variable_scope('out', reuse=reuse):
out = tf.layers.dense(fc1, units=1, activation=None, kernel_initializer=init)
return out
@staticmethod
def wrn(x, config, training, reuse=False, actv=tf.nn.relu):
# Implements W-28-10 wide residual network
# See Arxiv 1605.07146
network_width = 10 # k
block_multiplicity = 2 # n
filters = [16, 16, 32, 64]
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':True}
def residual_block(x, n_filters, actv, keep_prob, training, project_shortcut=False, first_block=False):
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':True}
if project_shortcut:
strides = [2,2] if not first_block else [1,1]
identity_map = tf.layers.conv2d(x, filters=n_filters, kernel_size=[1,1],
strides=strides, kernel_initializer=init, padding='same')
# identity_map = tf.layers.batch_normalization(identity_map, **kwargs)
else:
strides = [1,1]
identity_map = x
bn = tf.layers.batch_normalization(x, **kwargs)
conv = tf.layers.conv2d(bn, filters=n_filters, kernel_size=[3,3], activation=actv,
strides=strides, kernel_initializer=init, padding='same')
bn = tf.layers.batch_normalization(conv, **kwargs)
do = tf.layers.dropout(bn, rate=1-keep_prob, training=training)
conv = tf.layers.conv2d(do, filters=n_filters, kernel_size=[3,3], activation=actv,
kernel_initializer=init, padding='same')
out = tf.add(conv, identity_map)
return out
def residual_block_2(x, n_filters, actv, keep_prob, training, project_shortcut=False, first_block=False):
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':True}
prev_filters = x.get_shape().as_list()[-1]
if project_shortcut:
strides = [2,2] if not first_block else [1,1]
# identity_map = tf.layers.conv2d(x, filters=n_filters, kernel_size=[1,1],
# strides=strides, kernel_initializer=init, padding='same')
identity_map = tf.layers.average_pooling2d(x, strides, strides, 'valid')
identity_map = tf.pad(identity_map,
tf.constant([[0,0],[0,0],[0,0],[(n_filters-prev_filters)//2, (n_filters-prev_filters)//2]]))
# identity_map = tf.layers.batch_normalization(identity_map, **kwargs)
else:
strides = [1,1]
identity_map = x
x = tf.layers.batch_normalization(x, **kwargs)
x = tf.nn.relu(x)
x = tf.layers.conv2d(x, filters=n_filters, kernel_size=[3,3], strides=strides,
kernel_initializer=init, padding='same')
x = tf.layers.batch_normalization(x, **kwargs)
x = tf.nn.relu(x)
x = tf.layers.dropout(x, rate=1-keep_prob, training=training)
x = tf.layers.conv2d(x, filters=n_filters, kernel_size=[3,3],
kernel_initializer=init, padding='same')
out = tf.add(x, identity_map)
return out
with tf.variable_scope('wrn_conv', reuse=reuse):
# Initial convolution --------------------------------------------->
with tf.variable_scope('conv0', reuse=reuse):
conv = tf.layers.conv2d(x, filters[0], kernel_size=[3,3], activation=actv,
kernel_initializer=init, padding='same')
# Residual group 1 ------------------------------------------------>
rb = conv
f1 = filters[1]*network_width
for n in range(block_multiplicity):
with tf.variable_scope('group1/{}'.format(n), reuse=reuse):
project_shortcut = True if n==0 else False
rb = residual_block(rb, f1, actv, project_shortcut=project_shortcut,
keep_prob=config.conv_keep_prob, training=training, first_block=True)
# Residual group 2 ------------------------------------------------>
f2 = filters[2]*network_width
for n in range(block_multiplicity):
with tf.variable_scope('group2/{}'.format(n), reuse=reuse):
project_shortcut = True if n==0 else False
rb = residual_block(rb, f2, actv, project_shortcut=project_shortcut,
keep_prob=config.conv_keep_prob, training=training)
# Residual group 3 ------------------------------------------------>
f3 = filters[3]*network_width
for n in range(block_multiplicity):
with tf.variable_scope('group3/{}'.format(n), reuse=reuse):
project_shortcut = True if n==0 else False
rb = residual_block(rb, f3, actv, project_shortcut=project_shortcut,
keep_prob=config.conv_keep_prob, training=training)
# Avg pooling + output -------------------------------------------->
with tf.variable_scope('output', reuse=reuse):
bn = tf.nn.relu(tf.layers.batch_normalization(rb, **kwargs))
avp = tf.layers.average_pooling2d(bn, pool_size=[8,8], strides=[1,1], padding='valid')
flatten = tf.contrib.layers.flatten(avp)
out = tf.layers.dense(flatten, units=config.n_classes, kernel_initializer=init)
return out
@staticmethod
def old_encoder(x, config, training, C, reuse=False, actv=tf.nn.relu):
"""
Process image x ([512,1024]) into a feature map of size W/16 x H/16 x C
+ C: Bottleneck depth, controls bpp
+ Output: Projection onto C channels, C = {2,4,8,16}
"""
# proj_channels = [2,4,8,16]
init = tf.contrib.layers.xavier_initializer()
def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=actv, init=init):
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
with tf.variable_scope('encoder', reuse=reuse):
# Run convolutions
out = conv_block(x, kernel_size=3, strides=1, filters=160, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=320, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=480, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=640, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=800, actv=actv)
out = conv_block(out, kernel_size=3, strides=1, filters=960, actv=actv)
# Project channels onto lower-dimensional embedding space
W = tf.get_variable('W_channel_{}'.format(C), shape=[960,C], initializer=init)
feature_map = tf.einsum('ijkl,lm->ijkm', out, W) # feature_map = tf.tensordot(out, W, axes=((3),(0)))
# Feature maps have dimension W/16 x H/16 x C
return feature_map
================================================
FILE: cGAN/train.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import pandas as pd
import time, os, sys
import argparse
# User-defined
from network import Network
from utils import Utils
from data import Data
from model import Model
from config import config_train, directories
tf.logging.set_verbosity(tf.logging.ERROR)
def train(config, args):
start_time = time.time()
G_loss_best, D_loss_best = float('inf'), float('inf')
ckpt = tf.train.get_checkpoint_state(directories.checkpoints)
# Load data
print('Training on dataset', args.dataset)
if config.use_conditional_GAN:
print('Using conditional GAN')
paths, semantic_map_paths = Data.load_dataframe(directories.train, load_semantic_maps=True)
test_paths, test_semantic_map_paths = Data.load_dataframe(directories.test, load_semantic_maps=True)
else:
paths = Data.load_dataframe(directories.train)
test_paths = Data.load_dataframe(directories.test)
# Build graph
gan = Model(config, paths, name=args.name, dataset=args.dataset)
saver = tf.train.Saver()
if config.use_conditional_GAN:
feed_dict_test_init = {gan.test_path_placeholder: test_paths,
gan.test_semantic_map_path_placeholder: test_semantic_map_paths}
feed_dict_train_init = {gan.path_placeholder: paths,
gan.semantic_map_path_placeholder: semantic_map_paths}
else:
feed_dict_test_init = {gan.test_path_placeholder: test_paths}
feed_dict_train_init = {gan.path_placeholder: paths}
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
train_handle = sess.run(gan.train_iterator.string_handle())
test_handle = sess.run(gan.test_iterator.string_handle())
if args.restore_last and ckpt.model_checkpoint_path:
# Continue training saved model
saver.restore(sess, ckpt.model_checkpoint_path)
print('{} restored.'.format(ckpt.model_checkpoint_path))
else:
if args.restore_path:
new_saver = tf.train.import_meta_graph('{}.meta'.format(args.restore_path))
new_saver.restore(sess, args.restore_path)
print('{} restored.'.format(args.restore_path))
sess.run(gan.test_iterator.initializer, feed_dict=feed_dict_test_init)
for epoch in range(config.num_epochs):
sess.run(gan.train_iterator.initializer, feed_dict=feed_dict_train_init)
# Run diagnostics
G_loss_best, D_loss_best = Utils.run_diagnostics(gan, config, directories, sess, saver, train_handle,
start_time, epoch, args.name, G_loss_best, D_loss_best)
while True:
try:
# Update generator
# for _ in range(8):
feed_dict = {gan.training_phase: True, gan.handle: train_handle}
sess.run(gan.G_train_op, feed_dict=feed_dict)
# Update discriminator
step, _ = sess.run([gan.D_global_step, gan.D_train_op], feed_dict=feed_dict)
if step % config.diagnostic_steps == 0:
G_loss_best, D_loss_best = Utils.run_diagnostics(gan, config, directories, sess, saver, train_handle,
start_time, epoch, args.name, G_loss_best, D_loss_best)
Utils.single_plot(epoch, step, sess, gan, train_handle, args.name, config)
# for _ in range(4):
# sess.run(gan.G_opt_op, feed_dict=feed_dict)
except tf.errors.OutOfRangeError:
print('End of epoch!')
break
except KeyboardInterrupt:
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'{}_last.ckpt'.format(args.name)), global_step=epoch)
print('Interrupted, model saved to: ', save_path)
sys.exit()
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'{}_end.ckpt'.format(args.name)),
global_step=epoch)
print("Training Complete. Model saved to file: {} Time elapsed: {:.3f} s".format(save_path, time.time()-start_time))
def main(**kwargs):
parser = argparse.ArgumentParser()
parser.add_argument("-rl", "--restore_last", help="restore last saved model", action="store_true")
parser.add_argument("-r", "--restore_path", help="path to model to be restored", type=str)
parser.add_argument("-opt", "--optimizer", default="adam", help="Selected optimizer", type=str)
parser.add_argument("-name", "--name", default="gan-train", help="Checkpoint/Tensorboard label")
parser.add_argument("-ds", "--dataset", default="cityscapes", help="choice of training dataset. Currently only supports cityscapes/ADE20k", choices=set(("cityscapes", "ADE20k")), type=str)
args = parser.parse_args()
# Launch training
train(config_train, args)
if __name__ == '__main__':
main()
================================================
FILE: cGAN/utils.py
================================================
# -*- coding: utf-8 -*-
# Diagnostic helper functions for Tensorflow session
import tensorflow as tf
import numpy as np
import os, time
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
from config import directories
class Utils(object):
@staticmethod
def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=tf.nn.relu):
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
@staticmethod
def upsample_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=tf.nn.relu):
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d_transpose(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
@staticmethod
def residual_block(x, n_filters, kernel_size=3, strides=1, actv=tf.nn.relu):
init = tf.contrib.layers.xavier_initializer()
# kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
strides = [1,1]
identity_map = x
p = int((kernel_size-1)/2)
res = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = actv(tf.contrib.layers.instance_norm(res))
res = tf.pad(res, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = tf.contrib.layers.instance_norm(res)
assert res.get_shape().as_list() == identity_map.get_shape().as_list(), 'Mismatched shapes between input/output!'
out = tf.add(res, identity_map)
return out
@staticmethod
def get_available_gpus():
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
#return local_device_protos
print('Available GPUs:')
print([x.name for x in local_device_protos if x.device_type == 'GPU'])
@staticmethod
def scope_variables(name):
with tf.variable_scope(name):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=tf.get_variable_scope().name)
@staticmethod
def run_diagnostics(model, config, directories, sess, saver, train_handle, start_time, epoch, name, G_loss_best, D_loss_best):
t0 = time.time()
improved = ''
sess.run(tf.local_variables_initializer())
feed_dict_test = {model.training_phase: False, model.handle: train_handle}
try:
G_loss, D_loss, summary = sess.run([model.G_loss, model.D_loss, model.merge_op], feed_dict=feed_dict_test)
model.train_writer.add_summary(summary)
except tf.errors.OutOfRangeError:
G_loss, D_loss = float('nan'), float('nan')
if G_loss < G_loss_best and D_loss < D_loss_best:
G_loss_best, D_loss_best = G_loss, D_loss
improved = '[*]'
if epoch>5:
save_path = saver.save(sess,
os.path.join(directories.checkpoints_best, '{}_epoch{}.ckpt'.format(name, epoch)),
global_step=epoch)
print('Graph saved to file: {}'.format(save_path))
if epoch % 5 == 0 and epoch > 5:
save_path = saver.save(sess, os.path.join(directories.checkpoints, '{}_epoch{}.ckpt'.format(name, epoch)), global_step=epoch)
print('Graph saved to file: {}'.format(save_path))
print('Epoch {} | Generator Loss: {:.3f} | Discriminator Loss: {:.3f} | Rate: {} examples/s ({:.2f} s) {}'.format(epoch, G_loss, D_loss, int(config.batch_size/(time.time()-t0)), time.time() - start_time, improved))
return G_loss_best, D_loss_best
@staticmethod
def single_plot(epoch, global_step, sess, model, handle, name, config):
real = model.example
gen = model.reconstruction
# Generate images from noise, using the generator network.
r, g = sess.run([real, gen], feed_dict={model.training_phase:True, model.handle: handle})
images = list()
for im, imtype in zip([r,g], ['real', 'gen']):
im = ((im+1.0))/2 # [-1,1] -> [0,1]
im = np.squeeze(im)
im = im[:,:,:3]
images.append(im)
# Uncomment to plot real and generated samples separately
# f = plt.figure()
# plt.imshow(im)
# plt.axis('off')
# f.savefig("{}/gan_compression_{}_epoch{}_step{}_{}.pdf".format(directories.samples, name, epoch,
# global_step, imtype), format='pdf', dpi=720, bbox_inches='tight', pad_inches=0)
# plt.gcf().clear()
# plt.close(f)
comparison = np.hstack(images)
f = plt.figure()
plt.imshow(comparison)
plt.axis('off')
f.savefig("{}/gan_compression_{}_epoch{}_step{}_{}_comparison.pdf".format(directories.samples, name, epoch,
global_step, imtype), format='pdf', dpi=720, bbox_inches='tight', pad_inches=0)
plt.gcf().clear()
plt.close(f)
@staticmethod
def weight_decay(weight_decay, var_label='DW'):
"""L2 weight decay loss."""
costs = []
for var in tf.trainable_variables():
if var.op.name.find(r'{}'.format(var_label)) > 0:
costs.append(tf.nn.l2_loss(var))
return tf.multiply(weight_decay, tf.add_n(costs))
================================================
FILE: checkpoints/.gitignore
================================================
*
!.gitignore
!best/
================================================
FILE: compress.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import pandas as pd
import time, os, sys
import argparse
# User-defined
from network import Network
from utils import Utils
from data import Data
from model import Model
from config import config_test, directories
tf.logging.set_verbosity(tf.logging.ERROR)
def single_compress(config, args):
start = time.time()
ckpt = tf.train.get_checkpoint_state(directories.checkpoints)
assert (ckpt.model_checkpoint_path), 'Missing checkpoint file!'
if config.use_conditional_GAN:
print('Using conditional GAN')
paths, semantic_map_paths = np.array([args.image_path]), np.array([args.semantic_map_path])
else:
paths = np.array([args.image_path])
gan = Model(config, paths, name='single_compress', dataset=args.dataset, evaluate=True)
saver = tf.train.Saver()
if config.use_conditional_GAN:
feed_dict_init = {gan.path_placeholder: paths,
gan.semantic_map_path_placeholder: semantic_map_paths}
else:
feed_dict_init = {gan.path_placeholder: paths}
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
# Initialize variables
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
handle = sess.run(gan.train_iterator.string_handle())
if args.restore_last and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Most recent {} restored.'.format(ckpt.model_checkpoint_path))
else:
if args.restore_path:
new_saver = tf.train.import_meta_graph('{}.meta'.format(args.restore_path))
new_saver.restore(sess, args.restore_path)
print('Previous checkpoint {} restored.'.format(args.restore_path))
sess.run(gan.train_iterator.initializer, feed_dict=feed_dict_init)
eval_dict = {gan.training_phase: False, gan.handle: handle}
if args.output_path is None:
output = os.path.splitext(os.path.basename(args.image_path))
save_path = os.path.join(directories.samples, '{}_compressed.pdf'.format(output[0]))
else:
save_path = args.output_path
Utils.single_plot(0, 0, sess, gan, handle, save_path, config, single_compress=True)
print('Reconstruction saved to', save_path)
return
def main(**kwargs):
parser = argparse.ArgumentParser()
parser.add_argument("-rl", "--restore_last", help="restore last saved model", action="store_true")
parser.add_argument("-r", "--restore_path", help="path to model to be restored", type=str)
parser.add_argument("-i", "--image_path", help="path to image to compress", type=str)
parser.add_argument("-sm", "--semantic_map_path", help="path to corresponding semantic map", type=str)
parser.add_argument("-o", "--output_path", help="path to output image", type=str)
parser.add_argument("-ds", "--dataset", default="cityscapes", help="choice of training dataset. Currently only supports cityscapes/ADE20k", choices=set(("cityscapes", "ADE20k")), type=str)
args = parser.parse_args()
# Launch training
single_compress(config_test, args)
if __name__ == '__main__':
main()
================================================
FILE: config.py
================================================
#!/usr/bin/env python3
class config_train(object):
mode = 'gan-train'
num_epochs = 512
batch_size = 1
ema_decay = 0.999
G_learning_rate = 2e-4
D_learning_rate = 2e-4
lr_decay_rate = 2e-5
momentum = 0.9
weight_decay = 5e-4
noise_dim = 128
optimizer = 'adam'
kernel_size = 3
diagnostic_steps = 256
# WGAN
gradient_penalty = True
lambda_gp = 10
weight_clipping = False
max_c = 1e-2
n_critic_iterations = 20
# Compression
lambda_X = 12
channel_bottleneck = 8
sample_noise = True
use_vanilla_GAN = False
use_feature_matching_loss = True
upsample_dim = 256
multiscale = True
feature_matching_weight = 10
use_conditional_GAN = False
class config_test(object):
mode = 'gan-test'
num_epochs = 512
batch_size = 1
ema_decay = 0.999
G_learning_rate = 2e-4
D_learning_rate = 2e-4
lr_decay_rate = 2e-5
momentum = 0.9
weight_decay = 5e-4
noise_dim = 128
optimizer = 'adam'
kernel_size = 3
diagnostic_steps = 256
# WGAN
gradient_penalty = True
lambda_gp = 10
weight_clipping = False
max_c = 1e-2
n_critic_iterations = 5
# Compression
lambda_X = 12
channel_bottleneck = 8
sample_noise = True
use_vanilla_GAN = False
use_feature_matching_loss = True
upsample_dim = 256
multiscale = True
feature_matching_weight = 10
use_conditional_GAN = False
class directories(object):
train = 'data/cityscapes_paths_train.h5'
test = 'data/cityscapes_paths_test.h5'
val = 'data/cityscapes_paths_val.h5'
tensorboard = 'tensorboard'
checkpoints = 'checkpoints'
checkpoints_best = 'checkpoints/best'
samples = 'samples/cityscapes'
================================================
FILE: data/.gitignore
================================================
*
!.gitignore
!resize_cityscapes.sh
!cityscapes_paths_train.h5
!cityscapes_paths_test.h5
!cityscapes_paths_val.h5
================================================
FILE: data/resize_cityscapes.sh
================================================
#!/bin/bash
# Author: Grace Han
# In place resampling to 512 x 1024 px
# Requires imagemagick on a *nix system
# Modify according to your directory structure
for f in ./**/*.png; do
convert $f -resize 1024x512 $f
done
================================================
FILE: data.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import pandas as pd
from config import directories
class Data(object):
@staticmethod
def load_dataframe(filename, load_semantic_maps=False):
df = pd.read_hdf(filename, key='df').sample(frac=1).reset_index(drop=True)
if load_semantic_maps:
return df['path'].values, df['semantic_map_path'].values
else:
return df['path'].values
@staticmethod
def load_dataset(image_paths, batch_size, test=False, augment=False, downsample=False,
training_dataset='cityscapes', use_conditional_GAN=False, **kwargs):
def _augment(image):
# On-the-fly data augmentation
image = tf.image.random_brightness(image, max_delta=0.1)
image = tf.image.random_contrast(image, 0.5, 1.5)
image = tf.image.random_flip_left_right(image)
return image
def _parser(image_path, semantic_map_path=None):
def _aspect_preserving_width_resize(image, width=512):
height_i = tf.shape(image)[0]
# width_i = tf.shape(image)[1]
# ratio = tf.to_float(width_i) / tf.to_float(height_i)
# new_height = tf.to_int32(tf.to_float(height_i) / ratio)
new_height = height_i - tf.floormod(height_i, 16)
return tf.image.resize_image_with_crop_or_pad(image, new_height, width)
def _image_decoder(path):
im = tf.image.decode_png(tf.read_file(path), channels=3)
im = tf.image.convert_image_dtype(im, dtype=tf.float32)
return 2 * im - 1 # [0,1] -> [-1,1] (tanh range)
image = _image_decoder(image_path)
# Explicitly set the shape if you want a sanity check
# or if you are using your own custom dataset, otherwise
# the model is shape-agnostic as it is fully convolutional
# im.set_shape([512,1024,3]) # downscaled cityscapes
if use_conditional_GAN:
# Semantic map only enabled for cityscapes
semantic_map = _image_decoder(semantic_map_path)
if training_dataset == 'ADE20k':
image = _aspect_preserving_width_resize(image)
if use_conditional_GAN:
semantic_map = _aspect_preserving_width_resize(semantic_map)
# im.set_shape([None,512,3])
if use_conditional_GAN:
return image, semantic_map
else:
return image
print('Training on', training_dataset)
if use_conditional_GAN:
dataset = tf.data.Dataset.from_tensor_slices((image_paths, kwargs['semantic_map_paths']))
else:
dataset = tf.data.Dataset.from_tensor_slices(image_paths)
dataset = dataset.shuffle(buffer_size=8)
dataset = dataset.map(_parser)
dataset = dataset.cache()
dataset = dataset.batch(batch_size)
if test:
dataset = dataset.repeat()
return dataset
@staticmethod
def load_cGAN_dataset(image_paths, semantic_map_paths, batch_size, test=False, augment=False, downsample=False,
training_dataset='cityscapes'):
"""
Load image dataset with semantic label maps for conditional GAN
"""
def _parser(image_path, semantic_map_path):
def _aspect_preserving_width_resize(image, width=512):
# If training on ADE20k
height_i = tf.shape(image)[0]
new_height = height_i - tf.floormod(height_i, 16)
return tf.image.resize_image_with_crop_or_pad(image, new_height, width)
def _image_decoder(path):
im = tf.image.decode_png(tf.read_file(image_path), channels=3)
im = tf.image.convert_image_dtype(im, dtype=tf.float32)
return 2 * im - 1 # [0,1] -> [-1,1] (tanh range)
image, semantic_map = _image_decoder(image_path), _image_decoder(semantic_map_path)
print('Training on', training_dataset)
if training_dataset is 'ADE20k':
image = _aspect_preserving_width_resize(image)
semantic_map = _aspect_preserving_width_resize(semantic_map)
# im.set_shape([512,1024,3]) # downscaled cityscapes
return image, semantic_map
dataset = tf.data.Dataset.from_tensor_slices(image_paths, semantic_map_paths)
dataset = dataset.map(_parser)
dataset = dataset.shuffle(buffer_size=8)
dataset = dataset.batch(batch_size)
if test:
dataset = dataset.repeat()
return dataset
@staticmethod
def load_inference(filenames, labels, batch_size, resize=(32,32)):
# Single image estimation over multiple stochastic forward passes
def _preprocess_inference(image_path, label, resize=(32,32)):
# Preprocess individual images during inference
image_path = tf.squeeze(image_path)
image = tf.image.decode_png(tf.read_file(image_path))
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.image.resize_images(image, size=resize)
return image, label
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.map(_preprocess_inference)
dataset = dataset.batch(batch_size)
return dataset
================================================
FILE: model.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import glob, time, os
from network import Network
from data import Data
from config import directories
from utils import Utils
class Model():
def __init__(self, config, paths, dataset, name='gan_compression', evaluate=False):
# Build the computational graph
print('Building computational graph ...')
self.G_global_step = tf.Variable(0, trainable=False)
self.D_global_step = tf.Variable(0, trainable=False)
self.handle = tf.placeholder(tf.string, shape=[])
self.training_phase = tf.placeholder(tf.bool)
# >>> Data handling
self.path_placeholder = tf.placeholder(paths.dtype, paths.shape)
self.test_path_placeholder = tf.placeholder(paths.dtype)
self.semantic_map_path_placeholder = tf.placeholder(paths.dtype, paths.shape)
self.test_semantic_map_path_placeholder = tf.placeholder(paths.dtype)
train_dataset = Data.load_dataset(self.path_placeholder,
config.batch_size,
augment=False,
training_dataset=dataset,
use_conditional_GAN=config.use_conditional_GAN,
semantic_map_paths=self.semantic_map_path_placeholder)
test_dataset = Data.load_dataset(self.test_path_placeholder,
config.batch_size,
augment=False,
training_dataset=dataset,
use_conditional_GAN=config.use_conditional_GAN,
semantic_map_paths=self.test_semantic_map_path_placeholder,
test=True)
self.iterator = tf.data.Iterator.from_string_handle(self.handle,
train_dataset.output_types,
train_dataset.output_shapes)
self.train_iterator = train_dataset.make_initializable_iterator()
self.test_iterator = test_dataset.make_initializable_iterator()
if config.use_conditional_GAN:
self.example, self.semantic_map = self.iterator.get_next()
else:
self.example = self.iterator.get_next()
# Global generator: Encode -> quantize -> reconstruct
# =======================================================================================================>>>
with tf.variable_scope('generator'):
self.feature_map = Network.encoder(self.example, config, self.training_phase, config.channel_bottleneck)
self.w_hat = Network.quantizer(self.feature_map, config)
if config.use_conditional_GAN:
self.semantic_feature_map = Network.encoder(self.semantic_map, config, self.training_phase,
config.channel_bottleneck, scope='semantic_map')
self.w_hat_semantic = Network.quantizer(self.semantic_feature_map, config, scope='semantic_map')
self.w_hat = tf.concat([self.w_hat, self.w_hat_semantic], axis=-1)
if config.sample_noise is True:
print('Sampling noise...')
# noise_prior = tf.contrib.distributions.Uniform(-1., 1.)
# self.noise_sample = noise_prior.sample([tf.shape(self.example)[0], config.noise_dim])
noise_prior = tf.contrib.distributions.MultivariateNormalDiag(loc=tf.zeros([config.noise_dim]), scale_diag=tf.ones([config.noise_dim]))
v = noise_prior.sample(tf.shape(self.example)[0])
Gv = Network.dcgan_generator(v, config, self.training_phase, C=config.channel_bottleneck, upsample_dim=config.upsample_dim)
self.z = tf.concat([self.w_hat, Gv], axis=-1)
else:
self.z = self.w_hat
self.reconstruction = Network.decoder(self.z, config, self.training_phase, C=config.channel_bottleneck)
print('Real image shape:', self.example.get_shape().as_list())
print('Reconstruction shape:', self.reconstruction.get_shape().as_list())
if evaluate:
return
# Pass generated, real images to discriminator
# =======================================================================================================>>>
if config.use_conditional_GAN:
# Model conditional distribution
self.example = tf.concat([self.example, self.semantic_map], axis=-1)
self.reconstruction = tf.concat([self.reconstruction, self.semantic_map], axis=-1)
if config.multiscale:
D_x, D_x2, D_x4, *Dk_x = Network.multiscale_discriminator(self.example, config, self.training_phase,
use_sigmoid=config.use_vanilla_GAN, mode='real')
D_Gz, D_Gz2, D_Gz4, *Dk_Gz = Network.multiscale_discriminator(self.reconstruction, config, self.training_phase,
use_sigmoid=config.use_vanilla_GAN, mode='reconstructed', reuse=True)
else:
D_x = Network.discriminator(self.example, config, self.training_phase, use_sigmoid=config.use_vanilla_GAN)
D_Gz = Network.discriminator(self.reconstruction, config, self.training_phase, use_sigmoid=config.use_vanilla_GAN, reuse=True)
# Loss terms
# =======================================================================================================>>>
if config.use_vanilla_GAN is True:
# Minimize JS divergence
D_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_x,
labels=tf.ones_like(D_x)))
D_loss_gen = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_Gz,
labels=tf.zeros_like(D_Gz)))
self.D_loss = D_loss_real + D_loss_gen
# G_loss = max log D(G(z))
self.G_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_Gz,
labels=tf.ones_like(D_Gz)))
else:
# Minimize $\chi^2$ divergence
self.D_loss = tf.reduce_mean(tf.square(D_x - 1.)) + tf.reduce_mean(tf.square(D_Gz))
self.G_loss = tf.reduce_mean(tf.square(D_Gz - 1.))
if config.multiscale:
self.D_loss += tf.reduce_mean(tf.square(D_x2 - 1.)) + tf.reduce_mean(tf.square(D_x4 - 1.))
self.D_loss += tf.reduce_mean(tf.square(D_Gz2)) + tf.reduce_mean(tf.square(D_Gz4))
distortion_penalty = config.lambda_X * tf.losses.mean_squared_error(self.example, self.reconstruction)
self.G_loss += distortion_penalty
if config.use_feature_matching_loss: # feature extractor for generator
D_x_layers, D_Gz_layers = [j for i in Dk_x for j in i], [j for i in Dk_Gz for j in i]
feature_matching_loss = tf.reduce_sum([tf.reduce_mean(tf.abs(Dkx-Dkz)) for Dkx, Dkz in zip(D_x_layers, D_Gz_layers)])
self.G_loss += config.feature_matching_weight * feature_matching_loss
# Optimization
# =======================================================================================================>>>
G_opt = tf.train.AdamOptimizer(learning_rate=config.G_learning_rate, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=config.D_learning_rate, beta1=0.5)
theta_G = Utils.scope_variables('generator')
theta_D = Utils.scope_variables('discriminator')
# print('Generator parameters:', theta_G)
# print('Discriminator parameters:', theta_D)
G_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')
D_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='discriminator')
# Execute the update_ops before performing the train_step
with tf.control_dependencies(G_update_ops):
self.G_opt_op = G_opt.minimize(self.G_loss, name='G_opt', global_step=self.G_global_step, var_list=theta_G)
with tf.control_dependencies(D_update_ops):
self.D_opt_op = D_opt.minimize(self.D_loss, name='D_opt', global_step=self.D_global_step, var_list=theta_D)
G_ema = tf.train.ExponentialMovingAverage(decay=config.ema_decay, num_updates=self.G_global_step)
G_maintain_averages_op = G_ema.apply(theta_G)
D_ema = tf.train.ExponentialMovingAverage(decay=config.ema_decay, num_updates=self.D_global_step)
D_maintain_averages_op = D_ema.apply(theta_D)
with tf.control_dependencies(G_update_ops+[self.G_opt_op]):
self.G_train_op = tf.group(G_maintain_averages_op)
with tf.control_dependencies(D_update_ops+[self.D_opt_op]):
self.D_train_op = tf.group(D_maintain_averages_op)
# >>> Monitoring
# tf.summary.scalar('learning_rate', learning_rate)
tf.summary.scalar('generator_loss', self.G_loss)
tf.summary.scalar('discriminator_loss', self.D_loss)
tf.summary.scalar('distortion_penalty', distortion_penalty)
if config.use_feature_matching_loss:
tf.summary.scalar('feature_matching_loss', feature_matching_loss)
tf.summary.scalar('G_global_step', self.G_global_step)
tf.summary.scalar('D_global_step', self.D_global_step)
tf.summary.image('real_images', self.example[:,:,:,:3], max_outputs=4)
tf.summary.image('compressed_images', self.reconstruction[:,:,:,:3], max_outputs=4)
if config.use_conditional_GAN:
tf.summary.image('semantic_map', self.semantic_map, max_outputs=4)
self.merge_op = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(
os.path.join(directories.tensorboard, '{}_train_{}'.format(name, time.strftime('%d-%m_%I:%M'))), graph=tf.get_default_graph())
self.test_writer = tf.summary.FileWriter(
os.path.join(directories.tensorboard, '{}_test_{}'.format(name, time.strftime('%d-%m_%I:%M'))))
================================================
FILE: network.py
================================================
""" Modular components of computational graph
JTan 2018
"""
import tensorflow as tf
from utils import Utils
class Network(object):
@staticmethod
def encoder(x, config, training, C, reuse=False, actv=tf.nn.relu, scope='image'):
"""
Process image x ([512,1024]) into a feature map of size W/16 x H/16 x C
+ C: Bottleneck depth, controls bpp
+ Output: Projection onto C channels, C = {2,4,8,16}
"""
init = tf.contrib.layers.xavier_initializer()
print('<------------ Building global {} generator architecture ------------>'.format(scope))
def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=actv, init=init):
bn_kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
# x = tf.layers.batch_normalization(x, **bn_kwargs)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
with tf.variable_scope('encoder_{}'.format(scope), reuse=reuse):
# Run convolutions
f = [60, 120, 240, 480, 960]
x = tf.pad(x, [[0, 0], [3, 3], [3, 3], [0, 0]], 'REFLECT')
out = conv_block(x, filters=f[0], kernel_size=7, strides=1, padding='VALID', actv=actv)
out = conv_block(out, filters=f[1], kernel_size=3, strides=2, actv=actv)
out = conv_block(out, filters=f[2], kernel_size=3, strides=2, actv=actv)
out = conv_block(out, filters=f[3], kernel_size=3, strides=2, actv=actv)
out = conv_block(out, filters=f[4], kernel_size=3, strides=2, actv=actv)
# Project channels onto space w/ dimension C
# Feature maps have dimension W/16 x H/16 x C
out = tf.pad(out, [[0, 0], [1, 1], [1, 1], [0, 0]], 'REFLECT')
feature_map = conv_block(out, filters=C, kernel_size=3, strides=1, padding='VALID', actv=actv)
return feature_map
@staticmethod
def quantizer(w, config, reuse=False, temperature=1, L=5, scope='image'):
"""
Quantize feature map over L centers to obtain discrete $\hat{w}$
+ Centers: {-2,-1,0,1,2}
+ TODO: Toggle learnable centers?
"""
with tf.variable_scope('quantizer_{}'.format(scope, reuse=reuse)):
centers = tf.cast(tf.range(-2,3), tf.float32)
# Partition W into the Voronoi tesellation over the centers
w_stack = tf.stack([w for _ in range(L)], axis=-1)
w_hard = tf.cast(tf.argmin(tf.abs(w_stack - centers), axis=-1), tf.float32) + tf.reduce_min(centers)
smx = tf.nn.softmax(-1.0/temperature * tf.abs(w_stack - centers), dim=-1)
# Contract last dimension
w_soft = tf.einsum('ijklm,m->ijkl', smx, centers) # w_soft = tf.tensordot(smx, centers, axes=((-1),(0)))
# Treat quantization as differentiable for optimization
w_bar = tf.round(tf.stop_gradient(w_hard - w_soft) + w_soft)
return w_bar
@staticmethod
def decoder(w_bar, config, training, C, reuse=False, actv=tf.nn.relu, channel_upsample=960):
"""
Attempt to reconstruct the image from the quantized representation w_bar.
Generated image should be consistent with the true image distribution while
recovering the specific encoded image
+ C: Bottleneck depth, controls bpp - last dimension of encoder output
+ TODO: Concatenate quantized w_bar with noise sampled from prior
"""
init = tf.contrib.layers.xavier_initializer()
def residual_block(x, n_filters, kernel_size=3, strides=1, actv=actv):
init = tf.contrib.layers.xavier_initializer()
# kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
strides = [1,1]
identity_map = x
p = int((kernel_size-1)/2)
res = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = actv(tf.contrib.layers.instance_norm(res))
res = tf.pad(res, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = tf.contrib.layers.instance_norm(res)
assert res.get_shape().as_list() == identity_map.get_shape().as_list(), 'Mismatched shapes between input/output!'
out = tf.add(res, identity_map)
return out
def upsample_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=actv, batch_norm=False):
bn_kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d_transpose(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
if batch_norm is True:
x = tf.layers.batch_normalization(x, **bn_kwargs)
else:
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
# Project channel dimension of w_bar to higher dimension
# W_pc = tf.get_variable('W_pc_{}'.format(C), shape=[C, channel_upsample], initializer=init)
# upsampled = tf.einsum('ijkl,lm->ijkm', w_bar, W_pc)
with tf.variable_scope('decoder', reuse=reuse):
w_bar = tf.pad(w_bar, [[0, 0], [1, 1], [1, 1], [0, 0]], 'REFLECT')
upsampled = Utils.conv_block(w_bar, filters=960, kernel_size=3, strides=1, padding='VALID', actv=actv)
# Process upsampled feature map with residual blocks
res = residual_block(upsampled, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
res = residual_block(res, 960, actv=actv)
# Upsample to original dimensions - mirror decoder
f = [480, 240, 120, 60]
ups = upsample_block(res, f[0], 3, strides=[2,2], padding='same')
ups = upsample_block(ups, f[1], 3, strides=[2,2], padding='same')
ups = upsample_block(ups, f[2], 3, strides=[2,2], padding='same')
ups = upsample_block(ups, f[3], 3, strides=[2,2], padding='same')
ups = tf.pad(ups, [[0, 0], [3, 3], [3, 3], [0, 0]], 'REFLECT')
ups = tf.layers.conv2d(ups, 3, kernel_size=7, strides=1, padding='VALID')
out = tf.nn.tanh(ups)
return out
@staticmethod
def discriminator(x, config, training, reuse=False, actv=tf.nn.leaky_relu, use_sigmoid=False, ksize=4):
# x is either generator output G(z) or drawn from the real data distribution
# Patch-GAN discriminator based on arXiv 1711.11585
# bn_kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
in_kwargs = {'center':True, 'scale':True, 'activation_fn':actv}
print('Shape of x:', x.get_shape().as_list())
with tf.variable_scope('discriminator', reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=ksize, strides=2, padding='same', activation=actv)
c2 = tf.layers.conv2d(c1, 128, kernel_size=ksize, strides=2, padding='same')
c2 = actv(tf.contrib.layers.instance_norm(c2, **in_kwargs))
c3 = tf.layers.conv2d(c2, 256, kernel_size=ksize, strides=2, padding='same')
c3 = actv(tf.contrib.layers.instance_norm(c3, **in_kwargs))
c4 = tf.layers.conv2d(c3, 512, kernel_size=ksize, strides=2, padding='same')
c4 = actv(tf.contrib.layers.instance_norm(c4, **in_kwargs))
out = tf.layers.conv2d(c4, 1, kernel_size=ksize, strides=1, padding='same')
if use_sigmoid is True: # Otherwise use LS-GAN
out = tf.nn.sigmoid(out)
return out
@staticmethod
def multiscale_discriminator(x, config, training, actv=tf.nn.leaky_relu, use_sigmoid=False,
ksize=4, mode='real', reuse=False):
# x is either generator output G(z) or drawn from the real data distribution
# Multiscale + Patch-GAN discriminator architecture based on arXiv 1711.11585
print('<------------ Building multiscale discriminator architecture ------------>')
if mode == 'real':
print('Building discriminator D(x)')
elif mode == 'reconstructed':
print('Building discriminator D(G(z))')
else:
raise NotImplementedError('Invalid discriminator mode specified.')
# Downsample input
x2 = tf.layers.average_pooling2d(x, pool_size=3, strides=2, padding='same')
x4 = tf.layers.average_pooling2d(x2, pool_size=3, strides=2, padding='same')
print('Shape of x:', x.get_shape().as_list())
print('Shape of x downsampled by factor 2:', x2.get_shape().as_list())
print('Shape of x downsampled by factor 4:', x4.get_shape().as_list())
def discriminator(x, scope, actv=actv, use_sigmoid=use_sigmoid, ksize=ksize, reuse=reuse):
# Returns patch-GAN output + intermediate layers
with tf.variable_scope('discriminator_{}'.format(scope), reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=ksize, strides=2, padding='same', activation=actv)
c2 = Utils.conv_block(c1, filters=128, kernel_size=ksize, strides=2, padding='same', actv=actv)
c3 = Utils.conv_block(c2, filters=256, kernel_size=ksize, strides=2, padding='same', actv=actv)
c4 = Utils.conv_block(c3, filters=512, kernel_size=ksize, strides=2, padding='same', actv=actv)
out = tf.layers.conv2d(c4, 1, kernel_size=ksize, strides=1, padding='same')
if use_sigmoid is True: # Otherwise use LS-GAN
out = tf.nn.sigmoid(out)
return out, c1, c2, c3, c4
with tf.variable_scope('discriminator', reuse=reuse):
disc, *Dk = discriminator(x, 'original')
disc_downsampled_2, *Dk_2 = discriminator(x2, 'downsampled_2')
disc_downsampled_4, *Dk_4 = discriminator(x4, 'downsampled_4')
return disc, disc_downsampled_2, disc_downsampled_4, Dk, Dk_2, Dk_4
@staticmethod
def dcgan_generator(z, config, training, C, reuse=False, actv=tf.nn.relu, kernel_size=5, upsample_dim=256):
"""
Upsample noise to concatenate with quantized representation w_bar.
+ z: Drawn from latent distribution - [batch_size, noise_dim]
+ C: Bottleneck depth, controls bpp - last dimension of encoder output
"""
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
with tf.variable_scope('noise_generator', reuse=reuse):
# [batch_size, 4, 8, dim]
with tf.variable_scope('fc1', reuse=reuse):
h2 = tf.layers.dense(z, units=4 * 8 * upsample_dim, activation=actv, kernel_initializer=init) # cifar-10
h2 = tf.layers.batch_normalization(h2, **kwargs)
h2 = tf.reshape(h2, shape=[-1, 4, 8, upsample_dim])
# [batch_size, 8, 16, dim/2]
with tf.variable_scope('upsample1', reuse=reuse):
up1 = tf.layers.conv2d_transpose(h2, upsample_dim//2, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
up1 = tf.layers.batch_normalization(up1, **kwargs)
# [batch_size, 16, 32, dim/4]
with tf.variable_scope('upsample2', reuse=reuse):
up2 = tf.layers.conv2d_transpose(up1, upsample_dim//4, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
up2 = tf.layers.batch_normalization(up2, **kwargs)
# [batch_size, 32, 64, dim/8]
with tf.variable_scope('upsample3', reuse=reuse):
up3 = tf.layers.conv2d_transpose(up2, upsample_dim//8, kernel_size=kernel_size, strides=2, padding='same', activation=actv) # cifar-10
up3 = tf.layers.batch_normalization(up3, **kwargs)
with tf.variable_scope('conv_out', reuse=reuse):
out = tf.pad(up3, [[0, 0], [3, 3], [3, 3], [0, 0]], 'REFLECT')
out = tf.layers.conv2d(out, C, kernel_size=7, strides=1, padding='VALID')
return out
@staticmethod
def dcgan_discriminator(x, config, training, reuse=False, actv=tf.nn.relu):
# x is either generator output G(z) or drawn from the real data distribution
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
print('Shape of x:', x.get_shape().as_list())
x = tf.reshape(x, shape=[-1, 32, 32, 3])
# x = tf.reshape(x, shape=[-1, 28, 28, 1])
with tf.variable_scope('discriminator', reuse=reuse):
with tf.variable_scope('conv1', reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=5, strides=2, padding='same', activation=actv)
c1 = tf.layers.batch_normalization(c1, **kwargs)
with tf.variable_scope('conv2', reuse=reuse):
c2 = tf.layers.conv2d(c1, 128, kernel_size=5, strides=2, padding='same', activation=actv)
c2 = tf.layers.batch_normalization(c2, **kwargs)
with tf.variable_scope('fc1', reuse=reuse):
fc1 = tf.contrib.layers.flatten(c2)
# fc1 = tf.reshape(c2, shape=[-1, 8 * 8 * 128])
fc1 = tf.layers.dense(fc1, units=1024, activation=actv, kernel_initializer=init)
fc1 = tf.layers.batch_normalization(fc1, **kwargs)
with tf.variable_scope('out', reuse=reuse):
out = tf.layers.dense(fc1, units=2, activation=None, kernel_initializer=init)
return out
@staticmethod
def critic_grande(x, config, training, reuse=False, actv=tf.nn.relu, kernel_size=5, gradient_penalty=True):
# x is either generator output G(z) or drawn from the real data distribution
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
print('Shape of x:', x.get_shape().as_list())
x = tf.reshape(x, shape=[-1, 32, 32, 3])
# x = tf.reshape(x, shape=[-1, 28, 28, 1])
with tf.variable_scope('critic', reuse=reuse):
with tf.variable_scope('conv1', reuse=reuse):
c1 = tf.layers.conv2d(x, 64, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
if gradient_penalty is False:
c1 = tf.layers.batch_normalization(c1, **kwargs)
with tf.variable_scope('conv2', reuse=reuse):
c2 = tf.layers.conv2d(c1, 128, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
if gradient_penalty is False:
c2 = tf.layers.batch_normalization(c2, **kwargs)
with tf.variable_scope('conv3', reuse=reuse):
c3 = tf.layers.conv2d(c2, 256, kernel_size=kernel_size, strides=2, padding='same', activation=actv)
if gradient_penalty is False:
c3 = tf.layers.batch_normalization(c3, **kwargs)
with tf.variable_scope('fc1', reuse=reuse):
fc1 = tf.contrib.layers.flatten(c3)
# fc1 = tf.reshape(c2, shape=[-1, 8 * 8 * 128])
fc1 = tf.layers.dense(fc1, units=1024, activation=actv, kernel_initializer=init)
#fc1 = tf.layers.batch_normalization(fc1, **kwargs)
with tf.variable_scope('out', reuse=reuse):
out = tf.layers.dense(fc1, units=1, activation=None, kernel_initializer=init)
return out
@staticmethod
def wrn(x, config, training, reuse=False, actv=tf.nn.relu):
# Implements W-28-10 wide residual network
# See Arxiv 1605.07146
network_width = 10 # k
block_multiplicity = 2 # n
filters = [16, 16, 32, 64]
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':True}
def residual_block(x, n_filters, actv, keep_prob, training, project_shortcut=False, first_block=False):
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':True}
if project_shortcut:
strides = [2,2] if not first_block else [1,1]
identity_map = tf.layers.conv2d(x, filters=n_filters, kernel_size=[1,1],
strides=strides, kernel_initializer=init, padding='same')
# identity_map = tf.layers.batch_normalization(identity_map, **kwargs)
else:
strides = [1,1]
identity_map = x
bn = tf.layers.batch_normalization(x, **kwargs)
conv = tf.layers.conv2d(bn, filters=n_filters, kernel_size=[3,3], activation=actv,
strides=strides, kernel_initializer=init, padding='same')
bn = tf.layers.batch_normalization(conv, **kwargs)
do = tf.layers.dropout(bn, rate=1-keep_prob, training=training)
conv = tf.layers.conv2d(do, filters=n_filters, kernel_size=[3,3], activation=actv,
kernel_initializer=init, padding='same')
out = tf.add(conv, identity_map)
return out
def residual_block_2(x, n_filters, actv, keep_prob, training, project_shortcut=False, first_block=False):
init = tf.contrib.layers.xavier_initializer()
kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':True}
prev_filters = x.get_shape().as_list()[-1]
if project_shortcut:
strides = [2,2] if not first_block else [1,1]
# identity_map = tf.layers.conv2d(x, filters=n_filters, kernel_size=[1,1],
# strides=strides, kernel_initializer=init, padding='same')
identity_map = tf.layers.average_pooling2d(x, strides, strides, 'valid')
identity_map = tf.pad(identity_map,
tf.constant([[0,0],[0,0],[0,0],[(n_filters-prev_filters)//2, (n_filters-prev_filters)//2]]))
# identity_map = tf.layers.batch_normalization(identity_map, **kwargs)
else:
strides = [1,1]
identity_map = x
x = tf.layers.batch_normalization(x, **kwargs)
x = tf.nn.relu(x)
x = tf.layers.conv2d(x, filters=n_filters, kernel_size=[3,3], strides=strides,
kernel_initializer=init, padding='same')
x = tf.layers.batch_normalization(x, **kwargs)
x = tf.nn.relu(x)
x = tf.layers.dropout(x, rate=1-keep_prob, training=training)
x = tf.layers.conv2d(x, filters=n_filters, kernel_size=[3,3],
kernel_initializer=init, padding='same')
out = tf.add(x, identity_map)
return out
with tf.variable_scope('wrn_conv', reuse=reuse):
# Initial convolution --------------------------------------------->
with tf.variable_scope('conv0', reuse=reuse):
conv = tf.layers.conv2d(x, filters[0], kernel_size=[3,3], activation=actv,
kernel_initializer=init, padding='same')
# Residual group 1 ------------------------------------------------>
rb = conv
f1 = filters[1]*network_width
for n in range(block_multiplicity):
with tf.variable_scope('group1/{}'.format(n), reuse=reuse):
project_shortcut = True if n==0 else False
rb = residual_block(rb, f1, actv, project_shortcut=project_shortcut,
keep_prob=config.conv_keep_prob, training=training, first_block=True)
# Residual group 2 ------------------------------------------------>
f2 = filters[2]*network_width
for n in range(block_multiplicity):
with tf.variable_scope('group2/{}'.format(n), reuse=reuse):
project_shortcut = True if n==0 else False
rb = residual_block(rb, f2, actv, project_shortcut=project_shortcut,
keep_prob=config.conv_keep_prob, training=training)
# Residual group 3 ------------------------------------------------>
f3 = filters[3]*network_width
for n in range(block_multiplicity):
with tf.variable_scope('group3/{}'.format(n), reuse=reuse):
project_shortcut = True if n==0 else False
rb = residual_block(rb, f3, actv, project_shortcut=project_shortcut,
keep_prob=config.conv_keep_prob, training=training)
# Avg pooling + output -------------------------------------------->
with tf.variable_scope('output', reuse=reuse):
bn = tf.nn.relu(tf.layers.batch_normalization(rb, **kwargs))
avp = tf.layers.average_pooling2d(bn, pool_size=[8,8], strides=[1,1], padding='valid')
flatten = tf.contrib.layers.flatten(avp)
out = tf.layers.dense(flatten, units=config.n_classes, kernel_initializer=init)
return out
@staticmethod
def old_encoder(x, config, training, C, reuse=False, actv=tf.nn.relu):
"""
Process image x ([512,1024]) into a feature map of size W/16 x H/16 x C
+ C: Bottleneck depth, controls bpp
+ Output: Projection onto C channels, C = {2,4,8,16}
"""
# proj_channels = [2,4,8,16]
init = tf.contrib.layers.xavier_initializer()
def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=actv, init=init):
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
with tf.variable_scope('encoder', reuse=reuse):
# Run convolutions
out = conv_block(x, kernel_size=3, strides=1, filters=160, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=320, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=480, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=640, actv=actv)
out = conv_block(out, kernel_size=[3,3], strides=2, filters=800, actv=actv)
out = conv_block(out, kernel_size=3, strides=1, filters=960, actv=actv)
# Project channels onto lower-dimensional embedding space
W = tf.get_variable('W_channel_{}'.format(C), shape=[960,C], initializer=init)
feature_map = tf.einsum('ijkl,lm->ijkm', out, W) # feature_map = tf.tensordot(out, W, axes=((3),(0)))
# Feature maps have dimension W/16 x H/16 x C
return feature_map
================================================
FILE: samples/.gitignore
================================================
*
!.gitignore
================================================
FILE: tensorboard/.gitignore
================================================
*
!.gitignore
================================================
FILE: train.py
================================================
#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import pandas as pd
import time, os, sys
import argparse
# User-defined
from network import Network
from utils import Utils
from data import Data
from model import Model
from config import config_train, directories
tf.logging.set_verbosity(tf.logging.ERROR)
def train(config, args):
start_time = time.time()
G_loss_best, D_loss_best = float('inf'), float('inf')
ckpt = tf.train.get_checkpoint_state(directories.checkpoints)
# Load data
print('Training on dataset', args.dataset)
if config.use_conditional_GAN:
print('Using conditional GAN')
paths, semantic_map_paths = Data.load_dataframe(directories.train, load_semantic_maps=True)
test_paths, test_semantic_map_paths = Data.load_dataframe(directories.test, load_semantic_maps=True)
else:
paths = Data.load_dataframe(directories.train)
test_paths = Data.load_dataframe(directories.test)
# Build graph
gan = Model(config, paths, name=args.name, dataset=args.dataset)
saver = tf.train.Saver()
if config.use_conditional_GAN:
feed_dict_test_init = {gan.test_path_placeholder: test_paths,
gan.test_semantic_map_path_placeholder: test_semantic_map_paths}
feed_dict_train_init = {gan.path_placeholder: paths,
gan.semantic_map_path_placeholder: semantic_map_paths}
else:
feed_dict_test_init = {gan.test_path_placeholder: test_paths}
feed_dict_train_init = {gan.path_placeholder: paths}
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
train_handle = sess.run(gan.train_iterator.string_handle())
test_handle = sess.run(gan.test_iterator.string_handle())
if args.restore_last and ckpt.model_checkpoint_path:
# Continue training saved model
saver.restore(sess, ckpt.model_checkpoint_path)
print('{} restored.'.format(ckpt.model_checkpoint_path))
else:
if args.restore_path:
new_saver = tf.train.import_meta_graph('{}.meta'.format(args.restore_path))
new_saver.restore(sess, args.restore_path)
print('{} restored.'.format(args.restore_path))
sess.run(gan.test_iterator.initializer, feed_dict=feed_dict_test_init)
for epoch in range(config.num_epochs):
sess.run(gan.train_iterator.initializer, feed_dict=feed_dict_train_init)
# Run diagnostics
G_loss_best, D_loss_best = Utils.run_diagnostics(gan, config, directories, sess, saver, train_handle,
start_time, epoch, args.name, G_loss_best, D_loss_best)
while True:
try:
# Update generator
# for _ in range(8):
feed_dict = {gan.training_phase: True, gan.handle: train_handle}
sess.run(gan.G_train_op, feed_dict=feed_dict)
# Update discriminator
step, _ = sess.run([gan.D_global_step, gan.D_train_op], feed_dict=feed_dict)
if step % config.diagnostic_steps == 0:
G_loss_best, D_loss_best = Utils.run_diagnostics(gan, config, directories, sess, saver, train_handle,
start_time, epoch, args.name, G_loss_best, D_loss_best)
Utils.single_plot(epoch, step, sess, gan, train_handle, args.name, config)
# for _ in range(4):
# sess.run(gan.G_train_op, feed_dict=feed_dict)
except tf.errors.OutOfRangeError:
print('End of epoch!')
break
except KeyboardInterrupt:
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'{}_last.ckpt'.format(args.name)), global_step=epoch)
print('Interrupted, model saved to: ', save_path)
sys.exit()
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'{}_end.ckpt'.format(args.name)),
global_step=epoch)
print("Training Complete. Model saved to file: {} Time elapsed: {:.3f} s".format(save_path, time.time()-start_time))
def main(**kwargs):
parser = argparse.ArgumentParser()
parser.add_argument("-rl", "--restore_last", help="restore last saved model", action="store_true")
parser.add_argument("-r", "--restore_path", help="path to model to be restored", type=str)
parser.add_argument("-opt", "--optimizer", default="adam", help="Selected optimizer", type=str)
parser.add_argument("-name", "--name", default="gan-train", help="Checkpoint/Tensorboard label")
parser.add_argument("-ds", "--dataset", default="cityscapes", help="choice of training dataset. Currently only supports cityscapes/ADE20k", choices=set(("cityscapes", "ADE20k")), type=str)
args = parser.parse_args()
# Launch training
train(config_train, args)
if __name__ == '__main__':
main()
================================================
FILE: utils.py
================================================
# -*- coding: utf-8 -*-
# Diagnostic helper functions for Tensorflow session
import tensorflow as tf
import numpy as np
import os, time
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
from config import directories
class Utils(object):
@staticmethod
def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=tf.nn.relu):
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
@staticmethod
def upsample_block(x, filters, kernel_size=[3,3], strides=2, padding='same', actv=tf.nn.relu):
in_kwargs = {'center':True, 'scale': True}
x = tf.layers.conv2d_transpose(x, filters, kernel_size, strides=strides, padding=padding, activation=None)
x = tf.contrib.layers.instance_norm(x, **in_kwargs)
x = actv(x)
return x
@staticmethod
def residual_block(x, n_filters, kernel_size=3, strides=1, actv=tf.nn.relu):
init = tf.contrib.layers.xavier_initializer()
# kwargs = {'center':True, 'scale':True, 'training':training, 'fused':True, 'renorm':False}
strides = [1,1]
identity_map = x
p = int((kernel_size-1)/2)
res = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = actv(tf.contrib.layers.instance_norm(res))
res = tf.pad(res, [[0, 0], [p, p], [p, p], [0, 0]], 'REFLECT')
res = tf.layers.conv2d(res, filters=n_filters, kernel_size=kernel_size, strides=strides,
activation=None, padding='VALID')
res = tf.contrib.layers.instance_norm(res)
assert res.get_shape().as_list() == identity_map.get_shape().as_list(), 'Mismatched shapes between input/output!'
out = tf.add(res, identity_map)
return out
@staticmethod
def get_available_gpus():
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
#return local_device_protos
print('Available GPUs:')
print([x.name for x in local_device_protos if x.device_type == 'GPU'])
@staticmethod
def scope_variables(name):
with tf.variable_scope(name):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=tf.get_variable_scope().name)
@staticmethod
def run_diagnostics(model, config, directories, sess, saver, train_handle, start_time, epoch, name, G_loss_best, D_loss_best):
t0 = time.time()
improved = ''
sess.run(tf.local_variables_initializer())
feed_dict_test = {model.training_phase: False, model.handle: train_handle}
try:
G_loss, D_loss, summary = sess.run([model.G_loss, model.D_loss, model.merge_op], feed_dict=feed_dict_test)
model.train_writer.add_summary(summary)
except tf.errors.OutOfRangeError:
G_loss, D_loss = float('nan'), float('nan')
if G_loss < G_loss_best and D_loss < D_loss_best:
G_loss_best, D_loss_best = G_loss, D_loss
improved = '[*]'
if epoch>5:
save_path = saver.save(sess,
os.path.join(directories.checkpoints_best, '{}_epoch{}.ckpt'.format(name, epoch)),
global_step=epoch)
print('Graph saved to file: {}'.format(save_path))
if epoch % 5 == 0 and epoch > 5:
save_path = saver.save(sess, os.path.join(directories.checkpoints, '{}_epoch{}.ckpt'.format(name, epoch)), global_step=epoch)
print('Graph saved to file: {}'.format(save_path))
print('Epoch {} | Generator Loss: {:.3f} | Discriminator Loss: {:.3f} | Rate: {} examples/s ({:.2f} s) {}'.format(epoch, G_loss, D_loss, int(config.batch_size/(time.time()-t0)), time.time() - start_time, improved))
return G_loss_best, D_loss_best
@staticmethod
def single_plot(epoch, global_step, sess, model, handle, name, config, single_compress=False):
real = model.example
gen = model.reconstruction
# Generate images from noise, using the generator network.
r, g = sess.run([real, gen], feed_dict={model.training_phase:True, model.handle: handle})
images = list()
for im, imtype in zip([r,g], ['real', 'gen']):
im = ((im+1.0))/2 # [-1,1] -> [0,1]
im = np.squeeze(im)
im = im[:,:,:3]
images.append(im)
# Uncomment to plot real and generated samples separately
# f = plt.figure()
# plt.imshow(im)
# plt.axis('off')
# f.savefig("{}/gan_compression_{}_epoch{}_step{}_{}.pdf".format(directories.samples, name, epoch,
# global_step, imtype), format='pdf', dpi=720, bbox_inches='tight', pad_inches=0)
# plt.gcf().clear()
# plt.close(f)
comparison = np.hstack(images)
f = plt.figure()
plt.imshow(comparison)
plt.axis('off')
if single_compress:
f.savefig(name, format='pdf', dpi=720, bbox_inches='tight', pad_inches=0)
else:
f.savefig("{}/gan_compression_{}_epoch{}_step{}_{}_comparison.pdf".format(directories.samples, name, epoch,
global_step, imtype), format='pdf', dpi=720, bbox_inches='tight', pad_inches=0)
plt.gcf().clear()
plt.close(f)
@staticmethod
def weight_decay(weight_decay, var_label='DW'):
"""L2 weight decay loss."""
costs = []
for var in tf.trainable_variables():
if var.op.name.find(r'{}'.format(var_label)) > 0:
costs.append(tf.nn.l2_loss(var))
return tf.multiply(weight_decay, tf.add_n(costs))
gitextract_gpypn45i/ ├── .gitignore ├── LICENSE ├── README.md ├── cGAN/ │ ├── config.py │ ├── data.py │ ├── model.py │ ├── network.py │ ├── train.py │ └── utils.py ├── checkpoints/ │ └── .gitignore ├── compress.py ├── config.py ├── data/ │ ├── .gitignore │ ├── cityscapes_paths_test.h5 │ ├── cityscapes_paths_train.h5 │ ├── cityscapes_paths_val.h5 │ └── resize_cityscapes.sh ├── data.py ├── model.py ├── network.py ├── samples/ │ └── .gitignore ├── tensorboard/ │ └── .gitignore ├── train.py └── utils.py
SYMBOL INDEX (66 symbols across 13 files)
FILE: cGAN/config.py
class config_train (line 3) | class config_train(object):
class config_test (line 36) | class config_test(object):
class directories (line 69) | class directories(object):
FILE: cGAN/data.py
class Data (line 7) | class Data(object):
method load_dataframe (line 10) | def load_dataframe(filename, load_semantic_maps=False):
method load_dataset (line 19) | def load_dataset(image_paths, batch_size, test=False, augment=False, d...
method load_cGAN_dataset (line 86) | def load_cGAN_dataset(image_paths, semantic_map_paths, batch_size, tes...
method load_inference (line 128) | def load_inference(filenames, labels, batch_size, resize=(32,32)):
FILE: cGAN/model.py
class Model (line 12) | class Model():
method __init__ (line 13) | def __init__(self, config, paths, dataset, name='gan_compression', eva...
FILE: cGAN/network.py
class Network (line 7) | class Network(object):
method encoder (line 10) | def encoder(x, config, training, C, reuse=False, actv=tf.nn.relu, scop...
method quantizer (line 49) | def quantizer(w, config, reuse=False, temperature=1, L=5, scope='image'):
method decoder (line 73) | def decoder(w_bar, config, training, C, reuse=False, actv=tf.nn.relu, ...
method discriminator (line 152) | def discriminator(x, config, training, reuse=False, actv=tf.nn.leaky_r...
method multiscale_discriminator (line 178) | def multiscale_discriminator(x, config, training, actv=tf.nn.leaky_rel...
method dcgan_generator (line 223) | def dcgan_generator(z, config, training, C, reuse=False, actv=tf.nn.re...
method dcgan_discriminator (line 261) | def dcgan_discriminator(x, config, training, reuse=False, actv=tf.nn.r...
method critic_grande (line 291) | def critic_grande(x, config, training, reuse=False, actv=tf.nn.relu, k...
method wrn (line 327) | def wrn(x, config, training, reuse=False, actv=tf.nn.relu):
method old_encoder (line 432) | def old_encoder(x, config, training, C, reuse=False, actv=tf.nn.relu):
FILE: cGAN/train.py
function train (line 17) | def train(config, args):
function main (line 106) | def main(**kwargs):
FILE: cGAN/utils.py
class Utils (line 14) | class Utils(object):
method conv_block (line 17) | def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same...
method upsample_block (line 26) | def upsample_block(x, filters, kernel_size=[3,3], strides=2, padding='...
method residual_block (line 35) | def residual_block(x, n_filters, kernel_size=3, strides=1, actv=tf.nn....
method get_available_gpus (line 58) | def get_available_gpus():
method scope_variables (line 66) | def scope_variables(name):
method run_diagnostics (line 71) | def run_diagnostics(model, config, directories, sess, saver, train_han...
method single_plot (line 101) | def single_plot(epoch, global_step, sess, model, handle, name, config):
method weight_decay (line 137) | def weight_decay(weight_decay, var_label='DW'):
FILE: compress.py
function single_compress (line 17) | def single_compress(config, args):
function main (line 66) | def main(**kwargs):
FILE: config.py
class config_train (line 3) | class config_train(object):
class config_test (line 36) | class config_test(object):
class directories (line 69) | class directories(object):
FILE: data.py
class Data (line 7) | class Data(object):
method load_dataframe (line 10) | def load_dataframe(filename, load_semantic_maps=False):
method load_dataset (line 19) | def load_dataset(image_paths, batch_size, test=False, augment=False, d...
method load_cGAN_dataset (line 87) | def load_cGAN_dataset(image_paths, semantic_map_paths, batch_size, tes...
method load_inference (line 129) | def load_inference(filenames, labels, batch_size, resize=(32,32)):
FILE: model.py
class Model (line 12) | class Model():
method __init__ (line 13) | def __init__(self, config, paths, dataset, name='gan_compression', eva...
FILE: network.py
class Network (line 7) | class Network(object):
method encoder (line 10) | def encoder(x, config, training, C, reuse=False, actv=tf.nn.relu, scop...
method quantizer (line 49) | def quantizer(w, config, reuse=False, temperature=1, L=5, scope='image'):
method decoder (line 73) | def decoder(w_bar, config, training, C, reuse=False, actv=tf.nn.relu, ...
method discriminator (line 152) | def discriminator(x, config, training, reuse=False, actv=tf.nn.leaky_r...
method multiscale_discriminator (line 178) | def multiscale_discriminator(x, config, training, actv=tf.nn.leaky_rel...
method dcgan_generator (line 223) | def dcgan_generator(z, config, training, C, reuse=False, actv=tf.nn.re...
method dcgan_discriminator (line 261) | def dcgan_discriminator(x, config, training, reuse=False, actv=tf.nn.r...
method critic_grande (line 291) | def critic_grande(x, config, training, reuse=False, actv=tf.nn.relu, k...
method wrn (line 327) | def wrn(x, config, training, reuse=False, actv=tf.nn.relu):
method old_encoder (line 432) | def old_encoder(x, config, training, C, reuse=False, actv=tf.nn.relu):
FILE: train.py
function train (line 17) | def train(config, args):
function main (line 106) | def main(**kwargs):
FILE: utils.py
class Utils (line 14) | class Utils(object):
method conv_block (line 17) | def conv_block(x, filters, kernel_size=[3,3], strides=2, padding='same...
method upsample_block (line 26) | def upsample_block(x, filters, kernel_size=[3,3], strides=2, padding='...
method residual_block (line 35) | def residual_block(x, n_filters, kernel_size=3, strides=1, actv=tf.nn....
method get_available_gpus (line 58) | def get_available_gpus():
method scope_variables (line 66) | def scope_variables(name):
method run_diagnostics (line 71) | def run_diagnostics(model, config, directories, sess, saver, train_han...
method single_plot (line 101) | def single_plot(epoch, global_step, sess, model, handle, name, config,...
method weight_decay (line 140) | def weight_decay(weight_decay, var_label='DW'):
Condensed preview — 24 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (124K chars).
[
{
"path": ".gitignore",
"chars": 1258,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# Data\n#data/\n#checkpoints/\n#tensorboard/\n#sa"
},
{
"path": "LICENSE",
"chars": 1061,
"preview": "MIT License\n\nCopyright (c) 2018 JTan\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof th"
},
{
"path": "README.md",
"chars": 8226,
"preview": "# generative-compression\n\nTensorFlow Implementation for learned compression of images using Generative Adversarial Netwo"
},
{
"path": "cGAN/config.py",
"chars": 1852,
"preview": "#!/usr/bin/env python3\n\nclass config_train(object):\n mode = 'gan-train'\n num_epochs = 512\n batch_size = 1\n e"
},
{
"path": "cGAN/data.py",
"chars": 5654,
"preview": "#!/usr/bin/python3\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nfrom config import directories\n\nclass "
},
{
"path": "cGAN/model.py",
"chars": 10137,
"preview": "#!/usr/bin/python3\n \nimport tensorflow as tf\nimport numpy as np\nimport glob, time, os\n\nfrom network import Network\nfr"
},
{
"path": "cGAN/network.py",
"chars": 24156,
"preview": "\"\"\" Modular components of computational graph\n JTan 2018\n\"\"\"\nimport tensorflow as tf\nfrom utils import Utils\n\nclass N"
},
{
"path": "cGAN/train.py",
"chars": 5281,
"preview": "#!/usr/bin/python3\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport time, os, sys\nimport argparse\n\n"
},
{
"path": "cGAN/utils.py",
"chars": 5881,
"preview": "# -*- coding: utf-8 -*-\n# Diagnostic helper functions for Tensorflow session\n\nimport tensorflow as tf\nimport numpy as np"
},
{
"path": "checkpoints/.gitignore",
"chars": 22,
"preview": "*\n!.gitignore\n!best/\n\n"
},
{
"path": "compress.py",
"chars": 3323,
"preview": "#!/usr/bin/python3\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport time, os, sys\nimport argparse\n\n"
},
{
"path": "config.py",
"chars": 1763,
"preview": "#!/usr/bin/env python3\n\nclass config_train(object):\n mode = 'gan-train'\n num_epochs = 512\n batch_size = 1\n e"
},
{
"path": "data/.gitignore",
"chars": 115,
"preview": "*\n!.gitignore\n!resize_cityscapes.sh\n!cityscapes_paths_train.h5\n!cityscapes_paths_test.h5\n!cityscapes_paths_val.h5\n\n"
},
{
"path": "data/resize_cityscapes.sh",
"chars": 222,
"preview": "#!/bin/bash\n# Author: Grace Han\n# In place resampling to 512 x 1024 px\n# Requires imagemagick on a *nix system\n# Modify "
},
{
"path": "data.py",
"chars": 5652,
"preview": "#!/usr/bin/python3\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nfrom config import directories\n\nclass "
},
{
"path": "model.py",
"chars": 10175,
"preview": "#!/usr/bin/python3\n \nimport tensorflow as tf\nimport numpy as np\nimport glob, time, os\n\nfrom network import Network\nfr"
},
{
"path": "network.py",
"chars": 24156,
"preview": "\"\"\" Modular components of computational graph\n JTan 2018\n\"\"\"\nimport tensorflow as tf\nfrom utils import Utils\n\nclass N"
},
{
"path": "samples/.gitignore",
"chars": 15,
"preview": "*\n!.gitignore\n\n"
},
{
"path": "tensorboard/.gitignore",
"chars": 14,
"preview": "*\n!.gitignore\n"
},
{
"path": "train.py",
"chars": 5283,
"preview": "#!/usr/bin/python3\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport time, os, sys\nimport argparse\n\n"
},
{
"path": "utils.py",
"chars": 6040,
"preview": "# -*- coding: utf-8 -*-\n# Diagnostic helper functions for Tensorflow session\n\nimport tensorflow as tf\nimport numpy as np"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the Justin-Tan/generative-compression GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 24 files (117.5 KB), approximately 29.0k tokens, and a symbol index with 66 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.