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model.py
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model.py
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#!/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'))))