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WGAN_GP.py
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WGAN_GP.py
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#-*- coding: utf-8 -*-
from __future__ import division
import os
import time
import tensorflow as tf
import numpy as np
from ops import *
from utils import *
class WGAN_GP(object):
def __init__(self, sess, epoch, batch_size, dataset_name, checkpoint_dir, result_dir, log_dir):
self.sess = sess
self.dataset_name = dataset_name
self.checkpoint_dir = checkpoint_dir
self.result_dir = result_dir
self.log_dir = log_dir
self.epoch = epoch
self.batch_size = batch_size
self.model_name = "WGAN-GP" # name for checkpoint
if dataset_name == 'mnist' or dataset_name == 'fashion-mnist':
# parameters
self.input_height = 28
self.input_width = 28
self.output_height = 28
self.output_width = 28
self.z_dim = 62 # dimension of noise-vector
self.c_dim = 1
# WGAN_GP parameter
self.lambd = 0.25 # The higher value, the more stable, but the slower convergence
self.disc_iters = 1 # The number of critic iterations for one-step of generator
# train
self.learning_rate = 0.0002
self.beta1 = 0.5
# test
self.sample_num = 64 # number of generated images to be saved
# load mnist
self.data_X, self.data_y = load_mnist(self.dataset_name)
# get number of batches for a single epoch
self.num_batches = len(self.data_X) // self.batch_size
else:
raise NotImplementedError
def discriminator(self, x, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
# Architecture : (64)4c2s-(128)4c2s_BL-FC1024_BL-FC1_S
with tf.variable_scope("discriminator", reuse=reuse):
net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name='d_conv1'))
net = lrelu(bn(conv2d(net, 128, 4, 4, 2, 2, name='d_conv2'), is_training=is_training, scope='d_bn2'))
net = tf.reshape(net, [self.batch_size, -1])
net = lrelu(bn(linear(net, 1024, scope='d_fc3'), is_training=is_training, scope='d_bn3'))
out_logit = linear(net, 1, scope='d_fc4')
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def generator(self, z, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
# Architecture : FC1024_BR-FC7x7x128_BR-(64)4dc2s_BR-(1)4dc2s_S
with tf.variable_scope("generator", reuse=reuse):
net = tf.nn.relu(bn(linear(z, 1024, scope='g_fc1'), is_training=is_training, scope='g_bn1'))
net = tf.nn.relu(bn(linear(net, 128 * 7 * 7, scope='g_fc2'), is_training=is_training, scope='g_bn2'))
net = tf.reshape(net, [self.batch_size, 7, 7, 128])
net = tf.nn.relu(
bn(deconv2d(net, [self.batch_size, 14, 14, 64], 4, 4, 2, 2, name='g_dc3'), is_training=is_training,
scope='g_bn3'))
out = tf.nn.sigmoid(deconv2d(net, [self.batch_size, 28, 28, 1], 4, 4, 2, 2, name='g_dc4'))
return out
def build_model(self):
# some parameters
image_dims = [self.input_height, self.input_width, self.c_dim]
bs = self.batch_size
""" Graph Input """
# images
self.inputs = tf.placeholder(tf.float32, [bs] + image_dims, name='real_images')
# noises
self.z = tf.placeholder(tf.float32, [bs, self.z_dim], name='z')
""" Loss Function """
# output of D for real images
D_real, D_real_logits, _ = self.discriminator(self.inputs, is_training=True, reuse=False)
# output of D for fake images
G = self.generator(self.z, is_training=True, reuse=False)
D_fake, D_fake_logits, _ = self.discriminator(G, is_training=True, reuse=True)
# get loss for discriminator
d_loss_real = - tf.reduce_mean(D_real)
d_loss_fake = tf.reduce_mean(D_fake)
self.d_loss = d_loss_real + d_loss_fake
# get loss for generator
self.g_loss = - d_loss_fake
""" Gradient Penalty """
# This is borrowed from https://github.com/kodalinaveen3/DRAGAN/blob/master/DRAGAN.ipynb
alpha = tf.random_uniform(shape=self.inputs.get_shape(), minval=0.,maxval=1.)
differences = G - self.inputs # This is different from MAGAN
interpolates = self.inputs + (alpha * differences)
D_inter,_,_=self.discriminator(interpolates, is_training=True, reuse=True)
gradients = tf.gradients(D_inter, [interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
self.d_loss += self.lambd * gradient_penalty
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'd_' in var.name]
g_vars = [var for var in t_vars if 'g_' in var.name]
# optimizers
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
self.d_optim = tf.train.AdamOptimizer(self.learning_rate, beta1=self.beta1) \
.minimize(self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.learning_rate*5, beta1=self.beta1) \
.minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z, is_training=False, reuse=True)
""" Summary """
d_loss_real_sum = tf.summary.scalar("d_loss_real", d_loss_real)
d_loss_fake_sum = tf.summary.scalar("d_loss_fake", d_loss_fake)
d_loss_sum = tf.summary.scalar("d_loss", self.d_loss)
g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
# final summary operations
self.g_sum = tf.summary.merge([d_loss_fake_sum, g_loss_sum])
self.d_sum = tf.summary.merge([d_loss_real_sum, d_loss_sum])
def train(self):
# initialize all variables
tf.global_variables_initializer().run()
# graph inputs for visualize training results
self.sample_z = np.random.uniform(-1, 1, size=(self.batch_size , self.z_dim))
self.test_images = self.data_X[0:self.batch_size]
# saver to save model
self.saver = tf.train.Saver()
# summary writer
self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_name, self.sess.graph)
# restore check-point if it exits
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
start_epoch = (int)(checkpoint_counter / self.num_batches)
start_batch_id = checkpoint_counter - start_epoch * self.num_batches
counter = checkpoint_counter
print(" [*] Load SUCCESS")
else:
start_epoch = 0
start_batch_id = 0
counter = 1
print(" [!] Load failed...")
# loop for epoch
start_time = time.time()
for epoch in range(start_epoch, self.epoch):
# get batch data
for idx in range(start_batch_id, self.num_batches):
batch_images = self.data_X[idx*self.batch_size:(idx+1)*self.batch_size]
batch_z = np.random.uniform(-1, 1, [self.batch_size, self.z_dim]).astype(np.float32)
# update D network
_, summary_str, d_loss = self.sess.run([self.d_optim, self.d_sum, self.d_loss],
feed_dict={self.inputs: batch_images, self.z: batch_z})
self.writer.add_summary(summary_str, counter)
# update G network
if (counter-1) % self.disc_iters == 0:
batch_z = np.random.uniform(-1, 1, [self.batch_size, self.z_dim]).astype(np.float32)
_, summary_str, g_loss = self.sess.run([self.g_optim, self.g_sum, self.g_loss], feed_dict={self.inputs: batch_images, self.z: batch_z})
self.writer.add_summary(summary_str, counter)
counter += 1
# display training status
print("Epoch: [%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, self.num_batches, time.time() - start_time, d_loss, g_loss))
# save training results for every 300 steps
if np.mod(counter, 300) == 0:
samples = self.sess.run(self.fake_images,
feed_dict={self.z: self.sample_z, self.inputs: self.test_images})
tot_num_samples = min(self.sample_num, self.batch_size)
manifold_h = int(np.floor(np.sqrt(tot_num_samples)))
manifold_w = int(np.floor(np.sqrt(tot_num_samples)))
save_images(samples[:manifold_h * manifold_w, :, :, :], [manifold_h, manifold_w],
'./' + self.result_dir + '/' + self.model_name + '_train_{:02d}_{:04d}.png'.format(
epoch, idx))
# After an epoch, start_batch_id is set to zero
# non-zero value is only for the first epoch after loading pre-trained model
start_batch_id = 0
# save model
self.save(self.checkpoint_dir, counter)
# show temporal results
self.visualize_results(epoch)
# save model for final step
self.save(self.checkpoint_dir, counter)
def visualize_results(self, epoch):
tot_num_samples = min(self.sample_num, self.batch_size)
image_frame_dim = int(np.floor(np.sqrt(tot_num_samples)))
""" random condition, random noise """
z_sample = np.random.uniform(-1, 1, size=(self.batch_size, self.z_dim))
samples = self.sess.run(self.fake_images, feed_dict={self.z: z_sample})
save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],
self.result_dir + '/' + self.model_name + '_epoch%03d' % epoch + '_test_all_classes.png')
@property
def model_dir(self):
return "{}_{}_{}_{}".format(
self.dataset_name, self.batch_size,
self.output_height, self.output_width)
def save(self, checkpoint_dir, step):
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir, self.model_name)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,os.path.join(checkpoint_dir, self.model_name+'.model'), global_step=step)
def load(self, checkpoint_dir):
import re
print(" [*] Reading checkpoints...")
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir, self.model_name)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
counter = int(next(re.finditer("(\d+)(?!.*\d)",ckpt_name)).group(0))
print(" [*] Success to read {}".format(ckpt_name))
return True, counter
else:
print(" [*] Failed to find a checkpoint")
return False, 0