forked from MustafaMustafa/WassersteinGAN-TensorFlow
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model.py
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model.py
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"""
Modified version of https://github.com/carpedm20/DCGAN-tensorflow/blob/master/model.py
"""
import tensorflow as tf
import numpy as np
from ops import *
class DCGAN(object):
def __init__(self, sess, dataset, batch_size=64, sample_size = 64, output_size=64,
z_dim=100, z_dist='normal', gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024, c_dim=3):
"""
Args:
sess: TensorFlow session
dataset: name of dataset
batch_size: The size of batch. Should be specified before training.
output_size: (optional) The resolution in pixels of the images. [64]
z_dim: (optional) Dimension of dim for Z. [100]
z_dist: (optional) Generating function for z (prior). uniform or normal [uniform]
gf_dim: (optional) Dimension of gen filters in first conv layer. [64]
df_dim: (optional) Dimension of discrim filters in first conv layer. [64]
gfc_dim: (optional) Dimension of gen units for for fully connected layer. [1024]
dfc_dim: (optional) Dimension of discrim units for fully connected layer. [1024]
c_dim: (optional) Dimension of image color. For grayscale input, set to 1. [3]
"""
self.sess = sess
self.dataset = dataset
self.model_name = "DCGAN"
self.batch_size = batch_size
self.sample_size = sample_size
self.output_size = output_size
self.z_dim = z_dim
if z_dist == 'uniform':
self.z_gen = generator_prior(np.random.uniform, [-1,1])
elif z_dist == 'normal':
self.z_gen = generator_prior(np.random.normal, [0,1])
else:
print("Unknown generating function %s", z_dist)
exit(1)
self.gf_dim = gf_dim
self.df_dim = df_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.c_dim = c_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
self.g_bn0 = batch_norm(name='g_bn0')
self.g_bn1 = batch_norm(name='g_bn1')
self.g_bn2 = batch_norm(name='g_bn2')
self.g_bn3 = batch_norm(name='g_bn3')
self.build_model()
def build_model(self):
self.images = tf.placeholder(tf.float32, [self.batch_size] + [self.output_size, self.output_size, self.c_dim], name='real_images')
self.sample_images= tf.placeholder(tf.float32, [self.sample_size] + [self.output_size, self.output_size, self.c_dim], name='sample_images')
self.z = tf.placeholder(tf.float32, [None, self.z_dim], name='z')
self.z_sum = tf.summary.histogram("z", self.z)
self.G = self.generator(self.z)
self.D = self.discriminator(self.images)
self.D_ = self.discriminator(self.G, reuse=True)
self.sampler = self.sampler(self.z)
self.d_sum = tf.summary.histogram("d", self.D)
self.d__sum = tf.summary.histogram("d_", self.D_)
self.G_sum = tf.summary.image("G", self.G)
self.d_loss_real = tf.reduce_mean(tf.scalar_mul(-1, self.D))
self.d_loss_fake = tf.reduce_mean(self.D_)
self.g_loss = tf.reduce_mean(tf.scalar_mul(-1, self.D_))
self.d_loss_real_sum = tf.summary.scalar("d_loss_real", self.d_loss_real)
self.d_loss_fake_sum = tf.summary.scalar("d_loss_fake", self.d_loss_fake)
self.d_loss = self.d_loss_real + self.d_loss_fake
self.g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
self.d_loss_sum = tf.summary.scalar("d_loss", self.d_loss)
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'd_' in var.name]
self.g_vars = [var for var in t_vars if 'g_' in var.name]
self.epoch = tf.Variable(-1, name='epoch', trainable=False)
self.increment_epoch = tf.assign(self.epoch, self.epoch+1)
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.saver = tf.train.Saver(max_to_keep=1000)
def discriminator(self, image, y=None, reuse=False):
with tf.variable_scope("discriminator") as scope:
if reuse:
scope.reuse_variables()
h0 = lrelu(conv2d(image, self.df_dim, name='d_h0_conv'))
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = linear(tf.reshape(h3, [self.batch_size, -1]), 1, 'd_h3_lin')
return h4
def generator(self, z, y=None):
with tf.variable_scope("generator") as scope:
s = self.output_size
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(z, self.gf_dim*8*s16*s16, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(self.z_, [-1, s16, s16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(h0,
[self.batch_size, s8, s8, self.gf_dim*4], name='g_h1', with_w=True)
h1 = tf.nn.relu(self.g_bn1(self.h1))
h2, self.h2_w, self.h2_b = deconv2d(h1,
[self.batch_size, s4, s4, self.gf_dim*2], name='g_h2', with_w=True)
h2 = tf.nn.relu(self.g_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(h2,
[self.batch_size, s2, s2, self.gf_dim*1], name='g_h3', with_w=True)
h3 = tf.nn.relu(self.g_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(h3,
[self.batch_size, s, s, self.c_dim], name='g_h4', with_w=True)
return tf.nn.tanh(h4)
def sampler(self, z, y=None):
with tf.variable_scope("generator") as scope:
scope.reuse_variables()
s = self.output_size
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
# project `z` and reshape
h0 = tf.reshape(linear(z, self.gf_dim*8*s16*s16, 'g_h0_lin'),
[-1, s16, s16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(h0, train=False))
h1 = deconv2d(h0, [self.batch_size, s8, s8, self.gf_dim*4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1, train=False))
h2 = deconv2d(h1, [self.batch_size, s4, s4, self.gf_dim*2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2, train=False))
h3 = deconv2d(h2, [self.batch_size, s2, s2, self.gf_dim*1], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3, train=False))
h4 = deconv2d(h3, [self.batch_size, s, s, self.c_dim], name='g_h4')
return tf.nn.tanh(h4)
def save(self, checkpoint_dir, train_tag=''):
model_name = self.model_name + train_tag + ".model-epoch"
model_dir = "%s_%s_%s" % (self.dataset, self.batch_size, self.output_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,
os.path.join(checkpoint_dir, model_name),
global_step=self.epoch)
def load(self, checkpoint_dir):
print(" [*] Reading checkpoints...")
model_dir = "%s_%s_%s" % (self.dataset, self.batch_size, self.output_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
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))
print(" [*] Success to read {}".format(ckpt_name))
return True
else:
print(" [*] Failed to find a checkpoint")
return False