ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
d_loss = 0.
d_overpowered = False
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size) # recover n_epoch
ds_iter.pointer = saved_global_step % (len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x in range(ds_iter.iterate()):
#batch_x, _ = mnist.train.next_batch(model.batch_size)
batch_x, _ = ds_iter.next_batch()
#batch_x = batch_x.reshape(-1, model.n_input)
batch_x = np.reshape(iu.transform(batch_x, inv_type='127'),
(model.batch_size, model.height, model.width, model.channel))
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
if not d_overpowered:
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
def main():
start_time = time.time() # Clocking start
# MNIST Dataset load
#mnist = DataSet(ds_path="D:/DataSet/mnist/").data
# Loading Cifar-10 DataSet
ds = DataSet2(
height=32,
width=32,
channel=3,
ds_path=
"/media/shar/240A27640A2731EA/shared2/Awesome-GANs-master/BGAN/cifar/",
ds_name='cifar-10')
ds_iter = DataIterator(
x=iu.transform(ds.train_images, '127'),
y=ds.train_labels,
batch_size=train_step['batch_size'],
label_off=True
) # using label # maybe someday, i'll change this param's name
# Generated image save
test_images = iu.transform(ds.test_images[:100], inv_type='127')
iu.save_images(test_images,
size=[10, 10],
image_path=results['output'] + 'sample.png',
inv_type='127')
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# BGAN Model
model = bgan.BGAN(s)
# Initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
d_loss = 0.
d_overpowered = False
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
#batch_x, _ = mnist.train.next_batch(model.batch_size)
batch_x, _ = ds_iter.next_batch()
batch_x = batch_x.reshape(-1, model.n_input)
batch_z = np.random.uniform(
-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
if not d_overpowered:
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
d_overpowered = d_loss < g_loss / 2.
# Logging
if global_step % train_step['logging_interval'] == 0:
batch_x, _ = ds_iter.next_batch()
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, summary = s.run(
[model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Step %08d => " % global_step,
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g, feed_dict={
model.z: sample_z,
})
samples = np.reshape(samples, [-1] + model.image_shape[1:])
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_1{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step=global_step)
print(sample_dir)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# Loading Cifar-10 DataSet
ds = DataSet(height=32,
width=32,
channel=3,
ds_path="D:/DataSet/cifar/cifar-10-batches-py/",
ds_name='cifar-10')
ds_iter = DataIterator(
x=iu.transform(ds.train_images, '127'),
y=ds.train_labels,
batch_size=train_step['batch_size'],
label_off=True
) # using label # maybe someday, i'll change this param's name
# Generated image save
test_images = iu.transform(ds.test_images[:100], inv_type='127')
iu.save_images(test_images,
size=[10, 10],
image_path=results['output'] + 'sample.png',
inv_type='127')
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# WGAN Model
model = wgan.WGAN(s,
batch_size=train_step['batch_size'],
height=32,
width=32,
channel=3,
enable_gp=True) # WGAN-GP
# Initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %s" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for _ in range(ds_iter.num_batches):
# Update critic
model.critic = 5
if global_step % 500 == 0 or global_step < 25:
model.critic = 100
if model.EnableGP:
model.critic = 1
for _ in range(model.critic):
batch_x = ds_iter.next_batch()
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update d_clip
if not model.EnableGP:
s.run(model.d_clip)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
batch_x = ds_iter.next_batch()
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Logging
if global_step % train_step['logging_interval'] == 0:
summary = s.run(model.merged,
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print(
"[+] Epoch %04d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g, feed_dict={
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()