def gan_train(max_it, it_offset):
print("GAN iteration: " + str(max_it))
# total_it = it_offset + max_it
for it in range(it_offset, it_offset + max_it):
real_ipt, y = data_pool.batch(['img', 'label'])
if it > 30000:
_, _ = sess.run([d_step, g_step], feed_dict={real: real_ipt})
else:
_, _ = sess.run([d_step, g_step2],
feed_dict={real: real_ipt}) #ori g loss
if it % 10 == 0:
summary = sess.run(merged, feed_dict={real: real_ipt})
writer.add_summary(summary, it)
if it % 1000 == 0:
i = 0
for f in fake_set:
sample_imgs = sess.run(f)
# if normalize:
# for i in range(len(sample_imgs)):
sample_imgs = sample_imgs * 2. - 1.
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
# for imgs, name in zip(sample_imgs, list_of_names):
my_utils.saveSampleImgs(imgs=sample_imgs,
full_path=save_dir + "/" +
'sample-%d-%d.jpg' % (i, it),
row=8,
column=8)
i += 1
def gan_train(max_it, it_offset):
print("GAN iteration: " + str(max_it))
# total_it = it_offset + max_it
for it in range(it_offset, it_offset + max_it):
real_ipt, y = data_pool.batch(['img', 'label'])
# z_ipt = np.random.normal(size=[batch_size, z_dim])
# z_ipt = np.random.normal(size=[batch_size, z_dim])
# if it%700 ==0 and it >0:
# global cat_weight_init
# cat_weight_init = min(0.7, 1.3*cat_weight_init)
# print('cat weight', cat_weight_init)
_, _ = sess.run([d_step, g_step], feed_dict={real: real_ipt})
if it % 10 == 0:
summary = sess.run(merged, feed_dict={real: real_ipt})
writer.add_summary(summary, it)
if it % 1000 == 0:
import utils
i = 0
for f in fake_set:
sample_imgs = sess.run(f)
# if normalize:
# for i in range(len(sample_imgs)):
sample_imgs = sample_imgs * 2. - 1.
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
# for imgs, name in zip(sample_imgs, list_of_names):
my_utils.saveSampleImgs(imgs=sample_imgs,
full_path=save_dir + "/" +
'sample-%d-%d.jpg' % (i, it),
row=8,
column=8)
i += 1
def sample_once(it):
list_of_generators = [images_form_g1, images_form_g2, images_form_g3] # used for sampling images
list_of_names = ['g1-it%d.jpg' % it, 'g2-it%d.jpg' % it, 'g3-it%d.jpg' % it]
rows = 10
columns = 10
sample_imgs = sess.run(list_of_generators, feed_dict={z: np.random.normal(size=[rows * columns, z_dim]),
cat_1: [one_hot_labels[0] for _ in range(rows * columns)],
cat_2: [one_hot_labels[1] for _ in range(rows * columns)],
cat_3: [one_hot_labels[2] for _ in range(rows * columns)]})
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
for imgs, name in zip(sample_imgs, list_of_names):
my_utils.saveSampleImgs(imgs=imgs, full_path=save_dir + "/" + name, row=rows, column=columns)
def save_img(list_of_generators, list_of_names):
# list_of_generators = [images_for_1, images_for_2, images_for_tensorboard] # used for sampling images
# list_of_names = ['g1-it%d.jpg' % 1, 'g2-it%d.jpg' % 1, 'wgan-jpg']
rows = 10
columns = 10
sample_imgs = sess.run(
list_of_generators,
feed_dict={z: np.random.normal(size=[rows * columns, z_dim])})
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
for imgs, name in zip(sample_imgs, list_of_names):
my_utils.saveSampleImgs(imgs=imgs,
full_path=save_dir + "/" + name,
row=rows,
column=columns)
def sample_once():
import utils
# rows = 10
# columns = 10
# feed = {random_z: np.random.normal(size=[rows*columns, z_dim])}
sample_imgs = sess.run(fake)
# if normalize:
# for i in range(len(sample_imgs)):
sample_imgs = sample_imgs * 2. - 1.
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
# for imgs, name in zip(sample_imgs, list_of_names):
my_utils.saveSampleImgs(imgs=sample_imgs,
full_path=save_dir + "/" + 'sample.jpg',
row=50,
column=40)
def gan_train(max_it, it_offset):
print("GAN iteration: " + str(max_it))
for it in range(it_offset, it_offset + max_it):
real_ipt, y = data_pool.batch(['img', 'label'])
_, _ = sess.run([d_step,g_step], feed_dict={real: real_ipt, real_weight: real_weight_init})
if it % 10 == 0:
summary = sess.run(merged, feed_dict={real: real_ipt, real_weight: real_weight_init})
writer.add_summary(summary, it)
if it%1000 == 0:
i = 0
for f in fake_set:
sample_imgs = sess.run(f)
sample_imgs = sample_imgs * 2. - 1.
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
my_utils.saveSampleImgs(imgs=sample_imgs, full_path=save_dir + "/" + 'sample-%d-%d.jpg' % (i,it), row=8,
column=8)
i += 1
writer.add_summary(summary, it)
var = raw_input("Continue training for %d iterations?" % max_it)
if var.lower() == 'y':
training(max_it, it_offset + max_it)
total_it = 0
try:
training(max_it,0)
total_it = sess.run(global_step)
print("Total iterations: "+str(total_it))
except Exception, e:
traceback.print_exc()
finally:
var = raw_input("Save sample images?")
if var.lower() == 'y':
list_of_generators = [images_form_g1, images_form_g2] # used for sampling images
list_of_names = ['g1-it%d.jpg'%total_it,'g2-it%d.jpg'%total_it]
rows = 10
columns = 10
sample_imgs = sess.run(list_of_generators, feed_dict={z: np.random.normal(size=[rows*columns, z_dim])})
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
for imgs,name in zip(sample_imgs,list_of_names):
my_utils.saveSampleImgs(imgs=imgs, full_path=save_dir+"/"+name, row=rows,column=columns)
# save checkpoint
save_path = saver.save(sess, dir+"/checkpoint/model.ckpt")
print("Model saved in path: %s" % save_path)
print(" [*] Close main session!")
sess.close()
except Exception, e:
traceback.print_exc()
finally:
import utils
i = 0
for f in fake_set:
sample_imgs = sess.run(f)
# if normalize:
# for i in range(len(sample_imgs)):
sample_imgs = sample_imgs * 2. - 1.
save_dir = dir + "/sample_imgs"
utils.mkdir(save_dir + '/')
# for imgs, name in zip(sample_imgs, list_of_names):
my_utils.saveSampleImgs(imgs=sample_imgs,
full_path=save_dir + "/" +
'sample-%d.jpg' % i,
row=8,
column=8)
i += 1
# save checkpoint
save_path = saver.save(sess, dir + "/checkpoint/model.ckpt")
print("Model saved in path: %s" % save_path)
print(" [*] Close main session!")
sess.close()
def run(exp):
if exp == 'imsat-svhn':
print('Expriment IMSAT + GAN on SVHN, one experiments')
print('=====Exp 1=====')
