def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'birds':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=None,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.35)
# with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
y_dim=10,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
img_name = FLAGS.test_image
# Below function is only if input is 128x128
# dcgan.test(z=img_name, config=FLAGS)
dcgan.variable_size_test(z=img_name, config=FLAGS)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,y_dim=10,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir = FLAGS.sample_dir, mid_epoch = FLAGS.mid_epoch)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, input_size=FLAGS.input_size,
batch_size=FLAGS.batch_size, report_freq=FLAGS.report_freq,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir = FLAGS.sample_dir, mid_epoch = FLAGS.mid_epoch,
large_data_path = FLAGS.large_data_path, small_data_path = FLAGS.small_data_path)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
if FLAGS.visualize:
to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
[dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
[dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
[dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
[dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, y_dim=10, output_size=28, c_dim=1,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
print(dcgan.evaluate(6400))
if FLAGS.visualize:
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
y_dim=10)
else:
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
z_sample = np.random.uniform(-1,
1,
size=(FLAGS.batch_size, dcgan.z_dim))
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(
samples, [8, 8],
'./samples/test_%s.png' % strftime("%Y-%m-%d %H:%M:%S", gmtime()))
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
y_dim=10,
c_dim=1,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# set up GPU properties:
if FLAGS.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.gpu_id)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.4
config.allow_soft_placement = True
else:
config = tf.ConfigProto()
with tf.Session(config=config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
y_dim=10,
output_size=28,
c_dim=1,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
output_size=FLAGS.output_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
if FLAGS.visualize:
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.print(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir
)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception('[!] Train a model first, then run test mode')
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
# 首先是打印参数数据，然后判断输入图像的输出图像的宽是否指定，如果没有指定，则等于其图像的高。
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
#然后判断checkpoint和sample的文件是否存在，不存在则创建。
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#然后是设置session参数。tf.ConfigProto一般用在创建session的时候，用来对session进行参数配置
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
#运行session，首先判断处理的是哪个数据集，然后对应使用不同参数的DCGAN类，这个类会在model.py文件中定义。
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir)
#show所有与训练相关的变量。
show_all_variables()
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
train=FLAGS.train)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
df_dim=FLAGS.df_dim,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
train=FLAGS.train)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
dcgan.get_collisions(FLAGS,
nbatch=FLAGS.num_batches,
topK=FLAGS.topK)
def main(_):
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[No training data found to test on] ")
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
y_dim=10,
c_dim=1,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
assert (os.path.exists(FLAGS.checkpoint_dir))
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(sess,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
checkpoint_dir=FLAGS.checkpoint_dir,
lam=FLAGS.lam)
#dcgan.load(FLAGS.checkpoint_dir):
dcgan.complete(FLAGS)
show_all_variables()
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
with tf.device('/cpu:0'), tf.Session() as sess:
FLAGS.sess = sess
dcgan = DCGAN(y_dim=10)
def main():
# Define Data for training
dataX = glob(
os.path.join("./data", FLAGS.datasetX, FLAGS.input_fname_pattern))
dataY = glob(
os.path.join("./data", FLAGS.datasetY, FLAGS.input_fname_pattern))
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
dcgan = DCGAN(sess,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
c_dim=FLAGS.c_dim,
checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.train:
dcgan.train(FLAGS, dataX, dataY)
else: # INFERENCE
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# Render samples to "samples" folder
# Option 1 render manifold of samples with dim = n*n = number_of_samples
# Option 2 render imagens one by one
dcgan.get_samples(sample_dir=FLAGS.sample_dir, option=1)
print("====DONE=====")
def main(_):
if not os.path.exists(FLAGS.summary_dir):
os.makedirs(FLAGS.summary_dir)
runs = sorted(map(int, next(os.walk(FLAGS.summary_dir))[1]))
if len(runs) == 0:
run_nr = 0
else:
run_nr = runs[-1] + 1
run_folder = str(run_nr).zfill(3)
FLAGS.summary_dir = os.path.join(FLAGS.summary_dir, run_folder)
FLAGS.checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, run_folder)
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
dcgan = DCGAN(sess) # call a class
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.summary_dir):
os.makedirs(FLAGS.summary_dir)
dcgan.train(FLAGS, run_folder)
def main(_):
dcgan = DCGAN(height=FLAGS.height,
width=FLAGS.width,
batch_size=FLAGS.batch_size,
epochs=FLAGS.epochs,
learning_rate=FLAGS.learning_rate)
dcgan.train()
def main(_):
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
dcgan = DCGAN(sess,
input_height=FLAGS.input_height,
input_width=FLAGS.input_width,
output_height=FLAGS.output_height,
output_width=FLAGS.output_width,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
c_dim=1,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
print('Loading checkpoint hasn\'t been implemented yet.')
def main(_):
# パラメータ表示
pp.pprint(flags.FLAGS.__flags)
# ディレクトリがなければ作成
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# メモリ節約のため、最初に必要なGPUメモリをすべて確保するのではなく、
# 必要になった時点で確保するようにallow_growthをTrueに設定する。
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
z_dim=FLAGS.z_dim,
learning_rate=FLAGS.learning_rate,
beta1=FLAGS.beta1,
input_dir=FLAGS.input_dir,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
# 学習開始
dcgan.train(FLAGS)
def full_model(sess, xs, n="A"):
print("LOAD WITH", n)
with tf.variable_scope(n + "QQ"):
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
adversarial_path=FLAGS.adversarial_path,
ground_truth_path=FLAGS.ground_truth_path,
test_path=FLAGS.test_path,
save_path=FLAGS.save_path,
advin=xs,
c_dim=FLAGS.c_dim,
)
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
classified = classifier(dcgan.sampler)
#print('print xtest')
#print(sess.run(dcgan.sampler, {xs: x_test[:64]}))
#print('done')
return dcgan.sampler, classified
def main(_):
assert(os.path.exists(FLAGS.checkpointDir))
# Deal with input size default values
if not FLAGS.input_width:
FLAGS.input_width = FLAGS.input_height
def get_default_gpu_session(fraction=0.8):
if fraction > 1:
fraction = 0.8
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = fraction
return tf.Session(config=config)
with get_default_gpu_session(FLAGS.gpu_utilization) as sess:
dcgan_instance = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_file_extension,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpointDir,
lambda_val=FLAGS.lambda_val,
# Xx-GAN
dataset_name2=FLAGS.dataset2
)
dcgan_instance.inpaint(FLAGS)
def run_gan():
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
print(train_images.shape)
train_images = train_images.reshape(train_images.shape[0], 28, 28,
1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # Normalize images to [-1,1]
print(train_images.shape)
# Batch and shuffle the data
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(
BUFFER_SIZE).batch(BATCH_SIZE)
gan = DCGAN(gen_lr, disc_lr, batch_size=BATCH_SIZE, noise_dim=NOISE_DIM)
gan.create_generator()
gan.create_discriminator()
# Test generator
random_noise = tf.random.normal([1, NOISE_DIM])
generated_image = gan.generator(random_noise)
#plt.imshow(generated_image[0,:,:,0],cmap='gray')
#plt.show()
# Test Discriminator
prob = gan.discriminator(generated_image)
print("Probability of image being real: {}".format(sigmoid(prob)))
gan.set_noise_seed(num_examples_to_generate)
gan.set_checkpoint(path=save_ckpt_path)
gen_loss_array, disc_loss_array = gan.train(train_dataset, epochs=EPOCHS)
# Plot Discriminator Loss
plt.plot(range(EPOCHS), gen_loss_array)
plt.plot(range(EPOCHS), disc_loss_array)
plt.show()
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
output_size=FLAGS.output_size,
batch_size=FLAGS.batch_size,
sample_size=FLAGS.sample_size)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
if FLAGS.visualize:
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=7,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS)
def main(_):
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# Prevent tensorflow from allocating the totality of a GPU memory
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(sess, flags=FLAGS)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
visualize(sess, dcgan, FLAGS)
def generate_latent_walk(checkpoint_dir, dataset_name, output_size, n_epochs,
jump_length, output_file_name):
n_jumps = n_epochs * 64
boundary = 1.3
z = np.zeros((n_jumps, 100))
walk = np.zeros((n_jumps, output_size, output_size, 3))
for t in range(1, n_jumps):
jumps = mixture_pareto(mu=0.04, alpha=1.6, size=[1, 100])
# keep walk confined within hypercube with reflective boundaries
new_z = z[t - 1, :] + jumps
outside_domain_left = new_z < -boundary
outside_domain_right = new_z > boundary
new_z_2 = new_z[:]
new_z_2[outside_domain_left] = -boundary - (
new_z[outside_domain_left] + boundary)
new_z_2[outside_domain_right] = boundary - (
new_z[outside_domain_right] - boundary)
new_z[outside_domain_left] = new_z_2[outside_domain_left]
new_z[outside_domain_right] = new_z_2[outside_domain_right]
z[t, :] = new_z
with tf.Session() as sess:
dcgan = DCGAN(sess,
checkpoint_dir=checkpoint_dir,
dataset_name=dataset_name,
output_size=output_size,
is_train=False)
for e in range(n_epochs):
this_start = e * 64
this_end = (e + 1) * 64
walk[this_start:this_end, :, :, :] = dcgan.sample(
z[this_start:this_end, :])
save_latent_walk(walk, output_file_name)
return walk
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
image_size=108,
batch_size=64,
y_dim=None,
output_size=28,
c_dim=1,
dataset_name='mnist',
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
pass
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
pass
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
print('Will try to complete images...')
dcgan.complete()
print('Done!')
def main():
train_images, test_images, train_labels, test_labels = dataset.train_test_data(
'./PetImages_resize', batch_size)
train_size = len(train_labels)
test_size = len(test_labels)
train_dataset = dataset.create_dataset(train_images, train_labels)
train_dataset = train_dataset.cache().shuffle(
buffer_size=train_size).batch(batch_size).repeat(
num_epoch).make_one_shot_iterator().get_next()
test_dataset = dataset.create_dataset(test_images, test_labels)
test_dataset = test_dataset.cache().shuffle(
buffer_size=10).batch(test_size).make_one_shot_iterator().get_next()
with tf.Session() as sess:
model = DCGAN(sess,
train_dataset=train_dataset,
test_dataset=test_dataset,
train_size=train_size,
test_size=test_size,
batch_size=batch_size,
num_epoch=num_epoch)
model.build_model()
model.intialize_variables()
#model.create_image_from_generator()
model.train()
def main():
model = DCGAN()
solver = Solver(model,
num_epoch=10,
image_path='data/celeb_resized',
model_save_path='model/',
log_path='log/')
solver.train()
