def main(_):
pp.pprint(flags.FLAGS.__flags)
cfg_stage_i = config_from_yaml(FLAGS.cfg_stage_I)
cfg = config_from_yaml(FLAGS.cfg_stage_II)
if not os.path.exists(cfg.CHECKPOINT_DIR):
os.makedirs(cfg.CHECKPOINT_DIR)
if not os.path.exists(cfg.SAMPLE_DIR):
os.makedirs(cfg.SAMPLE_DIR)
if not os.path.exists(cfg.LOGS_DIR):
os.makedirs(cfg.LOGS_DIR)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
datadir = cfg.DATASET_DIR
dataset = TextDataset(datadir, 256)
filename_test = '%s/test' % datadir
dataset.test = dataset.get_data(filename_test)
filename_train = '%s/train' % datadir
dataset.train = dataset.get_data(filename_train)
with tf.Session(config=run_config) as sess:
if cfg.EVAL.FLAG:
stage_i = ConditionalGanStageI(cfg_stage_i, build_model=False)
stage_ii = ConditionalGan(stage_i, cfg, build_model=False)
stage_ii_eval = StageIIEval(
sess=sess,
model=stage_ii,
dataset=dataset,
cfg=cfg,
)
stage_ii_eval.evaluate_inception()
elif cfg.TRAIN.FLAG:
stage_i = ConditionalGanStageI(cfg_stage_i, build_model=False)
stage_ii = ConditionalGan(stage_i, cfg)
show_all_variables()
stage_ii_trainer = ConditionalGanTrainer(
sess=sess,
model=stage_ii,
dataset=dataset,
cfg=cfg,
cfg_stage_i=cfg_stage_i,
)
stage_ii_trainer.train()
else:
stage_i = ConditionalGanStageI(cfg_stage_i, build_model=False)
stage_ii = ConditionalGan(stage_i, cfg, build_model=False)
stage_ii_eval = StageIIVisualizer(
sess=sess,
model=stage_ii,
dataset=dataset,
cfg=cfg,
)
stage_ii_eval.visualize()
def main(_):
pp.pprint(flags.FLAGS.__flags)
cfg = config_from_yaml(FLAGS.cfg)
if not os.path.exists(cfg.CHECKPOINT_DIR):
os.makedirs(cfg.CHECKPOINT_DIR)
if not os.path.exists(cfg.LOGS_DIR):
os.makedirs(cfg.LOGS_DIR)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
datadir = cfg.DATASET_DIR
dataset = TextDataset(datadir, 299)
# We train inception on the test dataset which contains completely other classes from the train dataset
# (used in GAN training). This is needed for a correct evaluation of the Inception/FID score.
filename_test = '%s/test' % datadir
dataset.test = dataset.get_data(filename_test)
with tf.Session(config=run_config) as sess:
if cfg.TRAIN.FLAG:
stage_i_trainer = InceptionTrainer(
sess=sess,
dataset=dataset,
cfg=cfg,
)
stage_i_trainer.train()
else:
pass
def main(_):
pp.pprint(flags.FLAGS.__flags)
cfg = config_from_yaml(FLAGS.cfg)
if not os.path.exists(cfg.CHECKPOINT_DIR):
os.makedirs(cfg.CHECKPOINT_DIR)
if not os.path.exists(cfg.SAMPLE_DIR):
os.makedirs(cfg.SAMPLE_DIR)
if not os.path.exists(cfg.LOGS_DIR):
os.makedirs(cfg.LOGS_DIR)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
datadir = cfg.DATASET_DIR
dataset = TextDataset(datadir, 64)
# dataset = TextDataset(datadir, 512)
filename_test = '%s/test' % datadir
dataset._test = dataset.get_data(filename_test)
filename_train = '%s/train' % datadir
dataset.train = dataset.get_data(filename_train)
with tf.Session(config=run_config) as sess:
if cfg.EVAL.FLAG:
gancls = GanCls(cfg, build_model=False)
eval = GanClsEval(
sess=sess,
model=gancls,
dataset=dataset,
cfg=cfg)
eval.evaluate_inception()
elif cfg.TRAIN.FLAG:
gancls = GanCls(cfg)
show_all_variables()
gancls_trainer = GanClsTrainer(
sess=sess,
model=gancls,
dataset=dataset,
cfg=cfg,
)
gancls_trainer.train()
else:
gancls = GanCls(cfg, build_model=False)
gancls_visualiser = GanClsVisualizer(
sess=sess,
model=gancls,
dataset=dataset,
config=cfg,
)
gancls_visualiser.visualize()
def main(_):
pp.pprint(flags.FLAGS.__flags)
cfg = config_from_yaml(FLAGS.cfg)
if not os.path.exists(cfg.CHECKPOINT_DIR):
os.makedirs(cfg.CHECKPOINT_DIR)
if not os.path.exists(cfg.SAMPLE_DIR):
os.makedirs(cfg.SAMPLE_DIR)
if not os.path.exists(cfg.LOGS_DIR):
os.makedirs(cfg.LOGS_DIR)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
datadir = cfg.DATASET_DIR
dataset = TextDataset(datadir, 1)
filename_test = '%s/test' % datadir
dataset._test = dataset.get_data(filename_test)
filename_train = '%s/train' % datadir
dataset.train = dataset.get_data(filename_train)
with tf.Session(config=run_config) as sess:
stage_i = ConditionalGan(cfg)
show_all_variables()
if cfg.TRAIN.FLAG:
stage_i_trainer = ConditionalGanTrainer(
sess=sess,
model=stage_i,
dataset=dataset,
cfg=cfg,
)
stage_i_trainer.train()
else:
pass
flags = tf.app.flags
flags.DEFINE_string(
'cfg', './models/pggan/cfg/flowers.yml',
'Relative path to the config of the model [./models/pggan/cfg/flowers.yml]'
)
# flags.DEFINE_string('cfg', './models/pggan/cfg/birds.yml',
# 'Relative path to the config of the model [./models/pggan/cfg/birds.yml]')
FLAGS = flags.FLAGS
if __name__ == "__main__":
stage = [1, 2, 3, 4, 5, 6, 7]
all_samples = []
cfg = config_from_yaml(FLAGS.cfg)
batch_size = 64
z_dim = 128
datadir = cfg.DATASET_DIR
dataset = TextDataset(datadir, 64)
filename_test = '%s/test' % datadir
dataset.test = dataset.get_data(filename_test)
filename_train = '%s/train' % datadir
dataset.train = dataset.get_data(filename_train)
z_sample = np.random.standard_normal((batch_size, z_dim))
