pprint.pprint(cfg)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
datadir = 'Data/%s' % cfg.DATASET_NAME
dataset = TextDataset(datadir, cfg.EMBEDDING_TYPE, 1)
filename_test = '%s/test' % (datadir)
dataset.test = dataset.get_data(filename_test)
if cfg.TRAIN.FLAG:
filename_train = '%s/train' % (datadir)
dataset.train = dataset.get_data(filename_train)
ckt_logs_dir = "ckt_logs/%s/%s_%s" % (cfg.DATASET_NAME,
cfg.CONFIG_NAME, timestamp)
mkdir_p(ckt_logs_dir)
else:
s_tmp = cfg.TRAIN.PRETRAINED_MODEL
ckt_logs_dir = s_tmp[:s_tmp.find('.ckpt')]
model = CondGAN(image_shape=dataset.image_shape)
algo = CondGANTrainer(model=model,
dataset=dataset,
ckt_logs_dir=ckt_logs_dir)
if cfg.TRAIN.FLAG:
algo.train()
else:
''' For every input text embedding/sentence in the
training and test datasets, generate cfg.TRAIN.NUM_COPY
images with randomness from noise z and conditioning augmentation.'''
algo.evaluate()
dataset = TextDataset(datadir, cfg.EMBEDDING_TYPE, 1)
filename_test = '%s/test' % (datadir)
dataset.test = dataset.get_data(filename_test)
if cfg.TRAIN.FLAG:
filename_train = '%s/train' % (datadir)
dataset.train = dataset.get_data(filename_train)
ckt_logs_dir = "ckt_logs/%s/%s_%s" % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
mkdir_p(ckt_logs_dir)
else:
s_tmp = cfg.TRAIN.PRETRAINED_MODEL
ckt_logs_dir = s_tmp[:s_tmp.find('.ckpt')]
model = CondGAN(
image_shape=dataset.image_shape
)
algo = CondGANTrainer(
model=model,
dataset=dataset,
ckt_logs_dir=ckt_logs_dir
)
if cfg.TRAIN.FLAG:
algo.train()
else:
''' For every input text embedding/sentence in the
training and test datasets, generate cfg.TRAIN.NUM_COPY
images with randomness from noise z and conditioning augmentation.'''
algo.evaluate()