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 = "ckt_logs/%s/%s_%s" % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
mkdir_p(ckt_logs_dir)
#ckt_logs_dir = s_tmp[:s_tmp.find('.ckpt')]
model = CondGAN(lr_imsize=int(dataset.image_shape[0] /
dataset.hr_lr_ratio),
hr_lr_ratio=dataset.hr_lr_ratio)
algo = CondGANTrainer(model=model,
dataset=dataset,
ckt_logs_dir=ckt_logs_dir)
if cfg.TRAIN.FLAG:
#algo.train()
algo.train_classifier()
algo.batch_size = 100
alog.zero_shot_eval()
elif cfg.ZEROSHOT.FLAG:
'''
For every input image in test dataset, calculate conditional probability given
every sentence of all classes.
'''
algo.zero_shot_eval()
else:
datadir = 'Data/%s' % cfg.DATASET_NAME
dataset = TextDataset(datadir, cfg.EMBEDDING_TYPE, 4)
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(lr_imsize=int(dataset.image_shape[0] /
dataset.hr_lr_ratio),
hr_lr_ratio=dataset.hr_lr_ratio)
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()
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(
lr_imsize=int(dataset.image_shape[0] / dataset.hr_lr_ratio),
hr_lr_ratio=dataset.hr_lr_ratio
)
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()