superimage =\
np.concatenate([superimage, mid_padding, superimage2], axis=0)
top_padding = np.zeros((128, superimage.shape[1], 3))
superimage =\
np.concatenate([top_padding, superimage], axis=0)
fullpath = '%s/sentence%d.jpg' % (save_dir, startID + j)
superimage = drawCaption(np.uint8(superimage), captions_batch[j])
scipy.misc.imsave(fullpath, superimage)
if __name__ == "__main__":
args = parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.gpu_id != -1:
cfg.GPU_ID = args.gpu_id
if args.caption_path is not None:
cfg.TEST.CAPTION_PATH = args.caption_path
# Load text embeddings generated from the encoder
cap_path = cfg.TEST.CAPTION_PATH
t_file = torchfile.load(cap_path)
captions_list = t_file.raw_txt
embeddings = np.concatenate(t_file.fea_txt, axis=0)
num_embeddings = len(captions_list)
print('Successfully load sentences from: ', cap_path)
print('Total number of sentences:', num_embeddings)
print('num_embeddings:', num_embeddings, embeddings.shape)
# path to save generated samples
superimage =\
np.concatenate([superimage, mid_padding, superimage2], axis=0)
top_padding = np.zeros((128, superimage.shape[1], 3))
superimage =\
np.concatenate([top_padding, superimage], axis=0)
fullpath = '%s/sentence%d.jpg' % (save_dir, startID + j)
superimage = drawCaption(np.uint8(superimage), captions_batch[j])
scipy.misc.imsave(fullpath, superimage)
if __name__ == "__main__":
args = parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.gpu_id != -1:
cfg.GPU_ID = args.gpu_id
if args.caption_path is not None:
cfg.TEST.CAPTION_PATH = args.caption_path
# Load text embeddings generated from the encoder
cap_path = cfg.TEST.CAPTION_PATH
t_file = torchfile.load(cap_path)
captions_list = t_file.raw_txt
embeddings = np.concatenate(t_file.fea_txt, axis=0)
num_embeddings = len(captions_list)
print('Successfully load sentences from: ', cap_path)
print('Total number of sentences:', num_embeddings)
print('num_embeddings:', num_embeddings, embeddings.shape)
# path to save generated samples
parser.add_argument('--epoch', dest='epoch', default=600, type=int)
parser.add_argument('--batch_size',
dest='batch_size',
default=64,
type=int)
parser.add_argument('--dataset_dir', dest='dataset_dir', type=str)
# if len(sys.argv) == 1:
# parser.print_help()
# sys.exit(1)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
cfg_from_file("stageI/cfg/flowers.yml")
cfg.TRAIN.MAX_EPOCH = args.epoch
cfg.TRAIN.BATCH_SIZE = args.batch_size
print('Using config:')
pprint.pprint(cfg)
## now = datetime.datetime.now(dateutil.tz.tzlocal())
## timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
datadir = args.dataset_dir
dataset = TextDataset(datadir, cfg.EMBEDDING_TYPE, 1)
filename_test = '%s/test' % (datadir)
dataset.test = dataset.get_data(filename_test)
if cfg.TRAIN.FLAG:
mid_padding = np.zeros((64, superimage.shape[1], 3))
superimage =\
np.concatenate([superimage, mid_padding, superimage2], axis=0)
top_padding = np.zeros((128, superimage.shape[1], 3))
superimage =\
np.concatenate([top_padding, superimage], axis=0)
fullpath = '%s/sentence%d.jpg' % (save_dir, startID + j)
superimage = drawCaption(np.uint8(superimage), captions_batch[j])
scipy.misc.imsave(fullpath, superimage)
if __name__ == "__main__":
args = parse_args()
cfg_from_file("demo/cfg/flowers-demo.yml")
cfg.GPU_ID = 0
uid = args.uid
cfg.TEST.CAPTION_PATH = "Data/flowers/example_captions_%s.t7" % uid
cfg.TEST.PRETRAINED_MODEL = args.model_path
# Load text embeddings generated from the encoder
cap_path = cfg.TEST.CAPTION_PATH
t_file = torchfile.load(cap_path)
captions_list = t_file.raw_txt
embeddings = np.concatenate(t_file.fea_txt, axis=0)
num_embeddings = len(captions_list)
print('Successfully load sentences from: ', cap_path)
print('Total number of sentences:', num_embeddings)
print('num_embeddings:', num_embeddings, embeddings.shape)
# path to save generated samples
