def main():
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
evaluate(cfg, **args.__dict__)
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.TRAINER_ID = int(os.getenv("PADDLE_TRAINER_ID", 0))
cfg.NUM_TRAINERS = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
cfg.check_and_infer()
print_info(pprint.pformat(cfg))
train(cfg)
dataformats='HWC')
# add ground truth (label) images
grt = grts[i]
if grt is not None:
grt = grt[0:valid_shape[0], 0:valid_shape[1]]
grt_pil = PILImage.fromarray(grt.astype(np.uint8), mode='P')
grt_pil.putpalette(color_map)
grt_pil = grt_pil.resize((org_shape[1], org_shape[0]))
grt = np.array(grt_pil.convert("RGB"))
log_writer.add_image(
"Label/{}".format(img_name),
grt,
epoch,
dataformats='HWC')
# If in local_test mode, only visualize 5 images just for testing
# procedure
if local_test and img_cnt >= 5:
break
if __name__ == '__main__':
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
visualize(cfg, **args.__dict__)