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)
if args.enable_ce:
random.seed(0)
np.random.seed(0)
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))
name = cfg.TRAIN.MODEL_SAVE_DIR.replace("output/", "")
store_config = {
"model_name": cfg.MODEL.MODEL_NAME,
"solver": cfg.SOLVER.OPTIMIZER,
"lr": cfg.SOLVER.LR,
"lr_policy": cfg.SOLVER.LR_POLICY,
}
wandb.init(project="rs_segmentation",
name=name,
config=store_config,
dir=cfg.LOG_DIR,
notes="")
train(cfg)
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts is not None:
cfg.update_from_list(args.opts)
cfg.check_and_infer(reset_dataset=True)
print(pprint.pformat(cfg))
train(cfg)
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))
export_inference_model(args)
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)
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
cfg.check_and_infer(reset_dataset=True)
logger.info(pprint.pformat(cfg))
init_global_variable()
check_train_dataset()
init_global_variable()
check_val_dataset()
init_global_variable()
check_test_dataset()
inf_resize_value_check()
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
cfg.check_and_infer()
logger.info(pprint.pformat(cfg))
init_global_variable()
check_train_dataset()
init_global_variable()
check_val_dataset()
init_global_variable()
check_test_dataset()
inf_resize_value_check()
print("\nDetailed error information can be viewed in detail.log file.")
# BGR->RGB
img = cv2.imread(
os.path.join(cfg.DATASET.DATA_DIR,
img_names[i]))[..., ::-1]
log_writer.add_image("Images/{}".format(img_names[i]),
img,
epoch,
dataformats='HWC')
#add ground truth (label) images
if grt is not None:
log_writer.add_image("Label/{}".format(img_names[i]),
grt[..., ::-1],
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__)
