def main():
r"""Main function.
"""
# arguments
args = get_args()
print("Command line arguments:")
print(args)
# configurations
cfg = get_cfg_defaults()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
if args.inference:
update_inference_cfg(cfg)
cfg.freeze()
print("Configuration details:")
print(cfg)
if not os.path.exists(cfg.DATASET.OUTPUT_PATH):
print('Output directory: ', cfg.DATASET.OUTPUT_PATH)
os.makedirs(cfg.DATASET.OUTPUT_PATH)
save_all_cfg(cfg, cfg.DATASET.OUTPUT_PATH)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device: ", device)
mode = 'test' if args.inference else 'train'
trainer = Trainer(cfg, device, mode, args.checkpoint)
if args.inference:
trainer.test()
else:
trainer.train()
def main():
r"""Main function.
"""
# arguments
args = get_args()
print("Command line arguments:")
print(args)
# configurations
cfg = get_cfg_defaults()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
if args.inference:
update_inference_cfg(cfg)
cfg.freeze()
print("Configuration details:")
print(cfg)
if not os.path.exists(cfg.dataset.output_path):
print('Output directory: ', cfg.dataset.output_path)
os.makedirs(cfg.dataset.output_path)
save_all_cfg(cfg, cfg.dataset.output_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device: ", device)
cudnn.enabled = True
cudnn.benchmark = True
mode = 'test' if args.inference else 'train'
trainer = Trainer(cfg, device, mode, args.checkpoint)
if cfg.dataset.DO_CHUNK_TITLE == 0:
if args.inference:
trainer.test()
else:
trainer.train()
else:
trainer.run_chunk(mode)