| | |
| | | |
| | | |
| | | def main(**kwargs): |
| | | # preprocess_config(kwargs) |
| | | # import pdb; pdb.set_trace() |
| | | |
| | | # set random seed |
| | | tables.print() |
| | | set_all_random_seed(kwargs.get("seed", 0)) |
| | |
| | | logging.info(f"Loading pretrained params from {p}") |
| | | load_pretrained_model( |
| | | model=model, |
| | | init_param=p, |
| | | path=p, |
| | | ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True), |
| | | oss_bucket=kwargs.get("oss_bucket", None), |
| | | scope_map=kwargs.get("scope_map", None), |
| | | excludes=kwargs.get("excludes", None), |
| | | ) |
| | | else: |
| | | initialize(model, kwargs.get("init", "kaiming_normal")) |
| | |
| | | local_rank=local_rank, |
| | | use_ddp=use_ddp, |
| | | use_fsdp=use_fsdp, |
| | | output_dir=kwargs.get("output_dir", "./exp"), |
| | | resume=kwargs.get("resume", True), |
| | | **kwargs.get("train_conf"), |
| | | ) |
| | | trainer.run() |