| | |
| | | init_param = (init_param,) |
| | | logging.info("init_param is not None: %s", init_param) |
| | | for p in init_param: |
| | | logging.info(f"Loading pretrained params from {p}") |
| | | load_pretrained_model( |
| | | model=model, |
| | | 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), |
| | | ) |
| | | if os.path.exists(p): |
| | | logging.info(f"Loading pretrained params from {p}") |
| | | load_pretrained_model( |
| | | model=model, |
| | | 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: |
| | | logging.info(f"Checkpoint does not exist, init randomly: {p}") |
| | | else: |
| | | initialize(model, kwargs.get("init", "kaiming_normal")) |
| | | |