| | |
| | | def download_model(**kwargs): |
| | | model_hub = kwargs.get("model_hub", "ms") |
| | | if model_hub == "ms": |
| | | kwargs = download_fr_ms(**kwargs) |
| | | kwargs = download_from_ms(**kwargs) |
| | | |
| | | return kwargs |
| | | |
| | | def download_fr_ms(**kwargs): |
| | | def download_from_ms(**kwargs): |
| | | model_or_path = kwargs.get("model") |
| | | if model_or_path in name_maps_ms: |
| | | model_or_path = name_maps_ms[model_or_path] |
| | | model_revision = kwargs.get("model_revision") |
| | | if not os.path.exists(model_or_path): |
| | | model_or_path = get_or_download_model_dir(model_or_path, model_revision, is_training=kwargs.get("is_training")) |
| | | model_or_path = get_or_download_model_dir(model_or_path, model_revision, is_training=kwargs.get("is_training"), check_latest=kwargs.get("kwargs", True)) |
| | | |
| | | config = os.path.join(model_or_path, "config.yaml") |
| | | if os.path.exists(config) and os.path.exists(os.path.join(model_or_path, "model.pb")): |
| | |
| | | return OmegaConf.to_container(kwargs, resolve=True) |
| | | |
| | | def get_or_download_model_dir( |
| | | model, |
| | | model_revision=None, |
| | | is_training=False, |
| | | model, |
| | | model_revision=None, |
| | | is_training=False, |
| | | check_latest=True, |
| | | ): |
| | | """ Get local model directory or download model if necessary. |
| | | |
| | |
| | | |
| | | key = Invoke.LOCAL_TRAINER if is_training else Invoke.PIPELINE |
| | | |
| | | if os.path.exists(model): |
| | | if os.path.exists(model) and check_latest: |
| | | model_cache_dir = model if os.path.isdir( |
| | | model) else os.path.dirname(model) |
| | | try: |