Instructions to use Superxixixi/LoCoNet_ASD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Superxixixi/LoCoNet_ASD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Superxixixi/LoCoNet_ASD", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Superxixixi/LoCoNet_ASD", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # -*- coding: utf-8 -*- | |
| #================================================================ | |
| # Don't go gently into that good night. | |
| # | |
| # author: klaus | |
| # description: | |
| # | |
| #================================================================ | |
| import warnings | |
| from mmcv.cnn import MODELS as MMCV_MODELS | |
| from mmcv.utils import Registry | |
| from mmaction.utils import import_module_error_func | |
| MODELS = Registry('models', parent=MMCV_MODELS) | |
| BACKBONES = MODELS | |
| NECKS = MODELS | |
| HEADS = MODELS | |
| RECOGNIZERS = MODELS | |
| LOSSES = MODELS | |
| LOCALIZERS = MODELS | |
| try: | |
| from mmdet.models.builder import DETECTORS, build_detector | |
| except (ImportError, ModuleNotFoundError): | |
| # Define an empty registry and building func, so that can import | |
| DETECTORS = MODELS | |
| def build_detector(cfg, train_cfg, test_cfg): | |
| pass | |
| def build_backbone(cfg): | |
| """Build backbone.""" | |
| return BACKBONES.build(cfg) | |
| def build_head(cfg): | |
| """Build head.""" | |
| return HEADS.build(cfg) | |
| def build_recognizer(cfg, train_cfg=None, test_cfg=None): | |
| """Build recognizer.""" | |
| if train_cfg is not None or test_cfg is not None: | |
| warnings.warn( | |
| 'train_cfg and test_cfg is deprecated, ' | |
| 'please specify them in model. Details see this ' | |
| 'PR: https://github.com/open-mmlab/mmaction2/pull/629', UserWarning) | |
| assert cfg.get( | |
| 'train_cfg' | |
| ) is None or train_cfg is None, 'train_cfg specified in both outer field and model field' # noqa: E501 | |
| assert cfg.get( | |
| 'test_cfg' | |
| ) is None or test_cfg is None, 'test_cfg specified in both outer field and model field ' # noqa: E501 | |
| return RECOGNIZERS.build(cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) | |
| def build_loss(cfg): | |
| """Build loss.""" | |
| return LOSSES.build(cfg) | |
| def build_localizer(cfg): | |
| """Build localizer.""" | |
| return LOCALIZERS.build(cfg) | |
| def build_model(cfg, train_cfg=None, test_cfg=None): | |
| """Build model.""" | |
| args = cfg.copy() | |
| obj_type = args.pop('type') | |
| if obj_type in LOCALIZERS: | |
| return build_localizer(cfg) | |
| if obj_type in RECOGNIZERS: | |
| return build_recognizer(cfg, train_cfg, test_cfg) | |
| if obj_type in DETECTORS: | |
| if train_cfg is not None or test_cfg is not None: | |
| warnings.warn( | |
| 'train_cfg and test_cfg is deprecated, ' | |
| 'please specify them in model. Details see this ' | |
| 'PR: https://github.com/open-mmlab/mmaction2/pull/629', UserWarning) | |
| return build_detector(cfg, train_cfg, test_cfg) | |
| model_in_mmdet = ['FastRCNN'] | |
| if obj_type in model_in_mmdet: | |
| raise ImportError('Please install mmdet for spatial temporal detection tasks.') | |
| raise ValueError(f'{obj_type} is not registered in ' 'LOCALIZERS, RECOGNIZERS or DETECTORS') | |
| def build_neck(cfg): | |
| """Build neck.""" | |
| return NECKS.build(cfg) | |