Loading docs/source/api_doc/tagging/camie.rst +6 −0 Original line number Diff line number Diff line Loading @@ -13,3 +13,9 @@ get_camie_tags convert_camie_emb_to_prediction ---------------------------------------------------- .. autofunction:: convert_camie_emb_to_prediction imgutils/tagging/camie.py +54 −1 Original line number Diff line number Diff line Loading @@ -150,7 +150,7 @@ def _postprocess_embedding_values( :type embedding: numpy.ndarray :param model_name: Name of the model used :type model_name: str :param mode: Prediction mode for threshold selection :param mode: Prediction mode affecting threshold values :type mode: CamieModeTyping :param thresholds: Custom thresholds for tag selection :type thresholds: Optional[Union[float, Dict[str, float]]] Loading Loading @@ -232,6 +232,15 @@ def get_camie_tags( :return: Extracted tags and embeddings, follow the format from ``fmt``. :rtype: Any .. note:: Modes for selection: - ``balanced``: Balanced precision/recall - ``high_precision``: Higher precision thresholds - ``high_recall``: Higher recall thresholds - ``micro_opt``: Micro-optimized thresholds - ``macro_opt``: Macro-optimized thresholds .. note:: The fmt argument can include the following keys: Loading Loading @@ -349,6 +358,16 @@ def get_camie_tags( @ts_lru_cache() def _get_camie_emb_to_pred_model(model_name: str, is_refined: bool = False): """ Load embedding-to-prediction conversion model. :param model_name: Model variant name :type model_name: str :param is_refined: Use refined embeddings (True) or initial embeddings (False) :type is_refined: bool :return: ONNX model session for embedding conversion :rtype: onnxruntime.InferenceSession """ return open_onnx_model(hf_hub_download( repo_id=_REPO_ID, repo_type='model', Loading @@ -366,6 +385,40 @@ def convert_camie_emb_to_prediction( drop_overlap: bool = False, fmt: Any = ('rating', 'general', 'character'), ): """ Convert stored embeddings back to tag predictions. Useful for reprocessing existing embeddings with new thresholds or formats. :param emb: Embedding vector(s) from previous inference :type emb: np.ndarray :param model_name: Original model variant name :type model_name: str :param is_refined: Whether embeddings come from refined stage, otherwise from initial stage :type is_refined: bool :param mode: Threshold selection strategy :type mode: CamieModeTyping :param thresholds: Custom threshold values :type thresholds: Optional[Union[float, Dict[str, float]]] :param no_underline: Remove underscores from tag names :type no_underline: bool :param drop_overlap: Remove overlapping tags in general category :type drop_overlap: bool :param fmt: Output format specification :type fmt: Any :return: Formatted results matching original prediction format :rtype: Any .. note:: Modes for selection: - ``balanced``: Balanced precision/recall - ``high_precision``: Higher precision thresholds - ``high_recall``: Higher recall thresholds - ``micro_opt``: Micro-optimized thresholds - ``macro_opt``: Macro-optimized thresholds """ model = _get_camie_emb_to_pred_model(model_name=model_name, is_refined=is_refined) if len(emb.shape) == 1: logits, pred = model.run(["logits", "output"], {'embedding': emb[np.newaxis]}) Loading Loading
docs/source/api_doc/tagging/camie.rst +6 −0 Original line number Diff line number Diff line Loading @@ -13,3 +13,9 @@ get_camie_tags convert_camie_emb_to_prediction ---------------------------------------------------- .. autofunction:: convert_camie_emb_to_prediction
imgutils/tagging/camie.py +54 −1 Original line number Diff line number Diff line Loading @@ -150,7 +150,7 @@ def _postprocess_embedding_values( :type embedding: numpy.ndarray :param model_name: Name of the model used :type model_name: str :param mode: Prediction mode for threshold selection :param mode: Prediction mode affecting threshold values :type mode: CamieModeTyping :param thresholds: Custom thresholds for tag selection :type thresholds: Optional[Union[float, Dict[str, float]]] Loading Loading @@ -232,6 +232,15 @@ def get_camie_tags( :return: Extracted tags and embeddings, follow the format from ``fmt``. :rtype: Any .. note:: Modes for selection: - ``balanced``: Balanced precision/recall - ``high_precision``: Higher precision thresholds - ``high_recall``: Higher recall thresholds - ``micro_opt``: Micro-optimized thresholds - ``macro_opt``: Macro-optimized thresholds .. note:: The fmt argument can include the following keys: Loading Loading @@ -349,6 +358,16 @@ def get_camie_tags( @ts_lru_cache() def _get_camie_emb_to_pred_model(model_name: str, is_refined: bool = False): """ Load embedding-to-prediction conversion model. :param model_name: Model variant name :type model_name: str :param is_refined: Use refined embeddings (True) or initial embeddings (False) :type is_refined: bool :return: ONNX model session for embedding conversion :rtype: onnxruntime.InferenceSession """ return open_onnx_model(hf_hub_download( repo_id=_REPO_ID, repo_type='model', Loading @@ -366,6 +385,40 @@ def convert_camie_emb_to_prediction( drop_overlap: bool = False, fmt: Any = ('rating', 'general', 'character'), ): """ Convert stored embeddings back to tag predictions. Useful for reprocessing existing embeddings with new thresholds or formats. :param emb: Embedding vector(s) from previous inference :type emb: np.ndarray :param model_name: Original model variant name :type model_name: str :param is_refined: Whether embeddings come from refined stage, otherwise from initial stage :type is_refined: bool :param mode: Threshold selection strategy :type mode: CamieModeTyping :param thresholds: Custom threshold values :type thresholds: Optional[Union[float, Dict[str, float]]] :param no_underline: Remove underscores from tag names :type no_underline: bool :param drop_overlap: Remove overlapping tags in general category :type drop_overlap: bool :param fmt: Output format specification :type fmt: Any :return: Formatted results matching original prediction format :rtype: Any .. note:: Modes for selection: - ``balanced``: Balanced precision/recall - ``high_precision``: Higher precision thresholds - ``high_recall``: Higher recall thresholds - ``micro_opt``: Micro-optimized thresholds - ``macro_opt``: Macro-optimized thresholds """ model = _get_camie_emb_to_pred_model(model_name=model_name, is_refined=is_refined) if len(emb.shape) == 1: logits, pred = model.run(["logits", "output"], {'embedding': emb[np.newaxis]}) Loading
