Loading docs/source/api_doc/generic/attachment.rst 0 → 100644 +23 −0 Original line number Diff line number Diff line imgutils.generic.attachment ======================================= .. currentmodule:: imgutils.generic.attachment .. automodule:: imgutils.generic.attachment Attachment ----------------------------------------- .. autoclass:: Attachment :members: __init__, encoder_model, predict open_attachment ----------------------------------------- .. autofunction:: open_attachment docs/source/api_doc/generic/index.rst +1 −0 Original line number Diff line number Diff line Loading @@ -9,6 +9,7 @@ imgutils.generic .. toctree:: :maxdepth: 3 attachment classify enhance clip Loading imgutils/generic/attachment.py +117 −3 Original line number Diff line number Diff line """ This module provides functionality for handling attachments in machine learning models, particularly those hosted on Hugging Face's model hub. It includes tools for loading, managing and making predictions with ONNX models for classification, tagging and regression tasks. The module provides a caching mechanism for model loading and thread-safe operations for concurrent access to models and their metadata. An example of attachment models is `deepghs/eattach_monochrome_experiments <https://huggingface.co/deepghs/eattach_monochrome_experiments>`_. .. note:: If you want to train a custom attachment model for taggers, take a look at our framework `deepghs/emb_attachments <https://github.com/deepghs/emb_attachments>`_. """ import json import os from threading import Lock from typing import Optional, Any from typing import Optional, Any, Tuple import numpy as np from huggingface_hub import hf_hub_download from imgutils.utils import open_onnx_model, vreplace, ts_lru_cache from ..utils import open_onnx_model, vreplace, ts_lru_cache class Attachment: """ A class to manage machine learning model attachments from Hugging Face. This class handles model loading, caching, and prediction for various types of problems including classification, tagging, and regression. :param repo_id: The Hugging Face repository ID :type repo_id: str :param model_name: Name of the model :type model_name: str :param hf_token: Optional Hugging Face authentication token :type hf_token: Optional[str] """ def __init__(self, repo_id: str, model_name: str, hf_token: Optional[str] = None): """ Initialize the Attachment instance with repository and model information. """ self.repo_id = repo_id self.model_name = model_name self._meta_value = None Loading @@ -33,6 +65,12 @@ class Attachment: @property def _meta(self): """ Load and cache model metadata from the Hugging Face repository. :return: Model metadata as a dictionary :rtype: dict """ with self._model_lock: if self._meta_value is None: with open(hf_hub_download( Loading @@ -47,9 +85,21 @@ class Attachment: @property def encoder_model(self) -> str: """ Get the encoder model name from metadata. :return: Name of the encoder model :rtype: str """ return self._meta['encoder_model'] def _open_model(self): """ Load and cache the ONNX model from Hugging Face. :return: Loaded ONNX model :rtype: object """ with self._model_lock: if self._model is None: self._model = open_onnx_model(hf_hub_download( Loading @@ -61,12 +111,30 @@ class Attachment: return self._model def _predict_raw(self, embedding: np.ndarray): def _predict_raw(self, embedding: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """ Make raw predictions using the model. :param embedding: Input embedding array :type embedding: np.ndarray :return: Tuple of logits and predictions :rtype: Tuple[np.ndarray, np.ndarray] """ model = self._open_model() logits, prediction = model.run(['logits', 'prediction'], {'input': embedding}) return logits, prediction def _predict_classification(self, embedding: np.ndarray, fmt: Any = 'top'): """ Make classification predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ labels = np.array(self._meta['problem']['labels']) logits, prediction = self._predict_raw(embedding) retval = [] Loading @@ -87,6 +155,18 @@ class Attachment: return retval def _predict_tagging(self, embedding: np.ndarray, threshold: float = 0.3, fmt: Any = 'tags'): """ Make tagging predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param threshold: Confidence threshold for tag selection :type threshold: float :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ tags = np.array(self._meta['problem']['tags']) logits, prediction = self._predict_raw(embedding) retval = [] Loading @@ -103,6 +183,16 @@ class Attachment: return retval def _predict_regression(self, embedding: np.ndarray, fmt: Any = 'full'): """ Make regression predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ field_names = [name for name, _, _ in self._meta['problem']['fields']] logits, prediction = self._predict_raw(embedding) retval = [] Loading @@ -118,6 +208,15 @@ class Attachment: return retval def predict(self, embedding: np.ndarray, **kwargs): """ Make predictions based on the problem type (classification, tagging, or regression). :param embedding: Input embedding array :type embedding: np.ndarray :param kwargs: Additional arguments passed to specific prediction methods :return: Prediction results in specified format :raises ValueError: If embedding shape is invalid or problem type is unknown """ embedding = embedding.astype(np.float32) if len(embedding.shape) == 1: single = True Loading @@ -144,6 +243,21 @@ class Attachment: @ts_lru_cache() def open_attachment(repo_id: str, model_name: str, hf_token: Optional[str] = None) -> 'Attachment': """ Create and cache an Attachment instance. This function creates a new Attachment instance or returns a cached one if it was previously created with the same parameters. :param repo_id: The Hugging Face repository ID :type repo_id: str :param model_name: Name of the model :type model_name: str :param hf_token: Optional Hugging Face authentication token :type hf_token: Optional[str] :return: An Attachment instance :rtype: Attachment """ return Attachment( repo_id=repo_id, model_name=model_name, Loading Loading
docs/source/api_doc/generic/attachment.rst 0 → 100644 +23 −0 Original line number Diff line number Diff line imgutils.generic.attachment ======================================= .. currentmodule:: imgutils.generic.attachment .. automodule:: imgutils.generic.attachment Attachment ----------------------------------------- .. autoclass:: Attachment :members: __init__, encoder_model, predict open_attachment ----------------------------------------- .. autofunction:: open_attachment
docs/source/api_doc/generic/index.rst +1 −0 Original line number Diff line number Diff line Loading @@ -9,6 +9,7 @@ imgutils.generic .. toctree:: :maxdepth: 3 attachment classify enhance clip Loading
imgutils/generic/attachment.py +117 −3 Original line number Diff line number Diff line """ This module provides functionality for handling attachments in machine learning models, particularly those hosted on Hugging Face's model hub. It includes tools for loading, managing and making predictions with ONNX models for classification, tagging and regression tasks. The module provides a caching mechanism for model loading and thread-safe operations for concurrent access to models and their metadata. An example of attachment models is `deepghs/eattach_monochrome_experiments <https://huggingface.co/deepghs/eattach_monochrome_experiments>`_. .. note:: If you want to train a custom attachment model for taggers, take a look at our framework `deepghs/emb_attachments <https://github.com/deepghs/emb_attachments>`_. """ import json import os from threading import Lock from typing import Optional, Any from typing import Optional, Any, Tuple import numpy as np from huggingface_hub import hf_hub_download from imgutils.utils import open_onnx_model, vreplace, ts_lru_cache from ..utils import open_onnx_model, vreplace, ts_lru_cache class Attachment: """ A class to manage machine learning model attachments from Hugging Face. This class handles model loading, caching, and prediction for various types of problems including classification, tagging, and regression. :param repo_id: The Hugging Face repository ID :type repo_id: str :param model_name: Name of the model :type model_name: str :param hf_token: Optional Hugging Face authentication token :type hf_token: Optional[str] """ def __init__(self, repo_id: str, model_name: str, hf_token: Optional[str] = None): """ Initialize the Attachment instance with repository and model information. """ self.repo_id = repo_id self.model_name = model_name self._meta_value = None Loading @@ -33,6 +65,12 @@ class Attachment: @property def _meta(self): """ Load and cache model metadata from the Hugging Face repository. :return: Model metadata as a dictionary :rtype: dict """ with self._model_lock: if self._meta_value is None: with open(hf_hub_download( Loading @@ -47,9 +85,21 @@ class Attachment: @property def encoder_model(self) -> str: """ Get the encoder model name from metadata. :return: Name of the encoder model :rtype: str """ return self._meta['encoder_model'] def _open_model(self): """ Load and cache the ONNX model from Hugging Face. :return: Loaded ONNX model :rtype: object """ with self._model_lock: if self._model is None: self._model = open_onnx_model(hf_hub_download( Loading @@ -61,12 +111,30 @@ class Attachment: return self._model def _predict_raw(self, embedding: np.ndarray): def _predict_raw(self, embedding: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """ Make raw predictions using the model. :param embedding: Input embedding array :type embedding: np.ndarray :return: Tuple of logits and predictions :rtype: Tuple[np.ndarray, np.ndarray] """ model = self._open_model() logits, prediction = model.run(['logits', 'prediction'], {'input': embedding}) return logits, prediction def _predict_classification(self, embedding: np.ndarray, fmt: Any = 'top'): """ Make classification predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ labels = np.array(self._meta['problem']['labels']) logits, prediction = self._predict_raw(embedding) retval = [] Loading @@ -87,6 +155,18 @@ class Attachment: return retval def _predict_tagging(self, embedding: np.ndarray, threshold: float = 0.3, fmt: Any = 'tags'): """ Make tagging predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param threshold: Confidence threshold for tag selection :type threshold: float :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ tags = np.array(self._meta['problem']['tags']) logits, prediction = self._predict_raw(embedding) retval = [] Loading @@ -103,6 +183,16 @@ class Attachment: return retval def _predict_regression(self, embedding: np.ndarray, fmt: Any = 'full'): """ Make regression predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ field_names = [name for name, _, _ in self._meta['problem']['fields']] logits, prediction = self._predict_raw(embedding) retval = [] Loading @@ -118,6 +208,15 @@ class Attachment: return retval def predict(self, embedding: np.ndarray, **kwargs): """ Make predictions based on the problem type (classification, tagging, or regression). :param embedding: Input embedding array :type embedding: np.ndarray :param kwargs: Additional arguments passed to specific prediction methods :return: Prediction results in specified format :raises ValueError: If embedding shape is invalid or problem type is unknown """ embedding = embedding.astype(np.float32) if len(embedding.shape) == 1: single = True Loading @@ -144,6 +243,21 @@ class Attachment: @ts_lru_cache() def open_attachment(repo_id: str, model_name: str, hf_token: Optional[str] = None) -> 'Attachment': """ Create and cache an Attachment instance. This function creates a new Attachment instance or returns a cached one if it was previously created with the same parameters. :param repo_id: The Hugging Face repository ID :type repo_id: str :param model_name: Name of the model :type model_name: str :param hf_token: Optional Hugging Face authentication token :type hf_token: Optional[str] :return: An Attachment instance :rtype: Attachment """ return Attachment( repo_id=repo_id, model_name=model_name, Loading
