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"imgutils.metrics.psnr_": [[42, "module-imgutils.metrics.psnr_"]], "psnr() (in module imgutils.metrics.psnr_)": [[42, "imgutils.metrics.psnr_.psnr"]], "detect_text_with_ocr() (in module imgutils.ocr)": [[43, "imgutils.ocr.detect_text_with_ocr"]], "imgutils.ocr": [[43, "module-imgutils.ocr"]], "list_det_models() (in module imgutils.ocr)": [[43, "imgutils.ocr.list_det_models"]], "list_rec_models() (in module imgutils.ocr)": [[43, "imgutils.ocr.list_rec_models"]], "ocr() (in module imgutils.ocr)": [[43, "imgutils.ocr.ocr"]], "align_maxsize() (in module imgutils.operate.align)": [[44, "imgutils.operate.align.align_maxsize"]], "imgutils.operate.align": [[44, "module-imgutils.operate.align"]], "basecensor (class in imgutils.operate.censor_)": [[45, "imgutils.operate.censor_.BaseCensor"]], "blurcensor (class in imgutils.operate.censor_)": [[45, "imgutils.operate.censor_.BlurCensor"]], "colorcensor (class in imgutils.operate.censor_)": [[45, "imgutils.operate.censor_.ColorCensor"]], 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"emojibasedcensor (class in imgutils.operate.imgcensor)": [[46, "imgutils.operate.imgcensor.EmojiBasedCensor"]], "imagebasedcensor (class in imgutils.operate.imgcensor)": [[46, "imgutils.operate.imgcensor.ImageBasedCensor"]], "singleimage (class in imgutils.operate.imgcensor)": [[46, "imgutils.operate.imgcensor.SingleImage"]], "__init__() (imgutils.operate.imgcensor.singleimage method)": [[46, "imgutils.operate.imgcensor.SingleImage.__init__"]], "censor_area() (imgutils.operate.imgcensor.emojibasedcensor method)": [[46, "imgutils.operate.imgcensor.EmojiBasedCensor.censor_area"]], "censor_area() (imgutils.operate.imgcensor.imagebasedcensor method)": [[46, "imgutils.operate.imgcensor.ImageBasedCensor.censor_area"]], "find_for_area() (imgutils.operate.imgcensor.singleimage method)": [[46, "imgutils.operate.imgcensor.SingleImage.find_for_area"]], "height (imgutils.operate.imgcensor.singleimage property)": [[46, "imgutils.operate.imgcensor.SingleImage.height"]], "imgutils.operate.imgcensor": [[46, "module-imgutils.operate.imgcensor"]], "width (imgutils.operate.imgcensor.singleimage property)": [[46, "imgutils.operate.imgcensor.SingleImage.width"]], "imgutils.operate": [[47, "module-imgutils.operate"]], "imgutils.operate.squeeze": [[48, "module-imgutils.operate.squeeze"]], "squeeze() (in module imgutils.operate.squeeze)": [[48, "imgutils.operate.squeeze.squeeze"]], "squeeze_with_transparency() (in module imgutils.operate.squeeze)": [[48, "imgutils.operate.squeeze.squeeze_with_transparency"]], "dwpose_estimate() (in module imgutils.pose.dwpose)": [[49, "imgutils.pose.dwpose.dwpose_estimate"]], "imgutils.pose.dwpose": [[49, "module-imgutils.pose.dwpose"]], "left_ankle (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_ANKLE"]], "left_big_toe (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_BIG_TOE"]], "left_ear (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_EAR"]], "left_elbow (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_ELBOW"]], "left_eye (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_EYE"]], "left_heel (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_HEEL"]], "left_hip (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_HIP"]], "left_knee (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_KNEE"]], "left_shoulder (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_SHOULDER"]], "left_small_toe (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_SMALL_TOE"]], "left_wrist (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.LEFT_WRIST"]], "neck (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.NECK"]], "nose (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.NOSE"]], "op18keypointset (class in imgutils.pose.format)": [[50, "imgutils.pose.format.OP18KeyPointSet"]], "op18_body_max (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_BODY_MAX"]], "op18_body_min (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_BODY_MIN"]], "op18_face_max (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_FACE_MAX"]], "op18_face_min (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_FACE_MIN"]], "op18_left_foot_max (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_LEFT_FOOT_MAX"]], "op18_left_foot_min (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_LEFT_FOOT_MIN"]], "op18_left_hand_max (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_LEFT_HAND_MAX"]], "op18_left_hand_min (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_LEFT_HAND_MIN"]], "op18_right_foot_max (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_RIGHT_FOOT_MAX"]], "op18_right_foot_min (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_RIGHT_FOOT_MIN"]], "op18_right_hand_max (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_RIGHT_HAND_MAX"]], "op18_right_hand_min (in module imgutils.pose.format)": [[50, "imgutils.pose.format.OP18_RIGHT_HAND_MIN"]], "right_ankle (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.RIGHT_ANKLE"]], "right_big_toe (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.RIGHT_BIG_TOE"]], "right_ear (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.RIGHT_EAR"]], "right_elbow (imgutils.pose.format.openpose18 attribute)": [[50, "imgutils.pose.format.OpenPose18.RIGHT_ELBOW"]], "right_eye 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"create_torchvision_transforms() (in module imgutils.preprocess.torchvision)": [[56, "imgutils.preprocess.torchvision.create_torchvision_transforms"]], "imgutils.preprocess.torchvision": [[56, "module-imgutils.preprocess.torchvision"]], "parse_torchvision_transforms() (in module imgutils.preprocess.torchvision)": [[56, "imgutils.preprocess.torchvision.parse_torchvision_transforms"]], "register_torchvision_parse() (in module imgutils.preprocess.torchvision)": [[56, "imgutils.preprocess.torchvision.register_torchvision_parse"]], "register_torchvision_transform() (in module imgutils.preprocess.torchvision)": [[56, "imgutils.preprocess.torchvision.register_torchvision_transform"]], "notprocessortypeerror (class in imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.NotProcessorTypeError"]], "convert_to_size_dict() (in module imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.convert_to_size_dict"]], "create_bit_transforms() (in module 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"imgutils.preprocess.transformers.create_transforms_from_transformers"]], "create_transforms_from_vit_processor() (in module imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.create_transforms_from_vit_processor"]], "create_vit_transforms() (in module imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.create_vit_transforms"]], "get_size_dict() (in module imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.get_size_dict"]], "imgutils.preprocess.transformers": [[57, "module-imgutils.preprocess.transformers"]], "is_valid_size_dict() (in module imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.is_valid_size_dict"]], "register_creators_for_transformers() (in module imgutils.preprocess.transformers)": [[57, "imgutils.preprocess.transformers.register_creators_for_transformers"]], "backgroundimageset (class in imgutils.resource.background)": [[58, 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"module-imgutils.restore.nafnet"]], "restore_with_nafnet() (in module imgutils.restore.nafnet)": [[62, "imgutils.restore.nafnet.restore_with_nafnet"]], "imgutils.restore.scunet": [[63, "module-imgutils.restore.scunet"]], "restore_with_scunet() (in module imgutils.restore.scunet)": [[63, "imgutils.restore.scunet.restore_with_scunet"]], "imgutils.sd": [[64, "module-imgutils.sd"]], "sdmetadata (class in imgutils.sd.metadata)": [[65, "imgutils.sd.metadata.SDMetaData"]], "__str__() (imgutils.sd.metadata.sdmetadata method)": [[65, "imgutils.sd.metadata.SDMetaData.__str__"]], "get_sdmeta_from_image() (in module imgutils.sd.metadata)": [[65, "imgutils.sd.metadata.get_sdmeta_from_image"]], "imgutils.sd.metadata": [[65, "module-imgutils.sd.metadata"]], "parse_sdmeta_from_text() (in module imgutils.sd.metadata)": [[65, "imgutils.sd.metadata.parse_sdmeta_from_text"]], "pnginfo (imgutils.sd.metadata.sdmetadata property)": [[65, "imgutils.sd.metadata.SDMetaData.pnginfo"]], "save_image_with_sdmeta() 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-53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3242.21575 MHz\n", + "CPU Freq: 3123.1165 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/attachments/.doctrees/nbsphinx/information/environment.result.ipynb b/dev/attachments/.doctrees/nbsphinx/information/environment.result.ipynb index 338fe0d5a625b7a1d10885d64c1ba11d2f9e1c43..534b9848354bbc067eadfeb423bd438d6805e84d 100644 --- a/dev/attachments/.doctrees/nbsphinx/information/environment.result.ipynb +++ b/dev/attachments/.doctrees/nbsphinx/information/environment.result.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-03-06T17:29:43.243872Z", - "iopub.status.busy": "2025-03-06T17:29:43.243385Z", - "iopub.status.idle": "2025-03-06T17:29:44.409450Z", - "shell.execute_reply": "2025-03-06T17:29:44.408673Z" + "iopub.execute_input": "2025-03-07T05:50:13.979989Z", + "iopub.status.busy": "2025-03-07T05:50:13.979774Z", + "iopub.status.idle": "2025-03-07T05:50:15.145801Z", + "shell.execute_reply": "2025-03-07T05:50:15.145093Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3230.49575 MHz\n", + "CPU Freq: 3003.9987499999997 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/attachments/.doctrees/tutorials/installation/index.doctree b/dev/attachments/.doctrees/tutorials/installation/index.doctree index 7f5f6514bb53a7df927e93d10281e8c1cb5471b6..a009cdf8e8d63f53fe832756dc14b89b1ab882d5 100644 Binary files a/dev/attachments/.doctrees/tutorials/installation/index.doctree and b/dev/attachments/.doctrees/tutorials/installation/index.doctree differ diff --git a/dev/attachments/.doctrees/tutorials/quick_start/index.doctree b/dev/attachments/.doctrees/tutorials/quick_start/index.doctree index 001d07c4a960d48ac92b3a27f299b7aeb0e23888..6571974b65064549e9c240ce5741975b45d5eea2 100644 Binary files a/dev/attachments/.doctrees/tutorials/quick_start/index.doctree and b/dev/attachments/.doctrees/tutorials/quick_start/index.doctree differ diff --git a/dev/attachments/_modules/imgutils/generic/attachment.html b/dev/attachments/_modules/imgutils/generic/attachment.html new file mode 100644 index 0000000000000000000000000000000000000000..e3ffb746fb21e61d6f98eb886836aa921a919f51 --- /dev/null +++ b/dev/attachments/_modules/imgutils/generic/attachment.html @@ -0,0 +1,424 @@ + + + + + + + + imgutils.generic.attachment — imgutils 0.12.0 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +

Source code for imgutils.generic.attachment

+"""
+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, Tuple
+
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+from ..utils import open_onnx_model, vreplace, ts_lru_cache
+
+
+
[docs]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]
+    """
+
+
[docs]    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
+        self._model = None
+
+        self._hf_token = hf_token
+        self._global_lock = Lock()
+        self._model_lock = Lock()

+
+    def _get_hf_token(self) -> Optional[str]:
+        """
+        Retrieve the Hugging Face authentication token.
+
+        Checks both instance variable and environment for token presence.
+
+        :return: Authentication token if available
+        :rtype: Optional[str]
+        """
+        return self._hf_token or os.environ.get('HF_TOKEN')
+
+    @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(
+                        repo_id=self.repo_id,
+                        repo_type='model',
+                        filename=f'{self.model_name}/meta.json',
+                        token=self._get_hf_token(),
+                ), 'r') as f:
+                    self._meta_value = json.load(f)
+
+        return self._meta_value
+
+    @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(
+                    repo_id=self.repo_id,
+                    repo_type='model',
+                    filename=f'{self.model_name}/model.onnx',
+                    token=self._get_hf_token(),
+                ))
+
+        return self._model
+
+    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 = []
+        for logit, pred in zip(logits, prediction):
+            scores = dict(zip(labels, pred.tolist()))
+            maxidx = np.argmax(pred)
+            top_label, top_score = labels[maxidx].item(), pred[maxidx].item()
+            top = top_label, top_score
+            retval.append(vreplace(fmt, {
+                'scores': scores,
+                'top': top,
+                'top_label': top_label,
+                'top_score': top_score,
+                'logit': logit,
+                'prediction': pred,
+            }))
+
+        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 = []
+        for logit, pred in zip(logits, prediction):
+            selection = pred >= threshold
+            pvalues, ptags = pred[selection], tags[selection]
+            result = dict(zip(ptags.tolist(), pvalues.tolist()))
+            retval.append(vreplace(fmt, {
+                'tags': result,
+                'logit': logit,
+                'prediction': pred,
+            }))
+
+        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 = []
+        for logit, pred in zip(logits, prediction):
+            result = dict(zip(field_names, pred.tolist()))
+            retval.append(vreplace(fmt, {
+                'full': result,
+                'logit': logit,
+                'prediction': pred,
+                **{f'field/{key}': value for key, value in result.items()},
+            }))
+
+        return retval
+
+
[docs]    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
+            embedding = embedding[np.newaxis, ...]
+        elif len(embedding.shape) == 2:
+            single = False
+        else:
+            raise ValueError(f'Unexpected embedding shape - {embedding!r}.')
+
+        problem_type = self._meta['problem']['type']
+        if problem_type == 'classification':
+            result = self._predict_classification(embedding, **kwargs)
+        elif problem_type == 'tagging':
+            result = self._predict_tagging(embedding, **kwargs)
+        elif problem_type == 'regression':
+            result = self._predict_regression(embedding, **kwargs)
+        else:
+            raise ValueError(f'Unknown problem type - {problem_type!r}.')
+
+        if single:
+            result = result[0]
+        return result

+
+
+
[docs]@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,
+        hf_token=hf_token,
+    )

+
+ + +
+
+ +
+
+
+
+ +
+ + Other Versions + v: dev/attachments + + +
+
+
Tags
+
v0.10.0
+
v0.11.0
+
v0.11.1
+
v0.11.2
+
v0.12.0
+
+
+
Branches
+
dev/attachments
+
HEAD
+
dev/denormalize
+
main
+
+
+
+ + + \ No newline at end of file diff --git a/dev/attachments/_modules/imgutils/tagging/wd14.html b/dev/attachments/_modules/imgutils/tagging/wd14.html index 693942aec26259da9209e6ebdf8843d7a6666a7a..5c183eb2a9b4f44b625e3f1997a393f13bcb6aa8 100644 --- a/dev/attachments/_modules/imgutils/tagging/wd14.html +++ b/dev/attachments/_modules/imgutils/tagging/wd14.html @@ -111,7 +111,8 @@ project on Hugging Face. """ -from typing import List, Tuple, Any +from collections import defaultdict +from typing import List, Tuple, Any, Optional, Mapping, Dict, Union import numpy as np import onnxruntime @@ -123,7 +124,8 @@ from .format import remove_underline from .overlap import drop_overlap_tags from ..data import load_image, ImageTyping -from ..utils import open_onnx_model, vreplace, sigmoid, ts_lru_cache +from ..generic.attachment import open_attachment, Attachment +from ..utils import open_onnx_model, vreplace, sigmoid, ts_lru_cache, vnames SWIN_MODEL_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2" CONV_MODEL_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2" @@ -293,6 +295,7 @@ no_underline: bool = False, drop_overlap: bool = False, fmt: Any = ('rating', 'general', 'character'), + attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]] = None, ): """ Post-process the embedding and prediction results. @@ -315,9 +318,36 @@ :type no_underline: bool :param drop_overlap: Whether to drop overlapping tags. :type drop_overlap: bool - :param fmt: The format of the output. - :return: The post-processed results. + :param fmt: Output format specification defining which components to include + :type fmt: Any + :param attachments: Additional model attachments for extended tagging capabilities + :type attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]] + + :return: Processed tagging results in the specified format + :rtype: Any + :raises ValueError: If attachment configuration is invalid or incompatible """ + attachments = dict(attachments or {}) + d_attachments: Dict[str, Tuple[Attachment, dict]] = {} + for attach_name, attach_tpl in attachments.items(): + if '/' in attach_name: + raise ValueError(f'Invalid attachment register name, no \'/\' required - {attach_name!r}.') + + if len(attach_tpl) == 2: + (attach_repo_id, attach_model_name), attach_kwargs = attach_tpl, {} + elif len(attach_tpl) == 3: + attach_repo_id, attach_model_name, attach_kwargs = attach_tpl + else: + raise ValueError(f'Invalid attachment tuple for {attach_name!r}, ' + f'2 or 3 elements expected but {attach_tpl!r} found.') + attachment = open_attachment(repo_id=attach_repo_id, model_name=attach_model_name) + expected_encoder_model = f'wdtagger:{MODEL_NAMES[model_name]}' + if attachment.encoder_model != expected_encoder_model: + raise ValueError(f'Attachment encoder model not match, ' + f'{expected_encoder_model!r} expected but {attachment.encoder_model!r} found ' + f'for {attach_name!r}.') + d_attachments[attach_name] = (attachment, attach_kwargs) + assert len(pred.shape) == len(embedding.shape) == 1, \ f'Both pred and embeddings shapes should be 1-dim, ' \ f'but pred: {pred.shape!r}, embedding: {embedding.shape!r} actually found.' @@ -343,17 +373,27 @@ character_res = {x: v.item() for x, v in character_names if v > character_threshold} - return vreplace( - fmt, - { - 'rating': rating, - 'general': general_res, - 'character': character_res, - 'tag': {**general_res, **character_res}, - 'embedding': embedding.astype(np.float32), - 'prediction': pred.astype(np.float32), - } - ) + mapping_values = { + 'rating': rating, + 'general': general_res, + 'character': character_res, + 'tag': {**general_res, **character_res}, + 'embedding': embedding.astype(np.float32), + 'prediction': pred.astype(np.float32), + } + + d_attach_infers = defaultdict(list) + for vname in vnames(fmt): + if '/' in vname and vname.split('/', maxsplit=1)[0] in d_attachments: + attach_name, attach_fmt_name = vname.split('/', maxsplit=1) + d_attach_infers[attach_name].append(attach_fmt_name) + for attach_name, attach_infer_names in d_attach_infers.items(): + attachment, attach_kwargs = d_attachments[attach_name] + attach_infer_values = attachment.predict(embedding=embedding, fmt=attach_infer_names, **attach_kwargs) + attach_mapping_names = [f'{attach_name}/{name}' for name in attach_infer_names] + mapping_values.update(dict(zip(attach_mapping_names, attach_infer_values))) + + return vreplace(fmt, mapping_values)
[docs]def get_wd14_tags( @@ -366,6 +406,7 @@ no_underline: bool = False, drop_overlap: bool = False, fmt: Any = ('rating', 'general', 'character'), + attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]] = None, ): """ Get tags for an image using WD14 taggers. @@ -391,7 +432,13 @@ :type drop_overlap: bool :param fmt: Return format, default is ``('rating', 'general', 'character')``. ``embedding`` is also supported for feature extraction. + :type fmt: Any + :param attachments: Additional model attachments for extended tagging capabilities + :type attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]] + :return: Prediction result based on the provided fmt. + :rtype: Any + :raises ValueError: If attachment configuration is invalid or incompatible .. note:: The fmt argument can include the following keys: @@ -414,6 +461,44 @@ This embedding is valuable for constructing indices that enable rapid querying of images based on visual features within large-scale datasets. + .. note:: + The attachment system allows integration of additional tagging models to extend the base WD14 tagger's capabilities. + Attachments are specified using a dictionary with the following format: + + .. code-block:: python + + attachments = { + 'name': ('repo_id', 'model_name'), # Basic format + 'name': ('repo_id', 'model_name', {'threshold': 0.35}) # With additional parameters when predicting + } + + The ``fmt`` argument can include attachment results using the format 'name/key', where: + + - ``name``: The name specified in the attachments dictionary + - ``key``: The specific output type requested from the attachment + + For example: + + >>> from imgutils.tagging import get_wd14_tags + >>> + >>> # Using an attachment for additional style tagging + >>> results = get_wd14_tags( + ... 'image.jpg', + ... attachments={'monochrome': ('deepghs/eattach_monochrome_experiments', 'mlp_layer1_seed1')}, + ... fmt=('general', 'monochrome/scores') + ... ) + >>> + >>> # Results will include both base tags and attachment outputs + >>> print(results) + ( + {'1girl': 0.99, ...}, + {'monochrome': 0.999, 'normal': 0.001}, + ) + + Multiple attachments can be used simultaneously, and each attachment can provide multiple output types + through its fmt specification. Ensure that attachment models are compatible with the base WD14 model's + embedding format. + Example: Here are some images for example @@ -460,6 +545,7 @@ no_underline=no_underline, drop_overlap=drop_overlap, fmt=fmt, + attachments=attachments, )
@@ -476,6 +562,7 @@ no_underline: bool = False, drop_overlap: bool = False, fmt: Any = ('rating', 'general', 'character'), + attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]] = None, denormalize: bool = False, denormalizer_name: str = _DEFAULT_DENORMALIZER_NAME, ): @@ -501,12 +588,16 @@ :param drop_overlap: Remove overlapping tags to reduce redundancy :type drop_overlap: bool :param fmt: Specify return format structure for predictions, default is ``('rating', 'general', 'character')``. - :type fmt: tuple + :type fmt: Any + :param attachments: Additional model attachments for extended tagging capabilities + :type attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]] :param denormalize: Whether to denormalize the embedding before prediction :type denormalize: bool :param denormalizer_name: Name of the denormalizer to use if denormalization is enabled :type denormalizer_name: str :return: For single embeddings: prediction result based on fmt. For batches: list of prediction results. + :rtype: Any + :raises ValueError: If attachment configuration is invalid or incompatible .. note:: Only the embeddings not get normalized can be converted to understandable prediction result. @@ -557,6 +648,7 @@ no_underline=no_underline, drop_overlap=drop_overlap, fmt=fmt, + attachments=attachments, ) else: return [ @@ -571,6 +663,7 @@ no_underline=no_underline, drop_overlap=drop_overlap, fmt=fmt, + attachments=attachments, ) for pred_item, emb_item in zip(pred, emb) ] diff --git a/dev/attachments/_modules/index.html b/dev/attachments/_modules/index.html index 1d25f07f9126f8fcfcdf815a6569fd99194c335a..973529235d667186b990505ecb6244033b2be694 100644 --- a/dev/attachments/_modules/index.html +++ b/dev/attachments/_modules/index.html @@ -122,6 +122,7 @@
  • imgutils.edge.canny
  • imgutils.edge.lineart
  • imgutils.edge.lineart_anime
    • +
  • imgutils.generic.attachment
  • imgutils.generic.classify
  • imgutils.generic.clip
  • imgutils.generic.enhance
    • diff --git a/dev/attachments/_sources/api_doc/generic/attachment.rst.txt b/dev/attachments/_sources/api_doc/generic/attachment.rst.txt new file mode 100644 index 0000000000000000000000000000000000000000..4ba383b91d420c4afaa8667ce0ee056ec0c3be00 --- /dev/null +++ b/dev/attachments/_sources/api_doc/generic/attachment.rst.txt @@ -0,0 +1,23 @@ +imgutils.generic.attachment +======================================= + +.. currentmodule:: imgutils.generic.attachment + +.. automodule:: imgutils.generic.attachment + + + +Attachment +----------------------------------------- + +.. autoclass:: Attachment + :members: __init__, encoder_model, predict + + +open_attachment +----------------------------------------- + +.. autofunction:: open_attachment + + + diff --git a/dev/attachments/_sources/api_doc/generic/index.rst.txt b/dev/attachments/_sources/api_doc/generic/index.rst.txt index 70a62c32722aa90304ac074efd5f2683df6c42d9..cfd47df40c7e3b38c9006782c8ff89e72030780d 100644 --- a/dev/attachments/_sources/api_doc/generic/index.rst.txt +++ b/dev/attachments/_sources/api_doc/generic/index.rst.txt @@ -9,6 +9,7 @@ imgutils.generic .. toctree:: :maxdepth: 3 + attachment classify enhance clip diff --git a/dev/attachments/_sources/information/environment.result.ipynb.txt b/dev/attachments/_sources/information/environment.result.ipynb.txt index 338fe0d5a625b7a1d10885d64c1ba11d2f9e1c43..534b9848354bbc067eadfeb423bd438d6805e84d 100644 --- a/dev/attachments/_sources/information/environment.result.ipynb.txt +++ b/dev/attachments/_sources/information/environment.result.ipynb.txt @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-03-06T17:29:43.243872Z", - "iopub.status.busy": "2025-03-06T17:29:43.243385Z", - "iopub.status.idle": "2025-03-06T17:29:44.409450Z", - "shell.execute_reply": "2025-03-06T17:29:44.408673Z" + "iopub.execute_input": "2025-03-07T05:50:13.979989Z", + "iopub.status.busy": "2025-03-07T05:50:13.979774Z", + "iopub.status.idle": "2025-03-07T05:50:15.145801Z", + "shell.execute_reply": "2025-03-07T05:50:15.145093Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3230.49575 MHz\n", + "CPU Freq: 3003.9987499999997 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/attachments/api_doc/generic/attachment.html b/dev/attachments/api_doc/generic/attachment.html new file mode 100644 index 0000000000000000000000000000000000000000..a22a596e3a298069a3ead3555db654ec9a623630 --- /dev/null +++ b/dev/attachments/api_doc/generic/attachment.html @@ -0,0 +1,283 @@ + + + + + + + + + imgutils.generic.attachment — imgutils 0.12.0 documentation + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + + + +
    +

    imgutils.generic.attachment

    +

    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.

    +
    +

    Note

    +

    If you want to train a custom attachment model for taggers, +take a look at our framework deepghs/emb_attachments.

    +
    +
    +

    Attachment

    +
    +
    +class imgutils.generic.attachment.Attachment(repo_id: str, model_name: str, hf_token: str | None = None)[source]
    +

    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.

    +
    +
    Parameters:
    +
      +

    • repo_id (str) – The Hugging Face repository ID

      • +

    • model_name (str) – Name of the model

      • +

    • hf_token (Optional[str]) – Optional Hugging Face authentication token

      • +
    +
    +
    +
    +
    +__init__(repo_id: str, model_name: str, hf_token: str | None = None)[source]
    +

    Initialize the Attachment instance with repository and model information.

    +
    + +
    +
    +property encoder_model: str
    +

    Get the encoder model name from metadata.

    +
    +
    Returns:
    +

    Name of the encoder model

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    +
    +predict(embedding: ndarray, **kwargs)[source]
    +

    Make predictions based on the problem type (classification, tagging, or regression).

    +
    +
    Parameters:
    +
      +

    • embedding (np.ndarray) – Input embedding array

      • +

    • kwargs – Additional arguments passed to specific prediction methods

      • +
    +
    +
    Returns:
    +

    Prediction results in specified format

    +
    +
    Raises:
    +

    ValueError – If embedding shape is invalid or problem type is unknown

    +
    +
    +
    + +
    + +
    +
    +

    open_attachment

    +
    +
    +imgutils.generic.attachment.open_attachment(repo_id: str, model_name: str, hf_token: str | None = None) → Attachment[source]
    +

    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.

    +
    +
    Parameters:
    +
      +

    • repo_id (str) – The Hugging Face repository ID

      • +

    • model_name (str) – Name of the model

      • +

    • hf_token (Optional[str]) – Optional Hugging Face authentication token

      • +
    +
    +
    Returns:
    +

    An Attachment instance

    +
    +
    Return type:
    +

    Attachment

    +
    +
    +
    + +
    +
    + + + +
    +
    + +
    +
    +
    +
    + +
    + + Other Versions + v: dev/attachments + + +
    +
    +
    Tags
    +
    v0.10.0
    +
    v0.11.0
    +
    v0.11.1
    +
    v0.11.2
    +
    v0.12.0
    +
    +
    +
    Branches
    +
    dev/attachments
    +
    HEAD
    +
    dev/denormalize
    +
    main
    +
    +
    +
    + + + \ No newline at end of file diff --git a/dev/attachments/api_doc/generic/classify.html b/dev/attachments/api_doc/generic/classify.html index 4d350839f92a6f77f5696fa0728fdda146675d4b..d1e4ad76062980c6649988693974aa150505b0d8 100644 --- a/dev/attachments/api_doc/generic/classify.html +++ b/dev/attachments/api_doc/generic/classify.html @@ -23,7 +23,7 @@ - + @@ -62,6 +62,7 @@
  • imgutils.detect
  • imgutils.edge
  • imgutils.generic