Add metadata_info.py for metadata writer.
PiperOrigin-RevId: 480146881
This commit is contained in:
Yuqi Li
Copybara-Service
parent
62d2ae601e
commit
cbc7eb661b
21
mediapipe/tasks/python/metadata/metadata_writers/BUILD
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21
mediapipe/tasks/python/metadata/metadata_writers/BUILD
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# Placeholder for internal Python strict library compatibility macro.
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package(
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default_visibility = [
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"//visibility:public",
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],
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licenses = ["notice"],
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)
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py_library(
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name = "metadata_info",
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srcs = [
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"metadata_info.py",
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],
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srcs_version = "PY3",
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visibility = ["//visibility:public"],
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deps = [
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"//mediapipe/tasks/metadata:metadata_schema_py",
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"//mediapipe/tasks/metadata:schema_py",
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],
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)
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13
mediapipe/tasks/python/metadata/metadata_writers/__init__.py
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13
mediapipe/tasks/python/metadata/metadata_writers/__init__.py
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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@ -0,0 +1,446 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Helper classes for common model metadata information."""
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import csv
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import os
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from typing import List, Optional, Type
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from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb
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from mediapipe.tasks.metadata import schema_py_generated as _schema_fb
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# Min and max values for UINT8 tensors.
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_MIN_UINT8 = 0
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_MAX_UINT8 = 255
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# Default description for vocabulary files.
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_VOCAB_FILE_DESCRIPTION = ("Vocabulary file to convert natural language "
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"words to embedding vectors.")
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class GeneralMd:
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"""A container for common metadata information of a model.
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Attributes:
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name: name of the model.
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version: version of the model.
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description: description of what the model does.
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author: author of the model.
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licenses: licenses of the model.
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"""
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def __init__(self,
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name: Optional[str] = None,
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version: Optional[str] = None,
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description: Optional[str] = None,
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author: Optional[str] = None,
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licenses: Optional[str] = None) -> None:
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self.name = name
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self.version = version
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self.description = description
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self.author = author
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self.licenses = licenses
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def create_metadata(self) -> _metadata_fb.ModelMetadataT:
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"""Creates the model metadata based on the general model information.
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Returns:
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A Flatbuffers Python object of the model metadata.
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"""
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model_metadata = _metadata_fb.ModelMetadataT()
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model_metadata.name = self.name
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model_metadata.version = self.version
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model_metadata.description = self.description
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model_metadata.author = self.author
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model_metadata.license = self.licenses
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return model_metadata
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class AssociatedFileMd:
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"""A container for common associated file metadata information.
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Attributes:
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file_path: path to the associated file.
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description: description of the associated file.
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file_type: file type of the associated file [1].
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locale: locale of the associated file [2].
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L77
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L176
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"""
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def __init__(
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self,
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file_path: str,
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description: Optional[str] = None,
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file_type: Optional[int] = _metadata_fb.AssociatedFileType.UNKNOWN,
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locale: Optional[str] = None) -> None:
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self.file_path = file_path
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self.description = description
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self.file_type = file_type
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self.locale = locale
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def create_metadata(self) -> _metadata_fb.AssociatedFileT:
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"""Creates the associated file metadata.
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Returns:
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A Flatbuffers Python object of the associated file metadata.
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"""
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file_metadata = _metadata_fb.AssociatedFileT()
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file_metadata.name = os.path.basename(self.file_path)
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file_metadata.description = self.description
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file_metadata.type = self.file_type
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file_metadata.locale = self.locale
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return file_metadata
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class LabelFileMd(AssociatedFileMd):
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"""A container for label file metadata information."""
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_LABEL_FILE_DESCRIPTION = ("Labels for categories that the model can "
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"recognize.")
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_FILE_TYPE = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
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def __init__(self, file_path: str, locale: Optional[str] = None) -> None:
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"""Creates a LabelFileMd object.
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Args:
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file_path: file_path of the label file.
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locale: locale of the label file [1].
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L176
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"""
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super().__init__(file_path, self._LABEL_FILE_DESCRIPTION, self._FILE_TYPE,
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locale)
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class ScoreCalibrationMd:
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"""A container for score calibration [1] metadata information.
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
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"""
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_SCORE_CALIBRATION_FILE_DESCRIPTION = (
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"Contains sigmoid-based score calibration parameters. The main purposes "
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"of score calibration is to make scores across classes comparable, so "
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"that a common threshold can be used for all output classes.")
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_FILE_TYPE = _metadata_fb.AssociatedFileType.TENSOR_AXIS_SCORE_CALIBRATION
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def __init__(self,
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score_transformation_type: _metadata_fb.ScoreTransformationType,
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default_score: float, file_path: str) -> None:
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"""Creates a ScoreCalibrationMd object.
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Args:
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score_transformation_type: type of the function used for transforming the
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uncalibrated score before applying score calibration.
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default_score: the default calibrated score to apply if the uncalibrated
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score is below min_score or if no parameters were specified for a given
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index.
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file_path: file_path of the score calibration file [1].
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L133
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Raises:
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ValueError: if the score_calibration file is malformed.
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"""
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self._score_transformation_type = score_transformation_type
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self._default_score = default_score
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self._file_path = file_path
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# Sanity check the score calibration file.
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with open(self._file_path) as calibration_file:
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csv_reader = csv.reader(calibration_file, delimiter=",")
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for row in csv_reader:
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if row and len(row) != 3 and len(row) != 4:
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raise ValueError(
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f"Expected empty lines or 3 or 4 parameters per line in score"
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f" calibration file, but got {len(row)}.")
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if row and float(row[0]) < 0:
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raise ValueError(
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f"Expected scale to be a non-negative value, but got "
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f"{float(row[0])}.")
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def create_metadata(self) -> _metadata_fb.ProcessUnitT:
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"""Creates the score calibration metadata based on the information.
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Returns:
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A Flatbuffers Python object of the score calibration metadata.
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"""
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score_calibration = _metadata_fb.ProcessUnitT()
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score_calibration.optionsType = (
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_metadata_fb.ProcessUnitOptions.ScoreCalibrationOptions)
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options = _metadata_fb.ScoreCalibrationOptionsT()
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options.scoreTransformation = self._score_transformation_type
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options.defaultScore = self._default_score
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score_calibration.options = options
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return score_calibration
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def create_score_calibration_file_md(self) -> AssociatedFileMd:
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return AssociatedFileMd(self._file_path,
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self._SCORE_CALIBRATION_FILE_DESCRIPTION,
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self._FILE_TYPE)
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class TensorMd:
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"""A container for common tensor metadata information.
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Attributes:
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name: name of the tensor.
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description: description of what the tensor is.
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min_values: per-channel minimum value of the tensor.
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max_values: per-channel maximum value of the tensor.
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content_type: content_type of the tensor.
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associated_files: information of the associated files in the tensor.
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tensor_name: name of the corresponding tensor [1] in the TFLite model. It is
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used to locate the corresponding tensor and decide the order of the tensor
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metadata [2] when populating model metadata.
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[1]:
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https://github.com/tensorflow/tensorflow/blob/cb67fef35567298b40ac166b0581cd8ad68e5a3a/tensorflow/lite/schema/schema.fbs#L1129-L1136
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L623-L640
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"""
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def __init__(
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self,
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name: Optional[str] = None,
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description: Optional[str] = None,
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min_values: Optional[List[float]] = None,
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max_values: Optional[List[float]] = None,
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content_type: int = _metadata_fb.ContentProperties.FeatureProperties,
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associated_files: Optional[List[Type[AssociatedFileMd]]] = None,
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tensor_name: Optional[str] = None) -> None:
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self.name = name
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self.description = description
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self.min_values = min_values
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self.max_values = max_values
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self.content_type = content_type
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self.associated_files = associated_files
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self.tensor_name = tensor_name
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def create_metadata(self) -> _metadata_fb.TensorMetadataT:
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"""Creates the input tensor metadata based on the information.
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Returns:
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A Flatbuffers Python object of the input metadata.
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"""
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tensor_metadata = _metadata_fb.TensorMetadataT()
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tensor_metadata.name = self.name
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tensor_metadata.description = self.description
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# Create min and max values
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stats = _metadata_fb.StatsT()
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stats.max = self.max_values
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stats.min = self.min_values
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tensor_metadata.stats = stats
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# Create content properties
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content = _metadata_fb.ContentT()
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if self.content_type is _metadata_fb.ContentProperties.FeatureProperties:
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content.contentProperties = _metadata_fb.FeaturePropertiesT()
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elif self.content_type is _metadata_fb.ContentProperties.ImageProperties:
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content.contentProperties = _metadata_fb.ImagePropertiesT()
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elif self.content_type is (
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_metadata_fb.ContentProperties.BoundingBoxProperties):
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content.contentProperties = _metadata_fb.BoundingBoxPropertiesT()
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elif self.content_type is _metadata_fb.ContentProperties.AudioProperties:
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content.contentProperties = _metadata_fb.AudioPropertiesT()
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content.contentPropertiesType = self.content_type
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tensor_metadata.content = content
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# TODO: check if multiple label files have populated locale.
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# Create associated files
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if self.associated_files:
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tensor_metadata.associatedFiles = [
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file.create_metadata() for file in self.associated_files
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]
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return tensor_metadata
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class InputImageTensorMd(TensorMd):
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"""A container for input image tensor metadata information.
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Attributes:
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norm_mean: the mean value used in tensor normalization [1].
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norm_std: the std value used in the tensor normalization [1]. norm_mean and
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norm_std must have the same dimension.
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color_space_type: the color space type of the input image [2].
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L198
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"""
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# Min and max float values for image pixels.
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_MIN_PIXEL = 0.0
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_MAX_PIXEL = 255.0
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def __init__(
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self,
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name: Optional[str] = None,
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description: Optional[str] = None,
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norm_mean: Optional[List[float]] = None,
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norm_std: Optional[List[float]] = None,
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color_space_type: Optional[int] = _metadata_fb.ColorSpaceType.UNKNOWN,
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tensor_type: Optional["_schema_fb.TensorType"] = None) -> None:
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"""Initializes the instance of InputImageTensorMd.
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Args:
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name: name of the tensor.
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description: description of what the tensor is.
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norm_mean: the mean value used in tensor normalization [1].
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norm_std: the std value used in the tensor normalization [1]. norm_mean
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and norm_std must have the same dimension.
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color_space_type: the color space type of the input image [2].
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tensor_type: data type of the tensor.
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L198
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Raises:
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ValueError: if norm_mean and norm_std have different dimensions.
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"""
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if norm_std and norm_mean and len(norm_std) != len(norm_mean):
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raise ValueError(
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f"norm_mean and norm_std are expected to be the same dim. But got "
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f"{len(norm_mean)} and {len(norm_std)}")
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if tensor_type is _schema_fb.TensorType.UINT8:
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min_values = [_MIN_UINT8]
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max_values = [_MAX_UINT8]
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elif tensor_type is _schema_fb.TensorType.FLOAT32 and norm_std and norm_mean:
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min_values = [
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float(self._MIN_PIXEL - mean) / std
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for mean, std in zip(norm_mean, norm_std)
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]
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max_values = [
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float(self._MAX_PIXEL - mean) / std
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for mean, std in zip(norm_mean, norm_std)
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]
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else:
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# Uint8 and Float32 are the two major types currently. And Task library
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# doesn't support other types so far.
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min_values = None
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max_values = None
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super().__init__(name, description, min_values, max_values,
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_metadata_fb.ContentProperties.ImageProperties)
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self.norm_mean = norm_mean
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self.norm_std = norm_std
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self.color_space_type = color_space_type
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def create_metadata(self) -> _metadata_fb.TensorMetadataT:
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"""Creates the input image metadata based on the information.
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Returns:
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A Flatbuffers Python object of the input image metadata.
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"""
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tensor_metadata = super().create_metadata()
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tensor_metadata.content.contentProperties.colorSpace = self.color_space_type
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# Create normalization parameters
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if self.norm_mean and self.norm_std:
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normalization = _metadata_fb.ProcessUnitT()
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normalization.optionsType = (
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_metadata_fb.ProcessUnitOptions.NormalizationOptions)
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normalization.options = _metadata_fb.NormalizationOptionsT()
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normalization.options.mean = self.norm_mean
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normalization.options.std = self.norm_std
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tensor_metadata.processUnits = [normalization]
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return tensor_metadata
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class ClassificationTensorMd(TensorMd):
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"""A container for the classification tensor metadata information.
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Attributes:
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label_files: information of the label files [1] in the classification
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tensor.
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score_calibration_md: information of the score calibration operation [2] in
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the classification tensor.
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[1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
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"""
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# Min and max float values for classification results.
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_MIN_FLOAT = 0.0
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_MAX_FLOAT = 1.0
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def __init__(self,
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name: Optional[str] = None,
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description: Optional[str] = None,
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label_files: Optional[List[LabelFileMd]] = None,
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tensor_type: Optional[int] = None,
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score_calibration_md: Optional[ScoreCalibrationMd] = None,
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tensor_name: Optional[str] = None) -> None:
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"""Initializes the instance of ClassificationTensorMd.
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Args:
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name: name of the tensor.
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description: description of what the tensor is.
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label_files: information of the label files [1] in the classification
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tensor.
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tensor_type: data type of the tensor.
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score_calibration_md: information of the score calibration files operation
|
||||
[2] in the classification tensor.
|
||||
tensor_name: name of the corresponding tensor [3] in the TFLite model. It
|
||||
is used to locate the corresponding classification tensor and decide the
|
||||
order of the tensor metadata [4] when populating model metadata.
|
||||
[1]:
|
||||
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
|
||||
[2]:
|
||||
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
|
||||
[3]:
|
||||
https://github.com/tensorflow/tensorflow/blob/cb67fef35567298b40ac166b0581cd8ad68e5a3a/tensorflow/lite/schema/schema.fbs#L1129-L1136
|
||||
[4]:
|
||||
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L623-L640
|
||||
"""
|
||||
self.score_calibration_md = score_calibration_md
|
||||
|
||||
if tensor_type is _schema_fb.TensorType.UINT8:
|
||||
min_values = [_MIN_UINT8]
|
||||
max_values = [_MAX_UINT8]
|
||||
elif tensor_type is _schema_fb.TensorType.FLOAT32:
|
||||
min_values = [self._MIN_FLOAT]
|
||||
max_values = [self._MAX_FLOAT]
|
||||
else:
|
||||
# Uint8 and Float32 are the two major types currently. And Task library
|
||||
# doesn't support other types so far.
|
||||
min_values = None
|
||||
max_values = None
|
||||
|
||||
associated_files = label_files or []
|
||||
if self.score_calibration_md:
|
||||
associated_files.append(
|
||||
score_calibration_md.create_score_calibration_file_md())
|
||||
|
||||
super().__init__(name, description, min_values, max_values,
|
||||
_metadata_fb.ContentProperties.FeatureProperties,
|
||||
associated_files, tensor_name)
|
||||
|
||||
def create_metadata(self) -> _metadata_fb.TensorMetadataT:
|
||||
"""Creates the classification tensor metadata based on the information."""
|
||||
tensor_metadata = super().create_metadata()
|
||||
if self.score_calibration_md:
|
||||
tensor_metadata.processUnits = [
|
||||
self.score_calibration_md.create_metadata()
|
||||
]
|
||||
return tensor_metadata
|
||||
26
mediapipe/tasks/python/test/metadata/metadata_writers/BUILD
Normal file
26
mediapipe/tasks/python/test/metadata/metadata_writers/BUILD
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
# Placeholder for internal Python strict test compatibility macro.
|
||||
|
||||
package(
|
||||
default_visibility = [
|
||||
"//visibility:public",
|
||||
],
|
||||
licenses = ["notice"], # Apache 2.0
|
||||
)
|
||||
|
||||
py_test(
|
||||
name = "metadata_info_test",
|
||||
srcs = ["metadata_info_test.py"],
|
||||
data = [
|
||||
"//mediapipe/tasks/testdata/metadata:data_files",
|
||||
],
|
||||
python_version = "PY3",
|
||||
srcs_version = "PY3",
|
||||
deps = [
|
||||
"//mediapipe/tasks/metadata:metadata_schema_py",
|
||||
"//mediapipe/tasks/metadata:schema_py",
|
||||
"//mediapipe/tasks/python/metadata",
|
||||
"//mediapipe/tasks/python/metadata/metadata_writers:metadata_info",
|
||||
"//mediapipe/tasks/python/test:test_utils",
|
||||
"@flatbuffers//:runtime_py",
|
||||
],
|
||||
)
|
||||
|
|
@ -0,0 +1,343 @@
|
|||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""Tests for metadata info classes."""
|
||||
|
||||
import tempfile
|
||||
|
||||
from absl.testing import absltest
|
||||
from absl.testing import parameterized
|
||||
|
||||
import flatbuffers
|
||||
from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb
|
||||
from mediapipe.tasks.metadata import schema_py_generated as _schema_fb
|
||||
from mediapipe.tasks.python.metadata import metadata as _metadata
|
||||
from mediapipe.tasks.python.metadata.metadata_writers import metadata_info
|
||||
from mediapipe.tasks.python.test import test_utils
|
||||
|
||||
_SCORE_CALIBRATION_FILE = test_utils.get_test_data_path("score_calibration.txt")
|
||||
|
||||
|
||||
class GeneralMdTest(absltest.TestCase):
|
||||
|
||||
_EXPECTED_GENERAL_META_JSON = test_utils.get_test_data_path(
|
||||
"general_meta.json")
|
||||
|
||||
def test_create_metadata_should_succeed(self):
|
||||
general_md = metadata_info.GeneralMd(
|
||||
name="model",
|
||||
version="v1",
|
||||
description="A ML model.",
|
||||
author="MediaPipe",
|
||||
licenses="Apache")
|
||||
general_metadata = general_md.create_metadata()
|
||||
|
||||
# Create the Flatbuffers object and convert it to the json format.
|
||||
builder = flatbuffers.Builder(0)
|
||||
builder.Finish(
|
||||
general_metadata.Pack(builder),
|
||||
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
metadata_json = _metadata.convert_to_json(bytes(builder.Output()))
|
||||
|
||||
with open(self._EXPECTED_GENERAL_META_JSON, "r") as f:
|
||||
expected_json = f.read()
|
||||
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
class AssociatedFileMdTest(absltest.TestCase):
|
||||
|
||||
_EXPECTED_META_JSON = test_utils.get_test_data_path(
|
||||
"associated_file_meta.json")
|
||||
|
||||
def test_create_metadata_should_succeed(self):
|
||||
file_md = metadata_info.AssociatedFileMd(
|
||||
file_path="label.txt",
|
||||
description="The label file.",
|
||||
file_type=_metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS,
|
||||
locale="en")
|
||||
file_metadata = file_md.create_metadata()
|
||||
|
||||
# Create the Flatbuffers object and convert it to the json format.
|
||||
model_metadata = _metadata_fb.ModelMetadataT()
|
||||
model_metadata.associatedFiles = [file_metadata]
|
||||
builder = flatbuffers.Builder(0)
|
||||
builder.Finish(
|
||||
model_metadata.Pack(builder),
|
||||
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
metadata_json = _metadata.convert_to_json(bytes(builder.Output()))
|
||||
|
||||
with open(self._EXPECTED_META_JSON, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
class TensorMdTest(parameterized.TestCase):
|
||||
|
||||
_TENSOR_NAME = "input"
|
||||
_TENSOR_DESCRIPTION = "The input tensor."
|
||||
_TENSOR_MIN = 0
|
||||
_TENSOR_MAX = 1
|
||||
_LABEL_FILE_EN = "labels.txt"
|
||||
_LABEL_FILE_CN = "labels_cn.txt" # Locale label file in Chinese.
|
||||
_EXPECTED_FEATURE_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"feature_tensor_meta.json")
|
||||
_EXPECTED_IMAGE_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"image_tensor_meta.json")
|
||||
_EXPECTED_BOUNDING_BOX_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"bounding_box_tensor_meta.json")
|
||||
|
||||
@parameterized.named_parameters(
|
||||
{
|
||||
"testcase_name": "feature_tensor",
|
||||
"content_type": _metadata_fb.ContentProperties.FeatureProperties,
|
||||
"golden_json": _EXPECTED_FEATURE_TENSOR_JSON
|
||||
}, {
|
||||
"testcase_name": "image_tensor",
|
||||
"content_type": _metadata_fb.ContentProperties.ImageProperties,
|
||||
"golden_json": _EXPECTED_IMAGE_TENSOR_JSON
|
||||
}, {
|
||||
"testcase_name": "bounding_box_tensor",
|
||||
"content_type": _metadata_fb.ContentProperties.BoundingBoxProperties,
|
||||
"golden_json": _EXPECTED_BOUNDING_BOX_TENSOR_JSON
|
||||
})
|
||||
def test_create_metadata_should_succeed(self, content_type, golden_json):
|
||||
associated_file1 = metadata_info.AssociatedFileMd(
|
||||
file_path=self._LABEL_FILE_EN, locale="en")
|
||||
associated_file2 = metadata_info.AssociatedFileMd(
|
||||
file_path=self._LABEL_FILE_CN, locale="cn")
|
||||
|
||||
tensor_md = metadata_info.TensorMd(
|
||||
name=self._TENSOR_NAME,
|
||||
description=self._TENSOR_DESCRIPTION,
|
||||
min_values=[self._TENSOR_MIN],
|
||||
max_values=[self._TENSOR_MAX],
|
||||
content_type=content_type,
|
||||
associated_files=[associated_file1, associated_file2])
|
||||
tensor_metadata = tensor_md.create_metadata()
|
||||
|
||||
metadata_json = _metadata.convert_to_json(
|
||||
_create_dummy_model_metadata_with_tensor(tensor_metadata))
|
||||
with open(golden_json, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
class InputImageTensorMdTest(parameterized.TestCase):
|
||||
|
||||
_NAME = "image"
|
||||
_DESCRIPTION = "The input image."
|
||||
_NORM_MEAN = (0, 127.5, 255)
|
||||
_NORM_STD = (127.5, 127.5, 127.5)
|
||||
_COLOR_SPACE_TYPE = _metadata_fb.ColorSpaceType.RGB
|
||||
_EXPECTED_FLOAT_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"input_image_tensor_float_meta.json")
|
||||
_EXPECTED_UINT8_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"input_image_tensor_uint8_meta.json")
|
||||
_EXPECTED_UNSUPPORTED_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"input_image_tensor_unsupported_meta.json")
|
||||
|
||||
@parameterized.named_parameters(
|
||||
{
|
||||
"testcase_name": "float",
|
||||
"tensor_type": _schema_fb.TensorType.FLOAT32,
|
||||
"golden_json": _EXPECTED_FLOAT_TENSOR_JSON
|
||||
}, {
|
||||
"testcase_name": "uint8",
|
||||
"tensor_type": _schema_fb.TensorType.UINT8,
|
||||
"golden_json": _EXPECTED_UINT8_TENSOR_JSON
|
||||
}, {
|
||||
"testcase_name": "unsupported_tensor_type",
|
||||
"tensor_type": _schema_fb.TensorType.INT16,
|
||||
"golden_json": _EXPECTED_UNSUPPORTED_TENSOR_JSON
|
||||
})
|
||||
def test_create_metadata_should_succeed(self, tensor_type, golden_json):
|
||||
tesnor_md = metadata_info.InputImageTensorMd(
|
||||
name=self._NAME,
|
||||
description=self._DESCRIPTION,
|
||||
norm_mean=list(self._NORM_MEAN),
|
||||
norm_std=list(self._NORM_STD),
|
||||
color_space_type=self._COLOR_SPACE_TYPE,
|
||||
tensor_type=tensor_type)
|
||||
tensor_metadata = tesnor_md.create_metadata()
|
||||
|
||||
metadata_json = _metadata.convert_to_json(
|
||||
_create_dummy_model_metadata_with_tensor(tensor_metadata))
|
||||
with open(golden_json, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
def test_init_should_throw_exception_with_incompatible_mean_and_std(self):
|
||||
norm_mean = [0]
|
||||
norm_std = [1, 2]
|
||||
with self.assertRaises(ValueError) as error:
|
||||
metadata_info.InputImageTensorMd(norm_mean=norm_mean, norm_std=norm_std)
|
||||
self.assertEqual(
|
||||
f"norm_mean and norm_std are expected to be the same dim. But got "
|
||||
f"{len(norm_mean)} and {len(norm_std)}", str(error.exception))
|
||||
|
||||
|
||||
class ClassificationTensorMdTest(parameterized.TestCase):
|
||||
|
||||
_NAME = "probability"
|
||||
_DESCRIPTION = "The classification result tensor."
|
||||
_LABEL_FILE_EN = "labels.txt"
|
||||
_LABEL_FILE_CN = "labels_cn.txt" # Locale label file in Chinese.
|
||||
_CALIBRATION_DEFAULT_SCORE = 0.2
|
||||
_EXPECTED_FLOAT_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"classification_tensor_float_meta.json")
|
||||
_EXPECTED_UINT8_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"classification_tensor_uint8_meta.json")
|
||||
_EXPECTED_UNSUPPORTED_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"classification_tensor_unsupported_meta.json")
|
||||
|
||||
@parameterized.named_parameters(
|
||||
{
|
||||
"testcase_name": "float",
|
||||
"tensor_type": _schema_fb.TensorType.FLOAT32,
|
||||
"golden_json": _EXPECTED_FLOAT_TENSOR_JSON
|
||||
}, {
|
||||
"testcase_name": "uint8",
|
||||
"tensor_type": _schema_fb.TensorType.UINT8,
|
||||
"golden_json": _EXPECTED_UINT8_TENSOR_JSON
|
||||
}, {
|
||||
"testcase_name": "unsupported_tensor_type",
|
||||
"tensor_type": _schema_fb.TensorType.INT16,
|
||||
"golden_json": _EXPECTED_UNSUPPORTED_TENSOR_JSON
|
||||
})
|
||||
def test_create_metadata_should_succeed(self, tensor_type, golden_json):
|
||||
label_file_en = metadata_info.LabelFileMd(
|
||||
file_path=self._LABEL_FILE_EN, locale="en")
|
||||
label_file_cn = metadata_info.LabelFileMd(
|
||||
file_path=self._LABEL_FILE_CN, locale="cn")
|
||||
score_calibration_md = metadata_info.ScoreCalibrationMd(
|
||||
_metadata_fb.ScoreTransformationType.IDENTITY,
|
||||
self._CALIBRATION_DEFAULT_SCORE, _SCORE_CALIBRATION_FILE)
|
||||
|
||||
tesnor_md = metadata_info.ClassificationTensorMd(
|
||||
name=self._NAME,
|
||||
description=self._DESCRIPTION,
|
||||
label_files=[label_file_en, label_file_cn],
|
||||
tensor_type=tensor_type,
|
||||
score_calibration_md=score_calibration_md)
|
||||
tensor_metadata = tesnor_md.create_metadata()
|
||||
|
||||
metadata_json = _metadata.convert_to_json(
|
||||
_create_dummy_model_metadata_with_tensor(tensor_metadata))
|
||||
with open(golden_json, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
class ScoreCalibrationMdTest(absltest.TestCase):
|
||||
_DEFAULT_VALUE = 0.2
|
||||
_EXPECTED_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
"score_calibration_tensor_meta.json")
|
||||
_EXPECTED_MODEL_META_JSON = test_utils.get_test_data_path(
|
||||
"score_calibration_file_meta.json")
|
||||
|
||||
def test_create_metadata_should_succeed(self):
|
||||
score_calibration_md = metadata_info.ScoreCalibrationMd(
|
||||
_metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
|
||||
_SCORE_CALIBRATION_FILE)
|
||||
score_calibration_metadata = score_calibration_md.create_metadata()
|
||||
|
||||
metadata_json = _metadata.convert_to_json(
|
||||
_create_dummy_model_metadata_with_process_uint(
|
||||
score_calibration_metadata))
|
||||
with open(self._EXPECTED_TENSOR_JSON, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
def test_create_score_calibration_file_md_should_succeed(self):
|
||||
score_calibration_md = metadata_info.ScoreCalibrationMd(
|
||||
_metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
|
||||
_SCORE_CALIBRATION_FILE)
|
||||
score_calibration_file_md = (
|
||||
score_calibration_md.create_score_calibration_file_md())
|
||||
file_metadata = score_calibration_file_md.create_metadata()
|
||||
|
||||
# Create the Flatbuffers object and convert it to the json format.
|
||||
model_metadata = _metadata_fb.ModelMetadataT()
|
||||
model_metadata.associatedFiles = [file_metadata]
|
||||
builder = flatbuffers.Builder(0)
|
||||
builder.Finish(
|
||||
model_metadata.Pack(builder),
|
||||
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
metadata_json = _metadata.convert_to_json(bytes(builder.Output()))
|
||||
|
||||
with open(self._EXPECTED_MODEL_META_JSON, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
def test_create_score_calibration_file_fails_with_less_colunms(self):
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
malformed_calibration_file = test_utils.create_calibration_file(
|
||||
temp_dir, content="1.0,0.2")
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
ValueError,
|
||||
"Expected empty lines or 3 or 4 parameters per line in score" +
|
||||
" calibration file, but got 2."):
|
||||
metadata_info.ScoreCalibrationMd(
|
||||
_metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
|
||||
malformed_calibration_file)
|
||||
|
||||
def test_create_score_calibration_file_fails_with_negative_scale(self):
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
malformed_calibration_file = test_utils.create_calibration_file(
|
||||
temp_dir, content="-1.0,0.2,0.1")
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
ValueError,
|
||||
"Expected scale to be a non-negative value, but got -1.0."):
|
||||
metadata_info.ScoreCalibrationMd(
|
||||
_metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
|
||||
malformed_calibration_file)
|
||||
|
||||
|
||||
def _create_dummy_model_metadata_with_tensor(
|
||||
tensor_metadata: _metadata_fb.TensorMetadataT) -> bytes:
|
||||
# Create a dummy model using the tensor metadata.
|
||||
subgraph_metadata = _metadata_fb.SubGraphMetadataT()
|
||||
subgraph_metadata.inputTensorMetadata = [tensor_metadata]
|
||||
model_metadata = _metadata_fb.ModelMetadataT()
|
||||
model_metadata.subgraphMetadata = [subgraph_metadata]
|
||||
|
||||
# Create the Flatbuffers object and convert it to the json format.
|
||||
builder = flatbuffers.Builder(0)
|
||||
builder.Finish(
|
||||
model_metadata.Pack(builder),
|
||||
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
return bytes(builder.Output())
|
||||
|
||||
|
||||
def _create_dummy_model_metadata_with_process_uint(
|
||||
process_unit_metadata: _metadata_fb.ProcessUnitT) -> bytes:
|
||||
# Create a dummy model using the tensor metadata.
|
||||
subgraph_metadata = _metadata_fb.SubGraphMetadataT()
|
||||
subgraph_metadata.inputProcessUnits = [process_unit_metadata]
|
||||
model_metadata = _metadata_fb.ModelMetadataT()
|
||||
model_metadata.subgraphMetadata = [subgraph_metadata]
|
||||
|
||||
# Create the Flatbuffers object and convert it to the json format.
|
||||
builder = flatbuffers.Builder(0)
|
||||
builder.Finish(
|
||||
model_metadata.Pack(builder),
|
||||
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
return bytes(builder.Output())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
absltest.main()
|
||||
|
|
@ -43,3 +43,13 @@ def get_test_data_path(file_or_dirname: str) -> str:
|
|||
if f.endswith(file_or_dirname):
|
||||
return os.path.join(directory, f)
|
||||
raise ValueError("No %s in test directory" % file_or_dirname)
|
||||
|
||||
|
||||
def create_calibration_file(file_dir: str,
|
||||
file_name: str = "score_calibration.txt",
|
||||
content: str = "1.0,2.0,3.0,4.0") -> str:
|
||||
"""Creates the calibration file."""
|
||||
calibration_file = os.path.join(file_dir, file_name)
|
||||
with open(calibration_file, mode="w") as file:
|
||||
file.write(content)
|
||||
return calibration_file
|
||||
|
|
|
|||
28
mediapipe/tasks/testdata/metadata/BUILD
vendored
28
mediapipe/tasks/testdata/metadata/BUILD
vendored
|
|
@ -33,7 +33,21 @@ mediapipe_files(srcs = [
|
|||
|
||||
exports_files([
|
||||
"external_file",
|
||||
"general_meta.json",
|
||||
"golden_json.json",
|
||||
"associated_file_meta.json",
|
||||
"bounding_box_tensor_meta.json",
|
||||
"classification_tensor_float_meta.json",
|
||||
"classification_tensor_uint8_meta.json",
|
||||
"classification_tensor_unsupported_meta.json",
|
||||
"feature_tensor_meta.json",
|
||||
"image_tensor_meta.json",
|
||||
"input_image_tensor_float_meta.json",
|
||||
"input_image_tensor_uint8_meta.json",
|
||||
"input_image_tensor_unsupported_meta.json",
|
||||
"score_calibration.txt",
|
||||
"score_calibration_file_meta.json",
|
||||
"score_calibration_tensor_meta.json",
|
||||
])
|
||||
|
||||
filegroup(
|
||||
|
|
@ -51,7 +65,21 @@ filegroup(
|
|||
filegroup(
|
||||
name = "data_files",
|
||||
srcs = [
|
||||
"associated_file_meta.json",
|
||||
"bounding_box_tensor_meta.json",
|
||||
"classification_tensor_float_meta.json",
|
||||
"classification_tensor_uint8_meta.json",
|
||||
"classification_tensor_unsupported_meta.json",
|
||||
"external_file",
|
||||
"feature_tensor_meta.json",
|
||||
"general_meta.json",
|
||||
"golden_json.json",
|
||||
"image_tensor_meta.json",
|
||||
"input_image_tensor_float_meta.json",
|
||||
"input_image_tensor_uint8_meta.json",
|
||||
"input_image_tensor_unsupported_meta.json",
|
||||
"score_calibration.txt",
|
||||
"score_calibration_file_meta.json",
|
||||
"score_calibration_tensor_meta.json",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
10
mediapipe/tasks/testdata/metadata/associated_file_meta.json
vendored
Normal file
10
mediapipe/tasks/testdata/metadata/associated_file_meta.json
vendored
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "label.txt",
|
||||
"description": "The label file.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "en"
|
||||
}
|
||||
]
|
||||
}
|
||||
35
mediapipe/tasks/testdata/metadata/bounding_box_tensor_meta.json
vendored
Normal file
35
mediapipe/tasks/testdata/metadata/bounding_box_tensor_meta.json
vendored
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "input",
|
||||
"description": "The input tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "BoundingBoxProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
"max": [
|
||||
1.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"locale": "cn"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
52
mediapipe/tasks/testdata/metadata/classification_tensor_float_meta.json
vendored
Normal file
52
mediapipe/tasks/testdata/metadata/classification_tensor_float_meta.json
vendored
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "probability",
|
||||
"description": "The classification result tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "ScoreCalibrationOptions",
|
||||
"options": {
|
||||
"default_score": 0.2
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
1.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "cn"
|
||||
},
|
||||
{
|
||||
"name": "score_calibration.txt",
|
||||
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
|
||||
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
52
mediapipe/tasks/testdata/metadata/classification_tensor_uint8_meta.json
vendored
Normal file
52
mediapipe/tasks/testdata/metadata/classification_tensor_uint8_meta.json
vendored
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "probability",
|
||||
"description": "The classification result tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "ScoreCalibrationOptions",
|
||||
"options": {
|
||||
"default_score": 0.2
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
255.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "cn"
|
||||
},
|
||||
{
|
||||
"name": "score_calibration.txt",
|
||||
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
|
||||
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
46
mediapipe/tasks/testdata/metadata/classification_tensor_unsupported_meta.json
vendored
Normal file
46
mediapipe/tasks/testdata/metadata/classification_tensor_unsupported_meta.json
vendored
Normal file
|
|
@ -0,0 +1,46 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "probability",
|
||||
"description": "The classification result tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "ScoreCalibrationOptions",
|
||||
"options": {
|
||||
"default_score": 0.2
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_AXIS_LABELS",
|
||||
"locale": "cn"
|
||||
},
|
||||
{
|
||||
"name": "score_calibration.txt",
|
||||
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
|
||||
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
35
mediapipe/tasks/testdata/metadata/feature_tensor_meta.json
vendored
Normal file
35
mediapipe/tasks/testdata/metadata/feature_tensor_meta.json
vendored
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "input",
|
||||
"description": "The input tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
"max": [
|
||||
1.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"locale": "cn"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
7
mediapipe/tasks/testdata/metadata/general_meta.json
vendored
Normal file
7
mediapipe/tasks/testdata/metadata/general_meta.json
vendored
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
"name": "model",
|
||||
"description": "A ML model.",
|
||||
"version": "v1",
|
||||
"author": "MediaPipe",
|
||||
"license": "Apache"
|
||||
}
|
||||
35
mediapipe/tasks/testdata/metadata/image_tensor_meta.json
vendored
Normal file
35
mediapipe/tasks/testdata/metadata/image_tensor_meta.json
vendored
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "input",
|
||||
"description": "The input tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
"max": [
|
||||
1.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"locale": "cn"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
47
mediapipe/tasks/testdata/metadata/input_image_tensor_float_meta.json
vendored
Normal file
47
mediapipe/tasks/testdata/metadata/input_image_tensor_float_meta.json
vendored
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "image",
|
||||
"description": "The input image.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
"color_space": "RGB"
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "NormalizationOptions",
|
||||
"options": {
|
||||
"mean": [
|
||||
0.0,
|
||||
127.5,
|
||||
255.0
|
||||
],
|
||||
"std": [
|
||||
127.5,
|
||||
127.5,
|
||||
127.5
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
2.0,
|
||||
1.0,
|
||||
0.0
|
||||
],
|
||||
"min": [
|
||||
0.0,
|
||||
-1.0,
|
||||
-2.0
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
43
mediapipe/tasks/testdata/metadata/input_image_tensor_uint8_meta.json
vendored
Normal file
43
mediapipe/tasks/testdata/metadata/input_image_tensor_uint8_meta.json
vendored
Normal file
|
|
@ -0,0 +1,43 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "image",
|
||||
"description": "The input image.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
"color_space": "RGB"
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "NormalizationOptions",
|
||||
"options": {
|
||||
"mean": [
|
||||
0.0,
|
||||
127.5,
|
||||
255.0
|
||||
],
|
||||
"std": [
|
||||
127.5,
|
||||
127.5,
|
||||
127.5
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
255.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
37
mediapipe/tasks/testdata/metadata/input_image_tensor_unsupported_meta.json
vendored
Normal file
37
mediapipe/tasks/testdata/metadata/input_image_tensor_unsupported_meta.json
vendored
Normal file
|
|
@ -0,0 +1,37 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "image",
|
||||
"description": "The input image.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
"color_space": "RGB"
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "NormalizationOptions",
|
||||
"options": {
|
||||
"mean": [
|
||||
0.0,
|
||||
127.5,
|
||||
255.0
|
||||
],
|
||||
"std": [
|
||||
127.5,
|
||||
127.5,
|
||||
127.5
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
511
mediapipe/tasks/testdata/metadata/score_calibration.txt
vendored
Normal file
511
mediapipe/tasks/testdata/metadata/score_calibration.txt
vendored
Normal file
|
|
@ -0,0 +1,511 @@
|
|||
|
||||
0.9876328110694885,0.36622241139411926,0.5352765321731567,0.71484375
|
||||
0.9584911465644836,1.0602262020111084,0.2777034342288971,0.019999999552965164
|
||||
0.9698624014854431,0.8795201778411865,0.539591908454895,0.00390625
|
||||
0.7486230731010437,1.1876736879348755,2.552982807159424,0.019999999552965164
|
||||
0.9745277166366577,0.3739396333694458,0.4621727764606476,0.19921875
|
||||
0.9683839678764343,0.6996201276779175,0.7690851092338562,0.019999999552965164
|
||||
0.6875,0.31044548749923706,1.0056899785995483,0.019999999552965164
|
||||
0.9849396347999573,0.8532888889312744,-0.2361421436071396,0.03125
|
||||
0.9878578186035156,1.0118975639343262,0.13313621282577515,0.359375
|
||||
0.9915205836296082,0.4434199929237366,1.0268371105194092,0.05078125
|
||||
0.9370332360267639,0.4586562216281891,-0.08101099729537964,0.019999999552965164
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0.9876235723495483,0.7181832790374756,0.41931474208831787,0.019999999552965164
|
||||
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|
||||
0.990247905254364,0.9825950860977173,0.6003378033638,0.00390625
|
||||
0.9635987281799316,0.3707619905471802,-0.03457726538181305,0.0390625
|
||||
0.9924789071083069,1.485293984413147,0.5796234607696533,0.00390625
|
||||
0.9839015603065491,0.06343062222003937,1.9442640542984009,0.5
|
||||
0.9927193522453308,0.7006005048751831,0.3714500069618225,0.019999999552965164
|
||||
0.9870567321777344,0.869498610496521,1.5008329153060913,0.00390625
|
||||
0.9002388119697571,0.4945279657840729,-0.27996397018432617,0.0078125
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0.98891282081604,0.8541091680526733,0.5112633109092712,0.66796875
|
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0.9001862406730652,0.43330734968185425,0.3592444360256195,0.00390625
|
||||
0.958705723285675,0.7425220012664795,0.15833647549152374,0.00390625
|
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0.9910086989402771,0.9245886206626892,0.8454338908195496,0.01953125
|
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0.9912900328636169,1.3806378841400146,1.0953043699264526,0.99609375
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0.9887956976890564,1.0331758260726929,0.6490115523338318,0.640625
|
||||
0.8638584017753601,0.902369499206543,-0.2767508327960968,0.0078125
|
||||
0.7059138417243958,1.0,1.032223091723683e-11,0.019999999552965164
|
||||
0.9889519810676575,0.8361310362815857,0.811896800994873,0.03515625
|
||||
0.970467209815979,0.07315781712532043,0.20799599587917328,0.00390625
|
||||
0.9828550219535828,0.8393198251724243,0.6089786291122437,0.28515625
|
||||
0.9553551077842712,0.7775288820266724,-0.4464336037635803,0.046875
|
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0.9782186150550842,0.4313304126262665,0.4458310604095459,0.019999999552965164
|
||||
0.9371097087860107,0.9338632225990295,1.3358187675476074,0.019999999552965164
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||||
0.9861361384391785,0.24091234803199768,1.4301774501800537,0.80078125
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||||
0.9890525341033936,1.1365840435028076,0.3055979013442993,0.00390625
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0.957517683506012,0.058012738823890686,0.15909947454929352,0.046875
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0.9762251377105713,0.72292160987854,0.49151331186294556,0.019999999552965164
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||||
0.9875496625900269,0.9114606976509094,-0.5052767992019653,0.05859375
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||||
0.9715835452079773,0.8113637566566467,-2.0302956104278564,0.019999999552965164
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||||
0.9846333265304565,0.49688151478767395,0.7285738587379456,0.019999999552965164
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0.98553466796875,0.1484774351119995,1.3616747856140137,0.5859375
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||||
0.9866309762001038,1.0217945575714111,-0.8717418313026428,0.02734375
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0.9891880750656128,0.42588523030281067,0.7833192944526672,0.109375
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||||
0.9870361685752869,0.8525673151016235,1.2773776054382324,0.019999999552965164
|
||||
0.9897037744522095,0.8012522459030151,0.3973642885684967,0.109375
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||||
0.9828903079032898,1.1558295488357544,-0.6781614422798157,0.5859375
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||||
0.9924454689025879,1.1040401458740234,1.3243318796157837,0.019999999552965164
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0.9826735258102417,1.0064337253570557,-0.5324167013168335,0.38671875
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0.949999988079071,0.8152432441711426,0.6293236613273621,0.00390625
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||||
0.9905489087104797,0.9191447496414185,0.5621309876441956,0.019999999552965164
|
||||
0.9664857387542725,0.5995981693267822,-0.7409313321113586,0.01171875
|
||||
0.9847198724746704,0.8284208178520203,0.2851041555404663,0.9296875
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||||
0.9342833757400513,0.5566492676734924,0.6875373721122742,0.019999999552965164
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||||
0.8894915580749512,0.4102778434753418,0.37977635860443115,0.01953125
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||||
0.9870865941047668,0.44245558977127075,0.16041725873947144,0.10000000149011612
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||||
0.9890456795692444,1.1491310596466064,1.0844204425811768,0.01953125
|
||||
0.7304704785346985,0.12790271639823914,-0.1085965558886528,0.019999999552965164
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||||
0.9830618500709534,0.8738722205162048,-0.11583804339170456,0.0234375
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||||
0.9885876178741455,0.744857668876648,0.11028216779232025,0.01953125
|
||||
0.9575535655021667,0.3011772632598877,0.5136104226112366,0.00390625
|
||||
0.9298899173736572,1.1736249923706055,4.0247297286987305,0.09765625
|
||||
0.9907795190811157,1.0897759199142456,0.6261603236198425,0.019999999552965164
|
||||
0.9855174422264099,0.6543705463409424,0.08955699950456619,0.08984375
|
||||
0.976660430431366,0.5610390901565552,0.6389923095703125,0.0390625
|
||||
0.9870068430900574,0.80875563621521,-0.6651867032051086,0.08984375
|
||||
0.9652793407440186,0.5887689590454102,0.5353426933288574,0.0703125
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||||
0.9875175952911377,0.7699108123779297,0.876632034778595,0.019999999552965164
|
||||
0.9016479849815369,0.9994669556617737,0.30356451869010925,0.015625
|
||||
0.989987850189209,0.7350922226905823,0.8748764991760254,0.0078125
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0.983323335647583,0.8931586146354675,1.0226351022720337,0.01171875
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0.9914804100990295,0.9369975328445435,0.8283791542053223,0.019999999552965164
|
||||
0.9704275727272034,1.124052882194519,0.9457330107688904,0.019999999552965164
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||||
0.9867291450500488,0.9667392373085022,-0.6122757196426392,0.44140625
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||||
0.9887421131134033,0.7823470234870911,0.343982458114624,0.00390625
|
||||
0.9861542582511902,0.9171664118766785,0.35665032267570496,0.019999999552965164
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||||
0.9772396683692932,0.08705096691846848,1.7621256113052368,0.66796875
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0.9819098114967346,0.8605496883392334,0.5151250958442688,0.01171875
|
||||
0.982971727848053,0.5631197690963745,1.608361005783081,0.019999999552965164
|
||||
|
||||
0.9914254546165466,0.3850722908973694,1.4068152904510498,0.98828125
|
||||
0.9880355596542358,1.1387118101119995,1.4653834104537964,0.05859375
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||||
0.9586950540542603,1.7633997201919556,1.0344760417938232,0.019999999552965164
|
||||
0.9828103184700012,0.8817474842071533,0.7680216431617737,0.890625
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||||
0.9880233407020569,0.899823784828186,0.44692227244377136,0.19921875
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0.9862816333770752,0.8610615134239197,0.4195229709148407,0.03125
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0.9813369512557983,0.8014124631881714,1.1136316061019897,0.0078125
|
||||
0.9148907661437988,0.5909111499786377,1.2860896587371826,0.015625
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0.9865161776542664,0.8720636963844299,0.6233670115470886,0.015625
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||||
0.9786784648895264,0.48225611448287964,-0.005022380966693163,0.12109375
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0.9843324422836304,1.0519789457321167,-2.2056643962860107,0.03125
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||||
0.9688847064971924,0.8007095456123352,0.14495795965194702,0.1640625
|
||||
0.9724696278572083,0.9987169504165649,0.32869264483451843,0.019999999552965164
|
||||
0.9875112175941467,1.0948023796081543,2.15657114982605,0.03125
|
||||
0.9923174381256104,0.10759950429201126,0.6762840747833252,0.019999999552965164
|
||||
0.9666666388511658,0.6234443783760071,1.4971232414245605,0.0390625
|
||||
0.989655613899231,0.8248854279518127,0.4701078534126282,0.019999999552965164
|
||||
0.9753870368003845,0.6746605634689331,-0.23550045490264893,0.1640625
|
||||
0.9170913100242615,1.0504746437072754,2.7344093322753906,0.019999999552965164
|
||||
0.9821392297744751,1.4154850244522095,1.2012253999710083,0.019999999552965164
|
||||
0.9886221885681152,1.22860586643219,1.160277009010315,0.890625
|
||||
0.9877735376358032,0.6805673837661743,1.5975077152252197,0.359375
|
||||
0.9831939339637756,0.6648986339569092,1.1059051752090454,0.28515625
|
||||
0.950076162815094,0.724887490272522,0.316800057888031,0.019999999552965164
|
||||
0.9817547798156738,0.8619367480278015,-0.24251239001750946,0.109375
|
||||
0.9849069714546204,0.8399055004119873,1.7567216157913208,0.4000000059604645
|
||||
0.9821556806564331,0.8135135769844055,0.33616918325424194,0.0078125
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||||
0.8329862356185913,0.7938078045845032,1.0597797632217407,0.019999999552965164
|
||||
0.9856904149055481,0.05120579153299332,0.8267747759819031,0.5
|
||||
0.9766159057617188,0.7623113989830017,0.7656452059745789,0.09765625
|
||||
0.9885436296463013,0.9814053177833557,0.05546858534216881,0.00390625
|
||||
0.9900276064872742,0.9320858716964722,-0.36458709836006165,0.03125
|
||||
0.9058290123939514,0.7260504364967346,1.1726433038711548,0.019999999552965164
|
||||
0.9503811597824097,0.6632846593856812,0.7332696914672852,0.019999999552965164
|
||||
0.9846004247665405,0.6996731758117676,-0.8613988757133484,0.019999999552965164
|
||||
0.9897956252098083,0.8407823443412781,1.2952353954315186,0.76171875
|
||||
0.9898385405540466,0.7309674024581909,0.7317643761634827,0.019999999552965164
|
||||
0.9850022196769714,0.7537633180618286,0.3925366699695587,0.03125
|
||||
0.9858620762825012,0.9250133633613586,2.0220303535461426,0.9296875
|
||||
0.8120821714401245,0.3994182348251343,-0.4576922655105591,0.019999999552965164
|
||||
0.9496838450431824,0.8251343965530396,0.15125347673892975,0.019999999552965164
|
||||
0.9420520067214966,0.6087028384208679,1.0767998695373535,0.019999999552965164
|
||||
0.9899152517318726,0.8887513279914856,0.9602599143981934,0.019999999552965164
|
||||
0.9461711049079895,1.1373282670974731,0.6371906995773315,0.00390625
|
||||
0.9834751486778259,0.7226889729499817,0.8995278477668762,0.109375
|
||||
0.9850850105285645,1.2857465744018555,-2.2220215797424316,0.38671875
|
||||
0.9789451956748962,0.9153420925140381,0.12551555037498474,0.01171875
|
||||
0.8774109482765198,0.9271970987319946,0.5529487729072571,0.019999999552965164
|
||||
0.9074040651321411,0.920030951499939,0.40618932247161865,0.00390625
|
||||
0.9878932237625122,0.5347745418548584,0.8865230679512024,0.046875
|
||||
0.937852144241333,1.1346293687820435,-0.3324768841266632,0.019999999552965164
|
||||
0.7542195916175842,0.44728168845176697,0.45312440395355225,0.019999999552965164
|
||||
0.9915731549263,1.3838905096054077,-0.043990228325128555,0.01171875
|
||||
0.9284758567810059,0.4973248541355133,0.9887621998786926,0.019999999552965164
|
||||
0.9700435400009155,0.8664135336875916,1.0059133768081665,0.046875
|
||||
0.9667003750801086,0.7796391844749451,-0.10554620623588562,0.00390625
|
||||
0.9698932766914368,0.7340040802955627,0.4837290942668915,0.00390625
|
||||
0.973517894744873,0.9678344130516052,0.36683231592178345,0.00390625
|
||||
0.9770389795303345,0.8958415389060974,1.2423408031463623,0.015625
|
||||
0.9902989864349365,0.7568255066871643,0.9843511581420898,0.019999999552965164
|
||||
0.9908176064491272,0.8731094002723694,0.6906698346138,0.00390625
|
||||
0.9901729226112366,0.8561913371086121,0.8783953189849854,0.5859375
|
||||
9
mediapipe/tasks/testdata/metadata/score_calibration_file_meta.json
vendored
Normal file
9
mediapipe/tasks/testdata/metadata/score_calibration_file_meta.json
vendored
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
{
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "score_calibration.txt",
|
||||
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
|
||||
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
|
||||
}
|
||||
]
|
||||
}
|
||||
15
mediapipe/tasks/testdata/metadata/score_calibration_tensor_meta.json
vendored
Normal file
15
mediapipe/tasks/testdata/metadata/score_calibration_tensor_meta.json
vendored
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_process_units": [
|
||||
{
|
||||
"options_type": "ScoreCalibrationOptions",
|
||||
"options": {
|
||||
"score_transformation": "LOG",
|
||||
"default_score": 0.2
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
84
third_party/external_files.bzl
vendored
84
third_party/external_files.bzl
vendored
|
|
@ -22,12 +22,24 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/albert_with_metadata.tflite?generation=1661875651648830"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_associated_file_meta_json",
|
||||
sha256 = "5b2cba11ae893e1226af6570813955889e9f171d6d2c67b3e96ecb6b96d8c681",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/associated_file_meta.json?generation=1665422792304395"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_bert_text_classifier_tflite",
|
||||
sha256 = "1e5a550c09bff0a13e61858bcfac7654d7fcc6d42106b4f15e11117695069600",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/bert_text_classifier.tflite?generation=1663009542017720"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_bounding_box_tensor_meta_json",
|
||||
sha256 = "cc019cee86529955a24a3d43ca3d778fa366bcb90d67c8eaf55696789833841a",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/bounding_box_tensor_meta.json?generation=1665422797529909"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_BUILD",
|
||||
sha256 = "d2b2a8346202691d7f831887c84e9642e974f64ed67851d9a58cf15c94b1f6b3",
|
||||
|
|
@ -70,6 +82,24 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/cats_and_dogs_no_resizing.jpg?generation=1661875687251296"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_classification_tensor_float_meta_json",
|
||||
sha256 = "1d10b1c9c87eabac330651136804074ddc134779e94a73cf783207c3aa2a5619",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/classification_tensor_float_meta.json?generation=1665422803073223"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_classification_tensor_uint8_meta_json",
|
||||
sha256 = "74f4d64ee0017d11e0fdc975a88d974d73b72b889fd4d67992356052edde0f1e",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/classification_tensor_uint8_meta.json?generation=1665422808178685"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_classification_tensor_unsupported_meta_json",
|
||||
sha256 = "4810ad8a00f0078c6a693114d00f692aa70ff2d61030a6e516db1e654707e208",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/classification_tensor_unsupported_meta.json?generation=1665422813312699"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_coco_efficientdet_lite0_v1_1_0_quant_2021_09_06_tflite",
|
||||
sha256 = "dee1b4af055a644804d5594442300ecc9e4f7080c25b7c044c98f527eeabb6cf",
|
||||
|
|
@ -166,6 +196,18 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/face_landmark_with_attention.tflite?generation=1661875751615925"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_feature_tensor_meta_json",
|
||||
sha256 = "b2c30ddfd495956ce81085f8a143422f4310b002cfbf1c594ff2ee0576e29d6f",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/feature_tensor_meta.json?generation=1665422818797346"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_general_meta_json",
|
||||
sha256 = "b95363e4bae89b9c2af484498312aaad4efc7ff57c7eadcc4e5e7adca641445f",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/general_meta.json?generation=1665422822603848"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_golden_json_json",
|
||||
sha256 = "55c0c88748d099aa379930504df62c6c8f1d8874ea52d2f8a925f352c4c7f09c",
|
||||
|
|
@ -208,6 +250,30 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/hand_recrop.tflite?generation=1661875770633070"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_image_tensor_meta_json",
|
||||
sha256 = "aad86fde3defb379c82ff7ee48e50493a58529cdc0623cf0d7bf135c3577060e",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/image_tensor_meta.json?generation=1665422826106636"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_input_image_tensor_float_meta_json",
|
||||
sha256 = "426ecf5c3ace61db3936b950c3709daece15827ea21905ddbcdc81b1c6e70232",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_float_meta.json?generation=1665422829230563"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_input_image_tensor_uint8_meta_json",
|
||||
sha256 = "dc7ff86b606641e480c7d154b5f467e1f8c895f85733c73ba47a259a66ed187b",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_uint8_meta.json?generation=1665422832572887"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_input_image_tensor_unsupported_meta_json",
|
||||
sha256 = "443d436c2068df8201b9822c35e724acfd8004a788d388e7d74c38a2425c55df",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_unsupported_meta.json?generation=1665422835757143"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_iris_and_gaze_tflite",
|
||||
sha256 = "b6dcb860a92a3c7264a8e50786f46cecb529672cdafc17d39c78931257da661d",
|
||||
|
|
@ -472,6 +538,24 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/right_hands.jpg?generation=1661875908672404"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_score_calibration_file_meta_json",
|
||||
sha256 = "6a3c305620371f662419a496f75be5a10caebca7803b1e99d8d5d22ba51cda94",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration_file_meta.json?generation=1665422841236117"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_score_calibration_tensor_meta_json",
|
||||
sha256 = "24cbde7f76dd6a09a55d07f30493c2f254d61154eb2e8d18ed947ff56781186d",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration_tensor_meta.json?generation=1665422844327992"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_score_calibration_txt",
|
||||
sha256 = "34b0c51a8c79b4515bdd24e440c4b76a9f0fd01ef6385b36af983036e7be6271",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration.txt?generation=1665422847392804"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_segmentation_golden_rotation0_png",
|
||||
sha256 = "9ee993919b753118928ba2d14f7c5c83a6cfc23355e6943dac4ad81eedd73069",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user