Metadata Writer: add object detection metadata writer.
PiperOrigin-RevId: 513897494
This commit is contained in:
Yuqi Li
Copybara-Service
parent
13db1c55d3
commit
fe92d2e781
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@ -50,6 +50,12 @@ py_library(
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deps = [":metadata_writer"],
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)
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py_library(
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name = "object_detector",
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srcs = ["object_detector.py"],
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deps = [":metadata_writer"],
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)
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py_library(
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name = "model_asset_bundle_utils",
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srcs = ["model_asset_bundle_utils.py"],
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@ -349,6 +349,21 @@ class SentencePieceTokenizerMd:
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return tokenizer
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class ValueRangeMd:
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"""A container for value range metadata information."""
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def __init__(self, min_value: int, max_value: int) -> None:
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self.min_value = min_value
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self.max_value = max_value
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def create_metadata(self) -> _metadata_fb.ValueRangeT:
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"""Creates the value range metadata based on the information."""
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value_range_metadata = _metadata_fb.ValueRangeT()
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value_range_metadata.min = self.min_value
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value_range_metadata.max = self.max_value
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return value_range_metadata
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class TensorMd:
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"""A container for common tensor metadata information.
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@ -362,10 +377,12 @@ class TensorMd:
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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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content_range_md: information of content range [3]. [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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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L623-L640
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[3]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L385
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"""
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def __init__(
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@ -376,7 +393,9 @@ class TensorMd:
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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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tensor_name: Optional[str] = None,
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content_range_md: Optional[ValueRangeMd] = None,
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) -> 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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@ -384,6 +403,7 @@ class TensorMd:
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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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self.content_range_md = content_range_md
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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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@ -415,6 +435,8 @@ class TensorMd:
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content.contentPropertiesType = self.content_type
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tensor_metadata.content = content
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if self.content_range_md:
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tensor_metadata.content.range = self.content_range_md.create_metadata()
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# TODO: check if multiple label files have populated locale.
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# Create associated files
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@ -737,7 +759,9 @@ class ClassificationTensorMd(TensorMd):
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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,
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score_thresholding_md: Optional[ScoreThresholdingMd] = None) -> None:
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score_thresholding_md: Optional[ScoreThresholdingMd] = None,
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content_range_md: Optional[ValueRangeMd] = None,
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) -> None:
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"""Initializes the instance of ClassificationTensorMd.
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Args:
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@ -753,16 +777,18 @@ class ClassificationTensorMd(TensorMd):
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order of the tensor metadata [4] when populating model metadata.
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score_thresholding_md: information of the score thresholding [5] in the
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classification tensor.
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[1]:
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content_range_md: information of content range [6]. [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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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
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[3]:
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[3]:
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https://github.com/tensorflow/tensorflow/blob/cb67fef35567298b40ac166b0581cd8ad68e5a3a/tensorflow/lite/schema/schema.fbs#L1129-L1136
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[4]:
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[4]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L623-L640
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[5]:
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[5]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L468
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[6]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L385
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"""
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self.score_calibration_md = score_calibration_md
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self.score_thresholding_md = score_thresholding_md
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@ -784,9 +810,16 @@ class ClassificationTensorMd(TensorMd):
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associated_files.append(
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score_calibration_md.create_score_calibration_file_md())
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super().__init__(name, description, min_values, max_values,
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_metadata_fb.ContentProperties.FeatureProperties,
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associated_files, tensor_name)
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super().__init__(
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name,
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description,
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min_values,
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max_values,
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_metadata_fb.ContentProperties.FeatureProperties,
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associated_files,
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tensor_name,
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content_range_md,
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)
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def create_metadata(self) -> _metadata_fb.TensorMetadataT:
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"""Creates the classification tensor metadata based on the information."""
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@ -804,3 +837,188 @@ class ClassificationTensorMd(TensorMd):
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self.score_thresholding_md.create_metadata()
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]
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return tensor_metadata
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class LocationTensorMd(TensorMd):
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"""A container for the detection location tensor metadata information."""
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# The default order is {left, top, right, bottom}. Denote the order to be
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# {top, left, bottom, right}.
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_BOUNDING_BOX_INDEX = (1, 0, 3, 2)
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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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content_range_md: Optional[ValueRangeMd] = None,
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) -> None:
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super().__init__(
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name=name, description=description, content_range_md=content_range_md
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)
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def create_metadata(self) -> _metadata_fb.TensorMetadataT:
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"""Creates the detection location tensor metadata."""
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content = _metadata_fb.ContentT()
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content.contentPropertiesType = (
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_metadata_fb.ContentProperties.BoundingBoxProperties
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)
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properties = _metadata_fb.BoundingBoxPropertiesT()
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properties.index = list(self._BOUNDING_BOX_INDEX)
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properties.type = _metadata_fb.BoundingBoxType.BOUNDARIES
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properties.coordinateType = _metadata_fb.CoordinateType.RATIO
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content.contentProperties = properties
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if self.content_range_md:
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content.range = self.content_range_md.create_metadata()
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location_metadata = super().create_metadata()
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location_metadata.content = content
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return location_metadata
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class CategoryTensorMd(TensorMd):
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"""A container for the category tensor metadata information."""
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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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label_files: Optional[List[LabelFileMd]] = None,
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content_range_md: Optional[ValueRangeMd] = None,
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):
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"""Initializes a CategoryTensorMd object.
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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 category tensor.
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content_range_md: information of content range [2]. [1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L116
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L385
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"""
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# In category tensors, label files are in the type of TENSOR_VALUE_LABELS.
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if label_files:
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for file in label_files:
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file.file_type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS
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super().__init__(
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name=name,
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description=description,
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associated_files=label_files,
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content_range_md=content_range_md,
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)
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class DetectionOutputTensorsMd:
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"""A container for the output tensor metadata of detection models."""
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_LOCATION_NAME = "location"
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_LOCATION_DESCRIPTION = "The locations of the detected boxes."
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_CATRGORY_NAME = "category"
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_CATEGORY_DESCRIPTION = "The categories of the detected boxes."
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_SCORE_NAME = "score"
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_SCORE_DESCRIPTION = "The scores of the detected boxes."
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_NUMBER_NAME = "number of detections"
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_NUMBER_DESCRIPTION = "The number of the detected boxes."
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_CONTENT_VALUE_DIM = 2
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def __init__(
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self,
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model_buffer: bytearray,
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label_files: Optional[List[LabelFileMd]] = None,
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score_calibration_md: Optional[ScoreCalibrationMd] = None,
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) -> None:
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"""Initializes the instance of DetectionOutputTensorsMd.
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Args:
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model_buffer: A valid flatbuffer loaded from the TFLite model file.
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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 files operation
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[2] in the classification tensor. [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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content_range_md = ValueRangeMd(
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min_value=self._CONTENT_VALUE_DIM, max_value=self._CONTENT_VALUE_DIM
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)
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location_md = LocationTensorMd(
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name=self._LOCATION_NAME,
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description=self._LOCATION_DESCRIPTION,
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content_range_md=content_range_md,
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)
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category_md = CategoryTensorMd(
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name=self._CATRGORY_NAME,
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description=self._CATEGORY_DESCRIPTION,
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label_files=label_files,
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content_range_md=content_range_md,
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)
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score_md = ClassificationTensorMd(
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name=self._SCORE_NAME,
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description=self._SCORE_DESCRIPTION,
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score_calibration_md=score_calibration_md,
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content_range_md=content_range_md,
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)
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number_md = TensorMd(
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name=self._NUMBER_NAME, description=self._NUMBER_DESCRIPTION
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)
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# Get the tensor indices of tflite outputs and then gets the order of the
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# output metadata by the value of tensor indices. The output tensor indices
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# follow the order as [location, category, score,# detections]. For
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# instance, if the output indices are [601, 599, 598, 600], tensor names and
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# indices aligned as below:
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# - (598, location)
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# - (599, category)
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# - (600, score)
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# - (601, number of detections)
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# because of the op's ports of TFLITE_DETECTION_POST_PROCESS
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# (https://github.com/tensorflow/tensorflow/blob/a4fe268ea084e7d323133ed7b986e0ae259a2bc7/tensorflow/lite/kernels/detection_postprocess.cc#L47-L50).
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# Thus, the metadata of tensors are paired with output tensor indices & name
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# in this way.
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# Get the output tensor indices and names from the tflite model.
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tensor_indices_and_names = zip(
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writer_utils.get_output_tensor_indices(model_buffer),
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writer_utils.get_output_tensor_names(model_buffer),
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)
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# Sort by the output tensor indices.
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tensor_indices_and_names = sorted(tensor_indices_and_names)
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# Align tensor names with metadata.
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self._output_mds = [location_md, category_md, score_md, number_md]
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if len(self._output_mds) != len(tensor_indices_and_names):
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raise ValueError(
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"The size of TFLite output should be " + str(len(self._output_mds))
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)
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for i, output_md in enumerate(self._output_mds):
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output_md.tensor_name = tensor_indices_and_names[i][1]
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@property
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def output_mds(self) -> List[TensorMd]:
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return self._output_mds
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class TensorGroupMd:
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"""A container for a group of tensor metadata information."""
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def __init__(
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self, name: Optional[str] = None, tensor_names: Optional[List[str]] = None
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) -> None:
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"""Initializes a CategoryTensorMd object.
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Args:
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name: name of tensor group.
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tensor_names: Names of the tensors to group together, corresponding to
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TensorMetadata.name [1]. [1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L564
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"""
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self.name = name
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self.tensor_names = tensor_names
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def create_metadata(self) -> _metadata_fb.TensorGroupT:
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"""Creates the tensor group metadata."""
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group = _metadata_fb.TensorGroupT()
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group.name = self.name
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group.tensorNames = self.tensor_names
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return group
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@ -34,6 +34,10 @@ _INPUT_REGEX_TEXT_DESCRIPTION = ('Embedding vectors representing the input '
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'text to be processed.')
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_OUTPUT_CLASSIFICATION_NAME = 'score'
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_OUTPUT_CLASSIFICATION_DESCRIPTION = 'Score of the labels respectively.'
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# Detection tensor result to be grouped together.
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_DETECTION_GROUP_NAME = 'detection_result'
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# File name to export score calibration parameters.
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_SCORE_CALIBATION_FILENAME = 'score_calibration.txt'
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@dataclasses.dataclass
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@ -311,7 +315,8 @@ def _create_metadata_buffer(
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general_md: Optional[metadata_info.GeneralMd] = None,
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input_md: Optional[List[metadata_info.TensorMd]] = None,
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output_md: Optional[List[metadata_info.TensorMd]] = None,
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input_process_units: Optional[List[metadata_fb.ProcessUnitT]] = None
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input_process_units: Optional[List[metadata_fb.ProcessUnitT]] = None,
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output_group_md: Optional[List[metadata_info.TensorGroupMd]] = None,
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) -> bytearray:
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"""Creates a buffer of the metadata.
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@ -321,8 +326,11 @@ def _create_metadata_buffer(
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input_md: metadata information of the input tensors.
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output_md: metadata information of the output tensors.
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input_process_units: a lists of metadata of the input process units [1].
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[1]:
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output_group_md: a list of metadata of output tensor groups [2]; [1]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L655
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[2]:
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https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L677
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Returns:
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A buffer of the metadata.
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@ -359,6 +367,10 @@ def _create_metadata_buffer(
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subgraph_metadata.outputTensorMetadata = output_metadata
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if input_process_units:
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subgraph_metadata.inputProcessUnits = input_process_units
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if output_group_md:
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subgraph_metadata.outputTensorGroups = [
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m.create_metadata() for m in output_group_md
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]
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# Create the whole model metadata.
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if general_md is None:
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@ -402,6 +414,7 @@ class MetadataWriter(object):
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self._input_mds = []
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self._input_process_units = []
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self._output_mds = []
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self._output_group_mds = []
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self._associated_files = []
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self._temp_folder = tempfile.TemporaryDirectory()
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@ -583,27 +596,13 @@ class MetadataWriter(object):
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Returns:
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The current Writer instance to allow chained operation.
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"""
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calibration_md = None
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if score_calibration:
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calibration_md = metadata_info.ScoreCalibrationMd(
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score_transformation_type=score_calibration.transformation_type,
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default_score=score_calibration.default_score,
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file_path=self._export_calibration_file('score_calibration.txt',
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score_calibration.parameters))
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calibration_md = self._create_score_calibration_md(score_calibration)
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score_thresholding_md = None
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if score_thresholding:
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score_thresholding_md = metadata_info.ScoreThresholdingMd(
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score_thresholding.global_score_threshold)
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label_files = None
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if labels:
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label_files = []
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for item in labels.labels:
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label_files.append(
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metadata_info.LabelFileMd(
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self._export_labels(item.filename, item.names),
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locale=item.locale))
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label_files = self._create_label_file_md(labels)
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output_md = metadata_info.ClassificationTensorMd(
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name=name,
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description=description,
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@ -615,6 +614,41 @@ class MetadataWriter(object):
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self._output_mds.append(output_md)
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||||
return self
|
||||
|
||||
def add_detection_output(
|
||||
self,
|
||||
labels: Optional[Labels] = None,
|
||||
score_calibration: Optional[ScoreCalibration] = None,
|
||||
group_name: str = _DETECTION_GROUP_NAME,
|
||||
) -> 'MetadataWriter':
|
||||
"""Adds a detection head metadata for detection output tensor.
|
||||
|
||||
Args:
|
||||
labels: an instance of Labels helper class.
|
||||
score_calibration: an instance of ScoreCalibration helper class.
|
||||
group_name: name of output tensor group.
|
||||
|
||||
Returns:
|
||||
The current Writer instance to allow chained operation.
|
||||
"""
|
||||
calibration_md = self._create_score_calibration_md(score_calibration)
|
||||
label_files = self._create_label_file_md(labels)
|
||||
detection_output_mds = metadata_info.DetectionOutputTensorsMd(
|
||||
self._model_buffer,
|
||||
label_files=label_files,
|
||||
score_calibration_md=calibration_md,
|
||||
).output_mds
|
||||
self._output_mds.extend(detection_output_mds)
|
||||
# Outputs are location, category, score, number of detections.
|
||||
if len(detection_output_mds) != 4:
|
||||
raise ValueError('The size of detections output should be 4.')
|
||||
# The first 3 tensors (location, category, score) are grouped.
|
||||
group_md = metadata_info.TensorGroupMd(
|
||||
name=group_name,
|
||||
tensor_names=[output_md.name for output_md in detection_output_mds[:3]],
|
||||
)
|
||||
self._output_group_mds.append(group_md)
|
||||
return self
|
||||
|
||||
def add_feature_output(self,
|
||||
name: Optional[str] = None,
|
||||
description: Optional[str] = None) -> 'MetadataWriter':
|
||||
|
|
@ -639,7 +673,9 @@ class MetadataWriter(object):
|
|||
general_md=self._general_md,
|
||||
input_md=self._input_mds,
|
||||
output_md=self._output_mds,
|
||||
input_process_units=self._input_process_units)
|
||||
input_process_units=self._input_process_units,
|
||||
output_group_md=self._output_group_mds,
|
||||
)
|
||||
populator.load_metadata_buffer(metadata_buffer)
|
||||
if self._associated_files:
|
||||
populator.load_associated_files(self._associated_files)
|
||||
|
|
@ -683,6 +719,36 @@ class MetadataWriter(object):
|
|||
self._associated_files.append(filepath)
|
||||
return filepath
|
||||
|
||||
def _create_score_calibration_md(
|
||||
self, score_calibration: ScoreCalibration
|
||||
) -> Optional[metadata_info.ScoreCalibrationMd]:
|
||||
"""Creates the ScoreCalibrationMd object."""
|
||||
if score_calibration is None:
|
||||
return None
|
||||
return metadata_info.ScoreCalibrationMd(
|
||||
score_transformation_type=score_calibration.transformation_type,
|
||||
default_score=score_calibration.default_score,
|
||||
file_path=self._export_calibration_file(
|
||||
_SCORE_CALIBATION_FILENAME, score_calibration.parameters
|
||||
),
|
||||
)
|
||||
|
||||
def _create_label_file_md(
|
||||
self, labels: Optional[Labels] = None
|
||||
) -> Optional[List[metadata_info.LabelFileMd]]:
|
||||
"""Creates a list of LabelFileMd objects."""
|
||||
label_files = None
|
||||
if labels:
|
||||
label_files = []
|
||||
for item in labels.labels:
|
||||
label_files.append(
|
||||
metadata_info.LabelFileMd(
|
||||
self._export_labels(item.filename, item.names),
|
||||
locale=item.locale,
|
||||
)
|
||||
)
|
||||
return label_files
|
||||
|
||||
|
||||
class MetadataWriterBase:
|
||||
"""Base MetadataWriter class which contains the apis exposed to users.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,77 @@
|
|||
# Copyright 2023 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.
|
||||
# ==============================================================================
|
||||
"""Writes metadata and label file to the Object Detector models."""
|
||||
|
||||
from typing import List, Optional
|
||||
|
||||
from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
|
||||
|
||||
_MODEL_NAME = "ObjectDetector"
|
||||
_MODEL_DESCRIPTION = (
|
||||
"Identify which of a known set of objects might be present and provide "
|
||||
"information about their positions within the given image or a video "
|
||||
"stream."
|
||||
)
|
||||
|
||||
|
||||
class MetadataWriter(metadata_writer.MetadataWriterBase):
|
||||
"""MetadataWriter to write the metadata into the object detector."""
|
||||
|
||||
@classmethod
|
||||
def create(
|
||||
cls,
|
||||
model_buffer: bytearray,
|
||||
input_norm_mean: List[float],
|
||||
input_norm_std: List[float],
|
||||
labels: metadata_writer.Labels,
|
||||
score_calibration: Optional[metadata_writer.ScoreCalibration] = None,
|
||||
) -> "MetadataWriter":
|
||||
"""Creates MetadataWriter to write the metadata for image classifier.
|
||||
|
||||
The parameters required in this method are mandatory when using MediaPipe
|
||||
Tasks.
|
||||
|
||||
Example usage:
|
||||
metadata_writer = object_detector.Metadatawriter.create(model_buffer, ...)
|
||||
tflite_content, json_content = metadata_writer.populate()
|
||||
|
||||
When calling `populate` function in this class, it returns TfLite content
|
||||
and JSON content. Note that only the output TFLite is used for deployment.
|
||||
The output JSON content is used to interpret the metadata content.
|
||||
|
||||
Args:
|
||||
model_buffer: A valid flatbuffer loaded from the TFLite model file.
|
||||
input_norm_mean: the mean value used in the input tensor normalization
|
||||
[1].
|
||||
input_norm_std: the std value used in the input tensor normalizarion [1].
|
||||
labels: an instance of Labels helper class used in the output
|
||||
classification tensor [2].
|
||||
score_calibration: A container of the score calibration operation [3] in
|
||||
the classification tensor. Optional if the model does not use score
|
||||
calibration. [1]:
|
||||
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
|
||||
[2]:
|
||||
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
|
||||
[3]:
|
||||
https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
|
||||
|
||||
Returns:
|
||||
A MetadataWriter object.
|
||||
"""
|
||||
writer = metadata_writer.MetadataWriter(model_buffer)
|
||||
writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION)
|
||||
writer.add_image_input(input_norm_mean, input_norm_std)
|
||||
writer.add_detection_output(labels, score_calibration)
|
||||
return cls(writer)
|
||||
|
|
@ -61,6 +61,12 @@ def get_output_tensor_types(
|
|||
return tensor_types
|
||||
|
||||
|
||||
def get_output_tensor_indices(model_buffer: bytearray) -> List[int]:
|
||||
"""Gets a list of the output tensor indices."""
|
||||
subgraph = get_subgraph(model_buffer)
|
||||
return subgraph.OutputsAsNumpy()
|
||||
|
||||
|
||||
def get_subgraph(model_buffer: bytearray) -> _schema_fb.SubGraph:
|
||||
"""Gets the subgraph of the model.
|
||||
|
||||
|
|
|
|||
|
|
@ -73,3 +73,19 @@ py_test(
|
|||
srcs = ["model_asset_bundle_utils_test.py"],
|
||||
deps = ["//mediapipe/tasks/python/metadata/metadata_writers:model_asset_bundle_utils"],
|
||||
)
|
||||
|
||||
py_test(
|
||||
name = "object_detector_test",
|
||||
srcs = ["object_detector_test.py"],
|
||||
data = [
|
||||
"//mediapipe/tasks/testdata/metadata:data_files",
|
||||
"//mediapipe/tasks/testdata/metadata:model_files",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/tasks/metadata:metadata_schema_py",
|
||||
"//mediapipe/tasks/python/metadata",
|
||||
"//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer",
|
||||
"//mediapipe/tasks/python/metadata/metadata_writers:object_detector",
|
||||
"//mediapipe/tasks/python/test:test_utils",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
|
|
@ -403,6 +403,58 @@ class SentencePieceTokenizerMdTest(absltest.TestCase):
|
|||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
class CategoryTensorMdTest(parameterized.TestCase, absltest.TestCase):
|
||||
_NAME = "category"
|
||||
_DESCRIPTION = "The category tensor."
|
||||
_LABEL_FILE_EN = "labels.txt"
|
||||
_LABEL_FILE_CN = "labels_cn.txt" # Locale label file in Chinese.
|
||||
_EXPECTED_TENSOR_JSON = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, "category_tensor_float_meta.json")
|
||||
)
|
||||
|
||||
def test_create_metadata_should_succeed(self):
|
||||
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"
|
||||
)
|
||||
tensor_md = metadata_info.CategoryTensorMd(
|
||||
name=self._NAME,
|
||||
description=self._DESCRIPTION,
|
||||
label_files=[label_file_en, label_file_cn],
|
||||
)
|
||||
tensor_metadata = tensor_md.create_metadata()
|
||||
|
||||
metadata_json = _metadata.convert_to_json(
|
||||
_create_dummy_model_metadata_with_tensor(tensor_metadata)
|
||||
)
|
||||
with open(self._EXPECTED_TENSOR_JSON, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
class TensorGroupMdMdTest(absltest.TestCase):
|
||||
_NAME = "detection_result"
|
||||
_TENSOR_NAMES = ["location", "category", "score"]
|
||||
_EXPECTED_JSON = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, "tensor_group_meta.json")
|
||||
)
|
||||
|
||||
def test_create_metadata_should_succeed(self):
|
||||
tensor_group_md = metadata_info.TensorGroupMd(
|
||||
name=self._NAME, tensor_names=self._TENSOR_NAMES
|
||||
)
|
||||
tensor_group_metadata = tensor_group_md.create_metadata()
|
||||
|
||||
metadata_json = _metadata.convert_to_json(
|
||||
_create_dummy_model_metadata_with_tensor_group(tensor_group_metadata)
|
||||
)
|
||||
with open(self._EXPECTED_JSON, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
|
||||
def _create_dummy_model_metadata_with_tensor(
|
||||
tensor_metadata: _metadata_fb.TensorMetadataT) -> bytes:
|
||||
# Create a dummy model using the tensor metadata.
|
||||
|
|
@ -427,6 +479,24 @@ def _create_dummy_model_metadata_with_process_uint(
|
|||
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_tensor_group(
|
||||
tensor_group: _metadata_fb.TensorGroupT,
|
||||
) -> bytes:
|
||||
# Creates a dummy model using the tensor group.
|
||||
subgraph_metadata = _metadata_fb.SubGraphMetadataT()
|
||||
subgraph_metadata.outputTensorGroups = [tensor_group]
|
||||
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(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,112 @@
|
|||
# Copyright 2023 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_writer.object_detector."""
|
||||
|
||||
import os
|
||||
|
||||
from absl.testing import absltest
|
||||
from absl.testing import parameterized
|
||||
|
||||
from mediapipe.tasks.metadata import metadata_schema_py_generated as metadata_fb
|
||||
from mediapipe.tasks.python.metadata import metadata
|
||||
from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
|
||||
from mediapipe.tasks.python.metadata.metadata_writers import object_detector
|
||||
from mediapipe.tasks.python.test import test_utils
|
||||
|
||||
_TEST_DATA_DIR = "mediapipe/tasks/testdata/metadata"
|
||||
_LABEL_FILE = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, "labelmap.txt")
|
||||
)
|
||||
_LABEL_FILE_NAME = "labels.txt"
|
||||
_NORM_MEAN = 127.5
|
||||
_NORM_STD = 127.5
|
||||
|
||||
_MODEL_COCO = test_utils.get_test_data_path(
|
||||
os.path.join(
|
||||
_TEST_DATA_DIR,
|
||||
"coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_no_metadata.tflite",
|
||||
)
|
||||
)
|
||||
_SCORE_CALIBRATION_FILE = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, "score_calibration.csv")
|
||||
)
|
||||
_SCORE_CALIBRATION_FILENAME = "score_calibration.txt"
|
||||
_SCORE_CALIBRATION_DEFAULT_SCORE = 0.2
|
||||
_JSON_FOR_SCORE_CALIBRATION = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, "coco_ssd_mobilenet_v1_score_calibration.json")
|
||||
)
|
||||
|
||||
|
||||
class MetadataWriterTest(parameterized.TestCase, absltest.TestCase):
|
||||
|
||||
@parameterized.parameters(
|
||||
"ssd_mobilenet_v1_no_metadata",
|
||||
"efficientdet_lite0_v1",
|
||||
)
|
||||
def test_create_should_succeed(self, model_name):
|
||||
model_path = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, model_name + ".tflite")
|
||||
)
|
||||
with open(model_path, "rb") as f:
|
||||
model_buffer = f.read()
|
||||
writer = object_detector.MetadataWriter.create(
|
||||
model_buffer,
|
||||
[_NORM_MEAN],
|
||||
[_NORM_STD],
|
||||
labels=metadata_writer.Labels().add_from_file(_LABEL_FILE),
|
||||
)
|
||||
_, metadata_json = writer.populate()
|
||||
expected_json_path = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, model_name + ".json")
|
||||
)
|
||||
with open(expected_json_path, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
def test_create_with_score_calibration_should_succeed(self):
|
||||
with open(_MODEL_COCO, "rb") as f:
|
||||
model_buffer = f.read()
|
||||
writer = object_detector.MetadataWriter.create(
|
||||
model_buffer,
|
||||
[_NORM_MEAN],
|
||||
[_NORM_STD],
|
||||
labels=metadata_writer.Labels().add_from_file(_LABEL_FILE),
|
||||
score_calibration=metadata_writer.ScoreCalibration.create_from_file(
|
||||
metadata_fb.ScoreTransformationType.INVERSE_LOGISTIC,
|
||||
_SCORE_CALIBRATION_FILE,
|
||||
_SCORE_CALIBRATION_DEFAULT_SCORE,
|
||||
),
|
||||
)
|
||||
tflite_content, metadata_json = writer.populate()
|
||||
with open(_JSON_FOR_SCORE_CALIBRATION, "r") as f:
|
||||
expected_json = f.read()
|
||||
self.assertEqual(metadata_json, expected_json)
|
||||
|
||||
displayer = metadata.MetadataDisplayer.with_model_buffer(tflite_content)
|
||||
calibration_file_buffer = displayer.get_associated_file_buffer(
|
||||
_SCORE_CALIBRATION_FILENAME
|
||||
)
|
||||
with open(_SCORE_CALIBRATION_FILE, "rb") as f:
|
||||
expected_calibration_file_buffer = f.read()
|
||||
self.assertEqual(calibration_file_buffer, expected_calibration_file_buffer)
|
||||
|
||||
label_file_buffer = displayer.get_associated_file_buffer(_LABEL_FILE_NAME)
|
||||
with open(_LABEL_FILE, "rb") as f:
|
||||
expected_labelfile_buffer = f.read()
|
||||
self.assertEqual(label_file_buffer, expected_labelfile_buffer)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
absltest.main()
|
||||
20
mediapipe/tasks/testdata/metadata/BUILD
vendored
20
mediapipe/tasks/testdata/metadata/BUILD
vendored
|
|
@ -25,6 +25,12 @@ package(
|
|||
mediapipe_files(srcs = [
|
||||
"30k-clean.model",
|
||||
"bert_text_classifier_no_metadata.tflite",
|
||||
"category_tensor_float_meta.json",
|
||||
"coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_no_metadata.tflite",
|
||||
"coco_ssd_mobilenet_v1_score_calibration.json",
|
||||
"efficientdet_lite0_v1.json",
|
||||
"efficientdet_lite0_v1.tflite",
|
||||
"labelmap.txt",
|
||||
"mobile_ica_8bit-with-metadata.tflite",
|
||||
"mobile_ica_8bit-with-unsupported-metadata-version.tflite",
|
||||
"mobile_ica_8bit-without-model-metadata.tflite",
|
||||
|
|
@ -35,6 +41,10 @@ mediapipe_files(srcs = [
|
|||
"mobilenet_v2_1.0_224_quant_without_metadata.tflite",
|
||||
"mobilenet_v2_1.0_224_without_metadata.tflite",
|
||||
"movie_review.tflite",
|
||||
"score_calibration.csv",
|
||||
"ssd_mobilenet_v1_no_metadata.json",
|
||||
"ssd_mobilenet_v1_no_metadata.tflite",
|
||||
"tensor_group_meta.json",
|
||||
])
|
||||
|
||||
exports_files([
|
||||
|
|
@ -74,6 +84,8 @@ filegroup(
|
|||
srcs = [
|
||||
"30k-clean.model",
|
||||
"bert_text_classifier_no_metadata.tflite",
|
||||
"coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_no_metadata.tflite",
|
||||
"efficientdet_lite0_v1.tflite",
|
||||
"mobile_ica_8bit-with-metadata.tflite",
|
||||
"mobile_ica_8bit-with-unsupported-metadata-version.tflite",
|
||||
"mobile_ica_8bit-without-model-metadata.tflite",
|
||||
|
|
@ -83,6 +95,7 @@ filegroup(
|
|||
"mobilenet_v2_1.0_224_quant_without_metadata.tflite",
|
||||
"mobilenet_v2_1.0_224_without_metadata.tflite",
|
||||
"movie_review.tflite",
|
||||
"ssd_mobilenet_v1_no_metadata.tflite",
|
||||
],
|
||||
)
|
||||
|
||||
|
|
@ -94,9 +107,12 @@ filegroup(
|
|||
"bert_text_classifier_with_sentence_piece.json",
|
||||
"bert_tokenizer_meta.json",
|
||||
"bounding_box_tensor_meta.json",
|
||||
"category_tensor_float_meta.json",
|
||||
"classification_tensor_float_meta.json",
|
||||
"classification_tensor_uint8_meta.json",
|
||||
"classification_tensor_unsupported_meta.json",
|
||||
"coco_ssd_mobilenet_v1_score_calibration.json",
|
||||
"efficientdet_lite0_v1.json",
|
||||
"external_file",
|
||||
"feature_tensor_meta.json",
|
||||
"general_meta.json",
|
||||
|
|
@ -107,6 +123,7 @@ filegroup(
|
|||
"input_image_tensor_unsupported_meta.json",
|
||||
"input_text_tensor_default_meta.json",
|
||||
"input_text_tensor_meta.json",
|
||||
"labelmap.txt",
|
||||
"labels.txt",
|
||||
"mobilebert_vocab.txt",
|
||||
"mobilenet_v2_1.0_224.json",
|
||||
|
|
@ -114,10 +131,13 @@ filegroup(
|
|||
"movie_review.json",
|
||||
"movie_review_labels.txt",
|
||||
"regex_vocab.txt",
|
||||
"score_calibration.csv",
|
||||
"score_calibration.txt",
|
||||
"score_calibration_file_meta.json",
|
||||
"score_calibration_tensor_meta.json",
|
||||
"score_thresholding_meta.json",
|
||||
"sentence_piece_tokenizer_meta.json",
|
||||
"ssd_mobilenet_v1_no_metadata.json",
|
||||
"tensor_group_meta.json",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
33
mediapipe/tasks/testdata/metadata/category_tensor_float_meta.json
vendored
Normal file
33
mediapipe/tasks/testdata/metadata/category_tensor_float_meta.json
vendored
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "category",
|
||||
"description": "The category tensor.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_VALUE_LABELS",
|
||||
"locale": "en"
|
||||
},
|
||||
{
|
||||
"name": "labels_cn.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_VALUE_LABELS",
|
||||
"locale": "cn"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
140
mediapipe/tasks/testdata/metadata/coco_ssd_mobilenet_v1_score_calibration.json
vendored
Normal file
140
mediapipe/tasks/testdata/metadata/coco_ssd_mobilenet_v1_score_calibration.json
vendored
Normal file
|
|
@ -0,0 +1,140 @@
|
|||
{
|
||||
"name": "ObjectDetector",
|
||||
"description": "Identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream.",
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "image",
|
||||
"description": "Input image to be processed.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
"color_space": "RGB"
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "NormalizationOptions",
|
||||
"options": {
|
||||
"mean": [
|
||||
127.5
|
||||
],
|
||||
"std": [
|
||||
127.5
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
255.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"output_tensor_metadata": [
|
||||
{
|
||||
"name": "location",
|
||||
"description": "The locations of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "BoundingBoxProperties",
|
||||
"content_properties": {
|
||||
"index": [
|
||||
1,
|
||||
0,
|
||||
3,
|
||||
2
|
||||
],
|
||||
"type": "BOUNDARIES"
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "category",
|
||||
"description": "The categories of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_VALUE_LABELS"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "score",
|
||||
"description": "The scores of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "ScoreCalibrationOptions",
|
||||
"options": {
|
||||
"score_transformation": "INVERSE_LOGISTIC",
|
||||
"default_score": 0.2
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
},
|
||||
"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"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "number of detections",
|
||||
"description": "The number of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
}
|
||||
],
|
||||
"output_tensor_groups": [
|
||||
{
|
||||
"name": "detection_result",
|
||||
"tensor_names": [
|
||||
"location",
|
||||
"category",
|
||||
"score"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"min_parser_version": "1.2.0"
|
||||
}
|
||||
124
mediapipe/tasks/testdata/metadata/efficientdet_lite0_v1.json
vendored
Normal file
124
mediapipe/tasks/testdata/metadata/efficientdet_lite0_v1.json
vendored
Normal file
|
|
@ -0,0 +1,124 @@
|
|||
{
|
||||
"name": "ObjectDetector",
|
||||
"description": "Identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream.",
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "image",
|
||||
"description": "Input image to be processed.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
"color_space": "RGB"
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "NormalizationOptions",
|
||||
"options": {
|
||||
"mean": [
|
||||
127.5
|
||||
],
|
||||
"std": [
|
||||
127.5
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
255.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"output_tensor_metadata": [
|
||||
{
|
||||
"name": "score",
|
||||
"description": "The scores of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "location",
|
||||
"description": "The locations of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "BoundingBoxProperties",
|
||||
"content_properties": {
|
||||
"index": [
|
||||
1,
|
||||
0,
|
||||
3,
|
||||
2
|
||||
],
|
||||
"type": "BOUNDARIES"
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "number of detections",
|
||||
"description": "The number of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "category",
|
||||
"description": "The categories of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_VALUE_LABELS"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"output_tensor_groups": [
|
||||
{
|
||||
"name": "detection_result",
|
||||
"tensor_names": [
|
||||
"location",
|
||||
"category",
|
||||
"score"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"min_parser_version": "1.2.0"
|
||||
}
|
||||
90
mediapipe/tasks/testdata/metadata/labelmap.txt
vendored
Normal file
90
mediapipe/tasks/testdata/metadata/labelmap.txt
vendored
Normal file
|
|
@ -0,0 +1,90 @@
|
|||
person
|
||||
bicycle
|
||||
car
|
||||
motorcycle
|
||||
airplane
|
||||
bus
|
||||
train
|
||||
truck
|
||||
boat
|
||||
traffic light
|
||||
fire hydrant
|
||||
???
|
||||
stop sign
|
||||
parking meter
|
||||
bench
|
||||
bird
|
||||
cat
|
||||
dog
|
||||
horse
|
||||
sheep
|
||||
cow
|
||||
elephant
|
||||
bear
|
||||
zebra
|
||||
giraffe
|
||||
???
|
||||
backpack
|
||||
umbrella
|
||||
???
|
||||
???
|
||||
handbag
|
||||
tie
|
||||
suitcase
|
||||
frisbee
|
||||
skis
|
||||
snowboard
|
||||
sports ball
|
||||
kite
|
||||
baseball bat
|
||||
baseball glove
|
||||
skateboard
|
||||
surfboard
|
||||
tennis racket
|
||||
bottle
|
||||
???
|
||||
wine glass
|
||||
cup
|
||||
fork
|
||||
knife
|
||||
spoon
|
||||
bowl
|
||||
banana
|
||||
apple
|
||||
sandwich
|
||||
orange
|
||||
broccoli
|
||||
carrot
|
||||
hot dog
|
||||
pizza
|
||||
donut
|
||||
cake
|
||||
chair
|
||||
couch
|
||||
potted plant
|
||||
bed
|
||||
???
|
||||
dining table
|
||||
???
|
||||
???
|
||||
toilet
|
||||
???
|
||||
tv
|
||||
laptop
|
||||
mouse
|
||||
remote
|
||||
keyboard
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
???
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
89
mediapipe/tasks/testdata/metadata/score_calibration.csv
vendored
Normal file
89
mediapipe/tasks/testdata/metadata/score_calibration.csv
vendored
Normal file
|
|
@ -0,0 +1,89 @@
|
|||
|
||||
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
|
||||
0.9905818104743958,0.8670706152915955,0.012704282067716122,0.019999999552965164
|
||||
0.9080020189285278,0.8507471680641174,0.5081117749214172,0.019999999552965164
|
||||
0.985953152179718,0.9933826923370361,-0.8114940524101257,0.109375
|
||||
0.9819648861885071,1.12098228931427,-0.6330763697624207,0.01171875
|
||||
0.9025918245315552,0.7803755402565002,0.03275677561759949,0.08984375
|
||||
0.9863958954811096,0.11243592947721481,0.935604453086853,0.61328125
|
||||
0.9905291795730591,0.3710605800151825,0.708966851234436,0.359375
|
||||
0.9917052984237671,0.9596433043479919,0.19800108671188354,0.09765625
|
||||
0.8762937188148499,0.3449830114841461,0.5352474451065063,0.0078125
|
||||
0.9902125000953674,0.8918796181678772,-0.1306992471218109,0.26171875
|
||||
|
||||
0.9902340173721313,0.9177873134613037,-0.4322589933872223,0.019999999552965164
|
||||
0.9707600474357605,0.7028177976608276,0.9813734889030457,0.019999999552965164
|
||||
0.9823090434074402,1.0499590635299683,0.12045472860336304,0.0078125
|
||||
0.990516185760498,0.9449402093887329,1.3773189783096313,0.019999999552965164
|
||||
0.9875434041023254,0.577914297580719,1.282518982887268,0.0390625
|
||||
0.9821421504020691,0.0967339277267456,0.8279788494110107,0.47265625
|
||||
0.9875047206878662,0.9038218259811401,2.1208062171936035,0.38671875
|
||||
0.9857864379882812,0.8627446889877319,0.18189261853694916,0.019999999552965164
|
||||
0.9647751450538635,1.0752476453781128,-0.018294010311365128,0.0234375
|
||||
0.9830358624458313,0.5638481378555298,0.8346489667892456,0.019999999552965164
|
||||
0.9904966354370117,1.0160938501358032,-0.0573287308216095,0.00390625
|
||||
0.8458405137062073,0.4868394434452057,0.6617084741592407,0.019999999552965164
|
||||
0.9847381711006165,0.5939620137214661,0.008616370148956776,0.00390625
|
||||
0.9375938773155212,0.723095178604126,0.6635608077049255,0.019999999552965164
|
||||
0.9334303140640259,0.5689108967781067,0.37019580602645874,0.019999999552965164
|
||||
0.9716793894767761,1.0037211179733276,0.5898993611335754,0.02734375
|
||||
0.9197732210159302,0.46794334053993225,0.7365336418151855,0.640625
|
||||
0.9857497811317444,0.7299028635025024,0.9195274114608765,0.0390625
|
||||
0.8758038282394409,1.200216293334961,0.02580185979604721,0.019999999552965164
|
||||
0.9841026067733765,0.8050475716590881,0.9698556661605835,0.0078125
|
||||
0.9908539652824402,0.7911490201950073,0.19351358711719513,0.12109375
|
||||
0.9179956316947937,0.023991893976926804,0.35193610191345215,0.04296875
|
||||
0.9903728365898132,0.7744967341423035,0.2686336636543274,0.359375
|
||||
0.906022846698761,0.5766159892082214,1.0600007772445679,0.04296875
|
||||
0.9885554909706116,0.99117511510849,0.5611960291862488,0.4140625
|
||||
0.9906331896781921,1.1376535892486572,1.45369291305542,0.019999999552965164
|
||||
0.9640991687774658,0.5387894511222839,1.1824018955230713,0.019999999552965164
|
||||
0.9932155609130859,0.4347895085811615,1.3938102722167969,0.0078125
|
||||
0.9884702563285828,0.885567843914032,0.1556047648191452,0.1484375
|
||||
0.9891508221626282,0.04143073782324791,0.6111864447593689,0.0078125
|
||||
0.8935436010360718,0.2937895655632019,0.3215920031070709,0.00390625
|
||||
0.8327123522758484,0.8381986021995544,-0.026293788105249405,0.019999999552965164
|
||||
0.9839455485343933,0.9581400156021118,1.495324969291687,0.640625
|
||||
0.9904995560646057,0.9168422818183899,0.33293962478637695,0.015625
|
||||
0.9856975674629211,1.0433714389801025,0.5954801440238953,0.019999999552965164
|
||||
0.9942344427108765,0.7206616997718811,1.666426181793213,0.9609375
|
||||
0.8182767033576965,0.9546273946762085,0.5500107407569885,0.019999999552965164
|
||||
0.9631295800209045,0.6277880668640137,0.05952891707420349,0.05859375
|
||||
0.9819005727767944,1.0826934576034546,0.7444049715995789,0.30859375
|
||||
0.9884315133094788,1.0500890016555786,1.1161768436431885,0.019999999552965164
|
||||
0.9175815582275391,0.09232989698648453,1.596696138381958,0.47265625
|
||||
0.9868760108947754,0.903079628944397,-0.15774966776371002,0.8515625
|
||||
0.9866015911102295,0.7533788084983826,0.7489103078842163,0.03125
|
||||
0.8074312806129456,0.8615151643753052,0.40621864795684814,0.00390625
|
||||
0.9829285144805908,0.8954831957817078,0.4462486207485199,0.02734375
|
||||
0.9681841135025024,0.6257772445678711,0.43809664249420166,0.38671875
|
||||
0.9872947931289673,0.9947993159294128,0.9271130561828613,0.26171875
|
||||
0.7997345328330994,0.3995186686515808,-0.3755347430706024,0.019999999552965164
|
||||
0.9922754168510437,1.1357101202011108,-0.10267537832260132,0.5
|
||||
0.9861471652984619,0.8725204467773438,1.1657888889312744,0.019999999552965164
|
||||
0.9888646006584167,1.2098380327224731,-0.27832522988319397,0.05078125
|
||||
0.5641342997550964,1.0501892566680908,1.9519661664962769,0.019999999552965164
|
||||
0.9548168778419495,0.8971696496009827,1.378737449645996,0.00390625
|
||||
0.9875019788742065,0.8718118071556091,0.5476236939430237,0.0078125
|
||||
0.9725168347358704,0.6989551782608032,-1.3157455921173096,0.61328125
|
||||
0.9864014983177185,0.7576251029968262,-0.41650667786598206,0.00390625
|
||||
0.960071861743927,0.13068856298923492,0.4819187819957733,0.019999999552965164
|
||||
0.9849705100059509,0.7724528908729553,0.3877875804901123,0.03125
|
||||
0.9703006744384766,0.8848260641098022,-1.1767181158065796,0.80078125
|
||||
0.9837008714675903,0.7015050053596497,0.18209102749824524,0.00390625
|
||||
0.9579976797103882,0.053806986659765244,2.7309608459472656,0.4000000059604645
|
||||
0.9896979928016663,0.41135814785957336,0.5738034844398499,0.019999999552965164
|
||||
0.9853873252868652,0.5438565611839294,0.20562179386615753,0.02734375
|
||||
0.9784129858016968,0.6330984830856323,-0.1789831817150116,0.015625
|
||||
0.9375,0.855596125125885,-0.1933964192867279,0.019999999552965164
|
||||
0.9524176716804504,0.08709807693958282,0.6299692988395691,0.33203125
|
||||
|
124
mediapipe/tasks/testdata/metadata/ssd_mobilenet_v1_no_metadata.json
vendored
Normal file
124
mediapipe/tasks/testdata/metadata/ssd_mobilenet_v1_no_metadata.json
vendored
Normal file
|
|
@ -0,0 +1,124 @@
|
|||
{
|
||||
"name": "ObjectDetector",
|
||||
"description": "Identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream.",
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"input_tensor_metadata": [
|
||||
{
|
||||
"name": "image",
|
||||
"description": "Input image to be processed.",
|
||||
"content": {
|
||||
"content_properties_type": "ImageProperties",
|
||||
"content_properties": {
|
||||
"color_space": "RGB"
|
||||
}
|
||||
},
|
||||
"process_units": [
|
||||
{
|
||||
"options_type": "NormalizationOptions",
|
||||
"options": {
|
||||
"mean": [
|
||||
127.5
|
||||
],
|
||||
"std": [
|
||||
127.5
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"stats": {
|
||||
"max": [
|
||||
255.0
|
||||
],
|
||||
"min": [
|
||||
0.0
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"output_tensor_metadata": [
|
||||
{
|
||||
"name": "location",
|
||||
"description": "The locations of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "BoundingBoxProperties",
|
||||
"content_properties": {
|
||||
"index": [
|
||||
1,
|
||||
0,
|
||||
3,
|
||||
2
|
||||
],
|
||||
"type": "BOUNDARIES"
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "category",
|
||||
"description": "The categories of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
},
|
||||
"associated_files": [
|
||||
{
|
||||
"name": "labels.txt",
|
||||
"description": "Labels for categories that the model can recognize.",
|
||||
"type": "TENSOR_VALUE_LABELS"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "score",
|
||||
"description": "The scores of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
},
|
||||
"range": {
|
||||
"min": 2,
|
||||
"max": 2
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "number of detections",
|
||||
"description": "The number of the detected boxes.",
|
||||
"content": {
|
||||
"content_properties_type": "FeatureProperties",
|
||||
"content_properties": {
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
}
|
||||
}
|
||||
],
|
||||
"output_tensor_groups": [
|
||||
{
|
||||
"name": "detection_result",
|
||||
"tensor_names": [
|
||||
"location",
|
||||
"category",
|
||||
"score"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"min_parser_version": "1.2.0"
|
||||
}
|
||||
16
mediapipe/tasks/testdata/metadata/tensor_group_meta.json
vendored
Normal file
16
mediapipe/tasks/testdata/metadata/tensor_group_meta.json
vendored
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
{
|
||||
"subgraph_metadata": [
|
||||
{
|
||||
"output_tensor_groups": [
|
||||
{
|
||||
"name": "detection_result",
|
||||
"tensor_names": [
|
||||
"location",
|
||||
"category",
|
||||
"score"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
88
third_party/external_files.bzl
vendored
88
third_party/external_files.bzl
vendored
|
|
@ -94,6 +94,12 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/canned_gesture_classifier.tflite?generation=1668550473107417"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_category_tensor_float_meta_json",
|
||||
sha256 = "d0cbe95a99ffc57046d7e66cf194600d12899216a4d3bf1a3851811648005e38",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/category_tensor_float_meta.json?generation=1677522730922512"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_cat_jpg",
|
||||
sha256 = "2533197401eebe9410ea4d063f86c43fbd2666f3e8165a38aca155c0d09c21be",
|
||||
|
|
@ -160,6 +166,12 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite?generation=1661875692679200"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_coco_ssd_mobilenet_v1_1_0_quant_2018_06_29_no_metadata_tflite",
|
||||
sha256 = "e4b118e5e4531945de2e659742c7c590f7536f8d0ed26d135abcfe83b4779d13",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_no_metadata.tflite?generation=1677522735292070"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_coco_ssd_mobilenet_v1_1_0_quant_2018_06_29_tflite",
|
||||
sha256 = "61d598093ed03ed41aa47c3a39a28ac01e960d6a810a5419b9a5016a1e9c469b",
|
||||
|
|
@ -172,6 +184,12 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_with_dummy_score_calibration.tflite?generation=1662653237233967"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_coco_ssd_mobilenet_v1_score_calibration_json",
|
||||
sha256 = "f377600be924c29697477f9d739db9db5d712aec4a644548526912858db6a082",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/coco_ssd_mobilenet_v1_score_calibration.json?generation=1677522739770755"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_corrupted_mobilenet_v1_0_25_224_1_default_1_tflite",
|
||||
sha256 = "f0cbeb8061f4c693e20de779ce255af923508492e8a24f6db320845a52facb51",
|
||||
|
|
@ -190,6 +208,18 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/dummy_gesture_recognizer.task?generation=1665707319890725"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_efficientdet_lite0_v1_json",
|
||||
sha256 = "7a9e1fb625a6130a251e612637fc546cfc8cfabfadc7dbdade44c87f1d8996ca",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_v1.json?generation=1677522746026682"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_efficientdet_lite0_v1_tflite",
|
||||
sha256 = "f97efd21d6009a7b4b94b3e57baaeb77ec3225b42d32477f5003736a8084c081",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_v1.tflite?generation=1677522750449279"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_empty_vocab_for_regex_tokenizer_txt",
|
||||
sha256 = "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
|
||||
|
|
@ -238,6 +268,30 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/external_file.txt?generation=1661875736240688"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_face_blendshapes_generated_graph_pbtxt",
|
||||
sha256 = "92e016a08940e1f81752e749e1931f9e551fa9483786b8fba0624257e9c41d3d",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/face_blendshapes_generated_graph.pbtxt?generation=1677522753449135"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_face_blendshapes_in_landmarks_prototxt",
|
||||
sha256 = "f6a10fa5825f2eee695371a449c605698403c146c270b2cb7574512f3f9e4af8",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/face_blendshapes_in_landmarks.prototxt?generation=1677522757270549"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_face_blendshapes_out_prototxt",
|
||||
sha256 = "ea7740add8b87c9bd375eaa40a05b509eaca04f025cb6bdc7ca486e9fb32dfba",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/face_blendshapes_out.prototxt?generation=1677522761312644"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_face_blendshapes_tflite",
|
||||
sha256 = "4f36dded049db18d76048567439b2a7f58f1daabc00d78bfe8f3ad396a2d2082",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/face_blendshapes.tflite?generation=1677522764748685"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_face_detection_full_range_sparse_tflite",
|
||||
sha256 = "2c3728e6da56f21e21a320433396fb06d40d9088f2247c05e5635a688d45dfe1",
|
||||
|
|
@ -448,6 +502,12 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/knift_labelmap.txt?generation=1661875792821628"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_labelmap_txt",
|
||||
sha256 = "f8803ef7900160c629d570848dfda4175e21667bf7b71f73f8ece4938c9f2bf2",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/labelmap.txt?generation=1677522772140291"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_labels_txt",
|
||||
sha256 = "536feacc519de3d418de26b2effb4d75694a8c4c0063e36499a46fa8061e2da9",
|
||||
|
|
@ -726,8 +786,8 @@ def external_files():
|
|||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_portrait_expected_face_landmarks_with_attention_pbtxt",
|
||||
sha256 = "f2ccd889654b914996e4aab0d7831a3e73d3b63d6c14f6bac4bec5cd3415bce4",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/portrait_expected_face_landmarks_with_attention.pbtxt?generation=1676415475626542"],
|
||||
sha256 = "dae959456f001015278f3a1535bd03c9fa0990a3df951135645ce23293be0613",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/portrait_expected_face_landmarks_with_attention.pbtxt?generation=1677522777298874"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
|
|
@ -790,6 +850,12 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/right_hands_rotated.jpg?generation=1666037076873345"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_score_calibration_csv",
|
||||
sha256 = "3ff4962162387ab8851940d2f063ce2b3a4734a8893c007a3c92d11170b020c3",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration.csv?generation=1677522780749449"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_score_calibration_file_meta_json",
|
||||
sha256 = "6a3c305620371f662419a496f75be5a10caebca7803b1e99d8d5d22ba51cda94",
|
||||
|
|
@ -892,12 +958,30 @@ def external_files():
|
|||
urls = ["https://storage.googleapis.com/mediapipe-assets/ssdlite_object_detection.tflite?generation=1661875944118759"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_ssd_mobilenet_v1_no_metadata_json",
|
||||
sha256 = "89157590b736cf3f3247aa9c8be3570c2856f4981a1e9476117e7c629e7c4825",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/ssd_mobilenet_v1_no_metadata.json?generation=1677522786336455"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_ssd_mobilenet_v1_no_metadata_tflite",
|
||||
sha256 = "e4b118e5e4531945de2e659742c7c590f7536f8d0ed26d135abcfe83b4779d13",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/ssd_mobilenet_v1_no_metadata.tflite?generation=1677522790838583"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_ssd_mobilenet_v1_tflite",
|
||||
sha256 = "cbdecd08b44c5dea3821f77c5468e2936ecfbf43cde0795a2729fdb43401e58b",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/ssd_mobilenet_v1.tflite?generation=1661875947436302"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_tensor_group_meta_json",
|
||||
sha256 = "eea454ae15b0c4f7e1f84aad9700bc936627fe22a085d335a40269740bc33c69",
|
||||
urls = ["https://storage.googleapis.com/mediapipe-assets/tensor_group_meta.json?generation=1677522794324300"],
|
||||
)
|
||||
|
||||
http_file(
|
||||
name = "com_google_mediapipe_test_jpg",
|
||||
sha256 = "798a12a466933842528d8438f553320eebe5137f02650f12dd68706a2f94fb4f",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user