Add custom metadata for object detection model with out-of-graph nms.

PiperOrigin-RevId: 527083453

mediapipe/model_maker/python/vision/object_detector/object_detector.py

@@ -328,7 +328,7 @@ class ObjectDetector(classifier.Classifier):
       converter.target_spec.supported_ops = (tf.lite.OpsSet.TFLITE_BUILTINS,)
       tflite_model = converter.convert()
 
-    writer = object_detector_writer.MetadataWriter.create(
+    writer = object_detector_writer.MetadataWriter.create_for_models_with_nms(
         tflite_model,
         self._model_spec.mean_rgb,
         self._model_spec.stddev_rgb,

mediapipe/tasks/metadata/BUILD

@@ -36,3 +36,13 @@ flatbuffer_py_library(
     name = "image_segmenter_metadata_schema_py",
     srcs = ["image_segmenter_metadata_schema.fbs"],
 )
+
+flatbuffer_cc_library(
+    name = "object_detector_metadata_schema_cc",
+    srcs = ["object_detector_metadata_schema.fbs"],
+)
+
+flatbuffer_py_library(
+    name = "object_detector_metadata_schema_py",
+    srcs = ["object_detector_metadata_schema.fbs"],
+)

mediapipe/tasks/metadata/object_detector_metadata_schema.fbs

@@ -0,0 +1,98 @@
+// 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.
+
+namespace mediapipe.tasks;
+
+// ObjectDetectorOptions.min_parser_version indicates the minimum necessary
+// object detector metadata parser version to fully understand all fields in a
+// given metadata flatbuffer. This min_parser_version is specific for the
+// object detector metadata defined in this schema file.
+//
+// New fields and types will have associated comments with the schema version
+// for which they were added.
+//
+// Schema Semantic version: 1.0.0
+
+// This indicates the flatbuffer compatibility. The number will bump up when a
+// break change is applied to the schema, such as removing fields or adding new
+// fields to the middle of a table.
+file_identifier "V001";
+
+
+// History:
+// 1.0.0 - Initial version.
+
+// A fixed size anchor.
+table FixedAnchor {
+  x_center: float;
+  y_center: float;
+  width: float;
+  height: float;
+}
+
+// The schema for a list of anchors with fixed size.
+table FixedAnchorsSchema {
+  anchors: [FixedAnchor];
+}
+
+// The ssd anchors options used in the object detector.
+table SsdAnchorsOptions {
+  fixed_anchors_schema: FixedAnchorsSchema;
+}
+
+// The options for decoding the raw model output tensors. The options are mostly
+// used in TensorsToDetectionsCalculatorOptions.
+table TensorsDecodingOptions {
+  // The number of output classes predicted by the detection model.
+  num_classes: int;
+  // The number of output boxes predicted by the detection model.
+  num_boxes: int;
+  // The number of output values per boxes predicted by the detection
+  // model. The values contain bounding boxes, keypoints, etc.
+  num_coords: int;
+  // The offset of keypoint coordinates in the location tensor.
+  keypoint_coord_offset: int;
+  // The number of predicted keypoints.
+  num_keypoints: int;
+  // The dimension of each keypoint, e.g. number of values predicted for each
+  // keypoint.
+  num_values_per_keypoint: int;
+  // Parameters for decoding SSD detection model.
+  x_scale: float;
+  y_scale: float;
+  w_scale: float;
+  h_scale: float;
+  // Whether to apply exponential on box size.
+  apply_exponential_on_box_size: bool;
+  // Whether to apply sigmod function on the score.
+  sigmoid_score: bool;
+}
+
+table ObjectDetectorOptions {
+  // TODO: automatically populate min parser string.
+  // The minimum necessary object detector metadata parser version to fully
+  // understand all fields in a given metadata flatbuffer. This field is
+  // automatically populated by the MetadataPopulator when the metadata is
+  // populated into a TFLite model. This min_parser_version is specific for the
+  // object detector metadata defined in this schema file.
+  min_parser_version:string;
+
+  // The options of ssd anchors configs used by the detection model.
+  ssd_anchors_options:SsdAnchorsOptions;
+
+  // The tensors decoding options to convert raw tensors to detection results.
+  tensors_decoding_options:TensorsDecodingOptions;
+}
+
+root_type ObjectDetectorOptions;

mediapipe/tasks/python/metadata/metadata_writers/BUILD

@@ -67,7 +67,15 @@ py_library(
 py_library(
     name = "object_detector",
     srcs = ["object_detector.py"],
-    deps = [":metadata_writer"],
+    data = ["//mediapipe/tasks/metadata:object_detector_metadata_schema.fbs"],
+    deps = [
+        ":metadata_info",
+        ":metadata_writer",
+        "//mediapipe/tasks/metadata:metadata_schema_py",
+        "//mediapipe/tasks/metadata:object_detector_metadata_schema_py",
+        "//mediapipe/tasks/python/metadata",
+        "@flatbuffers//:runtime_py",
+    ],
 )
 
 py_library(

mediapipe/tasks/python/metadata/metadata_writers/metadata_info.py

@@ -17,6 +17,7 @@
 import abc
 import collections
 import csv
+import enum
 import os
 from typing import List, Optional, Type, Union
 
@@ -1004,6 +1005,84 @@ class DetectionOutputTensorsMd:
     return self._output_mds
 
 
+class RawDetectionOutputTensorsOrder(enum.Enum):
+  """Output tensors order for detection models without postprocessing.
+
+  Because it is not able to determined the order of output tensors for models
+  without postprocessing, it is needed to specify the output tensors order for
+  metadata writer.
+  """
+
+  UNSPECIFIED = 0
+  # The first tensor is score, and the second tensor is location.
+  SCORE_LOCATION = 1
+  # The first tensor is location, and the second tensor is score.
+  LOCATION_SCORE = 2
+
+
+class RawDetectionOutputTensorsMd:
+  """A container for the output tensor metadata of detection models without postprocessing."""
+
+  _LOCATION_NAME = "location"
+  _LOCATION_DESCRIPTION = "The locations of the detected boxes."
+  _SCORE_NAME = "score"
+  _SCORE_DESCRIPTION = "The scores of the detected boxes."
+  _CONTENT_VALUE_DIM = 2
+
+  def __init__(
+      self,
+      model_buffer: bytearray,
+      label_files: Optional[List[LabelFileMd]] = None,
+      output_tensors_order: RawDetectionOutputTensorsOrder = RawDetectionOutputTensorsOrder.UNSPECIFIED,
+  ) -> None:
+    """Initializes the instance of DetectionOutputTensorsMd.
+
+    Args:
+      model_buffer: A valid flatbuffer loaded from the TFLite model file.
+      label_files: information of the label files [1] in the classification
+        tensor.
+      output_tensors_order: the order of the output tensors.
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L9
+    """
+    # Get the output tensor indices and names from the tflite model.
+    tensor_indices_and_names = list(
+        zip(
+            writer_utils.get_output_tensor_indices(model_buffer),
+            writer_utils.get_output_tensor_names(model_buffer),
+        )
+    )
+    location_md = LocationTensorMd(
+        name=self._LOCATION_NAME,
+        description=self._LOCATION_DESCRIPTION,
+    )
+    score_md = ClassificationTensorMd(
+        name=self._SCORE_NAME,
+        description=self._SCORE_DESCRIPTION,
+        label_files=label_files,
+    )
+
+    if output_tensors_order == RawDetectionOutputTensorsOrder.SCORE_LOCATION:
+      self._output_mds = [score_md, location_md]
+    elif output_tensors_order == RawDetectionOutputTensorsOrder.LOCATION_SCORE:
+      self._output_mds = [location_md, score_md]
+    else:
+      raise ValueError(
+          f"Unsupported OutputTensorsOrder value: {output_tensors_order}"
+      )
+
+    if len(self._output_mds) != len(tensor_indices_and_names):
+      raise ValueError(
+          "The size of TFLite output should be " + str(len(self._output_mds))
+      )
+    for i, output_md in enumerate(self._output_mds):
+      output_md.tensor_name = tensor_indices_and_names[i][1]
+
+  @property
+  def output_mds(self) -> List[TensorMd]:
+    return self._output_mds
+
+
 class TensorGroupMd:
   """A container for a group of tensor metadata information."""
 

mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py

@@ -632,7 +632,7 @@ class MetadataWriter(object):
       score_calibration: Optional[ScoreCalibration] = None,
       group_name: str = _DETECTION_GROUP_NAME,
   ) -> 'MetadataWriter':
-    """Adds a detection head metadata for detection output tensor.
+    """Adds a detection head metadata for detection output tensor of models with postprocessing.
 
     Args:
       labels: an instance of Labels helper class.
@@ -661,6 +661,33 @@ class MetadataWriter(object):
     self._output_group_mds.append(group_md)
     return self
 
+  def add_raw_detection_output(
+      self,
+      labels: Optional[Labels] = None,
+      output_tensors_order: metadata_info.RawDetectionOutputTensorsOrder = metadata_info.RawDetectionOutputTensorsOrder.UNSPECIFIED,
+  ) -> 'MetadataWriter':
+    """Adds a detection head metadata for detection output tensor of models without postprocessing.
+
+    Args:
+      labels: an instance of Labels helper class.
+      output_tensors_order: the order of the output tensors. For models of
+        out-of-graph non-maximum-suppression only.
+
+    Returns:
+      The current Writer instance to allow chained operation.
+    """
+    label_files = self._create_label_file_md(labels)
+    detection_output_mds = metadata_info.RawDetectionOutputTensorsMd(
+        self._model_buffer,
+        label_files=label_files,
+        output_tensors_order=output_tensors_order,
+    ).output_mds
+    self._output_mds.extend(detection_output_mds)
+    # Outputs are location, score.
+    if len(detection_output_mds) != 2:
+      raise ValueError('The size of detections output should be 2.')
+    return self
+
   def add_segmentation_output(
       self,
       labels: Optional[Labels] = None,

mediapipe/tasks/python/metadata/metadata_writers/object_detector.py

@@ -14,8 +14,14 @@
 # ==============================================================================
 """Writes metadata and label file to the Object Detector models."""
 
+import dataclasses
 from typing import List, Optional
 
+import flatbuffers
+from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb
+from mediapipe.tasks.metadata import object_detector_metadata_schema_py_generated as _detector_metadata_fb
+from mediapipe.tasks.python.metadata import metadata
+from mediapipe.tasks.python.metadata.metadata_writers import metadata_info
 from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
 
 _MODEL_NAME = "ObjectDetector"
@@ -25,12 +31,187 @@ _MODEL_DESCRIPTION = (
     "stream."
 )
 
+# Metadata Schema file for object detector.
+_FLATC_METADATA_SCHEMA_FILE = metadata.get_path_to_datafile(
+    "../../../metadata/object_detector_metadata_schema.fbs",
+)
+
+# Metadata name in custom metadata field. The metadata name is used to get
+# object detector metadata from SubGraphMetadata.custom_metadata and shouldn't
+# be changed.
+_METADATA_NAME = "DETECTOR_METADATA"
+
+
+@dataclasses.dataclass
+class FixedAnchor:
+  """A fixed size anchor."""
+
+  x_center: float
+  y_center: float
+  width: Optional[float]
+  height: Optional[float]
+
+
+@dataclasses.dataclass
+class FixedAnchorsSchema:
+  """The schema for a list of anchors with fixed size."""
+
+  anchors: List[FixedAnchor]
+
+
+@dataclasses.dataclass
+class SsdAnchorsOptions:
+  """The ssd anchors options used in object detector model."""
+
+  fixed_anchors_schema: Optional[FixedAnchorsSchema]
+
+
+@dataclasses.dataclass
+class TensorsDecodingOptions:
+  """The decoding options to convert model output tensors to detections."""
+
+  # The number of output classes predicted by the detection model.
+  num_classes: int
+  # The number of output boxes predicted by the detection model.
+  num_boxes: int
+  # The number of output values per boxes predicted by the detection
+  # model. The values contain bounding boxes, keypoints, etc.
+  num_coords: int
+  # The offset of keypoint coordinates in the location tensor.
+  keypoint_coord_offset: int
+  # The number of predicted keypoints.
+  num_keypoints: int
+  # The dimension of each keypoint, e.g. number of values predicted for each
+  # keypoint.
+  num_values_per_keypoint: int
+  # Parameters for decoding SSD detection model.
+  x_scale: float
+  y_scale: float
+  w_scale: float
+  h_scale: float
+  # Whether to apply exponential on box size.
+  apply_exponential_on_box_size: bool
+  # Whether to apply sigmod function on the score.
+  sigmoid_score: bool
+
+
+# Create an individual method for getting the metadata json file, so that it can
+# be used as a standalone util.
+def convert_to_json(metadata_buffer: bytearray) -> str:
+  """Converts the metadata into a json string.
+
+  Args:
+    metadata_buffer: valid metadata buffer in bytes.
+
+  Returns:
+    Metadata in JSON format.
+
+  Raises:
+    ValueError: error occurred when parsing the metadata schema file.
+  """
+  return metadata.convert_to_json(
+      metadata_buffer,
+      custom_metadata_schema={_METADATA_NAME: _FLATC_METADATA_SCHEMA_FILE},
+  )
+
+
+class ObjectDetectorOptionsMd(metadata_info.CustomMetadataMd):
+  """Object detector options metadata."""
+
+  _METADATA_FILE_IDENTIFIER = b"V001"
+
+  def __init__(
+      self,
+      ssd_anchors_options: SsdAnchorsOptions,
+      tensors_decoding_options: TensorsDecodingOptions,
+  ) -> None:
+    """Creates an ObjectDetectorOptionsMd object.
+
+    Args:
+      ssd_anchors_options: the ssd anchors options associated to the object
+        detector model.
+      tensors_decoding_options: the tensors decoding options used to decode the
+        object detector model output.
+    """
+    if ssd_anchors_options.fixed_anchors_schema is None:
+      raise ValueError(
+          "Currently only support FixedAnchorsSchema, which cannot be found"
+          " in ssd_anchors_options."
+      )
+    self.ssd_anchors_options = ssd_anchors_options
+    self.tensors_decoding_options = tensors_decoding_options
+    super().__init__(name=_METADATA_NAME)
+
+  def create_metadata(self) -> _metadata_fb.CustomMetadataT:
+    """Creates the image segmenter options metadata.
+
+    Returns:
+      A Flatbuffers Python object of the custom metadata including object
+      detector options metadata.
+    """
+    detector_options = _detector_metadata_fb.ObjectDetectorOptionsT()
+
+    # Set ssd_anchors_options.
+    ssd_anchors_options = _detector_metadata_fb.SsdAnchorsOptionsT()
+    fixed_anchors_schema = _detector_metadata_fb.FixedAnchorsSchemaT()
+    fixed_anchors_schema.anchors = []
+    for anchor in self.ssd_anchors_options.fixed_anchors_schema.anchors:
+      anchor_t = _detector_metadata_fb.FixedAnchorT()
+      anchor_t.xCenter = anchor.x_center
+      anchor_t.yCenter = anchor.y_center
+      anchor_t.width = anchor.width
+      anchor_t.height = anchor.height
+      fixed_anchors_schema.anchors.append(anchor_t)
+    ssd_anchors_options.fixedAnchorsSchema = fixed_anchors_schema
+    detector_options.ssdAnchorsOptions = ssd_anchors_options
+
+    # Set tensors_decoding_options.
+    tensors_decoding_options = _detector_metadata_fb.TensorsDecodingOptionsT()
+    tensors_decoding_options.numClasses = (
+        self.tensors_decoding_options.num_classes
+    )
+    tensors_decoding_options.numBoxes = self.tensors_decoding_options.num_boxes
+    tensors_decoding_options.numCoords = (
+        self.tensors_decoding_options.num_coords
+    )
+    tensors_decoding_options.keypointCoordOffset = (
+        self.tensors_decoding_options.keypoint_coord_offset
+    )
+    tensors_decoding_options.numKeypoints = (
+        self.tensors_decoding_options.num_keypoints
+    )
+    tensors_decoding_options.numValuesPerKeypoint = (
+        self.tensors_decoding_options.num_values_per_keypoint
+    )
+    tensors_decoding_options.xScale = self.tensors_decoding_options.x_scale
+    tensors_decoding_options.yScale = self.tensors_decoding_options.y_scale
+    tensors_decoding_options.wScale = self.tensors_decoding_options.w_scale
+    tensors_decoding_options.hScale = self.tensors_decoding_options.h_scale
+    tensors_decoding_options.applyExponentialOnBoxSize = (
+        self.tensors_decoding_options.apply_exponential_on_box_size
+    )
+    tensors_decoding_options.sigmoidScore = (
+        self.tensors_decoding_options.sigmoid_score
+    )
+    detector_options.tensorsDecodingOptions = tensors_decoding_options
+
+    # Get the object detector options flatbuffer.
+    b = flatbuffers.Builder(0)
+    b.Finish(detector_options.Pack(b), self._METADATA_FILE_IDENTIFIER)
+    detector_options_buf = b.Output()
+
+    # Add the object detector options flatbuffer in custom metadata.
+    custom_metadata = _metadata_fb.CustomMetadataT()
+    custom_metadata.name = self.name
+    custom_metadata.data = detector_options_buf
+    return custom_metadata
+
 
 class MetadataWriter(metadata_writer.MetadataWriterBase):
   """MetadataWriter to write the metadata into the object detector."""
 
   @classmethod
-  def create(
+  def create_for_models_with_nms(
       cls,
       model_buffer: bytearray,
       input_norm_mean: List[float],
@@ -38,7 +219,9 @@ class MetadataWriter(metadata_writer.MetadataWriterBase):
       labels: metadata_writer.Labels,
       score_calibration: Optional[metadata_writer.ScoreCalibration] = None,
   ) -> "MetadataWriter":
-    """Creates MetadataWriter to write the metadata for image classifier.
+    """Creates MetadataWriter to write the metadata for object detector with postprocessing in the model.
+
+    This method create a metadata writer for the models with postprocessing [1].
 
     The parameters required in this method are mandatory when using MediaPipe
     Tasks.
@@ -54,18 +237,20 @@ class MetadataWriter(metadata_writer.MetadataWriterBase):
     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].
+        [2].
-      input_norm_std: the std value used in the input tensor normalizarion [1].
+      input_norm_std: the std value used in the input tensor normalizarion [2].
       labels: an instance of Labels helper class used in the output
-        classification tensor [2].
+        classification tensor [3].
-      score_calibration: A container of the score calibration operation [3] in
+      score_calibration: A container of the score calibration operation [4] 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
+        https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/detection_postprocess.cc
       [2]:
-        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
       [3]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
+      [4]:
         https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
 
     Returns:
@@ -76,3 +261,70 @@ class MetadataWriter(metadata_writer.MetadataWriterBase):
     writer.add_image_input(input_norm_mean, input_norm_std)
     writer.add_detection_output(labels, score_calibration)
     return cls(writer)
+
+  @classmethod
+  def create_for_models_without_nms(
+      cls,
+      model_buffer: bytearray,
+      input_norm_mean: List[float],
+      input_norm_std: List[float],
+      labels: metadata_writer.Labels,
+      ssd_anchors_options: SsdAnchorsOptions,
+      tensors_decoding_options: TensorsDecodingOptions,
+      output_tensors_order: metadata_info.RawDetectionOutputTensorsOrder = metadata_info.RawDetectionOutputTensorsOrder.UNSPECIFIED,
+  ) -> "MetadataWriter":
+    """Creates MetadataWriter to write the metadata for object detector without postprocessing in the model.
+
+    This method create a metadata writer for the models without postprocessing
+    [1].
+
+    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
+        [2].
+      input_norm_std: the std value used in the input tensor normalizarion [2].
+      labels: an instance of Labels helper class used in the output
+        classification tensor [3].
+      ssd_anchors_options: the ssd anchors options associated to the object
+        detector model.
+      tensors_decoding_options: the tensors decoding options used to decode the
+        object detector model output.
+      output_tensors_order: the order of the output tensors.
+      [1]:
+        https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/detection_postprocess.cc
+      [2]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
+      [3]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
+
+    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_raw_detection_output(
+        labels, output_tensors_order=output_tensors_order
+    )
+    option_md = ObjectDetectorOptionsMd(
+        ssd_anchors_options, tensors_decoding_options
+    )
+    writer.add_custom_metadata(option_md)
+    return cls(writer)
+
+  def populate(self) -> "tuple[bytearray, str]":
+    model_buf, _ = super().populate()
+    metadata_buf = metadata.get_metadata_buffer(model_buf)
+    json_content = convert_to_json(metadata_buf)
+    return model_buf, json_content

mediapipe/tasks/python/test/metadata/metadata_writers/BUILD

@@ -86,6 +86,7 @@ py_test(
     deps = [
         "//mediapipe/tasks/metadata:metadata_schema_py",
         "//mediapipe/tasks/python/metadata",
+        "//mediapipe/tasks/python/metadata/metadata_writers:metadata_info",
         "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer",
         "//mediapipe/tasks/python/metadata/metadata_writers:object_detector",
         "//mediapipe/tasks/python/test:test_utils",

mediapipe/tasks/python/test/metadata/metadata_writers/object_detector_test.py

@@ -14,6 +14,7 @@
 # ==============================================================================
 """Tests for metadata_writer.object_detector."""
 
+import csv
 import os
 
 from absl.testing import absltest
@@ -21,6 +22,7 @@ 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_info
 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
@@ -48,6 +50,39 @@ _JSON_FOR_SCORE_CALIBRATION = test_utils.get_test_data_path(
     os.path.join(_TEST_DATA_DIR, "coco_ssd_mobilenet_v1_score_calibration.json")
 )
 
+_EFFICIENTDET_LITE0_ANCHORS_FILE = test_utils.get_test_data_path(
+    os.path.join(_TEST_DATA_DIR, "efficientdet_lite0_fp16_no_nms_anchors.csv")
+)
+
+
+def read_ssd_anchors_from_csv(file_path):
+  with open(file_path, "r") as anchors_file:
+    csv_reader = csv.reader(anchors_file, delimiter=",")
+    parameters = []
+    for row in csv_reader:
+      if not row:
+        parameters.append(None)
+        continue
+      if len(row) != 4:
+        raise ValueError(
+            "Expected empty lines or 4 parameters per line in "
+            f"anchors file, but got {len(row)}."
+        )
+      parameters.append(row)
+  anchors = []
+  for parameter in parameters:
+    anchors.append(
+        object_detector.FixedAnchor(
+            x_center=float(parameter[1]),
+            y_center=float(parameter[0]),
+            width=float(parameter[3]),
+            height=float(parameter[2]),
+        )
+    )
+  return object_detector.SsdAnchorsOptions(
+      fixed_anchors_schema=object_detector.FixedAnchorsSchema(anchors)
+  )
+
 
 class MetadataWriterTest(parameterized.TestCase, absltest.TestCase):
 
@@ -61,24 +96,27 @@ class MetadataWriterTest(parameterized.TestCase, absltest.TestCase):
     )
     with open(model_path, "rb") as f:
       model_buffer = f.read()
-    writer = object_detector.MetadataWriter.create(
+    writer = (
+        object_detector.MetadataWriter.create_for_models_with_nms(
             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()
+      expected_json = f.read().strip()
     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(
+    writer = (
+        object_detector.MetadataWriter.create_for_models_with_nms(
             model_buffer,
             [_NORM_MEAN],
             [_NORM_STD],
@@ -89,9 +127,10 @@ class MetadataWriterTest(parameterized.TestCase, absltest.TestCase):
                 _SCORE_CALIBRATION_DEFAULT_SCORE,
             ),
         )
+    )
     tflite_content, metadata_json = writer.populate()
     with open(_JSON_FOR_SCORE_CALIBRATION, "r") as f:
-      expected_json = f.read()
+      expected_json = f.read().strip()
     self.assertEqual(metadata_json, expected_json)
 
     displayer = metadata.MetadataDisplayer.with_model_buffer(tflite_content)

mediapipe/tasks/testdata/metadata/BUILD

@@ -32,7 +32,7 @@ mediapipe_files(srcs = [
     "deeplabv3_with_activation.json",
     "deeplabv3_without_labels.json",
     "deeplabv3_without_metadata.tflite",
-    "efficientdet_lite0_v1.json",
+    "efficientdet_lite0_fp16_no_nms.tflite",
     "efficientdet_lite0_v1.tflite",
     "labelmap.txt",
     "mobile_ica_8bit-with-custom-metadata.tflite",
@@ -64,6 +64,8 @@ exports_files([
     "classification_tensor_float_meta.json",
     "classification_tensor_uint8_meta.json",
     "classification_tensor_unsupported_meta.json",
+    "efficientdet_lite0_fp16_no_nms_anchors.csv",
+    "efficientdet_lite0_fp16_no_nms.json",
     "feature_tensor_meta.json",
     "image_tensor_meta.json",
     "input_image_tensor_float_meta.json",
@@ -94,6 +96,7 @@ filegroup(
         "bert_text_classifier_no_metadata.tflite",
         "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_no_metadata.tflite",
         "deeplabv3_without_metadata.tflite",
+        "efficientdet_lite0_fp16_no_nms.tflite",
         "efficientdet_lite0_v1.tflite",
         "mobile_ica_8bit-with-custom-metadata.tflite",
         "mobile_ica_8bit-with-large-min-parser-version.tflite",
@@ -126,6 +129,7 @@ filegroup(
         "deeplabv3.json",
         "deeplabv3_with_activation.json",
         "deeplabv3_without_labels.json",
+        "efficientdet_lite0_fp16_no_nms_anchors.csv",
         "efficientdet_lite0_v1.json",
         "external_file",
         "feature_tensor_meta.json",

mediapipe/tasks/testdata/metadata/coco_ssd_mobilenet_v1_score_calibration.json

@@ -56,23 +56,20 @@
               "max": 2
             }
           },
-          "stats": {
+          "stats": {}
-          }
         },
         {
           "name": "category",
           "description": "The categories of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {},
-            },
             "range": {
               "min": 2,
               "max": 2
             }
           },
-          "stats": {
+          "stats": {},
-          },
           "associated_files": [
             {
               "name": "labels.txt",
@@ -86,8 +83,7 @@
           "description": "The scores of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {},
-            },
             "range": {
               "min": 2,
               "max": 2
@@ -102,8 +98,7 @@
               }
             }
           ],
-          "stats": {
+          "stats": {},
-          },
           "associated_files": [
             {
               "name": "score_calibration.txt",
@@ -117,11 +112,9 @@
           "description": "The number of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {}
-            }
           },
-          "stats": {
+          "stats": {}
-          }
         }
       ],
       "output_tensor_groups": [

mediapipe/tasks/testdata/metadata/efficientdet_lite0_v1.json

@@ -42,15 +42,13 @@
           "description": "The scores of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {},
-            },
             "range": {
               "min": 2,
               "max": 2
             }
           },
-          "stats": {
+          "stats": {}
-          }
         },
         {
           "name": "location",
@@ -71,34 +69,29 @@
               "max": 2
             }
           },
-          "stats": {
+          "stats": {}
-          }
         },
         {
           "name": "number of detections",
           "description": "The number of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {}
-            }
           },
-          "stats": {
+          "stats": {}
-          }
         },
         {
           "name": "category",
           "description": "The categories of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {},
-            },
             "range": {
               "min": 2,
               "max": 2
             }
           },
-          "stats": {
+          "stats": {},
-          },
           "associated_files": [
             {
               "name": "labels.txt",

mediapipe/tasks/testdata/metadata/ssd_mobilenet_v1_no_metadata.json

@@ -56,23 +56,20 @@
               "max": 2
             }
           },
-          "stats": {
+          "stats": {}
-          }
         },
         {
           "name": "category",
           "description": "The categories of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {},
-            },
             "range": {
               "min": 2,
               "max": 2
             }
           },
-          "stats": {
+          "stats": {},
-          },
           "associated_files": [
             {
               "name": "labels.txt",
@@ -86,26 +83,22 @@
           "description": "The scores of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {},
-            },
             "range": {
               "min": 2,
               "max": 2
             }
           },
-          "stats": {
+          "stats": {}
-          }
         },
         {
           "name": "number of detections",
           "description": "The number of the detected boxes.",
           "content": {
             "content_properties_type": "FeatureProperties",
-            "content_properties": {
+            "content_properties": {}
-            }
           },
-          "stats": {
+          "stats": {}
-          }
         }
       ],
       "output_tensor_groups": [

third_party/external_files.bzl

@@ -198,8 +198,8 @@ def external_files():
 
     http_file(
         name = "com_google_mediapipe_coco_ssd_mobilenet_v1_score_calibration_json",
-        sha256 = "f377600be924c29697477f9d739db9db5d712aec4a644548526912858db6a082",
+        sha256 = "a850674f9043bfc775527fee7f1b639f7fe0fb56e8d3ed2b710247967c888b09",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/coco_ssd_mobilenet_v1_score_calibration.json?generation=1677522739770755"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/coco_ssd_mobilenet_v1_score_calibration.json?generation=1682456086898538"],
     )
 
     http_file(
@@ -262,10 +262,28 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/dynamic_input_classifier.tflite?generation=1680543275416843"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_efficientdet_lite0_fp16_no_nms_anchors_csv",
+        sha256 = "284475a0f16e34afcc6c0fe68b05bd871aca5b20c83db0870c6a36dd63827176",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_fp16_no_nms_anchors.csv?generation=1682456090001817"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_efficientdet_lite0_fp16_no_nms_json",
+        sha256 = "dc3b333e41c43fb49ace048c25c18d0e34df78fb5ee77edbe169264368f78b92",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_fp16_no_nms.json?generation=1682456092938505"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_efficientdet_lite0_fp16_no_nms_tflite",
+        sha256 = "bcda125c96d3767bca894c8cbe7bc458379c9974c9fd8bdc6204e7124a74082a",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_fp16_no_nms.tflite?generation=1682456096034465"],
+    )
+
     http_file(
         name = "com_google_mediapipe_efficientdet_lite0_v1_json",
-        sha256 = "7a9e1fb625a6130a251e612637fc546cfc8cfabfadc7dbdade44c87f1d8996ca",
+        sha256 = "ef9706696a3ea5d87f4324ac56e877a92033d33e522c4b7d5a416fbcab24d8fc",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_v1.json?generation=1677522746026682"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/efficientdet_lite0_v1.json?generation=1682456098581704"],
     )
 
     http_file(
@@ -1158,8 +1176,8 @@ def external_files():
 
     http_file(
         name = "com_google_mediapipe_ssd_mobilenet_v1_no_metadata_json",
-        sha256 = "89157590b736cf3f3247aa9c8be3570c2856f4981a1e9476117e7c629e7c4825",
+        sha256 = "ae5a5971a1c3df705307448ef97c854d846b7e6f2183fb51015bd5af5d7deb0f",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/ssd_mobilenet_v1_no_metadata.json?generation=1677522786336455"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/ssd_mobilenet_v1_no_metadata.json?generation=1682456117002011"],
     )
 
     http_file(