/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

syntax = "proto2";

package mediapipe.tasks.components.processors.proto;

import "mediapipe/calculators/tensor/tensors_to_classification_calculator.proto";
import "mediapipe/framework/calculator.proto";
import "mediapipe/tasks/cc/components/calculators/classification_aggregation_calculator.proto";
import "mediapipe/tasks/cc/components/calculators/score_calibration_calculator.proto";

message ClassificationPostprocessingGraphOptions {
  extend mediapipe.CalculatorOptions {
    optional ClassificationPostprocessingGraphOptions ext = 460416950;
  }

  // Optional mapping between output tensor index and corresponding score
  // calibration options.
  map<int32, ScoreCalibrationCalculatorOptions> score_calibration_options = 4;

  // Options for the TensorsToClassification calculators (one per classification
  // head) encapsulated by the ClassificationPostprocessing subgraph.
  repeated mediapipe.TensorsToClassificationCalculatorOptions
      tensors_to_classifications_options = 1;

  // Options for the ClassificationAggregationCalculator encapsulated by the
  // ClassificationPostprocessing subgraph.
  optional mediapipe.ClassificationAggregationCalculatorOptions
      classification_aggregation_options = 2;

  // Whether output tensors are quantized (kTfLiteUint8) or not (kFloat32).
  optional bool has_quantized_outputs = 3;
}
