label_image 예제를 보면 get_top_n 에서

(prediction[i] + 128) / 256.0 나 prediction[i] / 255.0 

같은 quantization 범위에 맞춘 무언가가 보이는데 

TfLiteQuantizationParams.scale 와 TfLiteQuantizationParams.zero_point을 이용하면 자동화 가능할 느낌.

template <class T>
void get_top_n(T* prediction, int prediction_size, size_t num_results,
               float threshold, std::vector<std::pair<float, int>>* top_results,
               TfLiteType input_type) {
  // Will contain top N results in ascending order.
  std::priority_queue<std::pair<float, int>, std::vector<std::pair<float, int>>,
                      std::greater<std::pair<float, int>>>
      top_result_pq;

  const long count = prediction_size;  // NOLINT(runtime/int)
  float value = 0.0;

  for (int i = 0; i < count; ++i) {
    switch (input_type) {
      case kTfLiteFloat32:
        value = prediction[i];
        break;
      case kTfLiteInt8:
        value = (prediction[i] + 128) / 256.0;
        break;
      case kTfLiteUInt8:
        value = prediction[i] / 255.0;
        break;
      default:
        break;
    }
    // Only add it if it beats the threshold and has a chance at being in
    // the top N.
    if (value < threshold) {
      continue;
    }

    top_result_pq.push(std::pair<float, int>(value, i));

    // If at capacity, kick the smallest value out.
    if (top_result_pq.size() > num_results) {
      top_result_pq.pop();
    }
  }

  // Copy to output vector and reverse into descending order.
  while (!top_result_pq.empty()) {
    top_results->push_back(top_result_pq.top());
    top_result_pq.pop();
  }
  std::reverse(top_results->begin(), top_results->end());
}

 

netron 에서 보면 quantization 범위가 나오는데, 어딘가 저장하고는 있는 듯 해서 검색 중

// SupportedQuantizationTypes.
typedef enum TfLiteQuantizationType {
  // No quantization.
  kTfLiteNoQuantization = 0,
  // Affine quantization (with support for per-channel quantization).
  // Corresponds to TfLiteAffineQuantization.
  kTfLiteAffineQuantization = 1,
} TfLiteQuantizationType;

// Structure specifying the quantization used by the tensor, if-any.
typedef struct TfLiteQuantization {
  // The type of quantization held by params.
  TfLiteQuantizationType type;
  // Holds a reference to one of the quantization param structures specified
  // below.
  void* params;
} TfLiteQuantization;

// Legacy. Will be deprecated in favor of TfLiteAffineQuantization.
// If per-layer quantization is specified this field will still be populated in
// addition to TfLiteAffineQuantization.
// Parameters for asymmetric quantization. Quantized values can be converted
// back to float using:
//     real_value = scale * (quantized_value - zero_point)
typedef struct TfLiteQuantizationParams {
  float scale;
  int32_t zero_point;
} TfLiteQuantizationParams;

// Parameters for asymmetric quantization across a dimension (i.e per output
// channel quantization).
// quantized_dimension specifies which dimension the scales and zero_points
// correspond to.
// For a particular value in quantized_dimension, quantized values can be
// converted back to float using:
//     real_value = scale * (quantized_value - zero_point)
typedef struct TfLiteAffineQuantization {
  TfLiteFloatArray* scale;
  TfLiteIntArray* zero_point;
  int32_t quantized_dimension;
} TfLiteAffineQuantization;

typedef struct TfLiteTensor {
  TfLiteType type;
  TfLitePtrUnion data;
  TfLiteIntArray* dims;
  TfLiteQuantizationParams params;
  TfLiteAllocationType allocation_type;
  size_t bytes;
  const void* allocation;
  const char* name;
  struct TfLiteDelegate* delegate;
  TfLiteBufferHandle buffer_handle;
  bool data_is_stale;
  bool is_variable;
  TfLiteQuantization quantization;
  TfLiteSparsity* sparsity;
  const TfLiteIntArray* dims_signature;
} TfLiteTensor;

 

+

2021.05.26

typedef struct TfLiteIntArray {
  int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
#if (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
     __GNUC_MINOR__ >= 1) ||                                      \
    defined(HEXAGON) || (__clang_major__ == 7 && __clang_minor__ == 1)
  int data[0];
#else
  int data[];
#endif
} TfLiteIntArray;

// Fixed size list of floats. Used for per-channel quantization.
typedef struct TfLiteFloatArray {
  int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
// This also applies to the toolchain used for Qualcomm Hexagon DSPs.
#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
    __GNUC_MINOR__ >= 1
  float data[0];
#else
  float data[];
#endif
} TfLiteFloatArray;

 

class Interpreter {
  /// Invoke the interpreter (run the whole graph in dependency order).
  ///
  /// NOTE: It is possible that the interpreter is not in a ready state
  /// to evaluate (i.e. if a ResizeTensor() has been performed without an
  /// AllocateTensors().
  /// Returns status of success or failure.
  TfLiteStatus Invoke();

  /// WARNING: Experimental interface, subject to change
  Subgraph& primary_subgraph() {
    return *subgraphs_.front();  /// Safe as subgraphs_ always has 1 entry.
  }
  
  /// Read only access to list of inputs.
  const std::vector<int>& inputs() const { return primary_subgraph().inputs(); }
  
  /// Read only access to list of outputs.
  const std::vector<int>& outputs() const {
    return primary_subgraph().outputs();
  }
  
  // Subgraphs
  std::vector<std::unique_ptr<Subgraph>> subgraphs_;
};

[링크 : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/interpreter.h]

 

class Subgraph {
  // Array of indices representing the tensors that are inputs to the
  // interpreter.
  std::vector<int> inputs_;

  // Array of indices representing the tensors that are outputs to the
  // interpreter.
  std::vector<int> outputs_;
  
  // Read only access to list of inputs.
  std::vector<int>& inputs() { return inputs_; }

  // Read only access to list of inputs.
  const std::vector<int>& inputs() const { return inputs_; }

  // Read only access to list of outputs.
  std::vector<int>& outputs() { return outputs_; }

  // Read only access to list of outputs.
  const std::vector<int>& outputs() const { return outputs_; }
};

[링크 : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/core/subgraph.h]

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