inline void TransposeConv()

in src/tensorflow/lite/kernels/internal/reference/transpose_conv.h [114:212]
90 lines of code
17 McCabe index (conditional complexity)

inline void TransposeConv(
    const ConvParams& params, const RuntimeShape& input_shape,
    const uint8_t* input_data, const RuntimeShape& filter_shape,
    const uint8_t* filter_data, const RuntimeShape& bias_shape,
    const int32_t* bias_data, const RuntimeShape& output_shape,
    uint8_t* output_data, const RuntimeShape& im2col_shape,
    uint8_t* im2col_data, int32_t* scratch_buffer) {
  const int stride_width = params.stride_width;
  const int stride_height = params.stride_height;
  const int pad_width = params.padding_values.width;
  const int pad_height = params.padding_values.height;
  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
  (void)im2col_data;   // only used in optimized code.
  (void)im2col_shape;  // only used in optimized code.

  const int batches = MatchingDim(input_shape, 0, output_shape, 0);
  const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
  const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
  const int input_height = input_shape.Dims(1);
  const int input_width = input_shape.Dims(2);
  const int filter_height = filter_shape.Dims(1);
  const int filter_width = filter_shape.Dims(2);
  const int output_height = output_shape.Dims(1);
  const int output_width = output_shape.Dims(2);
  const int32_t input_offset = params.input_offset;
  const int32_t filter_offset = params.weights_offset;
  const int32_t output_offset = params.output_offset;
  const int32_t output_multiplier = params.output_multiplier;
  const int output_shift = params.output_shift;
  const int32_t output_activation_min = params.quantized_activation_min;
  const int32_t output_activation_max = params.quantized_activation_max;
  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
  if (bias_data) {
    TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
  }

  const int num_elements = output_shape.FlatSize();
  // We need to initialize scratch_buffer to all 0s, as we apply the same
  // 'scatter' based trick as in float version.
  memset(scratch_buffer, 0, num_elements * sizeof(int32_t));

  // Loop through input elements one at a time.
  for (int batch = 0; batch < batches; ++batch) {
    for (int in_y = 0; in_y < input_height; ++in_y) {
      for (int in_x = 0; in_x < input_width; ++in_x) {
        for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
          // Loop through the output elements it will influence.
          const int out_x_origin = (in_x * stride_width) - pad_width;
          const int out_y_origin = (in_y * stride_height) - pad_height;
          for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
            for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
              for (int out_channel = 0; out_channel < output_depth;
                   ++out_channel) {
                // Compute output element location.
                const int out_x = out_x_origin + filter_x;
                const int out_y = out_y_origin + filter_y;
                // We cannot accumulate out of bounds.
                if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) &&
                    (out_y < output_height)) {
                  uint8_t input_value = input_data[Offset(
                      input_shape, batch, in_y, in_x, in_channel)];
                  uint8_t filter_value =
                      filter_data[Offset(filter_shape, out_channel, filter_y,
                                         filter_x, in_channel)];
                  scratch_buffer[Offset(output_shape, batch, out_y, out_x,
                                        out_channel)] +=
                      (input_value + input_offset) *
                      (filter_value + filter_offset);
                }
              }
            }
          }
        }
      }
    }
  }
  for (int batch = 0; batch < batches; ++batch) {
    for (int out_y = 0; out_y < output_height; ++out_y) {
      for (int out_x = 0; out_x < output_width; ++out_x) {
        for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
          int32_t acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x,
                                              out_channel)];
          if (bias_data) {
            acc += bias_data[out_channel];
          }
          int32_t scaled_acc = MultiplyByQuantizedMultiplier(
              acc, output_multiplier, output_shift);
          scaled_acc += output_offset;
          scaled_acc = std::max(scaled_acc, output_activation_min);
          scaled_acc = std::min(scaled_acc, output_activation_max);
          output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
              static_cast<uint8_t>(scaled_acc);
        }
      }
    }
  }
}