Function Conv

tensorflow/lite/kernels/internal/optimized/multithreaded_conv.h:135–174

};

inline void Conv(const Eigen::ThreadPoolDevice& device,
                 const ConvParams& params, const RuntimeShape& input_shape,
                 const float* input_data, const RuntimeShape& filter_shape,
                 const float* filter_data, const RuntimeShape& bias_shape,
                 const float* bias_data, const RuntimeShape& output_shape,
                 float* output_data, const RuntimeShape& im2col_shape,
                 float* im2col_data) {
  // im2col data should not be generated for the multi-thread supporting case.
  TFLITE_DCHECK(!im2col_data);
  (void)im2col_shape;
  const int stride_width = params.stride_width;
  const int stride_height = params.stride_height;
  const PaddingType padding = params.padding_type;
  const int pad_width = params.padding_values.width;
  const int pad_height = params.padding_values.height;
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);

  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);
  EigenTensorConvFunctor<float> conv_functor;
  conv_functor(device, input_data, batches, input_height, input_width,
               input_depth, filter_data, filter_height, filter_width,
               output_depth, stride_height, stride_width, pad_height, pad_width,
               padding, output_data, output_height, output_width);

  optimized_ops::AddBiasAndEvalActivationFunction(
      output_activation_min, output_activation_max, bias_shape, bias_data,
      output_shape, output_data);
}

}  // namespace multithreaded_ops
}  // namespace tflite