tests/validation/CPP/DepthwiseConvolutionLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "DepthwiseConvolutionLayer.h"

 #include "ConvolutionLayer.h"
 #include "Utils.h"

 #include "tests/validation/CPP/Utils.h"
 #include "tests/validation/CPP/UtilsQuantizedAsymm.h"
 #include "tests/validation/FixedPoint.h"
 #include "tests/validation/Helpers.h"

 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 /** Perform a depthwise convolution
  *
  * - Three dimensions tensors
  * - Third dimention is number of channels
  * - Depths of input tensor and filter are equals
  * - Padding, stride and output shape "match"
  *
  */
 template <typename T, typename TB>
 SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info)
 {
     // Create reference
     SimpleTensor<T> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position() };

     // Compute reference
     const int filter_width  = weights.shape().x();
     const int filter_height = weights.shape().y();
     const int filter_plane  = filter_width * filter_height;
     const int input_width   = src.shape().x();
     const int input_height  = src.shape().y();
     const int input_depth   = src.shape().z();
     const int num_batches   = src.shape().total_size() / (input_width * input_height * input_depth);

     const int filter_half_width  = filter_width / 2;
     const int filter_half_height = filter_height / 2;

     const int pad_left   = std::min(static_cast<int>(conv_info.pad_left()), filter_half_width);
     const int pad_top    = std::min(static_cast<int>(conv_info.pad_top()), filter_half_height);
     const int pad_right  = std::min(static_cast<int>(conv_info.pad_right()), filter_half_width);
     const int pad_bottom = std::min(static_cast<int>(conv_info.pad_bottom()), filter_half_height);

     const int minimum_x = -pad_left + filter_half_width;
     const int minimum_y = -pad_top + filter_half_height;
     const int maximum_x = input_width + pad_left - filter_half_width + pad_right - filter_half_width;
     const int maximum_y = input_height + pad_top - filter_half_height + pad_bottom - filter_half_height;

     int out_pos = 0;
     for(int r = 0; r < num_batches; ++r)
     {
         for(int z = 0; z < input_depth; ++z)
         {
             for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second)
             {
                 for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first)
                 {
                     Coordinates coords(static_cast<int>(x), static_cast<int>(y), static_cast<int>(z), static_cast<int>(r));
                     size_t      filter_offset = filter_plane * z;

                     T val = 0;
                     for(int j = y - filter_half_height; j <= static_cast<int>(y + filter_half_height); ++j)
                     {
                         for(int i = x - filter_half_width; i <= static_cast<int>(x + filter_half_width); ++i)
                         {
                             coords.set(0, i);
                             coords.set(1, j);
                             val += *(weights.data() + filter_offset) * tensor_elem_at(src, coords, BorderMode::CONSTANT, 0.f);
                             ++filter_offset;
                         }
                     }
                     coords.set(0, x);
                     coords.set(1, y);
                     dst[out_pos++] = saturate_cast<T>(val + *static_cast<const TB *>(biases(Coordinates(z))));
                 }
             }
         }
     }

     return dst;
 }

 template <>
 SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape,
                                             const PadStrideInfo &conv_info)
 {
     // Create reference
     SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };

     const int   input_offset   = -src.quantization_info().offset;
     const float input_scale    = src.quantization_info().scale;
     const int   weights_offset = -weights.quantization_info().offset;
     const float weights_scale  = weights.quantization_info().scale;
     const int   output_offset  = dst.quantization_info().offset;
     const float output_scale   = dst.quantization_info().scale;

     int         output_multiplier;
     int         output_shift;
     const float multiplier = input_scale * weights_scale / output_scale;
     arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);

     // Compute reference
     const int filter_width  = weights.shape().x();
     const int filter_height = weights.shape().y();
     const int filter_plane  = filter_width * filter_height;
     const int input_width   = src.shape().x();
     const int input_height  = src.shape().y();
     const int input_depth   = src.shape().z();
     const int num_batches   = src.shape().total_size() / (input_width * input_height * input_depth);

     const int filter_half_size = filter_width / 2;
     const int pad_x            = std::min(filter_half_size, static_cast<int>(conv_info.pad().first));
     const int pad_y            = std::min(filter_half_size, static_cast<int>(conv_info.pad().second));
     const int minimum_x        = -pad_x + filter_half_size;
     const int minimum_y        = -pad_y + filter_half_size;

     int out_pos = 0;
     for(int r = 0; r < num_batches; ++r)
     {
         for(int z = 0; z < input_depth; ++z)
         {
             int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(z)));
             for(int y = minimum_y; y < input_height + pad_y - filter_half_size; y += conv_info.stride().second)
             {
                 for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first)
                 {
                     Coordinates coords(x, y, z);
                     int         filter_offset = filter_plane * z;

                     uint32_t val = 0;
                     for(int j = y - filter_half_size; j <= (y + filter_half_size); ++j)
                     {
                         for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i)
                         {
                             coords.set(0, i);
                             coords.set(1, j);
                             auto    in_val = tensor_elem_at<uint8_t>(src, coords, BorderMode::CONSTANT, 0);
                             uint8_t w_val  = *(weights.data() + filter_offset);
                             val += (in_val + input_offset) * (w_val + weights_offset);
                             ++filter_offset;
                         }
                     }
                     val += bias_val;
                     val = asymm_rounding_divide_by_pow2(asymm_int_mult(val, output_multiplier), output_shift);
                     val += output_offset;
                     val = std::max<int32_t>(val, 0);
                     val = std::min<int32_t>(val, 255);

                     // Store the result
                     dst[out_pos++] = val;
                 }
             }
         }
     }

     return dst;
 }

 template SimpleTensor<float> depthwise_convolution(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &biases, const TensorShape &dst_shape,
                                                    const PadStrideInfo &conv_info);
 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017 ARM Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "DepthwiseConvolutionLayer.h"

	#include "ConvolutionLayer.h"
	#include "Utils.h"

	#include "tests/validation/CPP/Utils.h"
	#include "tests/validation/CPP/UtilsQuantizedAsymm.h"
	#include "tests/validation/FixedPoint.h"
	#include "tests/validation/Helpers.h"

	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	/** Perform a depthwise convolution
	*
	* - Three dimensions tensors
	* - Third dimention is number of channels
	* - Depths of input tensor and filter are equals
	* - Padding, stride and output shape "match"
	*
	*/
	template <typename T, typename TB>
	SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info)
	{
	// Create reference
	SimpleTensor<T> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position() };

	// Compute reference
	const int filter_width = weights.shape().x();
	const int filter_height = weights.shape().y();
	const int filter_plane = filter_width * filter_height;
	const int input_width = src.shape().x();
	const int input_height = src.shape().y();
	const int input_depth = src.shape().z();
	const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);

	const int filter_half_width = filter_width / 2;
	const int filter_half_height = filter_height / 2;

	const int pad_left = std::min(static_cast<int>(conv_info.pad_left()), filter_half_width);
	const int pad_top = std::min(static_cast<int>(conv_info.pad_top()), filter_half_height);
	const int pad_right = std::min(static_cast<int>(conv_info.pad_right()), filter_half_width);
	const int pad_bottom = std::min(static_cast<int>(conv_info.pad_bottom()), filter_half_height);

	const int minimum_x = -pad_left + filter_half_width;
	const int minimum_y = -pad_top + filter_half_height;
	const int maximum_x = input_width + pad_left - filter_half_width + pad_right - filter_half_width;
	const int maximum_y = input_height + pad_top - filter_half_height + pad_bottom - filter_half_height;

	int out_pos = 0;
	for(int r = 0; r < num_batches; ++r)
	{
	for(int z = 0; z < input_depth; ++z)
	{
	for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second)
	{
	for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first)
	{
	Coordinates coords(static_cast<int>(x), static_cast<int>(y), static_cast<int>(z), static_cast<int>(r));
	size_t filter_offset = filter_plane * z;

	T val = 0;
	for(int j = y - filter_half_height; j <= static_cast<int>(y + filter_half_height); ++j)
	{
	for(int i = x - filter_half_width; i <= static_cast<int>(x + filter_half_width); ++i)
	{
	coords.set(0, i);
	coords.set(1, j);
	val += (weights.data() + filter_offset) tensor_elem_at(src, coords, BorderMode::CONSTANT, 0.f);
	++filter_offset;
	}
	}
	coords.set(0, x);
	coords.set(1, y);
	dst[out_pos++] = saturate_cast<T>(val + static_cast<const TB >(biases(Coordinates(z))));
	}
	}
	}
	}

	return dst;
	}

	template <>
	SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape,
	const PadStrideInfo &conv_info)
	{
	// Create reference
	SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };

	const int input_offset = -src.quantization_info().offset;
	const float input_scale = src.quantization_info().scale;
	const int weights_offset = -weights.quantization_info().offset;
	const float weights_scale = weights.quantization_info().scale;
	const int output_offset = dst.quantization_info().offset;
	const float output_scale = dst.quantization_info().scale;

	int output_multiplier;
	int output_shift;
	const float multiplier = input_scale * weights_scale / output_scale;
	arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);

	// Compute reference
	const int filter_width = weights.shape().x();
	const int filter_height = weights.shape().y();
	const int filter_plane = filter_width * filter_height;
	const int input_width = src.shape().x();
	const int input_height = src.shape().y();
	const int input_depth = src.shape().z();
	const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);

	const int filter_half_size = filter_width / 2;
	const int pad_x = std::min(filter_half_size, static_cast<int>(conv_info.pad().first));
	const int pad_y = std::min(filter_half_size, static_cast<int>(conv_info.pad().second));
	const int minimum_x = -pad_x + filter_half_size;
	const int minimum_y = -pad_y + filter_half_size;

	int out_pos = 0;
	for(int r = 0; r < num_batches; ++r)
	{
	for(int z = 0; z < input_depth; ++z)
	{
	int32_t bias_val = static_cast<const int32_t >(biases(Coordinates(z)));
	for(int y = minimum_y; y < input_height + pad_y - filter_half_size; y += conv_info.stride().second)
	{
	for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first)
	{
	Coordinates coords(x, y, z);
	int filter_offset = filter_plane * z;

	uint32_t val = 0;
	for(int j = y - filter_half_size; j <= (y + filter_half_size); ++j)
	{
	for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i)
	{
	coords.set(0, i);
	coords.set(1, j);
	auto in_val = tensor_elem_at<uint8_t>(src, coords, BorderMode::CONSTANT, 0);
	uint8_t w_val = *(weights.data() + filter_offset);
	val += (in_val + input_offset) * (w_val + weights_offset);
	++filter_offset;
	}
	}
	val += bias_val;
	val = asymm_rounding_divide_by_pow2(asymm_int_mult(val, output_multiplier), output_shift);
	val += output_offset;
	val = std::max<int32_t>(val, 0);
	val = std::min<int32_t>(val, 255);

	// Store the result
	dst[out_pos++] = val;
	}
	}
	}
	}

	return dst;
	}

	template SimpleTensor<float> depthwise_convolution(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &biases, const TensorShape &dst_shape,
	const PadStrideInfo &conv_info);
	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute