tests/validation/reference/Convolution3d.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2021 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.
  */
 #ifndef ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H
 #define ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H

 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "support/Requires.h"
 #include "tests/validation/Helpers.h"
 #include "tests/validation/reference/UtilsQuantizedAsymm.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace convolution_3d
 {
 namespace detail
 {
 inline bool is_valid_pixel(int i, int min, int max)
 {
     return (i >= min && i < max);
 }

 // 3D convolution for floating point type
 template < typename T, typename TW, typename TB, typename std::enable_if < validation::is_floating_point<T>::value &&validation::is_floating_point<TW>::value
                                                                            &&validation::is_floating_point<TB>::value,
                                                                            int >::type = 0 >
 inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
                           int i_offset, int w_offset, int b_offset, int o_offset,
                           int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1, int filter_id = 0)
 {
     ARM_COMPUTE_UNUSED(filter_id);
     const T *in_ptr  = in.data() + i_offset;
     const TW *w_ptr   = weights.data() + w_offset;
     const TB *b_ptr   = bias.data() + b_offset;
     T        *out_ptr = out.data() + o_offset;

     const int half_width_weights_start  = width_weights / 2;
     const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
     const int half_height_weights_start = height_weights / 2;
     const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;

     // Reset accumulator
     T acc(0);

     // Compute a 2D convolution for each IFM and accumulate the result
     for(int ifm = 0; ifm < depth_in; ++ifm)
     {
         // Compute the offset for the input slice
         const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;

         // Compute 2D convolution
         for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
         {
             for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
             {
                 // Check if the pixel is out-of-bound
                 if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in))
                 {
                     const int idx = xk + half_width_weights_start;
                     const int idy = yk + half_height_weights_start;

                     const T  i_value = in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in];
                     const TW w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];

                     acc += i_value * w_value;
                 }
             }
         }
     }

     // Accumulate the bias and store the result
     *out_ptr = acc + (*b_ptr);
 }

 // 3D convolution for QASYMM8 type
 template < typename T, typename TW, typename TB, ARM_COMPUTE_REQUIRES_TA((std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) &&(std::is_same<TW, uint8_t>::value
                                                                          || std::is_same<TW, int8_t>::value)) >
 inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
                           int i_offset, int w_offset, int b_offset, int o_offset,
                           int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1, int filter_id = 0)
 {
     const T *in_ptr  = in.data() + i_offset;
     const TW *w_ptr   = weights.data() + w_offset;
     const TB *b_ptr   = bias.data() + b_offset;
     T        *out_ptr = out.data() + o_offset;

     const UniformQuantizationInfo iq_info = in.quantization_info().uniform();
     const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();
     const UniformQuantizationInfo oq_info = out.quantization_info().uniform();

     const int   input_offset   = -iq_info.offset;
     const float input_scale    = iq_info.scale;
     int         weights_offset = -wq_info.offset;
     float       weights_scale  = wq_info.scale;
     if(is_data_type_quantized_per_channel(weights.data_type()))
     {
         if(is_data_type_quantized_asymmetric(weights.data_type()))
         {
             weights_offset = weights.quantization_info().offset()[filter_id];
         }
         else
         {
             weights_offset = 0;
         }
         weights_scale = weights.quantization_info().scale()[filter_id];
     }
     const int   output_offset = oq_info.offset;
     const float output_scale  = oq_info.scale;

     int         output_multiplier = 0;
     int         output_shift      = 0;
     const float multiplier        = input_scale * weights_scale / output_scale;
     arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);

     const int half_width_weights_start  = width_weights / 2;
     const int half_width_weights_end    = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
     const int half_height_weights_start = height_weights / 2;
     const int half_height_weights_end   = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;

     // Reset accumulator
     int32_t acc(0);

     // Compute a 2D convolution for each IFM and accumulate the result
     for(int ifm = 0; ifm < depth_in; ++ifm)
     {
         // Compute the offset for the input slice
         const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;

         // Compute 2D convolution
         for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
         {
             for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
             {
                 // Check if the pixel is out-of-bound
                 if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in))
                 {
                     const int idx = xk + half_width_weights_start;
                     const int idy = yk + half_height_weights_start;

                     const int32_t i_value = in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in];
                     const int32_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
                     acc += (i_value + input_offset) * (w_value + weights_offset);
                 }
             }
         }
     }

     // Accumulate the bias
     acc += (*b_ptr);

     // Quantize down
     acc = validation::quantize_down_scale_by_fixedpoint(acc, output_multiplier, output_shift, output_offset,
                                                         std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max());

     // Store the result
     *out_ptr = acc;
 }
 } // namespace detail
 } // namespace convolution_3d
 } // namespace test
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H */
	/*
	* Copyright (c) 2017-2021 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.
	*/
	#ifndef ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H
	#define ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H

	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "support/Requires.h"
	#include "tests/validation/Helpers.h"
	#include "tests/validation/reference/UtilsQuantizedAsymm.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace convolution_3d
	{
	namespace detail
	{
	inline bool is_valid_pixel(int i, int min, int max)
	{
	return (i >= min && i < max);
	}

	// 3D convolution for floating point type
	template < typename T, typename TW, typename TB, typename std::enable_if < validation::is_floating_point<T>::value &&validation::is_floating_point<TW>::value
	&&validation::is_floating_point<TB>::value,
	int >::type = 0 >
	inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
	int i_offset, int w_offset, int b_offset, int o_offset,
	int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1, int filter_id = 0)
	{
	ARM_COMPUTE_UNUSED(filter_id);
	const T *in_ptr = in.data() + i_offset;
	const TW *w_ptr = weights.data() + w_offset;
	const TB *b_ptr = bias.data() + b_offset;
	T *out_ptr = out.data() + o_offset;

	const int half_width_weights_start = width_weights / 2;
	const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
	const int half_height_weights_start = height_weights / 2;
	const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;

	// Reset accumulator
	T acc(0);

	// Compute a 2D convolution for each IFM and accumulate the result
	for(int ifm = 0; ifm < depth_in; ++ifm)
	{
	// Compute the offset for the input slice
	const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;

	// Compute 2D convolution
	for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
	{
	for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
	{
	// Check if the pixel is out-of-bound
	if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in))
	{
	const int idx = xk + half_width_weights_start;
	const int idy = yk + half_height_weights_start;

	const T i_value = in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in];
	const TW w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];

	acc += i_value * w_value;
	}
	}
	}
	}

	// Accumulate the bias and store the result
	out_ptr = acc + (b_ptr);
	}

	// 3D convolution for QASYMM8 type
	template < typename T, typename TW, typename TB, ARM_COMPUTE_REQUIRES_TA((std::is_same<T, uint8_t>::value \|\| std::is_same<T, int8_t>::value) &&(std::is_same<TW, uint8_t>::value
	\|\| std::is_same<TW, int8_t>::value)) >
	inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
	int i_offset, int w_offset, int b_offset, int o_offset,
	int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1, int filter_id = 0)
	{
	const T *in_ptr = in.data() + i_offset;
	const TW *w_ptr = weights.data() + w_offset;
	const TB *b_ptr = bias.data() + b_offset;
	T *out_ptr = out.data() + o_offset;

	const UniformQuantizationInfo iq_info = in.quantization_info().uniform();
	const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();
	const UniformQuantizationInfo oq_info = out.quantization_info().uniform();

	const int input_offset = -iq_info.offset;
	const float input_scale = iq_info.scale;
	int weights_offset = -wq_info.offset;
	float weights_scale = wq_info.scale;
	if(is_data_type_quantized_per_channel(weights.data_type()))
	{
	if(is_data_type_quantized_asymmetric(weights.data_type()))
	{
	weights_offset = weights.quantization_info().offset()[filter_id];
	}
	else
	{
	weights_offset = 0;
	}
	weights_scale = weights.quantization_info().scale()[filter_id];
	}
	const int output_offset = oq_info.offset;
	const float output_scale = oq_info.scale;

	int output_multiplier = 0;
	int output_shift = 0;
	const float multiplier = input_scale * weights_scale / output_scale;
	arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);

	const int half_width_weights_start = width_weights / 2;
	const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
	const int half_height_weights_start = height_weights / 2;
	const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;

	// Reset accumulator
	int32_t acc(0);

	// Compute a 2D convolution for each IFM and accumulate the result
	for(int ifm = 0; ifm < depth_in; ++ifm)
	{
	// Compute the offset for the input slice
	const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;

	// Compute 2D convolution
	for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
	{
	for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
	{
	// Check if the pixel is out-of-bound
	if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in))
	{
	const int idx = xk + half_width_weights_start;
	const int idy = yk + half_height_weights_start;

	const int32_t i_value = in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in];
	const int32_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
	acc += (i_value + input_offset) * (w_value + weights_offset);
	}
	}
	}
	}

	// Accumulate the bias
	acc += (*b_ptr);

	// Quantize down
	acc = validation::quantize_down_scale_by_fixedpoint(acc, output_multiplier, output_shift, output_offset,
	std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max());

	// Store the result
	*out_ptr = acc;
	}
	} // namespace detail
	} // namespace convolution_3d
	} // namespace test
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H */