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/*
* Copyright (c) 2017-2019 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/misc/Requires.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.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, 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 */
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