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/*
* 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 "ConvolutionLayer.h"
#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/half.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
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 std::enable_if<is_floating_point<T>::value, int>::type = 0>
void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int fixed_point_position)
{
ARM_COMPUTE_UNUSED(fixed_point_position);
const int half_width_weights = width_weights / 2;
const int half_height_weights = height_weights / 2;
// 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; yk <= half_height_weights; ++yk)
{
for(int xk = -half_width_weights; xk <= half_width_weights; ++xk)
{
// Check if the pixel is out-of-bound
if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
{
const int idx = xk + half_width_weights;
const int idy = yk + half_height_weights;
const T i_value = in[offset_slice_in + xk + yk * width_in];
const T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights];
acc += i_value * w_value;
}
}
}
}
// Accumulate the bias and store the result
*out = acc + (*bias);
}
// 3D convolution for fixed point type
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights,
int fixed_point_position)
{
const int half_width_weights = width_weights / 2;
const int half_height_weights = height_weights / 2;
using namespace fixed_point_arithmetic;
using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
// Reset accumulator
fixed_point<promoted_type> acc(0, fixed_point_position);
// 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; yk <= half_height_weights; ++yk)
{
for(int xk = -half_width_weights; xk <= half_width_weights; ++xk)
{
// Check if the pixel is out-of-bound
if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
{
const int idx = xk + half_width_weights;
const int idy = yk + half_height_weights;
const fixed_point<promoted_type> i_value(in[offset_slice_in + xk + yk * width_in], fixed_point_position, true);
const fixed_point<promoted_type> w_value(weights[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true);
const fixed_point<promoted_type> iw = i_value * w_value;
acc = iw + acc;
}
}
}
}
// Get the bias
const fixed_point<promoted_type> b(*bias, fixed_point_position, true);
// Accumulate the bias and covert back
acc = acc + b;
fixed_point<T> res(acc);
*out = res.raw();
}
} // namespace
template <typename T>
SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, const PadStrideInfo &info)
{
// Create reference
SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position() };
// Compute reference
const int width_in = src.shape().x();
const int height_in = src.shape().y();
const int depth_in = src.shape().z();
const int width_out = dst.shape().x();
const int height_out = dst.shape().y();
const int depth_out = dst.shape().z();
const int width_weights = weights.shape().x();
const int height_weights = weights.shape().y();
const int depth_weights = weights.shape().z();
const int pad_xi = std::min(static_cast<int>(info.pad().first), width_weights / 2);
const int pad_yi = std::min(static_cast<int>(info.pad().second), height_weights / 2);
const int start_xi = width_weights / 2 - pad_xi;
const int start_yi = height_weights / 2 - pad_yi;
const int end_xi = width_in - start_xi;
const int end_yi = height_in - start_yi;
const int stride_xi = info.stride().first;
const int stride_yi = info.stride().second;
const int num_batches = src.shape().total_size() / (width_in * height_in * depth_in);
for(int r = 0; r < num_batches; ++r)
{
for(int yi = start_yi; yi < end_yi; yi += stride_yi)
{
for(int xi = start_xi; xi < end_xi; xi += stride_xi)
{
for(int ofm = 0; ofm < depth_out; ++ofm)
{
// Compute input and output offsets
const int offset_in = r * width_in * height_in * depth_in;
const int xo = (xi - start_xi) / stride_xi;
const int yo = (yi - start_yi) / stride_yi;
const int offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out;
// Compute 3D convolution
convolution3d(src.data() + offset_in,
weights.data() + ofm * width_weights * height_weights * depth_weights,
bias.data() + ofm,
dst.data() + offset_out,
xi, yi,
width_in, height_in, depth_in,
width_weights, height_weights,
src.fixed_point_position());
}
}
}
}
return dst;
}
template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
const PadStrideInfo &info);
template SimpleTensor<half_float::half> convolution_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias,
const TensorShape &output_shape, const PadStrideInfo &info);
template SimpleTensor<qint8_t> convolution_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &output_shape,
const PadStrideInfo &info);
template SimpleTensor<qint16_t> convolution_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &output_shape,
const PadStrideInfo &info);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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