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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 "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp"
namespace winograd
{
template <>
template <>
void WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::execute(
const int n_output_channels,
const int n_input_channels,
const float* const input, // NOTE: Data in HWIO order
float* const output,
const int matrix_stride,
const int matrix_row_stride
)
{
// Get pointers to each cell of the weight tensor
const auto weight_col_stride = n_input_channels * n_output_channels;
const float *inptrs[3];
for (int j = 0; j < 3; j++)
{
inptrs[j] = input + j*weight_col_stride;
}
// For each input channel
for (int ic = 0; ic < n_input_channels; ic++)
{
float *outptr = output + ic * matrix_row_stride;
// For each output channel
int channels_remaining = n_output_channels;
for (; channels_remaining; channels_remaining--)
{
// Matrices used and computed in this kernel
float w[3], V[inner_tile_cols];
// Read weights
for (int j = 0; j < 3; j++)
{
w[j] = *(inptrs[j]++);
}
// Compute V = w WT
V[0] = (w[0]*-1) / 36.0f;
V[1] = (w[1]*-1 + w[0]*1 + w[2]*1) / 48.0f;
V[2] = (w[0]*1 + w[1]*1 + w[2]*1) / 48.0f;
V[3] = (w[0]*-1 + w[2]*-4 + w[1]*2) / 120.0f;
V[4] = (w[0]*-1 + w[2]*-4 + w[1]*-2) / 120.0f;
V[5] = (w[1]*-3 + w[2]*9 + w[0]*1) / 720.0f;
V[6] = (w[1]*3 + w[2]*9 + w[0]*1) / 720.0f;
V[7] = (w[2]*1) / 1;
// Store the transformed weights
for (int j = 0; j < inner_tile_cols; j++)
{
*(outptr + j*matrix_stride) = V[j];
}
outptr++;
}
}
}
template <>
template <>
int WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
{
(void) shape;
return 0; // TODO
}
template <>
template <>
void WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::execute(
const int n_output_channels,
const int n_input_channels,
const float* const input, // NOTE: Data in HWIO order
float* const output,
const int matrix_stride,
const int matrix_row_stride
)
{
// Redirect to the 1xN implementation
WinogradGEMM<1, 6, 1, 3>::template WeightsTransform<float>::execute(
n_output_channels, n_input_channels, input, output, matrix_stride,
matrix_row_stride
);
}
template <>
template <>
int WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
{
(void) shape;
return 0; // TODO
}
template struct WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>;
template struct WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>;
}
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