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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/NEWeightsReshapeKernel.h"
#include "arm_compute/core/Dimensions.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
using namespace arm_compute;
namespace
{
template <typename T>
void weights_reshape(const ITensor *input, const ITensor *bias, ITensor *output, const Window &window)
{
const unsigned int kernel_size = input->info()->dimension(0);
const unsigned int kernel_depth = input->info()->dimension(2);
const unsigned int input_stride_x = input->info()->strides_in_bytes().x();
const unsigned int input_stride_y = input->info()->strides_in_bytes().y();
const unsigned int input_stride_z = input->info()->strides_in_bytes().z();
const unsigned int output_stride_y = output->info()->strides_in_bytes().y();
// Create iterators
Iterator in(input, window);
execute_window_loop(window, [&](const Coordinates & id)
{
// Get column index
const int kernel_idx = id[3];
const int kernel_idz = id[4];
// Setup pointers
const uint8_t *tmp_input_ptr = in.ptr();
uint8_t *tmp_output_ptr = output->ptr_to_element(Coordinates(kernel_idx, 0, kernel_idz));
const uint8_t *curr_input_row_ptr = tmp_input_ptr;
const uint8_t *curr_input_depth_ptr = tmp_input_ptr;
// Linearize volume
for(unsigned int d = 0; d < kernel_depth; ++d)
{
for(unsigned int j = 0; j < kernel_size; ++j)
{
for(unsigned int i = 0; i < kernel_size; ++i)
{
*(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(tmp_input_ptr));
tmp_input_ptr += input_stride_x;
tmp_output_ptr += output_stride_y;
}
curr_input_row_ptr += input_stride_y;
tmp_input_ptr = curr_input_row_ptr;
}
curr_input_depth_ptr += input_stride_z;
curr_input_row_ptr = curr_input_depth_ptr;
tmp_input_ptr = curr_input_depth_ptr;
}
// Add bias
if(bias != nullptr)
{
*(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(kernel_idx, kernel_idz))));
}
},
in);
}
} // namespace
NEWeightsReshapeKernel::NEWeightsReshapeKernel()
: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr)
{
}
void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != input->info()->dimension(1));
const DataType dt = input->info()->data_type();
const int fixed_point_position = input->info()->fixed_point_position();
TensorShape output_shape{ input->info()->tensor_shape() };
output_shape.collapse(3);
const size_t tmp_dim = output_shape[0];
output_shape.set(0, output_shape[1]);
output_shape.set(1, tmp_dim + (bias != nullptr ? 1 : 0));
// Set data type and shape for output tensor if not yet configured
set_data_type_if_unknown(*output->info(), dt);
set_fixed_point_position_if_zero(*output->info(), fixed_point_position);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
if(bias != nullptr)
{
TensorShape bias_shape{ input->info()->tensor_shape()[3] };
// Set data type and shape for bias tensor if not yet configured
set_data_type_if_unknown(*bias->info(), dt);
set_fixed_point_position_if_zero(*bias->info(), fixed_point_position);
set_shape_if_empty(*bias->info(), bias_shape);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(bias->info()->tensor_shape(), bias_shape);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::F32, DataType::QS8);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
_input = input;
_bias = bias;
_output = output;
switch(_input->info()->data_type())
{
case DataType::F32:
{
_func = &weights_reshape<uint32_t>;
break;
}
case DataType::QS8:
{
_func = &weights_reshape<uint8_t>;
break;
}
default:
{
ARM_COMPUTE_ERROR_ON("Data type not supported");
break;
}
}
// Configure kernel
Window window = calculate_max_window(*input->info(), Steps());
window.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));
window.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
window.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
// The NEConvolutionLayerWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped
output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
INEKernel::configure(window);
}
void NEWeightsReshapeKernel::run(const Window &window)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
(*_func)(_input, _bias, _output, window);
}
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