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/*
* Copyright (c) 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.
*/
#include "arm_compute/core/NEON/kernels/NESpaceToDepthLayerKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <arm_neon.h>
#include <cstdint>
using namespace arm_compute::misc::shape_calculator;
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, int32_t block_shape)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
ARM_COMPUTE_RETURN_ERROR_ON(block_shape < 1);
// Validate output if initialized
if(output->total_size() != 0)
{
const DataLayout data_layout = input->data_layout();
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
const int idx_batch = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_width] % block_shape != 0);
ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_height] % block_shape != 0);
ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_batch] != output->tensor_shape()[idx_batch]);
ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape()[idx_channel] % (block_shape * block_shape) != 0);
ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape().total_size() != output->tensor_shape().total_size());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
return Status{};
}
} // namespace
NESpaceToDepthLayerKernel::NESpaceToDepthLayerKernel()
: _input(nullptr), _output(nullptr), _block_shape()
{
}
void NESpaceToDepthLayerKernel::configure(const ITensor *input, ITensor *output, int32_t block_shape)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
TensorShape output_shape = misc::shape_calculator::compute_space_to_depth_shape(input->info(), block_shape);
auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), block_shape));
_input = input;
_block_shape = block_shape;
_output = output;
// Configure kernel window
Window win = calculate_max_window(*output->info(), Steps());
INEKernel::configure(win);
}
Status NESpaceToDepthLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, int32_t block_shape)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, block_shape));
return Status{};
}
void NESpaceToDepthLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window);
const DataLayout data_layout = _input->info()->data_layout();
const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
const int element_size = _input->info()->element_size();
const size_t channel_size = _input->info()->dimension(channel_idx);
Window slice_out = window.first_slice_window_3D();
int batch_id = 0;
// Main loop for NCHW and NHWC
if(_output->info()->data_layout() == DataLayout::NCHW)
{
do
{
Iterator out(_output, slice_out);
execute_window_loop(slice_out, [&](const Coordinates & id)
{
const size_t channel_id = id.z();
const size_t in_x = id.x() * _block_shape + (channel_id / channel_size) % _block_shape;
const size_t in_y = id.y() * _block_shape + (channel_id / channel_size) / _block_shape;
const int z = channel_id % channel_size;
Coordinates input_coords{ in_x, in_y, z, batch_id };
memcpy(out.ptr(), _input->ptr_to_element(input_coords), element_size);
},
out);
++batch_id;
}
while(window.slide_window_slice_3D(slice_out));
}
else
{
do
{
Iterator out(_output, slice_out);
execute_window_loop(slice_out, [&](const Coordinates & id)
{
const size_t channel_id = id.x();
const size_t in_x = id.y() * _block_shape + (channel_id / channel_size) % _block_shape;
const size_t in_y = id.z() * _block_shape + (channel_id / channel_size) / _block_shape;
const int z = channel_id % channel_size;
Coordinates input_coords{ z, in_x, in_y, batch_id };
memcpy(out.ptr(), _input->ptr_to_element(input_coords), element_size);
},
out);
++batch_id;
}
while(window.slide_window_slice_3D(slice_out));
}
}
} // namespace arm_compute
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