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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.
*/
#include "arm_compute/runtime/CL/functions/CLDepthConcatenateLayer.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "support/ToolchainSupport.h"
using namespace arm_compute;
CLDepthConcatenateLayer::CLDepthConcatenateLayer() // NOLINT
: _inputs_vector(),
_concat_kernels_vector(),
_border_handlers_vector(),
_num_inputs(0)
{
}
void CLDepthConcatenateLayer::configure(const std::vector<ICLTensor *> &inputs_vector, ICLTensor *output) // NOLINT
{
_num_inputs = inputs_vector.size();
std::vector<ITensorInfo *> inputs_vector_info;
for(unsigned int i = 0; i < _num_inputs; i++)
{
inputs_vector_info.emplace_back(inputs_vector.at(i)->info());
}
_concat_kernels_vector = arm_compute::support::cpp14::make_unique<CLDepthConcatenateLayerKernel[]>(_num_inputs);
_border_handlers_vector = arm_compute::support::cpp14::make_unique<CLFillBorderKernel[]>(_num_inputs);
TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector_info, Window::DimZ);
// Output auto inizialitation if not yet initialized
auto_init_if_empty(*output->info(), output_shape, 1, inputs_vector[0]->info()->data_type());
ARM_COMPUTE_ERROR_THROW_ON(CLDepthConcatenateLayer::validate(inputs_vector_info, output->info()));
unsigned int depth_offset = 0;
for(unsigned int i = 0; i < _num_inputs; i++)
{
_concat_kernels_vector[i].configure(inputs_vector.at(i), depth_offset, output);
_border_handlers_vector[i].configure(inputs_vector.at(i), _concat_kernels_vector[i].border_size(), BorderMode::CONSTANT, PixelValue());
depth_offset += inputs_vector.at(i)->info()->dimension(2);
}
// Set valid region from shape
output->info()->set_valid_region(ValidRegion(Coordinates(), output_shape));
}
Status CLDepthConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_RETURN_ERROR_ON(inputs_vector.size() < 2);
// Output auto inizialitation if not yet initialized
TensorInfo tmp_output_info = *output->clone();
TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, Window::DimZ);
auto_init_if_empty(tmp_output_info, output_shape, 1, inputs_vector[0]->data_type());
unsigned int depth_offset = 0;
for(const auto &input : inputs_vector)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
ARM_COMPUTE_RETURN_ON_ERROR(CLDepthConcatenateLayerKernel::validate(input, depth_offset, &tmp_output_info));
depth_offset += input->dimension(2);
}
return Status{};
}
void CLDepthConcatenateLayer::run()
{
cl::CommandQueue q = CLScheduler::get().queue();
for(unsigned i = 0; i < _num_inputs; i++)
{
CLScheduler::get().enqueue(_border_handlers_vector[i], false);
CLScheduler::get().enqueue(_concat_kernels_vector[i], true);
}
}
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