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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 <algorithm>
#include <vector>
#include "arm_compute/graph/nodes/DepthConcatenateLayer.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/functions/CLDepthConcatenate.h"
#include "arm_compute/runtime/NEON/functions/NEDepthConcatenate.h"
#include "arm_compute/runtime/Tensor.h"
#include "support/ToolchainSupport.h"
#include "utils/TypePrinter.h"
using namespace arm_compute::graph;
namespace
{
template <typename DepthConcatenationType, typename TensorType, TargetHint hint>
std::unique_ptr<arm_compute::IFunction> instantiate_function(std::vector<arm_compute::ITensor *> inputs, arm_compute::ITensor *output)
{
auto depth_concat = arm_compute::support::cpp14::make_unique<DepthConcatenationType>();
std::vector<TensorType *> casted_inputs;
std::transform(inputs.begin(), inputs.end(), std::back_inserter(casted_inputs), [](arm_compute::ITensor * input)
{
return dynamic_cast<TensorType *>(input);
});
depth_concat->configure(
casted_inputs,
dynamic_cast<TensorType *>(output));
return std::move(depth_concat);
}
template <TargetHint hint>
std::unique_ptr<arm_compute::IFunction> instantiate(std::vector<arm_compute::ITensor *> inputs, arm_compute::ITensor *output);
template <>
std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(std::vector<arm_compute::ITensor *> inputs, arm_compute::ITensor *output)
{
return instantiate_function<arm_compute::CLDepthConcatenate, arm_compute::ICLTensor, TargetHint::OPENCL>(std::move(inputs), output);
}
template <>
std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(std::vector<arm_compute::ITensor *> inputs, arm_compute::ITensor *output)
{
return instantiate_function<arm_compute::NEDepthConcatenate, arm_compute::ITensor, TargetHint::NEON>(std::move(inputs), output);
}
} // namespace
std::unique_ptr<arm_compute::IFunction> DepthConcatenateLayer::instantiate_node(GraphContext &ctx, std::vector<arm_compute::ITensor *> inputs, arm_compute::ITensor *output)
{
std::unique_ptr<arm_compute::IFunction> func;
_hint = ctx.hints().target_hint();
_inputs = std::move(inputs);
_output = output;
if(_hint == TargetHint::OPENCL)
{
func = instantiate<TargetHint::OPENCL>(_inputs, _output);
}
else
{
func = instantiate<TargetHint::NEON>(_inputs, _output);
}
return func;
}
void DepthConcatenateLayer::print_info()
{
if(_hint == TargetHint::OPENCL)
{
std::cout << "Instantiating NEDepthConcatenate";
}
else
{
std::cout << "Instantiating CLDepthConcatenate";
}
for(const auto &i : _inputs)
{
std::cout << " Input: " << i->info()->tensor_shape();
}
std::cout << " Output: " << _output->info()->tensor_shape();
}
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