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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/graph/nodes/ConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
#include "support/ToolchainSupport.h"
#include "utils/TypePrinter.h"
using namespace arm_compute::graph;
namespace
{
template <typename ConvolutionType, typename TensorType, Hint hint>
std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
bool weights_are_loaded = weights.tensor() != nullptr;
bool biases_are_loaded = biases.tensor() != nullptr;
auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
conv->configure(
dynamic_cast<TensorType *>(input),
dynamic_cast<TensorType *>(weights.set_target(hint)),
dynamic_cast<TensorType *>(biases.set_target(hint)),
dynamic_cast<TensorType *>(output),
conv_info, weights_info);
if(!weights_are_loaded)
{
weights.allocate_and_fill_if_needed();
}
if(!biases_are_loaded)
{
biases.allocate_and_fill_if_needed();
}
return std::move(conv);
}
template <Hint hint>
std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info);
template <>
std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
}
template <>
std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info);
}
} // namespace
std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output)
{
if(_weights.tensor() == nullptr)
{
_weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, input->info()->dimension(2), _ofm), input->info()->num_channels(), input->info()->data_type(),
input->info()->fixed_point_position()));
}
if(_biases.tensor() == nullptr)
{
_biases.set_info(TensorInfo(TensorShape(_ofm), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
}
std::unique_ptr<arm_compute::IFunction> func;
_hint = hint;
_input = input;
_output = output;
if(_hint == Hint::OPENCL)
{
func = instantiate<Hint::OPENCL>(input, _weights, _biases, output, _conv_info, _weights_info);
}
else
{
func = instantiate<Hint::NEON>(input, _weights, _biases, output, _conv_info, _weights_info);
}
return func;
}
void ConvolutionLayer::print_info()
{
if(_hint == Hint::OPENCL)
{
std::cout << "Instantiating CLConvolutionLayer";
}
else
{
std::cout << "Instantiating NEConvolutionLayer";
}
std::cout << " Type: " << _input->info()->data_type() << " Input Shape: " << _input->info()->tensor_shape() << " Weights shape: " << _weights.info().tensor_shape() << " Biases Shape: " <<
_biases.info().tensor_shape() << " Output Shape: " << _output->info()->tensor_shape() << " PadStrideInfo: " << _conv_info << "WeightsInfo: " << _weights_info << std::endl;
}
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