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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/BatchNormalizationLayer.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"
#include "arm_compute/runtime/Tensor.h"
#include "support/ToolchainSupport.h"
#include "utils/TypePrinter.h"
using namespace arm_compute::graph;
namespace
{
template <typename BatchBatchNormalizationLayer, typename TensorType, TargetHint target_hint>
std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
{
auto norm = arm_compute::support::cpp14::make_unique<BatchBatchNormalizationLayer>();
norm->configure(
dynamic_cast<TensorType *>(input),
dynamic_cast<TensorType *>(output),
dynamic_cast<TensorType *>(mean.set_target(target_hint)),
dynamic_cast<TensorType *>(var.set_target(target_hint)),
dynamic_cast<TensorType *>(beta.set_target(target_hint)),
dynamic_cast<TensorType *>(gamma.set_target(target_hint)),
epsilon);
return std::move(norm);
}
template <TargetHint target_hint>
std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon);
template <>
std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma,
float epsilon)
{
return instantiate_function<arm_compute::CLBatchNormalizationLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, output, mean, var, beta, gamma, epsilon);
}
template <>
std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
{
return instantiate_function<arm_compute::NEBatchNormalizationLayer, arm_compute::ITensor, TargetHint::NEON>(input, output, mean, var, beta, gamma, epsilon);
}
} // namespace
std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
{
ARM_COMPUTE_ERROR_ON(input == nullptr || input->tensor() == nullptr);
ARM_COMPUTE_ERROR_ON(output == nullptr || output->tensor() == nullptr);
std::unique_ptr<arm_compute::IFunction> func;
_target_hint = ctx.hints().target_hint();
arm_compute::ITensor *in = input->tensor();
arm_compute::ITensor *out = output->tensor();
unsigned int batch_norm_size = in->info()->dimension(2);
if(_mean.tensor() == nullptr)
{
_mean.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
if(_var.tensor() == nullptr)
{
_var.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
if(_beta.tensor() == nullptr)
{
_beta.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
if(_gamma.tensor() == nullptr)
{
_gamma.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
if(_target_hint == TargetHint::OPENCL)
{
func = instantiate<TargetHint::OPENCL>(in, out, _mean, _var, _beta, _gamma, _epsilon);
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLBatchNormalizationLayer");
}
else
{
func = instantiate<TargetHint::NEON>(in, out, _mean, _var, _beta, _gamma, _epsilon);
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEBatchNormalizationLayer");
}
ARM_COMPUTE_LOG_GRAPH_INFO(" Data Type: " << in->info()->data_type()
<< " Input shape: " << in->info()->tensor_shape()
<< " Output shape: " << out->info()->tensor_shape()
<< std::endl);
return func;
}
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