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/*
* Copyright (c) 2017-2018 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/graph/Error.h"
#include "arm_compute/graph/NodeContext.h"
#include "arm_compute/graph/OperationRegistry.h"
#include "support/ToolchainSupport.h"
using namespace arm_compute::graph;
std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
{
ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output);
arm_compute::ITensor *in = input->tensor();
arm_compute::ITensor *out = output->tensor();
_target_hint = ctx.hints().target_hint();
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()));
}
bool mean_is_loaded = _mean.tensor() != nullptr;
bool var_is_loaded = _var.tensor() != nullptr;
bool gamma_is_loaded = _gamma.tensor() != nullptr;
bool beta_is_loaded = _beta.tensor() != nullptr;
// Set mean, var, gamma and beta target
_mean.set_target(_target_hint);
_var.set_target(_target_hint);
_gamma.set_target(_target_hint);
_beta.set_target(_target_hint);
// Create node context
NodeContext node_ctx(OperationType::BatchNormalizationLayer);
node_ctx.set_target(_target_hint);
node_ctx.add_input(in);
node_ctx.add_input(_mean.tensor());
node_ctx.add_input(_var.tensor());
node_ctx.add_input(_beta.tensor());
node_ctx.add_input(_gamma.tensor());
node_ctx.add_output(out);
node_ctx.add_parameter<float>("epsilon", _epsilon);
node_ctx.add_parameter<ActivationLayerInfo>("act_info", _act_info);
// Configure operation
auto func = OperationRegistry::get().find_operation(OperationType::BatchNormalizationLayer, _target_hint)->configure(node_ctx);
// Fill tensors
if(!mean_is_loaded)
{
_mean.allocate_and_fill_if_needed();
}
if(!var_is_loaded)
{
_var.allocate_and_fill_if_needed();
}
if(!gamma_is_loaded)
{
_gamma.allocate_and_fill_if_needed();
}
if(!beta_is_loaded)
{
_beta.allocate_and_fill_if_needed();
}
// Get function
return func;
}
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