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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/FullyConnectedLayer.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;
namespace
{
TensorShape calculate_fullyconnected_layer_output_shape(const TensorShape &input_shape, unsigned int output_neurons)
{
// Note: Only 1D batch space is supported at the moment
unsigned int batches = input_shape[1];
if(input_shape.num_dimensions() > 2)
{
batches = input_shape[3];
}
return TensorShape(output_neurons, batches);
}
} // namespace
std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::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();
if(_weights.tensor() == nullptr)
{
unsigned int num_weights = 1;
unsigned int num_dimensions = in->info()->num_dimensions();
// Ignore the batch dimension if there is one:
if(num_dimensions == 2 || num_dimensions == 4)
{
num_dimensions--;
}
for(unsigned int i = 0; i < num_dimensions; i++)
{
num_weights *= in->info()->dimension(i);
}
_weights.set_info(TensorInfo(TensorShape(num_weights, _num_neurons), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
if(_biases.tensor() == nullptr)
{
_biases.set_info(TensorInfo(TensorShape(_num_neurons), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
// Auto configure output
arm_compute::auto_init_if_empty(*out->info(),
calculate_fullyconnected_layer_output_shape(in->info()->tensor_shape(), _num_neurons),
in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position());
bool weights_are_loaded = _weights.tensor() != nullptr;
bool biases_are_loaded = _biases.tensor() != nullptr;
// Create node context
NodeContext node_ctx(OperationType::FullyConnectedLayer);
node_ctx.set_target(_target_hint);
node_ctx.add_input(in);
node_ctx.add_input(_weights.set_target(_target_hint));
node_ctx.add_input(_biases.set_target(_target_hint));
node_ctx.add_output(out);
// Configure operation
auto func = OperationRegistry::get().find_operation(OperationType::FullyConnectedLayer, _target_hint)->configure(node_ctx);
// Fill biases
if(!weights_are_loaded)
{
_weights.allocate_and_fill_if_needed();
}
if(!biases_are_loaded)
{
_biases.allocate_and_fill_if_needed();
}
// Get function
return func;
}
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