arm_compute v17.12
diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp
index 6b21810..219e0f9 100644
--- a/src/graph/nodes/FullyConnectedLayer.cpp
+++ b/src/graph/nodes/FullyConnectedLayer.cpp
@@ -23,12 +23,10 @@
*/
#include "arm_compute/graph/nodes/FullyConnectedLayer.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Logger.h"
-#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
-#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
+#include "arm_compute/graph/Error.h"
+#include "arm_compute/graph/NodeContext.h"
+#include "arm_compute/graph/OperationRegistry.h"
#include "support/ToolchainSupport.h"
-#include "utils/TypePrinter.h"
using namespace arm_compute::graph;
@@ -44,52 +42,20 @@
}
return TensorShape(output_neurons, batches);
}
-template <typename FullyConnectedType, typename TensorType, TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output)
-{
- bool weights_are_loaded = weights.tensor() != nullptr;
- bool biases_are_loaded = biases.tensor() != nullptr;
-
- auto conv = arm_compute::support::cpp14::make_unique<FullyConnectedType>();
- conv->configure(
- dynamic_cast<TensorType *>(input),
- dynamic_cast<TensorType *>(weights.set_target(target_hint)),
- dynamic_cast<TensorType *>(biases.set_target(target_hint)),
- dynamic_cast<TensorType *>(output));
- 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 <TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output);
-
-template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output)
-{
- return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, weights, biases, output);
-}
-
-template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output)
-{
- return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, TargetHint::NEON>(input, weights, biases, output);
-}
} // namespace
-std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output)
+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 = input->info()->num_dimensions();
+ unsigned int num_dimensions = in->info()->num_dimensions();
// Ignore the batch dimension if there is one:
if(num_dimensions == 2 || num_dimensions == 4)
{
@@ -97,40 +63,44 @@
}
for(unsigned int i = 0; i < num_dimensions; i++)
{
- num_weights *= input->info()->dimension(i);
+ num_weights *= in->info()->dimension(i);
}
- _weights.set_info(TensorInfo(TensorShape(num_weights, _num_neurons), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ _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), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ _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(*output->info(),
- calculate_fullyconnected_layer_output_shape(input->info()->tensor_shape(), _num_neurons),
- input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position());
+ 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());
- std::unique_ptr<arm_compute::IFunction> func;
- _target_hint = ctx.hints().target_hint();
+ bool weights_are_loaded = _weights.tensor() != nullptr;
+ bool biases_are_loaded = _biases.tensor() != nullptr;
- if(_target_hint == TargetHint::OPENCL)
+ // 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)
{
- func = instantiate<TargetHint::OPENCL>(input, _weights, _biases, output);
- ARM_COMPUTE_LOG("Instantiating CLFullyConnectedLayer");
+ _weights.allocate_and_fill_if_needed();
}
- else
+ if(!biases_are_loaded)
{
- func = instantiate<TargetHint::NEON>(input, _weights, _biases, output);
- ARM_COMPUTE_LOG("Instantiating NEFullyConnectedLayer");
+ _biases.allocate_and_fill_if_needed();
}
- ARM_COMPUTE_LOG(" 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()
- << std::endl);
-
+ // Get function
return func;
}