arm_compute v18.05
diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp
index 3742150..d94a785 100644
--- a/src/graph/nodes/FullyConnectedLayer.cpp
+++ b/src/graph/nodes/FullyConnectedLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,86 +21,89 @@
* 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/nodes/FullyConnectedLayerNode.h"
-#include "arm_compute/graph/Error.h"
-#include "arm_compute/graph/NodeContext.h"
-#include "arm_compute/graph/OperationRegistry.h"
-#include "support/ToolchainSupport.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/INodeVisitor.h"
-using namespace arm_compute::graph;
-
-namespace
+namespace arm_compute
{
-TensorShape calculate_fullyconnected_layer_output_shape(const TensorShape &input_shape, unsigned int output_neurons)
+namespace graph
+{
+FullyConnectedLayerNode::FullyConnectedLayerNode(unsigned int num_outputs)
+ : _num_outputs(num_outputs)
+{
+ _input_edges.resize(3, EmptyEdgeID);
+ _outputs.resize(1, NullTensorID);
+}
+
+TensorDescriptor FullyConnectedLayerNode::compute_weights_descriptor(const TensorDescriptor &input_descriptor,
+ unsigned int num_outputs)
+{
+ unsigned int num_weights = 1;
+ unsigned int num_dimensions = input_descriptor.shape.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 *= input_descriptor.shape[i];
+ }
+
+ TensorDescriptor weights_descriptor = input_descriptor;
+ weights_descriptor.shape = TensorShape(num_weights, num_outputs);
+
+ return weights_descriptor;
+}
+
+TensorDescriptor FullyConnectedLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+ unsigned int num_outputs)
{
// Note: Only 1D batch space is supported at the moment
- unsigned int batches = input_shape[1];
- if(input_shape.num_dimensions() > 2)
+ unsigned int batches = input_descriptor.shape[1];
+ if(input_descriptor.shape.num_dimensions() > 2)
{
- batches = input_shape[3];
+ batches = input_descriptor.shape[3];
}
- return TensorShape(output_neurons, batches);
-}
-} // namespace
-std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
+ TensorDescriptor output_descriptor = input_descriptor;
+ output_descriptor.shape = TensorShape(num_outputs, batches);
+
+ return output_descriptor;
+}
+
+bool FullyConnectedLayerNode::forward_descriptors()
{
- 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)
+ if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
- 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()));
+ Tensor *dst = output(0);
+ ARM_COMPUTE_ERROR_ON(dst == nullptr);
+ dst->desc() = configure_output(0);
+ return true;
}
- 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;
+ return false;
}
+
+TensorDescriptor FullyConnectedLayerNode::configure_output(size_t idx) const
+{
+ ARM_COMPUTE_UNUSED(idx);
+ const Tensor *src = input(0);
+ ARM_COMPUTE_ERROR_ON(src == nullptr);
+
+ return compute_output_descriptor(src->desc(), _num_outputs);
+}
+
+NodeType FullyConnectedLayerNode::type() const
+{
+ return NodeType::FullyConnectedLayer;
+}
+
+void FullyConnectedLayerNode::accept(INodeVisitor &v)
+{
+ v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file