COMPMID-363 Add Graph library support

Change-Id: Ie841419bf65d0e06bdfe0bdd2d8d4e0bb3631e54
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/87931
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

1 files changed, 130 insertions, 0 deletions

diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp
new file mode 100644
index 0000000000..8d244cb515
--- /dev/null
+++ b/src/graph/nodes/FullyConnectedLayer.cpp
@@ -0,0 +1,130 @@
+/*
+ * 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/FullyConnectedLayer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
+#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
+#include "support/ToolchainSupport.h"
+#include "utils/TypePrinter.h"
+
+using namespace arm_compute::graph;
+
+namespace
+{
+template <typename FullyConnectedType, typename TensorType, Hint 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(hint)),
+        dynamic_cast<TensorType *>(biases.set_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 <Hint                          hint>
+std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output);
+
+template <>
+std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output)
+{
+    return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output);
+}
+
+template <>
+std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output)
+{
+    return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output);
+}
+} // namespace
+
+std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output)
+{
+    if(_weights.tensor() == nullptr)
+    {
+        unsigned int num_weights    = 1;
+        unsigned int num_dimensions = input->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 *= input->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()));
+    }
+    if(_biases.tensor() == nullptr)
+    {
+        _biases.set_info(TensorInfo(TensorShape(_num_neurons), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+    }
+
+    arm_compute::auto_init_if_empty(*output->info(), TensorShape(_num_neurons, input->info()->dimension(1)), input->info()->num_channels(), input->info()->data_type(),
+                                    input->info()->fixed_point_position());
+
+    std::unique_ptr<arm_compute::IFunction> func;
+    _hint   = hint;
+    _input  = input;
+    _output = output;
+
+    if(_hint == Hint::OPENCL)
+    {
+        func = instantiate<Hint::OPENCL>(input, _weights, _biases, output);
+    }
+    else
+    {
+        func = instantiate<Hint::NEON>(input, _weights, _biases, output);
+    }
+
+    return func;
+}
+
+void FullyConnectedLayer::print_info()
+{
+    if(_hint == Hint::OPENCL)
+    {
+        std::cout << "Instantiating CLFullyConnectedLayer";
+    }
+    else
+    {
+        std::cout << "Instantiating NEFullyConnectedLayer";
+    }
+    std::cout << " 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;
+}