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>
diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp
new file mode 100644
index 0000000..b80bf93
--- /dev/null
+++ b/src/graph/nodes/ConvolutionLayer.cpp
@@ -0,0 +1,117 @@
+/*
+ * 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/ConvolutionLayer.h"
+
+#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
+#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
+#include "support/ToolchainSupport.h"
+#include "utils/TypePrinter.h"
+
+using namespace arm_compute::graph;
+
+namespace
+{
+template <typename ConvolutionType, typename TensorType, Hint hint>
+std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+ bool weights_are_loaded = weights.tensor() != nullptr;
+ bool biases_are_loaded = biases.tensor() != nullptr;
+
+ auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
+ conv->configure(
+ dynamic_cast<TensorType *>(input),
+ dynamic_cast<TensorType *>(weights.set_target(hint)),
+ dynamic_cast<TensorType *>(biases.set_target(hint)),
+ dynamic_cast<TensorType *>(output),
+ conv_info, weights_info);
+ 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, const PadStrideInfo &conv_info, const WeightsInfo &weights_info);
+
+template <>
+std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+ return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
+}
+
+template <>
+std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+ return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info);
+}
+} // namespace
+
+std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output)
+{
+ if(_weights.tensor() == nullptr)
+ {
+ _weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, input->info()->dimension(2), _ofm), input->info()->num_channels(), input->info()->data_type(),
+ input->info()->fixed_point_position()));
+ }
+ if(_biases.tensor() == nullptr)
+ {
+ _biases.set_info(TensorInfo(TensorShape(_ofm), 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, _conv_info, _weights_info);
+ }
+ else
+ {
+ func = instantiate<Hint::NEON>(input, _weights, _biases, output, _conv_info, _weights_info);
+ }
+
+ return func;
+}
+
+void ConvolutionLayer::print_info()
+{
+ if(_hint == Hint::OPENCL)
+ {
+ std::cout << "Instantiating CLConvolutionLayer";
+ }
+ else
+ {
+ std::cout << "Instantiating NEConvolutionLayer";
+ }
+ 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() << " PadStrideInfo: " << _conv_info << "WeightsInfo: " << _weights_info << std::endl;
+}