arm_compute v17.10
Change-Id: If1489af40eccd0219ede8946577afbf04db31b29
diff --git a/src/graph/nodes/BatchNormalizationLayer.cpp b/src/graph/nodes/BatchNormalizationLayer.cpp
new file mode 100644
index 0000000..a6a990f
--- /dev/null
+++ b/src/graph/nodes/BatchNormalizationLayer.cpp
@@ -0,0 +1,110 @@
+/*
+ * 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/BatchNormalizationLayer.h"
+
+#include "arm_compute/core/Logger.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h"
+#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "support/ToolchainSupport.h"
+#include "utils/TypePrinter.h"
+
+using namespace arm_compute::graph;
+
+namespace
+{
+template <typename BatchBatchNormalizationLayer, typename TensorType, TargetHint target_hint>
+std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
+{
+ auto norm = arm_compute::support::cpp14::make_unique<BatchBatchNormalizationLayer>();
+ norm->configure(
+ dynamic_cast<TensorType *>(input),
+ dynamic_cast<TensorType *>(output),
+ dynamic_cast<TensorType *>(mean.set_target(target_hint)),
+ dynamic_cast<TensorType *>(var.set_target(target_hint)),
+ dynamic_cast<TensorType *>(beta.set_target(target_hint)),
+ dynamic_cast<TensorType *>(gamma.set_target(target_hint)),
+ epsilon);
+
+ return std::move(norm);
+}
+
+template <TargetHint target_hint>
+std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon);
+
+template <>
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
+{
+ return instantiate_function<arm_compute::CLBatchNormalizationLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, output, mean, var, beta, gamma, epsilon);
+}
+
+template <>
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
+{
+ return instantiate_function<arm_compute::NEBatchNormalizationLayer, arm_compute::ITensor, TargetHint::NEON>(input, output, mean, var, beta, gamma, epsilon);
+}
+} // namespace
+
+std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output)
+{
+ std::unique_ptr<arm_compute::IFunction> func;
+ _target_hint = ctx.hints().target_hint();
+
+ unsigned int batch_norm_size = input->info()->dimension(2);
+ if(_mean.tensor() == nullptr)
+ {
+ _mean.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ }
+ if(_var.tensor() == nullptr)
+ {
+ _var.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ }
+ if(_beta.tensor() == nullptr)
+ {
+ _beta.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ }
+ if(_gamma.tensor() == nullptr)
+ {
+ _gamma.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ }
+
+ if(_target_hint == TargetHint::OPENCL)
+ {
+ func = instantiate<TargetHint::OPENCL>(input, output, _mean, _var, _beta, _gamma, _epsilon);
+ ARM_COMPUTE_LOG("Instantiating CLBatchNormalizationLayer");
+ }
+ else
+ {
+ func = instantiate<TargetHint::NEON>(input, output, _mean, _var, _beta, _gamma, _epsilon);
+ ARM_COMPUTE_LOG("Instantiating NEBatchNormalizationLayer");
+ }
+
+ ARM_COMPUTE_LOG(" Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << std::endl);
+
+ return func;
+}
\ No newline at end of file