1 files changed, 128 insertions, 0 deletions
diff --git a/libs/kernel/acl/src/neon/Pooling.cpp b/libs/kernel/acl/src/neon/Pooling.cpp
new file mode 100644
index 000000000..5c58ae0b5
--- /dev/null
+++ b/libs/kernel/acl/src/neon/Pooling.cpp
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <OperationsUtils.h>
+#include <arm_compute/core/TensorShape.h>
+#include <arm_compute/core/TensorInfo.h>
+#include "../IO_accessor.h"
+#include "../shape.h"
+#include "../NEUniqueTensor.h"
+
+#include <cassert>
+
+namespace nnfw {
+namespace kernel {
+namespace acl {
+namespace neon {
+
+bool maxPoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
+                 int32_t padding_left, int32_t padding_right,
+                 int32_t padding_top, int32_t padding_bottom,
+                 int32_t stride_width, int32_t stride_height,
+                 int32_t filter_width, int32_t filter_height,
+                 int32_t activation,
+                 float* outputData, const nnfw::rt::Shape& outputShape)
+{
+  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
+  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
+
+  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
+
+  arm_compute::PadStrideInfo pad_info = arm_compute::PadStrideInfo(stride_width, stride_height,
+                                              padding_left, padding_right,
+                                              padding_top, padding_bottom,
+                                              arm_compute::DimensionRoundingType::FLOOR);
+
+  arm_compute::PoolingLayerInfo maxpool_info = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::MAX,
+                                                        arm_compute::Size2D(filter_width,filter_height),
+                                                        pad_info, false);
+
+  NEUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
+  NEUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
+
+  auto pool_f = std::make_shared<arm_compute::NEPoolingLayer>();
+  pool_f->configure(input.ptr(), output.ptr(), maxpool_info);
+
+  fns.emplace_back(pool_f);
+
+  util::insertFusedActivationLayer<NEUniqueTensor, arm_compute::NEActivationLayer>(output, activation, fns);
+
+  input.allocate();
+  output.allocate();
+
+  TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
+
+  for (const auto &fn : fns)
+  {
+    fn->run();
+  }
+
+  TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
+
+  return true;
+}
+
+bool averagePoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
+                 int32_t padding_left, int32_t padding_right,
+                 int32_t padding_top, int32_t padding_bottom,
+                 int32_t stride_width, int32_t stride_height,
+                 int32_t filter_width, int32_t filter_height,
+                 int32_t activation,
+                 float* outputData, const nnfw::rt::Shape& outputShape)
+{
+  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
+  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
+
+  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
+
+  arm_compute::PadStrideInfo pad_info = arm_compute::PadStrideInfo(stride_width, stride_height,
+                                              padding_left, padding_right,
+                                              padding_top, padding_bottom,
+                                              arm_compute::DimensionRoundingType::FLOOR);
+
+  arm_compute::PoolingLayerInfo pool_info = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::AVG,
+                                                        arm_compute::Size2D(filter_width,filter_height),
+                                                        pad_info, true);
+
+  NEUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
+  NEUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
+
+  auto pool_f = std::make_shared<arm_compute::NEPoolingLayer>();
+  pool_f->configure(input.ptr(), output.ptr(), pool_info);
+
+  fns.emplace_back(pool_f);
+
+  util::insertFusedActivationLayer<NEUniqueTensor, arm_compute::NEActivationLayer>(output, activation, fns);
+
+  input.allocate();
+  output.allocate();
+
+  TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
+
+  for (const auto &fn : fns)
+  {
+    fn->run();
+  }
+
+  TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
+
+  return true;
+}
+
+} // namespace neon
+} // namespace acl
+} // namespace kernel
+} // namespace nnfw