From c7ad62faf9e95bc6a8a9cea6affc24ca4250a74e Mon Sep 17 00:00:00 2001
From: =?utf8?q?=EC=98=A4=ED=98=95=EC=84=9D/On-Device=20Lab=28SR=29/Staff?=
 =?utf8?q?=20Engineer/=EC=82=BC=EC=84=B1=EC=A0=84=EC=9E=90?=
 <hseok82.oh@samsung.com>
Date: Wed, 5 Jun 2019 19:17:49 +0900
Subject: [PATCH] [Interp] Introduce average pool operation in interpreter
 (#5357)

Introduce average pool operation for float type in interpreter

Signed-off-by: Hyeongseok Oh <hseok82.oh@samsung.com>
---
 .../core/src/exec/interp/operations/AvgPool2D.cc   | 86 +++++++++++++++++++++-
 1 file changed, 85 insertions(+), 1 deletion(-)

diff --git a/runtimes/neurun/core/src/exec/interp/operations/AvgPool2D.cc b/runtimes/neurun/core/src/exec/interp/operations/AvgPool2D.cc
index c65a6a7..9afb1b3 100644
--- a/runtimes/neurun/core/src/exec/interp/operations/AvgPool2D.cc
+++ b/runtimes/neurun/core/src/exec/interp/operations/AvgPool2D.cc
@@ -14,7 +14,14 @@
  * limitations under the License.
  */
 
+#include <cker/operation/AveragePool.h>
+
+#include "OperationUtil.h"
+
 #include "exec/interp/Registration.h"
+#include "model/operation/AvgPool2DNode.h"
+#include "util/Utils.h"
+#include "util/Padding.h"
 
 namespace neurun
 {
@@ -22,10 +29,87 @@ namespace exec
 {
 namespace interp
 {
+namespace avgpool2d
+{
+
+void prepareAvgPool2D(ExecEnv *env, const model::Operation &node)
+{
+  auto maxpool_node = reinterpret_cast<const model::operation::AvgPool2DNode &>(node);
+
+  const auto in_index = node.getInputs().at(0);
+  const auto out_index = node.getOutputs().at(0);
+
+  const auto in_tensor = env->tensorAt(in_index);
+  UNUSED_RELEASE(in_tensor);
+
+  assert(in_tensor->num_dimensions() == 4);
+
+  // TODO handle unspecified output shape:
+  //      calculate output shape using ifm shape, kernel width/height, padding, stride
+  const auto output_info = env->model().operands.at(out_index).info();
+  assert(output_info.total_size() != 0);
+  env->allocateIfNeeded(out_index, output_info);
+
+  auto out_tensor = env->tensorAt(out_index);
+  UNUSED_RELEASE(out_tensor);
+
+  // Handle same ifm & ofm data type only
+  assert(in_tensor->data_type() == out_tensor->data_type());
+  assert(out_tensor->num_dimensions() == 4);
+}
+
+void invoke(const ITensor *in_tensor, const ITensor *out_tensor,
+            const model::operation::AvgPool2DNode::Param &param)
+{
+  const auto ifm_shape = in_tensor->tensorInfo().shape().asFeature();
+  const auto ofm_shape = out_tensor->tensorInfo().shape().asFeature();
+  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
+                                                      param.stride, param.kw, param.kh);
+  // Calculate
+  nnfw::cker::AveragePoolParams cker_param;
+  calculateActivationRange(param.activation, &cker_param.float_activation_min,
+                           &cker_param.float_activation_max);
+  cker_param.filter_width = param.kw;
+  cker_param.filter_height = param.kh;
+  cker_param.padding_values.width = padding.left;
+  cker_param.padding_values.height = padding.top;
+  cker_param.stride_width = param.stride.horizontal;
+  cker_param.stride_height = param.stride.vertical;
+
+  const auto in_shape = convertShape(in_tensor->tensorInfo().shape());
+  const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
+  const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO());
+  float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer());
+
+  nnfw::cker::AveragePool(cker_param, in_shape, in_ptr, out_shape, out_ptr);
+}
+
+void invokeAvgPool2D(const ExecEnv *env, const model::Operation &node)
+{
+  auto maxpool_node = reinterpret_cast<const model::operation::AvgPool2DNode &>(node);
+
+  const auto in_index = node.getInputs().at(0);
+  const auto out_index = node.getOutputs().at(0);
+
+  // Check lhs shape is same with rhs (with broadcast)
+  const auto in_tensor = env->tensorAt(in_index);
+  const auto out_tensor = env->tensorAt(out_index);
+
+  const auto data_type = in_tensor->data_type();
+  if (data_type == model::DataType::FLOAT32)
+  {
+    invoke(in_tensor, out_tensor, maxpool_node.param());
+  }
+  else
+  {
+    throw std::runtime_error{"NYI: Support float only"};
+  }
+}
+} // namespace maxpool2d
 
 OpKernel *getAvgPool2DNode()
 {
-  static OpKernel kernel = {nullptr, nullptr};
+  static OpKernel kernel = {avgpool2d::prepareAvgPool2D, avgpool2d::invokeAvgPool2D};
   return &kernel;
 }
 
-- 
2.7.4