1 files changed, 129 insertions, 0 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/AvgPool2D.cc b/runtime/neurun/core/src/exec/interp/operations/AvgPool2D.cc
new file mode 100644
index 000000000..bd396491f
--- /dev/null
+++ b/runtime/neurun/core/src/exec/interp/operations/AvgPool2D.cc
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2019 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 <cker/operation/AveragePool.h>
+
+#include "OperationUtil.h"
+
+#include "exec/interp/Registration.h"
+#include "ir/operation/AvgPool2D.h"
+#include "util/Utils.h"
+#include "util/Padding.h"
+#include "util/ShapeInference.h"
+#include "misc/polymorphic_downcast.h"
+
+namespace neurun
+{
+namespace exec
+{
+namespace interp
+{
+namespace avgpool2d
+{
+
+void prepareAvgPool2D(ExecEnv *env, const ir::Operation &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);
+
+  const auto output_info = env->graph().operands().at(out_index).info();
+  if (output_info.total_size() == 0)
+  {
+    // Handle unspecified output shape
+    const auto &avgpool_node =
+        nnfw::misc::polymorphic_downcast<const ir::operation::AvgPool2D &>(node);
+    const auto infered_output_shapes =
+        shape_inference::inferAvgPoolShape(in_tensor->tensorInfo().shape(), avgpool_node.param());
+    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
+  }
+  else
+  {
+    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 ir::operation::AvgPool2D::Param &param)
+{
+  // TODO Support NCHW frontend
+  const auto ifm_shape = in_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
+  const auto ofm_shape = out_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
+  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
+                                                      param.stride, param.kw, param.kh);
+  // Calculate
+  nnfw::cker::PoolParams 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 ir::Operation &node)
+{
+  const auto &avgpool_node =
+      nnfw::misc::polymorphic_downcast<const ir::operation::AvgPool2D &>(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 == ir::DataType::FLOAT32)
+  {
+    invoke(in_tensor, out_tensor, avgpool_node.param());
+  }
+  else
+  {
+    throw std::runtime_error{"NYI: Support float only"};
+  }
+}
+} // namespace avgpool2d
+
+OpKernel *getAvgPool2D()
+{
+  static OpKernel kernel = {avgpool2d::prepareAvgPool2D, avgpool2d::invokeAvgPool2D};
+  return &kernel;
+}
+
+} // namespace interp
+} // namespace exec
+} // namespace neurun