1 files changed, 60 insertions, 0 deletions
diff --git a/compiler/mir-onnx-importer/Op/ReduceMean.cpp b/compiler/mir-onnx-importer/Op/ReduceMean.cpp
new file mode 100644
index 000000000..ec43bffb4
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+++ b/compiler/mir-onnx-importer/Op/ReduceMean.cpp
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+/*
+ * 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 "ReduceMean.h"
+
+#include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
+
+#include "mir/ops/ReduceMeanOp.h"
+
+#include <numeric>
+
+namespace mir_onnx
+{
+
+void convertReduceMeanV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
+{
+  const auto inputs = context->getNodeInputs(onnx_node);
+  assert(inputs.size() == 1);
+
+  const auto axes = getAttributeValue<std::vector<std::int64_t>>(onnx_node, "axes");
+  const auto keepdims = getAttributeValue<int64_t>(onnx_node, "keepdims", 1);
+
+  std::vector<int32_t> reduce_dims;
+  if (axes.empty())
+  { // reduce over all dimensions
+    reduce_dims.resize(inputs[0]->getShape().rank());
+    std::iota(reduce_dims.begin(), reduce_dims.end(), 0);
+  }
+  else
+  {
+    auto rank = inputs[0]->getShape().rank();
+
+    std::transform(axes.begin(), axes.end(), std::back_inserter(reduce_dims),
+                   [rank](int64_t axis) { return axis < 0 ? axis + rank : axis; });
+  }
+  // Keep the reduced dimension or not, default 1 mean keep reduced dimension.
+  bool keep_dims = static_cast<bool>(keepdims);
+
+  mir::Graph *graph = context->getGraph();
+  auto result =
+      createOp<mir::ops::ReduceMeanOp>(graph, inputs[0], reduce_dims, keep_dims)->getOutput(0);
+
+  context->setNodeOutputs(onnx_node, {result});
+}
+
+} // namespace mir_onnx