1 files changed, 56 insertions, 0 deletions
diff --git a/compiler/mir-onnx-importer/Op/Pad.cpp b/compiler/mir-onnx-importer/Op/Pad.cpp
new file mode 100644
index 000000000..c3d3a6860
--- /dev/null
+++ b/compiler/mir-onnx-importer/Op/Pad.cpp
@@ -0,0 +1,56 @@
+/*
+ * 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 "Pad.h"
+
+#include "ONNXHelpers.h"
+
+#include "mir/ops/PadOp.h"
+
+namespace mir_onnx
+{
+
+std::vector<mir::Operation::Output *>
+PadNodeConverter::convert(const onnx::NodeProto &onnx_node,
+                          const std::vector<mir::Operation::Output *> &inputs,
+                          mir::Graph *graph) const
+{
+  bool found;
+  float value;
+  std::tie(found, value) = getFloatAttribute(onnx_node, "value");
+  assert(found);
+  auto padsAtt = findAttribute(onnx_node, "pads");
+  assert(padsAtt);
+  auto modeAtt = findAttribute(onnx_node, "mode");
+  assert(modeAtt);
+  auto mode = modeAtt->s();
+  const mir::Scalar scalar(reinterpret_cast<const char *>(&value), mir::DTYPE::FLOAT32,
+                           sizeof(float));
+  assert(padsAtt->ints_size() > 0);
+  int axis_size = padsAtt->ints_size() / 2;
+  std::vector<std::pair<int32_t, int32_t>> vec(axis_size);
+  auto *data = padsAtt->ints().data();
+  for (int i = 0; i < axis_size; i++)
+  {
+    auto pair = std::make_pair(data[i], data[axis_size + i]);
+    vec[i] = pair;
+  }
+  auto result =
+      createOp<mir::ops::PadOp>(graph, inputs[0], inputs[0]->getShape().rank(), vec, scalar);
+  return {result->getOutput(0)};
+}
+
+} // namespace mir_onnx