1 files changed, 109 insertions, 0 deletions
diff --git a/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc b/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc
new file mode 100644
index 000000000..5fe5e3993
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+++ b/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * 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 "ConcatLayer.h"
+
+#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
+#include "kernel/cpu/OperationUtils.h"
+
+namespace neurun
+{
+namespace kernel
+{
+namespace cpu
+{
+
+ConcatLayer::ConcatLayer()
+    : _inputDataPtrs(), _outputData(nullptr), _axis(0), _inputShapes(), _outputShape(),
+      _inputType(OperandType::SCALAR_FLOAT32)
+{
+  // DO NOTHING
+}
+
+bool ConcatLayer::concatenationFloat32()
+{
+  int num_inputs = _inputShapes.size();
+  std::vector<::tflite::Dims<4> *> inputDimsPtr(num_inputs);
+  std::vector<::tflite::Dims<4>> inputDims(num_inputs);
+  for (int i = 0; i < num_inputs; i++)
+  {
+    inputDims[i] = convertShapeToDims(_inputShapes[i]);
+    inputDimsPtr[i] = &inputDims[i];
+  }
+
+  std::vector<const float *> inputFloatPtrs;
+
+  for (auto ptr : _inputDataPtrs)
+  {
+    inputFloatPtrs.emplace_back(reinterpret_cast<const float *>(ptr));
+  }
+
+  ::tflite::optimized_ops::Concatenation<::tflite::FusedActivationFunctionType::kNone, float>(
+      getNumberOfDimensions(_outputShape) - _axis - 1, inputFloatPtrs.data(), inputDimsPtr.data(),
+      num_inputs, reinterpret_cast<float *>(_outputData), convertShapeToDims(_outputShape));
+  return true;
+}
+bool ConcatLayer::concatenationQuant8()
+{
+  int num_inputs = _inputShapes.size();
+  std::vector<::tflite::Dims<4> *> inputDimsPtr(num_inputs);
+  std::vector<::tflite::Dims<4>> inputDims(num_inputs);
+  for (int i = 0; i < num_inputs; i++)
+  {
+    inputDims[i] = convertShapeToDims(_inputShapes[i]);
+    inputDimsPtr[i] = &inputDims[i];
+  }
+  ::tflite::optimized_ops::Concatenation<::tflite::FusedActivationFunctionType::kNone, uint8_t>(
+      getNumberOfDimensions(_outputShape) - _axis - 1, _inputDataPtrs.data(), inputDimsPtr.data(),
+      num_inputs, _outputData, convertShapeToDims(_outputShape));
+  return true;
+}
+
+void ConcatLayer::configure(const std::vector<const uint8_t *> &inputDataPtrs,
+                            const std::vector<Shape> &inputShapes, int32_t axis,
+                            uint8_t *outputData, const Shape outputShape)
+{
+  _inputDataPtrs = inputDataPtrs;
+
+  for (auto shape : inputShapes)
+  {
+    _inputShapes.emplace_back(shape);
+    _inputType = shape.type;
+  }
+
+  _axis = axis;
+
+  _outputData = outputData;
+  _outputShape = outputShape;
+}
+
+void ConcatLayer::run()
+{
+  if (_inputType == OperandType::TENSOR_FLOAT32)
+  {
+    concatenationFloat32();
+  }
+  else if (_inputType == OperandType::TENSOR_QUANT8_ASYMM)
+  {
+    throw std::runtime_error{"ConcatLayer : Not tested for TENSOR_QUANT8_ASYMM"};
+    // concatenationQuant8();
+  }
+}
+
+} // namespace cpu
+} // namespace kernel
+} // namespace neurun