1 files changed, 103 insertions, 0 deletions
diff --git a/runtimes/neurun/src/kernel/cpu/OperationUtils.h b/runtimes/neurun/src/kernel/cpu/OperationUtils.h
new file mode 100644
index 000000000..5914d04e3
--- /dev/null
+++ b/runtimes/neurun/src/kernel/cpu/OperationUtils.h
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2018 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.
+ */
+
+#ifndef __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
+#define __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
+
+#include <NeuralNetworks.h>
+
+#include <iostream>
+#include <limits>
+#include <vector>
+
+#include "tensorflow/contrib/lite/kernels/internal/types.h"
+#include "graph/operand/Object.h"
+#include "graph/operand/DataType.h"
+
+using OperandType = neurun::graph::operand::DataType;
+
+namespace neurun
+{
+namespace kernel
+{
+namespace cpu
+{
+
+struct Shape
+{
+  OperandType type;
+  std::vector<uint32_t> dimensions;
+  float scale;
+  int32_t offset;
+};
+
+uint32_t getNumberOfDimensions(const Shape &shape);
+
+uint32_t getNumberOfElements(const Shape &shape);
+
+uint32_t getSizeOfDimension(const Shape &shape, uint32_t dimensionIdx);
+
+inline ::tflite::Dims<4> convertShapeToDims(const Shape &shape)
+{
+  // nnAssert(shape.dimensions.size() <= 4);
+  ::tflite::Dims<4> dims;
+  // The dimensions are reversed in Dims<4>.
+  for (int i = 0; i < 4; ++i)
+  {
+    int src = static_cast<int>(shape.dimensions.size()) - i - 1;
+    if (src >= 0)
+    {
+      dims.sizes[i] = static_cast<int>(getSizeOfDimension(shape, src));
+    }
+    else
+    {
+      dims.sizes[i] = 1;
+    }
+  }
+  dims.strides[0] = 1;
+  for (int i = 1; i < 4; i++)
+  {
+    dims.strides[i] = dims.strides[i - 1] * dims.sizes[i - 1];
+  }
+  return dims;
+}
+
+__wur bool QuantizeMultiplierSmallerThanOne(double double_multiplier, int32_t *quantized_multiplier,
+                                            int32_t *right_shift);
+
+__wur bool GetQuantizedConvolutionMultipler(const Shape &inputShape, const Shape &filterShape,
+                                            const Shape &biasShape, const Shape &outputShape,
+                                            float *multiplier);
+__wur bool QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
+                                            int *left_shift);
+
+void CalculateActivationRangeFloat(int32_t activation, float *activation_min,
+                                   float *activation_max);
+
+void CalculateActivationRangeUint8(int32_t activation, const Shape &outputShape, int32_t *act_min,
+                                   int32_t *act_max);
+
+int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
+
+Shape getShape(const ::neurun::graph::operand::Object &o);
+
+uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions);
+
+} // namespace cpu
+} // namespace kernel
+} // namespace neurun
+
+#endif // __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__