1 files changed, 152 insertions, 0 deletions
diff --git a/runtime/neurun/backend/cpu/kernel/OperationUtils.h b/runtime/neurun/backend/cpu/kernel/OperationUtils.h
new file mode 100644
index 000000000..b9e8c8974
--- /dev/null
+++ b/runtime/neurun/backend/cpu/kernel/OperationUtils.h
@@ -0,0 +1,152 @@
+/*
+ * 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 <iostream>
+#include <limits>
+#include <vector>
+
+#include <cker/Shape.h>
+
+#include "ir/Operand.h"
+#include "ir/DataType.h"
+#include <ir/InternalType.h>
+
+using OperandType = neurun::ir::DataType;
+
+namespace neurun
+{
+namespace backend
+{
+namespace cpu
+{
+namespace kernel
+{
+
+struct TensorDescriptor
+{
+  OperandType type;
+  std::vector<uint32_t> dimensions;
+  float scale;
+  int32_t offset;
+};
+
+union DataPtr {
+  uint8_t *u8;
+  int8_t *i8;
+  int32_t *i32;
+  float *f;
+  void *v;
+};
+
+uint32_t getNumberOfDimensions(const TensorDescriptor &descr);
+
+uint32_t getNumberOfElements(const TensorDescriptor &descr);
+
+uint32_t getSizeOfDimension(const TensorDescriptor &descr, uint32_t dimensionIdx);
+
+inline nnfw::cker::Shape convertToExtendedCkerShape(const TensorDescriptor &descr)
+{
+  std::vector<int32_t> raw_shape;
+  raw_shape.resize(4);
+
+  uint32_t src = 4 - descr.dimensions.size();
+  for (uint32_t i = 0; i < 4; ++i)
+  {
+    if (i < src)
+    {
+      raw_shape[i] = 1;
+    }
+    else
+    {
+      raw_shape[i] = descr.dimensions[i - src];
+    }
+  }
+
+  return nnfw::cker::GetShape(raw_shape);
+}
+
+inline nnfw::cker::Shape convertTensorDescriptorToCkerShape(const TensorDescriptor &descr)
+{
+  std::vector<int32_t> raw_shape;
+  raw_shape.resize(4);
+
+  for (uint32_t i = 0; i < 4; ++i)
+  {
+    if (i >= descr.dimensions.size())
+    {
+      raw_shape[i] = 1;
+    }
+    else
+    {
+      raw_shape[i] = descr.dimensions[i];
+    }
+  }
+
+  return nnfw::cker::GetShape(raw_shape);
+}
+
+inline int32_t getAxis(uint32_t rank, int32_t axis, ir::Layout frontend_layout)
+{
+  auto ret = axis;
+
+  if (axis < 0)
+  {
+    ret += rank;
+  }
+
+  // NCHW -> NHWC
+  if (frontend_layout == ir::Layout::NCHW)
+  {
+    int32_t permutation[4] = {0, 3, 1, 2};
+    ret = permutation[ret];
+  }
+
+  return ret;
+}
+
+void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift);
+
+void GetQuantizedConvolutionMultiplier(const TensorDescriptor &inputDescr,
+                                       const TensorDescriptor &filterDescr,
+                                       const TensorDescriptor &biasDescr,
+                                       const TensorDescriptor &outputDescr, float *multiplier);
+
+void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
+                                      int *left_shift);
+
+void CalculateActivationRangeFloat(ir::Activation activation, float *activation_min,
+                                   float *activation_max);
+
+void CalculateActivationRangeUint8(ir::Activation activation, const TensorDescriptor &outputDescr,
+                                   int32_t *act_min, int32_t *act_max);
+
+bool HaveSameShapes(const TensorDescriptor *input1, const TensorDescriptor *input2);
+
+int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
+
+TensorDescriptor getTensorDescriptor(const ir::Operand &o, ir::Layout frontend_layout);
+
+uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions);
+
+} // namespace kernel
+} // namespace cpu
+} // namespace backend
+} // namespace neurun
+
+#endif // __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__