Imported Upstream version 1.1.0upstream/1.1.0submit/tizen/20200304.094649submit/tizen/20200304.093946submit/tizen/20200304.092919accepted/tizen/unified/20200305.051107

1 files changed, 0 insertions, 165 deletions

diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc
deleted file mode 100644
index ff2f79309..000000000
--- a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc
+++ /dev/null
@@ -1,165 +0,0 @@
-/*
- * 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.
- */
-
-#include "internal/arm_compute/Cast.h"
-
-#include "internal/Swizzle.h"
-
-::arm_compute::Coordinates getARMComputeAxises(uint32_t rank)
-{
-  ::arm_compute::Coordinates res{};
-
-  res.set_num_dimensions(rank);
-
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    res.set(axis, ToARMComputeAxis(rank, axis).value());
-  }
-
-  return res;
-}
-
-::arm_compute::Coordinates asARMComputeCoordinates(const ::arm_compute::Coordinates &runtime_coord,
-                                                   const ::arm_compute::Coordinates &axises)
-{
-  ::arm_compute::Coordinates id{};
-  assert(runtime_coord.num_dimensions() == axises.num_dimensions());
-  for (size_t i = 0; i < runtime_coord.num_dimensions(); ++i)
-  {
-    id.set(axises[i], runtime_coord[i]);
-  }
-  return id;
-}
-
-// Restructure runtime_permutationVector to ACL_permutationVector
-::arm_compute::PermutationVector getARMComputePermutationVector(uint32_t rank,
-                                                                const int32_t *runtime_pv)
-{
-  // rank upto 4 is supported
-  assert(rank <= 4);
-  assert(runtime_pv != nullptr);
-
-  int new_pv[4] = {0};
-  ::arm_compute::Coordinates axises = getARMComputeAxises(rank);
-
-  if (rank == 4)
-  {
-    /**
-    axises = {3,1,0,2}
-       NNAPI      PermutationVector
-    N   0               3
-    H   1               1
-    W   2               0
-    C   3               2
-    **/
-    new_pv[0] = axises[runtime_pv[2]];
-    new_pv[1] = axises[runtime_pv[1]];
-    new_pv[2] = axises[runtime_pv[3]];
-    new_pv[3] = axises[runtime_pv[0]];
-  }
-  else
-  {
-    /**
-    mapping/axises = {rank-1 to 0}
-    CHW --------> WHC
-    or
-    WH ----------> HW
-    **/
-    for (int id = 0; id < rank; ++id)
-    {
-      new_pv[id] = axises[runtime_pv[rank - id - 1]];
-    }
-  }
-
-  return ::arm_compute::PermutationVector{new_pv[0], new_pv[1], new_pv[2], new_pv[3]};
-}
-
-::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape,
-                                         bool apply_dim_correction)
-{
-  const uint32_t rank = shape.rank();
-
-  ::arm_compute::TensorShape res{};
-
-  res.set_num_dimensions(rank);
-
-  for (uint32_t axis = 0; axis < rank; ++axis)
-  {
-    // NOTE In some cases, in incorrect dimensions is required.
-    // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of
-    // LSTM is used as the weight of the FullyConnected.
-    // The FullyConnected's weight must be greater or equal than 2-dimensions.
-    // However, if the dimension correction is applied to input_to_input_weights with input_size
-    // equal to 1, it will be changed to 1-D.
-    // So input_to_input_weights is not used by the weight of FullyConnected.
-    res.set(ToARMComputeAxis(rank, axis).value(), shape.dim(axis), apply_dim_correction);
-  }
-
-  return res;
-}
-
-::arm_compute::DataType asDataType(const int32_t type)
-{
-  switch (type)
-  {
-    case ANEURALNETWORKS_FLOAT32:
-    case ANEURALNETWORKS_TENSOR_FLOAT32:
-      return ::arm_compute::DataType::F32;
-    case ANEURALNETWORKS_INT32:
-    case ANEURALNETWORKS_TENSOR_INT32:
-      return ::arm_compute::DataType::S32;
-    case ANEURALNETWORKS_UINT32:
-      return ::arm_compute::DataType::U32;
-    case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM:
-      return ::arm_compute::DataType::QASYMM8;
-    default:
-      throw std::runtime_error("Not supported, yet");
-      break;
-  }
-}
-
-::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code)
-{
-  switch (code)
-  {
-    case ANEURALNETWORKS_FUSED_NONE:
-      return ::arm_compute::ActivationLayerInfo{};
-    case ANEURALNETWORKS_FUSED_RELU:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
-    case ANEURALNETWORKS_FUSED_RELU1:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
-    case ANEURALNETWORKS_FUSED_RELU6:
-      return ::arm_compute::ActivationLayerInfo{
-          ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f};
-    default:
-      throw std::runtime_error("Not supported, yet");
-      break;
-  }
-}
-
-::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset)
-{
-  return ::arm_compute::QuantizationInfo(scale, offset);
-}
-
-::arm_compute::TensorInfo asTensorInfo(const ::arm_compute::TensorShape &shape, const int32_t type,
-                                       const float scale, const int32_t zeroPoint)
-{
-  return ::arm_compute::TensorInfo(shape, 1, asDataType(type),
-                                   asQuantizationInfo(scale, zeroPoint));
-}