1 files changed, 0 insertions, 87 deletions
diff --git a/runtimes/neurun/src/backend/acl_cl/Convert.cc b/runtimes/neurun/src/backend/acl_cl/Convert.cc
deleted file mode 100644
index ed0a089c4..000000000
--- a/runtimes/neurun/src/backend/acl_cl/Convert.cc
+++ /dev/null
@@ -1,87 +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 "Convert.h"
-
-#include "Swizzle.h"
-#include "model/operand/DataType.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-::arm_compute::TensorShape asTensorShape(const ::neurun::model::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 ::neurun::model::operand::DataType &type)
-{
-  switch (type)
-  {
-    case ::neurun::model::operand::DataType::SCALAR_FLOAT32:
-    case ::neurun::model::operand::DataType::TENSOR_FLOAT32:
-      return ::arm_compute::DataType::F32;
-    case ::neurun::model::operand::DataType::SCALAR_INT32:
-    case ::neurun::model::operand::DataType::TENSOR_INT32:
-      return ::arm_compute::DataType::S32;
-    case ::neurun::model::operand::DataType::SCALAR_UINT32:
-      return ::arm_compute::DataType::U32;
-    case ::neurun::model::operand::DataType::TENSOR_QUANT8_ASYMM:
-      return ::arm_compute::DataType::QASYMM8;
-    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 ::neurun::model::operand::Shape &shape,
-                                       const ::neurun::model::operand::TypeInfo &typeInfo)
-{
-  return ::arm_compute::TensorInfo(asTensorShape(shape), 1, asDataType(typeInfo.type()),
-                                   asQuantizationInfo(typeInfo.scale(), typeInfo.offset()));
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun