1 files changed, 0 insertions, 123 deletions
diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp
deleted file mode 100644
index 2b8d82706..000000000
--- a/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2017-2018 ARM Limited.
- *
- * 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 "arm_compute/runtime/CL/functions/CLReduceOperation.h"
-
-#include "arm_compute/core/CL/kernels/CLReduceOperationKernel.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-
-using namespace arm_compute;
-
-CLReduceOperation::CLReduceOperation()
-    : _input(nullptr), _output(nullptr), _axis(), _interm_tensors(), _reduce_kernels()
-{
-}
-
-Status CLReduceOperation::validate(const ITensorInfo *input, const ITensorInfo *output,
-                                   const std::set<uint32_t> &axis, const ReduceOperation &op)
-{
-  const size_t num_of_kernels = axis.size();
-  const size_t num_of_interm_tensors = num_of_kernels - 1;
-
-  // Create temporary tensor infos
-  auto interm_tensors =
-      arm_compute::support::cpp14::make_unique<TensorInfo[]>(num_of_interm_tensors);
-
-  // Create intermediate tensor info
-  TensorShape shape{input->tensor_shape()};
-
-  auto it = axis.begin();
-  for (size_t i = 0; i < num_of_interm_tensors; ++i, ++it)
-  {
-    shape.set(*it, 1);
-    interm_tensors[i].set_data_type(input->data_type());
-    interm_tensors[i].set_tensor_shape(shape);
-    interm_tensors[i].set_num_channels(input->num_channels());
-  }
-
-  // Set a vector that is ordered ITensorInfo sequentially.
-  std::vector<const ITensorInfo *> tensors;
-  tensors.emplace_back(input);
-  for (size_t i = 0; i < num_of_interm_tensors; ++i)
-  {
-    tensors.emplace_back(interm_tensors.get() + i);
-  }
-  tensors.emplace_back(output);
-
-  // Validate ReduceOperation only on all kernels
-  it = axis.begin();
-  for (size_t i = 0; i < num_of_kernels; ++i, ++it)
-  {
-    ARM_COMPUTE_RETURN_ON_ERROR(
-        CLReduceOperationKernel::validate(tensors[i], tensors[i + 1], *it, op));
-  }
-
-  return Status{};
-}
-
-void CLReduceOperation::configure(ICLTensor *input, ICLTensor *output,
-                                  const std::set<uint32_t> &axis, ReduceOperation op)
-{
-  ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), axis, op));
-
-  _axis = axis;
-
-  _input = input;
-  _output = output;
-
-  // NOTE The axis must have no duplication.
-  const size_t num_of_kernels = axis.size();
-  const size_t num_of_interm_tensors = num_of_kernels - 1;
-
-  _interm_tensors = arm_compute::support::cpp14::make_unique<CLTensor[]>(num_of_interm_tensors);
-  _reduce_kernels =
-      arm_compute::support::cpp14::make_unique<CLReduceOperationKernel[]>(num_of_kernels);
-
-  TensorShape shape{input->info()->tensor_shape()};
-  auto it = axis.begin();
-  for (size_t i = 0; i < num_of_interm_tensors; ++i, ++it)
-  {
-    shape.set(*it, 1);
-    _interm_tensors[i].allocator()->init(
-        TensorInfo(shape, input->info()->num_channels(), input->info()->data_type()));
-    _interm_tensors[i].allocator()->allocate();
-  }
-
-  // Set a vector that is ordered ICLTensors sequentially.
-  std::vector<ICLTensor *> tensors;
-  tensors.emplace_back(input);
-  for (size_t i = 0; i < num_of_interm_tensors; ++i)
-  {
-    tensors.emplace_back(_interm_tensors.get() + i);
-  }
-  tensors.emplace_back(output);
-
-  // Apply ReduceOperation on all kernels
-  it = axis.begin();
-  for (size_t i = 0; i < num_of_kernels; ++i, ++it)
-  {
-    _reduce_kernels[i].configure(tensors[i], tensors[i + 1], *it, op);
-  }
-}
-
-void CLReduceOperation::run()
-{
-  const size_t num_of_kernels = _axis.size();
-  for (size_t i = 0; i < num_of_kernels; ++i)
-  {
-    CLScheduler::get().enqueue(_reduce_kernels[i]);
-  }
-}