1 files changed, 0 insertions, 130 deletions
diff --git a/libs/kernel/acl/src/cl/Pooling.cpp b/libs/kernel/acl/src/cl/Pooling.cpp
deleted file mode 100644
index e22eacccc..000000000
--- a/libs/kernel/acl/src/cl/Pooling.cpp
+++ /dev/null
@@ -1,130 +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 <OperationsUtils.h>
-#include <arm_compute/core/TensorShape.h>
-#include <arm_compute/core/TensorInfo.h>
-#include "../IO_accessor.h"
-#include "../shape.h"
-#include "../CLUniqueTensor.h"
-
-#include <cassert>
-
-namespace nnfw {
-namespace kernel {
-namespace acl {
-
-bool maxPoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
-                 int32_t padding_left, int32_t padding_right,
-                 int32_t padding_top, int32_t padding_bottom,
-                 int32_t stride_width, int32_t stride_height,
-                 int32_t filter_width, int32_t filter_height,
-                 int32_t activation,
-                 float* outputData, const nnfw::rt::Shape& outputShape)
-{
-  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
-  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
-
-  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
-
-  arm_compute::PadStrideInfo pad_info = arm_compute::PadStrideInfo(stride_width, stride_height,
-                                              padding_left, padding_right,
-                                              padding_top, padding_bottom,
-                                              arm_compute::DimensionRoundingType::FLOOR);
-
-  arm_compute::PoolingLayerInfo maxpool_info = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::MAX,
-                                                        arm_compute::Size2D(filter_width,filter_height),
-                                                        pad_info, false);
-
-  CLUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
-  CLUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
-
-  auto pool_f = std::make_shared<arm_compute::CLPoolingLayer>();
-  pool_f->configure(input.ptr(), output.ptr(), maxpool_info);
-
-  fns.emplace_back(pool_f);
-
-  input.allocate();
-  output.allocate();
-
-  util::insertFusedActivationLayer<CLUniqueTensor, arm_compute::CLActivationLayer>(output, activation, fns);
-
-  TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
-
-  for (const auto &fn : fns)
-  {
-    fn->run();
-  }
-
-  arm_compute::CLScheduler::get().sync();
-
-  TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
-
-  return true;
-}
-
-bool averagePoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
-                 int32_t padding_left, int32_t padding_right,
-                 int32_t padding_top, int32_t padding_bottom,
-                 int32_t stride_width, int32_t stride_height,
-                 int32_t filter_width, int32_t filter_height,
-                 int32_t activation,
-                 float* outputData, const nnfw::rt::Shape& outputShape)
-{
-  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
-  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
-
-  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
-
-  arm_compute::PadStrideInfo pad_info = arm_compute::PadStrideInfo(stride_width, stride_height,
-                                              padding_left, padding_right,
-                                              padding_top, padding_bottom,
-                                              arm_compute::DimensionRoundingType::FLOOR);
-
-  arm_compute::PoolingLayerInfo pool_info = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::AVG,
-                                                        arm_compute::Size2D(filter_width,filter_height),
-                                                        pad_info, true);
-
-  CLUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
-  CLUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
-
-  auto pool_f = std::make_shared<arm_compute::CLPoolingLayer>();
-  pool_f->configure(input.ptr(), output.ptr(), pool_info);
-
-  fns.emplace_back(pool_f);
-
-  input.allocate();
-  output.allocate();
-
-  util::insertFusedActivationLayer<CLUniqueTensor, arm_compute::CLActivationLayer>(output, activation, fns);
-
-  TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
-
-  for (const auto &fn : fns)
-  {
-    fn->run();
-  }
-
-  arm_compute::CLScheduler::get().sync();
-
-  TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
-
-  return true;
-}
-
-} // namespace acl
-} // namespace kernel
-} // namespace nnfw