1 files changed, 0 insertions, 113 deletions
diff --git a/libs/kernel/acl/src/cl/Conv2D.cpp b/libs/kernel/acl/src/cl/Conv2D.cpp
deleted file mode 100644
index 4783bdc1d..000000000
--- a/libs/kernel/acl/src/cl/Conv2D.cpp
+++ /dev/null
@@ -1,113 +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 <NeuralNetworks.h>
-
-#include <arm_compute/core/TensorShape.h>
-#include <arm_compute/core/TensorInfo.h>
-
-#include <util/environment.h>
-
-#include "../IO_accessor.h"
-#include "../util.h"
-#include "../shape.h"
-#include "../CLUniqueTensor.h"
-#include "../support.h"
-
-#include "util/feature/TextFormatter.h"
-
-#include "support/nnapi/feature/Reader.h"
-
-namespace nnfw {
-namespace kernel {
-namespace acl {
-
-static int verbose = 0;
-
-bool convFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
-                 const float* filterData, const nnfw::rt::Shape& filterShape,
-                 const float* biasData, const nnfw::rt::Shape& biasShape,
-                 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 activation,
-                 float* outputData, const nnfw::rt::Shape& outputShape)
-{
-  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
-  arm_compute::TensorShape filter_shape = util::fromNNShape(filterShape);
-  arm_compute::TensorShape bias_shape = util::fromVectorNNShape(biasShape);
-  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
-  arm_compute::PadStrideInfo conv_info = arm_compute::PadStrideInfo(stride_width, stride_height,
-                                              padding_left, padding_right,
-                                              padding_top, padding_bottom,
-                                              arm_compute::DimensionRoundingType::FLOOR);
-
-  CLUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
-  CLUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
-  CLUniqueTensor bias(arm_compute::TensorInfo(bias_shape, arm_compute::Format::F32));
-  CLUniqueTensor filter(arm_compute::TensorInfo(filter_shape, arm_compute::Format::F32));
-
-  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
-
-  auto conv_f = std::make_shared<arm_compute::CLConvolutionLayer>();
-
-  conv_f->configure(input.ptr(), filter.ptr(), bias.ptr(), output.ptr(), conv_info);
-
-  fns.emplace_back(conv_f);
-
-  util::insertFusedActivationLayer<CLUniqueTensor, arm_compute::CLActivationLayer>(output, activation, fns);
-
-  input.allocate();
-  output.allocate();
-  bias.allocate();
-  filter.allocate();
-
-  TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
-  TensorAccess<BiasAccessor>(bias.ref(), biasData, biasShape);
-  TensorAccess<WeightAccessor>(filter.ref(), filterData, filterShape);
-
-  nnfw::util::env::IntAccessor("CONV2D_VERBOSE").access(verbose);
-  if (verbose)
-  {
-    input.ref().map();
-    auto ifm_shape = nnfw::support::nnapi::feature::asFeatureShape(inputShape);
-    nnfw::support::nnapi::feature::Reader<float> nnapi_ifm_reader{ifm_shape, inputData};
-    nnfw::support::acl::feature::Reader<float> acl_ifm_reader{input.ptr()};
-
-    std::cout << "NNAPI IFM:" << std::endl;
-    std::cout << nnfw::util::feature::TextFormatter<float>{ifm_shape, nnapi_ifm_reader} << std::endl;
-
-    std::cout << "ARM Compute IFM:" << std::endl;
-    std::cout << nnfw::util::feature::TextFormatter<float>{ifm_shape, acl_ifm_reader} << std::endl;
-    input.ref().unmap();
-  }
-
-  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