1 files changed, 0 insertions, 105 deletions
diff --git a/compute/cker/include/cker/operation/optimized/AveragePool.h b/compute/cker/include/cker/operation/optimized/AveragePool.h
deleted file mode 100644
index d94a5811a..000000000
--- a/compute/cker/include/cker/operation/optimized/AveragePool.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright 2018 The TensorFlow Authors. 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.
- */
-
-#ifndef __NNFW_CKER_OPTIMIZED_AVERAGE_POOL_H__
-#define __NNFW_CKER_OPTIMIZED_AVERAGE_POOL_H__
-
-#if defined(CKER_OPTIMIZED_EIGEN)
-
-#include "cker/eigen/Utils.h"
-#include "cker/Shape.h"
-#include "cker/Types.h"
-#include "cker/Utils.h"
-#include <Eigen/Core>
-
-namespace nnfw
-{
-namespace cker
-{
-namespace optimized
-{
-
-// TODO Change to apply neon for this function if it is faster
-inline void AveragePool(const PoolParams &params, const Shape &input_shape, const float *input_data,
-                        const Shape &output_shape, float *output_data)
-{
-  assert(input_shape.DimensionsCount() == 4);
-  assert(output_shape.DimensionsCount() == 4);
-  const int batches = MatchingDim(input_shape, 0, output_shape, 0);
-  const int input_height = input_shape.Dims(1);
-  const int input_width = input_shape.Dims(2);
-  const int output_height = output_shape.Dims(1);
-  const int output_width = output_shape.Dims(2);
-  const int stride_height = params.stride_height;
-  const int stride_width = params.stride_width;
-
-  // TODO(benoitjacob) make this a proper reference impl without Eigen!
-  const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape);
-  auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape);
-  // TODO(benoitjacob) get rid of the dynamic memory allocation here!
-  Eigen::VectorXf out_count(out_mat.cols());
-  out_count.setZero();
-  // Prefill the output to 0.
-  out_mat.setZero();
-  for (int b = 0; b < batches; ++b)
-  {
-    for (int h = 0; h < input_height; ++h)
-    {
-      for (int w = 0; w < input_width; ++w)
-      {
-        // (h_start, h_end) * (w_start, w_end) is the range that the input
-        // vector projects to.
-        int hpad = h + params.padding_values.height;
-        int wpad = w + params.padding_values.width;
-        int h_start =
-            (hpad < params.filter_height) ? 0 : (hpad - params.filter_height) / stride_height + 1;
-        int h_end = std::min(hpad / stride_height + 1, output_height);
-        int w_start =
-            (wpad < params.filter_width) ? 0 : (wpad - params.filter_width) / stride_width + 1;
-        int w_end = std::min(wpad / stride_width + 1, output_width);
-        // compute elementwise sum
-        for (int ph = h_start; ph < h_end; ++ph)
-        {
-          for (int pw = w_start; pw < w_end; ++pw)
-          {
-            int out_offset = NodeOffset(b, ph, pw, output_height, output_width);
-            out_mat.col(out_offset) += in_mat.col(NodeOffset(b, h, w, input_height, input_width));
-            out_count(out_offset)++;
-          }
-        }
-      }
-    }
-  }
-  // Divide the output by the actual number of elements being averaged over
-  assert(out_count.minCoeff() > 0);
-  out_mat.array().rowwise() /= out_count.transpose().array();
-
-  const int flat_size = output_shape.FlatSize();
-  for (int i = 0; i < flat_size; ++i)
-  {
-    output_data[i] = ActivationFunctionWithMinMax(output_data[i], params.float_activation_min,
-                                                  params.float_activation_max);
-  }
-}
-
-} // namespace optimized
-} // namespace cker
-} // namespace nnfw
-
-#endif // defined(CKER_OPTIMIZED_EIGEN)
-
-#endif // __NNFW_CKER_OPTIMIZED_AVERAGE_POOL_H__