1 files changed, 0 insertions, 178 deletions
diff --git a/arm_compute/core/NEON/kernels/convolution/common/tensor.hpp b/arm_compute/core/NEON/kernels/convolution/common/tensor.hpp
deleted file mode 100644
index ad0a677a8f..0000000000
--- a/arm_compute/core/NEON/kernels/convolution/common/tensor.hpp
+++ /dev/null
@@ -1,178 +0,0 @@
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#pragma once
-#include <cstdlib>
-#include <random>
-
-#include "alloc.hpp"
-
-enum TensorOrder
-{
-  NHWC,  ///< [Batch x Height x Width x Channels]
-  NCHW,  ///< [Batch x Channels x Height x Width]
-};
-
-struct Tensor4DShape
-{
-  int n_batches, n_rows, n_cols, n_channels;
-  TensorOrder ordering;
-
-  // Create a new tensor with the default (NHWC) ordering
-  inline Tensor4DShape(
-    const int n_batches,
-    const int n_rows,
-    const int n_cols,
-    const int n_channels,
-    const TensorOrder ordering=NHWC
-  ) : n_batches(n_batches),
-      n_rows(n_rows),
-      n_cols(n_cols),
-      n_channels(n_channels),
-      ordering(ordering)
-  {
-  }
-
-  inline int index(const int n, const int i, const int j, const int c) const
-  {
-    if (this->ordering == NHWC)
-    {
-      return ((n*this->n_rows + i)*this->n_cols + j)*this->n_channels + c;
-    }
-    else  // NCHW
-    {
-      return ((n*this->n_channels + c)*this->n_rows + i)*this->n_cols + j;
-    }
-  }
-
-  inline int size() const
-  {
-    return n_batches * n_rows * n_cols * n_channels;
-  }
-
-  inline bool TestEq(const Tensor4DShape& other) const
-  {
-    return (n_batches == other.n_batches &&
-            n_rows == other.n_rows &&
-            n_cols == other.n_cols &&
-            n_channels == other.n_channels);
-  }
-};
-
-
-enum WeightOrder
-{
-  HWIO,  ///< [Height x Width x Input channels x Output channels]
-  OIHW,  ///< [Output channels x Input channels x Height x Width]
-};
-
-struct KernelShape
-{
-  int n_output_channels, n_rows, n_cols, n_input_channels;
-  WeightOrder ordering;
-
-  inline KernelShape(
-    const int n_output_channels,
-    const int n_rows,
-    const int n_cols,
-    const int n_input_channels,
-    const WeightOrder ordering=HWIO
-  ) : n_output_channels(n_output_channels),
-      n_rows(n_rows),
-      n_cols(n_cols),
-      n_input_channels(n_input_channels),
-      ordering(ordering)
-  {
-  }
-
-  inline int index(int oc, int i, int j, int ic) const
-  {
-    if (this->ordering == HWIO)
-    {
-      return ((i*this->n_cols + j)*this->n_input_channels + ic)*this->n_output_channels + oc;
-    }
-    else  // OIHW
-    {
-      return ((oc*this->n_input_channels + ic)*this->n_rows + i)*this->n_cols + j;
-    }
-  }
-
-  inline int size(void) const
-  {
-    return n_output_channels * n_rows * n_cols * n_input_channels;
-  }
-};
-
-
-template <typename ShapeT, typename T>
-class Tensor4D final
-{
-  public:
-    Tensor4D(ShapeT shape) :
-      shape(shape),
-      _data(reinterpret_cast<T*>(ALLOCATE(size_bytes())))
-    {
-        Clear();
-    }
-
-    Tensor4D(const Tensor4D<ShapeT, T>&) = delete;
-    Tensor4D operator=(const Tensor4D<ShapeT, T>&) = delete;
-
-    ~Tensor4D() {
-      free(_data);
-    }
-
-    inline T* ptr() const {
-      return _data;
-    }
-
-    inline size_t size_bytes() const {
-      return shape.size() * sizeof(T);
-    }
-
-    /* Extract an element of the tensor.
-     *
-     * If the shape is a Tensor4DShape then the index is given as batch, row,
-     * column and channel.  If the shape is a KernelShape then the index is
-     * given as output channel, row, column and input channel.
-     */
-    inline T& element(const int a, const int b, const int c, const int d) const
-    {
-      return _data[shape.index(a, b, c, d)];
-    }
-
-    inline void Clear() {
-      Fill(static_cast<T>(0));
-    }
-
-    inline void Fill(T val) {
-      for (int i = 0; i < shape.size(); i++)
-        _data[i] = val;
-    }
-
-    const ShapeT shape;
-
-  private:
-    T* const _data;
-};