1 files changed, 0 insertions, 193 deletions
diff --git a/libs/kernel/acl/src/util.h b/libs/kernel/acl/src/util.h
deleted file mode 100644
index 48ed02783..000000000
--- a/libs/kernel/acl/src/util.h
+++ /dev/null
@@ -1,193 +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.
- */
-
-#ifndef __NNFW_KERNEL_ACL_UTIL_H__
-#define __NNFW_KERNEL_ACL_UTIL_H__
-#include <OperationsUtils.h>
-
-#include <cmath>
-#include <cassert>
-#include <functional>
-
-namespace nnfw {
-namespace kernel {
-namespace acl {
-namespace util {
-
-// TODO: make a separate module.
-class TensorWrapper {
-public:
-  TensorWrapper(std::vector<uint32_t> dims,
-      OperandType type = OperandType::FLOAT32,
-      float scale = 1.0,
-      int32_t offset = 0)
-  :_shape{type, dims, scale, offset}
-  {
-
-    // currently, we support only FLOAT32 for now.
-    assert( type == OperandType::FLOAT32);
-
-    uint32_t size_bytes = sizeof(float);
-
-    _num_elems = 1;
-    for( auto& d: dims ) {
-      _num_elems *= d;
-    }
-
-    _data = new uint8_t[_num_elems * size_bytes];
-  }
-
-  ~TensorWrapper() {
-    delete [] _data;
-  }
-
-  const nnfw::rt::Shape shape() const {
-    return _shape;
-  }
-
-  uint32_t num_elems() const { return _num_elems; }
-
-  template<class T>
-  T at(const uint32_t& idx) const {
-    return reinterpret_cast<T*>(_data)[idx];
-  }
-
-  template<class T>
-  T& at(const uint32_t& idx) {
-    return reinterpret_cast<T*>(_data)[idx];
-  }
-
-  template<class T>
-  T* ptr() { return reinterpret_cast<T*>(_data); }
-
-  void initValue(float f) {
-    for( uint32_t i = 0; i < _num_elems; ++i ) {
-      at<float>(i) = f;
-    }
-  }
-
-  typedef std::function<float(uint32_t n, uint32_t c, uint32_t h, uint32_t w)> funcInit4;
-  void initValue(funcInit4 f) {
-    assert(_shape.dimensions.size() == 4);
-
-    int N = _shape.dimensions[0];
-    int H = _shape.dimensions[1];
-    int W = _shape.dimensions[2];
-    int C = _shape.dimensions[3];
-
-    for(int n = 0; n < N; ++n) {
-      for(int h = 0; h < H; ++h) {
-        for(int w = 0; w < W; ++w) {
-          for(int c = 0; c < C; ++c) {
-            uint32_t offset = n*H*W*C + h*W*C + w*C + c;
-            at<float>(offset) = f(n,c,h,w);
-          }
-        }
-      }
-    }
-  }
-
-  typedef std::function<float(uint32_t c, uint32_t h, uint32_t w)> funcInit3;
-  void initValue(funcInit3 f) {
-    assert(_shape.dimensions.size() == 3);
-
-    int C = _shape.dimensions[0];
-    int H = _shape.dimensions[1];
-    int W = _shape.dimensions[2];
-
-    for(int h = 0; h < H; ++h) {
-      for(int w = 0; w < W; ++w) {
-        for(int c = 0; c < C; ++c) {
-          uint32_t offset = h*W*C + w*C + c;
-          at<float>(offset) = f(c,h,w);
-        }
-      }
-    }
-  }
-
-  typedef std::function<float(uint32_t h, uint32_t w)> funcInit2;
-  void initValue(funcInit2 f) {
-    assert(_shape.dimensions.size() == 2);
-
-    int H = _shape.dimensions[0];
-    int W = _shape.dimensions[1];
-
-    for(int h = 0; h < H; ++h) {
-      for(int w = 0; w < W; ++w) {
-        uint32_t offset = h*W + w;
-        at<float>(offset) = f(h,w);
-      }
-    }
-  }
-
-  typedef std::function<float(uint32_t w)> funcInit1;
-  void initValue(funcInit1 f) {
-    assert(_shape.dimensions.size() == 1);
-
-    int W = _shape.dimensions[0];
-
-    for(int w = 0; w < W; ++w) {
-      uint32_t offset = w;
-      at<float>(offset) = f(w);
-    }
-  }
-
-  void initValue(std::vector<float> v) {
-    assert(v.size() == _num_elems);
-    for( uint32_t i = 0; i < _num_elems; ++i ) {
-      at<float>(i) = v[i];
-    }
-  }
-
-  bool operator==(const TensorWrapper &t) const {
-    // compare the shape
-    assert(num_elems() == t.num_elems());
-    assert(_shape.type == t.shape().type);
-    assert(_shape.scale == t.shape().scale);
-    assert(_shape.offset == t.shape().offset);
-    assert(_shape.dimensions == t.shape().dimensions);
-
-    // currently, we support only FLOAT32.
-    assert(_shape.type == OperandType::FLOAT32);
-
-    for( uint32_t i = 0; i < _num_elems; ++i ) {
-      if( std::fabs(static_cast<float>(at<float>(i) - t.at<float>(i))) > 0.001f ) {
-        std::cout << "Comparing [" << i << "] " << at<float>(i) << "," << t.at<float>(i) << std::endl;
-        return false;
-      }
-    }
-
-    return true;
-  }
-
-private:
-  nnfw::rt::Shape _shape;
-  uint32_t           _num_elems;
-  uint8_t*           _data;
-};
-
-void initData(float* data, int num, float value);
-bool compareData(const float* result, const float* expected, const nnfw::rt::Shape& shape);
-void initData_Increasing(float* data, int num, float value);
-
-void NCHW2NHWC(const float* nchw, float* nhwc, const nnfw::rt::Shape& shape);
-
-} // namespace util
-} // namespace acl
-} // namespace kernel
-} // namespace nnfw
-
-#endif // __NNFW_KERNEL_ACL_UTIL_H__