458 lines
13 KiB
C++
458 lines
13 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_SIMPLE_TENSOR_H
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#define ARM_COMPUTE_TEST_SIMPLE_TENSOR_H
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/Utils.h"
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#include "support/MemorySupport.h"
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#include "tests/IAccessor.h"
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#include "tests/Utils.h"
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#include <algorithm>
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#include <array>
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#include <cstddef>
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#include <cstdint>
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#include <functional>
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#include <memory>
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#include <stdexcept>
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#include <utility>
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namespace arm_compute
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{
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namespace test
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{
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class RawTensor;
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/** Simple tensor object that stores elements in a consecutive chunk of memory.
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*
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* It can be created by either loading an image from a file which also
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* initialises the content of the tensor or by explcitly specifying the size.
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* The latter leaves the content uninitialised.
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*
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* Furthermore, the class provides methods to convert the tensor's values into
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* different image format.
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*/
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template <typename T>
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class SimpleTensor : public IAccessor
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{
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public:
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/** Create an uninitialised tensor. */
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SimpleTensor() = default;
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/** Create an uninitialised tensor of the given @p shape and @p format.
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*
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* @param[in] shape Shape of the new raw tensor.
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* @param[in] format Format of the new raw tensor.
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*/
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SimpleTensor(TensorShape shape, Format format);
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/** Create an uninitialised tensor of the given @p shape and @p data type.
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*
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* @param[in] shape Shape of the new raw tensor.
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* @param[in] data_type Data type of the new raw tensor.
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* @param[in] num_channels (Optional) Number of channels (default = 1).
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* @param[in] quantization_info (Optional) Quantization info for asymmetric quantization (default = empty).
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* @param[in] data_layout (Optional) Data layout of the tensor (default = NCHW).
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*/
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SimpleTensor(TensorShape shape, DataType data_type,
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int num_channels = 1,
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QuantizationInfo quantization_info = QuantizationInfo(),
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DataLayout data_layout = DataLayout::NCHW);
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/** Create a deep copy of the given @p tensor.
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*
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* @param[in] tensor To be copied tensor.
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*/
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SimpleTensor(const SimpleTensor &tensor);
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/** Create a deep copy of the given @p tensor.
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*
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* @param[in] tensor To be copied tensor.
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*
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* @return a copy of the given tensor.
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*/
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SimpleTensor &operator=(SimpleTensor tensor);
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/** Allow instances of this class to be move constructed */
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SimpleTensor(SimpleTensor &&) = default;
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/** Default destructor. */
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~SimpleTensor() = default;
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/** Tensor value type */
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using value_type = T;
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/** Tensor buffer pointer type */
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using Buffer = std::unique_ptr<value_type[]>;
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friend class RawTensor;
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/** Return value at @p offset in the buffer.
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*
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* @param[in] offset Offset within the buffer.
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*
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* @return value in the buffer.
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*/
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T &operator[](size_t offset);
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/** Return constant value at @p offset in the buffer.
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*
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* @param[in] offset Offset within the buffer.
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*
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* @return constant value in the buffer.
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*/
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const T &operator[](size_t offset) const;
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/** Shape of the tensor.
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*
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* @return the shape of the tensor.
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*/
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TensorShape shape() const override;
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/** Size of each element in the tensor in bytes.
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*
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* @return the size of each element in the tensor in bytes.
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*/
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size_t element_size() const override;
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/** Total size of the tensor in bytes.
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*
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* @return the total size of the tensor in bytes.
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*/
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size_t size() const override;
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/** Image format of the tensor.
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*
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* @return the format of the tensor.
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*/
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Format format() const override;
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/** Data layout of the tensor.
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*
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* @return the data layout of the tensor.
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*/
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DataLayout data_layout() const override;
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/** Data type of the tensor.
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*
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* @return the data type of the tensor.
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*/
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DataType data_type() const override;
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/** Number of channels of the tensor.
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*
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* @return the number of channels of the tensor.
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*/
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int num_channels() const override;
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/** Number of elements of the tensor.
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*
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* @return the number of elements of the tensor.
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*/
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int num_elements() const override;
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/** Available padding around the tensor.
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*
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* @return the available padding around the tensor.
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*/
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PaddingSize padding() const override;
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/** Quantization info in case of asymmetric quantized type
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*
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* @return
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*/
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QuantizationInfo quantization_info() const override;
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/** Constant pointer to the underlying buffer.
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*
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* @return a constant pointer to the data.
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*/
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const T *data() const;
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/** Pointer to the underlying buffer.
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*
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* @return a pointer to the data.
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*/
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T *data();
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/** Read only access to the specified element.
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*
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* @param[in] coord Coordinates of the desired element.
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*
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* @return A pointer to the desired element.
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*/
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const void *operator()(const Coordinates &coord) const override;
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/** Access to the specified element.
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*
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* @param[in] coord Coordinates of the desired element.
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*
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* @return A pointer to the desired element.
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*/
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void *operator()(const Coordinates &coord) override;
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/** Swaps the content of the provided tensors.
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*
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* @param[in, out] tensor1 Tensor to be swapped.
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* @param[in, out] tensor2 Tensor to be swapped.
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*/
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template <typename U>
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friend void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2);
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protected:
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Buffer _buffer{ nullptr };
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TensorShape _shape{};
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Format _format{ Format::UNKNOWN };
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DataType _data_type{ DataType::UNKNOWN };
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int _num_channels{ 0 };
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QuantizationInfo _quantization_info{};
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DataLayout _data_layout{ DataLayout::UNKNOWN };
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};
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template <typename T1, typename T2>
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SimpleTensor<T1> copy_tensor(const SimpleTensor<T2> &tensor)
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{
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SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
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tensor.num_channels(),
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tensor.quantization_info(),
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tensor.data_layout());
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for(size_t n = 0; n < size_t(st.num_elements()); n++)
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{
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st.data()[n] = static_cast<T1>(tensor.data()[n]);
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}
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return st;
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}
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template <typename T1, typename T2, typename std::enable_if<std::is_same<T1, T2>::value, int>::type = 0>
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SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor)
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{
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SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
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tensor.num_channels(),
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tensor.quantization_info(),
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tensor.data_layout());
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memcpy((void *)st.data(), (const void *)tensor.data(), size_t(st.num_elements() * sizeof(T1)));
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return st;
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}
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template < typename T1, typename T2, typename std::enable_if < (std::is_same<T1, half>::value || std::is_same<T2, half>::value), int >::type = 0 >
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SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor)
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{
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SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
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tensor.num_channels(),
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tensor.quantization_info(),
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tensor.data_layout());
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for(size_t n = 0; n < size_t(st.num_elements()); n++)
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{
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st.data()[n] = half_float::detail::half_cast<T1, T2>(tensor.data()[n]);
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}
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return st;
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}
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template <typename T>
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SimpleTensor<T>::SimpleTensor(TensorShape shape, Format format)
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: _buffer(nullptr),
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_shape(shape),
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_format(format),
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_quantization_info(),
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_data_layout(DataLayout::NCHW)
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{
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_num_channels = num_channels();
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_buffer = support::cpp14::make_unique<T[]>(num_elements() * _num_channels);
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}
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template <typename T>
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SimpleTensor<T>::SimpleTensor(TensorShape shape, DataType data_type, int num_channels, QuantizationInfo quantization_info, DataLayout data_layout)
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: _buffer(nullptr),
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_shape(shape),
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_data_type(data_type),
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_num_channels(num_channels),
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_quantization_info(quantization_info),
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_data_layout(data_layout)
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{
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_buffer = support::cpp14::make_unique<T[]>(this->_shape.total_size() * _num_channels);
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}
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template <typename T>
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SimpleTensor<T>::SimpleTensor(const SimpleTensor &tensor)
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: _buffer(nullptr),
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_shape(tensor.shape()),
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_format(tensor.format()),
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_data_type(tensor.data_type()),
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_num_channels(tensor.num_channels()),
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_quantization_info(tensor.quantization_info()),
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_data_layout(tensor.data_layout())
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{
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_buffer = support::cpp14::make_unique<T[]>(tensor.num_elements() * _num_channels);
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std::copy_n(tensor.data(), this->_shape.total_size() * _num_channels, _buffer.get());
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}
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template <typename T>
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SimpleTensor<T> &SimpleTensor<T>::operator=(SimpleTensor tensor)
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{
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swap(*this, tensor);
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return *this;
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}
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template <typename T>
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T &SimpleTensor<T>::operator[](size_t offset)
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{
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return _buffer[offset];
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}
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template <typename T>
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const T &SimpleTensor<T>::operator[](size_t offset) const
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{
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return _buffer[offset];
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}
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template <typename T>
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TensorShape SimpleTensor<T>::shape() const
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{
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return _shape;
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}
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template <typename T>
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size_t SimpleTensor<T>::element_size() const
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{
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return num_channels() * element_size_from_data_type(data_type());
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}
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template <typename T>
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QuantizationInfo SimpleTensor<T>::quantization_info() const
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{
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return _quantization_info;
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}
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template <typename T>
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size_t SimpleTensor<T>::size() const
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{
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const size_t size = std::accumulate(_shape.cbegin(), _shape.cend(), 1, std::multiplies<size_t>());
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return size * element_size();
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}
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template <typename T>
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Format SimpleTensor<T>::format() const
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{
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return _format;
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}
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template <typename T>
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DataLayout SimpleTensor<T>::data_layout() const
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{
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return _data_layout;
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}
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template <typename T>
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DataType SimpleTensor<T>::data_type() const
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{
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if(_format != Format::UNKNOWN)
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{
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return data_type_from_format(_format);
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}
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else
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{
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return _data_type;
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}
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}
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template <typename T>
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int SimpleTensor<T>::num_channels() const
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{
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switch(_format)
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{
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case Format::U8:
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case Format::U16:
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case Format::S16:
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case Format::U32:
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case Format::S32:
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case Format::F16:
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case Format::F32:
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return 1;
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// Because the U and V channels are subsampled
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// these formats appear like having only 2 channels:
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case Format::YUYV422:
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case Format::UYVY422:
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return 2;
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case Format::UV88:
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return 2;
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case Format::RGB888:
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return 3;
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case Format::RGBA8888:
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return 4;
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case Format::UNKNOWN:
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return _num_channels;
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//Doesn't make sense for planar formats:
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case Format::NV12:
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case Format::NV21:
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case Format::IYUV:
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case Format::YUV444:
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default:
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return 0;
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}
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}
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template <typename T>
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int SimpleTensor<T>::num_elements() const
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{
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return _shape.total_size();
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}
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template <typename T>
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PaddingSize SimpleTensor<T>::padding() const
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{
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return PaddingSize(0);
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}
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template <typename T>
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const T *SimpleTensor<T>::data() const
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{
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return _buffer.get();
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}
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template <typename T>
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T *SimpleTensor<T>::data()
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{
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return _buffer.get();
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}
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template <typename T>
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const void *SimpleTensor<T>::operator()(const Coordinates &coord) const
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{
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return _buffer.get() + coord2index(_shape, coord) * _num_channels;
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}
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template <typename T>
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void *SimpleTensor<T>::operator()(const Coordinates &coord)
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{
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return _buffer.get() + coord2index(_shape, coord) * _num_channels;
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}
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template <typename U>
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void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2)
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{
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// Use unqualified call to swap to enable ADL. But make std::swap available
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// as backup.
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using std::swap;
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swap(tensor1._shape, tensor2._shape);
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swap(tensor1._format, tensor2._format);
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swap(tensor1._data_type, tensor2._data_type);
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swap(tensor1._num_channels, tensor2._num_channels);
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swap(tensor1._quantization_info, tensor2._quantization_info);
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swap(tensor1._buffer, tensor2._buffer);
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}
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_SIMPLE_TENSOR_H */
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