| /* |
| * Copyright (c) 2017-2018 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. |
| */ |
| #ifndef __ARM_COMPUTE_TEST_SIMPLE_TENSOR_H__ |
| #define __ARM_COMPUTE_TEST_SIMPLE_TENSOR_H__ |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "support/ToolchainSupport.h" |
| #include "tests/IAccessor.h" |
| #include "tests/Utils.h" |
| |
| #include <algorithm> |
| #include <array> |
| #include <cstddef> |
| #include <cstdint> |
| #include <functional> |
| #include <memory> |
| #include <stdexcept> |
| #include <utility> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| class RawTensor; |
| |
| /** Simple tensor object that stores elements in a consecutive chunk of memory. |
| * |
| * It can be created by either loading an image from a file which also |
| * initialises the content of the tensor or by explcitly specifying the size. |
| * The latter leaves the content uninitialised. |
| * |
| * Furthermore, the class provides methods to convert the tensor's values into |
| * different image format. |
| */ |
| template <typename T> |
| class SimpleTensor : public IAccessor |
| { |
| public: |
| /** Create an uninitialised tensor. */ |
| SimpleTensor() = default; |
| |
| /** Create an uninitialised tensor of the given @p shape and @p format. |
| * |
| * @param[in] shape Shape of the new raw tensor. |
| * @param[in] format Format of the new raw tensor. |
| */ |
| SimpleTensor(TensorShape shape, Format format); |
| |
| /** Create an uninitialised tensor of the given @p shape and @p data type. |
| * |
| * @param[in] shape Shape of the new raw tensor. |
| * @param[in] data_type Data type of the new raw tensor. |
| * @param[in] num_channels (Optional) Number of channels (default = 1). |
| * @param[in] quantization_info (Optional) Quantization info for asymmetric quantization (default = empty). |
| * @param[in] data_layout (Optional) Data layout of the tensor (default = NCHW). |
| */ |
| SimpleTensor(TensorShape shape, DataType data_type, |
| int num_channels = 1, |
| QuantizationInfo quantization_info = QuantizationInfo(), |
| DataLayout data_layout = DataLayout::NCHW); |
| |
| /** Create a deep copy of the given @p tensor. |
| * |
| * @param[in] tensor To be copied tensor. |
| */ |
| SimpleTensor(const SimpleTensor &tensor); |
| |
| /** Create a deep copy of the given @p tensor. |
| * |
| * @param[in] tensor To be copied tensor. |
| * |
| * @return a copy of the given tensor. |
| */ |
| SimpleTensor &operator=(SimpleTensor tensor); |
| /** Allow instances of this class to be move constructed */ |
| SimpleTensor(SimpleTensor &&) = default; |
| /** Default destructor. */ |
| ~SimpleTensor() = default; |
| |
| /** Tensor value type */ |
| using value_type = T; |
| /** Tensor buffer pointer type */ |
| using Buffer = std::unique_ptr<value_type[]>; |
| |
| friend class RawTensor; |
| |
| /** Return value at @p offset in the buffer. |
| * |
| * @param[in] offset Offset within the buffer. |
| * |
| * @return value in the buffer. |
| */ |
| T &operator[](size_t offset); |
| |
| /** Return constant value at @p offset in the buffer. |
| * |
| * @param[in] offset Offset within the buffer. |
| * |
| * @return constant value in the buffer. |
| */ |
| const T &operator[](size_t offset) const; |
| |
| /** Shape of the tensor. |
| * |
| * @return the shape of the tensor. |
| */ |
| TensorShape shape() const override; |
| /** Size of each element in the tensor in bytes. |
| * |
| * @return the size of each element in the tensor in bytes. |
| */ |
| size_t element_size() const override; |
| /** Total size of the tensor in bytes. |
| * |
| * @return the total size of the tensor in bytes. |
| */ |
| size_t size() const override; |
| /** Image format of the tensor. |
| * |
| * @return the format of the tensor. |
| */ |
| Format format() const override; |
| /** Data layout of the tensor. |
| * |
| * @return the data layout of the tensor. |
| */ |
| DataLayout data_layout() const override; |
| /** Data type of the tensor. |
| * |
| * @return the data type of the tensor. |
| */ |
| DataType data_type() const override; |
| /** Number of channels of the tensor. |
| * |
| * @return the number of channels of the tensor. |
| */ |
| int num_channels() const override; |
| /** Number of elements of the tensor. |
| * |
| * @return the number of elements of the tensor. |
| */ |
| int num_elements() const override; |
| /** Available padding around the tensor. |
| * |
| * @return the available padding around the tensor. |
| */ |
| PaddingSize padding() const override; |
| /** Quantization info in case of asymmetric quantized type |
| * |
| * @return |
| */ |
| QuantizationInfo quantization_info() const override; |
| |
| /** Constant pointer to the underlying buffer. |
| * |
| * @return a constant pointer to the data. |
| */ |
| const T *data() const; |
| |
| /** Pointer to the underlying buffer. |
| * |
| * @return a pointer to the data. |
| */ |
| T *data(); |
| |
| /** Read only access to the specified element. |
| * |
| * @param[in] coord Coordinates of the desired element. |
| * |
| * @return A pointer to the desired element. |
| */ |
| const void *operator()(const Coordinates &coord) const override; |
| |
| /** Access to the specified element. |
| * |
| * @param[in] coord Coordinates of the desired element. |
| * |
| * @return A pointer to the desired element. |
| */ |
| void *operator()(const Coordinates &coord) override; |
| |
| /** Swaps the content of the provided tensors. |
| * |
| * @param[in, out] tensor1 Tensor to be swapped. |
| * @param[in, out] tensor2 Tensor to be swapped. |
| */ |
| template <typename U> |
| friend void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2); |
| |
| protected: |
| Buffer _buffer{ nullptr }; |
| TensorShape _shape{}; |
| Format _format{ Format::UNKNOWN }; |
| DataType _data_type{ DataType::UNKNOWN }; |
| int _num_channels{ 0 }; |
| QuantizationInfo _quantization_info{}; |
| DataLayout _data_layout{ DataLayout::UNKNOWN }; |
| }; |
| |
| template <typename T1, typename T2> |
| SimpleTensor<T1> copy_tensor(const SimpleTensor<T2> &tensor) |
| { |
| SimpleTensor<T1> st(tensor.shape(), tensor.data_type(), |
| tensor.num_channels(), |
| tensor.quantization_info(), |
| tensor.data_layout()); |
| for(size_t n = 0; n < size_t(st.num_elements()); n++) |
| { |
| st.data()[n] = static_cast<T1>(tensor.data()[n]); |
| } |
| return st; |
| } |
| |
| template <typename T1, typename T2, typename std::enable_if<std::is_same<T1, T2>::value, int>::type = 0> |
| SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor) |
| { |
| SimpleTensor<T1> st(tensor.shape(), tensor.data_type(), |
| tensor.num_channels(), |
| tensor.quantization_info(), |
| tensor.data_layout()); |
| memcpy((void *)st.data(), (const void *)tensor.data(), size_t(st.num_elements() * sizeof(T1))); |
| return st; |
| } |
| |
| template < typename T1, typename T2, typename std::enable_if < (std::is_same<T1, half>::value || std::is_same<T2, half>::value), int >::type = 0 > |
| SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor) |
| { |
| SimpleTensor<T1> st(tensor.shape(), tensor.data_type(), |
| tensor.num_channels(), |
| tensor.quantization_info(), |
| tensor.data_layout()); |
| for(size_t n = 0; n < size_t(st.num_elements()); n++) |
| { |
| st.data()[n] = half_float::detail::half_cast<T1, T2>(tensor.data()[n]); |
| } |
| return st; |
| } |
| |
| template <typename T> |
| SimpleTensor<T>::SimpleTensor(TensorShape shape, Format format) |
| : _buffer(nullptr), |
| _shape(shape), |
| _format(format), |
| _quantization_info(), |
| _data_layout(DataLayout::NCHW) |
| { |
| _num_channels = num_channels(); |
| _buffer = support::cpp14::make_unique<T[]>(num_elements() * _num_channels); |
| } |
| |
| template <typename T> |
| SimpleTensor<T>::SimpleTensor(TensorShape shape, DataType data_type, int num_channels, QuantizationInfo quantization_info, DataLayout data_layout) |
| : _buffer(nullptr), |
| _shape(shape), |
| _data_type(data_type), |
| _num_channels(num_channels), |
| _quantization_info(quantization_info), |
| _data_layout(data_layout) |
| { |
| _buffer = support::cpp14::make_unique<T[]>(num_elements() * this->num_channels()); |
| } |
| |
| template <typename T> |
| SimpleTensor<T>::SimpleTensor(const SimpleTensor &tensor) |
| : _buffer(nullptr), |
| _shape(tensor.shape()), |
| _format(tensor.format()), |
| _data_type(tensor.data_type()), |
| _num_channels(tensor.num_channels()), |
| _quantization_info(tensor.quantization_info()), |
| _data_layout(tensor.data_layout()) |
| { |
| _buffer = support::cpp14::make_unique<T[]>(tensor.num_elements() * num_channels()); |
| std::copy_n(tensor.data(), num_elements() * num_channels(), _buffer.get()); |
| } |
| |
| template <typename T> |
| SimpleTensor<T> &SimpleTensor<T>::operator=(SimpleTensor tensor) |
| { |
| swap(*this, tensor); |
| |
| return *this; |
| } |
| |
| template <typename T> |
| T &SimpleTensor<T>::operator[](size_t offset) |
| { |
| return _buffer[offset]; |
| } |
| |
| template <typename T> |
| const T &SimpleTensor<T>::operator[](size_t offset) const |
| { |
| return _buffer[offset]; |
| } |
| |
| template <typename T> |
| TensorShape SimpleTensor<T>::shape() const |
| { |
| return _shape; |
| } |
| |
| template <typename T> |
| size_t SimpleTensor<T>::element_size() const |
| { |
| return num_channels() * element_size_from_data_type(data_type()); |
| } |
| |
| template <typename T> |
| QuantizationInfo SimpleTensor<T>::quantization_info() const |
| { |
| return _quantization_info; |
| } |
| |
| template <typename T> |
| size_t SimpleTensor<T>::size() const |
| { |
| const size_t size = std::accumulate(_shape.cbegin(), _shape.cend(), 1, std::multiplies<size_t>()); |
| return size * element_size(); |
| } |
| |
| template <typename T> |
| Format SimpleTensor<T>::format() const |
| { |
| return _format; |
| } |
| |
| template <typename T> |
| DataLayout SimpleTensor<T>::data_layout() const |
| { |
| return _data_layout; |
| } |
| |
| template <typename T> |
| DataType SimpleTensor<T>::data_type() const |
| { |
| if(_format != Format::UNKNOWN) |
| { |
| return data_type_from_format(_format); |
| } |
| else |
| { |
| return _data_type; |
| } |
| } |
| |
| template <typename T> |
| int SimpleTensor<T>::num_channels() const |
| { |
| switch(_format) |
| { |
| case Format::U8: |
| case Format::U16: |
| case Format::S16: |
| case Format::U32: |
| case Format::S32: |
| case Format::F16: |
| case Format::F32: |
| return 1; |
| // Because the U and V channels are subsampled |
| // these formats appear like having only 2 channels: |
| case Format::YUYV422: |
| case Format::UYVY422: |
| return 2; |
| case Format::UV88: |
| return 2; |
| case Format::RGB888: |
| return 3; |
| case Format::RGBA8888: |
| return 4; |
| case Format::UNKNOWN: |
| return _num_channels; |
| //Doesn't make sense for planar formats: |
| case Format::NV12: |
| case Format::NV21: |
| case Format::IYUV: |
| case Format::YUV444: |
| default: |
| return 0; |
| } |
| } |
| |
| template <typename T> |
| int SimpleTensor<T>::num_elements() const |
| { |
| return _shape.total_size(); |
| } |
| |
| template <typename T> |
| PaddingSize SimpleTensor<T>::padding() const |
| { |
| return PaddingSize(0); |
| } |
| |
| template <typename T> |
| const T *SimpleTensor<T>::data() const |
| { |
| return _buffer.get(); |
| } |
| |
| template <typename T> |
| T *SimpleTensor<T>::data() |
| { |
| return _buffer.get(); |
| } |
| |
| template <typename T> |
| const void *SimpleTensor<T>::operator()(const Coordinates &coord) const |
| { |
| return _buffer.get() + coord2index(_shape, coord) * _num_channels; |
| } |
| |
| template <typename T> |
| void *SimpleTensor<T>::operator()(const Coordinates &coord) |
| { |
| return _buffer.get() + coord2index(_shape, coord) * _num_channels; |
| } |
| |
| template <typename U> |
| void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2) |
| { |
| // Use unqualified call to swap to enable ADL. But make std::swap available |
| // as backup. |
| using std::swap; |
| swap(tensor1._shape, tensor2._shape); |
| swap(tensor1._format, tensor2._format); |
| swap(tensor1._data_type, tensor2._data_type); |
| swap(tensor1._num_channels, tensor2._num_channels); |
| swap(tensor1._quantization_info, tensor2._quantization_info); |
| swap(tensor1._buffer, tensor2._buffer); |
| } |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* __ARM_COMPUTE_TEST_SIMPLE_TENSOR_H__ */ |