| /* | |
| * Copyright (c) 2017-2022 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_TENSOR_LIBRARY_H | |
| #define ARM_COMPUTE_TEST_TENSOR_LIBRARY_H | |
| #include "arm_compute/core/Coordinates.h" | |
| #include "arm_compute/core/Error.h" | |
| #include "arm_compute/core/Helpers.h" | |
| #include "arm_compute/core/TensorInfo.h" | |
| #include "arm_compute/core/TensorShape.h" | |
| #include "arm_compute/core/Types.h" | |
| #include "arm_compute/core/Window.h" | |
| #include "support/Random.h" | |
| #include "tests/RawTensor.h" | |
| #include "tests/TensorCache.h" | |
| #include "tests/Utils.h" | |
| #include "tests/framework/Exceptions.h" | |
| #include "utils/Utils.h" | |
| #include <algorithm> | |
| #include <cstddef> | |
| #include <fstream> | |
| #include <random> | |
| #include <string> | |
| #include <type_traits> | |
| #include <vector> | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| /** Factory class to create and fill tensors. | |
| * | |
| * Allows to initialise tensors from loaded images or by specifying the shape | |
| * explicitly. Furthermore, provides methods to fill tensors with the content of | |
| * loaded images or with random values. | |
| */ | |
| class AssetsLibrary final | |
| { | |
| public: | |
| using RangePair = std::pair<float, float>; | |
| public: | |
| /** Initialises the library with a @p path to the assets directory. | |
| * Furthermore, sets the seed for the random generator to @p seed. | |
| * | |
| * @param[in] path Path to load assets from. | |
| * @param[in] seed Seed used to initialise the random number generator. | |
| */ | |
| AssetsLibrary(std::string path, std::random_device::result_type seed); | |
| /** Path to assets directory used to initialise library. | |
| * | |
| * @return the path to the assets directory. | |
| */ | |
| std::string path() const; | |
| /** Seed that is used to fill tensors with random values. | |
| * | |
| * @return the initial random seed. | |
| */ | |
| std::random_device::result_type seed() const; | |
| /** Provides a tensor shape for the specified image. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * | |
| * @return the tensor shape for the specified image. | |
| */ | |
| TensorShape get_image_shape(const std::string &name); | |
| /** Provides a constant raw tensor for the specified image. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * | |
| * @return a raw tensor for the specified image. | |
| */ | |
| const RawTensor &get(const std::string &name) const; | |
| /** Provides a raw tensor for the specified image. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * | |
| * @return a raw tensor for the specified image. | |
| */ | |
| RawTensor get(const std::string &name); | |
| /** Creates an uninitialised raw tensor with the given @p data_type and @p | |
| * num_channels. The shape is derived from the specified image. | |
| * | |
| * @param[in] name Image file used to initialise the tensor. | |
| * @param[in] data_type Data type used to initialise the tensor. | |
| * @param[in] num_channels Number of channels used to initialise the tensor. | |
| * | |
| * @return a raw tensor for the specified image. | |
| */ | |
| RawTensor get(const std::string &name, DataType data_type, int num_channels = 1) const; | |
| /** Provides a contant raw tensor for the specified image after it has been | |
| * converted to @p format. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] format Format used to look up the raw tensor. | |
| * | |
| * @return a raw tensor for the specified image. | |
| */ | |
| const RawTensor &get(const std::string &name, Format format) const; | |
| /** Provides a raw tensor for the specified image after it has been | |
| * converted to @p format. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] format Format used to look up the raw tensor. | |
| * | |
| * @return a raw tensor for the specified image. | |
| */ | |
| RawTensor get(const std::string &name, Format format); | |
| /** Provides a contant raw tensor for the specified channel after it has | |
| * been extracted form the given image. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] channel Channel used to look up the raw tensor. | |
| * | |
| * @note The channel has to be unambiguous so that the format can be | |
| * inferred automatically. | |
| * | |
| * @return a raw tensor for the specified image channel. | |
| */ | |
| const RawTensor &get(const std::string &name, Channel channel) const; | |
| /** Provides a raw tensor for the specified channel after it has been | |
| * extracted form the given image. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] channel Channel used to look up the raw tensor. | |
| * | |
| * @note The channel has to be unambiguous so that the format can be | |
| * inferred automatically. | |
| * | |
| * @return a raw tensor for the specified image channel. | |
| */ | |
| RawTensor get(const std::string &name, Channel channel); | |
| /** Provides a constant raw tensor for the specified channel after it has | |
| * been extracted form the given image formatted to @p format. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] format Format used to look up the raw tensor. | |
| * @param[in] channel Channel used to look up the raw tensor. | |
| * | |
| * @return a raw tensor for the specified image channel. | |
| */ | |
| const RawTensor &get(const std::string &name, Format format, Channel channel) const; | |
| /** Provides a raw tensor for the specified channel after it has been | |
| * extracted form the given image formatted to @p format. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] format Format used to look up the raw tensor. | |
| * @param[in] channel Channel used to look up the raw tensor. | |
| * | |
| * @return a raw tensor for the specified image channel. | |
| */ | |
| RawTensor get(const std::string &name, Format format, Channel channel); | |
| /** Puts garbage values all around the tensor for testing purposes | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] distribution Distribution used to fill the tensor's surroundings. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| */ | |
| template <typename T, typename D> | |
| void fill_borders_with_garbage(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const; | |
| /** Fills the specified @p tensor with random values drawn from @p | |
| * distribution. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] distribution Distribution used to fill the tensor. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| * | |
| * @note The @p distribution has to provide operator(Generator &) which | |
| * will be used to draw samples. | |
| */ | |
| template <typename T, typename D> | |
| void fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const; | |
| template <typename T, typename D> | |
| void fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const; | |
| /** Fills the specified @p raw tensor with random values drawn from @p | |
| * distribution. | |
| * | |
| * @param[in, out] vec To be filled vector. | |
| * @param[in] distribution Distribution used to fill the tensor. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| * | |
| * @note The @p distribution has to provide operator(Generator &) which | |
| * will be used to draw samples. | |
| */ | |
| template <typename T, typename D> | |
| void fill(std::vector<T> &vec, D &&distribution, std::random_device::result_type seed_offset) const; | |
| /** Fills the specified @p raw tensor with random values drawn from @p | |
| * distribution. | |
| * | |
| * @param[in, out] raw To be filled raw. | |
| * @param[in] distribution Distribution used to fill the tensor. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| * | |
| * @note The @p distribution has to provide operator(Generator &) which | |
| * will be used to draw samples. | |
| */ | |
| template <typename D> | |
| void fill(RawTensor &raw, D &&distribution, std::random_device::result_type seed_offset) const; | |
| /** Fills the specified @p tensor with the content of the specified image | |
| * converted to the given format. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] name Image file used to fill the tensor. | |
| * @param[in] format Format of the image used to fill the tensor. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| template <typename T> | |
| void fill(T &&tensor, const std::string &name, Format format) const; | |
| /** Fills the raw tensor with the content of the specified image | |
| * converted to the given format. | |
| * | |
| * @param[in, out] raw To be filled raw tensor. | |
| * @param[in] name Image file used to fill the tensor. | |
| * @param[in] format Format of the image used to fill the tensor. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| void fill(RawTensor &raw, const std::string &name, Format format) const; | |
| /** Fills the specified @p tensor with the content of the specified channel | |
| * extracted from the given image. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] name Image file used to fill the tensor. | |
| * @param[in] channel Channel of the image used to fill the tensor. | |
| * | |
| * @note The channel has to be unambiguous so that the format can be | |
| * inferred automatically. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| template <typename T> | |
| void fill(T &&tensor, const std::string &name, Channel channel) const; | |
| /** Fills the raw tensor with the content of the specified channel | |
| * extracted from the given image. | |
| * | |
| * @param[in, out] raw To be filled raw tensor. | |
| * @param[in] name Image file used to fill the tensor. | |
| * @param[in] channel Channel of the image used to fill the tensor. | |
| * | |
| * @note The channel has to be unambiguous so that the format can be | |
| * inferred automatically. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| void fill(RawTensor &raw, const std::string &name, Channel channel) const; | |
| /** Fills the specified @p tensor with the content of the specified channel | |
| * extracted from the given image after it has been converted to the given | |
| * format. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] name Image file used to fill the tensor. | |
| * @param[in] format Format of the image used to fill the tensor. | |
| * @param[in] channel Channel of the image used to fill the tensor. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| template <typename T> | |
| void fill(T &&tensor, const std::string &name, Format format, Channel channel) const; | |
| /** Fills the raw tensor with the content of the specified channel | |
| * extracted from the given image after it has been converted to the given | |
| * format. | |
| * | |
| * @param[in, out] raw To be filled raw tensor. | |
| * @param[in] name Image file used to fill the tensor. | |
| * @param[in] format Format of the image used to fill the tensor. | |
| * @param[in] channel Channel of the image used to fill the tensor. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| void fill(RawTensor &raw, const std::string &name, Format format, Channel channel) const; | |
| /** Fills the specified @p tensor with the content of the raw tensor. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] raw Raw tensor used to fill the tensor. | |
| * | |
| * @warning No check is performed that the specified format actually | |
| * matches the format of the tensor. | |
| */ | |
| template <typename T> | |
| void fill(T &&tensor, RawTensor raw) const; | |
| /** Fill a tensor with uniform distribution | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| */ | |
| template <typename T> | |
| void fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const; | |
| /** Fill a tensor with uniform distribution | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| * @param[in] low lowest value in the range (inclusive) | |
| * @param[in] high highest value in the range (inclusive) | |
| * | |
| * @note @p low and @p high must be of the same type as the data type of @p tensor | |
| */ | |
| template <typename T, typename D> | |
| void fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset, D low, D high) const; | |
| /** Fill a tensor with uniform distribution across the specified range | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. | |
| * @param[in] excluded_range_pairs Ranges to exclude from the generator | |
| */ | |
| template <typename T> | |
| void fill_tensor_uniform_ranged(T &&tensor, | |
| std::random_device::result_type seed_offset, | |
| const std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) const; | |
| /** Fills the specified @p tensor with data loaded from .npy (numpy binary) in specified path. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] name Data file. | |
| * | |
| * @note The numpy array stored in the binary .npy file must be row-major in the sense that it | |
| * must store elements within a row consecutively in the memory, then rows within a 2D slice, | |
| * then 2D slices within a 3D slice and so on. Note that it imposes no restrictions on what | |
| * indexing convention is used in the numpy array. That is, the numpy array can be either fortran | |
| * style or C style as long as it adheres to the rule above. | |
| * | |
| * More concretely, the orders of dimensions for each style are as follows: | |
| * C-style (numpy default): | |
| * array[HigherDims..., Z, Y, X] | |
| * Fortran style: | |
| * array[X, Y, Z, HigherDims...] | |
| */ | |
| template <typename T> | |
| void fill_layer_data(T &&tensor, std::string name) const; | |
| /** Fill a tensor with a constant value | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] value Value to be assigned to all elements of the input tensor. | |
| * | |
| * @note @p value must be of the same type as the data type of @p tensor | |
| */ | |
| template <typename T, typename D> | |
| void fill_tensor_value(T &&tensor, D value) const; | |
| /** Fill a tensor with a given vector with static values. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] values A vector containing values | |
| * | |
| * To cope with various size tensors, the vector size doens't have to be | |
| * the same as tensor's size. If the size of the tensor is larger than the vector, | |
| * the iterator the vector will keep iterating and wrap around. If the vector is | |
| * larger, values located after the required size won't be used. | |
| */ | |
| template <typename T, typename DataType> | |
| void fill_static_values(T &&tensor, const std::vector<DataType> &values) const; | |
| // Function type to generate a number to fill tensors. | |
| template <typename ResultType> | |
| using GeneratorFunctionType = std::function<ResultType(void)>; | |
| /** Fill a tensor with a value generator function. | |
| * | |
| * @param[in, out] tensor To be filled tensor. | |
| * @param[in] generate_value A function that generates values. | |
| */ | |
| template <typename T, typename ResultType> | |
| void fill_with_generator(T &&tensor, const GeneratorFunctionType<ResultType> &generate_value) const; | |
| private: | |
| // Function prototype to convert between image formats. | |
| using Converter = void (*)(const RawTensor &src, RawTensor &dst); | |
| // Function prototype to extract a channel from an image. | |
| using Extractor = void (*)(const RawTensor &src, RawTensor &dst); | |
| // Function prototype to load an image file. | |
| using Loader = RawTensor (*)(const std::string &path); | |
| const Converter &get_converter(Format src, Format dst) const; | |
| const Converter &get_converter(DataType src, Format dst) const; | |
| const Converter &get_converter(Format src, DataType dst) const; | |
| const Converter &get_converter(DataType src, DataType dst) const; | |
| const Extractor &get_extractor(Format format, Channel) const; | |
| const Loader &get_loader(const std::string &extension) const; | |
| /** Creates a raw tensor from the specified image. | |
| * | |
| * @param[in] name To be loaded image file. | |
| * | |
| * @note If use_single_image is true @p name is ignored and the user image | |
| * is loaded instead. | |
| */ | |
| RawTensor load_image(const std::string &name) const; | |
| /** Provides a raw tensor for the specified image and format. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] format Format used to look up the raw tensor. | |
| * | |
| * If the tensor has already been requested before the cached version will | |
| * be returned. Otherwise the tensor will be added to the cache. | |
| * | |
| * @note If use_single_image is true @p name is ignored and the user image | |
| * is loaded instead. | |
| */ | |
| const RawTensor &find_or_create_raw_tensor(const std::string &name, Format format) const; | |
| /** Provides a raw tensor for the specified image, format and channel. | |
| * | |
| * @param[in] name Image file used to look up the raw tensor. | |
| * @param[in] format Format used to look up the raw tensor. | |
| * @param[in] channel Channel used to look up the raw tensor. | |
| * | |
| * If the tensor has already been requested before the cached version will | |
| * be returned. Otherwise the tensor will be added to the cache. | |
| * | |
| * @note If use_single_image is true @p name is ignored and the user image | |
| * is loaded instead. | |
| */ | |
| const RawTensor &find_or_create_raw_tensor(const std::string &name, Format format, Channel channel) const; | |
| mutable TensorCache _cache{}; | |
| mutable arm_compute::Mutex _format_lock{}; | |
| mutable arm_compute::Mutex _channel_lock{}; | |
| const std::string _library_path; | |
| std::random_device::result_type _seed; | |
| }; | |
| namespace detail | |
| { | |
| template <typename T> | |
| inline std::vector<std::pair<T, T>> convert_range_pair(const std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) | |
| { | |
| std::vector<std::pair<T, T>> converted; | |
| std::transform(excluded_range_pairs.begin(), | |
| excluded_range_pairs.end(), | |
| std::back_inserter(converted), | |
| [](const AssetsLibrary::RangePair & p) | |
| { | |
| return std::pair<T, T>(static_cast<T>(p.first), static_cast<T>(p.second)); | |
| }); | |
| return converted; | |
| } | |
| /* Read npy header and check the payload is suitable for the specified type and shape | |
| * | |
| * @param[in] stream ifstream of the npy file | |
| * @param[in] expect_typestr Expected typestr | |
| * @param[in] expect_shape Shape of tensor expected to receive the data | |
| * | |
| * @note Advances stream to the beginning of the data payload | |
| */ | |
| void validate_npy_header(std::ifstream &stream, const std::string &expect_typestr, const TensorShape &expect_shape); | |
| } // namespace detail | |
| template <typename T, typename D> | |
| void AssetsLibrary::fill_borders_with_garbage(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const | |
| { | |
| const PaddingSize padding_size = tensor.padding(); | |
| Window window; | |
| window.set(0, Window::Dimension(-padding_size.left, tensor.shape()[0] + padding_size.right, 1)); | |
| if(tensor.shape().num_dimensions() > 1) | |
| { | |
| window.set(1, Window::Dimension(-padding_size.top, tensor.shape()[1] + padding_size.bottom, 1)); | |
| } | |
| std::mt19937 gen(_seed + seed_offset); | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| TensorShape shape = tensor.shape(); | |
| // If outside of valid region | |
| if(id.x() < 0 || id.x() >= static_cast<int>(shape.x()) || id.y() < 0 || id.y() >= static_cast<int>(shape.y())) | |
| { | |
| using ResultType = typename std::remove_reference<D>::type::result_type; | |
| const ResultType value = distribution(gen); | |
| void *const out_ptr = tensor(id); | |
| store_value_with_data_type(out_ptr, value, tensor.data_type()); | |
| } | |
| }); | |
| } | |
| template <typename T, typename D> | |
| void AssetsLibrary::fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const | |
| { | |
| using DistributionType = typename std::remove_reference<D>::type; | |
| using ResultType = typename DistributionType::result_type; | |
| std::mt19937 gen(_seed + seed_offset); | |
| TensorShape shape(tensor.shape()); | |
| const uint32_t num_boxes = tensor.num_elements() / 4; | |
| // Iterate over all elements | |
| DistributionType size_dist{ ResultType(0.f), ResultType(1.f) }; | |
| for(uint32_t element_idx = 0; element_idx < num_boxes * 4; element_idx += 4) | |
| { | |
| const ResultType delta = size_dist(gen); | |
| const ResultType epsilon = size_dist(gen); | |
| const ResultType left = distribution(gen); | |
| const ResultType top = distribution(gen); | |
| const ResultType right = left + delta; | |
| const ResultType bottom = top + epsilon; | |
| const std::tuple<ResultType, ResultType, ResultType, ResultType> box(left, top, right, bottom); | |
| Coordinates x1 = index2coord(shape, element_idx); | |
| Coordinates y1 = index2coord(shape, element_idx + 1); | |
| Coordinates x2 = index2coord(shape, element_idx + 2); | |
| Coordinates y2 = index2coord(shape, element_idx + 3); | |
| ResultType &target_value_x1 = reinterpret_cast<ResultType *>(tensor(x1))[0]; | |
| ResultType &target_value_y1 = reinterpret_cast<ResultType *>(tensor(y1))[0]; | |
| ResultType &target_value_x2 = reinterpret_cast<ResultType *>(tensor(x2))[0]; | |
| ResultType &target_value_y2 = reinterpret_cast<ResultType *>(tensor(y2))[0]; | |
| store_value_with_data_type(&target_value_x1, std::get<0>(box), tensor.data_type()); | |
| store_value_with_data_type(&target_value_y1, std::get<1>(box), tensor.data_type()); | |
| store_value_with_data_type(&target_value_x2, std::get<2>(box), tensor.data_type()); | |
| store_value_with_data_type(&target_value_y2, std::get<3>(box), tensor.data_type()); | |
| } | |
| fill_borders_with_garbage(tensor, distribution, seed_offset); | |
| } | |
| template <typename T, typename D> | |
| void AssetsLibrary::fill(std::vector<T> &vec, D &&distribution, std::random_device::result_type seed_offset) const | |
| { | |
| ARM_COMPUTE_ERROR_ON_MSG(vec.empty(), "Vector must not be empty"); | |
| using ResultType = typename std::remove_reference<D>::type::result_type; | |
| std::mt19937 gen(_seed + seed_offset); | |
| for(size_t i = 0; i < vec.size(); ++i) | |
| { | |
| const ResultType value = distribution(gen); | |
| vec[i] = value; | |
| } | |
| } | |
| template <typename T, typename ResultType> | |
| void AssetsLibrary::fill_with_generator(T &&tensor, const GeneratorFunctionType<ResultType> &generate_value) const | |
| { | |
| const bool is_nhwc = tensor.data_layout() == DataLayout::NHWC; | |
| TensorShape shape(tensor.shape()); | |
| if(is_nhwc) | |
| { | |
| // Ensure that the equivalent tensors will be filled for both data layouts | |
| permute(shape, PermutationVector(1U, 2U, 0U)); | |
| } | |
| // Iterate over all elements | |
| const uint32_t num_elements = tensor.num_elements(); | |
| for(uint32_t element_idx = 0; element_idx < num_elements; ++element_idx) | |
| { | |
| Coordinates id = index2coord(shape, element_idx); | |
| if(is_nhwc) | |
| { | |
| // Write in the correct id for permuted shapes | |
| permute(id, PermutationVector(2U, 0U, 1U)); | |
| } | |
| // Iterate over all channels | |
| for(int channel = 0; channel < tensor.num_channels(); ++channel) | |
| { | |
| const ResultType value = generate_value(); | |
| ResultType &target_value = reinterpret_cast<ResultType *>(tensor(id))[channel]; | |
| store_value_with_data_type(&target_value, value, tensor.data_type()); | |
| } | |
| } | |
| } | |
| template <typename T, typename D> | |
| void AssetsLibrary::fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const | |
| { | |
| using ResultType = typename std::remove_reference<D>::type::result_type; | |
| std::mt19937 gen(_seed + seed_offset); | |
| GeneratorFunctionType<ResultType> number_generator = [&]() | |
| { | |
| const ResultType value = distribution(gen); | |
| return value; | |
| }; | |
| fill_with_generator(tensor, number_generator); | |
| fill_borders_with_garbage(tensor, distribution, seed_offset); | |
| } | |
| template <typename T, typename DataType> | |
| void AssetsLibrary::fill_static_values(T &&tensor, const std::vector<DataType> &values) const | |
| { | |
| auto it = values.begin(); | |
| GeneratorFunctionType<DataType> get_next_value = [&]() | |
| { | |
| const DataType value = *it; | |
| ++it; | |
| if(it == values.end()) | |
| { | |
| it = values.begin(); | |
| } | |
| return value; | |
| }; | |
| fill_with_generator(tensor, get_next_value); | |
| } | |
| template <typename D> | |
| void AssetsLibrary::fill(RawTensor &raw, D &&distribution, std::random_device::result_type seed_offset) const | |
| { | |
| std::mt19937 gen(_seed + seed_offset); | |
| for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) | |
| { | |
| using ResultType = typename std::remove_reference<D>::type::result_type; | |
| const ResultType value = distribution(gen); | |
| store_value_with_data_type(raw.data() + offset, value, raw.data_type()); | |
| } | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill(T &&tensor, const std::string &name, Format format) const | |
| { | |
| const RawTensor &raw = get(name, format); | |
| for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) | |
| { | |
| const Coordinates id = index2coord(raw.shape(), offset / raw.element_size()); | |
| const RawTensor::value_type *const raw_ptr = raw.data() + offset; | |
| const auto out_ptr = static_cast<RawTensor::value_type *>(tensor(id)); | |
| std::copy_n(raw_ptr, raw.element_size(), out_ptr); | |
| } | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill(T &&tensor, const std::string &name, Channel channel) const | |
| { | |
| fill(std::forward<T>(tensor), name, get_format_for_channel(channel), channel); | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill(T &&tensor, const std::string &name, Format format, Channel channel) const | |
| { | |
| const RawTensor &raw = get(name, format, channel); | |
| for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) | |
| { | |
| const Coordinates id = index2coord(raw.shape(), offset / raw.element_size()); | |
| const RawTensor::value_type *const raw_ptr = raw.data() + offset; | |
| const auto out_ptr = static_cast<RawTensor::value_type *>(tensor(id)); | |
| std::copy_n(raw_ptr, raw.element_size(), out_ptr); | |
| } | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill(T &&tensor, RawTensor raw) const | |
| { | |
| for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) | |
| { | |
| const Coordinates id = index2coord(raw.shape(), offset / raw.element_size()); | |
| const RawTensor::value_type *const raw_ptr = raw.data() + offset; | |
| const auto out_ptr = static_cast<RawTensor::value_type *>(tensor(id)); | |
| std::copy_n(raw_ptr, raw.element_size(), out_ptr); | |
| } | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const | |
| { | |
| switch(tensor.data_type()) | |
| { | |
| case DataType::U8: | |
| case DataType::QASYMM8: | |
| { | |
| std::uniform_int_distribution<unsigned int> distribution_u8(std::numeric_limits<uint8_t>::lowest(), std::numeric_limits<uint8_t>::max()); | |
| fill(tensor, distribution_u8, seed_offset); | |
| break; | |
| } | |
| case DataType::S8: | |
| case DataType::QSYMM8: | |
| case DataType::QSYMM8_PER_CHANNEL: | |
| case DataType::QASYMM8_SIGNED: | |
| { | |
| std::uniform_int_distribution<int> distribution_s8(std::numeric_limits<int8_t>::lowest(), std::numeric_limits<int8_t>::max()); | |
| fill(tensor, distribution_s8, seed_offset); | |
| break; | |
| } | |
| case DataType::U16: | |
| { | |
| std::uniform_int_distribution<uint16_t> distribution_u16(std::numeric_limits<uint16_t>::lowest(), std::numeric_limits<uint16_t>::max()); | |
| fill(tensor, distribution_u16, seed_offset); | |
| break; | |
| } | |
| case DataType::S16: | |
| case DataType::QSYMM16: | |
| { | |
| std::uniform_int_distribution<int16_t> distribution_s16(std::numeric_limits<int16_t>::lowest(), std::numeric_limits<int16_t>::max()); | |
| fill(tensor, distribution_s16, seed_offset); | |
| break; | |
| } | |
| case DataType::U32: | |
| { | |
| std::uniform_int_distribution<uint32_t> distribution_u32(std::numeric_limits<uint32_t>::lowest(), std::numeric_limits<uint32_t>::max()); | |
| fill(tensor, distribution_u32, seed_offset); | |
| break; | |
| } | |
| case DataType::S32: | |
| { | |
| std::uniform_int_distribution<int32_t> distribution_s32(std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max()); | |
| fill(tensor, distribution_s32, seed_offset); | |
| break; | |
| } | |
| case DataType::U64: | |
| { | |
| std::uniform_int_distribution<uint64_t> distribution_u64(std::numeric_limits<uint64_t>::lowest(), std::numeric_limits<uint64_t>::max()); | |
| fill(tensor, distribution_u64, seed_offset); | |
| break; | |
| } | |
| case DataType::S64: | |
| { | |
| std::uniform_int_distribution<int64_t> distribution_s64(std::numeric_limits<int64_t>::lowest(), std::numeric_limits<int64_t>::max()); | |
| fill(tensor, distribution_s64, seed_offset); | |
| break; | |
| } | |
| case DataType::BFLOAT16: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| arm_compute::utils::uniform_real_distribution_16bit<bfloat16> distribution_bf16{ -1000.f, 1000.f }; | |
| fill(tensor, distribution_bf16, seed_offset); | |
| break; | |
| } | |
| case DataType::F16: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution_f16{ -100.f, 100.f }; | |
| fill(tensor, distribution_f16, seed_offset); | |
| break; | |
| } | |
| case DataType::F32: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| std::uniform_real_distribution<float> distribution_f32(-1000.f, 1000.f); | |
| fill(tensor, distribution_f32, seed_offset); | |
| break; | |
| } | |
| case DataType::F64: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| std::uniform_real_distribution<double> distribution_f64(-1000.f, 1000.f); | |
| fill(tensor, distribution_f64, seed_offset); | |
| break; | |
| } | |
| case DataType::SIZET: | |
| { | |
| std::uniform_int_distribution<size_t> distribution_sizet(std::numeric_limits<size_t>::lowest(), std::numeric_limits<size_t>::max()); | |
| fill(tensor, distribution_sizet, seed_offset); | |
| break; | |
| } | |
| default: | |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); | |
| } | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill_tensor_uniform_ranged(T &&tensor, | |
| std::random_device::result_type seed_offset, | |
| const std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) const | |
| { | |
| using namespace arm_compute::utils::random; | |
| switch(tensor.data_type()) | |
| { | |
| case DataType::U8: | |
| case DataType::QASYMM8: | |
| { | |
| const auto converted_pairs = detail::convert_range_pair<uint32_t>(excluded_range_pairs); | |
| RangedUniformDistribution<uint32_t> distribution_u8(std::numeric_limits<uint8_t>::lowest(), | |
| std::numeric_limits<uint8_t>::max(), | |
| converted_pairs); | |
| fill(tensor, distribution_u8, seed_offset); | |
| break; | |
| } | |
| case DataType::S8: | |
| case DataType::QSYMM8: | |
| { | |
| const auto converted_pairs = detail::convert_range_pair<int32_t>(excluded_range_pairs); | |
| RangedUniformDistribution<int32_t> distribution_s8(std::numeric_limits<int8_t>::lowest(), | |
| std::numeric_limits<int8_t>::max(), | |
| converted_pairs); | |
| fill(tensor, distribution_s8, seed_offset); | |
| break; | |
| } | |
| case DataType::U16: | |
| { | |
| const auto converted_pairs = detail::convert_range_pair<uint16_t>(excluded_range_pairs); | |
| RangedUniformDistribution<uint16_t> distribution_u16(std::numeric_limits<uint16_t>::lowest(), | |
| std::numeric_limits<uint16_t>::max(), | |
| converted_pairs); | |
| fill(tensor, distribution_u16, seed_offset); | |
| break; | |
| } | |
| case DataType::S16: | |
| case DataType::QSYMM16: | |
| { | |
| const auto converted_pairs = detail::convert_range_pair<int16_t>(excluded_range_pairs); | |
| RangedUniformDistribution<int16_t> distribution_s16(std::numeric_limits<int16_t>::lowest(), | |
| std::numeric_limits<int16_t>::max(), | |
| converted_pairs); | |
| fill(tensor, distribution_s16, seed_offset); | |
| break; | |
| } | |
| case DataType::U32: | |
| { | |
| const auto converted_pairs = detail::convert_range_pair<uint32_t>(excluded_range_pairs); | |
| RangedUniformDistribution<uint32_t> distribution_u32(std::numeric_limits<uint32_t>::lowest(), | |
| std::numeric_limits<uint32_t>::max(), | |
| converted_pairs); | |
| fill(tensor, distribution_u32, seed_offset); | |
| break; | |
| } | |
| case DataType::S32: | |
| { | |
| const auto converted_pairs = detail::convert_range_pair<int32_t>(excluded_range_pairs); | |
| RangedUniformDistribution<int32_t> distribution_s32(std::numeric_limits<int32_t>::lowest(), | |
| std::numeric_limits<int32_t>::max(), | |
| converted_pairs); | |
| fill(tensor, distribution_s32, seed_offset); | |
| break; | |
| } | |
| case DataType::BFLOAT16: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| const auto converted_pairs = detail::convert_range_pair<bfloat16>(excluded_range_pairs); | |
| RangedUniformDistribution<bfloat16> distribution_bf16(bfloat16(-1000.f), bfloat16(1000.f), converted_pairs); | |
| fill(tensor, distribution_bf16, seed_offset); | |
| break; | |
| } | |
| case DataType::F16: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| const auto converted_pairs = detail::convert_range_pair<half>(excluded_range_pairs); | |
| RangedUniformDistribution<half> distribution_f16(half(-100.f), half(100.f), converted_pairs); | |
| fill(tensor, distribution_f16, seed_offset); | |
| break; | |
| } | |
| case DataType::F32: | |
| { | |
| // It doesn't make sense to check [-inf, inf], so hard code it to a big number | |
| const auto converted_pairs = detail::convert_range_pair<float>(excluded_range_pairs); | |
| RangedUniformDistribution<float> distribution_f32(-1000.f, 1000.f, converted_pairs); | |
| fill(tensor, distribution_f32, seed_offset); | |
| break; | |
| } | |
| default: | |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); | |
| } | |
| } | |
| template <typename T, typename D> | |
| void AssetsLibrary::fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset, D low, D high) const | |
| { | |
| switch(tensor.data_type()) | |
| { | |
| case DataType::U8: | |
| case DataType::QASYMM8: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<uint8_t, D>::value)); | |
| std::uniform_int_distribution<uint32_t> distribution_u8(low, high); | |
| fill(tensor, distribution_u8, seed_offset); | |
| break; | |
| } | |
| case DataType::S8: | |
| case DataType::QSYMM8: | |
| case DataType::QASYMM8_SIGNED: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<int8_t, D>::value)); | |
| std::uniform_int_distribution<int32_t> distribution_s8(low, high); | |
| fill(tensor, distribution_s8, seed_offset); | |
| break; | |
| } | |
| case DataType::U16: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<uint16_t, D>::value)); | |
| std::uniform_int_distribution<uint16_t> distribution_u16(low, high); | |
| fill(tensor, distribution_u16, seed_offset); | |
| break; | |
| } | |
| case DataType::S16: | |
| case DataType::QSYMM16: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<int16_t, D>::value)); | |
| std::uniform_int_distribution<int16_t> distribution_s16(low, high); | |
| fill(tensor, distribution_s16, seed_offset); | |
| break; | |
| } | |
| case DataType::U32: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<uint32_t, D>::value)); | |
| std::uniform_int_distribution<uint32_t> distribution_u32(low, high); | |
| fill(tensor, distribution_u32, seed_offset); | |
| break; | |
| } | |
| case DataType::S32: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<int32_t, D>::value)); | |
| std::uniform_int_distribution<int32_t> distribution_s32(low, high); | |
| fill(tensor, distribution_s32, seed_offset); | |
| break; | |
| } | |
| case DataType::U64: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<uint64_t, D>::value)); | |
| std::uniform_int_distribution<uint64_t> distribution_u64(low, high); | |
| fill(tensor, distribution_u64, seed_offset); | |
| break; | |
| } | |
| case DataType::S64: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<int64_t, D>::value)); | |
| std::uniform_int_distribution<int64_t> distribution_s64(low, high); | |
| fill(tensor, distribution_s64, seed_offset); | |
| break; | |
| } | |
| case DataType::BFLOAT16: | |
| { | |
| arm_compute::utils::uniform_real_distribution_16bit<bfloat16> distribution_bf16{ float(low), float(high) }; | |
| fill(tensor, distribution_bf16, seed_offset); | |
| break; | |
| } | |
| case DataType::F16: | |
| { | |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution_f16{ float(low), float(high) }; | |
| fill(tensor, distribution_f16, seed_offset); | |
| break; | |
| } | |
| case DataType::F32: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<float, D>::value)); | |
| std::uniform_real_distribution<float> distribution_f32(low, high); | |
| fill(tensor, distribution_f32, seed_offset); | |
| break; | |
| } | |
| case DataType::F64: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<double, D>::value)); | |
| std::uniform_real_distribution<double> distribution_f64(low, high); | |
| fill(tensor, distribution_f64, seed_offset); | |
| break; | |
| } | |
| case DataType::SIZET: | |
| { | |
| ARM_COMPUTE_ERROR_ON(!(std::is_same<size_t, D>::value)); | |
| std::uniform_int_distribution<size_t> distribution_sizet(low, high); | |
| fill(tensor, distribution_sizet, seed_offset); | |
| break; | |
| } | |
| default: | |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); | |
| } | |
| } | |
| template <typename T> | |
| void AssetsLibrary::fill_layer_data(T &&tensor, std::string name) const | |
| { | |
| #ifdef _WIN32 | |
| const std::string path_separator("\\"); | |
| #else /* _WIN32 */ | |
| const std::string path_separator("/"); | |
| #endif /* _WIN32 */ | |
| const std::string path = _library_path + path_separator + name; | |
| // Open file | |
| std::ifstream stream(path, std::ios::in | std::ios::binary); | |
| if(!stream.good()) | |
| { | |
| throw framework::FileNotFound("Could not load npy file: " + path); | |
| } | |
| validate_npy_header(stream, tensor.data_type(), tensor.shape()); | |
| // Read data | |
| if(tensor.padding().empty()) | |
| { | |
| // If tensor has no padding read directly from stream. | |
| stream.read(reinterpret_cast<char *>(tensor.data()), tensor.size()); | |
| } | |
| else | |
| { | |
| // If tensor has padding accessing tensor elements through execution window. | |
| Window window; | |
| window.use_tensor_dimensions(tensor.shape()); | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| stream.read(reinterpret_cast<char *>(tensor(id)), tensor.element_size()); | |
| }); | |
| } | |
| } | |
| template <typename T, typename D> | |
| void AssetsLibrary::fill_tensor_value(T &&tensor, D value) const | |
| { | |
| fill_tensor_uniform(tensor, 0, value, value); | |
| } | |
| } // namespace test | |
| } // namespace arm_compute | |
| #endif /* ARM_COMPUTE_TEST_TENSOR_LIBRARY_H */ |