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Diffstat (limited to 'tests/validation_old/AssetsLibrary.h')
| -rw-r--r-- | tests/validation_old/AssetsLibrary.h | 674 |
1 files changed, 0 insertions, 674 deletions
diff --git a/tests/validation_old/AssetsLibrary.h b/tests/validation_old/AssetsLibrary.h deleted file mode 100644 index 6945aa6fe1..0000000000 --- a/tests/validation_old/AssetsLibrary.h +++ /dev/null @@ -1,674 +0,0 @@ -/* - * Copyright (c) 2017 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 "tests/RawTensor.h" -#include "tests/TensorCache.h" -#include "tests/Utils.h" -#include "tests/validation_old/half.h" - -#include <algorithm> -#include <cstddef> -#include <fstream> -#include <random> -#include <string> -#include <type_traits> - -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: - /** Initialises the library with a @p path to the image directory. - * Furthermore, sets the seed for the random generator to @p seed. - * - * @param[in] path Path to load images from. - * @param[in] seed Seed used to initialise the random number generator. - */ - AssetsLibrary(std::string path, std::random_device::result_type seed); - - /** Seed that is used to fill tensors with random values. */ - 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. - */ - TensorShape get_image_shape(const std::string &name); - - /** Provides a contant raw tensor for the specified image. - * - * @param[in] name Image file used to look up the raw tensor. - */ - 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. - */ - 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. - */ - 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. - */ - 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. - */ - 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. - */ - 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. - */ - 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. - */ - 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. - */ - 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; - - /** 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; - - /** Fill a tensor with uniform distribution across the range of its type - * - * @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 across the a 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] 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; - - /** Fills the specified @p tensor with data loaded from binary in specified path. - * - * @param[in, out] tensor To be filled tensor. - * @param[in] name Data file. - */ - template <typename T> - void fill_layer_data(T &&tensor, std::string name) 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 std::mutex _format_lock{}; - mutable std::mutex _channel_lock{}; - const std::string _library_path; - std::random_device::result_type _seed; -}; - -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)); - window.set(1, Window::Dimension(-padding_size.top, tensor.shape()[1] + padding_size.bottom, 1)); - - std::mt19937 gen(_seed); - - 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(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const -{ - Window window; - for(unsigned int d = 0; d < tensor.shape().num_dimensions(); ++d) - { - window.set(d, Window::Dimension(0, tensor.shape()[d], 1)); - } - - std::mt19937 gen(_seed + seed_offset); - - //FIXME: Replace with normal loop - execute_window_loop(window, [&](const Coordinates & id) - { - 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()); - }); - - fill_borders_with_garbage(tensor, distribution, seed_offset); -} - -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_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const -{ - switch(tensor.data_type()) - { - case DataType::U8: - { - std::uniform_int_distribution<uint8_t> 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::QS8: - { - std::uniform_int_distribution<int8_t> 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::QS16: - { - 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::F16: - { - // It doesn't make sense to check [-inf, inf], so hard code it to a big number - std::uniform_real_distribution<float> 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, 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: - { - ARM_COMPUTE_ERROR_ON(!(std::is_same<uint8_t, D>::value)); - std::uniform_int_distribution<uint8_t> distribution_u8(low, high); - fill(tensor, distribution_u8, seed_offset); - break; - } - case DataType::S8: - case DataType::QS8: - { - ARM_COMPUTE_ERROR_ON(!(std::is_same<int8_t, D>::value)); - std::uniform_int_distribution<int8_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::QS16: - { - 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::F16: - { - std::uniform_real_distribution<float> distribution_f16(low, 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 file(path, std::ios::in | std::ios::binary); - if(!file.good()) - { - throw std::runtime_error("Could not load binary data: " + path); - } - - Window window; - for(unsigned int d = 0; d < tensor.shape().num_dimensions(); ++d) - { - window.set(d, Window::Dimension(0, tensor.shape()[d], 1)); - } - - //FIXME : Replace with normal loop - execute_window_loop(window, [&](const Coordinates & id) - { - float val; - file.read(reinterpret_cast<char *>(&val), sizeof(float)); - void *const out_ptr = tensor(id); - store_value_with_data_type(out_ptr, val, tensor.data_type()); - }); -} -} // namespace test -} // namespace arm_compute -#endif /* __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__ */ |