arm_compute v17.09

Change-Id: I4bf8f4e6e5f84ce0d5b6f5ba570d276879f42a81

1 files changed, 736 insertions, 0 deletions

diff --git a/tests/AssetsLibrary.h b/tests/AssetsLibrary.h
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+/*
+ * 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 "libnpy/npy.hpp"
+#include "tests/RawTensor.h"
+#include "tests/TensorCache.h"
+#include "tests/Utils.h"
+#include "tests/framework/Exceptions.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:
+    /** 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 .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;
+
+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);
+
+    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;
+
+    std::vector<unsigned long> shape;
+
+    // Open file
+    std::ifstream stream(path, std::ios::in | std::ios::binary);
+    if(!stream.good())
+    {
+        throw framework::FileNotFound("Could not load npy file: " + path);
+    }
+    // Check magic bytes and version number
+    unsigned char v_major = 0;
+    unsigned char v_minor = 0;
+    npy::read_magic(stream, &v_major, &v_minor);
+
+    // Read header
+    std::string header;
+    if(v_major == 1 && v_minor == 0)
+    {
+        header = npy::read_header_1_0(stream);
+    }
+    else if(v_major == 2 && v_minor == 0)
+    {
+        header = npy::read_header_2_0(stream);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Unsupported file format version");
+    }
+
+    // Parse header
+    bool        fortran_order = false;
+    std::string typestr;
+    npy::ParseHeader(header, typestr, &fortran_order, shape);
+
+    // Check if the typestring matches the given one
+    std::string expect_typestr = get_typestring(tensor.data_type());
+    ARM_COMPUTE_ERROR_ON_MSG(typestr != expect_typestr, "Typestrings mismatch");
+
+    // Validate tensor shape
+    ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor.shape().num_dimensions(), "Tensor ranks mismatch");
+    if(fortran_order)
+    {
+        for(size_t i = 0; i < shape.size(); ++i)
+        {
+            ARM_COMPUTE_ERROR_ON_MSG(tensor.shape()[i] != shape[i], "Tensor dimensions mismatch");
+        }
+    }
+    else
+    {
+        for(size_t i = 0; i < shape.size(); ++i)
+        {
+            ARM_COMPUTE_ERROR_ON_MSG(tensor.shape()[i] != shape[shape.size() - i - 1], "Tensor dimensions mismatch");
+        }
+    }
+
+    // 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());
+        });
+    }
+}
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__ */