diff options
Diffstat (limited to 'tests/Utils.h')
| -rw-r--r-- | tests/Utils.h | 343 |
1 files changed, 81 insertions, 262 deletions
diff --git a/tests/Utils.h b/tests/Utils.h index 1cc3cb8b37..7b831468d3 100644 --- a/tests/Utils.h +++ b/tests/Utils.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 ARM Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -26,8 +26,6 @@ #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Error.h" -#include "arm_compute/core/HOGInfo.h" -#include "arm_compute/core/PyramidInfo.h" #include "arm_compute/core/Size2D.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" @@ -40,11 +38,6 @@ #include "arm_compute/runtime/CL/CLScheduler.h" #endif /* ARM_COMPUTE_CL */ -#ifdef ARM_COMPUTE_GC -#include "arm_compute/core/GLES_COMPUTE/OpenGLES.h" -#include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h" -#endif /* ARM_COMPUTE_GC */ - #include <cmath> #include <cstddef> #include <limits> @@ -55,6 +48,7 @@ #include <type_traits> #include <vector> +#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h" #include "arm_compute/runtime/CPP/CPPScheduler.h" #include "arm_compute/runtime/RuntimeContext.h" @@ -140,7 +134,7 @@ using make_unsigned_conditional_t = typename std::conditional<std::is_integral<T // clang-format on // *INDENT-ON* -} +} // namespace traits /** Look up the format corresponding to a channel. * @@ -246,101 +240,6 @@ inline ValidRegion shape_to_valid_region(const TensorShape &a_shape, bool border return valid_region; } -/** Create a valid region for Gaussian Pyramid Half based on tensor shape and valid region at level "i - 1" and border mode - * - * @note The border size is 2 in case of Gaussian Pyramid Half - * - * @param[in] a_shape Shape used at level "i - 1" of Gaussian Pyramid Half - * @param[in] a_valid_region Valid region used at level "i - 1" of Gaussian Pyramid Half - * @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined. - * - * return The valid region for the level "i" of Gaussian Pyramid Half - */ -inline ValidRegion shape_to_valid_region_gaussian_pyramid_half(const TensorShape &a_shape, const ValidRegion &a_valid_region, bool border_undefined = false) -{ - constexpr int border_size = 2; - - ValidRegion valid_region{ Coordinates(), a_shape }; - - Coordinates &anchor = valid_region.anchor; - TensorShape &shape = valid_region.shape; - - // Compute tensor shape for level "i" of Gaussian Pyramid Half - // dst_width = (src_width + 1) * 0.5f - // dst_height = (src_height + 1) * 0.5f - shape.set(0, (a_shape[0] + 1) * 0.5f); - shape.set(1, (a_shape[1] + 1) * 0.5f); - - if(border_undefined) - { - ARM_COMPUTE_ERROR_ON(shape.num_dimensions() < 2); - - // Compute the left and top invalid borders - float invalid_border_left = static_cast<float>(a_valid_region.anchor.x() + border_size) / 2.0f; - float invalid_border_top = static_cast<float>(a_valid_region.anchor.y() + border_size) / 2.0f; - - // For the new anchor point we can have 2 cases: - // 1) If the width/height of the tensor shape is odd, we have to take the ceil value of (a_valid_region.anchor.x() + border_size) / 2.0f or (a_valid_region.anchor.y() + border_size / 2.0f - // 2) If the width/height of the tensor shape is even, we have to take the floor value of (a_valid_region.anchor.x() + border_size) / 2.0f or (a_valid_region.anchor.y() + border_size) / 2.0f - // In this manner we should be able to propagate correctly the valid region along all levels of the pyramid - invalid_border_left = (a_shape[0] % 2) ? std::ceil(invalid_border_left) : std::floor(invalid_border_left); - invalid_border_top = (a_shape[1] % 2) ? std::ceil(invalid_border_top) : std::floor(invalid_border_top); - - // Set the anchor point - anchor.set(0, static_cast<int>(invalid_border_left)); - anchor.set(1, static_cast<int>(invalid_border_top)); - - // Compute shape - // Calculate the right and bottom invalid borders at the previous level of the pyramid - const float prev_invalid_border_right = static_cast<float>(a_shape[0] - (a_valid_region.anchor.x() + a_valid_region.shape[0])); - const float prev_invalid_border_bottom = static_cast<float>(a_shape[1] - (a_valid_region.anchor.y() + a_valid_region.shape[1])); - - // Calculate the right and bottom invalid borders at the current level of the pyramid - const float invalid_border_right = std::ceil((prev_invalid_border_right + static_cast<float>(border_size)) / 2.0f); - const float invalid_border_bottom = std::ceil((prev_invalid_border_bottom + static_cast<float>(border_size)) / 2.0f); - - const int valid_shape_x = std::max(0, static_cast<int>(shape.x()) - static_cast<int>(invalid_border_left) - static_cast<int>(invalid_border_right)); - const int valid_shape_y = std::max(0, static_cast<int>(shape.y()) - static_cast<int>(invalid_border_top) - static_cast<int>(invalid_border_bottom)); - - shape.set(0, valid_shape_x); - shape.set(1, valid_shape_y); - } - - return valid_region; -} - -/** Create a valid region for Laplacian Pyramid based on tensor shape and valid region at level "i - 1" and border mode - * - * @note The border size is 2 in case of Laplacian Pyramid - * - * @param[in] a_shape Shape used at level "i - 1" of Laplacian Pyramid - * @param[in] a_valid_region Valid region used at level "i - 1" of Laplacian Pyramid - * @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined. - * - * return The valid region for the level "i" of Laplacian Pyramid - */ -inline ValidRegion shape_to_valid_region_laplacian_pyramid(const TensorShape &a_shape, const ValidRegion &a_valid_region, bool border_undefined = false) -{ - ValidRegion valid_region = shape_to_valid_region_gaussian_pyramid_half(a_shape, a_valid_region, border_undefined); - - if(border_undefined) - { - const BorderSize gaussian5x5_border(2); - - auto border_left = static_cast<int>(gaussian5x5_border.left); - auto border_right = static_cast<int>(gaussian5x5_border.right); - auto border_top = static_cast<int>(gaussian5x5_border.top); - auto border_bottom = static_cast<int>(gaussian5x5_border.bottom); - - valid_region.anchor.set(0, valid_region.anchor[0] + border_left); - valid_region.anchor.set(1, valid_region.anchor[1] + border_top); - valid_region.shape.set(0, std::max(0, static_cast<int>(valid_region.shape[0]) - border_right - border_left)); - valid_region.shape.set(1, std::max(0, static_cast<int>(valid_region.shape[1]) - border_top - border_bottom)); - } - - return valid_region; -} - /** Write the value after casting the pointer according to @p data_type. * * @warning The type of the value must match the specified data type. @@ -519,6 +418,21 @@ inline bool is_in_valid_region(const ValidRegion &valid_region, Coordinates coor /** Create and initialize a tensor of the given type. * + * @param[in] info Tensor information to be used to create the tensor + * @param[in] ctx (Optional) Pointer to the runtime context. + * + * @return Initialized tensor of given type. + */ +template <typename T> +inline T create_tensor(const TensorInfo &info, IRuntimeContext *ctx = nullptr) +{ + T tensor(ctx); + tensor.allocator()->init(info); + return tensor; +} + +/** Create and initialize a tensor of the given type. + * * @param[in] shape Tensor shape. * @param[in] data_type Data type. * @param[in] num_channels (Optional) Number of channels. @@ -536,9 +450,8 @@ inline T create_tensor(const TensorShape &shape, DataType data_type, int num_cha TensorInfo info(shape, num_channels, data_type); info.set_quantization_info(quantization_info); info.set_data_layout(data_layout); - tensor.allocator()->init(info); - return tensor; + return create_tensor<T>(info, ctx); } /** Create and initialize a tensor of the given type. @@ -554,114 +467,7 @@ inline T create_tensor(const TensorShape &shape, Format format, IRuntimeContext { TensorInfo info(shape, format); - T tensor(ctx); - tensor.allocator()->init(info); - - return tensor; -} - -/** Create and initialize a multi-image of the given type. - * - * @param[in] shape Tensor shape. - * @param[in] format Format type. - * - * @return Initialized tensor of given type. - */ -template <typename T> -inline T create_multi_image(const TensorShape &shape, Format format) -{ - T multi_image; - multi_image.init(shape.x(), shape.y(), format); - - return multi_image; -} - -/** Create and initialize a HOG (Histogram of Oriented Gradients) of the given type. - * - * @param[in] hog_info HOGInfo object - * - * @return Initialized HOG of given type. - */ -template <typename T> -inline T create_HOG(const HOGInfo &hog_info) -{ - T hog; - hog.init(hog_info); - - return hog; -} - -/** Create and initialize a Pyramid of the given type. - * - * @param[in] pyramid_info The PyramidInfo object. - * - * @return Initialized Pyramid of given type. - */ -template <typename T> -inline T create_pyramid(const PyramidInfo &pyramid_info) -{ - T pyramid; - pyramid.init_auto_padding(pyramid_info); - - return pyramid; -} - -/** Initialize a convolution matrix. - * - * @param[in, out] conv The input convolution matrix. - * @param[in] width The width of the convolution matrix. - * @param[in] height The height of the convolution matrix. - * @param[in] seed The random seed to be used. - */ -inline void init_conv(int16_t *conv, unsigned int width, unsigned int height, std::random_device::result_type seed) -{ - std::mt19937 gen(seed); - std::uniform_int_distribution<int16_t> distribution_int16(-32768, 32767); - - for(unsigned int i = 0; i < width * height; ++i) - { - conv[i] = distribution_int16(gen); - } -} - -/** Initialize a separable convolution matrix. - * - * @param[in, out] conv The input convolution matrix. - * @param[in] width The width of the convolution matrix. - * @param[in] height The height of the convolution matrix. - * @param[in] seed The random seed to be used. - */ -inline void init_separable_conv(int16_t *conv, unsigned int width, unsigned int height, std::random_device::result_type seed) -{ - std::mt19937 gen(seed); - // Set it between -128 and 127 to ensure the matrix does not overflow - std::uniform_int_distribution<int16_t> distribution_int16(-128, 127); - - int16_t *conv_row = new int16_t[width]; - int16_t *conv_col = new int16_t[height]; - - conv_row[0] = conv_col[0] = 1; - for(unsigned int i = 1; i < width; ++i) - { - conv_row[i] = distribution_int16(gen); - } - - for(unsigned int i = 1; i < height; ++i) - { - conv_col[i] = distribution_int16(gen); - } - - // Multiply two matrices - for(unsigned int i = 0; i < width; ++i) - { - for(unsigned int j = 0; j < height; ++j) - { - conv[i * width + j] = conv_col[i] * conv_row[j]; - } - } - - delete[] conv_row; - delete[] conv_col; + return create_tensor<T>(info, ctx); } /** Create a vector with a uniform distribution of floating point values across the specified range. @@ -688,44 +494,6 @@ inline std::vector<T> generate_random_real(unsigned int num_values, T min, T max return v; } -/** Create a vector of random keypoints for pyramid representation. - * - * @param[in] shape The shape of the input tensor. - * @param[in] num_keypoints The number of keypoints to be created. - * @param[in] seed The random seed to be used. - * @param[in] num_levels The number of pyramid levels. - * - * @return A vector that contains the requested number of random keypoints - */ -inline std::vector<KeyPoint> generate_random_keypoints(const TensorShape &shape, size_t num_keypoints, std::random_device::result_type seed, size_t num_levels = 1) -{ - std::vector<KeyPoint> keypoints; - std::mt19937 gen(seed); - - // Calculate distribution bounds - const auto min = static_cast<int>(std::pow(2, num_levels)); - const auto max_width = static_cast<int>(shape.x()); - const auto max_height = static_cast<int>(shape.y()); - - ARM_COMPUTE_ERROR_ON(min > max_width || min > max_height); - - // Create distributions - std::uniform_int_distribution<> dist_w(min, max_width); - std::uniform_int_distribution<> dist_h(min, max_height); - - for(unsigned int i = 0; i < num_keypoints; i++) - { - KeyPoint keypoint; - keypoint.x = dist_w(gen); - keypoint.y = dist_h(gen); - keypoint.tracking_status = 1; - - keypoints.push_back(keypoint); - } - - return keypoints; -} - template <typename T, typename ArrayAccessor_T> inline void fill_array(ArrayAccessor_T &&array, const std::vector<T> &v) { @@ -806,18 +574,69 @@ inline void sync_if_necessary() template <typename TensorType> inline void sync_tensor_if_necessary(TensorType &tensor) { -#ifdef ARM_COMPUTE_GC - if(opengles31_is_available() && std::is_same<typename std::decay<TensorType>::type, arm_compute::GCTensor>::value) - { - // Force sync the tensor by calling map and unmap. - IGCTensor &t = dynamic_cast<IGCTensor &>(tensor); - t.map(); - t.unmap(); - } -#else /* ARM_COMPUTE_GC */ ARM_COMPUTE_UNUSED(tensor); -#endif /* ARM_COMPUTE_GC */ } + +/** Construct and return object for dimensions' state filled with the given value + * + * @param[in] value The value to fill + * + * @return Constructed class + */ +inline ITensorInfo::TensorDimsState construct_dims_state(int32_t value) +{ + auto states = ITensorInfo::TensorDimsState{}; + std::fill(states.begin(), states.end(), value); + return states; +} + +/** Construct and return object for dimensions' state filled with the value for dynamic state + * + * @return Constructed class filled with the value for dynamic state + */ +inline ITensorInfo::TensorDimsState construct_dynamic_dims_state() +{ + return construct_dims_state(ITensorInfo::get_dynamic_state_value()); +} + +/** Construct and return object for dimensions' state filled with the value for non-dynamic state + * + * @return Constructed class filled with the value for non-dynamic state + */ +inline ITensorInfo::TensorDimsState construct_static_dims_state() +{ + return construct_dims_state(ITensorInfo::get_static_state_value()); +} + +/** Set the dimension states of the given tensor to dynamic + * + * @param[in] t The tensor to set to dynamic state + * + */ +template <typename TensorType> +void set_tensor_dynamic(TensorType &t) +{ + t.info()->set_tensor_dims_state(construct_dynamic_dims_state()); +} + +/** Set the dimension states of the given tensor to state + * + * @param[in] t The tensor to set to static state + * + */ +template <typename TensorType> +void set_tensor_static(TensorType &t) +{ + t.info()->set_tensor_dims_state(construct_static_dims_state()); +} + +inline experimental::dynamic_fusion::Conv2dAttributes convert_pad_stride_info_to_conv_attr(const PadStrideInfo &info, const Size2D &dialation) +{ + const Padding2D info_pad(info.pad_left(), info.pad_right(), info.pad_top(), info.pad_bottom()); + const Size2D info_stride(info.stride().first, info.stride().second); + return arm_compute::experimental::dynamic_fusion::Conv2dAttributes().pad(info_pad).stride(info_stride).dilation(dialation); +} + } // namespace test } // namespace arm_compute #endif /* ARM_COMPUTE_TEST_UTILS_H */ |