| /* |
| * 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_VALIDATION_HELPERS_H__ |
| #define __ARM_COMPUTE_TEST_VALIDATION_HELPERS_H__ |
| |
| #include "ILutAccessor.h" |
| #include "Types.h" |
| #include "ValidationUserConfiguration.h" |
| |
| #include "arm_compute/core/Types.h" |
| |
| #include <random> |
| #include <type_traits> |
| #include <utility> |
| #include <vector> |
| |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| #include <arm_fp16.h> |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| /** Helper function to get the testing range for each activation layer. |
| * |
| * @param[in] activation Activation function to test. |
| * @param[in] fixed_point_position (Optional) Number of bits for the fractional part. Defaults to 1. |
| * |
| * @return A pair containing the lower upper testing bounds for a given function. |
| */ |
| template <typename T> |
| inline std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::ActivationFunction activation, int fixed_point_position = 1) |
| { |
| bool is_float = std::is_same<T, float>::value; |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| is_float = is_float || std::is_same<T, float16_t>::value; |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| |
| std::pair<T, T> bounds; |
| |
| // Set initial values |
| if(is_float) |
| { |
| bounds = std::make_pair(-255.f, 255.f); |
| } |
| else |
| { |
| bounds = std::make_pair(std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max()); |
| } |
| |
| // Reduce testing ranges |
| switch(activation) |
| { |
| case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| // Reduce range as exponent overflows |
| if(is_float) |
| { |
| bounds.first = -40.f; |
| bounds.second = 40.f; |
| } |
| else |
| { |
| bounds.first = -(1 << (fixed_point_position)); |
| bounds.second = 1 << (fixed_point_position); |
| } |
| break; |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| // Reduce range as exponent overflows |
| if(!is_float) |
| { |
| bounds.first = -(1 << (fixed_point_position)); |
| bounds.second = 1 << (fixed_point_position); |
| } |
| break; |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| // Reduce range as sqrt should take a non-negative number |
| bounds.first = (is_float) ? 0 : 1; |
| break; |
| default: |
| break; |
| } |
| return bounds; |
| } |
| /** Helper function to get the testing range for batch normalization layer. |
| * |
| * @param[in] fixed_point_position (Optional) Number of bits for the fractional part. Defaults to 1. |
| * |
| * @return A pair containing the lower upper testing bounds. |
| */ |
| template <typename T> |
| std::pair<T, T> get_batchnormalization_layer_test_bounds(int fixed_point_position = 1) |
| { |
| bool is_float = std::is_floating_point<T>::value; |
| std::pair<T, T> bounds; |
| |
| // Set initial values |
| if(is_float) |
| { |
| bounds = std::make_pair(-1.f, 1.f); |
| } |
| else |
| { |
| bounds = std::make_pair(1, 1 << (fixed_point_position)); |
| } |
| |
| return bounds; |
| } |
| |
| /** Fill mask with the corresponding given pattern. |
| * |
| * @param[in,out] mask Mask to be filled according to pattern |
| * @param[in] cols Columns (width) of mask |
| * @param[in] rows Rows (height) of mask |
| * @param[in] pattern Pattern to fill the mask according to |
| */ |
| inline void fill_mask_from_pattern(uint8_t *mask, int cols, int rows, MatrixPattern pattern) |
| { |
| unsigned int v = 0; |
| std::mt19937 gen(user_config.seed.get()); |
| std::bernoulli_distribution dist(0.5); |
| |
| for(int r = 0; r < rows; ++r) |
| { |
| for(int c = 0; c < cols; ++c, ++v) |
| { |
| uint8_t val = 0; |
| |
| switch(pattern) |
| { |
| case MatrixPattern::BOX: |
| val = 255; |
| break; |
| case MatrixPattern::CROSS: |
| val = ((r == (rows / 2)) || (c == (cols / 2))) ? 255 : 0; |
| break; |
| case MatrixPattern::DISK: |
| val = (((r - rows / 2.0f + 0.5f) * (r - rows / 2.0f + 0.5f)) / ((rows / 2.0f) * (rows / 2.0f)) + ((c - cols / 2.0f + 0.5f) * (c - cols / 2.0f + 0.5f)) / ((cols / 2.0f) * |
| (cols / 2.0f))) <= 1.0f ? 255 : 0; |
| break; |
| case MatrixPattern::OTHER: |
| val = (dist(gen) ? 0 : 255); |
| break; |
| default: |
| return; |
| } |
| |
| mask[v] = val; |
| } |
| } |
| |
| if(pattern == MatrixPattern::OTHER) |
| { |
| std::uniform_int_distribution<uint8_t> distribution_u8(0, ((cols * rows) - 1)); |
| mask[distribution_u8(gen)] = 255; |
| } |
| } |
| |
| /** Calculate output tensor shape give a vector of input tensor to concatenate |
| * |
| * @param[in] input_shapes Shapes of the tensors to concatenate across depth. |
| * |
| * @return The shape of output concatenated tensor. |
| */ |
| inline TensorShape calculate_depth_concatenate_shape(std::vector<TensorShape> input_shapes) |
| { |
| TensorShape out_shape = input_shapes.at(0); |
| |
| unsigned int max_x = 0; |
| unsigned int max_y = 0; |
| unsigned int depth = 0; |
| |
| for(auto const &shape : input_shapes) |
| { |
| max_x = std::max<unsigned int>(shape.x(), max_x); |
| max_y = std::max<unsigned int>(shape.y(), max_y); |
| depth += shape.z(); |
| } |
| |
| out_shape.set(0, max_x); |
| out_shape.set(1, max_y); |
| out_shape.set(2, depth); |
| |
| return out_shape; |
| } |
| |
| /** Create a vector of random ROIs. |
| * |
| * @param[in] shape The shape of the input tensor. |
| * @param[in] pool_info The ROI pooling information. |
| * @param[in] num_rois The number of ROIs to be created. |
| * @param[in] seed The random seed to be used. |
| * |
| * @return A vector that contains the requested number of random ROIs |
| */ |
| std::vector<ROI> generate_random_rois(const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, unsigned int num_rois, std::random_device::result_type seed); |
| |
| /** Helper function to fill the Lut random by a ILutAccessor. |
| * |
| * @param[in,out] table Accessor at the Lut. |
| * |
| */ |
| template <typename T> |
| void fill_lookuptable(T &&table) |
| { |
| std::mt19937 generator(user_config.seed.get()); |
| std::uniform_int_distribution<typename T::value_type> distribution(std::numeric_limits<typename T::value_type>::min(), std::numeric_limits<typename T::value_type>::max()); |
| |
| for(int i = std::numeric_limits<typename T::value_type>::min(); i <= std::numeric_limits<typename T::value_type>::max(); i++) |
| { |
| table[i] = distribution(generator); |
| } |
| } |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* __ARM_COMPUTE_TEST_VALIDATION_HELPERS_H__ */ |