| /* |
| * 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. |
| */ |
| #include "tests/validation/Helpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| void fill_mask_from_pattern(uint8_t *mask, int cols, int rows, MatrixPattern pattern) |
| { |
| unsigned int v = 0; |
| std::mt19937 gen(library->seed()); |
| 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; |
| } |
| } |
| |
| TensorShape calculate_depth_concatenate_shape(const std::vector<TensorShape> &input_shapes) |
| { |
| ARM_COMPUTE_ERROR_ON(input_shapes.empty()); |
| |
| TensorShape out_shape = input_shapes[0]; |
| |
| size_t max_x = 0; |
| size_t max_y = 0; |
| size_t depth = 0; |
| |
| for(const auto &shape : input_shapes) |
| { |
| max_x = std::max(shape.x(), max_x); |
| max_y = std::max(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; |
| } |
| |
| HarrisCornersParameters harris_corners_parameters() |
| { |
| HarrisCornersParameters params; |
| |
| std::mt19937 gen(library->seed()); |
| std::uniform_real_distribution<float> threshold_dist(0.f, 0.01f); |
| std::uniform_real_distribution<float> sensitivity(0.04f, 0.15f); |
| std::uniform_real_distribution<float> euclidean_distance(0.f, 30.f); |
| std::uniform_int_distribution<uint8_t> int_dist(0, 255); |
| |
| params.threshold = threshold_dist(gen); |
| params.sensitivity = sensitivity(gen); |
| params.min_dist = euclidean_distance(gen); |
| params.constant_border_value = int_dist(gen); |
| |
| return params; |
| } |
| |
| SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<uint8_t> &src) |
| { |
| const QuantizationInfo &quantization_info = src.quantization_info(); |
| SimpleTensor<float> dst{ src.shape(), DataType::F32, 1, 0 }; |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = quantization_info.dequantize(src[i]); |
| } |
| return dst; |
| } |
| |
| SimpleTensor<uint8_t> convert_to_asymmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info) |
| { |
| SimpleTensor<uint8_t> dst{ src.shape(), DataType::QASYMM8, 1, 0, quantization_info }; |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = quantization_info.quantize(src[i]); |
| } |
| return dst; |
| } |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |