| /* | |
| * Copyright (c) 2017-2019 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 "Validation.h" | |
| #include "arm_compute/core/Coordinates.h" | |
| #include "arm_compute/core/Error.h" | |
| #include "arm_compute/core/TensorShape.h" | |
| #include "arm_compute/core/Types.h" | |
| #include "arm_compute/runtime/Tensor.h" | |
| #include <array> | |
| #include <cmath> | |
| #include <cstddef> | |
| #include <cstdint> | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| namespace validation | |
| { | |
| namespace | |
| { | |
| /** Get the data from *ptr after casting according to @p data_type and then convert the data to double. | |
| * | |
| * @param[in] ptr Pointer to value. | |
| * @param[in] data_type Data type of both values. | |
| * | |
| * @return The data from the ptr after converted to double. | |
| */ | |
| double get_double_data(const void *ptr, DataType data_type) | |
| { | |
| if(ptr == nullptr) | |
| { | |
| ARM_COMPUTE_ERROR("Can't dereference a null pointer!"); | |
| } | |
| switch(data_type) | |
| { | |
| case DataType::U8: | |
| return *reinterpret_cast<const uint8_t *>(ptr); | |
| case DataType::S8: | |
| return *reinterpret_cast<const int8_t *>(ptr); | |
| case DataType::U16: | |
| return *reinterpret_cast<const uint16_t *>(ptr); | |
| case DataType::S16: | |
| return *reinterpret_cast<const int16_t *>(ptr); | |
| case DataType::U32: | |
| return *reinterpret_cast<const uint32_t *>(ptr); | |
| case DataType::S32: | |
| return *reinterpret_cast<const int32_t *>(ptr); | |
| case DataType::U64: | |
| return *reinterpret_cast<const uint64_t *>(ptr); | |
| case DataType::S64: | |
| return *reinterpret_cast<const int64_t *>(ptr); | |
| case DataType::F16: | |
| return *reinterpret_cast<const half *>(ptr); | |
| case DataType::F32: | |
| return *reinterpret_cast<const float *>(ptr); | |
| case DataType::F64: | |
| return *reinterpret_cast<const double *>(ptr); | |
| case DataType::SIZET: | |
| return *reinterpret_cast<const size_t *>(ptr); | |
| default: | |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); | |
| } | |
| } | |
| void check_border_element(const IAccessor &tensor, const Coordinates &id, | |
| const BorderMode &border_mode, const void *border_value, | |
| int64_t &num_elements, int64_t &num_mismatches) | |
| { | |
| const size_t channel_size = element_size_from_data_type(tensor.data_type()); | |
| const auto ptr = static_cast<const uint8_t *>(tensor(id)); | |
| if(border_mode == BorderMode::REPLICATE) | |
| { | |
| Coordinates border_id{ id }; | |
| if(id.x() < 0) | |
| { | |
| border_id.set(0, 0); | |
| } | |
| else if(static_cast<size_t>(id.x()) >= tensor.shape().x()) | |
| { | |
| border_id.set(0, tensor.shape().x() - 1); | |
| } | |
| if(id.y() < 0) | |
| { | |
| border_id.set(1, 0); | |
| } | |
| else if(static_cast<size_t>(id.y()) >= tensor.shape().y()) | |
| { | |
| border_id.set(1, tensor.shape().y() - 1); | |
| } | |
| border_value = tensor(border_id); | |
| } | |
| // Iterate over all channels within one element | |
| for(int channel = 0; channel < tensor.num_channels(); ++channel) | |
| { | |
| const size_t channel_offset = channel * channel_size; | |
| const double target = get_double_data(ptr + channel_offset, tensor.data_type()); | |
| const double reference = get_double_data(static_cast<const uint8_t *>(border_value) + channel_offset, tensor.data_type()); | |
| if(!compare<AbsoluteTolerance<double>>(target, reference)) | |
| { | |
| ARM_COMPUTE_TEST_INFO("id = " << id); | |
| ARM_COMPUTE_TEST_INFO("channel = " << channel); | |
| ARM_COMPUTE_TEST_INFO("target = " << std::setprecision(5) << target); | |
| ARM_COMPUTE_TEST_INFO("reference = " << std::setprecision(5) << reference); | |
| ARM_COMPUTE_EXPECT_EQUAL(target, reference, framework::LogLevel::DEBUG); | |
| ++num_mismatches; | |
| } | |
| ++num_elements; | |
| } | |
| } | |
| } // namespace | |
| void validate(const arm_compute::ValidRegion ®ion, const arm_compute::ValidRegion &reference) | |
| { | |
| ARM_COMPUTE_EXPECT_EQUAL(region.anchor.num_dimensions(), reference.anchor.num_dimensions(), framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(region.shape.num_dimensions(), reference.shape.num_dimensions(), framework::LogLevel::ERRORS); | |
| for(unsigned int d = 0; d < region.anchor.num_dimensions(); ++d) | |
| { | |
| ARM_COMPUTE_EXPECT_EQUAL(region.anchor[d], reference.anchor[d], framework::LogLevel::ERRORS); | |
| } | |
| for(unsigned int d = 0; d < region.shape.num_dimensions(); ++d) | |
| { | |
| ARM_COMPUTE_EXPECT_EQUAL(region.shape[d], reference.shape[d], framework::LogLevel::ERRORS); | |
| } | |
| } | |
| void validate(const arm_compute::PaddingSize &padding, const arm_compute::PaddingSize &reference) | |
| { | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.top, reference.top, framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.right, reference.right, framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.bottom, reference.bottom, framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.left, reference.left, framework::LogLevel::ERRORS); | |
| } | |
| void validate(const arm_compute::PaddingSize &padding, const arm_compute::PaddingSize &width_reference, const arm_compute::PaddingSize &height_reference) | |
| { | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.top, height_reference.top, framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.right, width_reference.right, framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.bottom, height_reference.bottom, framework::LogLevel::ERRORS); | |
| ARM_COMPUTE_EXPECT_EQUAL(padding.left, width_reference.left, framework::LogLevel::ERRORS); | |
| } | |
| void validate(const IAccessor &tensor, const void *reference_value) | |
| { | |
| ARM_COMPUTE_ASSERT(reference_value != nullptr); | |
| int64_t num_mismatches = 0; | |
| int64_t num_elements = 0; | |
| const size_t channel_size = element_size_from_data_type(tensor.data_type()); | |
| // Iterate over all elements, e.g. U8, S16, RGB888, ... | |
| const uint32_t tensor_num_elements = tensor.num_elements(); | |
| for(uint32_t element_idx = 0; element_idx < tensor_num_elements; ++element_idx) | |
| { | |
| const Coordinates id = index2coord(tensor.shape(), element_idx); | |
| const auto ptr = static_cast<const uint8_t *>(tensor(id)); | |
| // Iterate over all channels within one element | |
| for(int channel = 0; channel < tensor.num_channels(); ++channel) | |
| { | |
| const size_t channel_offset = channel * channel_size; | |
| const double target = get_double_data(ptr + channel_offset, tensor.data_type()); | |
| const double reference = get_double_data(reference_value, tensor.data_type()); | |
| if(!compare<AbsoluteTolerance<double>>(target, reference)) | |
| { | |
| ARM_COMPUTE_TEST_INFO("id = " << id); | |
| ARM_COMPUTE_TEST_INFO("channel = " << channel); | |
| ARM_COMPUTE_TEST_INFO("target = " << std::setprecision(5) << target); | |
| ARM_COMPUTE_TEST_INFO("reference = " << std::setprecision(5) << reference); | |
| ARM_COMPUTE_EXPECT_EQUAL(target, reference, framework::LogLevel::DEBUG); | |
| ++num_mismatches; | |
| } | |
| ++num_elements; | |
| } | |
| } | |
| if(num_elements > 0) | |
| { | |
| const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f; | |
| ARM_COMPUTE_TEST_INFO(num_mismatches << " values (" << std::fixed << std::setprecision(2) << percent_mismatches << "%) mismatched"); | |
| ARM_COMPUTE_EXPECT_EQUAL(num_mismatches, 0, framework::LogLevel::ERRORS); | |
| } | |
| } | |
| void validate(const IAccessor &tensor, BorderSize border_size, const BorderMode &border_mode, const void *border_value) | |
| { | |
| if(border_mode == BorderMode::UNDEFINED) | |
| { | |
| return; | |
| } | |
| else if(border_mode == BorderMode::CONSTANT) | |
| { | |
| ARM_COMPUTE_ASSERT(border_value != nullptr); | |
| } | |
| int64_t num_mismatches = 0; | |
| int64_t num_elements = 0; | |
| const int slice_size = tensor.shape()[0] * tensor.shape()[1]; | |
| for(int element_idx = 0; element_idx < tensor.num_elements(); element_idx += slice_size) | |
| { | |
| Coordinates id = index2coord(tensor.shape(), element_idx); | |
| // Top border | |
| for(int y = -border_size.top; y < 0; ++y) | |
| { | |
| id.set(1, y); | |
| for(int x = -border_size.left; x < static_cast<int>(tensor.shape()[0]) + static_cast<int>(border_size.right); ++x) | |
| { | |
| id.set(0, x); | |
| check_border_element(tensor, id, border_mode, border_value, num_elements, num_mismatches); | |
| } | |
| } | |
| // Bottom border | |
| for(int y = tensor.shape()[1]; y < static_cast<int>(tensor.shape()[1]) + static_cast<int>(border_size.bottom); ++y) | |
| { | |
| id.set(1, y); | |
| for(int x = -border_size.left; x < static_cast<int>(tensor.shape()[0]) + static_cast<int>(border_size.right); ++x) | |
| { | |
| id.set(0, x); | |
| check_border_element(tensor, id, border_mode, border_value, num_elements, num_mismatches); | |
| } | |
| } | |
| // Left/right border | |
| for(int y = 0; y < static_cast<int>(tensor.shape()[1]); ++y) | |
| { | |
| id.set(1, y); | |
| // Left border | |
| for(int x = -border_size.left; x < 0; ++x) | |
| { | |
| id.set(0, x); | |
| check_border_element(tensor, id, border_mode, border_value, num_elements, num_mismatches); | |
| } | |
| // Right border | |
| for(int x = tensor.shape()[0]; x < static_cast<int>(tensor.shape()[0]) + static_cast<int>(border_size.right); ++x) | |
| { | |
| id.set(0, x); | |
| check_border_element(tensor, id, border_mode, border_value, num_elements, num_mismatches); | |
| } | |
| } | |
| } | |
| if(num_elements > 0) | |
| { | |
| const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f; | |
| ARM_COMPUTE_TEST_INFO(num_mismatches << " values (" << std::fixed << std::setprecision(2) << percent_mismatches << "%) mismatched"); | |
| ARM_COMPUTE_EXPECT_EQUAL(num_mismatches, 0, framework::LogLevel::ERRORS); | |
| } | |
| } | |
| void validate(std::vector<unsigned int> classified_labels, std::vector<unsigned int> expected_labels) | |
| { | |
| ARM_COMPUTE_EXPECT_EQUAL(classified_labels.size(), expected_labels.size(), framework::LogLevel::ERRORS); | |
| int64_t num_mismatches = 0; | |
| const int num_elements = std::min(classified_labels.size(), expected_labels.size()); | |
| for(int i = 0; i < num_elements; ++i) | |
| { | |
| if(classified_labels[i] != expected_labels[i]) | |
| { | |
| ++num_mismatches; | |
| ARM_COMPUTE_EXPECT_EQUAL(classified_labels[i], expected_labels[i], framework::LogLevel::DEBUG); | |
| } | |
| } | |
| if(num_elements > 0) | |
| { | |
| const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f; | |
| ARM_COMPUTE_TEST_INFO(num_mismatches << " values (" << std::fixed << std::setprecision(2) << percent_mismatches << "%) mismatched"); | |
| ARM_COMPUTE_EXPECT_EQUAL(num_mismatches, 0, framework::LogLevel::ERRORS); | |
| } | |
| } | |
| } // namespace validation | |
| } // namespace test | |
| } // namespace arm_compute |