| /* |
| * 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 |
