COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae

1 files changed, 359 insertions, 0 deletions

diff --git a/tests/validation/Validation.cpp b/tests/validation/Validation.cpp
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+++ b/tests/validation/Validation.cpp
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+/*
+ * 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 "Validation.h"
+
+#include "IAccessor.h"
+#include "RawTensor.h"
+#include "TypePrinter.h"
+#include "Utils.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/FixedPoint.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/runtime/Tensor.h"
+
+#include <array>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <iomanip>
+
+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)
+{
+    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::QS8:
+            return *reinterpret_cast<const qint8_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);
+#if ENABLE_FP16
+        case DataType::F16:
+            return *reinterpret_cast<const float16_t *>(ptr);
+#endif
+        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 };
+        border_id.set(1, 0);
+        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 ref            = get_double_data(static_cast<const uint8_t *>(border_value) + channel_offset, tensor.data_type());
+        const double difference     = target - ref;
+
+        BOOST_TEST_INFO("id = " << id);
+        BOOST_TEST_INFO("channel = " << channel);
+        BOOST_TEST_INFO("reference = " << std::setprecision(5) << ref);
+        BOOST_TEST_INFO("target = " << std::setprecision(5) << target);
+        BOOST_TEST_WARN(difference == 0);
+
+        if(difference != 0.f)
+        {
+            ++num_mismatches;
+        }
+
+        ++num_elements;
+    }
+}
+
+void check_single_element(const Coordinates &id, const IAccessor &tensor, const RawTensor &reference, float tolerance_value,
+                          uint64_t wrap_range, int min_channels, size_t channel_size, int64_t &num_mismatches, int64_t &num_elements)
+{
+    const auto ptr     = static_cast<const uint8_t *>(tensor(id));
+    const auto ref_ptr = static_cast<const uint8_t *>(reference(id));
+
+    // Iterate over all channels within one element
+    for(int channel = 0; channel < min_channels; ++channel)
+    {
+        const size_t channel_offset = channel * channel_size;
+        const double target         = get_double_data(ptr + channel_offset, reference.data_type());
+        const double ref            = get_double_data(ref_ptr + channel_offset, reference.data_type());
+        const double difference     = target - ref;
+
+        BOOST_TEST_INFO("id = " << id);
+        BOOST_TEST_INFO("channel = " << channel);
+        BOOST_TEST_INFO("reference = " << std::setprecision(5) << ref);
+        BOOST_TEST_INFO("target = " << std::setprecision(5) << target);
+        BOOST_TEST_WARN(difference == 0);
+
+        if(std::abs(difference) > tolerance_value)
+        {
+            // If no special cases for tolerating wrappping cases
+            // or the special case of wrapping exceeds tolerance_value
+            if(wrap_range == 0 || (wrap_range - std::abs(difference)) > tolerance_value)
+            {
+                ++num_mismatches;
+            }
+        }
+        ++num_elements;
+    }
+}
+} // namespace
+
+void validate(const arm_compute::ValidRegion &region, const arm_compute::ValidRegion &reference)
+{
+    BOOST_TEST(region.anchor.num_dimensions() == reference.anchor.num_dimensions());
+    BOOST_TEST(region.shape.num_dimensions() == reference.shape.num_dimensions());
+
+    for(unsigned int d = 0; d < region.anchor.num_dimensions(); ++d)
+    {
+        BOOST_TEST(region.anchor[d] == reference.anchor[d]);
+    }
+
+    for(unsigned int d = 0; d < region.shape.num_dimensions(); ++d)
+    {
+        BOOST_TEST(region.shape[d] == reference.shape[d]);
+    }
+}
+
+void validate(const arm_compute::PaddingSize &padding, const arm_compute::PaddingSize &reference)
+{
+    BOOST_TEST(padding.top == reference.top);
+    BOOST_TEST(padding.right == reference.right);
+    BOOST_TEST(padding.bottom == reference.bottom);
+    BOOST_TEST(padding.left == reference.left);
+}
+
+void validate(const IAccessor &tensor, const RawTensor &reference, float tolerance_value, float tolerance_number, uint64_t wrap_range)
+{
+    // Validate with valid region covering the entire shape
+    validate(tensor, reference, shape_to_valid_region(tensor.shape()), tolerance_value, tolerance_number, wrap_range);
+}
+
+void validate(const IAccessor &tensor, const RawTensor &reference, const ValidRegion &valid_region, float tolerance_value, float tolerance_number, uint64_t wrap_range)
+{
+    int64_t num_mismatches = 0;
+    int64_t num_elements   = 0;
+
+    BOOST_TEST(tensor.element_size() == reference.element_size());
+    BOOST_TEST(tensor.format() == reference.format());
+    BOOST_TEST(tensor.data_type() == reference.data_type());
+    BOOST_TEST(tensor.num_channels() == reference.num_channels());
+    BOOST_TEST(compare_dimensions(tensor.shape(), reference.shape()));
+
+    const int    min_elements = std::min(tensor.num_elements(), reference.num_elements());
+    const int    min_channels = std::min(tensor.num_channels(), reference.num_channels());
+    const size_t channel_size = element_size_from_data_type(reference.data_type());
+
+    // Iterate over all elements within valid region, e.g. U8, S16, RGB888, ...
+    for(int element_idx = 0; element_idx < min_elements; ++element_idx)
+    {
+        const Coordinates id = index2coord(reference.shape(), element_idx);
+        if(is_in_valid_region(valid_region, id))
+        {
+            check_single_element(id, tensor, reference, tolerance_value, wrap_range, min_channels, channel_size, num_mismatches, num_elements);
+        }
+    }
+
+    const int64_t absolute_tolerance_number = tolerance_number * num_elements;
+    const float   percent_mismatches        = static_cast<float>(num_mismatches) / num_elements * 100.f;
+
+    BOOST_TEST(num_mismatches <= absolute_tolerance_number,
+               num_mismatches << " values (" << std::setprecision(2) << percent_mismatches
+               << "%) mismatched (maximum tolerated " << std::setprecision(2) << tolerance_number << "%)");
+}
+
+void validate(const IAccessor &tensor, const void *reference_value)
+{
+    BOOST_TEST_REQUIRE((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, ...
+    for(int 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 ref            = get_double_data(reference_value, tensor.data_type());
+            const double difference     = target - ref;
+
+            BOOST_TEST_INFO("id = " << id);
+            BOOST_TEST_INFO("channel = " << channel);
+            BOOST_TEST_INFO("reference = " << std::setprecision(5) << ref);
+            BOOST_TEST_INFO("target = " << std::setprecision(5) << target);
+            BOOST_TEST_WARN(difference == 0);
+
+            if(difference != 0.f)
+            {
+                ++num_mismatches;
+            }
+
+            ++num_elements;
+        }
+    }
+
+    const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f;
+
+    BOOST_TEST(num_mismatches == 0,
+               num_mismatches << " values (" << std::setprecision(2) << percent_mismatches << "%) mismatched");
+}
+
+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)
+    {
+        BOOST_TEST((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);
+            }
+        }
+    }
+
+    const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f;
+
+    BOOST_TEST(num_mismatches == 0,
+               num_mismatches << " values (" << std::setprecision(2) << percent_mismatches << "%) mismatched");
+}
+
+void validate(std::vector<unsigned int> classified_labels, std::vector<unsigned int> expected_labels)
+{
+    BOOST_TEST(expected_labels.size() != 0);
+    BOOST_TEST(classified_labels.size() == expected_labels.size());
+
+    for(unsigned int i = 0; i < expected_labels.size(); ++i)
+    {
+        BOOST_TEST(classified_labels[i] == expected_labels[i]);
+    }
+}
+} // namespace validation
+} // namespace test
+} // namespace arm_compute