| /* | |
| * Copyright (c) 2019-2020 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 "arm_compute/core/Types.h" | |
| #include "arm_compute/runtime/CPP/functions/CPPDetectionPostProcessLayer.h" | |
| #include "arm_compute/runtime/Tensor.h" | |
| #include "arm_compute/runtime/TensorAllocator.h" | |
| #include "tests/NEON/Accessor.h" | |
| #include "tests/PaddingCalculator.h" | |
| #include "tests/datasets/ShapeDatasets.h" | |
| #include "tests/framework/Asserts.h" | |
| #include "tests/framework/Macros.h" | |
| #include "tests/framework/datasets/Datasets.h" | |
| #include "tests/validation/Validation.h" | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| namespace validation | |
| { | |
| namespace | |
| { | |
| template <typename U, typename T> | |
| inline void fill_tensor(U &&tensor, const std::vector<T> &v) | |
| { | |
| std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); | |
| } | |
| template <typename D, typename U, typename T> | |
| inline void quantize_and_fill_tensor(U &&tensor, const std::vector<T> &v) | |
| { | |
| QuantizationInfo qi = tensor.quantization_info(); | |
| std::vector<D> quantized; | |
| quantized.reserve(v.size()); | |
| for(auto elem : v) | |
| { | |
| quantized.emplace_back(Qasymm8QuantizationHelper<D>::quantize(elem, qi)); | |
| } | |
| std::memcpy(tensor.data(), quantized.data(), sizeof(D) * quantized.size()); | |
| } | |
| template <typename T> | |
| inline int calc_qinfo(const float min, const float max, float &scale) | |
| { | |
| const auto qmin = std::numeric_limits<T>::min(); | |
| const auto qmax = std::numeric_limits<T>::max(); | |
| const float f_qmin = qmin; | |
| const float f_qmax = qmax; | |
| scale = (max - min) / (f_qmax - f_qmin); | |
| const float offset_from_min = f_qmin - min / scale; | |
| const float offset_from_max = f_qmax - max / scale; | |
| const float offset_from_min_error = std::abs(f_qmin) + std::abs(min / scale); | |
| const float offset_from_max_error = std::abs(f_qmax) + std::abs(max / scale); | |
| const float f_offset = offset_from_min_error < offset_from_max_error ? offset_from_min : offset_from_max; | |
| T tmp_offset; | |
| if(f_offset < f_qmin) | |
| { | |
| tmp_offset = qmin; | |
| } | |
| else if(f_offset > f_qmax) | |
| { | |
| tmp_offset = qmax; | |
| } | |
| else | |
| { | |
| tmp_offset = static_cast<T>(arm_compute::support::cpp11::round(f_offset)); | |
| } | |
| return static_cast<int>(tmp_offset); | |
| } | |
| inline QuantizationInfo qinfo_scaleoffset_from_minmax(DataType data_type, const float min, const float max) | |
| { | |
| int offset = 0; | |
| float scale = 0; | |
| // Continue only if [min,max] is a valid range and not a point | |
| if(min != max) | |
| { | |
| if(data_type == DataType::QASYMM8_SIGNED) | |
| { | |
| offset = calc_qinfo<int8_t>(min, max, scale); | |
| } | |
| else | |
| { | |
| offset = calc_qinfo<uint8_t>(min, max, scale); | |
| } | |
| } | |
| return QuantizationInfo(scale, offset); | |
| } | |
| inline void base_test_case(DetectionPostProcessLayerInfo info, DataType data_type, const SimpleTensor<float> &expected_output_boxes, | |
| const SimpleTensor<float> &expected_output_classes, const SimpleTensor<float> &expected_output_scores, const SimpleTensor<float> &expected_num_detection, | |
| AbsoluteTolerance<float> tolerance_boxes = AbsoluteTolerance<float>(0.1f), AbsoluteTolerance<float> tolerance_others = AbsoluteTolerance<float>(0.1f)) | |
| { | |
| Tensor box_encoding = create_tensor<Tensor>(TensorShape(4U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(data_type, -1.0f, 1.0f)); | |
| Tensor class_prediction = create_tensor<Tensor>(TensorShape(3U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(data_type, 0.0f, 1.0f)); | |
| Tensor anchors = create_tensor<Tensor>(TensorShape(4U, 6U), data_type, 1, qinfo_scaleoffset_from_minmax(data_type, 0.0f, 100.5f)); | |
| box_encoding.allocator()->allocate(); | |
| class_prediction.allocator()->allocate(); | |
| anchors.allocator()->allocate(); | |
| std::vector<float> box_encoding_vector = | |
| { | |
| 0.0f, 1.0f, 0.0f, 0.0f, | |
| 0.0f, -1.0f, 0.0f, 0.0f, | |
| 0.0f, 0.0f, 0.0f, 0.0f, | |
| 0.0f, 0.0f, 0.0f, 0.0f, | |
| 0.0f, 1.0f, 0.0f, 0.0f, | |
| 0.0f, 0.0f, 0.0f, 0.0f | |
| }; | |
| std::vector<float> class_prediction_vector = | |
| { | |
| 0.0f, 0.7f, 0.68f, | |
| 0.0f, 0.6f, 0.5f, | |
| 0.0f, 0.9f, 0.83f, | |
| 0.0f, 0.91f, 0.97f, | |
| 0.0f, 0.5f, 0.4f, | |
| 0.0f, 0.31f, 0.22f | |
| }; | |
| std::vector<float> anchors_vector = | |
| { | |
| 0.4f, 0.4f, 1.1f, 1.1f, | |
| 0.4f, 0.4f, 1.1f, 1.1f, | |
| 0.4f, 0.4f, 1.1f, 1.1f, | |
| 0.4f, 10.4f, 1.1f, 1.1f, | |
| 0.4f, 10.4f, 1.1f, 1.1f, | |
| 0.4f, 100.4f, 1.1f, 1.1f | |
| }; | |
| // Fill the tensors with random pre-generated values | |
| switch(data_type) | |
| { | |
| case DataType::F32: | |
| { | |
| fill_tensor(Accessor(box_encoding), box_encoding_vector); | |
| fill_tensor(Accessor(class_prediction), class_prediction_vector); | |
| fill_tensor(Accessor(anchors), anchors_vector); | |
| } | |
| break; | |
| case DataType::QASYMM8: | |
| { | |
| quantize_and_fill_tensor<uint8_t>(Accessor(box_encoding), box_encoding_vector); | |
| quantize_and_fill_tensor<uint8_t>(Accessor(class_prediction), class_prediction_vector); | |
| quantize_and_fill_tensor<uint8_t>(Accessor(anchors), anchors_vector); | |
| } | |
| break; | |
| case DataType::QASYMM8_SIGNED: | |
| { | |
| quantize_and_fill_tensor<int8_t>(Accessor(box_encoding), box_encoding_vector); | |
| quantize_and_fill_tensor<int8_t>(Accessor(class_prediction), class_prediction_vector); | |
| quantize_and_fill_tensor<int8_t>(Accessor(anchors), anchors_vector); | |
| } | |
| break; | |
| default: | |
| return; | |
| } | |
| // Determine the output through the CPP kernel | |
| Tensor output_boxes; | |
| Tensor output_classes; | |
| Tensor output_scores; | |
| Tensor num_detection; | |
| CPPDetectionPostProcessLayer detection; | |
| detection.configure(&box_encoding, &class_prediction, &anchors, &output_boxes, &output_classes, &output_scores, &num_detection, info); | |
| output_boxes.allocator()->allocate(); | |
| output_classes.allocator()->allocate(); | |
| output_scores.allocator()->allocate(); | |
| num_detection.allocator()->allocate(); | |
| // Run the kernel | |
| detection.run(); | |
| // Validate against the expected output | |
| // Validate output boxes | |
| validate(Accessor(output_boxes), expected_output_boxes, tolerance_boxes); | |
| // Validate detection classes | |
| validate(Accessor(output_classes), expected_output_classes, tolerance_others); | |
| // Validate detection scores | |
| validate(Accessor(output_scores), expected_output_scores, tolerance_others); | |
| // Validate num detections | |
| validate(Accessor(num_detection), expected_num_detection, tolerance_others); | |
| } | |
| } // namespace | |
| TEST_SUITE(CPP) | |
| TEST_SUITE(DetectionPostProcessLayer) | |
| // *INDENT-OFF* | |
| // clang-format off | |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("BoxEncodingsInfo", { TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 10U, 3U), 1, DataType::F32), // Mismatching batch_size | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::S8), // Unsupported data type | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), // Wrong Detection Info | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), // Wrong boxes dimensions | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8), // Wrong score dimension | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8_SIGNED)}), // Wrong score dimension | |
| framework::dataset::make("ClassPredsInfo",{ TensorInfo(TensorShape(3U ,10U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U ,10U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U ,10U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U ,10U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U ,10U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U ,10U), 1, DataType::QASYMM8), | |
| TensorInfo(TensorShape(3U ,10U), 1, DataType::QASYMM8_SIGNED)})), | |
| framework::dataset::make("AnchorsInfo",{ TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8), | |
| TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8_SIGNED)})), | |
| framework::dataset::make("OutputBoxInfo", { TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::S8), | |
| TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U, 5U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32)})), | |
| framework::dataset::make("OuputClassesInfo",{ TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(6U, 1U), 1, DataType::F32)})), | |
| framework::dataset::make("OutputScoresInfo",{ TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(6U, 1U), 1, DataType::F32)})), | |
| framework::dataset::make("NumDetectionsInfo",{ TensorInfo(TensorShape(1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U), 1, DataType::F32)})), | |
| framework::dataset::make("DetectionPostProcessLayerInfo",{ DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}), | |
| DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}), | |
| DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}), | |
| DetectionPostProcessLayerInfo(3, 1, 0.0f, 1.5f, 2, {0.0f,0.1f,0.1f,0.1f}), | |
| DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}), | |
| DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f})})), | |
| framework::dataset::make("Expected", {true, false, false, false, false, false })), | |
| box_encodings_info, classes_info, anchors_info, output_boxes_info, output_classes_info,output_scores_info, num_detection_info, detect_info, expected) | |
| { | |
| const Status status = CPPDetectionPostProcessLayer::validate(&box_encodings_info.clone()->set_is_resizable(false), | |
| &classes_info.clone()->set_is_resizable(false), | |
| &anchors_info.clone()->set_is_resizable(false), | |
| &output_boxes_info.clone()->set_is_resizable(false), | |
| &output_classes_info.clone()->set_is_resizable(false), | |
| &output_scores_info.clone()->set_is_resizable(false), &num_detection_info.clone()->set_is_resizable(false), detect_info); | |
| ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); | |
| } | |
| // clang-format on | |
| // *INDENT-ON* | |
| TEST_SUITE(F32) | |
| TEST_CASE(Float_general, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 3.f }); | |
| // Run base test | |
| base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection); | |
| } | |
| TEST_CASE(Float_fast, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/, | |
| false /*use_regular_nms*/, 1 /*detections_per_class*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 3.f }); | |
| // Run base test | |
| base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection); | |
| } | |
| TEST_CASE(Float_regular, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/, | |
| true /*use_regular_nms*/, 1 /*detections_per_class*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.91f, 0.0f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 2.f }); | |
| // Run test | |
| base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection); | |
| } | |
| TEST_SUITE_END() // F32 | |
| TEST_SUITE(QASYMM8) | |
| TEST_CASE(Quantized_general, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 3.f }); | |
| // Run test | |
| base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f)); | |
| } | |
| TEST_CASE(Quantized_fast, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/, | |
| false /*use_regular_nms*/, 1 /*detections_per_class*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 3.f }); | |
| // Run base test | |
| base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f)); | |
| } | |
| TEST_CASE(Quantized_regular, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/, | |
| true /*use_regular_nms*/, 1 /*detections_per_class*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.95f, 0.91f, 0.0f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 2.f }); | |
| // Run test | |
| base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f)); | |
| } | |
| TEST_SUITE_END() // QASYMM8 | |
| TEST_SUITE(QASYMM8_SIGNED) | |
| TEST_CASE(Quantized_general, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 3.f }); | |
| // Run test | |
| base_test_case(info, DataType::QASYMM8_SIGNED, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f)); | |
| } | |
| TEST_CASE(Quantized_fast, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/, | |
| false /*use_regular_nms*/, 1 /*detections_per_class*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 3.f }); | |
| // Run base test | |
| base_test_case(info, DataType::QASYMM8_SIGNED, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f)); | |
| } | |
| TEST_CASE(Quantized_regular, framework::DatasetMode::ALL) | |
| { | |
| DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/, | |
| 0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/, | |
| true /*use_regular_nms*/, 1 /*detections_per_class*/); | |
| // Fill expected detection boxes | |
| SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32); | |
| fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f }); | |
| // Fill expected detection classes | |
| SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f }); | |
| // Fill expected detection scores | |
| SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32); | |
| fill_tensor(expected_output_scores, std::vector<float> { 0.95f, 0.91f, 0.0f }); | |
| // Fill expected num detections | |
| SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32); | |
| fill_tensor(expected_num_detection, std::vector<float> { 2.f }); | |
| // Run test | |
| base_test_case(info, DataType::QASYMM8_SIGNED, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f)); | |
| } | |
| TEST_SUITE_END() // QASYMM8_SIGNED | |
| TEST_SUITE_END() // DetectionPostProcessLayer | |
| TEST_SUITE_END() // CPP | |
| } // namespace validation | |
| } // namespace test | |
| } // namespace arm_compute |