| /* |
| * Copyright (c) 2019-2021 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/NEON/functions/NEDetectionPostProcessLayer.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 U, typename T> |
| inline void quantize_and_fill_tensor(U &&tensor, const std::vector<T> &v) |
| { |
| QuantizationInfo qi = tensor.quantization_info(); |
| std::vector<uint8_t> quantized; |
| quantized.reserve(v.size()); |
| for(auto elem : v) |
| { |
| quantized.emplace_back(quantize_qasymm8(elem, qi)); |
| } |
| std::memcpy(tensor.data(), quantized.data(), sizeof(uint8_t) * quantized.size()); |
| } |
| inline QuantizationInfo qinfo_scaleoffset_from_minmax(const float min, const float max) |
| { |
| int offset = 0; |
| float scale = 0; |
| const uint8_t qmin = std::numeric_limits<uint8_t>::min(); |
| const uint8_t qmax = std::numeric_limits<uint8_t>::max(); |
| const float f_qmin = qmin; |
| const float f_qmax = qmax; |
| |
| // Continue only if [min,max] is a valid range and not a point |
| if(min != max) |
| { |
| 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; |
| |
| uint8_t uint8_offset = 0; |
| if(f_offset < f_qmin) |
| { |
| uint8_offset = qmin; |
| } |
| else if(f_offset > f_qmax) |
| { |
| uint8_offset = qmax; |
| } |
| else |
| { |
| uint8_offset = static_cast<uint8_t>(support::cpp11::round(f_offset)); |
| } |
| offset = uint8_offset; |
| } |
| 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(-1.0f, 1.0f)); |
| Tensor class_prediction = create_tensor<Tensor>(TensorShape(3U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(0.0f, 1.0f)); |
| Tensor anchors = create_tensor<Tensor>(TensorShape(4U, 6U), data_type, 1, qinfo_scaleoffset_from_minmax(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 |
| if(data_type == DataType::F32) |
| { |
| fill_tensor(Accessor(box_encoding), box_encoding_vector); |
| fill_tensor(Accessor(class_prediction), class_prediction_vector); |
| fill_tensor(Accessor(anchors), anchors_vector); |
| } |
| else |
| { |
| quantize_and_fill_tensor(Accessor(box_encoding), box_encoding_vector); |
| quantize_and_fill_tensor(Accessor(class_prediction), class_prediction_vector); |
| quantize_and_fill_tensor(Accessor(anchors), anchors_vector); |
| } |
| |
| // Determine the output through the Compute Library operator |
| Tensor output_boxes; |
| Tensor output_classes; |
| Tensor output_scores; |
| Tensor num_detection; |
| NEDetectionPostProcessLayer 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(NEON) |
| 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 |
| 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)})), |
| 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)})), |
| 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 = NEDetectionPostProcessLayer::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_END() // DetectionPostProcessLayer |
| TEST_SUITE_END() // Neon |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |