From a7acb3cbabeb66ce647684466a04c96b2963c9c9 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Tue, 8 Jan 2019 13:48:44 +0000 Subject: COMPMID-1849: Implement CPPDetectionPostProcessLayer * Add DetectionPostProcessLayer * Add DetectionPostProcessLayer at the graph Change-Id: I7e56f6cffc26f112d26dfe74853085bb8ec7d849 Signed-off-by: Isabella Gottardi Reviewed-on: https://review.mlplatform.org/c/1639 Reviewed-by: Giuseppe Rossini Tested-by: Arm Jenkins --- tests/validation/CPP/DetectionPostProcessLayer.cpp | 390 +++++++++++++++++++++ 1 file changed, 390 insertions(+) create mode 100644 tests/validation/CPP/DetectionPostProcessLayer.cpp (limited to 'tests') diff --git a/tests/validation/CPP/DetectionPostProcessLayer.cpp b/tests/validation/CPP/DetectionPostProcessLayer.cpp new file mode 100644 index 0000000000..51f3452b3d --- /dev/null +++ b/tests/validation/CPP/DetectionPostProcessLayer.cpp @@ -0,0 +1,390 @@ +/* + * Copyright (c) 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 "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 +inline void fill_tensor(U &&tensor, const std::vector &v) +{ + std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); +} +template +inline void quantize_and_fill_tensor(U &&tensor, const std::vector &v) +{ + QuantizationInfo qi = tensor.quantization_info(); + std::vector 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::min(); + const uint8_t qmax = std::numeric_limits::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(std::round(f_offset)); + } + offset = uint8_offset; + } + return QuantizationInfo(scale, offset); +} + +inline void base_test_case(DetectionPostProcessLayerInfo info, DataType data_type, const SimpleTensor &expected_output_boxes, + const SimpleTensor &expected_output_classes, const SimpleTensor &expected_output_scores, const SimpleTensor &expected_num_detection, + AbsoluteTolerance tolerance_boxes = AbsoluteTolerance(0.1f), AbsoluteTolerance tolerance_others = AbsoluteTolerance(0.1f)) +{ + Tensor box_encoding = create_tensor(TensorShape(4U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(-1.0f, 1.0f)); + Tensor class_prediction = create_tensor(TensorShape(3U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(0.0f, 1.0f)); + Tensor anchors = create_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 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 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 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 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 + 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 = 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 expected_output_boxes(TensorShape(4U, 3U), DataType::F32); + fill_tensor(expected_output_boxes, std::vector { -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 expected_output_classes(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_classes, std::vector { 1.0f, 0.0f, 0.0f }); + // Fill expected detection scores + SimpleTensor expected_output_scores(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_scores, std::vector { 0.97f, 0.95f, 0.31f }); + // Fill expected num detections + SimpleTensor expected_num_detection(TensorShape(1U), DataType::F32); + fill_tensor(expected_num_detection, std::vector { 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 expected_output_boxes(TensorShape(4U, 3U), DataType::F32); + fill_tensor(expected_output_boxes, std::vector { -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 expected_output_classes(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_classes, std::vector { 1.0f, 0.0f, 0.0f }); + // Fill expected detection scores + SimpleTensor expected_output_scores(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_scores, std::vector { 0.97f, 0.95f, 0.31f }); + // Fill expected num detections + SimpleTensor expected_num_detection(TensorShape(1U), DataType::F32); + fill_tensor(expected_num_detection, std::vector { 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 expected_output_boxes(TensorShape(4U, 3U), DataType::F32); + fill_tensor(expected_output_boxes, std::vector { -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 expected_output_classes(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_classes, std::vector { 1.0f, 0.0f, 0.0f }); + // Fill expected detection scores + SimpleTensor expected_output_scores(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_scores, std::vector { 0.97f, 0.91f, 0.0f }); + // Fill expected num detections + SimpleTensor expected_num_detection(TensorShape(1U), DataType::F32); + fill_tensor(expected_num_detection, std::vector { 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 expected_output_boxes(TensorShape(4U, 3U), DataType::F32); + fill_tensor(expected_output_boxes, std::vector { -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 expected_output_classes(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_classes, std::vector { 1.0f, 0.0f, 0.0f }); + // Fill expected detection scores + SimpleTensor expected_output_scores(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_scores, std::vector { 0.97f, 0.95f, 0.31f }); + // Fill expected num detections + SimpleTensor expected_num_detection(TensorShape(1U), DataType::F32); + fill_tensor(expected_num_detection, std::vector { 3.f }); + // Run test + base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance(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 expected_output_boxes(TensorShape(4U, 3U), DataType::F32); + fill_tensor(expected_output_boxes, std::vector { -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 expected_output_classes(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_classes, std::vector { 1.0f, 0.0f, 0.0f }); + // Fill expected detection scores + SimpleTensor expected_output_scores(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_scores, std::vector { 0.97f, 0.95f, 0.31f }); + // Fill expected num detections + SimpleTensor expected_num_detection(TensorShape(1U), DataType::F32); + fill_tensor(expected_num_detection, std::vector { 3.f }); + + // Run base test + base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance(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 expected_output_boxes(TensorShape(4U, 3U), DataType::F32); + fill_tensor(expected_output_boxes, std::vector { -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 expected_output_classes(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_classes, std::vector { 1.0f, 0.0f, 0.0f }); + // Fill expected detection scores + SimpleTensor expected_output_scores(TensorShape(3U), DataType::F32); + fill_tensor(expected_output_scores, std::vector { 0.95f, 0.91f, 0.0f }); + // Fill expected num detections + SimpleTensor expected_num_detection(TensorShape(1U), DataType::F32); + fill_tensor(expected_num_detection, std::vector { 2.f }); + + // Run test + base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance(0.3f)); +} + +TEST_SUITE_END() // QASYMM8 + +TEST_SUITE_END() // DetectionPostProcessLayer +TEST_SUITE_END() // CPP +} // namespace validation +} // namespace test +} // namespace arm_compute -- cgit v1.2.1