| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "../TfLiteParser.hpp" |
| |
| #include <boost/test/unit_test.hpp> |
| #include "test/GraphUtils.hpp" |
| |
| #include "ParserFlatbuffersFixture.hpp" |
| #include "ParserPrototxtFixture.hpp" |
| #include "ParserHelper.hpp" |
| |
| BOOST_AUTO_TEST_SUITE(TensorflowLiteParser) |
| |
| struct DetectionPostProcessFixture : ParserFlatbuffersFixture |
| { |
| explicit DetectionPostProcessFixture(const std::string& custom_options) |
| { |
| /* |
| The following values were used for the custom_options: |
| use_regular_nms = true |
| max_classes_per_detection = 1 |
| detections_per_class = 1 |
| nms_score_threshold = 0.0 |
| nms_iou_threshold = 0.5 |
| max_detections = 3 |
| max_detections = 3 |
| num_classes = 2 |
| h_scale = 5 |
| w_scale = 5 |
| x_scale = 10 |
| y_scale = 10 |
| */ |
| m_JsonString = R"( |
| { |
| "version": 3, |
| "operator_codes": [{ |
| "builtin_code": "CUSTOM", |
| "custom_code": "TFLite_Detection_PostProcess" |
| }], |
| "subgraphs": [{ |
| "tensors": [{ |
| "shape": [1, 6, 4], |
| "type": "UINT8", |
| "buffer": 0, |
| "name": "box_encodings", |
| "quantization": { |
| "min": [0.0], |
| "max": [255.0], |
| "scale": [1.0], |
| "zero_point": [ 1 ] |
| } |
| }, |
| { |
| "shape": [1, 6, 3], |
| "type": "UINT8", |
| "buffer": 1, |
| "name": "scores", |
| "quantization": { |
| "min": [0.0], |
| "max": [255.0], |
| "scale": [0.01], |
| "zero_point": [0] |
| } |
| }, |
| { |
| "shape": [6, 4], |
| "type": "UINT8", |
| "buffer": 2, |
| "name": "anchors", |
| "quantization": { |
| "min": [0.0], |
| "max": [255.0], |
| "scale": [0.5], |
| "zero_point": [0] |
| } |
| }, |
| { |
| "type": "FLOAT32", |
| "buffer": 3, |
| "name": "detection_boxes", |
| "quantization": {} |
| }, |
| { |
| "type": "FLOAT32", |
| "buffer": 4, |
| "name": "detection_classes", |
| "quantization": {} |
| }, |
| { |
| "type": "FLOAT32", |
| "buffer": 5, |
| "name": "detection_scores", |
| "quantization": {} |
| }, |
| { |
| "type": "FLOAT32", |
| "buffer": 6, |
| "name": "num_detections", |
| "quantization": {} |
| } |
| ], |
| "inputs": [0, 1, 2], |
| "outputs": [3, 4, 5, 6], |
| "operators": [{ |
| "opcode_index": 0, |
| "inputs": [0, 1, 2], |
| "outputs": [3, 4, 5, 6], |
| "builtin_options_type": 0, |
| "custom_options": [)" + custom_options + R"(], |
| "custom_options_format": "FLEXBUFFERS" |
| }] |
| }], |
| "buffers": [{}, |
| {}, |
| { "data": [ 1, 1, 2, 2, |
| 1, 1, 2, 2, |
| 1, 1, 2, 2, |
| 1, 21, 2, 2, |
| 1, 21, 2, 2, |
| 1, 201, 2, 2]}, |
| {}, |
| {}, |
| {}, |
| {}, |
| ] |
| } |
| )"; |
| } |
| }; |
| |
| struct ParseDetectionPostProcessCustomOptions : DetectionPostProcessFixture |
| { |
| private: |
| static armnn::DetectionPostProcessDescriptor GenerateDescriptor() |
| { |
| static armnn::DetectionPostProcessDescriptor descriptor; |
| descriptor.m_UseRegularNms = true; |
| descriptor.m_MaxDetections = 3u; |
| descriptor.m_MaxClassesPerDetection = 1u; |
| descriptor.m_DetectionsPerClass = 1u; |
| descriptor.m_NumClasses = 2u; |
| descriptor.m_NmsScoreThreshold = 0.0f; |
| descriptor.m_NmsIouThreshold = 0.5f; |
| descriptor.m_ScaleH = 5.0f; |
| descriptor.m_ScaleW = 5.0f; |
| descriptor.m_ScaleX = 10.0f; |
| descriptor.m_ScaleY = 10.0f; |
| |
| return descriptor; |
| } |
| |
| public: |
| ParseDetectionPostProcessCustomOptions() |
| : DetectionPostProcessFixture( |
| GenerateDetectionPostProcessJsonString(GenerateDescriptor())) |
| {} |
| }; |
| |
| BOOST_FIXTURE_TEST_CASE( ParseDetectionPostProcess, ParseDetectionPostProcessCustomOptions ) |
| { |
| Setup(); |
| |
| // Inputs |
| using UnquantizedContainer = std::vector<float>; |
| UnquantizedContainer boxEncodings = |
| { |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 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, 1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }; |
| |
| UnquantizedContainer scores = |
| { |
| 0.0f, 0.9f, 0.8f, |
| 0.0f, 0.75f, 0.72f, |
| 0.0f, 0.6f, 0.5f, |
| 0.0f, 0.93f, 0.95f, |
| 0.0f, 0.5f, 0.4f, |
| 0.0f, 0.3f, 0.2f |
| }; |
| |
| // Outputs |
| UnquantizedContainer detectionBoxes = |
| { |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }; |
| |
| UnquantizedContainer detectionClasses = { 1.0f, 0.0f, 0.0f }; |
| UnquantizedContainer detectionScores = { 0.95f, 0.93f, 0.0f }; |
| |
| UnquantizedContainer numDetections = { 2.0f }; |
| |
| // Quantize inputs and outputs |
| using QuantizedContainer = std::vector<uint8_t>; |
| QuantizedContainer quantBoxEncodings = QuantizedVector<uint8_t>(1.0f, 1, boxEncodings); |
| QuantizedContainer quantScores = QuantizedVector<uint8_t>(0.01f, 0, scores); |
| |
| std::map<std::string, QuantizedContainer> input = |
| { |
| { "box_encodings", quantBoxEncodings }, |
| { "scores", quantScores } |
| }; |
| |
| std::map<std::string, UnquantizedContainer> output = |
| { |
| { "detection_boxes", detectionBoxes}, |
| { "detection_classes", detectionClasses}, |
| { "detection_scores", detectionScores}, |
| { "num_detections", numDetections} |
| }; |
| |
| RunTest<armnn::DataType::QuantisedAsymm8, armnn::DataType::Float32>(0, input, output); |
| } |
| |
| BOOST_FIXTURE_TEST_CASE(DetectionPostProcessGraphStructureTest, ParseDetectionPostProcessCustomOptions) |
| { |
| /* |
| Inputs: box_encodings scores |
| \ / |
| DetectionPostProcess |
| / / \ \ |
| / / \ \ |
| Outputs: detection detection detection num_detections |
| boxes classes scores |
| */ |
| |
| ReadStringToBinary(); |
| |
| armnn::INetworkPtr network = m_Parser->CreateNetworkFromBinary(m_GraphBinary); |
| |
| auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec()); |
| |
| auto optimizedNetwork = boost::polymorphic_downcast<armnn::OptimizedNetwork*>(optimized.get()); |
| auto graph = optimizedNetwork->GetGraph(); |
| |
| // Check the number of layers in the graph |
| BOOST_TEST((graph.GetNumInputs() == 2)); |
| BOOST_TEST((graph.GetNumOutputs() == 4)); |
| BOOST_TEST((graph.GetNumLayers() == 7)); |
| |
| // Input layers |
| armnn::Layer* boxEncodingLayer = GetFirstLayerWithName(graph, "box_encodings"); |
| BOOST_TEST((boxEncodingLayer->GetType() == armnn::LayerType::Input)); |
| BOOST_TEST(CheckNumberOfInputSlot(boxEncodingLayer, 0)); |
| BOOST_TEST(CheckNumberOfOutputSlot(boxEncodingLayer, 1)); |
| |
| armnn::Layer* scoresLayer = GetFirstLayerWithName(graph, "scores"); |
| BOOST_TEST((scoresLayer->GetType() == armnn::LayerType::Input)); |
| BOOST_TEST(CheckNumberOfInputSlot(scoresLayer, 0)); |
| BOOST_TEST(CheckNumberOfOutputSlot(scoresLayer, 1)); |
| |
| // DetectionPostProcess layer |
| armnn::Layer* detectionPostProcessLayer = GetFirstLayerWithName(graph, "DetectionPostProcess:0:0"); |
| BOOST_TEST((detectionPostProcessLayer->GetType() == armnn::LayerType::DetectionPostProcess)); |
| BOOST_TEST(CheckNumberOfInputSlot(detectionPostProcessLayer, 2)); |
| BOOST_TEST(CheckNumberOfOutputSlot(detectionPostProcessLayer, 4)); |
| |
| // Output layers |
| armnn::Layer* detectionBoxesLayer = GetFirstLayerWithName(graph, "detection_boxes"); |
| BOOST_TEST((detectionBoxesLayer->GetType() == armnn::LayerType::Output)); |
| BOOST_TEST(CheckNumberOfInputSlot(detectionBoxesLayer, 1)); |
| BOOST_TEST(CheckNumberOfOutputSlot(detectionBoxesLayer, 0)); |
| |
| armnn::Layer* detectionClassesLayer = GetFirstLayerWithName(graph, "detection_classes"); |
| BOOST_TEST((detectionClassesLayer->GetType() == armnn::LayerType::Output)); |
| BOOST_TEST(CheckNumberOfInputSlot(detectionClassesLayer, 1)); |
| BOOST_TEST(CheckNumberOfOutputSlot(detectionClassesLayer, 0)); |
| |
| armnn::Layer* detectionScoresLayer = GetFirstLayerWithName(graph, "detection_scores"); |
| BOOST_TEST((detectionScoresLayer->GetType() == armnn::LayerType::Output)); |
| BOOST_TEST(CheckNumberOfInputSlot(detectionScoresLayer, 1)); |
| BOOST_TEST(CheckNumberOfOutputSlot(detectionScoresLayer, 0)); |
| |
| armnn::Layer* numDetectionsLayer = GetFirstLayerWithName(graph, "num_detections"); |
| BOOST_TEST((numDetectionsLayer->GetType() == armnn::LayerType::Output)); |
| BOOST_TEST(CheckNumberOfInputSlot(numDetectionsLayer, 1)); |
| BOOST_TEST(CheckNumberOfOutputSlot(numDetectionsLayer, 0)); |
| |
| // Check the connections |
| armnn::TensorInfo boxEncodingTensor(armnn::TensorShape({ 1, 6, 4 }), armnn::DataType::QuantisedAsymm8, 1, 1); |
| armnn::TensorInfo scoresTensor(armnn::TensorShape({ 1, 6, 3 }), armnn::DataType::QuantisedAsymm8, |
| 0.00999999978f, 0); |
| |
| armnn::TensorInfo detectionBoxesTensor(armnn::TensorShape({ 1, 3, 4 }), armnn::DataType::Float32, 0, 0); |
| armnn::TensorInfo detectionClassesTensor(armnn::TensorShape({ 1, 3 }), armnn::DataType::Float32, 0, 0); |
| armnn::TensorInfo detectionScoresTensor(armnn::TensorShape({ 1, 3 }), armnn::DataType::Float32, 0, 0); |
| armnn::TensorInfo numDetectionsTensor(armnn::TensorShape({ 1} ), armnn::DataType::Float32, 0, 0); |
| |
| BOOST_TEST(IsConnected(boxEncodingLayer, detectionPostProcessLayer, 0, 0, boxEncodingTensor)); |
| BOOST_TEST(IsConnected(scoresLayer, detectionPostProcessLayer, 0, 1, scoresTensor)); |
| BOOST_TEST(IsConnected(detectionPostProcessLayer, detectionBoxesLayer, 0, 0, detectionBoxesTensor)); |
| BOOST_TEST(IsConnected(detectionPostProcessLayer, detectionClassesLayer, 1, 0, detectionClassesTensor)); |
| BOOST_TEST(IsConnected(detectionPostProcessLayer, detectionScoresLayer, 2, 0, detectionScoresTensor)); |
| BOOST_TEST(IsConnected(detectionPostProcessLayer, numDetectionsLayer, 3, 0, numDetectionsTensor)); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |
