初始化项目
This commit is contained in:
BBIT-Kai
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[property]
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enable=1
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#Width height used for configuration to which below configs are configured
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config-width=1920
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config-height=1080
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osd-mode=2
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display-font-size=12
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[line-crossing-stream-0]
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enable=1
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# 箭头尾部、箭头头部、横线开始、横线结束、550;550;700;800;200;650;1700;650 # 测试视频
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line-crossing-LineA=950;400;1050;600;800;460;1060;460
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line-crossing-LineB=1040;630;1200;900;870;750;1280;720
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class-id=0
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extended=0
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# mode=loose、strict、balanced
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mode=loose
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[line-crossing-stream-1]
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enable=1
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line-crossing-LineA=930;210;850;430;780;330;1000;330
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line-crossing-LineB=750;550;620;900;340;680;990;700
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class-id=0
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extended=0
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mode=loose
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BaseConfig:
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minDetectorConfidence: 0.0430 # 如果检测器检测框的置信度低于此值，则不会被用于跟踪
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TargetManagement:
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enableBboxUnClipping: 1 # 如果检测框可能被图像边界裁剪，则解除裁剪
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preserveStreamUpdateOrder: 0 # 分配新目标ID时，是否保持输入流顺序，以在多次运行中保持目标ID确定性
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maxTargetsPerStream: 150 # 每个流可跟踪的最大目标数。建议设置大于10。注意：此值应包括以阴影模式跟踪的目标。最大值取决于GPU内存容量
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# [目标创建与终止策略]
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minIouDiff4NewTarget: 0.7418 # 如果新检测到的对象与已有目标的IOU高于此阈值，则丢弃该新对象
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minTrackerConfidence: 0.4009 # 如果对象跟踪器的置信度低于此值，则以阴影模式跟踪。有效范围：[0.0, 1.0]
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probationAge: 2 # 目标年龄超过此值，则视为有效目标
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maxShadowTrackingAge: 51 # 阴影跟踪的最大长度。如果阴影跟踪年龄超过此值，跟踪器将被终止
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earlyTerminationAge: 1 # 如果阴影跟踪年龄在试验期达到此阈值，则目标会提前终止
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TrajectoryManagement:
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useUniqueID: 0 # 分配跟踪器ID时是否使用64位长唯一ID
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DataAssociator:
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dataAssociatorType: 0 # 数据关联器类型 { DEFAULT=0 }
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associationMatcherType: 1 # 匹配算法类型 { GREEDY=0, CASCADED=1 }
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checkClassMatch: 1 # 是否只将同类对象关联，默认值：true
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# [关联度指标：有效候选阈值]
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minMatchingScore4Overall: 0.4290 # 总匹配分数最小值
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minMatchingScore4SizeSimilarity: 0.3627 # 检测框尺寸相似度最小分数
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minMatchingScore4Iou: 0.2575 # IOU最小分数
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minMatchingScore4VisualSimilarity: 0.5356 # 视觉相似度最小分数
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# [关联度指标：权重]
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matchingScoreWeight4VisualSimilarity: 0.3370 # 视觉相似度权重（相关响应比率）
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matchingScoreWeight4SizeSimilarity: 0.4354 # 尺寸相似度权重
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matchingScoreWeight4Iou: 0.3656 # IOU权重
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# [关联度指标：试探性检测] 仅使用IOU相似度进行试探性检测匹配
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tentativeDetectorConfidence: 0.2008 # 如果检测置信度低于此值但高于minDetectorConfidence，则视为试探性检测
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minMatchingScore4TentativeIou: 0.5296 # 匹配目标与试探性检测的最小IOU阈值
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StateEstimator:
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stateEstimatorType: 1 # 状态估计器类型 { DUMMY=0, SIMPLE=1, REGULAR=2 }
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# [动态建模]
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processNoiseVar4Loc: 1.5110 # 检测框中心的过程噪声方差
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processNoiseVar4Size: 1.3159 # 检测框尺寸的过程噪声方差
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processNoiseVar4Vel: 0.0300 # 速度的过程噪声方差
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measurementNoiseVar4Detector: 3.0283 # 检测器检测的测量噪声方差
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measurementNoiseVar4Tracker: 8.1505 # 跟踪器定位的测量噪声方差
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VisualTracker:
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visualTrackerType: 1 # 视觉跟踪器类型 { DUMMY=0, NvDCF=1 }
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# [NvDCF：特征提取]
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useColorNames: 1 # 是否使用ColorNames特征
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useHog: 0 # 是否使用HOG（方向梯度直方图）特征
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featureImgSizeLevel: 2 # 特征图尺寸等级。有效范围：{1, 2, 3, 4, 5}，从最小到最大
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featureFocusOffsetFactor_y: -0.2000 # 汉宁窗口中心相对于特征高度的偏移量。中心在垂直方向移动 (featureFocusOffsetFactor_y * featureMatSize.height)
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# [NvDCF：相关滤波器]
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filterLr: 0.0750 # DCF滤波器指数移动平均学习率，有效范围：[0.0, 1.0]
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filterChannelWeightsLr: 0.1000 # 特征通道权重的学习率，有效范围：[0.0, 1.0]
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gaussianSigma: 0.7500 # 创建DCF滤波器时期望响应的高斯标准差（像素）
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source-list:
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- main: rtsp://admin:cda1b2c3@192.168.1.6:554/Streaming/Channels/101 # 正1
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side: rtsp://admin:cda1b2c3@192.168.1.4:554/Streaming/Channels/101 # 辅2
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- main: rtsp://admin:cda1b2c3@192.168.1.7:554/Streaming/Channels/101 # 正2
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side: rtsp://admin:cda1b2c3@192.168.1.8:554/Streaming/Channels/101 # 辅1
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# 测试环境
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# - main: file:///opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/sentinel/dist/source/traffic_test.mp4
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# side: rtsp://admin:cda1b2c3@10.0.4.41:554/Streaming/Channels/101
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vehicle_type:
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# 0：不限制 1：轿跑车 2：大型车辆 3：轿车 4：SUV 5：卡车 6：面包车/厢型车
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# 7：大型车辆 + 卡车（生产）
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type: 0
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output:
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## 1:file ouput 2:fake output 3:eglsink output 4:rtsp output
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type: 4
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## 0: H264 encoder 1:H265 encoder
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enc: 0
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## encoder type 0=Hardware 1=Software
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enc-type: 0
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bitrate: 4000000
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##The file name without suffix
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filename: test
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primary-gie:
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#0:nvinfer, 1:nvinfeserver
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plugin-type: 0
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##For car detection
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config-file-path: pgie_0_traffic_cam_net.yml
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unique-id: 1
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#If there is ROI
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analytics:
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enable: 1
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config-file: config_nvdsanalytics.txt
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secondary-gie1:
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unique-id: 2
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#0:nvinfer, 1:nvinfeserver
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plugin-type: 0
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config-file-path: sgie_1_vehicle_type.yml
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process-mode: 2
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tracker:
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enable: 1
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tracker-width: 640
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tracker-height: 384
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gpu-id: 0
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ll-lib-file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
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ll-config-file: config_tracker.yml
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enable-batch-process: 1
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source-list:
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- main: rtsp://admin:cda1b2c3@192.168.1.6:554/Streaming/Channels/101 # 正1
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side: rtsp://admin:cda1b2c3@192.168.1.4:554/Streaming/Channels/101 # 辅2
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- main: rtsp://admin:cda1b2c3@192.168.1.7:554/Streaming/Channels/101 # 正2
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side: rtsp://admin:cda1b2c3@192.168.1.8:554/Streaming/Channels/101 # 辅1
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# 测试环境
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# - main: file:///opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/sentinel/dist/source/traffic_test.mp4
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# side: rtsp://admin:cda1b2c3@10.0.4.41:554/Streaming/Channels/101
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vehicle_type:
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# 0：不限制 1：轿跑车 2：大型车辆 3：轿车 4：SUV 5：卡车 6：面包车/厢型车
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# 7：大型车辆 + 卡车（生产）
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type: 7
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output:
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## 1:file ouput 2:fake output 3:eglsink output 4:rtsp output
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type: 2
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## 0: H264 encoder 1:H265 encoder
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enc: 0
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## encoder type 0=Hardware 1=Software
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enc-type: 0
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bitrate: 6000000
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##The file name without suffix
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filename: test
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primary-gie:
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#0:nvinfer, 1:nvinfeserver
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plugin-type: 0
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##For car detection
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config-file-path: pgie_0_traffic_cam_net.yml
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unique-id: 1
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#If there is ROI
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analytics:
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enable: 1
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config-file: config_nvdsanalytics.txt
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secondary-gie1:
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unique-id: 2
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#0:nvinfer, 1:nvinfeserver
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plugin-type: 0
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config-file-path: sgie_1_vehicle_type.yml
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process-mode: 2
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tracker:
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enable: 1
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tracker-width: 640
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tracker-height: 384
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gpu-id: 0
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ll-lib-file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
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ll-config-file: config_tracker.yml
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enable-batch-process: 1
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property:
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# 官方文档中的配置=============================
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gpu-id: 0
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infer-dims: 3;544;960
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net-scale-factor: 0.00392156862745098
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tlt-model-key: tlt_encode
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# 模型网络类型 0: Detector 1: Classifier 2: Segmentation 3: Instance Segmentation
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network-type: 0
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num-detected-classes: 4
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# 模型颜色格式 0：RGB 1：BGR 2：灰度
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model-color-format: 0
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# 保持宽高比 0：不保持 1：保持
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maintain-aspect-ratio: 0
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# 是否输出张量元数据 0：否 1：是
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output-tensor-meta: 0
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# 文件路径配置=============================
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onnx-file: ../models/traffic_cam_net/resnet18_trafficcamnet_pruned.onnx
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labelfile-path: ../models/traffic_cam_net/labels.txt
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model-engine-file: ../models/traffic_cam_net/resnet18_trafficcamnet_pruned.onnx_b4_gpu0_fp16.engine
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gie-unique-id: 1
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batch-size: 4
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# 推理模式配置 1：整帧 2：上游ROI （1：主推理 2：次推理（基于主推理））
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process-mode: 1
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## 0=FP32, 1=INT8, 2=FP16 mode
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network-mode: 2
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# 推理间隔 0：每帧 1：每1帧 2：每2帧
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interval: 0
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output-blob-names: output_cov/Sigmoid:0;output_bbox/BiasAdd:0
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# 集群模式 0: OpenCV groupRectangles() 1: DBSCAN 2: Non Maximum Suppression 3: DBSCAN + NMS Hybrid 4: No clustering
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cluster-mode: 2
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# 计算硬件 0: Platform default – GPU (dGPU), VIC (Jetson) 1: GPU 2: VIC (Jetson only)
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scaling-compute-hw: 0
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# 设置具体类别NMS等参数
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class-attrs-all:
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# 保留的最多对象数
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topk: 20
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# 两个方案之间的最大 IOU 分数，超过该分数后，置信度较低的方案将被拒绝。
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nms-iou-threshold: 0.5
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# 预聚类阈值，范围为0到1。较高的值会导致更多的边界框被聚类在一起。
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pre-cluster-threshold: 0.2
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## Per class configurations
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class-attrs-0:
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topk: 20
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nms-iou-threshold: 0.6
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pre-cluster-threshold: 0.4
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property:
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gpu-id: 0
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gie-unique-id: 2
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net-scale-factor: 1
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scaling-compute-hw: 0
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network-type: 1
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infer-dims: 3;224;224
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onnx-file: ../models/vehicle_type_net/resnet18_pruned.onnx
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model-engine-file: ../models/vehicle_type_net/resnet18_pruned.onnx_b4_gpu0_fp16.engine
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# model-engine-file: ../models/vehicle_type_net/resnet18_pruned.onnx_b4_gpu0_int8.engine
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labelfile-path: ../models/vehicle_type_net/labels.txt
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int8-calib-file: ../models/vehicle_type_net/resnet18_pruned_int8.txt
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batch-size: 4
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network-mode: 2
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input-object-min-width: 10
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input-object-min-height: 10
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model-color-format: 1
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# 基于模型的编号 0此处为车辆检测模型
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operate-on-gie-id: 1
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# 基于模型类别的编号 0此处为车辆类别
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operate-on-class-ids: 0
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# 分类器异步模式启动与否
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classifier-async-mode: 1
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# 分类器阈值，高于该值则认为分类成功
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classifier-threshold: 0.51
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# 基于上游ROI进行分类
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process-mode: 2
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#scaling-filter: 0
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car
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bicycle
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person
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road_sign
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TRT-100300-EntropyCalibration2
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input_1:0: 3c010a14
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conv1/convolution:0: 3d0b7086
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Reshape__113:0_output: 31a60d5b
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ONNXTRT_Broadcast_output: 31a60d5b
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conv1/BiasAdd:0: 3d0b7086
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bn_conv1/batchnorm/mul__10_output: 3d62b50e
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bn_conv1/batchnorm/mul_1:0: 3dd92c80
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bn_conv1/batchnorm/sub__11_output: 3c296b24
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bn_conv1/batchnorm/add_1:0: 3de3af36
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activation_1/Relu:0: 3de355a8
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block_1a_conv_shortcut/BiasAdd:0: 3d14edf6
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block_1a_bn_shortcut/batchnorm/mul__22_output: 3e788ca2
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block_1a_bn_shortcut/batchnorm/mul_1:0: 3d88cab4
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block_1a_bn_shortcut/batchnorm/sub__23_output: 3cac2012
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block_1a_bn_shortcut/batchnorm/add_1:0: 3cb23b6f
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block_1a_conv_1/convolution:0: 3e429cd3
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Reshape__115:0_output: 31e11e29
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ONNXTRT_Broadcast_1_output: 31e11e29
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block_1a_conv_1/BiasAdd:0: 3e429cd3
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block_1a_bn_1/batchnorm/mul__14_output: 3d97cfd7
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block_1a_bn_1/batchnorm/mul_1:0: 3e18ceef
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block_1a_bn_1/batchnorm/sub__15_output: 3ccaa65b
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block_1a_bn_1/batchnorm/add_1:0: 3dec091e
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block_1a_relu_1/Relu:0: 3d3b8c04
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block_1a_conv_2/convolution:0: 3db7871d
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Reshape__117:0_output: 31ae4bc8
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ONNXTRT_Broadcast_3_output: 31ae4bc8
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block_1a_conv_2/BiasAdd:0: 3db7871d
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block_1a_bn_2/batchnorm/mul__20_output: 3e456d78
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block_1a_bn_2/batchnorm/mul_1:0: 3e00e329
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block_1a_bn_2/batchnorm/sub__21_output: 3d9d559e
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block_1a_bn_2/batchnorm/add_1:0: 3d4cab2b
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add_1/add:0: 3d71098b
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block_1a_relu/Relu:0: 3d76aa90
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block_1b_conv_shortcut/BiasAdd:0: 3cbcc767
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block_1b_bn_shortcut/batchnorm/mul__34_output: 3dbd9ab8
|
||||
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|
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|
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ONNXTRT_Broadcast_23_output: 31c5e0aa
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ONNXTRT_Broadcast_29_output: 31ee2bc9
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ONNXTRT_Broadcast_31_output: 31fba56d
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|
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Reshape__149:0_output: 3b4a4f27
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ONNXTRT_Broadcast_36_output: 3b4a4f27
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+1
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||||
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Load Diff
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||||
TRT-100200-EntropyCalibration2
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
block_3a_bn_1/batchnorm/add_1:0: 3d842efc
|
||||
block_3a_relu_1/Relu:0: 3d8ceacf
|
||||
block_3a_conv_2/convolution:0: 3e217f38
|
||||
block_3a_bn_2/batchnorm/mul__50_output: 3be940f8
|
||||
block_3a_bn_2/batchnorm/mul_1:0: 3dbdbb69
|
||||
block_3a_bn_2/batchnorm/sub__51_output: 3cb023d4
|
||||
block_3a_bn_2/batchnorm/add_1:0: 3db581fe
|
||||
add_5/add:0: 3dd75d84
|
||||
block_3a_relu/Relu:0: 3ddea79e
|
||||
block_3b_conv_shortcut/convolution:0: 3e19e097
|
||||
block_3b_bn_shortcut/batchnorm/mul__61_output: 3bcf5e9f
|
||||
block_3b_bn_shortcut/batchnorm/mul_1:0: 3da454d0
|
||||
block_3b_bn_shortcut/batchnorm/sub__62_output: 3c98f85c
|
||||
block_3b_bn_shortcut/batchnorm/add_1:0: 3dad9067
|
||||
block_3b_conv_1/convolution:0: 3e8da30e
|
||||
block_3b_bn_1/batchnorm/mul__55_output: 3b232571
|
||||
block_3b_bn_1/batchnorm/mul_1:0: 3d914223
|
||||
block_3b_bn_1/batchnorm/sub__56_output: 3cd36c80
|
||||
block_3b_bn_1/batchnorm/add_1:0: 3d808ddb
|
||||
block_3b_relu_1/Relu:0: 3d5675a8
|
||||
block_3b_conv_2/convolution:0: 3de53def
|
||||
block_3b_bn_2/batchnorm/mul__59_output: 3c39af2b
|
||||
block_3b_bn_2/batchnorm/mul_1:0: 3dbbc34d
|
||||
block_3b_bn_2/batchnorm/sub__60_output: 3cbc6410
|
||||
block_3b_bn_2/batchnorm/add_1:0: 3da23173
|
||||
add_6/add:0: 3df37510
|
||||
block_3b_relu/Relu:0: 3de2cddd
|
||||
block_4a_conv_shortcut/convolution:0: 3d89462c
|
||||
block_4a_bn_shortcut/batchnorm/mul__70_output: 3c0d611e
|
||||
block_4a_bn_shortcut/batchnorm/mul_1:0: 3d668aa7
|
||||
block_4a_bn_shortcut/batchnorm/sub__71_output: 3c4ed7d8
|
||||
block_4a_bn_shortcut/batchnorm/add_1:0: 3d5c6f62
|
||||
block_4a_conv_1/convolution:0: 3e40745d
|
||||
block_4a_bn_1/batchnorm/mul__64_output: 3b37f2b3
|
||||
block_4a_bn_1/batchnorm/mul_1:0: 3d5c139a
|
||||
block_4a_bn_1/batchnorm/sub__65_output: 3c6e55b3
|
||||
block_4a_bn_1/batchnorm/add_1:0: 3d5bbd27
|
||||
block_4a_relu_1/Relu:0: 3d4477a8
|
||||
block_4a_conv_2/convolution:0: 3d7a43ae
|
||||
block_4a_bn_2/batchnorm/mul__68_output: 3c4fa08a
|
||||
block_4a_bn_2/batchnorm/mul_1:0: 3d983e2e
|
||||
block_4a_bn_2/batchnorm/sub__69_output: 3c689005
|
||||
block_4a_bn_2/batchnorm/add_1:0: 3d888944
|
||||
add_7/add:0: 3db32129
|
||||
block_4a_relu/Relu:0: 3d9ac9cf
|
||||
block_4b_conv_shortcut/convolution:0: 3d3061f8
|
||||
block_4b_bn_shortcut/batchnorm/mul__79_output: 3d0c4910
|
||||
block_4b_bn_shortcut/batchnorm/mul_1:0: 3e0db980
|
||||
block_4b_bn_shortcut/batchnorm/sub__80_output: 3c41ab03
|
||||
block_4b_bn_shortcut/batchnorm/add_1:0: 3dfac1bc
|
||||
block_4b_conv_1/convolution:0: 3dfc1387
|
||||
block_4b_bn_1/batchnorm/mul__73_output: 3be0b5cb
|
||||
block_4b_bn_1/batchnorm/mul_1:0: 3d919cd2
|
||||
block_4b_bn_1/batchnorm/sub__74_output: 3cce84b6
|
||||
block_4b_bn_1/batchnorm/add_1:0: 3d85b56d
|
||||
block_4b_relu_1/Relu:0: 3d73296f
|
||||
block_4b_conv_2/convolution:0: 3dba0111
|
||||
block_4b_bn_2/batchnorm/mul__77_output: 3d147ef3
|
||||
block_4b_bn_2/batchnorm/mul_1:0: 3e668945
|
||||
block_4b_bn_2/batchnorm/sub__78_output: 3c5dce3e
|
||||
block_4b_bn_2/batchnorm/add_1:0: 3e73ea14
|
||||
add_8/add:0: 3e8bcec3
|
||||
block_4b_relu/Relu:0: 3e90d884
|
||||
avg_pool/AvgPool:0: 3e90d884
|
||||
flatten/Reshape:0: 3cf618fd
|
||||
predictions/kernel/read__81_output: 3c23d576
|
||||
predictions/MatMul:0: 3ea93495
|
||||
predictions/bias/read__82_output: 3c021685
|
||||
ONNXTRT_Broadcast_output: 3c021685
|
||||
predictions/BiasAdd:0: 3e9dfa2f
|
||||
ONNXTRT_flattenTensor_output: 3e9dfa2f
|
||||
predictions/Softmax:0: 3c010a14
|
||||
predictions/Softmax_output: 3c010a14
|
||||
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Reference in New Issue
Block a user