alex@jetson:~/DeepStream-Yolo$ deepstream-app -c deepstream_app_config.txt WARNING: Deserialize engine failed because file path: /home/alex/DeepStream-Yolo/model_b1_gpu0_int8.engine open error 0:00:03.101678864 57852 0xfffef8002380 WARN nvinfer gstnvinfer.cpp:677:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() [UID = 1]: deserialize engine from file :/home/alex/DeepStream-Yolo/model_b1_gpu0_int8.engine failed 0:00:03.304207311 57852 0xfffef8002380 WARN nvinfer gstnvinfer.cpp:677:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() [UID = 1]: deserialize backend context from engine from file :/home/alex/DeepStream-Yolo/model_b1_gpu0_int8.engine failed, try rebuild 0:00:03.304275504 57852 0xfffef8002380 INFO nvinfer gstnvinfer.cpp:680:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() [UID = 1]: Trying to create engine from model files WARNING: INT8 calibration file not specified/accessible. INT8 calibration can be done through setDynamicRange API in 'NvDsInferCreateNetwork' implementation WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible. Loading pre-trained weights Loading weights of yolov5n complete Total weights read: 1881661 Building YOLO network Layer Input Shape Output Shape WeightPtr (0) conv_silu [3, 640, 640] [16, 320, 320] 1792 (1) conv_silu [16, 320, 320] [32, 160, 160] 6528 (2) conv_silu [32, 160, 160] [16, 160, 160] 7104 (3) route: 1 - [32, 160, 160] - (4) conv_silu [32, 160, 160] [16, 160, 160] 7680 (5) conv_silu [16, 160, 160] [16, 160, 160] 8000 (6) conv_silu [16, 160, 160] [16, 160, 160] 10368 (7) shortcut_add_linear: 4 [16, 160, 160] [16, 160, 160] - (8) route: 7, 2 - [32, 160, 160] - (9) conv_silu [32, 160, 160] [32, 160, 160] 11520 (10) conv_silu [32, 160, 160] [64, 80, 80] 30208 (11) conv_silu [64, 80, 80] [32, 80, 80] 32384 (12) route: 10 - [64, 80, 80] - (13) conv_silu [64, 80, 80] [32, 80, 80] 34560 (14) conv_silu [32, 80, 80] [32, 80, 80] 35712 (15) conv_silu [32, 80, 80] [32, 80, 80] 45056 (16) shortcut_add_linear: 13 [32, 80, 80] [32, 80, 80] - (17) conv_silu [32, 80, 80] [32, 80, 80] 46208 (18) conv_silu [32, 80, 80] [32, 80, 80] 55552 (19) shortcut_add_linear: 16 [32, 80, 80] [32, 80, 80] - (20) route: 19, 11 - [64, 80, 80] - (21) conv_silu [64, 80, 80] [64, 80, 80] 59904 (22) conv_silu [64, 80, 80] [128, 40, 40] 134144 (23) conv_silu [128, 40, 40] [64, 40, 40] 142592 (24) route: 22 - [128, 40, 40] - (25) conv_silu [128, 40, 40] [64, 40, 40] 151040 (26) conv_silu [64, 40, 40] [64, 40, 40] 155392 (27) conv_silu [64, 40, 40] [64, 40, 40] 192512 (28) shortcut_add_linear: 25 [64, 40, 40] [64, 40, 40] - (29) conv_silu [64, 40, 40] [64, 40, 40] 196864 (30) conv_silu [64, 40, 40] [64, 40, 40] 233984 (31) shortcut_add_linear: 28 [64, 40, 40] [64, 40, 40] - (32) conv_silu [64, 40, 40] [64, 40, 40] 238336 (33) conv_silu [64, 40, 40] [64, 40, 40] 275456 (34) shortcut_add_linear: 31 [64, 40, 40] [64, 40, 40] - (35) route: 34, 23 - [128, 40, 40] - (36) conv_silu [128, 40, 40] [128, 40, 40] 292352 (37) conv_silu [128, 40, 40] [256, 20, 20] 588288 (38) conv_silu [256, 20, 20] [128, 20, 20] 621568 (39) route: 37 - [256, 20, 20] - (40) conv_silu [256, 20, 20] [128, 20, 20] 654848 (41) conv_silu [128, 20, 20] [128, 20, 20] 671744 (42) conv_silu [128, 20, 20] [128, 20, 20] 819712 (43) shortcut_add_linear: 40 [128, 20, 20] [128, 20, 20] - (44) route: 43, 38 - [256, 20, 20] - (45) conv_silu [256, 20, 20] [256, 20, 20] 886272 (46) conv_silu [256, 20, 20] [128, 20, 20] 919552 (47) maxpool [128, 20, 20] [128, 20, 20] - (48) maxpool [128, 20, 20] [128, 20, 20] - (49) maxpool [128, 20, 20] [128, 20, 20] - (50) route: 46, 47, 48, 49 - [512, 20, 20] - (51) conv_silu [512, 20, 20] [256, 20, 20] 1051648 (52) conv_silu [256, 20, 20] [128, 20, 20] 1084928 (53) upsample [128, 20, 20] [128, 40, 40] - (54) route: 53, 36 - [256, 40, 40] - (55) conv_silu [256, 40, 40] [64, 40, 40] 1101568 (56) route: 54 - [256, 40, 40] - (57) conv_silu [256, 40, 40] [64, 40, 40] 1118208 (58) conv_silu [64, 40, 40] [64, 40, 40] 1122560 (59) conv_silu [64, 40, 40] [64, 40, 40] 1159680 (60) route: 59, 55 - [128, 40, 40] - (61) conv_silu [128, 40, 40] [128, 40, 40] 1176576 (62) conv_silu [128, 40, 40] [64, 40, 40] 1185024 (63) upsample [64, 40, 40] [64, 80, 80] - (64) route: 63, 21 - [128, 80, 80] - (65) conv_silu [128, 80, 80] [32, 80, 80] 1189248 (66) route: 64 - [128, 80, 80] - (67) conv_silu [128, 80, 80] [32, 80, 80] 1193472 (68) conv_silu [32, 80, 80] [32, 80, 80] 1194624 (69) conv_silu [32, 80, 80] [32, 80, 80] 1203968 (70) route: 69, 65 - [64, 80, 80] - (71) conv_silu [64, 80, 80] [64, 80, 80] 1208320 (72) conv_silu [64, 80, 80] [64, 40, 40] 1245440 (73) route: 72, 62 - [128, 40, 40] - (74) conv_silu [128, 40, 40] [64, 40, 40] 1253888 (75) route: 73 - [128, 40, 40] - (76) conv_silu [128, 40, 40] [64, 40, 40] 1262336 (77) conv_silu [64, 40, 40] [64, 40, 40] 1266688 (78) conv_silu [64, 40, 40] [64, 40, 40] 1303808 (79) route: 78, 74 - [128, 40, 40] - (80) conv_silu [128, 40, 40] [128, 40, 40] 1320704 (81) conv_silu [128, 40, 40] [128, 20, 20] 1468672 (82) route: 81, 52 - [256, 20, 20] - (83) conv_silu [256, 20, 20] [128, 20, 20] 1501952 (84) route: 82 - [256, 20, 20] - (85) conv_silu [256, 20, 20] [128, 20, 20] 1535232 (86) conv_silu [128, 20, 20] [128, 20, 20] 1552128 (87) conv_silu [128, 20, 20] [128, 20, 20] 1700096 (88) route: 87, 83 - [256, 20, 20] - (89) conv_silu [256, 20, 20] [256, 20, 20] 1766656 (90) route: 71 - [64, 80, 80] - (91) conv_logistic [64, 80, 80] [255, 80, 80] 1783231 (92) yolo [255, 80, 80] - - (93) route: 80 - [128, 40, 40] - (94) conv_logistic [128, 40, 40] [255, 40, 40] 1816126 (95) yolo [255, 40, 40] - - (96) route: 89 - [256, 20, 20] - (97) conv_logistic [256, 20, 20] [255, 20, 20] 1881661 (98) yolo [255, 20, 20] - - Output YOLO blob names: yolo_93 yolo_96 yolo_99 Total number of YOLO layers: 260 Building YOLO network complete Building the TensorRT Engine NOTE: letter_box is set in cfg file, make sure to set maintain-aspect-ratio=1 in config_infer file to get better accuracy File does not exist: /home/alex/DeepStream-Yolo/calib.table OpenCV is required to run INT8 calibrator deepstream-app: yolo.cpp:98: nvinfer1::ICudaEngine* Yolo::createEngine(nvinfer1::IBuilder*, nvinfer1::IBuilderConfig*): Assertion `0' failed. Aborted (core dumped)