TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
硬件平台: Jesson xavier NX
etPack4.4；cuDNN 8.0；CUDA 10.2；OpenCV 4.1.1；Visionworks 1.6；python3 3.6.9 ；TensorRT 7.1.3.4
修改config
Config config_v3;
config_v3.net_type = YOLOV3;
config_v3.file_model_cfg = "../configs/yolov3.cfg";
config_v3.file_model_weights = "../configs/yolov3-voc_11000.weights";
config_v3.calibration_image_list_file_txt = "../configs/calibration_images.txt";
config_v3.inference_precison =INT8;
config_v3.detect_thresh = 0.5;
模型是由darkent 训练的
File does not exist : ../configs/yolov3-voc_11000-kINT8-batch1.engine
Loading pre-trained weights...
Loading complete!
layer inp_size out_size weightPtr
(1) conv-bn-leaky 3 x 416 x 416 32 x 416 x 416 992
(2) conv-bn-leaky 32 x 416 x 416 64 x 208 x 208 19680
(3) conv-bn-leaky 64 x 208 x 208 32 x 208 x 208 21856
(4) conv-bn-leaky 32 x 208 x 208 64 x 208 x 208 40544
(5) skip 64 x 208 x 208 64 x 208 x 208 -
(6) conv-bn-leaky 64 x 208 x 208 128 x 104 x 104 114784
(7) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 123232
(8) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 197472
(9) skip 128 x 104 x 104 128 x 104 x 104 -
(10) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 205920
(11) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 280160
(12) skip 128 x 104 x 104 128 x 104 x 104 -
(13) conv-bn-leaky 128 x 104 x 104 256 x 52 x 52 576096
(14) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 609376
(15) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 905312
(16) skip 256 x 52 x 52 256 x 52 x 52 -
(17) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 938592
(18) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1234528
(19) skip 256 x 52 x 52 256 x 52 x 52 -
(20) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1267808
(21) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1563744
(22) skip 256 x 52 x 52 256 x 52 x 52 -
(23) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1597024
(24) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1892960
(25) skip 256 x 52 x 52 256 x 52 x 52 -
(26) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1926240
(27) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2222176
(28) skip 256 x 52 x 52 256 x 52 x 52 -
(29) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2255456
(30) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2551392
(31) skip 256 x 52 x 52 256 x 52 x 52 -
(32) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2584672
(33) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2880608
(34) skip 256 x 52 x 52 256 x 52 x 52 -
(35) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2913888
(36) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 3209824
(37) skip 256 x 52 x 52 256 x 52 x 52 -
(38) conv-bn-leaky 256 x 52 x 52 512 x 26 x 26 4391520
(39) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 4523616
(40) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 5705312
(41) skip 512 x 26 x 26 512 x 26 x 26 -
(42) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 5837408
(43) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 7019104
(44) skip 512 x 26 x 26 512 x 26 x 26 -
(45) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 7151200
(46) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 8332896
(47) skip 512 x 26 x 26 512 x 26 x 26 -
(48) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 8464992
(49) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 9646688
(50) skip 512 x 26 x 26 512 x 26 x 26 -
(51) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 9778784
(52) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 10960480
(53) skip 512 x 26 x 26 512 x 26 x 26 -
(54) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 11092576
(55) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 12274272
(56) skip 512 x 26 x 26 512 x 26 x 26 -
(57) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 12406368
(58) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 13588064
(59) skip 512 x 26 x 26 512 x 26 x 26 -
(60) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 13720160
(61) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 14901856
(62) skip 512 x 26 x 26 512 x 26 x 26 -
(63) conv-bn-leaky 512 x 26 x 26 1024 x 13 x 13 19624544
(64) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 20150880
(65) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 24873568
(66) skip 1024 x 13 x 13 1024 x 13 x 13 -
(67) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 25399904
(68) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 30122592
(69) skip 1024 x 13 x 13 1024 x 13 x 13 -
(70) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 30648928
(71) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 35371616
(72) skip 1024 x 13 x 13 1024 x 13 x 13 -
(73) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 35897952
(74) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 40620640
(75) skip 1024 x 13 x 13 1024 x 13 x 13 -
(76) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 41146976
(77) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 45869664
(78) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 46396000
(79) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 51118688
(80) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 51645024
(81) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 56367712
(82) conv-linear 1024 x 13 x 13 255 x 13 x 13 56629087
(83) yolo 255 x 13 x 13 255 x 13 x 13 56629087
(84) route - 512 x 13 x 13 56629087
(85) conv-bn-leaky 512 x 13 x 13 256 x 13 x 13 56761183
(86) upsample 256 x 13 x 13 256 x 26 x 26 -
(87) route - 768 x 26 x 26 56761183
(88) conv-bn-leaky 768 x 26 x 26 256 x 26 x 26 56958815
(89) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 58140511
(90) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 58272607
(91) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 59454303
(92) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 59586399
(93) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 60768095
(94) conv-linear 512 x 26 x 26 255 x 26 x 26 60898910
(95) yolo 255 x 26 x 26 255 x 26 x 26 60898910
(96) route - 256 x 26 x 26 60898910
(97) conv-bn-leaky 256 x 26 x 26 128 x 26 x 26 60932190
(98) upsample 128 x 26 x 26 128 x 52 x 52 -
(99) route - 384 x 52 x 52 60932190
(100) conv-bn-leaky 384 x 52 x 52 128 x 52 x 52 60981854
(101) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61277790
(102) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61311070
(103) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61607006
(104) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61640286
(105) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61936222
(106) conv-linear 256 x 52 x 52 255 x 52 x 52 62001757
(107) yolo 255 x 52 x 52 255 x 52 x 52 62001757
Number of unused weights left : -409260
yolo-trt: /home/inspur/pc_work/mount_mmcblk1/yolo-tensorrt/modules/yolo.cpp:429: void Yolo::createYOLOEngine(nvinfer1::DataType, Int8EntropyCalibrator*): Assertion `0' failed.
Aborted (core dumped)
可以帮忙看看是啥问题么,已经修改config_v3.inference_precison 为FP32 或者是FP16也一样有问题
硬件平台: Jesson xavier NX etPack4.4；cuDNN 8.0；CUDA 10.2；OpenCV 4.1.1；Visionworks 1.6；python3 3.6.9 ；TensorRT 7.1.3.4 修改config Config config_v3; config_v3.net_type = YOLOV3; config_v3.file_model_cfg = "../configs/yolov3.cfg"; config_v3.file_model_weights = "../configs/yolov3-voc_11000.weights"; config_v3.calibration_image_list_file_txt = "../configs/calibration_images.txt"; config_v3.inference_precison =INT8; config_v3.detect_thresh = 0.5; 模型是由darkent 训练的 File does not exist : ../configs/yolov3-voc_11000-kINT8-batch1.engine Loading pre-trained weights... Loading complete! layer inp_size out_size weightPtr (1) conv-bn-leaky 3 x 416 x 416 32 x 416 x 416 992
(2) conv-bn-leaky 32 x 416 x 416 64 x 208 x 208 19680 (3) conv-bn-leaky 64 x 208 x 208 32 x 208 x 208 21856 (4) conv-bn-leaky 32 x 208 x 208 64 x 208 x 208 40544 (5) skip 64 x 208 x 208 64 x 208 x 208 - (6) conv-bn-leaky 64 x 208 x 208 128 x 104 x 104 114784 (7) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 123232 (8) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 197472 (9) skip 128 x 104 x 104 128 x 104 x 104 - (10) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 205920 (11) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 280160 (12) skip 128 x 104 x 104 128 x 104 x 104 - (13) conv-bn-leaky 128 x 104 x 104 256 x 52 x 52 576096 (14) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 609376 (15) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 905312 (16) skip 256 x 52 x 52 256 x 52 x 52 - (17) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 938592 (18) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1234528 (19) skip 256 x 52 x 52 256 x 52 x 52 - (20) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1267808 (21) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1563744 (22) skip 256 x 52 x 52 256 x 52 x 52 - (23) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1597024 (24) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1892960 (25) skip 256 x 52 x 52 256 x 52 x 52 - (26) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1926240 (27) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2222176 (28) skip 256 x 52 x 52 256 x 52 x 52 - (29) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2255456 (30) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2551392 (31) skip 256 x 52 x 52 256 x 52 x 52 - (32) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2584672 (33) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2880608 (34) skip 256 x 52 x 52 256 x 52 x 52 - (35) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2913888 (36) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 3209824 (37) skip 256 x 52 x 52 256 x 52 x 52 - (38) conv-bn-leaky 256 x 52 x 52 512 x 26 x 26 4391520 (39) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 4523616 (40) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 5705312 (41) skip 512 x 26 x 26 512 x 26 x 26 - (42) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 5837408 (43) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 7019104 (44) skip 512 x 26 x 26 512 x 26 x 26 - (45) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 7151200 (46) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 8332896 (47) skip 512 x 26 x 26 512 x 26 x 26 - (48) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 8464992 (49) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 9646688 (50) skip 512 x 26 x 26 512 x 26 x 26 - (51) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 9778784 (52) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 10960480 (53) skip 512 x 26 x 26 512 x 26 x 26 - (54) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 11092576 (55) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 12274272 (56) skip 512 x 26 x 26 512 x 26 x 26 - (57) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 12406368 (58) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 13588064 (59) skip 512 x 26 x 26 512 x 26 x 26 - (60) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 13720160 (61) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 14901856 (62) skip 512 x 26 x 26 512 x 26 x 26 - (63) conv-bn-leaky 512 x 26 x 26 1024 x 13 x 13 19624544 (64) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 20150880 (65) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 24873568 (66) skip 1024 x 13 x 13 1024 x 13 x 13 - (67) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 25399904 (68) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 30122592 (69) skip 1024 x 13 x 13 1024 x 13 x 13 - (70) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 30648928 (71) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 35371616 (72) skip 1024 x 13 x 13 1024 x 13 x 13 - (73) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 35897952 (74) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 40620640 (75) skip 1024 x 13 x 13 1024 x 13 x 13 - (76) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 41146976 (77) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 45869664 (78) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 46396000 (79) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 51118688 (80) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 51645024 (81) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 56367712 (82) conv-linear 1024 x 13 x 13 255 x 13 x 13 56629087 (83) yolo 255 x 13 x 13 255 x 13 x 13 56629087 (84) route - 512 x 13 x 13 56629087 (85) conv-bn-leaky 512 x 13 x 13 256 x 13 x 13 56761183 (86) upsample 256 x 13 x 13 256 x 26 x 26 - (87) route - 768 x 26 x 26 56761183 (88) conv-bn-leaky 768 x 26 x 26 256 x 26 x 26 56958815 (89) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 58140511 (90) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 58272607 (91) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 59454303 (92) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 59586399 (93) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 60768095 (94) conv-linear 512 x 26 x 26 255 x 26 x 26 60898910 (95) yolo 255 x 26 x 26 255 x 26 x 26 60898910 (96) route - 256 x 26 x 26 60898910 (97) conv-bn-leaky 256 x 26 x 26 128 x 26 x 26 60932190 (98) upsample 128 x 26 x 26 128 x 52 x 52 - (99) route - 384 x 52 x 52 60932190 (100) conv-bn-leaky 384 x 52 x 52 128 x 52 x 52 60981854 (101) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61277790 (102) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61311070 (103) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61607006 (104) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61640286 (105) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61936222 (106) conv-linear 256 x 52 x 52 255 x 52 x 52 62001757 (107) yolo 255 x 52 x 52 255 x 52 x 52 62001757 Number of unused weights left : -409260 yolo-trt: /home/inspur/pc_work/mount_mmcblk1/yolo-tensorrt/modules/yolo.cpp:429: void Yolo::createYOLOEngine(nvinfer1::DataType, Int8EntropyCalibrator*): Assertion `0' failed. Aborted (core dumped) 可以帮忙看看是啥问题么,已经修改config_v3.inference_precison 为FP32 或者是FP16也一样有问题