自制的KITTI数据集训练闪退问题

[18845108835]

作者您好，我在ue4中自制了KITTI数据集（照片像素为(1242,375,3)），但是在训练的时候在train.py文件的 for iter_num, data in enumerate(dataloader_train):闪退，但是换成kitti3D数据集，便可以正常运行，想请教您一下是什么原因？

[Owen-Liuyuxuan]

请补充一下错误信息。特别是尝试把number_works设置为0之后，单线程采集数据收集更干净的错误信息。

[18845108835]

谢谢您的建议，昨天经过核实后，发现是自制数据集的问题，P2的最后一个数多了一个空格，已经解决闪退的问题啦，再次感谢您的建议~~~
现在出现了新的问题，描述如下，麻烦您啦：
Backend TkAgg is interactive backend. Turning interactive mode on.
clean up the recorder directory of C:\Users\FangGZ\Desktop\yolo3D_first\workdirs\Stereo3D\log\firstconfig=config/Stereo3D.py
-1
number of trained parameters of the model: 107466592
Found evaluate function evaluate_kitti_obj
Num training images: 2969
PSM Cos Volume takes 0.009999275207519531 seconds at call time 1
PSM Cos Volume takes 0.011998891830444336 seconds at call time 2
PSM Cos Volume takes 0.004999637603759766 seconds at call time 3
Cost Volume takes 0.006999969482421875 seconds at call time 1
PSM Cos Volume takes 0.012000083923339844 seconds at call time 4
PSM Cos Volume takes 0.006999969482421875 seconds at call time 5
Cost Volume takes 0.008991718292236328 seconds at call time 2
PSM Cos Volume takes 0.010965347290039062 seconds at call time 6
PSM Cos Volume takes 0.005000114440917969 seconds at call time 7
Cost Volume takes 0.004999876022338867 seconds at call time 3
PSM Cos Volume takes 0.015001296997070312 seconds at call time 8
PSM Cos Volume takes 0.007007122039794922 seconds at call time 9
Cost Volume takes 0.007995367050170898 seconds at call time 4
PSM Cos Volume takes 0.01299905776977539 seconds at call time 10
PSM Cos Volume takes 0.007002115249633789 seconds at call time 11
Cost Volume takes 0.010006427764892578 seconds at call time 5
PSM Cos Volume takes 0.011986494064331055 seconds at call time 12
PSM Cos Volume takes 0.008991479873657227 seconds at call time 13
Cost Volume takes 0.009996414184570312 seconds at call time 6
PSM Cos Volume takes 0.01297307014465332 seconds at call time 14
PSM Cos Volume takes 0.006989955902099609 seconds at call time 15
Cost Volume takes 0.00999903678894043 seconds at call time 7
PSM Cos Volume takes 0.013988256454467773 seconds at call time 16
PSM Cos Volume takes 0.007997989654541016 seconds at call time 17
Cost Volume takes 0.007001399993896484 seconds at call time 8
PSM Cos Volume takes 0.011969327926635742 seconds at call time 18
PSM Cos Volume takes 0.006997585296630859 seconds at call time 19
Cost Volume takes 0.009976387023925781 seconds at call time 9
/**** start testing after training epoch 9 ******/
clean up the recorder directory of C:\Users\FangGZ\Desktop\yolo3D_first\workdirs\Stereo3D\output\validation\data
rebuild C:\Users\FangGZ\Desktop\yolo3D_first\workdirs\Stereo3D\output\validation\data
0%| | 0/31 [00:00<?, ?it/s]C:\Users\FangGZ\Desktop\yolo3D_first\data\stereo_dataset.py:166: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\cb\pytorch_1000000000000\work\torch\csrc\utils\tensor_new.cpp:248.)
return torch.from_numpy(left_images).float(), torch.from_numpy(right_images).float(), torch.tensor(
C:\Users\FangGZ\Desktop\yolo3D_first\networks\pipelines\testers.py:39: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
scores, bbox, obj_index = module([left_images.cuda().float().contiguous(), right_images.cuda().float().contiguous(), torch.tensor(P2).cuda().float(), torch.tensor(P3).cuda().float()])
C:\Users\FangGZ\Desktop\yolo3D_first\networks\lib\PSM_cost_volume.py:82: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead.
cost = Variable(
C:\Users\FangGZ\Desktop\yolo3D_first\networks\lib\PSM_cost_volume.py:49: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead.
cost = Variable(
100%|██████████| 31/31 [00:06<00:00, 4.47it/s]
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2022.3.3\plugins\python\helpers\pydev\pydevconsole.py", line 364, in runcode
coro = func()
File "", line 1, in
File "C:\Program Files\JetBrains\PyCharm 2022.3.3\plugins\python\helpers\pydev_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2022.3.3\plugins\python\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:\Users\FangGZ\Desktop\yolo3D_first\train.py", line 190, in
main()
File "C:\Users\FangGZ\Desktop\yolo3D_first\train.py", line 180, in main
evaluate_detection(cfg, detector.module if is_distributed else detector, dataset_val, writer, epoch_num)
File "C:\Users\FangGZ\anaconda3\envs\pytorch\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\Users\FangGZ\Desktop\yolo3D_first\networks\pipelines\evaluators.py", line 89, in evaluate_kitti_obj
result_texts = evaluate(
File "C:\Users\FangGZ\Desktop\yolo3D_first\evaluator\kitti\evaluate.py", line 23, in evaluate
result_texts.append(get_official_eval_result(gt_annos, dt_annos, current_class))
File "C:\Users\FangGZ\Desktop\yolo3D_first\evaluator\kitti\eval.py", line 759, in get_official_eval_result
metrics = do_eval_v3(
File "C:\Users\FangGZ\Desktop\yolo3D_first\evaluator\kitti\eval.py", line 662, in do_eval_v3
ret = eval_class(
File "C:\Users\FangGZ\Desktop\yolo3D_first\evaluator\kitti\eval.py", line 505, in eval_class
rets = calculate_iou_partly(
File "C:\Users\FangGZ\Desktop\yolo3D_first\evaluator\kitti\eval.py", line 387, in calculate_iou_partly
gt_boxes = np.concatenate([a["bbox"] for a in gt_annos_part], 0)
File "<array_function internals>", line 180, in concatenate
ValueError: need at least one array to concatenate

[18845108835]

感觉评估函数出了一些问题，正在改动，不知道您有什么建议嘞，期待您的回复>.<