训练时报错，完成一个epoch是显示

[666zhouzhou6666]

2024-10-13 23:12:30 | ERROR | yolox.core.launch:98 - An error has been caught in function 'launch', process 'MainProcess' (14668), thread 'MainThread' (35416):
Traceback (most recent call last):

File "D:\Gugexaizai\YOLOX-0.2.0\tools\train.py", line 129, in
launch(
└ <function launch at 0x000001950AB11870>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\core\launch.py", line 98, in launch
main_func(*args)
│ └ (╒══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <function main at 0x000001950BFAB640>

File "D:\Gugexaizai\YOLOX-0.2.0\tools\train.py", line 114, in main
trainer.train()
│ └ <function Trainer.train at 0x000001950BF2AD40>
└ <yolox.core.trainer.Trainer object at 0x000001950BF77010>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\core\trainer.py", line 72, in train
self.train_in_epoch()
│ └ <function Trainer.train_in_epoch at 0x000001950BF51120>
└ <yolox.core.trainer.Trainer object at 0x000001950BF77010>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\core\trainer.py", line 82, in train_in_epoch
self.after_epoch()
│ └ <function Trainer.after_epoch at 0x000001950BFAAC20>
└ <yolox.core.trainer.Trainer object at 0x000001950BF77010>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\core\trainer.py", line 207, in after_epoch
self.evaluate_and_save_model()
│ └ <function Trainer.evaluate_and_save_model at 0x000001950BFAAEF0>
└ <yolox.core.trainer.Trainer object at 0x000001950BF77010>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\core\trainer.py", line 302, in evaluate_and_save_model
ap50_95, ap50, summary = self.exp.eval(
│ │ └ <function Exp.eval at 0x000001950BFABD90>
│ └ ╒══════════════════╤═════════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <yolox.core.trainer.Trainer object at 0x000001950BF77010>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\exp\yolox_base.py", line 285, in eval
return evaluator.evaluate(model, is_distributed, half)
│ │ │ │ └ False
│ │ │ └ False
│ │ └ YOLOX(
│ │ (backbone): YOLOPAFPN(
│ │ (backbone): CSPDarknet(
│ │ (stem): Focus(
│ │ (conv): BaseConv(
│ │ (conv): ...
│ └ <function VOCEvaluator.evaluate at 0x000001950BFA9D80>
└ <yolox.evaluators.voc_evaluator.VOCEvaluator object at 0x0000019514545810>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\evaluators\voc_evaluator.py", line 128, in evaluate
eval_results = self.evaluate_prediction(data_list, statistics)
│ │ │ └ tensor([ 1.0139, 0.1483, 15.0000], device='cuda:0')
│ │ └ {0: (None, None, None), 1: (None, None, None), 2: (None, None, None), 3: (None, None, None), 4: (None, None, None), 5: (None,...
│ └ <function VOCEvaluator.evaluate_prediction at 0x000001950BFA9EA0>
└ <yolox.evaluators.voc_evaluator.VOCEvaluator object at 0x0000019514545810>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\evaluators\voc_evaluator.py", line 205, in evaluate_prediction
mAP50, mAP70 = self.dataloader.dataset.evaluate_detections(
│ │ │ └ <function VOCDetection.evaluate_detections at 0x000001950BFAA710>
│ │ └ <yolox.data.datasets.voc.VOCDetection object at 0x0000019514585390>
│ └ <torch.utils.data.dataloader.DataLoader object at 0x00000195145449D0>
└ <yolox.evaluators.voc_evaluator.VOCEvaluator object at 0x0000019514545810>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\data\datasets\voc.py", line 265, in evaluate_detections
self._write_voc_results_file(all_boxes)
│ │ └ [[array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), arr...
│ └ <function VOCDetection._write_voc_results_file at 0x000001950BFAA830>
└ <yolox.data.datasets.voc.VOCDetection object at 0x0000019514585390>

File "D:\Gugexaizai\YOLOX-0.2.0\yolox\data\datasets\voc.py", line 299, in _write_voc_results_file
if dets == []:
└ array([], shape=(0, 5), dtype=float32)

ValueError: operands could not be broadcast together with shapes (0,5) (0,)

[ChialinSung]

2024-11-07 11:16:42 | INFO | yolox.core.trainer:218 - ---> start train epoch1
2024-11-07 11:16:48 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 10/134, gpu mem: 9301Mb, mem: 24.6Gb, iter_time: 0.677s, data_time: 0.059s, total_loss: 15.8, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 10.0, cls_loss: 2.0, lr: 1.392e-07, size: 1280, ETA: 7:33:23
2024-11-07 11:16:54 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 20/134, gpu mem: 9301Mb, mem: 24.5Gb, iter_time: 0.592s, data_time: 0.007s, total_loss: 17.0, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 11.3, cls_loss: 2.0, lr: 5.569e-07, size: 1440, ETA: 7:04:50
2024-11-07 11:17:01 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 30/134, gpu mem: 9301Mb, mem: 24.5Gb, iter_time: 0.695s, data_time: 0.187s, total_loss: 14.9, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 9.1, cls_loss: 2.0, lr: 1.253e-06, size: 1312, ETA: 7:18:08
2024-11-07 11:17:07 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 40/134, gpu mem: 9301Mb, mem: 24.5Gb, iter_time: 0.587s, data_time: 0.027s, total_loss: 14.3, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 8.6, cls_loss: 2.0, lr: 2.228e-06, size: 1408, ETA: 7:06:43
2024-11-07 11:17:14 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 50/134, gpu mem: 9301Mb, mem: 24.8Gb, iter_time: 0.690s, data_time: 0.246s, total_loss: 14.3, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 8.7, cls_loss: 1.7, lr: 3.481e-06, size: 1184, ETA: 7:13:41
2024-11-07 11:17:20 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 60/134, gpu mem: 9301Mb, mem: 24.6Gb, iter_time: 0.579s, data_time: 0.269s, total_loss: 12.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 7.1, cls_loss: 2.1, lr: 5.012e-06, size: 1408, ETA: 7:05:49
2024-11-07 11:17:27 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 70/134, gpu mem: 9301Mb, mem: 24.7Gb, iter_time: 0.698s, data_time: 0.148s, total_loss: 10.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 5.8, cls_loss: 1.7, lr: 6.822e-06, size: 1376, ETA: 7:11:35
2024-11-07 11:17:33 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 80/134, gpu mem: 9301Mb, mem: 24.6Gb, iter_time: 0.575s, data_time: 0.113s, total_loss: 11.0, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 5.6, cls_loss: 1.7, lr: 8.911e-06, size: 1216, ETA: 7:05:39
2024-11-07 11:17:39 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 90/134, gpu mem: 9301Mb, mem: 24.6Gb, iter_time: 0.683s, data_time: 0.414s, total_loss: 11.1, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 6.0, cls_loss: 1.5, lr: 1.128e-05, size: 1280, ETA: 7:09:01
2024-11-07 11:17:45 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 100/134, gpu mem: 9301Mb, mem: 24.7Gb, iter_time: 0.597s, data_time: 0.098s, total_loss: 10.3, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 5.5, cls_loss: 1.2, lr: 1.392e-05, size: 1248, ETA: 7:05:53
2024-11-07 11:17:52 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 110/134, gpu mem: 9301Mb, mem: 24.9Gb, iter_time: 0.678s, data_time: 0.413s, total_loss: 9.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.5, cls_loss: 1.2, lr: 1.685e-05, size: 1280, ETA: 7:08:14
2024-11-07 11:17:58 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 120/134, gpu mem: 9301Mb, mem: 24.8Gb, iter_time: 0.583s, data_time: 0.169s, total_loss: 9.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 5.0, cls_loss: 1.5, lr: 2.005e-05, size: 1120, ETA: 7:04:55
2024-11-07 11:18:05 | INFO | yolox.core.trainer:270 - epoch: 1/300, iter: 130/134, gpu mem: 9301Mb, mem: 24.9Gb, iter_time: 0.685s, data_time: 0.371s, total_loss: 9.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 5.1, cls_loss: 1.2, lr: 2.353e-05, size: 1408, ETA: 7:07:20
2024-11-07 11:18:07 | INFO | yolox.core.trainer:402 - Save weights to ./YOLOX_outputs/drd_exp
100%|###########################################| 34/34 [00:04<00:00, 7.30it/s]
2024-11-07 11:18:13 | INFO | yolox.evaluators.voc_evaluator:144 - Evaluate in main process...
Writing LA VOC results file
2024-11-07 11:18:13 | ERROR | yolox.core.trainer:79 - Exception in training:
2024-11-07 11:18:13 | INFO | yolox.core.trainer:200 - Training of experiment is done and the best AP is 0.00
2024-11-07 11:18:13 | ERROR | yolox.core.launch:98 - An error has been caught in function 'launch', process 'MainProcess' (28400), thread 'MainThread' (139758362833024):
Traceback (most recent call last):

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/tools/train.py", line 138, in
launch(
└ <function launch at 0x7f1ad420b640>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/core/launch.py", line 98, in launch
main_func(*args)
│ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <function main at 0x7f1ac2640550>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/tools/train.py", line 118, in main
trainer.train()
│ └ <function Trainer.train at 0x7f1ac24a1090>
└ <yolox.core.trainer.Trainer object at 0x7f1ac24a81c0>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/core/trainer.py", line 77, in train
self.train_in_epoch()
│ └ <function Trainer.train_in_epoch at 0x7f1ac24a1900>
└ <yolox.core.trainer.Trainer object at 0x7f1ac24a81c0>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/core/trainer.py", line 88, in train_in_epoch
self.after_epoch()
│ └ <function Trainer.after_epoch at 0x7f1ac24a1c60>
└ <yolox.core.trainer.Trainer object at 0x7f1ac24a81c0>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/core/trainer.py", line 237, in after_epoch
self.evaluate_and_save_model()
│ └ <function Trainer.evaluate_and_save_model at 0x7f1ac24a1f30>
└ <yolox.core.trainer.Trainer object at 0x7f1ac24a81c0>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/core/trainer.py", line 355, in evaluate_and_save_model
(ap50_95, ap50, summary), predictions = self.exp.eval(
│ │ └ <function Exp.eval at 0x7f1ac24a1870>
│ └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <yolox.core.trainer.Trainer object at 0x7f1ac24a81c0>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/exp/yolox_base.py", line 353, in eval
return evaluator.evaluate(model, is_distributed, half, return_outputs=return_outputs)
│ │ │ │ │ └ True
│ │ │ │ └ False
│ │ │ └ False
│ │ └ YOLOX(
│ │ (backbone): YOLOPAFPN(
│ │ (backbone): CSPDarknet(
│ │ (stem): Focus(
│ │ (conv): BaseConv(
│ │ (conv): ...
│ └ <function VOCEvaluator.evaluate at 0x7f1ac2487b50>
└ <yolox.evaluators.voc_evaluator.VOCEvaluator object at 0x7f1ab0324550>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/evaluators/voc_evaluator.py", line 114, in evaluate
eval_results = self.evaluate_prediction(data_list, statistics)
│ │ │ └ tensor([ 1.5594, 0.1323, 33.0000], device='cuda:0')
│ │ └ {0: (tensor([[ 532.7663, 388.5279, 1316.4070, 2144.0371],
│ │ [2980.8784, 2769.1257, 4348.3936, 3396.9895],
│ │ [175...
│ └ <function VOCEvaluator.evaluate_prediction at 0x7f1ac2487c70>
└ <yolox.evaluators.voc_evaluator.VOCEvaluator object at 0x7f1ab0324550>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/evaluators/voc_evaluator.py", line 186, in evaluate_prediction
mAP50, mAP70 = self.dataloader.dataset.evaluate_detections(all_boxes, tempdir)
│ │ │ │ │ └ '/tmp/tmpq5xf9vjr'
│ │ │ │ └ [[array([[1.07199146e+03, 4.26259369e+02, 1.16201208e+03, 8.33316711e+02,
│ │ │ │ 5.14281727e-02],
│ │ │ │ [2.27488110e+03, 4....
│ │ │ └ <function VOCDetection.evaluate_detections at 0x7f1ac24a0670>
│ │ └ <yolox.data.datasets.voc.VOCDetection object at 0x7f1ab03251e0>
│ └ <torch.utils.data.dataloader.DataLoader object at 0x7f1ab0324ee0>
└ <yolox.evaluators.voc_evaluator.VOCEvaluator object at 0x7f1ab0324550>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/data/datasets/voc.py", line 230, in evaluate_detections
self._write_voc_results_file(all_boxes)
│ │ └ [[array([[1.07199146e+03, 4.26259369e+02, 1.16201208e+03, 8.33316711e+02,
│ │ 5.14281727e-02],
│ │ [2.27488110e+03, 4....
│ └ <function VOCDetection._write_voc_results_file at 0x7f1ac24a0790>
└ <yolox.data.datasets.voc.VOCDetection object at 0x7f1ab03251e0>

File "/home/wsjc/S2021/songjl/220430/yolox/YOLOX-main/yolox/data/datasets/voc.py", line 264, in _write_voc_results_file
if dets == []:
└ array([[1.07199146e+03, 4.26259369e+02, 1.16201208e+03, 8.33316711e+02,
5.14281727e-02],
[2.27488110e+03, 4.09...

ValueError: operands could not be broadcast together with shapes (11,5) (0,)
same question, plz help me ,thanks!

[ChialinSung]

把if dets == []:改成if(dets.shape[0]==0):
这样就解决了！