博主，数据集能告诉我下吗！ about changeable HOT 15 CLOSED

博主你好，我在使用的过程中发现，当num_works = 4时会报这样的错误
File "E:/yolox-trans/train.py", line 234, in
fit_one_epoch(model_train, model, yolo_loss, loss_history, optimizer, epoch,
File "E:\yolox-trans\utils\utils_fit.py", line 14, in fit_one_epoch
for iteration, batch in enumerate(gen):
File "D:\miniconda\envs\py38\lib\site-packages\torch\utils\data\dataloader.py", line 435, in next
data = self._next_data()
File "D:\miniconda\envs\py38\lib\site-packages\torch\utils\data\dataloader.py", line 1085, in _next_data
return self._process_data(data)
File "D:\miniconda\envs\py38\lib\site-packages\torch\utils\data\dataloader.py", line 1111, in _process_data
data.reraise()
File "D:\miniconda\envs\py38\lib\site-packages\torch_utils.py", line 428, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 3.

File "D:\miniconda\envs\py38\lib\site-packages\changeable\dataloader.py", line 41, in call
images = default_collate(images)

with open('pcb_classes.txt', 'r')as f:		# 类别名文件，每行一个类别名
    lines = f.readlines()
    classes_name = tuple([line.rstrip('\n') for line in lines])

dataset = VOCDataset(root='PCB',		# voc数据集根目录
                     classes_name=classes_name,
                     is_train=True,
                     transforms=Compose([		# 这里添加了所有的数据增强方式，只做例子演示用。
                         Resize((300, 300)),
                         AdaptiveResize((300, 300)),
                         Scaled(1.1),
                         CropIou(0.5),
                         CropSize((300, 300)),
                         DivideStds((1,1,1)),
                         SubtractMeans((0,0,0)),
                         GaussNoise(),
                         SalePepperNoise(),
                         GaussBlur(),
                         MotionBlue(),
                         Cutout(),
                         RandomFlipLR(),
                         RandomFlipUD(),
                         ShuffleChannels(),
                         ChangeContrast(),
                         ChangeHue(),
                         ChangeBrightness(),
                         ChangeSaturation(),
                         ConvertBoxesToPercentage(),
                         ConvertBoxesToValue(),
                         ConvertBoxesForm('xyxy', 'cxcywh'),
                         ConvertBoxesForm('cxcywh', 'xyxy'),
                     ])
                     )

anchors = AnchorsGenerator(image_size=(600, 600),
                           feature_maps_size=((76, 76), (38, 38), (19, 19)),
                           anchors_size=(((10, 13), (16, 30), (33, 23)),
                                         ((30, 61), (62, 45), (59, 119)),
                                         ((116, 90), (156, 198), (373, 326))),
                           form='xyxy',
                           clip=True
                           )

anchors_assigner = AnchorsAssignerWH(anchors, 3)

loader = dataloader(dataset, batch_size=4, resize=(600, 600),  use_mosaic=True, anchors_assigner=anchors_assigner, shuffle=True, num_workers=4)

for i, (img, box, lab, ids) in enumerate(loader):
    print(i)
    print(img.size())
    print(box.size())
    print(lab.size())
    img, box, lab, id = img[0], box[0], lab[0], ids[0]
    img = img.permute((1, 2, 0)).numpy()
    box, lab = box.numpy(), lab.numpy()
    box, lab = box[lab>0], lab[lab>0]
    img = draw_boxes(img, box, lab, label_name=classes_name)
    plot_image(img)

## 将 shuffle =False， 不打乱数据顺序，num_workers = 1 使用一个进程去处理数据。
loader = dataloader(dataset, batch_size=4, resize=(600, 600),  use_mosaic=True, anchors_assigner=anchors_assigner, shuffle=False, num_workers=1)

for i, (img, box, lab, ids) in enumerate(loader):
    print(ids)