YOLOv5 训练报错记录（持续更新中...） |
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1、AttributeError: Can’t get attribute ‘C3’
AttributeError: Can’t get attribute ‘C3’ on
models/common.py 中没有 C3，全局查找，发现在 models/experimental.py 里，把这个类粘过来就行了 2、RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation. Traceback (most recent call last): File "train.py", line 453, in train(hyp, opt, device, tb_writer) File "train.py", line 70, in train model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create File "/home/zhangli/code/yolov5.3/models/yolo.py", line 85, in __init__ self._initialize_biases() # only run once File "/home/zhangli/code/yolov5.3/models/yolo.py", line 143, in _initialize_biases b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.修改 models/yolo.py 中的 _initialize_biases 函数为： def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. m = self.model[-1] # Detect() module for mi, s in zip(m.m, m.stride): # from b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) with torch.no_grad(): b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) 3、AssertionError: WARNING: No labels found in xxx… Can not train without labels. WARNING: No labels found in /home/zhangli/data/voc/Allride_2D_20210804/JPEGImages/. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data Traceback (most recent call last): File "train.py", line 453, in train(hyp, opt, device, tb_writer) File "train.py", line 161, in train dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, File "/home/zhangli/code/yolov5.3/utils/datasets.py", line 53, in create_dataloader dataset = LoadImagesAndLabels(path, imgsz, batch_size, File "/home/zhangli/code/yolov5.3/utils/datasets.py", line 432, in __init__ assert not augment, '%s. Can not train without labels.' % s AssertionError: WARNING: No labels found in /home/zhangli/data/voc/Allride_2D_20210804/JPEGImages/. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data. Can not train without labels.找到 utils/datasets.py,搜索 Define labels，将 images 修改为 JPEGImages，修改后如下 # Define labels self.label_files = [x.replace('JPEGImages', 'labels').replace(os.path.splitext(x)[-1], '.txt') for x in self.img_files] 4、PyTorch版本降级 / 安装指定版本PyTorchhttps://pytorch.org/get-started/previous-versions/ 5、AttributeError: Can’t get attribute ‘SiLU’ Traceback (most recent call last): File "train.py", line 453, in train(hyp, opt, device, tb_writer) File "train.py", line 69, in train ckpt = torch.load(weights, map_location=device) # load checkpoint File "/home/zhangli/anaconda3/envs/yolov5.3/lib/python3.8/site-packages/torch/serialization.py", line 584, in load return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) File "/home/zhangli/anaconda3/envs/yolov5.3/lib/python3.8/site-packages/torch/serialization.py", line 842, in _load result = unpickler.load() AttributeError: Can't get attribute 'SiLU' onpytorch版本问题（1.7以上似乎不会有这个问题），可将SiLU粘过来 class SiLU(Module): # export-friendly version of nn.SiLU() @staticmethod def forward(x): return x * torch.sigmoid(x) 6、RuntimeError: Unable to find a valid cuDNN algorithm to run convolutionbatch-size过大，调小即可 7、non-normalized or out of bounds coordinate labels坐标没有归一化，把YOLOv5-v5.0里的相关内容拿过来，若数据存在non-normalized or out of bounds coordinate labels问题，忽略即可 |
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