from torch import string_classes 如果有,你需要移除或更正这个导入语句。根据你的实际需求,你可能需要从正确的库或模块中导入 string_classes,或者这个导入本身就是不必要的。 如果string_classes非torch官方提供的属性或方法,请查找其可能的自定义来源或第三方库: 由于string_classes 不是torch 的一部分,它可能是...
from torch._six import string_classes as string_classes ModuleNotFoundError: No module named 'torch._six' 解决方法,将torch._six.string_classes替换为str: # if isinstance(root, torch._six.string_classes): # ... if isinstance(root, str): ... 参考: 解决No module named ’torch._six‘问题...
找到这个位置if isinstance(root, torch._six.string_classes):,发现是在判断是否为str类型,所以将torch._six.string_classes改为str,这样就没报错了。 __EOF__
from torch._six import string_classes, int_classes 改成: int_classes = int string_classes = str版权声明:本文为weixin_48262500原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。 本文链接:https://blog.csdn.net/weixin_48262500/article/details/122782895智能...
报错解决——ImportError: cannot import name ‘container_abcs‘ from ‘torch._six‘ (/home/csj/anaconda3/envs/p,将fromtorch._siximportcontainer_abcs,string_classes,int_classes改成:string_classes=strint_classes=intimportcollections.abcascontainer_abcs
nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.zeros(1)] * self.nl # init grid a = torch.tensor(anchors).float()....
在某些情况下,可能无法或不想降级PyTorch版本。此时,可以尝试使用兼容代码。例如,将from torch._six import string_classes替换为from six import string_classes。 方法三:手动安装six模块 如果以上方法都无法解决问题,可以尝试手动安装six模块。在命令行中运行以下命令: ...
elif isinstance(batch, string_classes): return batch elif isinstance(batch, collections.Mapping): return {k: pin_memory_batch(sample) for k, sample in batch.items()} elif isinstance(batch, collections.Sequence): return [pin_memory_batch(sample) for sample in batch] ...
(map_location,string_classes):map_location=torch.device(map_location)elifnot(map_locationisNoneorisinstance(map_location,torch.device)):raiseValueError("map_location should be either None, string or torch.device, ""but got type: "+str(type(map_location)))if(str(map_location).startswith('...
class torch.nn.AdaptiveLogSoftmaxWithLoss(in_features, n_classes, cutoffs, div_value=4.0, head_bias=False)[source] Efficient softmax approximation as described in Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou. Adapti...