Question

参考github：fast-bert

我以前使用Bert模型运行以下笔记本来预测多标签分类，这意味着我不需要GPU驱动程序，而是可以使用CPU内存，这是jupyter笔记本multilabel

的参考

这不是内存问题，如何解决此错误？

随着RAM大小的增加，CPU的数量也会增加，

我选择n1-standard-4（6个vCPU，26 GB内存）机器类型。

示例代码：

我已经删除了使用“ cpu ”而不是“ cuda ”

device = torch.device('cuda')
if torch.cuda.device_count() > 1:
    args.multi_gpu = True
else:
    args.multi_gpu = False

到

torch.device('cpu')

错误日志：

Traceback (most recent call last):-------------------------------------------------------------| 0.00% [0/63 00:00<00:00]
  File "bert/run.py", line 146, in <module>
    learner.fit(args.num_train_epochs, args.learning_rate, validate=True)
  File "/home/pt4_gcp/.local/lib/python3.7/site-packages/fast_bert/learner_cls.py", line 397, in fit
    outputs = self.model(**inputs)
  File "/home/pt4_gcp/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/pt4_gcp/.local/lib/python3.7/site-packages/fast_bert/modeling.py", line 205, in forward
    logits.view(-1, self.num_labels), labels.view(-1, self.num_labels)
  File "/home/pt4_gcp/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/pt4_gcp/.local/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 617, in forward
    reduction=self.reduction)
  File "/home/pt4_gcp/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 2433, in binary_cross_entropy_with_logits
    raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([64, 3])) must be the same as input size (torch.Size([32, 3]))

Answer 1

这似乎对于输入[32]和目标[64]具有不同数量的实例。请确保您具有与输入数量相同的目标变量数量。

ValueError：目标大小（torch.Size（[64，3]））必须与输入大小（torch.Size（[32，3]））相同

1 个答案: