bias, 0) model.apply(init_weights()) 数据出问题或者网络参数出问题,在计算中出现极大或异常的数值而导致nan inf def init_weights(m): classname = m.__class__.__name__ if classname.find('Linear') != -1: nn.init.kaiming_normal_(m.weight) model.apply(init_weights())...
正如在PengJv Nie's answer中已经指出的，这两个计算是不一样的，matmul(recon_1.t(), x)导致1433...
这两个计算是不一样的，matmul(recon_1.t(), x)导致1433×1433形状的结果，matmul(recon_1, x.t...
🐛 Bug np.std(4) returns 0 whereas torch.std(torch.tensor(4)) returns NaN. This causes numerical instabilities in certain situations. To Reproduce import numpy as np np.std(4) # returns 0 import torch torch.std(torch.tensor(4.)) # returns...
torch version: 1.13.1+cu117 cuda version: 11.7 res[ 0 ] max= tensor(nan, device='cuda:0') res[ 0 ] min= tensor(nan, device='cuda:0') res[ 1 ] max= tensor(nan, device='cuda:0') res[ 1 ] min= tensor(nan, device='cuda:0') res[ 2 ] max= tensor(nan, device='cuda:0...
以下文章来源于BOTManJL ，作者BOT Man What you don't use you don't pay for. (zero-overhead...
# 需要导入模块: import torch [as 别名]# 或者: from torch importisfinite[as 别名]defdetect_nan_tensors(self, loss: Tensor)->None:model = self.get_model()# check if loss is nanifnottorch.isfinite(loss).all():raiseValueError('The loss returned in `training_step` is nan or inf.')#...
!该 api 接受的输入,需确保第一个维度指代的是不同的样本;比如上例,意思是输入 X 有3 个样本,每个样本的数据是 1 维的tensor 基于此,我们可以用其实现对于图片数据,逐像素损失函数值的计算,注意逐像素损失最后除以的是像素点总数, torch 这里给的 \rm reduction=mean 也指的是将张量所有元素和相加再除以...
def move_to_cpu(sample): def _move_to_cpu(tensor): # PyTorch has poor support for half tensors (float16) on CPU. # Move any such tensors to float32. if tensor.dtype in {torch.bfloat16, torch.float16}: tensor = tensor.to(dtype=torch.float32) return tensor.cpu() return apply_...
If args is a Tensor, this is equivalent to having called it with a 1-ary tuple of that Tensor. (Note: passing keyword arguments to the model is not currently supported. Give us a shout if you need it.) f –a file-like object (has to implement fileno that returns a file descriptor...