new_tensor(data, dtype=None, device=None, requires_grad=False) → Tensor new_full(size, fill_value, dtype=None, device=None, requires_grad=False) → Tensor new_empty(size, dtype=None, device=None, requires_grad=False) → Tensor new_ones(size, dtype=None, device=None, requires_grad=Fal...
🐛 Describe the bug The following trivial example using torch.compile(dynamic=True) fails to compile. It never ends compiling, stalling forever on PyTorch 2.1.2. import torch import torchvision.models as models device = torch.device("cuda...
If type is a string describing a Tensor type, different from the type name of the given Tensor, returns a new Tensor of the specified type, whose contents corresponds to the contents of the original Tensor, casted to the given type (//memory copy occurs, with possible loss of precision//...
Therefore tensor.new_tensor(x) is equivalent to x.clone().detach() and tensor.new_tensor(x, requires_grad=True) is equivalent to x.clone().detach().requires_grad_(True). The equivalents using clone() and detach() are recommended.
repeat(neq))) self.Av = Parameter(dTensor(neq*nx).uniform_()) self.Asz = torch.Size([neq, nx]) self.b = Variable(torch.ones(neq).double().cuda()) Example #15Source File: dev_pdipm.py From lcp-physics with Apache License 2.0 5 votes def sparse_solve_kkt_inverse(H_, A_, ...
对于eager执行,每个tape会记录当前所执行的操作,这个tape只对当前计算有效,并计算相应的梯度。PyTorch也是动态图模式,但是与TensorFlow不同,它是每个需要计算Tensor会拥有grad_fn以追踪历史操作的梯度。 TensorFlow 2.0引入的eager提高了代码的简洁性,而且更容易debug。但是对于性能来说,eager执行相比Graph模式会有一定的损失...
Returns a new tensor that is anarrowed version ofinputtensor. The dimensiondimis input fromstarttostart+length. The returned tensor andinputtensor share the same underlying storage. Parameters input (Tensor) – the tensor tonarrow dim (int) – the dimension along which tonarrow ...
type_as(tensor), tensor], dim=dim), dim=dim, eps=eps ) if dim == 0: return return_tensor[:-1] elif dim == 1: return return_tensor[:, :-1] elif dim == 2: return return_tensor[:, :, :-1] else: raise RuntimeError("Cumprod on dimension 3 and more is not implemented") ...
UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector. 说明: 每张卡上的loss都是要汇总到第0张卡上求梯度,更新好以后把权重分发到其余卡。但是为什么会出现这个warning,这其实和nn.DataParallel中最后一个参数dim有关, 其表示te...
simple audio I/O for pytorch. Contribute to faroit/torchaudio development by creating an account on GitHub.