WebTensor. cpu (memory_format = torch.preserve_format) → Tensor ¶ Returns a copy of this object in CPU memory. If this object is already in CPU memory and on the correct device, … WebIf data is already a tensor with the requested dtype and device then data itself is returned, but if data is a tensor with a different dtype or device then it’s copied as if using …
torch.Tensor.copy_ — PyTorch 2.0 documentation
Web1 day ago · 🐛 Describe the bug Bit of a weird one, not sure if this is something interesting but just in case: import torch torch.tensor([torch.tensor(0)]) # works fine … WebIn a PyTorch setting, as you say, if you want a fresh copy of a tensor object to use in a completely different setting with no relationship or effect on its parent, you should use … dr martin cohen evicore
torch.as_tensor — PyTorch 2.0 documentation
Webtorch.to(other, non_blocking=False, copy=False) → Tensor. Returns a Tensor with same torch.dtype and torch.device as the Tensor other. When non_blocking, tries to convert … Webtorch.Tensor.clone — PyTorch 2.0 documentation torch.Tensor.clone Tensor.clone(*, memory_format=torch.preserve_format) → Tensor See torch.clone () Next Previous © … Web1 day ago · I tried one solution using extremely large masked tensors, e.g. x_masked = masked_tensor (x [:, :, None, :].repeat ( (1, 1, M, 1)), masks [None, None, :, :].repeat ( (b, c, 1, 1))) out = torch.mean (x_masked, -1).get_data () and while this is lightning fast, it results in extremely large tensors and is unusable. dr martin concord family medicine