I double-checked, and the torch.as_tensor(bytearray(...), dtype = torch.int16) actually doesn't do the right thing and constructs a tensor of (u)int8 elements first and then casts them to int16. Filed an issue
Will be cast to a torch.LongTensor internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values. values (array_...
import torch # 1、tensor.view调整tensor的形状 a = torch.arange(0, 6) b1 = a.view(2, 3) b2 = a.view(-1, 2) # 当某一维为-1时,回自动计算它的大小 print(b1) print(b2) b3 = b1.unsqueeze(1) print(b3) print(b3.size()) # 注意形状,在第1维(下标从0开始)上增加“1” b4 = ...
bitwise_not_() → Tensor bmm(batch2) → Tensor bool() → Tensor byte() → Tensor cauchy_(median=0, sigma=1, *, generator=None) → Tensor ceil() → Tensor ceil_() → Tensor char() → Tensor cholesky(upper=False) → Tensor cholesky_inverse(upper=False) → Tensor cholesky_solve(inpu...
>>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]])) tensor([[ 1, 2, 3], [ 4, 5, 6]]) 注意:torch.tensor()始终复制数据。如果您有Tensor数据并且只想更改其requires_grad标志,请使用requires_grad_()或detach()来避免复制。如果您有一个numpy array 并想要避免copy,请使用torch.as_ten...
Addingtorch.serialization.add_safe_globals([bytes, bytearray])at the beginning doesn't help. The doc fortorch.loadsays: weights_only: Indicates whether unpickler should be restricted to loading only tensors, primitive types, dictionaries and any types added via :func:`torch.serialization.add_safe...
// aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a)inlineat::Tensorbroadcast_to(constat::Tensor&self,at::IntArrayRefsize){returnat::_ops::broadcast_to::call(self,c10::fromIntArrayRefSlow(size));}namespacesymint{template<typenameT,typename=std::enable_if_t<std::is_...
tensor (Tensor)– buffer to be registered. Example: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 复制 >>> self.register_buffer('running_mean', torch.zeros(num_features)) register_forward_hook(hook)[source] Registers a forward hook on the module. The hook will be called every time ...
torch.addcmul(tensor, value=1, tensor1, tensor2, out=None)→ Tensor用tensor2对tensor1逐元素相乘,并对结果乘以标量值value然后加到tensor。张量的形状不需要匹配,但元素数量必须一致。如果输入是FloatTensor or DoubleTensor类型,则value 必须为实数,否则须为整数。
cdopt python path_to/Download_model.py --model_path model --model_name facebook/opt-30b --revision main 最後,將模型成品上傳至 Amazon S3 儲存貯體。 aws s3 cp opt{your_s3_bucket}/opt --recursive 您現在應該將模型成品存放在 Amazon S3 中,準備好部署到 SageMaker AI 端點。