切片 torch.tensor_split(input, indices_or_sections, dim=0) → List of Tensors 是按照索引拆分。相当于你指定了拆分位置的下标; 组合/拼接 torch.cat(tensors, dim=0, ***, out=None) → Tensor 拼接tensor 序列,可以指定dim 组合/拼接 torch.stack(tensors, dim=0, ***, out=None) → Tensor ...
reduction: one of available reductions ('min', 'max', 'sum', 'mean', 'prod'), case-sensitive alternatively, a callable f(tensor, reduced_axes) -> tensor can be provided. This allows using various reductions, examples: np.max, tf.reduce_logsumexp, torch.var, etc. axes_lengths: any ...
placeholderrepresents a function input. Thenameattribute specifies the name this value will take on.targetis similarly the name of the argument.argsholds either: 1) nothing, or 2) a single argument denoting the default parameter of the function input.kwargsis don’t-care. Placeholders correspond ...
A sparse tensor can be uncoalesced, in that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: torch.sparse. Parameters indices (array_like)– Initial data for the tensor. Can be a list, tuple, NumPy ndarray, ...
cholesky_solve(input2, upper=False) → Tensor chunk(chunks, dim=0) → List of Tensors clamp(min, max) → Tensor clamp_(min, max) → Tensor clone() → Tensor contiguous() → Tensor copy_(src, non_blocking=False) → Tensor cos() → Tensor ...
torch.full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor Indexing, Slicing, Joining, Mutating Ops torch.chunk(input, chunks, dim=0) → List of Tensors torch.gather(input, dim, index, out=None, sparse_grad=False) → Te...
The intermediate representation is the container for the operations that were recorded during symbolic tracing. It consists of a list of Nodes that represent function inputs, callsites (to functions, methods, or torch.nn.Module instances), and return values. More information about the IR can be ...
RuntimeError python value of type ‘…’ cannot be used as a value. 使用全局变量时会出现这种问题。 RuntimeError all inputs of range must be ‘…’, found Tensor (inferred) in argument。 torchscript函数默认的参数类型都是torch.tensor。当你想使用别的类型时,你需要明确的给出指定。比如 ...
We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if x.sum() > 0: def forward(self, x: Tensor, h: Tensor) -> Tuple[Tensor, Tensor]: dg = self.dg linear...
tensor= torch.cat(list_of_tensors, dim=0)tensor= torch.stack(list_of_tensors, dim=0) 将整数标记转换成独热(one-hot)编码 PyTorch 中的标记默认从 0 开始。 N = tensor.size(0) one_hot = torch.zeros(N, num_classes).long() one_hot.scatter_(dim=1, index=torch.unsqueeze(tensor,dim=1...