list of tensors is also accepted, those should be of the same type and shape pattern: string, reduction pattern reduction: one of available reductions ('min', 'max', 'sum', 'mean', 'prod'), case-sensitive alter
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...
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 ...
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, ...
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...
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...
It's not returned byforward, it's either created and passed in for each batch, or created in theforwardcall. I used a list of tensors because that turned out to be faster than narrowing and concatenating in each time step by a small amount. That shouldn't be relevant - that's how ...
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...
sum_dim_IntList::call(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) + 0x17dd (0x7fd0e26e677d in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so) frame #29: ./reproduce_sum() [0x4077cb] frame #30: main + 0x9d0 (0x404...
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 ...