torch.Tensor是一个包含单一数据类型元素的多维矩阵。Torch定义了9种CPU张量类型和9种GPU张量类型: Data type dtype CPU tensor GPU tensor 32-bit floating point torch.float32 or torch.float torch.FloatTensor torch.cuda.FloatTensor 64-bit floating point torch.float64 or torch.double torch.DoubleTensor tor...
>>> tensor.new_tensor(data) tensor([[ 0, 1], [ 2, 3]], dtype=torch.int8) .new_tensor(data) 的 . 前面是一个tensor, 返回一个新的Tensor,data作为张量数据。 默认情况下,返回的Tensor与此张量具有相同的torch.dtype和torch.device。 warning: new_tensor()始终复制数据。如果有Tensor数据并且想要...
>>> a = torch.randn(4) >>> a tensor([-0.9732, -0.3497, 0.6245, 0.4022]) >>> b = torch.randn(4, 1) >>> b tensor([[ 0.3743], [-1.7724], [-0.5811], [-0.8017]]) >>> torch.add(a, 10, b) tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], [-18.6971, -18.0736, -17.0994, -...
Assigning a Tensor doesn’t have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. If there was no such class as Parameter, these temporaries would get registered too. Parameters data (Tensor)– parameter tensor. ...
Tensor(batch.batch_size) \ .type_as(memory_bank) \ .long() \ .fill_(memory_bank.size(0)) return src, enc_states, memory_bank, src_lengths Example #2Source File: box_decomposition.py From botorch with MIT License 6 votes def _get_augmented_pareto_front_indices(self) -> Tensor: r...
# Ensure that data is not a view of a larger tensor: if isinstance(row, Tensor): row = row.clone() return pickle.dumps(row) buffer = io.BytesIO() torch.save(row, buffer) return buffer.getvalue() def _deserialize(self, row: bytes) -> Any: return pickle.loads(row) return torch...
🐛 Describe the bug import torch cfn = torch.compile(torch.cumsum) for n in [100, 10, 100]: print(torch.sum(cfn(torch.full((n,), float('inf'), device='cuda', dtype=torch.float64), -1))) prints tensor(inf, device='cuda:0', dtype=torch.floa...
keep_prob==1): #set keep_prob=1 to turn off dropblock return x if self.gamma is None: self.gamma = self.calculate_gamma(x) if x.type() == 'torch.cuda.HalfTensor': #TODO: not fully support for FP16 now FP16 = True x = x.float() else: FP16 = False p = torch.ones_like...
data(Tensor) – parameter tensor. requires_grad(bool,optional) – if the parameter requires gradient. SeeExcluding subgraphs from backwardfor more details. Default: True Containers Module classtorch.nn.Module[source] Base class for all neural network modules. ...
_solve(input, LU_data, LU_pivots, out=None) → Tensor torch.matmul(input, other, out=None) → Tensor torch.matrix_power(input, n) → Tensor torch.matrix_rank(input, tol=None, bool symmetric=False) → Tensor (input, mat2, out=None) → Tensor ...