x=torch.randn(5,4,dtype=torch.float32) x = torch.tensor([1,2,3,4]) x = torch.tensor(np.arange(10)) x = torch.randn(3,4) y = torch.rand_like(x) # Torch之间的运算 # torch默认的操作是elements和broadcasting的 X = torch.randn(3,4) y= torch.rand(1,4) # torch.tensor之间的...
Tensor.repeat: lambda self, *size: -1, Tensor.requires_grad_: lambda self, requires_grad=True: -1, Tensor.reshape_as: lambda self, other: -1, Tensor.resize: lambda self, *size: -1, Tensor.resize_: lambda self, size: -1, Tensor.resize_as: lambda self, other: -1, Tensor.resiz...
numpy(), atol=1) # AssertionError: # Not equal to tolerance rtol=1e-07, atol=1 # Mismatched elements: 1 / 1 (100%) # Max absolute difference: inf # Max relative difference: nan # x: array(-1.570796+9.903487j, dtype=complex64) # y: array(0.+infj, dtype=complex64)``` ### ...
Source File: repeat_factor.py From Parsing-R-CNN with MIT License 6 votes def __iter__(self): if self.shuffle: # deterministically shuffle based on epoch g = torch.Generator() g.manual_seed(self.epoch) indices = self._get_epoch_indices(g) randperm = torch.randperm(len(indices), ...
repeat(neq))) self.Av = Parameter(dTensor(neq*nx).uniform_()) self.Asz = torch.Size([neq, nx]) self.b = Variable(torch.ones(neq).double().cuda()) Example #15Source File: dev_pdipm.py From lcp-physics with Apache License 2.0 5 votes def sparse_solve_kkt_inverse(H_, A_, ...
repeat((1, n_frame)).unsqueeze(0) ) # size (1, n_fft, n_frame) window_envelop = torch.nn.functional.fold( window_sq, (1, (n_frame - 1) * hop_length + n_fft), (1, n_fft), stride=(1, hop_length) ).squeeze(2) # size (1, 1, expected_signal_len) expected_signal_...
Takes the two operands (scalar or tensor, both may contain floating point number) whose elements are to be divided (operand 1 by operand 2) as arguments. Parameters --- t1: tensor or scalar The first operand whose values are divided (may be floats) t2: tensor...
torch.repeat_interleave() torch.repeat_interleave(repeats) → Tensor torch.tensordot(a, b, dims=2)[source] torch.trace(input) → Tensor torch.tril(input, diagonal=0, out=None) → Tensor torch.tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) → ...
Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:`(N, C_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where .. math:: H_{out} = (H_{in} - 1) \times \text{...
repeat(*sizes) → Tensor repeat_interleave(repeats, dim=None) → Tensor requires_grad() requires_grad_(requires_grad=True) → Tensor reshape(*shape) → Tensor reshape_as(other) → Tensor resize_(*sizes) → Tensor resize_as_(tensor) → Tensor ...