View in context As the Palmer, lighted by a domestic with a torch, past through the intricate combination of apartments of this large and irregular mansion, the cupbearer coming behind him whispered in his ear, that if he had no objection to a cup of good mead in his apartment, there we...
view_as_real()仅支持具有complex dtypes的张量。 例子: >>>x=torch.randn(4, dtype=torch.cfloat)>>>x tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)])>>>torch.view_as_real(x) tensor([[0.4737,-0.3839], [-0.2098,-0.6699], [0.3470,-0.9451], [-0...
Test code: importtorchself=torch.randn([1,1,1,1],dtype=torch.complex64)other=torch.randn([1,1,1,1,2],dtype=torch.float64)self.view_as(other) Error log: Traceback (most recent call last): File "/home/yonghyeon/pytorch_err_case_py/view_as.py", line 6, in <module> self.view_...
view_as_real() is only supported for tensors with complex dtypes. Parameters input (Tensor)– the input tensor. Example:: >>> x=torch.randn(4, dtype=torch.cfloat) >>> x tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)]) >>> torch.view_as_real...
257 view = p.grad.to_dense().view(-1) 258 else: --> 259 view = p.grad.view(-1) 260 if torch.is_complex(view): 261 view = torch.view_as_real(view).view(-1) RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across...
tensor([1.+3.j, 2.+4.j]) torch.complex64 True 1. 2. 3. torch.is_conj( 输入 ) 返回“输入”是否是复数tensor的共轭比特的view x = torch.tensor([1+2j]) y = x.conj() torch.is_conj(y) 1. 2. 3. True 1. torch.is_floating_point( 输入 ) 返回“输入”是否是浮点类型 x = to...
view(batch_size, -1) if data_range is None: # by default use max, same as fastmri data_range = gt.max(dim=1)[0]# - gt.min(dim=1)[0] mse_err = (abs(gt - pred) ** 2).mean(1) psnr_val = 10 * torch.log10(data_range ** 2 / mse_err) if reduce: return psnr_val...
is_complex()) else x.double(), memory_format=torch.legacy_contiguous_format) if gen_non_contig_grad_outputs: y = torch.testing.make_non_contiguous(y) return y.requires_grad_() outputs = _as_tuple(func(*tupled_inputs)) tupled_grad_outputs = tuple(randn_like(x) for x in outputs) ...
native_functions=native_functions, --yaml解析结果 grouped_native_functions=grouped_native_functions, -- SchemaKind融合后结果。 structured_native_functions=structured_native_functions, --SchemaKind融合后,抽取带GROUP的数据 view_groups=view_groups, --view 相关数据 ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importifft[as 别名]defforward(self, x):bsn =1batchSize, dim, h, w = x.data.shape x_flat = x.permute(0,2,3,1).contiguous().view(-1, dim)# batchsize,h, w, dim,y = torch.ones(batchSize, self.output_dim, device=...