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...
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...
torch.view_as_real(tensor) if not isinstance(tensor, torch.nn.UninitializedParameter) and tensor.is_complex() else tensor )def _element_size(dtype): """ Returns the element size for a dtype, in bytes """ if not isinstance(dtype, torch.dtype): ...
Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:867.) return _VF.stft(input, n_fft, hop_length, win_length, wi...
grad_scale = (grad_input * grad_scale.float()).view(-1, *grad_input.shape).sum(0)return(grad_scale,None,None) 开发者ID:emilemathieu,项目名称:pvae,代码行数:27,代码来源:hyperbolic_radius.py 示例4: mean ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importlga...
multinomial(prob_prev, 1).view(-1)) prob_prev = torch.exp(outputs[-1].data) # fetch prev distribution: shape Nx(M+1) it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1).index_select(0, sample_ind)) else: it = seq[:, i-1].clone() # break if all ...
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...
# view.values() # del view[name] # view.items() # view.keys() # len(view) class OrderedDictWrapper(object): def __init__(self, module): self.module_ref = weakref.ref(module) @property def module(self): r = self.module_ref() if r is None: raise RuntimeError("_parameters or...
4. view() 5. unsqueeze() 二、mask掩码替换—masked_fill() 三、矩阵乘法( 点积运算 )—torch.matmul() 四、模块类中的 forward方法调用 五、模型中可优化参数 1. 查看模型中可学习(优化)的参数—model.named_parameters() 2. 将普通张量转换为模型可学习的参数—nn.Parameter() ...