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...
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(x) tensor([[ 0.4737, -0.3839], [-0.2098, -0.6699], [ 0.3470, -0.9451], [-0.5174, -1.3136]]) 此...
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...
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 ...
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...
sample: # x: [batch_size] - word index (next input) x = torch.multinomial(self.softmax(out / self.temperature), 1).view(-1) # Greedy sampling else: # x: [batch_size] - word index (next input) _, x = out.max(dim=1) return x ...
# Each OrderedDict needs to support: # x not in view # x in view # view[name] = ... # 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 ...
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() ...
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...
# compute_jacobian only works for pure real # or pure imaginary delta def compute_gradient(delta): # we currently assume that the norm of delta equals eps assert(delta == eps or delta == (eps * 1j)) def fn_out(): if not is_mkldnn: # x is a view into input and so this works...