| import torch
|
| from typing import Optional
|
|
|
|
|
| def init_weights(m, mean=0.0, std=0.01):
|
| classname = m.__class__.__name__
|
| if classname.find("Conv") != -1:
|
| m.weight.data.normal_(mean, std)
|
|
|
|
|
| def get_padding(kernel_size, dilation=1):
|
| return int((kernel_size * dilation - dilation) / 2)
|
|
|
|
|
| def convert_pad_shape(pad_shape):
|
| l = pad_shape[::-1]
|
| pad_shape = [item for sublist in l for item in sublist]
|
| return pad_shape
|
|
|
|
|
| def slice_segments(
|
| x: torch.Tensor, ids_str: torch.Tensor, segment_size: int = 4, dim: int = 2
|
| ):
|
| if dim == 2:
|
| ret = torch.zeros_like(x[:, :segment_size])
|
| elif dim == 3:
|
| ret = torch.zeros_like(x[:, :, :segment_size])
|
|
|
| for i in range(x.size(0)):
|
| idx_str = ids_str[i].item()
|
| idx_end = idx_str + segment_size
|
| if dim == 2:
|
| ret[i] = x[i, idx_str:idx_end]
|
| else:
|
| ret[i] = x[i, :, idx_str:idx_end]
|
|
|
| return ret
|
|
|
|
|
| def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
| b, d, t = x.size()
|
| if x_lengths is None:
|
| x_lengths = t
|
| ids_str_max = x_lengths - segment_size + 1
|
| ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long)
|
| ret = slice_segments(x, ids_str, segment_size, dim=3)
|
| return ret, ids_str
|
|
|
|
|
| @torch.jit.script
|
| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
| n_channels_int = n_channels[0]
|
| in_act = input_a + input_b
|
| t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
| acts = t_act * s_act
|
| return acts
|
|
|
|
|
| def sequence_mask(length: torch.Tensor, max_length: Optional[int] = None):
|
| if max_length is None:
|
| max_length = length.max()
|
| x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
| return x.unsqueeze(0) < length.unsqueeze(1)
|
|
|
|
|
| def grad_norm(parameters, norm_type: float = 2.0):
|
| if isinstance(parameters, torch.Tensor):
|
| parameters = [parameters]
|
|
|
| parameters = [p for p in parameters if p.grad is not None]
|
|
|
| if not parameters:
|
| return 0.0
|
|
|
| return torch.linalg.vector_norm(
|
| torch.stack([p.grad.norm(norm_type) for p in parameters]), ord=norm_type
|
| ).item()
|
|
|
