| from typing import List, Optional, Tuple, Iterator
|
|
|
| import torch
|
|
|
|
|
| def call_weight_data_normal_if_Conv(m: torch.nn.Module):
|
| classname = m.__class__.__name__
|
| if classname.find("Conv") != -1:
|
| mean = 0.0
|
| std = 0.01
|
| m.weight.data.normal_(mean, std)
|
|
|
|
|
| def get_padding(kernel_size: int, dilation=1) -> int:
|
| return int((kernel_size * dilation - dilation) / 2)
|
|
|
|
|
| def slice_on_last_dim(
|
| x: torch.Tensor,
|
| start_indices: List[int],
|
| segment_size=4,
|
| ) -> torch.Tensor:
|
| new_shape = [*x.shape]
|
| new_shape[-1] = segment_size
|
| ret = torch.empty(new_shape, device=x.device)
|
| for i in range(x.size(0)):
|
| idx_str = start_indices[i]
|
| idx_end = idx_str + segment_size
|
| ret[i, ..., :] = x[i, ..., idx_str:idx_end]
|
| return ret
|
|
|
|
|
| def rand_slice_segments_on_last_dim(
|
| x: torch.Tensor,
|
| x_lengths: int = None,
|
| segment_size=4,
|
| ) -> Tuple[torch.Tensor, List[int]]:
|
| b, _, t = x.size()
|
| if x_lengths is None:
|
| x_lengths = t
|
| ids_str_max = x_lengths - segment_size + 1
|
| ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
| ret = slice_on_last_dim(x, ids_str, segment_size)
|
| return ret, ids_str
|
|
|
|
|
| @torch.jit.script
|
| def activate_add_tanh_sigmoid_multiply(
|
| input_a: torch.Tensor, input_b: torch.Tensor, n_channels: int
|
| ) -> torch.Tensor:
|
| in_act = input_a + input_b
|
| t_act = torch.tanh(in_act[:, :n_channels, :])
|
| s_act = torch.sigmoid(in_act[:, n_channels:, :])
|
| acts = t_act * s_act
|
| return acts
|
|
|
|
|
| def sequence_mask(
|
| length: torch.Tensor,
|
| max_length: Optional[int] = None,
|
| ) -> torch.BoolTensor:
|
| if max_length is None:
|
| max_length = int(length.max())
|
| x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
| return x.unsqueeze(0) < length.unsqueeze(1)
|
|
|
|
|
| def total_grad_norm(
|
| parameters: Iterator[torch.nn.Parameter],
|
| norm_type: float = 2.0,
|
| ) -> float:
|
| norm_type = float(norm_type)
|
| total_norm = 0.0
|
|
|
| for p in parameters:
|
| if p.grad is None:
|
| continue
|
| param_norm = p.grad.data.norm(norm_type)
|
| total_norm += float(param_norm.item()) ** norm_type
|
| total_norm = total_norm ** (1.0 / norm_type)
|
|
|
| return total_norm
|
|
|
