from typing import Optional
|
|
import torch
|
import torch.nn as nn
|
|
import numpy as np
|
|
|
class MakePadMask(nn.Module):
|
def __init__(self, max_seq_len=512, flip=True):
|
super().__init__()
|
if flip:
|
self.mask_pad = torch.Tensor(1 - np.tri(max_seq_len)).type(torch.bool)
|
else:
|
self.mask_pad = torch.Tensor(np.tri(max_seq_len)).type(torch.bool)
|
|
def forward(self, lengths, xs=None, length_dim=-1, maxlen=None):
|
"""Make mask tensor containing indices of padded part.
|
This implementation creates the same mask tensor with original make_pad_mask,
|
which can be converted into onnx format.
|
Dimension length of xs should be 2 or 3.
|
"""
|
if length_dim == 0:
|
raise ValueError("length_dim cannot be 0: {}".format(length_dim))
|
|
if xs is not None and len(xs.shape) == 3:
|
if length_dim == 1:
|
lengths = lengths.unsqueeze(1).expand(*xs.transpose(1, 2).shape[:2])
|
else:
|
lengths = lengths.unsqueeze(1).expand(*xs.shape[:2])
|
|
if maxlen is not None:
|
m = maxlen
|
elif xs is not None:
|
m = xs.shape[-1]
|
else:
|
m = torch.max(lengths)
|
|
mask = self.mask_pad[lengths - 1][..., :m].type(torch.float32)
|
|
if length_dim == 1:
|
return mask.transpose(1, 2)
|
else:
|
return mask
|
|
|
class sequence_mask(nn.Module):
|
def __init__(self, max_seq_len=512, flip=True):
|
super().__init__()
|
|
def forward(self, lengths, max_seq_len=None, dtype=torch.float32, device=None):
|
if max_seq_len is None:
|
max_seq_len = lengths.max()
|
row_vector = torch.arange(0, max_seq_len, 1).to(lengths.device)
|
matrix = torch.unsqueeze(lengths, dim=-1)
|
mask = row_vector < matrix
|
|
return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
|
|
|
def normalize(
|
input: torch.Tensor, p: float = 2.0, dim: int = 1, out: Optional[torch.Tensor] = None
|
) -> torch.Tensor:
|
if out is None:
|
denom = input.norm(p, dim, keepdim=True).expand_as(input)
|
return input / denom
|
else:
|
denom = input.norm(p, dim, keepdim=True).expand_as(input)
|
return torch.div(input, denom, out=out)
|
|
|
def subsequent_mask(size: torch.Tensor):
|
return torch.ones(size, size).tril()
|
|
|
def MakePadMask_test():
|
feats_length = torch.tensor([10]).type(torch.long)
|
mask_fn = MakePadMask()
|
mask = mask_fn(feats_length)
|
print(mask)
|
|
|
if __name__ == "__main__":
|
MakePadMask_test()
|
