| | import numpy as np |
| | import torch |
| | from torch import nn |
| |
|
| |
|
| | class PositionalEncoding(nn.Module): |
| |
|
| | def __init__(self, d_model, dropout=0.1, max_len=5000, batch_first=False): |
| | super().__init__() |
| | self.batch_first = batch_first |
| |
|
| | self.dropout = nn.Dropout(p=dropout) |
| |
|
| | pe = torch.zeros(max_len, d_model) |
| | position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) |
| | div_term = torch.exp(torch.arange( |
| | 0, d_model, 2).float() * (-np.log(10000.0) / d_model)) |
| | pe[:, 0::2] = torch.sin(position * div_term) |
| | pe[:, 1::2] = torch.cos(position * div_term) |
| | pe = pe.unsqueeze(0).transpose(0, 1) |
| |
|
| | self.register_buffer("pe", pe) |
| |
|
| | def forward(self, x): |
| | |
| | if self.batch_first: |
| | x = x + self.pe.permute(1, 0, 2)[:, : x.shape[1], :] |
| | else: |
| | x = x + self.pe[: x.shape[0], :] |
| | return self.dropout(x) |
| |
|
