| 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) |
|
|
