| import torch | |
| import torch.nn as nn | |
| import math | |
| pe_scaling = 0.1 # Hyperparameter, 0.1 value was used from training | |
| class PositionalEncoding(nn.Module): | |
| def __init__(self, d_model, max_len): | |
| super().__init__() | |
| self.register_buffer("pe", self._generate_pe(max_len, d_model)) | |
| def _generate_pe(self, max_len, d_model): | |
| pe = torch.zeros(max_len, d_model) | |
| position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1) | |
| div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) | |
| pe[:, 0::2] = torch.sin(position * div_term) | |
| pe[:, 1::2] = torch.cos(position * div_term) | |
| pe = (pe + 1) / 2 | |
| pe = pe_scaling * pe | |
| return pe.unsqueeze(0) | |
| def forward(self, x): | |
| x = x + self.pe[:, :x.size(1), :].to(x.device) | |
| return x |