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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):
# not used in the final model
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)
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