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import torch
import torch.nn as nn
import numpy as np
class SinusoidalPositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(SinusoidalPositionalEncoding, self).__init__()
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.arange(0, d_model, 2).float()
div_term = div_term * (-np.log(10000.0) / d_model)
div_term = torch.exp(div_term)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
# T, 1, D
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.shape[0]]
return self.dropout(x)
class LearnedPositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(LearnedPositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
self.pe = nn.Parameter(torch.randn(max_len, 1, d_model))
def forward(self, x):
x = x + self.pe[:x.shape[0]]
return self.dropout(x)
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