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