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import numpy as np |
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import torch |
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from torch import nn |
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class PositionalEncoding(nn.Module): |
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def __init__(self, d_model, dropout=0.1, max_len=5000, batch_first=False): |
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super().__init__() |
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self.batch_first = batch_first |
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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.exp(torch.arange( |
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0, d_model, 2).float() * (-np.log(10000.0) / d_model)) |
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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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self.register_buffer("pe", pe) |
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def forward(self, x): |
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if self.batch_first: |
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x = x + self.pe.permute(1, 0, 2)[:, : x.shape[1], :] |
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else: |
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x = x + self.pe[: x.shape[0], :] |
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return self.dropout(x) |
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