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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=600):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
# vanilla sinusoidal encoding
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() * (-math.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)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, x.shape[1], :]
return self.dropout(x)
def enc_dec_mask(T, S, frame_width=2, expansion=0, device='cuda'):
mask = torch.ones(T, S)
for i in range(T):
mask[i, max(0, (i - expansion) * frame_width):(i + expansion + 1) * frame_width] = 0
return (mask == 1).to(device=device)
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