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Neural Machine Tranlation using Local Attention
I noticed pad with window in the encoder section,
def pad_with_window_size(self, batch):
size = batch.size()
n = len(size)
if n == 2:
length, batch_size = size
padded_length = length + (2 * self.window_size + 1)
padded = torch.empty((padded_length, batch_size), dtype=torch.long, device=self.device)
padded[:self.window_size, :] = self.pad
padded[self.window_size:self.window_size + length, :] = batch
padded[-(self.window_size + 1):, :] = self.pad
elif n == 3:
length, batch_size, hidden = size
padded_length = length + (2 * self.window_size + 1)
padded = torch.empty((padded_length, batch_size, hidden), dtype=torch.long, device=self.device)
padded[:self.window_size, :, :] = self.pad
padded[self.window_size:self.window_size + length, :, :] = batch
padded[-(self.window_size + 1):, :, :] = self.pad
else:
raise Exception(f'Cannot pad batch with {n} dimensions.')
return padded
When calculating the attention, pad with window on the output of the encoder, that is when n==3, can this achieve the same effect? But this will seriously hurt my results when I try to do this.
Hi, I want to ask some about your code.
In 176-188 lines in model.py, why to do this:
if start < self.window_size:
d = self.window_size - start
score[i, j, :d] = epsilon
if end > li + self.window_size:
d = (li + self.window_size) - end
score[i, j, d:] = epsilon
Shouldn’t it judge whether the selected window is beyond length?
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