remove comments

layers/attention.py

@@ -146,12 +146,7 @@ class AttentionRNNCell(nn.Module):
         if t == 0:
             self.alignment_model.reset()
             self.win_idx = 0
-        # Feed it to RNN
-        # s_i = f(y_{i-1}, c_{i}, s_{i-1})
         rnn_output = self.rnn_cell(torch.cat((memory, context), -1), rnn_state)
-        # Alignment
-        # (batch, max_time)
-        # e_{ij} = a(s_{i-1}, h_j)
         if self.align_model is 'b':
             alignment = self.alignment_model(annots, rnn_output)
         else:
@@ -169,13 +164,7 @@ class AttentionRNNCell(nn.Module):
                 alignment[:, front_win:] = -float("inf")
             # Update the window
             self.win_idx = torch.argmax(alignment,1).long()[0].item()
-        # Normalize context weight
-        # alignment = F.softmax(alignment, dim=-1)
-        # alignment = 5 * alignment
         alignment = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
-        # Attention context vector
-        # (batch, 1, dim)
-        # c_i = \sum_{j=1}^{T_x} \alpha_{ij} h_j
         context = torch.bmm(alignment.unsqueeze(1), annots)
         context = context.squeeze(1)
         return rnn_output, context, alignment