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af2130e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
class SeqKD(nn.Module):
"""
NLL loss with label smoothing.
"""
def __init__(self, T=1):
super(SeqKD, self).__init__()
self.kdloss = nn.KLDivLoss(reduction='batchmean')
self.T = T
def forward(self, prediction_logits, ref_logits, use_blank=True):
start_idx = 0 if use_blank else 1
prediction_logits = F.log_softmax(prediction_logits[:, :, start_idx:]/self.T, dim=-1) \
.view(-1, ref_logits.shape[2] - start_idx)
ref_probs = F.softmax(ref_logits[:, :, start_idx:]/self.T, dim=-1) \
.view(-1, ref_logits.shape[2] - start_idx)
loss = self.kdloss(prediction_logits, ref_probs)*self.T*self.T
# mask_probs = F.softmax(ref_logits[:, :, 1:], dim=-1).view(-1, ref_logits.shape[2] - 1)
# mask = torch.max(mask_probs, dim=1)[0] > 0.5
# if torch.sum(mask) != 0:
# loss = torch.sum(torch.sum(loss, dim=1) * mask) / torch.sum(mask)
# else:
# loss = torch.sum(torch.sum(loss, dim=1) * mask)
return loss
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