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import torch
import torch.nn.functional as F
from torch.nn import Module
class ForwardSumLoss(Module):
def __init__(self, blank_logprob=-1, loss_scale=1.0):
super().__init__()
self.log_softmax = torch.nn.LogSoftmax(dim=-1)
self.ctc_loss = torch.nn.CTCLoss(zero_infinity=True, blank=16)
self.blank_logprob = blank_logprob
self.loss_scale = loss_scale
def forward(self, attn_logprob, in_lens, out_lens):
key_lens = in_lens
query_lens = out_lens
max_key_len = attn_logprob.size(-1)
# Reorder input to [query_len, batch_size, key_len]
attn_logprob = attn_logprob.squeeze(1)
attn_logprob = attn_logprob.permute(1, 0, 2)
# Add blank label
attn_logprob = F.pad(input=attn_logprob, pad=(1, 0, 0, 0, 0, 0), value=self.blank_logprob)
# Convert to log probabilities
# Note: Mask out probs beyond key_len
key_inds = torch.arange(max_key_len + 1, device=attn_logprob.device, dtype=torch.long)
attn_logprob.masked_fill_(key_inds.view(1, 1, -1) > key_lens.view(1, -1, 1), -1e15) # key_inds >= key_lens+1
attn_logprob = self.log_softmax(attn_logprob)
# Target sequences
target_seqs = key_inds[1:].unsqueeze(0)
target_seqs = target_seqs.repeat(key_lens.numel(), 1)
# Evaluate CTC loss
cost = self.ctc_loss(attn_logprob, target_seqs, input_lengths=query_lens, target_lengths=key_lens)
cost *= self.loss_scale
return cost |