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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionCTCLoss(torch.nn.Module):
def __init__(self, blank_logprob=-1):
super(AttentionCTCLoss, self).__init__()
self.log_softmax = torch.nn.LogSoftmax(dim=-1)
self.blank_logprob = blank_logprob
self.CTCLoss = nn.CTCLoss(zero_infinity=True)
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), # key_inds >= key_lens+1
-float("inf"))
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.CTCLoss(
attn_logprob, target_seqs,
input_lengths=query_lens, target_lengths=key_lens)
return cost
class AttentionBinarizationLoss(torch.nn.Module):
def __init__(self):
super(AttentionBinarizationLoss, self).__init__()
def forward(self, hard_attention, soft_attention, eps=1e-12):
log_sum = torch.log(torch.clamp(soft_attention[hard_attention == 1],
min=eps)).sum()
return -log_sum / hard_attention.sum()
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