From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
funasr/losses/label_smoothing_loss.py | 12 ++++++------
1 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/funasr/losses/label_smoothing_loss.py b/funasr/losses/label_smoothing_loss.py
index 8f63df9..385025d 100644
--- a/funasr/losses/label_smoothing_loss.py
+++ b/funasr/losses/label_smoothing_loss.py
@@ -8,7 +8,7 @@
import torch
from torch import nn
-from funasr.modules.nets_utils import make_pad_mask
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
class LabelSmoothingLoss(nn.Module):
@@ -50,8 +50,8 @@
"""
assert x.size(2) == self.size
batch_size = x.size(0)
- x = x.view(-1, self.size)
- target = target.view(-1)
+ x = x.contiguous().view(-1, self.size)
+ target = target.contiguous().view(-1)
with torch.no_grad():
true_dist = x.clone()
true_dist.fill_(self.smoothing / (self.size - 1))
@@ -75,10 +75,10 @@
self.criterion = criterion
def forward(self, pred, label, lengths):
- pad_mask = make_pad_mask(lengths, maxlen=pred.shape[1])
+ pad_mask = make_pad_mask(lengths, maxlen=pred.shape[1]).to(pred.device)
loss = self.criterion(pred, label)
denom = (~pad_mask).sum() if self.normalize_length else pred.shape[0]
- return loss.masked_fill(pad_mask, 0).sum() / denom
+ return loss.masked_fill(pad_mask.unsqueeze(-1), 0).sum() / denom
class NllLoss(nn.Module):
@@ -97,7 +97,7 @@
normalize_length=False,
criterion=nn.NLLLoss(reduction='none'),
):
- """Construct an LabelSmoothingLoss object."""
+ """Construct an NllLoss object."""
super(NllLoss, self).__init__()
self.criterion = criterion
self.padding_idx = padding_idx
--
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