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Running
on
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Running
on
Zero
File size: 1,181 Bytes
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from torch import nn
class CTCHead(nn.Module):
def __init__(
self, in_channels, out_channels=6625, fc_decay=0.0004, mid_channels=None, return_feats=False, **kwargs
):
super(CTCHead, self).__init__()
if mid_channels is None:
self.fc = nn.Linear(
in_channels,
out_channels,
bias=True,
)
else:
self.fc1 = nn.Linear(
in_channels,
mid_channels,
bias=True,
)
self.fc2 = nn.Linear(
mid_channels,
out_channels,
bias=True,
)
self.out_channels = out_channels
self.mid_channels = mid_channels
self.return_feats = return_feats
def forward(self, x, labels=None):
if self.mid_channels is None:
predicts = self.fc(x)
else:
x = self.fc1(x)
predicts = self.fc2(x)
if self.return_feats:
result = {}
result["ctc"] = predicts
result["ctc_neck"] = x
else:
result = predicts
return result
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