m.bias.zero_()
m.weight.fill_(1.0)
- def forward(self, bs, mode='standard'):
+ def forward(self, bs, mode="standard"):
bs.x = F.pad(bs.x, (1, -1))
bs = self.embedding(bs)
- if mode=='standard':
+ if mode == "standard":
bs = self.trunk(bs)
bs = self.readout(bs)
- elif mode=='head':
+ elif mode == "head":
bs = self.trunk(bs)
- elif mode=='deep':
+ elif mode == "deep":
r = []
for l in self.trunk:
bs = l(bs)
- r += [ bs.slice() ]
+ r += [bs.slice()]
bs = BracketedSequence(torch.cat(r, -1))
else:
raise ValueError