def greedy_decode(self, inpute, inputc, src_pad_mask=None, context_mask=None, max_len=512, fill_pad=False, sample=False):
bsize = inpute.size(0)
sos_emb = self.get_sos_emb(inpute)
sqrt_isize = sqrt(sos_emb.size(-1))
out = sos_emb * sqrt_isize
if self.pemb is not None:
out = out + self.pemb.get_pos(0)
if self.drop is not None:
out = self.drop(out)
states = {}
for _tmp, net in enumerate(self.nets):
out, _state = net(inpute, None, inputc, src_pad_mask, context_mask, None, out)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
out = self.classifier(out)
wds = SampleMax(out.softmax(-1), dim=-1, keepdim=False) if sample else out.argmax(dim=-1)
trans = [wds]
done_trans = wds.eq(2)
for i in range(1, max_len):
out = self.wemb(wds) * sqrt_isize
if self.pemb is not None:
out = out + self.pemb.get_pos(i)
if self.drop is not None:
out = self.drop(out)
for _tmp, net in enumerate(self.nets):
out, _state = net(inpute, states[_tmp], inputc, src_pad_mask, None, context_mask, out)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
out = self.classifier(out)
wds = SampleMax(out.softmax(-1), dim=-1, keepdim=False) if sample else out.argmax(dim=-1)
trans.append(wds.masked_fill(done_trans, 0) if fill_pad else wds)
done_trans = done_trans | wds.eq(2)
if all_done(done_trans, bsize):
break
return torch.cat(trans, 1)
def greedy_decode(self, inpute, src_pad_mask=None, max_len=512, fill_pad=False, sample=False):
bsize = inpute.size(0)
out = self.get_sos_emb(inpute)
# out: input to the decoder for the first step (bsize, 1, isize)
if self.drop is not None:
out = self.drop(out)
out, statefl = self.flayer(out, "init", True)
states = {}
if self.projector:
inpute = self.projector(inpute)
attn = self.attn(out.unsqueeze(1), inpute, src_pad_mask).squeeze(1)
for _tmp, net in enumerate(self.nets):
out, _state = net(out, attn, "init", True)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
# out: (bsize, nwd)
out = self.classifier(torch.cat((out, attn), -1))
# wds: (bsize)
wds = SampleMax(out.softmax(-1), dim=-1, keepdim=False) if sample else out.argmax(dim=-1)
trans = [wds]
# done_trans: (bsize)
done_trans = wds.eq(2)
for i in range(1, max_len):
out = self.wemb(wds)
if self.drop is not None:
out = self.drop(out)
out, statefl = self.flayer(out, statefl)
attn = self.attn(out.unsqueeze(1), inpute, src_pad_mask).squeeze(1)
for _tmp, net in enumerate(self.nets):
out, _state = net(out, attn, states[_tmp])
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
out = self.classifier(torch.cat((out, attn), -1))
wds = SampleMax(out.softmax(-1), dim=-1, keepdim=False) if sample else out.argmax(dim=-1)
trans.append(wds.masked_fill(done_trans, pad_id) if fill_pad else wds)
done_trans = done_trans | wds.eq(2)
if all_done(done_trans, bsize):
break
return torch.stack(trans, 1)
def greedy_decode(self,
inpute,
src_pad_mask=None,
max_len=512,
fill_pad=False,
sample=False):
bsize, seql, isize = inpute[0].size()
sqrt_isize = sqrt(isize)
outs = []
for model, inputu in zip(self.nets, inpute):
sos_emb = model.get_sos_emb(inputu)
# out: input to the decoder for the first step (bsize, 1, isize)
out = sos_emb * sqrt_isize
if model.pemb is not None:
out = out + model.pemb.get_pos(0)
if model.drop is not None:
out = model.drop(out)
states = {}
for _tmp, net in enumerate(model.nets):
out, _state = net(inputu, None, src_pad_mask, None, out)
states[_tmp] = _state
if model.out_normer is not None:
out = model.out_normer(out)
# outs: [(bsize, 1, nwd)...]
outs.append(model.classifier(out).softmax(dim=-1))
out = torch.stack(outs).mean(0)
wds = SampleMax(out, dim=-1, keepdim=False) if sample else out.argmax(
dim=-1)
trans = [wds]
# done_trans: (bsize, 1)
done_trans = wds.eq(2)
for i in range(1, max_len):
outs = []
for model, inputu in zip(self.nets, inpute):
out = model.wemb(wds) * sqrt_isize
if model.pemb is not None:
out = out + model.pemb.get_pos(i)
if model.drop is not None:
out = model.drop(out)
for _tmp, net in enumerate(model.nets):
out, _state = net(inputu, states[_tmp], src_pad_mask, None,
out)
states[_tmp] = _state
if model.out_normer is not None:
out = model.out_normer(out)
# outs: [(bsize, 1, nwd)...]
outs.append(model.classifier(out).softmax(dim=-1))
out = torch.stack(outs).mean(0)
wds = SampleMax(out, dim=-1,
keepdim=False) if sample else out.argmax(dim=-1)
trans.append(
wds.masked_fill(done_trans, pad_id) if fill_pad else wds)
done_trans = done_trans | wds.eq(2)
if all_done(done_trans, bsize):
break
return torch.cat(trans, 1)
def greedy_decode(self,
inpute,
src_pad_mask=None,
max_len=512,
fill_pad=False,
sample=False):
bsize = inpute.size(0)
sos_emb = self.get_sos_emb(inpute)
sqrt_isize = sqrt(sos_emb.size(-1))
# out: input to the decoder for the first step (bsize, 1, isize)
out = sos_emb * sqrt_isize
if self.pemb is not None:
out = out + self.pemb.get_pos(0)
if self.drop is not None:
out = self.drop(out)
states = {}
for _tmp, (net,
inputu) in enumerate(zip(self.nets, inpute.unbind(dim=-1))):
out, _state = net(inputu, None, src_pad_mask, None, out, True)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
out = self.classifier(out)
wds = SampleMax(out.softmax(-1), dim=-1,
keepdim=False) if sample else out.argmax(dim=-1)
trans = [wds]
# done_trans: (bsize, 1)
done_trans = wds.eq(2)
for i in range(1, max_len):
out = self.wemb(wds) * sqrt_isize
if self.pemb is not None:
out = out + self.pemb.get_pos(i)
if self.drop is not None:
out = self.drop(out)
for _tmp, (net, inputu) in enumerate(
zip(self.nets, inpute.unbind(dim=-1))):
out, _state = net(inputu, states[_tmp], src_pad_mask, None,
out, True)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
# out: (bsize, 1, nwd)
out = self.classifier(out)
wds = SampleMax(out.softmax(-1), dim=-1,
keepdim=False) if sample else out.argmax(dim=-1)
trans.append(
wds.masked_fill(done_trans, pad_id) if fill_pad else wds)
done_trans = done_trans | wds.eq(2)
if all_done(done_trans, bsize):
break
return torch.cat(trans, 1)
def greedy_decode(self,
inpute,
src_pad_mask=None,
max_len=512,
fill_pad=False,
sample=False):
bsize = inpute.size(0)
out = self.get_sos_emb(inpute)
if self.drop is not None:
out = self.drop(out)
states = {}
for _tmp, net in enumerate(self.nets):
out, _state = net(inpute,
"init",
src_pad_mask=src_pad_mask,
query_unit=out)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
out = self.classifier(out)
wds = SampleMax(out.softmax(-1), dim=-1,
keepdim=False) if sample else out.argmax(dim=-1)
trans = [wds]
done_trans = wds.eq(2)
for i in range(1, max_len):
out = self.wemb(wds)
if self.drop is not None:
out = self.drop(out)
for _tmp, net in enumerate(self.nets):
out, _state = net(inpute,
states[_tmp],
src_pad_mask=src_pad_mask,
query_unit=out)
states[_tmp] = _state
if self.out_normer is not None:
out = self.out_normer(out)
out = self.classifier(out)
wds = SampleMax(out.softmax(-1), dim=-1,
keepdim=False) if sample else out.argmax(dim=-1)
trans.append(
wds.masked_fill(done_trans, pad_id) if fill_pad else wds)
done_trans = done_trans | wds.eq(2)
if all_done(done_trans, bsize):
break
return torch.cat(trans, 1)
