def run_spinn(self, example, use_internal_parser=False, validate_transitions=True):
state_both, transition_acc, transition_loss = super(SentencePairModel, self).run_spinn(
example, use_internal_parser, validate_transitions)
batch_size = len(state_both) / 2
h_premise = get_h(torch.cat(state_both[:batch_size], 0), self.hidden_dim)
h_hypothesis = get_h(torch.cat(state_both[batch_size:], 0), self.hidden_dim)
return [h_premise, h_hypothesis], transition_acc, transition_loss
def run_spinn(self,
example,
use_internal_parser=False,
validate_transitions=True):
state, transition_acc, transition_loss = super(
SentenceModel, self).run_spinn(example, use_internal_parser,
validate_transitions)
h = get_h(torch.cat(state, 0), self.hidden_dim)
return [h], transition_acc, transition_loss
def reduce_phase_hook(self, lefts, rights, trackings, reduce_stacks, r_idxs=None):
for idx, stack in izip(r_idxs, reduce_stacks):
h = get_h(stack[-1], self.hidden_dim)
assert h.size() == torch.Size([1, self.hidden_dim]), 'hsize: {}'.format(h.size())
self.states[idx].append(h)
def shift_phase_hook(self, tops, trackings, stacks, idxs):
# print 'shift_phase_hook...'
for idx, stack in izip(idxs, stacks):
h = get_h(stack[-1], self.hidden_dim)
assert h.size() == torch.Size([1, self.hidden_dim]), 'hsize: {}'.format(h.size())
self.states[idx].append(h)