def beamsearch_hamcycle(pred, W, beam_size=2):
N = W.size(-1)
batch_size = W.size(0)
BS = BeamSearch(beam_size, batch_size, N)
trans_probs = pred.gather(1, BS.get_current_state())
for step in range(N-1):
BS.advance(trans_probs, step + 1)
trans_probs = pred.gather(1, BS.get_current_state())
ends = torch.zeros(batch_size, 1).type(dtype_l)
# extract paths
Paths = BS.get_hyp(ends)
# Compute cost of path
Costs = compute_cost_path(Paths, W)
return Costs, Paths
def _beam_search_decoding(self, imgs, beam_size):
B = imgs.size(0)
# use batch_size*beam_size as new Batch
imgs = tile(imgs, beam_size, dim=0)
enc_outs, hiddens = self.model.encode(imgs)
dec_states, O_t = self.model.init_decoder(enc_outs, hiddens)
new_B = imgs.size(0)
# first decoding step's input
tgt = torch.ones(new_B, 1).long() * START_TOKEN
beam = BeamSearch(beam_size, B)
for t in range(self.max_len):
tgt = beam.current_predictions.unsqueeze(1)
dec_states, O_t, probs = self.step_decoding(
dec_states, O_t, enc_outs, tgt)
log_probs = torch.log(probs)
beam.advance(log_probs)
any_beam_is_finished = beam.is_finished.any()
if any_beam_is_finished:
beam.update_finished()
if beam.done:
break
select_indices = beam.current_origin
if any_beam_is_finished:
# Reorder states
h, c = dec_states
h = h.index_select(0, select_indices)
c = c.index_select(0, select_indices)
dec_states = (h, c)
O_t = O_t.index_select(0, select_indices)
# get results
formulas_idx = torch.stack([hyps[1] for hyps in beam.hypotheses],
dim=0)
results = self._idx2formulas(formulas_idx)
return results
