def create_node(idx, lex, word_idx, h, depth):
node = TensorNode(Node(lex, idx, ""))
node.label = lex + '-' + str(idx)
node.word_idx = word_idx.clone()
node.h = (h[0].clone(), h[1].clone())
node.depth = depth
return node
def greedy_search(self, fc_feats, att_feats, vocab, max_seq_length):
fc_feats, att_feats, p_att_feats = self._prepare_feature(
fc_feats, att_feats)
states_r, probs_r = self.from_scratch(fc_feats, att_feats)
logprob, word_idx_r = probs_r.max(dim=1)
lex_r = vocab.idx2word[word_idx_r.item()]
word_idx_r = torch.LongTensor([word_idx_r])
node_r = TensorNode(Node(lex_r, 1, ""))
node_r.word_idx = word_idx_r
node_r.h = states_r
node_r.logprob = logprob.item()
tree = Tree()
tree.root = node_r
tree.nodes.update({node_r.idx - 1: node_r})
queue = Queue()
queue.put(tree.root)
# print(tree.root.lex)
while not queue.empty() and len(tree.nodes) <= max_seq_length:
node = queue.get()
if node.lex == '<EOB>':
continue
idx = len(tree.nodes) + 1
lc = TensorNode(Node("", idx, ""))
lc.parent = node
self.complete_node(lc, vocab, att_feats, p_att_feats)
node.lc = lc
lc.depth = node.depth + 1
mc = TensorNode(Node("", idx + 1, ""))
mc.parent = node
mc.left_brother = lc
self.complete_node(mc, vocab, att_feats, p_att_feats)
node.mc = mc
mc.depth = node.depth + 1
lc.right_brother = mc
rc = TensorNode(Node("", idx + 2, ""))
rc.parent = node
rc.left_brother = mc
self.complete_node(rc, vocab, att_feats, p_att_feats)
node.rc = rc
rc.depth = node.depth + 1
mc.right_brother = rc
tree.nodes.update({lc.idx - 1: lc})
tree.nodes.update({mc.idx - 1: mc})
tree.nodes.update({rc.idx - 1: rc})
queue.put(lc)
queue.put(mc)
queue.put(rc)
return tree
def greedy_search(self, fc_feat, vocab, max_seq_length):
h_r, probs_r = self.from_scratch(fc_feat)
_, word_idx_r = probs_r.max(dim=1)
lex_r = vocab.idx2word[word_idx_r.item()]
word_idx_r = torch.LongTensor([word_idx_r])
node_r = TensorNode(Node(lex_r, 1, ""))
node_r.word_idx = word_idx_r
node_r.h = h_r
tree = Tree()
tree.root = node_r
tree.nodes.update({node_r.idx - 1: node_r})
tree.model = self
tree.vocab = vocab
tree.max_seq_length = max_seq_length
tree.bfs_breed()
return tree
def beam_search(self,
fc_feat,
vocab,
max_seq_length,
global_beam_size,
local_beam_size,
depth_normalization_factor=0.8):
(h_r, c_r), logprobs_r = self.from_scratch(fc_feat)
ys, ix = torch.sort(logprobs_r, 1, True)
candidates = []
for c in range(global_beam_size):
local_logprob = ys[0, c].item()
candidates.append({'c': ix[0, c], 'p': local_logprob})
candidate_trees = []
for c in range(global_beam_size):
lex = vocab.idx2word[candidates[c]['c'].item()]
node = TensorNode(Node(lex, 1, ""))
node.word_idx = candidates[c]['c']
node.h = h_r.clone(), c_r.clone()
node.logprob = candidates[c]['p']
tree = Tree()
tree.root = node
tree.logprob = candidates[c]['p']
tree.nodes.update({node.idx - 1: node})
candidate_trees.append(tree)
# for t in range((max_seq_length - 1) // 3):
completed_sentences = []
# for t in range(10):
while True:
new_candidates = []
for tree_idx, tree in enumerate(candidate_trees):
# print(tree.__str__())
# print([_node.lex for _node in tree.nodes.values()])
dead_tree = True
for node in tree.nodes.values():
if node.lc is None and node.lex != '<EOB>':
dead_tree = False
if dead_tree:
temp_candidate_dict = {
'p': tree.logprob,
'idx': tree_idx,
'dead_tree': True
}
new_candidates.append(temp_candidate_dict)
print("done sentence: {}".format(tree.__str__()))
continue
new_candidates_per_tree = []
for node in tree.nodes.values():
current_depth = node.depth
# find L(local size) groups children of this node via chain beam search
if node.lc is not None or node.lex == '<EOB>':
continue
local_candidates = []
(hs, cs), logprobs = self.beam_step(
state_a=(node.h[0].clone(), node.h[1].clone()),
x_a=node.word_idx.clone())
ys, ix = torch.sort(logprobs, 1, True)
for c in range(local_beam_size):
local_logprob = ys[0, c].item()
local_candidates.append({
'c': ix[0, c],
'p': local_logprob,
'seq_p': [local_logprob]
})
# print("input: father ({}), left sibling (None)".format(node.lex))
# print("output candidates: {}".format([vocab.idx2word[cdd['c'].item()] for cdd in local_candidates]))
m_local_candidates = []
for c in range(local_beam_size):
(_h, _c), _logprobs = self.beam_step(
state_a=(node.h[0].clone(), node.h[1].clone()),
x_a=node.word_idx.clone(),
state_f=(hs.clone(), cs.clone()),
x_f=local_candidates[c]['c'].clone())
_ys, _ix = torch.sort(_logprobs, 1, True)
for q in range(local_beam_size):
local_logprob = _ys[0, q].item()
entry = {
'c': [local_candidates[c]['c'], _ix[0, q]],
'p':
local_logprob + local_candidates[c]['p'],
'seq_p':
local_candidates[c]['seq_p'] + [local_logprob],
'state': (_h, _c)
}
m_local_candidates.append(entry)
# print("input: father ({}), left sibling ({})".format(node.lex, vocab.idx2word[local_candidates[c]['c'].item()]))
# print("output candidates: {}".format(
# [vocab.idx2word[cdd['c'][1].item()] for cdd in m_local_candidates]))
m_local_candidates = sorted(m_local_candidates,
key=lambda x: -x['p'])
m_local_candidates = m_local_candidates[:local_beam_size]
# print([{'c': cdd['c'], 'p': cdd['p'], 'state_len': len(cdd['state']), 'state_0_len': len(cdd['state'][0])} for cdd in m_local_candidates])
r_local_candidates = []
for c in range(local_beam_size):
f_state = (m_local_candidates[c]['state'][0].clone(),
m_local_candidates[c]['state'][1].clone())
(_h, _c), _logprobs = self.beam_step(
state_a=(node.h[0].clone(), node.h[1].clone()),
x_a=node.word_idx.clone(),
state_f=f_state,
x_f=m_local_candidates[c]['c'][-1])
_ys, _ix = torch.sort(_logprobs, 1, True)
for q in range(local_beam_size):
local_logprob = _ys[0, q].item()
entry = {
'c': [
_.clone()
for _ in m_local_candidates[c]['c']
],
'p':
local_logprob + m_local_candidates[c]['p'],
'seq_p':
m_local_candidates[c]['seq_p'] +
[local_logprob],
'state':
[(m_local_candidates[c]['state'][0].clone(),
m_local_candidates[c]['state'][1].clone()),
(_h.clone(), _c.clone())]
}
entry['c'].append(_ix[0, q].clone())
r_local_candidates.append(entry)
r_local_candidates = sorted(r_local_candidates,
key=lambda x: -x['p'])
r_local_candidates = r_local_candidates[:local_beam_size]
# print([{'c': cdd['c'], 'p': cdd['p'], 'state_len': len(cdd['state']), 'state_0_len': len(cdd['state'][0])} for cdd in r_local_candidates])
for candidate in r_local_candidates:
candidate['state'].insert(0, (hs, cs))
# this should be proceed after selecting top-L's combination
# candidate['p'] += tree.logprob
# candidate['idx'] = tree_idx
candidate['fid'] = node.idx
# candidate['dead_tree'] = False
new_candidates_per_tree.append(r_local_candidates)
# Now we get a list with length of (len(nodes) - len(<EOB>))
# And the number of combination is local_batch_size ** length
# inefficient but accurate method, exhaustivity
# still using beam search
combination_candidates = []
for i in range(len(new_candidates_per_tree)):
if i == 0:
for j in range(local_beam_size):
combination_candidates.append({
'p':
new_candidates_per_tree[i][j]['p'],
'seq': [j]
})
continue
new_combination_candidates = []
for p in range(local_beam_size):
for q in range(local_beam_size):
local_combination_logprob = new_candidates_per_tree[
i][q]['p'] + combination_candidates[p]['p']
local_seq = combination_candidates[p]['seq'] + [q]
new_combination_candidates.append({
'p': local_combination_logprob,
'seq': local_seq
})
new_combination_candidates = sorted(
new_combination_candidates, key=lambda x: -x['p'])
new_combination_candidates = new_combination_candidates[:
local_beam_size]
combination_candidates = new_combination_candidates
# print(len(combination_candidates))
done_combination_candidates = []
for i in range(local_beam_size):
done_combination_candidates.append({
'p':
combination_candidates[i]['p'] + tree.logprob,
'combination': [
new_candidates_per_tree[i][wi] for i, wi in
enumerate(combination_candidates[i]['seq'])
],
'dead_tree':
False,
'idx':
tree_idx
})
new_candidates.extend(done_combination_candidates)
# print(len(new_candidates))
new_candidates = sorted(new_candidates, key=lambda x: -x['p'])
new_candidates = new_candidates[:global_beam_size]
new_candidate_trees = []
for _candidate in new_candidates:
if _candidate['dead_tree']:
new_candidate_trees.append(
candidate_trees[tree_idx].clone())
continue
tree_idx = _candidate['idx']
new_tree = candidate_trees[tree_idx].clone()
for candidate in _candidate['combination']:
f_node = new_tree.nodes[candidate['fid'] - 1]
# print(f_node.__repr__())
idx = len(new_tree.nodes) + 1
lex = [vocab.idx2word[_.item()] for _ in candidate['c']]
seq_p = candidate['seq_p']
lc = TensorNode(Node(lex[0], idx, ""))
lc.parent = f_node
lc.word_idx = candidate['c'][0].clone()
f_node.lc = lc
lc.depth = f_node.depth + 1
lc.h = tuple_clone(candidate['state'][0])
lc.logprob = seq_p[0]
mc = TensorNode(Node(lex[1], idx + 1, ""))
mc.parent = f_node
mc.left_brother = lc
mc.word_idx = candidate['c'][1].clone()
f_node.mc = mc
mc.depth = f_node.depth + 1
mc.h = tuple_clone(candidate['state'][1])
lc.right_brother = mc
mc.logprob = seq_p[1]
rc = TensorNode(Node(lex[2], idx + 2, ""))
rc.parent = f_node
rc.left_brother = mc
rc.word_idx = candidate['c'][2].clone()
f_node.rc = rc
rc.depth = f_node.depth + 1
rc.h = tuple_clone(candidate['state'][2])
mc.right_brother = rc
rc.logprob = seq_p[2]
new_tree.nodes.update({lc.idx - 1: lc})
new_tree.nodes.update({mc.idx - 1: mc})
new_tree.nodes.update({rc.idx - 1: rc})
new_tree.logprob += candidate['p']
# with open('beam_search.dot', 'w') as f:
# f.write(new_tree.graphviz())
#
# exit(0)
dead_tree = True
for node in new_tree.nodes.values():
if node.lc is None and node.lex != '<EOB>':
dead_tree = False
if dead_tree:
# new_tree.logprob /= len(new_tree.nodes)**3.0
completed_sentences.append(new_tree)
else:
new_candidate_trees.append(new_tree)
candidate_trees = new_candidate_trees
# if len(candidate_trees) == 0:
# break
break_flag = True
for tree in candidate_trees:
if len(tree.nodes) < max_seq_length:
break_flag = False
if break_flag:
break
return candidate_trees, completed_sentences
def _sample(self, fc_feats, vocab, max_seq_length):
state_a = self.init_state(1)
xt_a = self.fc_embed(fc_feats)
state_f = self.init_state(1)
xt_f = self.init_input(1)
output, state = self.md_lstm(xt_a, xt_f, state_a, state_f)
logits = self.logit(output)
logprobs = F.log_softmax(logits, dim=1)
logprob, word_idx_r = logprobs.max(dim=1)
word_idx_r = torch.LongTensor([word_idx_r])
node_r = TensorNode(Node("", 1, ""))
node_r.word_idx = word_idx_r
node_r.word_embed = self.embed(word_idx_r)
# node_r.h = output, state
node_r.h = state
node_r.logprob = logprob.item()
tree = Tree()
tree.root = node_r
tree.nodes.update({node_r.idx - 1: node_r})
eob_idx = vocab('<EOB>')
queue = Queue()
queue.put(tree.root)
while not queue.empty() and len(tree.nodes) <= max_seq_length:
node = queue.get()
if node.word_idx.item() == eob_idx:
continue
idx = len(tree.nodes) + 1
lc = TensorNode(Node("", idx, ""))
lc.parent = node
self.complete_node(lc)
node.lc = lc
lc.depth = node.depth + 1
mc = TensorNode(Node("", idx + 1, ""))
mc.parent = node
mc.left_brother = lc
self.complete_node(mc)
node.mc = mc
mc.depth = node.depth + 1
lc.right_brother = mc
rc = TensorNode(Node("", idx + 2, ""))
rc.parent = node
rc.left_brother = mc
self.complete_node(rc)
node.rc = rc
rc.depth = node.depth + 1
mc.right_brother = rc
tree.nodes.update({lc.idx - 1: lc})
tree.nodes.update({mc.idx - 1: mc})
tree.nodes.update({rc.idx - 1: rc})
queue.put(lc)
queue.put(mc)
queue.put(rc)
for node in tree.nodes.values():
node.lex = vocab.idx2word[node.word_idx.item()]
node.label = node.lex + '-' + str(node.idx)
return tree