def match(self, tree):
try:
if tree.label() != 'ROOT':
raise IndexError
if tree[0].label() != 'SBARQ':
raise IndexError
if tree[0][0][0].label() != 'WRB':
raise IndexError
if tree[0][0][0][0].lower() != 'when':
raise IndexError
if tree[0][1].label() != 'SQ':
raise IndexError
if tree[0][1][0].label() != 'VBD':
raise IndexError
if tree[0][1][1].label() != 'NP':
raise IndexError
if tree[0][1][2].label() != 'VP':
raise IndexError
part = Pattern.Part()
part.object = ParentedTree.fromstring(str(tree[0][1][1]))
part.property = ParentedTree.fromstring(str(Tree('VP', [
Tree.fromstring(str(tree[0][0][0])),
Tree.fromstring(str(tree[0][1][0])),
Tree.fromstring(str(tree[0][1][2]))
])))
return [part]
except IndexError:
return []
def walker(self, parent):
if parent.label() == 'IN' and parent.leaves() == ["of"]:
pos = parent.parent().treeposition()
part = Pattern.Part()
part.object = ParentedTree.fromstring(str(parent.right_sibling()))
part.property = ParentedTree.fromstring(str(self.get_query_tree()))
part.property[pos[:-1]].remove(part.property[pos])
self._parts.append(part)
for child in parent:
if isinstance(child, ParentedTree):
self.walker(child)
def walker(self, parent):
if parent.label() == 'IN' and parent.leaves() == ["of"]:
pos = parent.parent().treeposition()
part = Pattern.Part()
part.object = ParentedTree.fromstring(str(parent.right_sibling()))
part.property = ParentedTree.fromstring(str(self.get_query_tree()))
part.property[pos[:-1]].remove(part.property[pos])
self._parts.append(part)
for child in parent:
if isinstance(child, ParentedTree):
self.walker(child)
def run(self, args):
input_text = args["input"]["text"][0]
input_parse = args["input"]["parse"][0]
output_parse = args["output"]["parse"][0]
doc_list = args["doc_list"]
tuples = self.get_io_files([input_text, input_parse, output_parse], doc_list)
for files in tuples:
indexed_parses = []
in_text_file, in_parse_file, out_parse_file = files
text = self.read_file(in_text_file)
parses = self.read_file(in_parse_file)
parses = json.loads(parses)
leafreader = LeafReader(text)
for parse in parses:
tree = ParentedTree.fromstring(parse, read_leaf=leafreader.read_leaf)
indexed_parses.append(tree.pprint(margin=float("inf")))
# in-json parses
output = json.dumps(indexed_parses)
self.write_file(output, out_parse_file)
def match(self, *args, **kwargs):
Pattern.match(self, *args, **kwargs)
try:
if self.get_query_tree().label() != "ROOT":
raise IndexError
if self.get_query_tree()[0].label() != "SBARQ":
raise IndexError
if self.get_query_tree()[0][0].label() != "WHNP":
raise IndexError
if self.get_query_tree()[0][0][0].label() != "WP":
raise IndexError
if self.get_query_tree()[0][0][0][0].lower() != self._keyword:
raise IndexError
if self.get_query_tree()[0][1].label() != "SQ":
raise IndexError
if len(self.get_query_tree()[0][1]) < 2:
raise IndexError
part = Pattern.Part()
part.object = ParentedTree.fromstring(
str(self.get_query_tree()[0][1][1]))
self._parts.append(part)
return self._parts
except IndexError:
return []
def extract_independent_clauses(input_sent, predictor):
output = predictor.predict(sentence=input_sent)
tree_str = output["trees"]
t = ParentedTree.fromstring(tree_str)
candidate_nodes = list(t.subtrees(filter=lambda x: filt_r(x) or filt_l(x)))
for node in candidate_nodes:
if node.parent() in candidate_nodes:
candidate_nodes.remove(node.parent())
sub_sentences = []
for candidate in candidate_nodes:
temp = []
for subtree in candidate:
temp += subtree.leaves()
sub_sentences.append(temp)
sub_sentences = sub_sentences if sub_sentences else [t.leaves()]
sentences = []
for sentence in sub_sentences:
temp = ""
for i, word in enumerate(sentence):
if i == 0:
temp += word[0].title() + word[1:]
elif word in [".", "!", "?", ",", ";"]:
temp += word
else:
temp += " " + word
temp = temp.replace(" ’", "’")
temp = temp.replace(" n’", "n’")
sentences.append(temp)
return sentences
def match(self, *args, **kwargs):
Pattern.match(self, *args, **kwargs)
try:
if self.get_query_tree().label() != "ROOT":
raise IndexError
if self.get_query_tree()[0].label() != "SBARQ":
raise IndexError
if self.get_query_tree()[0][0].label() != "WHNP":
raise IndexError
if self.get_query_tree()[0][0][0].label() != "WP":
raise IndexError
if self.get_query_tree()[0][0][0][0].lower() != self._keyword:
raise IndexError
if self.get_query_tree()[0][1].label() != "SQ":
raise IndexError
if len(self.get_query_tree()[0][1]) < 2:
raise IndexError
part = Pattern.Part()
part.object = ParentedTree.fromstring(str(self.get_query_tree()[0][1][1]))
self._parts.append(part)
return self._parts
except IndexError:
return []
def replace_terminals_with_indices(treestring):
''' Replaces each terminal in the tree read from a string with an index in the sentence '''
tree = ParentedTree.fromstring(treestring)
for idx, _ in enumerate(tree.leaves()):
tree_location = tree.leaf_treeposition(idx)
non_terminal = tree[tree_location[:-1]]
non_terminal[0] = str(idx)
return tree
def generate(sent, synt, tmpls, synpg_model, pg_model, args):
with torch.no_grad():
# convert syntax to tag sequence
tagss = np.zeros((len(tmpls), args.max_sent_len), dtype=np.long)
tags_ = ParentedTree.fromstring(synt)
tags_ = getleaf(tags_)
tags_ = [
dictionary.word2idx[f"<{w}>"] for w in tags_
if f"<{w}>" in dictionary.word2idx
]
tagss[:, :len(tags_)] = tags_[:args.max_sent_len]
tagss = torch.from_numpy(tagss).cuda()
# generate parses from tag sequence and templates
parse_idxs = pg_model.generate(tagss,
tmpls,
args.max_synt_len,
temp=args.temp)
# add <sos> and remove tokens after <eos>
synts = np.zeros((len(tmpls), args.max_synt_len + 2), dtype=np.long)
synts[:, 0] = 1
for i in range((len(tmpls))):
parse_idx = parse_idxs[i].cpu().numpy()
eos_pos = np.where(parse_idx == dictionary.word2idx["<eos>"])[0]
eos_pos = eos_pos[0] + 1 if len(eos_pos) > 0 else len(idx)
synts[i, 1:eos_pos + 1] = parse_idx[:eos_pos]
synts = torch.from_numpy(synts).cuda()
# bpe segment and convert sentence to tensor
sents = np.zeros((len(tmpls), args.max_sent_len), dtype=np.long)
sent_ = bpe.segment(sent).split()
sent_ = [
dictionary.word2idx[w]
if w in dictionary.word2idx else dictionary.word2idx["<unk>"]
for w in sent_
]
sents[:, :len(sent_)] = sent_[:args.max_sent_len]
sents = torch.from_numpy(sents).cuda()
# generate paraphrases from sentence and generated parses
output_idxs = synpg_model.generate(sents,
synts,
args.max_sent_len,
temp=args.temp)
output_idxs = output_idxs.cpu().numpy()
paraphrases = [
reverse_bpe(synt2str(output_idxs[i], dictionary).split())
for i in range(len(tmpls))
]
return paraphrases
def generate(model, data, loader, dictionary, bpe, args):
model.eval()
with open(os.path.join(args.output_dir, f"target_sents.txt"), "w") as fp1, \
open(os.path.join(args.output_dir, f"target_synts.txt"), "w") as fp2, \
open(os.path.join(args.output_dir, f"outputs.txt"), "w") as fp3:
with torch.no_grad():
iterator = tqdm(loader, total=len(loader))
for it, data_idxs in enumerate(iterator):
data_idxs = data_idxs.numpy()
sents_ = data[0][data_idxs] # sents1
targs_ = data[1][data_idxs] # sents2
synts_ = data[3][data_idxs] # synts2
batch_size = len(sents_)
sents = np.zeros((batch_size, args.max_sent_len),
dtype=np.long)
synts = np.zeros((batch_size, args.max_synt_len + 2),
dtype=np.long)
for i in range(batch_size):
sent_ = sents_[i]
sent_ = bpe.segment(sent_).split()
sent_ = [
dictionary.word2idx[w] if w in dictionary.word2idx else
dictionary.word2idx["<unk>"] for w in sent_
]
sents[i, :len(sent_)] = sent_
synt_ = synts_[i]
synt_ = ParentedTree.fromstring(synt_)
synt_ = deleaf(synt_)
synt_ = [
dictionary.word2idx[f"<{w}>"] for w in synt_
if f"<{w}>" in dictionary.word2idx
]
synt_ = [dictionary.word2idx["<sos>"]
] + synt_ + [dictionary.word2idx["<eos>"]]
synts[i, :len(synt_)] = synt_
sents = torch.from_numpy(sents).cuda()
synts = torch.from_numpy(synts).cuda()
idxs = model.generate(sents,
synts,
sents.size(1),
sample=args.sample,
temp=args.temp)
for sent, idx, targ, synt_ in zip(sents_,
idxs.cpu().numpy(), targs_,
synts_):
fp1.write(targ + '\n')
fp2.write(synt_ + '\n')
fp3.write(
reverse_bpe(synt2str(idx, dictionary).split()) + '\n')
def evaluate(model, data, loader, criterion, dictionary, bpe, args):
model.eval()
total_loss = 0.0
max_it = len(loader)
with torch.no_grad():
for it, data_idxs in enumerate(loader):
data_idxs = np.sort(data_idxs.numpy())
# get batch of raw sentences and raw syntax
sents_ = data[0][data_idxs]
synts_ = data[1][data_idxs]
batch_size = len(sents_)
# initialize tensors
sents = np.zeros((batch_size, args.max_sent_len), dtype=np.long) # words without position
synts = np.zeros((batch_size, args.max_synt_len+2), dtype=np.long) # syntax
targs = np.zeros((batch_size, args.max_sent_len+2), dtype=np.long) # target output
for i in range(batch_size):
# bpe segment and convert to tensor
sent_ = sents_[i]
sent_ = bpe.segment(sent_).split()
sent_ = [dictionary.word2idx[w] if w in dictionary.word2idx else dictionary.word2idx["<unk>"] for w in sent_]
sents[i, :len(sent_)] = sent_
# add <sos> and <eos> for target output
targ_ = [dictionary.word2idx["<sos>"]] + sent_ + [dictionary.word2idx["<eos>"]]
targs[i, :len(targ_)] = targ_
# parse syntax and convert to tensor
synt_ = synts_[i]
synt_ = ParentedTree.fromstring(synt_)
synt_ = deleaf(synt_)
synt_ = [dictionary.word2idx[f"<{w}>"] for w in synt_ if f"<{w}>" in dictionary.word2idx]
synt_ = [dictionary.word2idx["<sos>"]] + synt_ + [dictionary.word2idx["<eos>"]]
synts[i, :len(synt_)] = synt_
sents = torch.from_numpy(sents).cuda()
synts = torch.from_numpy(synts).cuda()
targs = torch.from_numpy(targs).cuda()
# forward
outputs = model(sents, synts, targs)
# calculate loss
targs_ = targs[:, 1:].contiguous().view(-1)
outputs_ = outputs.contiguous().view(-1, outputs.size(-1))
loss = criterion(outputs_, targs_)
total_loss += loss.item()
return total_loss / max_it
def generate(epoch, eit, model, data, loader, dictionary, bpe, args, max_it=10):
model.eval()
with open(os.path.join(args.output_dir, "sents_valid_epoch{:02d}_it{:06d}.txt".format(epoch, eit)), "w") as fp:
with torch.no_grad():
for it, data_idxs in enumerate(loader):
if it >= max_it:
break
data_idxs = np.sort(data_idxs.numpy())
# get batch of raw sentences and raw syntax
sents_ = data[0][data_idxs]
synts_ = data[1][data_idxs]
batch_size = len(sents_)
# initialize tensors
sents = np.zeros((batch_size, args.max_sent_len), dtype=np.long) # words without position
synts = np.zeros((batch_size, args.max_synt_len+2), dtype=np.long) # syntax
targs = np.zeros((batch_size, args.max_sent_len+2), dtype=np.long) # target output
for i in range(batch_size):
# bpe segment and convert to tensor
sent_ = sents_[i]
sent_ = bpe.segment(sent_).split()
sent_ = [dictionary.word2idx[w] if w in dictionary.word2idx else dictionary.word2idx["<unk>"] for w in sent_]
sents[i, :len(sent_)] = sent_
# add <sos> and <eos> for target output
targ_ = [dictionary.word2idx["<sos>"]] + sent_ + [dictionary.word2idx["<eos>"]]
targs[i, :len(targ_)] = targ_
# parse syntax and convert to tensor
synt_ = synts_[i]
synt_ = ParentedTree.fromstring(synt_)
synt_ = deleaf(synt_)
synt_ = [dictionary.word2idx[f"<{w}>"] for w in synt_ if f"<{w}>" in dictionary.word2idx]
synt_ = [dictionary.word2idx["<sos>"]] + synt_ + [dictionary.word2idx["<eos>"]]
synts[i, :len(synt_)] = synt_
sents = torch.from_numpy(sents).cuda()
synts = torch.from_numpy(synts).cuda()
targs = torch.from_numpy(targs).cuda()
# generate
idxs = model.generate(sents, synts, sents.size(1), temp=args.temp)
# write output
for sent, idx, synt in zip(sents.cpu().numpy(), idxs.cpu().numpy(), synts.cpu().numpy()):
fp.write(synt2str(synt[1:], dictionary)+'\n')
fp.write(sent2str(sent, dictionary)+'\n')
fp.write(synt2str(idx, dictionary)+'\n')
fp.write("--\n")
def process_tree(tree_str, label):
example = None
try:
#print("getting tree")
tree = ParentedTree.fromstring(tree_str.__str__())
#print("before get_relation")
example = get_relation(tree, label)
#print(example.e1)
except ValueError as err:
#print(err)
pass
return example
def parse_parented_tree(tree_string):
""" Construct a tree from a constituent parse tree string.
Args:
tree_string (str): A constituent parse tree in bracket notation
Returns:
nltk.ParentedTree: A parse tree corresponding to the parse tree string.
"""
try:
return ParentedTree(tree_string)
except TypeError:
return ParentedTree.fromstring(tree_string)
def __init__(self, filename, postagged='./data/postagged-files',
parsed='./data/parsed-files',
dependency='./data/dep-files'):
self.filename = filename
postagged_file = os.path.join(postagged, filename+'.tag')
parsed_file = os.path.join(parsed, filename+'.parse')
dep_file = os.path.join(dependency, filename+'.parse.dep')
self.tagged_sents = [x.strip() for x in open(postagged_file) if x.strip()]
self.parsed_sents = [ParentedTree.fromstring(x) for x in open(parsed_file) if x.strip()]
self.dep_sents = [DepTree.fromstring(x)
for x in open(dep_file).read().strip().split('\n\n')
if x.strip()]
assert len(self.tagged_sents) == len(self.parsed_sents)
def parse2edus(parse):
"""
将成分句法树切割为 EDU
:param parse: 成分句法树 Bracket 格式文本， e.g. '( (IP (NP (PN 我)) (VP (VV 爱) (NP (NR 北京) (NR 天安门)))))'
:return: structure.tree.EDU 生成器
"""
pipeline = get_pipeline()
segmenter = pipeline.segmenter
parse = ParentedTree.fromstring(parse)
childs = list(parse.subtrees(lambda t: t.height() == 2 and t.label() != '-NONE-'))
text = ''.join([child[0] for child in childs])
sentence = Sentence((0, len(text)), text, parse=parse)
return segmenter.cut_edu(sentence)
def read_file(file_):
"""
"""
trees = []
with open(file_, "r", encoding="utf-8") as f:
lines = f.readlines()
for line in lines:
tree = ParentedTree.fromstring(line)
trees.append(tree)
return trees
def template2tensor(templates, max_tmpl_len, dictionary):
tmpls = np.zeros((len(templates), max_tmpl_len + 2), dtype=np.long)
for i, tp in enumerate(templates):
tmpl_ = ParentedTree.fromstring(tp)
tree2tmpl(tmpl_, 1, 2)
tmpl_ = str(tmpl_).replace(")", " )").replace("(", "( ").split(" ")
tmpl_ = [
dictionary.word2idx[f"<{w}>"] for w in tmpl_
if f"<{w}>" in dictionary.word2idx
]
tmpl_ = [dictionary.word2idx["<sos>"]
] + tmpl_ + [dictionary.word2idx["<eos>"]]
tmpls[i, :len(tmpl_)] = tmpl_
tmpls = torch.from_numpy(tmpls).cuda()
return tmpls
def file_to_trees(filename):
"""Reads the parse trees in the given file and returns them as a list of
ParentedTree objects.
A depth attribute is added for each node in the full tree.
Args:
filename: The base name of the input document.
Returns:
A list of ParentedTree objects.
"""
tree_filepath = 'data/parsed-files/' + filename + '.head.rel.tokenized.raw.parse'
sent_trees = []
with open(tree_filepath) as tree_file:
for line in tree_file:
if not line.startswith('\n'):
tree = ParentedTree.fromstring(line)
add_depth(tree)
sent_trees.append(tree)
return sent_trees
def cut_edu(self, sent: Sentence) -> List[EDU]:
if not hasattr(sent, "parse"):
print(sent.text)
parse = self.parser.parse(sent.text)
else:
parse = getattr(sent, "parse")
parse = ParentedTree.fromstring(parse.pformat())
children = list(
parse.subtrees(
lambda t: t.height() == 2 and t.label() != '-NONE-'))
edus = []
last_edu_words = []
last_edu_tags = []
offset = 0
for child in children:
if child[0] == '-LRB-':
child[0] = '('
if child[0] == '-RRB-':
child[0] = ')'
last_edu_words.append(child[0])
last_edu_tags.append(child.label())
if child[0] in self._eos or (child[0] in self.candidate and
self.model.predict(offset, parse)):
text = "".join(last_edu_words)
edu = EDU([TEXT(text)])
setattr(edu, "words", last_edu_words)
setattr(edu, "tags", last_edu_tags)
edus.append(edu)
last_edu_words = []
last_edu_tags = []
offset += len(child[0])
if last_edu_words:
text = "".join(last_edu_words)
edu = EDU([TEXT(text)])
setattr(edu, "words", last_edu_words)
setattr(edu, "tags", last_edu_tags)
edus.append(edu)
return edus
def get_phrases(sentence):
chunking_temp = []
chunking = []
sent_tagged = pos_tag(sentence)
res = get_tregex(sentence, 'ROOT')
if (res):
tree = ParentedTree.fromstring(res['0']['match'])
print(tree.pretty_print())
list_removed = []
traverse_tree(tree, chunking_temp)
print(chunking_temp)
#fix overlap string
for x in range(0, len(chunking_temp)):
p_x, tagged_x = chunking_temp[x]
for y in range(x + 1, len(chunking_temp)):
p_y = chunking_temp[y][0]
if (tagged_x == 'VP' or tagged_x == 'S' or tagged_x == 'SBAR'):
#do intersection
p_x = p_x.replace(p_y, '').strip()
else:
if p_y in p_x and p_y not in list_removed:
list_removed.append(p_y)
#chunking_temp[x][0] = re.sub(r" ", " ", chunking_temp[x][0])
#if(chunking_temp[x][0] != ''):
if (p_x not in list_removed and p_x != ''):
tags = []
for s in sent_tagged:
if s[0] in p_x:
tags.append(s[1])
chunking.append((p_x, tagged_x, ' '.join(tags)))
return chunking
def batch_extract(file):
'''
extract the isolate event from the sentences in the txt files
file: txt with (ROOT (S ...) ) sentences
'''
tree = ""
events = []
# open the input file with the sentences
with open(file, "r") as file:
# read a sentence
sentence = file.readline()
while sentence:
sentence = sentence.strip("\n")
try:
# make a tree from the sentence
tree = ParentedTree.fromstring(sentence)
event = extract_isolate(tree)
events.append(event)
except Exception as e:
pass
sentence = file.readline()
return events
def toNLTKtree(str):
newTree = ParentedTree.fromstring(str)
return newTree
def parse_indexify_transformations(in_p, out_p, label_voc, args):
in_trimmed_seqs = []
in_seqs = []
out_trimmed_seqs = []
out_seqs = []
max_trans_size = 0
for idx in range(len(in_p)):
# very rarely, a tree is invalid
try:
in_trimmed = ParentedTree.fromstring(in_p[idx])
in_orig = ParentedTree.fromstring(in_p[idx])
out_trimmed = ParentedTree.fromstring(out_p[idx])
out_orig = ParentedTree.fromstring(out_p[idx])
except:
continue
out_dh = parse_tree_level_dropout(out_trimmed, args.tree_level_dropout)
parse_tree_level_dropout(in_trimmed, args.tree_level_dropout, level=out_dh)
in_orig = deleaf(in_orig)
in_trimmed = deleaf(in_trimmed)
out_orig = deleaf(out_orig)
out_trimmed = deleaf(out_trimmed)
if max_trans_size < len(in_orig):
max_trans_size = len(in_orig)
if max_trans_size < len(out_orig):
max_trans_size = len(out_orig)
# only consider instances where top-level of input parse != top-level output
if in_trimmed != out_trimmed:
# make sure everything is invocab
try:
x = [label_voc[z] for z in in_orig]
x = [label_voc[z] for z in out_orig]
in_seqs.append(in_orig)
out_seqs.append(out_orig)
out_trimmed_seqs.append(out_trimmed)
in_trimmed_seqs.append(in_trimmed)
except:
pass
# no syntactic transformations in the batch!
if len(in_seqs) == 0:
return None
# otherwise, indexify and return
else:
in_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
out_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
in_trimmed_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
out_trimmed_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
in_lengths = []
out_lengths = []
out_trimmed_lengths = []
in_trimmed_lengths = []
for idx in range(len(in_seqs)):
curr_in = in_seqs[idx]
in_trans_np[idx, :len(curr_in)] = [label_voc[z] for z in curr_in]
in_lengths.append(len(curr_in))
curr_out = out_seqs[idx]
out_trans_np[idx, :len(curr_out)] = [label_voc[z] for z in curr_out]
out_lengths.append(len(curr_out))
curr_trimmed_in = in_trimmed_seqs[idx]
in_trimmed_np[idx, :len(curr_trimmed_in)] = [label_voc[z] for z in curr_trimmed_in]
in_trimmed_lengths.append(len(curr_trimmed_in))
curr_trimmed_out = out_trimmed_seqs[idx]
out_trimmed_np[idx, :len(curr_trimmed_out)] = [label_voc[z] for z in curr_trimmed_out]
out_trimmed_lengths.append(len(curr_trimmed_out))
# cut off extra padding
in_trans_np = in_trans_np[:, :np.max(in_lengths)]
out_trans_np = out_trans_np[:, :np.max(out_lengths)]
in_trimmed_np = in_trimmed_np[:, :np.max(in_trimmed_lengths)]
out_trimmed_np = out_trimmed_np[:, :np.max(out_trimmed_lengths)]
return in_trans_np, out_trans_np, in_trimmed_np, out_trimmed_np,\
np.array(in_lengths, dtype='int32'), np.array(out_lengths, dtype='int32'),\
np.array(in_trimmed_lengths, dtype='int32'), np.array(out_trimmed_lengths, dtype='int32')
def indexify_transformations(in_p, out_p, label_voc, args):
in_seqs = []
out_seqs = []
mismatch_inds = []
max_trans_size = 0
for idx in range(len(in_p)):
# very rarely, a tree is invalid
try:
in_tree = ParentedTree.fromstring(in_p[idx])
out_tree = ParentedTree.fromstring(out_p[idx])
except:
continue
if args.tree_dropout > 0:
tree_dropout(in_tree, args.tree_dropout, 0)
tree_dropout(out_tree, args.tree_dropout, 0)
elif args.tree_level_dropout > 0:
parse_tree_level_dropout(in_tree, args.tree_level_dropout)
parse_tree_level_dropout(out_tree, args.tree_level_dropout)
in_full_trans = deleaf(in_tree)
out_full_trans = deleaf(out_tree)
if max_trans_size < len(in_full_trans):
max_trans_size = len(in_full_trans)
if max_trans_size < len(out_full_trans):
max_trans_size = len(out_full_trans)
# only consider instances where input syntax differs from output syntax
if in_full_trans != out_full_trans:
# make sure everything is invocab
try:
x = [label_voc[z] for z in in_full_trans]
x = [label_voc[z] for z in out_full_trans]
in_seqs.append(in_full_trans)
out_seqs.append(out_full_trans)
mismatch_inds.append(idx)
except:
pass
# no syntactic transformations in the batch!
if len(in_seqs) == 0:
return None
# otherwise, indexify and return
else:
in_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
out_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
in_lengths = []
out_lengths = []
for idx in range(len(in_seqs)):
curr_in = in_seqs[idx]
in_trans_np[idx, :len(curr_in)] = [label_voc[z] for z in curr_in]
in_lengths.append(len(curr_in))
curr_out = out_seqs[idx]
out_trans_np[idx, :len(curr_out)] = [label_voc[z] for z in curr_out]
out_lengths.append(len(curr_out))
return in_trans_np, out_trans_np, mismatch_inds,\
np.array(in_lengths, dtype='int32'), np.array(out_lengths, dtype='int32')
'''
Main function.
'''
if __name__ == "__main__":
print("Beginning parse of PTB.ext...")
data = []
num_lines = rawgencount("PTB.ext")
with open('PTB.ext', encoding='utf-8') as f:
for sent_tree in tqdm(f, total=num_lines):
# Parse this sent_tree into an NLTK tree object
tree = ParentedTree.fromstring(sent_tree)
# Get all phrases in this tree
for phrase in get_coordphrases(tree):
conjuncts = phrase[0]
conjunction = phrase[1]
phrase_cat = phrase[2]
phrase_text = phrase[3]
sent_text = get_tree_text(tree)
# Only include two-termed coordinations
if len(conjuncts) != 2:
continue
row = []
def encode_data(text, parsed_repr, bpe, pp_vocab, parse_gen_voc, parse_net,
tp_templates, tp_template_lens, net, rev_label_voc,
rev_pp_vocab):
stime = time.time()
ssent = ' '.join(text.split())
seg_sent = bpe.segment(ssent.lower()).split()
results = []
results.append(reverse_bpe(seg_sent))
# encode sentence using pp_vocab, leave one word for EOS
seg_sent = [pp_vocab[w] for w in seg_sent if w in pp_vocab]
# add EOS
seg_sent.append(pp_vocab['EOS'])
torch_sent = Variable(
torch.from_numpy(np.array(seg_sent, dtype='int32')).long().cuda())
torch_sent_len = torch.from_numpy(np.array([len(seg_sent)],
dtype='int32')).long().cuda()
# encode parse using parse vocab
parse_tree = ParentedTree.fromstring(parsed_repr.strip())
parse_tree = deleaf(parse_tree)
np_parse = np.array([parse_gen_voc[w] for w in parse_tree], dtype='int32')
torch_parse = Variable(torch.from_numpy(np_parse).long().cuda())
torch_parse_len = torch.from_numpy(
np.array([len(parse_tree)], dtype='int32')).long().cuda()
# generate full parses from templates
beam_dict = parse_net.batch_beam_search(torch_parse.unsqueeze(0),
tp_templates,
torch_parse_len[:],
tp_template_lens,
parse_gen_voc['EOP'],
beam_size=3,
max_steps=150)
seq_lens = []
seqs = []
for b_idx in beam_dict:
prob, _, _, seq = beam_dict[b_idx][0]
seq = seq[:-1] # chop off EOP
seq_lens.append(len(seq))
seqs.append(seq)
np_parses = np.zeros((len(seqs), max(seq_lens)), dtype='int32')
for z, seq in enumerate(seqs):
np_parses[z, :seq_lens[z]] = seq
tp_parses = Variable(torch.from_numpy(np_parses).long().cuda())
tp_len = torch.from_numpy(np.array(seq_lens, dtype='int32')).long().cuda()
# generate paraphrases from parses
try:
beam_dict = net.batch_beam_search(torch_sent.unsqueeze(0),
tp_parses,
torch_sent_len[:],
tp_len,
pp_vocab['EOS'],
beam_size=3,
max_steps=40)
for b_idx in beam_dict:
prob, _, _, seq = beam_dict[b_idx][0]
gen_parse = ' '.join([rev_label_voc[z] for z in seqs[b_idx]])
gen_sent = ' '.join([rev_pp_vocab[w] for w in seq[:-1]])
results.append(reverse_bpe(gen_sent.split()))
except:
print("beam search OOM")
print(traceback.format_exc())
return results
def main():
files = glob.glob(TMP_PATH + "*.info.xml")
OUT = open("out.out", "w")
EXAMPLE_OUT = open("examples/" + SOURCE + "_" + LABEL + ".json", "w")
num_examples = 0
total_files = len(files)
no_tlinks = 0
print("[", file=EXAMPLE_OUT)
for file in files:
print(file, file=OUT)
soup = BeautifulSoup(open(file), "html.parser")
sentence = soup.sentence
print(sentence.string, file=OUT)
# store tokens in a list.
tokens = []
for token in soup.tokens.find_all("t"):
text = token.string.rsplit("\"", 3)[0].split("\"", 3)[-1]
if text[0] == " ":
text = text[1:]
tokens.append(text)
print(file=OUT)
# parse events
# <event id="e1" eiid="ei1" offset="2" string="said" tense="PAST"
# aspect="NONE" class="REPORTING" polarity="POS" modality="" happen=""
# lowerBoundDuration="" upperBoundDuration=""
# />
eid_dict = {}
eiid_dict = {}
for event in soup.events.find_all("event"):
text = event["string"]
token_pos = event["offset"]
eid_dict[event["id"]] = text
eiid_dict[event["eiid"]] = text
print(text, file=OUT)
# parse timexes
# <timex tid="t1" text="autumn" offset="19" length="1" type="DATE"
# value="XXXX-FA" temporalFunction="false"/>
timex_dict = {}
for timex in soup.timexes.find_all("timex"):
text = timex["text"]
timex_dict[timex["tid"].strip()] = text
print(text, file=OUT)
print(file=OUT)
tlinks = soup.find_all("tlink")
if len(tlinks) == 0:
no_tlinks += 1
print("NO TLINKS", file=OUT)
else:
headers = ["e1", "e2", "relation"]
table = []
e1s = []
e2s = []
rels = []
for tlink in tlinks:
e1 = tlink["event1"]
e2 = tlink["event2"]
if e1 in eid_dict:
e1 = eid_dict[e1]
elif e1 in eiid_dict:
e1 = eiid_dict[e1]
elif e1 in timex_dict:
e1 = timex_dict[e1]
else:
print("ERROR: Can't find e1", file=OUT)
print(eiid_dict)
if e2 in eid_dict:
e2 = eid_dict[e2]
elif e2 in eiid_dict:
e2 = eiid_dict[e2]
elif e2 in timex_dict:
e2 = timex_dict[e2]
else:
print("ERROR: Can't find e2", file=OUT)
# print(e1, "\t", e2, "\t", tlink["relation"])
table.append([e1, e2, tlink["relation"]])
print(tabulate(table, headers=headers), file=OUT)
print(file=OUT)
parse = soup.parse.string
t = ParentedTree.fromstring(parse)
example = get_relation(t, LABEL)
if example:
if num_examples > 0:
print(",", file=EXAMPLE_OUT)
print(example.to_json(), file=EXAMPLE_OUT, end="")
num_examples += 1
print(parse, file=OUT)
print(file=OUT)
print("\n]", file=EXAMPLE_OUT)
print("total files: ", total_files)
print("files without tlinks: ", no_tlinks)
print("files with failed event parsing: ", failed_event_parse)
print("files with successful event parsing: ",
total_files - failed_event_parse)
def __populate_Parses(lang, parsejson, new_parsedict):
"""
"""
# start CoreNLP servers for UD1
from stanfordnlp.server import CoreNLPClient
cwd = os.getcwd()
version = 'stanford-corenlp-full-2018-10-05'
corenlp_path = re.findall(r'\S*/marta-v2',
cwd)[0] + '/04_utils/' + version
os.environ["CORENLP_HOME"] = corenlp_path
if lang == 'en':
lang = {} # i.e. CoreNLP defaults to English model
corenlpclient_UD1 = CoreNLPClient(properties={
'ssplit.isOneSentence': True,
'tokenize.whitespace': True
},
annotators=[
'tokenize', 'ssplit', 'pos',
'parse', 'depparse',
'udfeats'
],
memory='2G',
be_quiet=True,
max_char_length=100000,
output_format='conllu')
# parse annotator is necessary to obtain udfeats (for postags)
if lang == 'fr':
lang = 'french'
corenlpclient_UD1 = CoreNLPClient(
properties=lang,
annotators=[
'tokenize', 'ssplit', 'pos', 'parse', 'depparse', 'udfeats'
],
memory='2G',
be_quiet=True,
max_char_length=100000,
output_format='conllu'
) # note that udfeats (for postags) currently works for english only https://stanfordnlp.github.io/CoreNLP/udfeats.html
if lang == 'zh':
lang = 'chinese'
corenlpclient_UD1 = CoreNLPClient(properties=lang,
annotators=[
'tokenize', 'ssplit', 'pos',
'parse', 'depparse',
'udfeats'
],
memory='2G',
be_quiet=True,
max_char_length=100000,
output_format='conllu')
# note that udfeats (for postags) currently works for english only https://stanfordnlp.github.io/CoreNLP/udfeats.html
# begin processing
for DocID in parsejson:
print('Now processing: ', dataset, DocID)
sentence_offset = 0 # this is the 4th element in a TokenList
# obtain the gold constituency parses for the document.
ConstTrees = __obtain_ConstTrees_Gold(
DocID, readpath='./03_data/{}/{}tbRoot/{}/', lang=LANG)
for sentence in parsejson[DocID]['sentences']:
# 1. create a ParsePDTB object
__parsepdtb = ParsePDTB(
lang=LANG,
docid=DocID,
sentid=sentence_offset,
gold_consttree=ConstTrees[sentence_offset],
pdtb_version=PDTB_VERSION)
# 2. add to .RawText and .Words
__parsepdtb.RawText = " ".join(
[word[0] for word in sentence['words']])
__parsepdtb.Words = sentence['words']
# 3. add to ConstTree_Auto. generate parse if missing
if sentence['parsetree'] == '(())\n':
_parse = a2_parsers._parse_rawtext2consttree(
LANG, __parsepdtb.RawText, tokenized=True)
__parsepdtb.ConstTree_Auto = _parse
else:
__parsepdtb.ConstTree_Auto = sentence['parsetree']
# 3. write to temp file, for converting to SD/UD1 in next steps
with open('./02_modelbuilding/02_output/input_temp.parser',
'w+') as f:
f.write(__parsepdtb.ConstTree_Gold)
# 4. convert constituency parse to gold UD 1.0 and add to DepTree_UD1_Gold
a2_parsers.convert_const2dep(
LANG,
dataset,
filename='',
readpath='/02_modelbuilding/02_output/input_temp.parser',
writepath='/02_modelbuilding/02_output/output_temp.parser',
format_='UD1',
usage='experiments')
with open('./02_modelbuilding/02_output/output_temp.parser',
'r') as f:
UD1_Gold_conllu = f.read()
def __conllu2tuple(conllu_doc):
"""helper function to convert CoNLL format into 3-tuple used by CoNLL 2016 organisers to store dependency parses
"""
to_list = conllu_doc.split('\n')
tokenlist = [
i.split('\t')[1] + '-' + i.split('\t')[0]
for i in to_list if i != ''
] # convert CoNLL line to <wordform>-<token num>
tokenlist.insert(0,
'ROOT-0') # add a root token to the start
deptree_gold = [
[
i.split('\t')[7], tokenlist[int(i.split('\t')[6])],
i.split('\t')[1] + '-' + i.split('\t')[0]
] for i in to_list if i != ''
] # convert to CoNLL 2016 dependencies format
return deptree_gold
__parsepdtb.DepTree_UD1_Gold = __conllu2tuple(UD1_Gold_conllu)
# 5. automatically generate UD 1.0 constituency parse (from raw text), place into same 3-tuple format as CoNLL 2016 Shared Task,and add to DepTree_UD1_Auto
UD1_Auto_conllu = corenlpclient_UD1.annotate(
__parsepdtb.RawText)
__parsepdtb.DepTree_UD1_Auto = __conllu2tuple(UD1_Auto_conllu)
# 6. add PTB-style and UD pos tags to .Words. Each of the variable below contain a list comprising 2-tuples. each tuple is (<wordform>, <part of speech>)
globals()['pos_PTBGold'] = [
i for i in ParentedTree.fromstring(
__parsepdtb.ConstTree_Gold).pos() if i[-1] != '-NONE-'
] # gold PTB parses have traces and these causes misalignment with the surface form. we drop these since parsers don't predict traces (Johannsen & Søgaard, 2013)
globals()['pos_PTBAuto'] = ParentedTree.fromstring(
__parsepdtb.ConstTree_Auto).pos()
globals()['pos_UDGold'] = [(i.split('\t')[1], i.split('\t')[3])
for i in UD1_Gold_conllu.split('\n')
if i != '']
globals()['pos_UDAuto'] = [(i.split('\t')[1], i.split('\t')[3])
for i in UD1_Auto_conllu.split('\n')
if i != '']
for postagset in ['PTBGold', 'PTBAuto', 'UDGold', 'UDAuto']:
try:
_tagset = globals()['pos_' + postagset]
assert len(_tagset) == len(__parsepdtb.Words)
for idx in range(len(__parsepdtb.Words)):
# add the part of speech as a new key in the dictionary for the token in .Words
__parsepdtb.Words[idx][1].update(
{'PartOfSpeech_' + postagset: _tagset[idx][1]})
except AssertionError as e:
e.args += (
postagset.upper() +
" is not of the same size as the .Words attribute for this sentence.",
)
print(e)
print("Continuing to attempt alignment of tokens.")
_words = [i[0] for i in __parsepdtb.Words]
_words_maxidx = len(_words) - 1
#'drop' the additional tokens in _tagset
_tagset = [i for i in _tagset if i[0] in _words]
_words_curridx = -1 # start with -1
for idx in range(len(_tagset)):
_words_curridx += 1
while __parsepdtb.Words[_words_curridx][
0] != _tagset[idx][
0] and _words_curridx < _words_maxidx:
__parsepdtb.Words[_words_curridx][1].update(
{
'PartOfSpeech_' + postagset:
'ParserError'
}
) # place a marker identifying the missing pos tag as an error from parsing
_words_curridx += 1
__parsepdtb.Words[_words_curridx][1].update(
{'PartOfSpeech_' + postagset: _tagset[idx][1]})
continue
# raise
sentence_offset += 1 # increase sentence offset before moving to handle next sentence
try:
new_parsedict[DocID].append(__parsepdtb)
except:
new_parsedict[DocID] = [__parsepdtb]
# shut down the CoreNLP servers
corenlpclient_UD1.stop()
def test_read_indexed_tree(self):
leafreader = IndexedLeafReader()
tree = ParentedTree.fromstring('(S1 (S (S (NP (PRP I|0|1)) (VP (VBP have|2|6) (NP (DT a|3|4) (NN book|9|13)))) (. .|13|14)))',
read_leaf=leafreader.read_leaf)
print(tree.pprint(margin=float("inf")))
def encode_data(out_file):
fn = ['idx', 'template', 'generated_parse', 'sentence']
ofile = codecs.open(out_file, 'w', 'utf-8')
out = csv.DictWriter(ofile, delimiter='\t', fieldnames=fn)
out.writerow(dict((x,x) for x in fn))
# read parsed data
infile = codecs.open(args.parsed_input_file, 'r', 'utf-8', errors='ignore')
inrdr = csv.DictReader(infile, delimiter='\t')
# loop over sentences and transform them
for d_idx, ex in enumerate(inrdr):
stime = time.time()
ssent = ' '.join(ex['tokens'].split())
seg_sent = bpe.segment(ssent.lower()).split()
# write gold sentence
out.writerow({'idx': ex['idx'],
'template':'GOLD', 'generated_parse':ex['parse'],
'sentence':reverse_bpe(seg_sent)})
# encode sentence using pp_vocab, leave one word for EOS
seg_sent = [pp_vocab[w] for w in seg_sent if w in pp_vocab]
# add EOS
seg_sent.append(pp_vocab['EOS'])
if args.gpu >= 0:
torch_sent = Variable(torch.from_numpy(np.array(seg_sent, dtype='int32')).long().cuda())
torch_sent_len = torch.from_numpy(np.array([len(seg_sent)], dtype='int32')).long().cuda()
else:
torch_sent = Variable(torch.from_numpy(np.array(seg_sent, dtype='int32')).long())
torch_sent_len = torch.from_numpy(np.array([len(seg_sent)], dtype='int32')).long()
# encode parse using parse vocab
parse_tree = ParentedTree.fromstring(ex['parse'].strip())
parse_tree = deleaf(parse_tree)
np_parse = np.array([parse_gen_voc[w] for w in parse_tree], dtype='int32')
if args.gpu >= 0:
torch_parse = Variable(torch.from_numpy(np_parse).long().cuda())
torch_parse_len = torch.from_numpy(np.array([len(parse_tree)], dtype='int32')).long().cuda()
else:
torch_parse = Variable(torch.from_numpy(np_parse).long())
torch_parse_len = torch.from_numpy(np.array([len(parse_tree)], dtype='int32')).long()
# generate full parses from templates
beam_dict = parse_net.batch_beam_search(torch_parse.unsqueeze(0), tp_templates,
torch_parse_len[:], tp_template_lens, parse_gen_voc['EOP'], beam_size=3, max_steps=150)
seq_lens = []
seqs = []
for b_idx in beam_dict:
prob,_,_,seq = beam_dict[b_idx][0]
seq = seq[:-1] # chop off EOP
seq_lens.append(len(seq))
seqs.append(seq)
np_parses = np.zeros((len(seqs), max(seq_lens)), dtype='int32')
for z, seq in enumerate(seqs):
np_parses[z, :seq_lens[z]] = seq
if args.gpu >= 0:
tp_parses = Variable(torch.from_numpy(np_parses).long().cuda())
tp_len = torch.from_numpy(np.array(seq_lens, dtype='int32')).long().cuda()
else:
tp_parses = Variable(torch.from_numpy(np_parses).long())
tp_len = torch.from_numpy(np.array(seq_lens, dtype='int32')).long()
# generate paraphrases from parses
# try:
beam_dict = net.batch_beam_search(torch_sent.unsqueeze(0), tp_parses,
torch_sent_len[:], tp_len, pp_vocab['EOS'], beam_size=3, max_steps=40)
for b_idx in beam_dict:
prob,_,_,seq = beam_dict[b_idx][0]
gen_parse = ' '.join([rev_label_voc[z] for z in seqs[b_idx]])
gen_sent = ' '.join([rev_pp_vocab[w] for w in seq[:-1]])
out.writerow({'idx': ex['idx'],
'template':templates[b_idx], 'generated_parse':gen_parse,
'sentence':reverse_bpe(gen_sent.split())})
# except:
# print('beam search OOM')
print(d_idx, time.time() - stime)
dest="parse",
default="data/projectsyndicate/projectsyndicate.truecased.de.20.parse",
help="german parses parse persentence")
opt.add_option("-m", dest="map", default="data/projectsyndicate/de-negra.map")
opt.add_option("-s", dest="word2unipos", default="data/projectsyndicate/de.pos")
(options, _) = opt.parse_args()
word2pos = {}
f = codecs.open(options.parse, 'r', 'utf-8')
posmap = {}
for line in codecs.open(options.map, 'r', 'utf-8').readlines():
de_pos, uni_pos = line.split()
posmap[de_pos] = uni_pos
with f:
for line in f:
t = ParentedTree.fromstring(line.strip())
for pos_token in t.subtrees(lambda t: t.height() == 2):
pos_token = str(pos_token)[1:-1]
(pos, token) = pos_token.split()
token = token.encode('utf-8')
s = word2pos.get(token, set())
s.add(posmap[pos])
word2pos[token] = s
f.close()
w = codecs.open(options.word2unipos, 'w', 'utf-8')
for token, set_pos in word2pos.items():
print token, ':', ','.join(set_pos)
w.write(token.encode('utf-8') + '\t' + ','.join(set_pos) + '\n')
w.flush()
w.close()
def tree_to_ptree(tree: nltk.Tree):
tree_str = tree.__str__()
ptree = PTree.fromstring(tree_str)
return ptree
def __init__(self, parse_string):
self.__tree = ParentedTree.fromstring(parse_string)
args = get_args()
i = 1
tot = str(len(args.input_files))
for file in args.input_files:
print("(" + str(i) + "/" + tot + ")")
print("Gathering coordination stats from " + file + "...")
sents = pd.read_csv(file)
data = []
for index, row in tqdm(sents.iterrows(), total=len(sents.index)):
parse_tree = row["Sentence Parse Tree"]
tree = ParentedTree.fromstring(parse_tree)
sent = get_tree_text(tree)
for coord in get_simple_coordphrases(tree):
category1 = coord[0][0]
conjunct1 = coord[0][1]
conjunction = coord[1]
category2 = coord[2][0]
conjunct2 = coord[2][1]
data.append([category1, conjunct1, category2, conjunct2,
conjunction, sent, parse_tree])
columns = ['1st Conjunct Category', '1st Conjunct Text',
'2nd Conjunct Category', '2nd Conjunct Text',
'Conjunction', 'Sentence Text', 'Sentence Parse Tree']
df = pd.DataFrame(data, columns=columns)
df.drop_duplicates(inplace=True)
def test_read_normal_tree(self):
leafreader = LeafReader('I have a book.')
tree = ParentedTree.fromstring('(S1 (S (S (NP (PRP I)) (VP (VBP have) (NP (DT a) (NN book)))) (. .)))',
read_leaf=leafreader.read_leaf)
print(tree.pprint(margin=float("inf")))
def match(self, tree):
if not isinstance(tree, ParentedTree):
raise AttributeError
self._query_tree = ParentedTree.fromstring(str(tree))
def train(epoch, model, train_data, valid_data, train_loader, valid_loader, optimizer, criterion, dictionary, bpe, args):
timer = Timer()
n_it = len(train_loader)
for it, data_idxs in enumerate(train_loader):
model.train()
data_idxs = np.sort(data_idxs.numpy())
# get batch of raw sentences and raw syntax
sents_ = train_data[0][data_idxs]
synts_ = train_data[1][data_idxs]
batch_size = len(sents_)
# initialize tensors
sents = np.zeros((batch_size, args.max_sent_len), dtype=np.long) # words without position
synts = np.zeros((batch_size, args.max_synt_len+2), dtype=np.long) # syntax
targs = np.zeros((batch_size, args.max_sent_len+2), dtype=np.long) # target output
for i in range(batch_size):
# bpe segment and convert to tensor
sent_ = sents_[i]
sent_ = bpe.segment(sent_).split()
sent_ = [dictionary.word2idx[w] if w in dictionary.word2idx else dictionary.word2idx["<unk>"] for w in sent_]
sents[i, :len(sent_)] = sent_
# add <sos> and <eos> for target output
targ_ = [dictionary.word2idx["<sos>"]] + sent_ + [dictionary.word2idx["<eos>"]]
targs[i, :len(targ_)] = targ_
# parse syntax and convert to tensor
synt_ = synts_[i]
synt_ = ParentedTree.fromstring(synt_)
synt_ = deleaf(synt_)
synt_ = [dictionary.word2idx[f"<{w}>"] for w in synt_ if f"<{w}>" in dictionary.word2idx]
synt_ = [dictionary.word2idx["<sos>"]] + synt_ + [dictionary.word2idx["<eos>"]]
synts[i, :len(synt_)] = synt_
sents = torch.from_numpy(sents).cuda()
synts = torch.from_numpy(synts).cuda()
targs = torch.from_numpy(targs).cuda()
# forward
outputs = model(sents, synts, targs)
# calculate loss
targs_ = targs[:, 1:].contiguous().view(-1)
outputs_ = outputs.contiguous().view(-1, outputs.size(-1))
optimizer.zero_grad()
loss = criterion(outputs_, targs_)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
if it % args.log_interval == 0:
# print current loss
valid_loss = evaluate(model, valid_data, valid_loader, criterion, dictionary, bpe, args)
print("| ep {:2d}/{} | it {:3d}/{} | {:5.2f} s | loss {:.4f} | g_norm {:.6f} | valid loss {:.4f} |".format(
epoch, args.n_epoch, it, n_it, timer.get_time_from_last(), loss.item(), model.grad_norm, valid_loss))
if it % args.gen_interval == 0:
# generate output to args.output_dir
generate(epoch, it, model, valid_data, valid_loader, dictionary, bpe, args)
if it % args.save_interval == 0:
# save model to args.model_dir
torch.save(model.state_dict(), os.path.join(args.model_dir, "synpg_epoch{:02d}.pt".format(epoch)))
