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spaCy
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Fix usage of PyTorch BiLSTM in ud_train

This commit is contained in:
Matthew Honnibal 2018-09-13 22:54:59 +00:00
parent afeddfff26
commit 8f2a6367e9
1 changed files with 20 additions and 5 deletions
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25
spacy/cli/ud_train.py
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@ -32,6 +32,11 @@ from .. import lang
from ..lang import zh
from ..lang import ja
try:
import torch
except ImportError:
torch = None
################
# Data reading #
@ -207,6 +212,14 @@ def write_conllu(docs, file_):
file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
file_.write("# text = {text}\n".format(text=sent.text))
for k, token in enumerate(sent):
if token.head.i > sent[-1].i or token.head.i < sent[0].i:
for word in doc[sent[0].i-10 : sent[0].i]:
print(word.i, word.head.i, word.text, word.dep_)
for word in sent:
print(word.i, word.head.i, word.text, word.dep_)
for word in doc[sent[-1].i : sent[-1].i+10]:
print(word.i, word.head.i, word.text, word.dep_)
raise ValueError("Invalid parse: head outside sentence (%s)" % token.text)
file_.write(token._.get_conllu_lines(k) + '\n')
file_.write('\n')
@ -290,9 +303,12 @@ def initialize_pipeline(nlp, docs, golds, config, device):
for tag in gold.tags:
if tag is not None:
nlp.tagger.add_label(tag)
if torch is not None and device != -1:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
return nlp.begin_training(
lambda: golds_to_gold_tuples(docs, golds), device=device,
subword_features=config.subword_features, conv_depth=config.conv_depth)
subword_features=config.subword_features, conv_depth=config.conv_depth,
bilstm_depth=config.bilstm_depth)
########################
@ -356,12 +372,12 @@ class TreebankPaths(object):
parses_dir=("Directory to write the development parses", "positional", None, Path),
config=("Path to json formatted config file", "option", "C", Path),
limit=("Size limit", "option", "n", int),
use_gpu=("Use GPU", "option", "g", int),
gpu_device=("Use GPU", "option", "g", int),
use_oracle_segments=("Use oracle segments", "flag", "G", int),
vectors_dir=("Path to directory with pre-trained vectors, named e.g. en/",
"option", "v", Path),
)
def main(ud_dir, parses_dir, corpus, config=None, limit=0, use_gpu=-1, vectors_dir=None,
def main(ud_dir, parses_dir, corpus, config=None, limit=0, gpu_device=-1, vectors_dir=None,
use_oracle_segments=False):
spacy.util.fix_random_seed()
lang.zh.Chinese.Defaults.use_jieba = False
@ -381,7 +397,7 @@ def main(ud_dir, parses_dir, corpus, config=None, limit=0, use_gpu=-1, vectors_d
max_doc_length=config.max_doc_length,
limit=limit)
optimizer = initialize_pipeline(nlp, docs, golds, config, use_gpu)
optimizer = initialize_pipeline(nlp, docs, golds, config, gpu_device)
batch_sizes = compounding(config.min_batch_size, config.max_batch_size, 1.001)
beam_prob = compounding(0.2, 0.8, 1.001)
@ -415,7 +431,6 @@ def main(ud_dir, parses_dir, corpus, config=None, limit=0, use_gpu=-1, vectors_d
parsed_docs, scores = evaluate(nlp, paths.dev.text,
paths.dev.conllu, out_path)
print_progress(i, losses, scores)
_render_parses(i, parsed_docs[:50])
def _render_parses(i, to_render):