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importos
importsys
importtime
importasciitree
importcollections
fromconcurrent.futuresimportThreadPoolExecutor
PROJECT_ROOT=os.path.abspath(os.path.dirname(__file__))
runfiles_path=os.path.join(PROJECT_ROOT, 'models', 'syntaxnet', 'bazel-bin', 'syntaxnet', 'parser_eval.runfiles')
tensorflow_path=os.path.join(PROJECT_ROOT, 'models', 'syntaxnet', 'bazel-bin', 'syntaxnet', 'parser_eval.runfiles',
'external', 'tf')
sys.path.append(runfiles_path)
sys.path.append(tensorflow_path)
importtensorflowastf
fromtensorflow.python.platformimporttf_loggingaslogging
fromsyntaxnetimportsentence_pb2, structured_graph_builder
fromsyntaxnet.opsimportgen_parser_ops
input_file_path=os.path.join(PROJECT_ROOT, "input-file.txt")
output_file_path=os.path.join(PROJECT_ROOT, "output-file.txt")
parser_path=os.path.join(PROJECT_ROOT, 'models', 'syntaxnet', 'bazel-bin', 'syntaxnet', 'parser_eval')
mcparseface_path=os.path.join(PROJECT_ROOT, 'models', 'syntaxnet', 'syntaxnet', 'models', 'parsey_mcparseface')
tagger_params_path=os.path.join(mcparseface_path, 'tagger-params')
parser_params_path=os.path.join(mcparseface_path, 'parser-params')
task_context_path=os.path.join(PROJECT_ROOT, "custom_context.pbtxt")
"""
PARSER_EVAL=bazel-bin/syntaxnet/parser_eval
MODEL_DIR=syntaxnet/models/parsey_mcparseface
[[ "$1" == "--conll" ]] && INPUT_FORMAT=stdin-conll || INPUT_FORMAT=stdin
$PARSER_EVAL \
--input=$INPUT_FORMAT \
--output=stdout-conll \
--hidden_layer_sizes=64 \
--arg_prefix=brain_tagger \
--graph_builder=structured \
--task_context=$MODEL_DIR/context.pbtxt \
--model_path=$MODEL_DIR/tagger-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr \
| \
$PARSER_EVAL \
--input=stdin-conll \
--output=stdout-conll \
--hidden_layer_sizes=512,512 \
--arg_prefix=brain_parser \
--graph_builder=structured \
--task_context=$MODEL_DIR/context.pbtxt \
--model_path=$MODEL_DIR/parser-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr \
| \
bazel-bin/syntaxnet/conll2tree \
--task_context=$MODEL_DIR/context.pbtxt \
--alsologtostderr
"""
defparse_sentence(sentence):
tagged_sentence=_perform_action_in_thread(action="brain_tagger", sentence=sentence)
parsed_sentence=_perform_action_in_thread(action="brain_parser", sentence=tagged_sentence)
sentence_dict=_get_sentence_dict_in_thread(parsed_sentence)
returnsentence_dict
defto_dict(sentence):
"""Builds a dictionary representing the parse tree of a sentence.
Args:
sentence: Sentence protocol buffer to represent.
Returns:
Dictionary mapping tokens to children.
"""
token_str= ['%s %s %s'% (token.word, token.tag, token.label)
fortokeninsentence.token]
children= [[] fortokeninsentence.token]
root=-1
foriinrange(0, len(sentence.token)):
token=sentence.token[i]
iftoken.head==-1:
root=i
else:
children[token.head].append(i)
def_get_dict(i):
d=collections.OrderedDict()
forcinchildren[i]:
d[token_str[c]] =_get_dict(c)
returnd
tree=collections.OrderedDict()
tree[token_str[root]] =_get_dict(root)
returntree
def_get_sentence_dict_in_thread(sentence_input):
# The file doesn't get written unless the calling thread dies.
# So this is a hack to be able to read from the file we are writing to.
input_file=open(input_file_path, mode="w")
input_file.write(sentence_input)
input_file.close()
executor=ThreadPoolExecutor(max_workers=1)
future=executor.submit(_get_sentence_dict)
returnfuture.result()
def_get_sentence_dict():
logging.set_verbosity(logging.INFO)
withtf.Session() assess:
src=gen_parser_ops.document_source(batch_size=32,
corpus_name='input-from-file-conll',
task_context=task_context_path)
sentence=sentence_pb2.Sentence()
result_dict=None
whileTrue:
documents, finished=sess.run(src)
fordindocuments:
sentence.ParseFromString(d)
d=to_dict(sentence)
result_dict=d
iffinished:
break
returnresult_dict
def_perform_action_in_thread(action=None, sentence=None):
# The file doesn't get written unless the calling thread dies.
# So this is a hack to be able to read from the file we are writing to.
input_file=open(input_file_path, mode="w")
input_file.write(sentence)
input_file.close()
executor=ThreadPoolExecutor(max_workers=1)
future=executor.submit(_perform_action, action)
future.result()
output_file=open(output_file_path, mode="r")
result=output_file.read()
output_file.close()
returnresult
def_perform_action(action=None):
arg_prefix=action
task_context=task_context_path
ifaction=="brain_tagger":
hidden_layer_sizes= [64]
model_path=tagger_params_path
output='output-to-file'
input='input-from-file'
elifaction=="brain_parser":
hidden_layer_sizes= [512, 512]
model_path=parser_params_path
output='output-to-file-conll'
input='input-from-file-conll'
else:
raiseException("Do not recognize action %s"%action)
withtf.Session() assess:
feature_sizes, domain_sizes, embedding_dims, num_actions=sess.run(
gen_parser_ops.feature_size(task_context=task_context,
arg_prefix=arg_prefix))
beam_size=8
max_steps=1000
batch_size=1024
slim_model=True
parser=structured_graph_builder.StructuredGraphBuilder(
num_actions,
feature_sizes,
domain_sizes,
embedding_dims,
hidden_layer_sizes,
gate_gradients=True,
arg_prefix=arg_prefix,
beam_size=beam_size,
max_steps=max_steps)
parser.AddEvaluation(task_context,
batch_size,
corpus_name=input,
evaluation_max_steps=max_steps)
withtf.Session() assess:
parser.AddSaver(slim_model)
sess.run(parser.inits.values())
parser.saver.restore(sess, model_path)
sink_documents=tf.placeholder(tf.string)
sink=gen_parser_ops.document_sink(sink_documents,
task_context=task_context,
corpus_name=output)
t=time.time()
num_epochs=None
num_tokens=0
num_correct=0
num_documents=0
whileTrue:
tf_eval_epochs, tf_eval_metrics, tf_documents=sess.run([
parser.evaluation['epochs'],
parser.evaluation['eval_metrics'],
parser.evaluation['documents'],
])
iflen(tf_documents):
logging.info('Processed %d documents', len(tf_documents))
num_documents+=len(tf_documents)
sess.run(sink, feed_dict={sink_documents: tf_documents})
num_tokens+=tf_eval_metrics[0]
num_correct+=tf_eval_metrics[1]
ifnum_epochsisNone:
num_epochs=tf_eval_epochs
elifnum_epochs<tf_eval_epochs:
break
logging.info('Total processed documents: %d', num_documents)
ifnum_tokens>0:
eval_metric=100.0*num_correct/num_tokens
logging.info('num correct tokens: %d', num_correct)
logging.info('total tokens: %d', num_tokens)
logging.info('Seconds elapsed in evaluation: %.2f, '
'eval metric: %.2f%%', time.time() -t, eval_metric)
"""
/usr/local/bin/python /Users/plowman/projects/parseface/models/syntaxnet/bazel-bin/syntaxnet/parser_eval.runfiles/syntaxnet/parser_eval.py
--input=stdin --output=stdout-conll --hidden_layer_sizes=64 --arg_prefix=brain_tagger
--graph_builder=structured --task_context=syntaxnet/models/parsey_mcparseface/context.pbtxt
--model_path=syntaxnet/models/parsey_mcparseface/tagger-params --slim_model --batch_size=1024
--alsologtostderr
"""
defpretty_print_dict(input_dict):
tr=asciitree.LeftAligned()
printtr(input_dict)
if__name__=="__main__":
parsed_sentence_dict=parse_sentence("Maybe there was once a human who looked like you, and somewhere along the "
"line you killed him and took his place. And your superiors don't know.")
print("OrderedDict: %s"%parsed_sentence_dict)
pretty_print_dict(parsed_sentence_dict)