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import sys |
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import json |
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import os |
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import io |
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import collections |
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import argparse |
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import logging |
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from e2edutch import conll |
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from e2edutch import minimize |
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from e2edutch import util |
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from e2edutch import coref_model as cm |
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from e2edutch import naf |
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import tensorflow.compat.v1 as tf |
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logger = logging.getLogger('e2edutch') |
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class Predictor(object): |
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""" |
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A predictor object loads a pretrained e2e model to predict coreferences. |
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It can be used to predict coreferences on tokenized text. |
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""" |
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def __init__(self, model_name='final', config=None, verbose=False): |
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if verbose: |
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logger.setLevel(logging.INFO) |
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if config: |
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self.config = config |
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else: |
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# if no configuration is provided, try to get a default config. |
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self.config = util.initialize_from_env(model_name=model_name) |
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# Clear tensorflow context: |
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tf.reset_default_graph() |
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self.session = tf.compat.v1.Session() |
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try: |
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self.model = cm.CorefModel(self.config) |
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self.model.restore(self.session) |
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except ValueError: |
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raise Exception("Trying to reload the model while the previous " + |
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"session hasn't been ended. Close the existing " + |
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"session with predictor.end_session()") |
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def predict(self, example): |
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""" |
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Predict coreference spans for a tokenized text. |
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Args: |
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example (dict): dict with the following fields: |
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sentences ([[str]]) |
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doc_id (str) |
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clusters ([[(int, int)]]) (optional) |
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Returns: |
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[[(int, int)]]: a list of clusters. The items of the cluster are |
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spans, denoted by their start end end token index |
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""" |
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tensorized_example = self.model.tensorize_example( |
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example, is_training=False) |
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feed_dict = {i: t for i, t in zip( |
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self.model.input_tensors, tensorized_example)} |
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_, _, _, top_span_starts, top_span_ends, top_antecedents, top_antecedent_scores = self.session.run( |
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self.model.predictions, feed_dict=feed_dict) |
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predicted_antecedents = self.model.get_predicted_antecedents( |
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top_antecedents, top_antecedent_scores) |
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predicted_clusters, _ = self.model.get_predicted_clusters( |
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top_span_starts, top_span_ends, predicted_antecedents) |
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return predicted_clusters |
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def end_session(self): |
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""" |
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Close the session, clearing the tensorflow model context. |
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""" |
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self.session.close() |
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tf.reset_default_graph() |
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def get_parser(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('config') |
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parser.add_argument('input_filename') |
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parser.add_argument('-o', '--output_file', |
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type=argparse.FileType('w'), default=sys.stdout) |
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parser.add_argument('-f', '--format_out', default='conll', |
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choices=['conll', 'jsonlines', 'naf']) |
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parser.add_argument('-c', '--word_col', type=int, default=2) |
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parser.add_argument('--cfg_file', |
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type=str, |
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default=None, |
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help="config file") |
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parser.add_argument('-v', '--verbose', action='store_true') |
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return parser |
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def read_jsonlines(input_filename): |
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for line in open(input_filename).readlines(): |
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example = json.loads(line) |
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yield example |
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def main(args=None): |
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parser = get_parser() |
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args = parser.parse_args() |
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if args.verbose: |
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logger.setLevel(logging.INFO) |
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# Input file in .jsonlines format or .conll. |
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input_filename = args.input_filename |
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ext_input = os.path.splitext(input_filename)[-1] |
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if ext_input not in ['.conll', '.jsonlines', '.txt', '.naf']: |
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raise Exception( |
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'Input file should be .naf, .conll, .txt or .jsonlines, but is {}.' |
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.format(ext_input)) |
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if ext_input == '.conll': |
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labels = collections.defaultdict(set) |
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stats = collections.defaultdict(int) |
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docs = minimize.minimize_partition( |
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input_filename, labels, stats, args.word_col) |
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elif ext_input == '.jsonlines': |
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docs = read_jsonlines(input_filename) |
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elif ext_input == '.naf': |
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naf_obj = naf.get_naf(input_filename) |
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jsonlines_obj, term_ids, tok_ids = naf.get_jsonlines(naf_obj) |
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docs = [jsonlines_obj] |
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else: |
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text = open(input_filename).read() |
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docs = [util.create_example(text)] |
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output_file = args.output_file |
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config = util.initialize_from_env(cfg_file=args.cfg_file, model_cfg_file=args.config) |
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predictor = Predictor(config=config) |
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sentences = {} |
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predictions = {} |
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for example_num, example in enumerate(docs): |
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example["predicted_clusters"], _ = predictor.predict(example) |
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if args.format_out == 'jsonlines': |
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output_file.write(json.dumps(example)) |
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output_file.write("\n") |
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else: |
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predictions[example['doc_key']] = example["predicted_clusters"] |
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sentences[example['doc_key']] = example["sentences"] |
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if example_num % 100 == 0: |
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logger.info("Decoded {} examples.".format(example_num + 1)) |
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if args.format_out == 'conll': |
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conll.output_conll(output_file, sentences, predictions) |
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elif args.format_out == 'naf': |
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# Check number of docs - what to do if multiple? |
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# Create naf obj if input format was not naf |
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if ext_input != '.naf': |
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# To do: add linguistic processing layers for terms and tokens |
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logger.warn( |
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'Outputting NAF when input was not naf,' |
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+ 'no dependency information available') |
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for doc_key in sentences: |
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naf_obj, term_ids = naf.get_naf_from_sentences( |
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sentences[doc_key]) |
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naf_obj = naf.create_coref_layer( |
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naf_obj, predictions[doc_key], term_ids) |
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naf_obj = naf.add_linguistic_processors(naf_obj) |
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buffer = io.BytesIO() |
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naf_obj.dump(buffer) |
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output_file.write(buffer.getvalue().decode('utf-8')) |
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# To do, make sepearate outputs? |
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# TO do, use dependency information from conll? |
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else: |
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# We only have one input doc |
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naf_obj = naf.create_coref_layer( |
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naf_obj, example["predicted_clusters"], term_ids) |
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naf_obj = naf.add_linguistic_processors(naf_obj) |
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buffer = io.BytesIO() |
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naf_obj.dump(buffer) |
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output_file.write(buffer.getvalue().decode('utf-8')) |
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if __name__ == "__main__": |
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main() |
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Filter-Bubble /
e2e-Dutch
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