91 строка
4.6 KiB
Python
91 строка
4.6 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT license.
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from settings import ProblemTypes, version
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import os
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import argparse
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import logging
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from ModelConf import ModelConf
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from problem import Problem
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from LearningMachine import LearningMachine
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def main(params):
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conf = ModelConf('predict', params.conf_path, version, params, mode=params.mode)
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problem = Problem('predict', conf.problem_type, conf.input_types, None,
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with_bos_eos=conf.add_start_end_for_seq, tagging_scheme=conf.tagging_scheme, tokenizer=conf.tokenizer,
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remove_stopwords=conf.remove_stopwords, DBC2SBC=conf.DBC2SBC, unicode_fix=conf.unicode_fix)
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if os.path.isfile(conf.saved_problem_path):
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problem.load_problem(conf.saved_problem_path)
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logging.info("Problem loaded!")
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logging.debug("Problem loaded from %s" % conf.saved_problem_path)
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else:
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raise Exception("Problem does not exist!")
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if len(conf.predict_fields_post_check) > 0:
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for field_to_chk in conf.predict_fields_post_check:
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field, target = field_to_chk.split('@')
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if not problem.output_dict.has_cell(target):
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raise Exception("The target %s of %s does not exist in the training data." % (target, field_to_chk))
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lm = LearningMachine('predict', conf, problem, vocab_info=None, initialize=False, use_gpu=conf.use_gpu)
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lm.load_model(conf.previous_model_path)
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if params.predict_mode == 'batch':
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logging.info('Predicting %s with the model saved at %s' % (conf.predict_data_path, conf.previous_model_path))
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if params.predict_mode == 'batch':
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lm.predict(conf.predict_data_path, conf.predict_output_path, conf.predict_file_columns, conf.predict_fields)
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logging.info("Predict done! The predict result: %s" % conf.predict_output_path)
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elif params.predict_mode == 'interactive':
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print('='*80)
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task_type = str(ProblemTypes[problem.problem_type]).split('.')[1]
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sample_format = list(conf.predict_file_columns.keys())
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target_ = conf.conf['inputs'].get('target', None)
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target_list = list(target_) if target_ else []
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for single_element in sample_format[:]:
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if single_element in target_list:
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sample_format.remove(single_element)
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predict_file_columns = {}
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for index, single in enumerate(sample_format):
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predict_file_columns[single] = index
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print('Enabling Interactive Inference Mode for %s Task...' % (task_type.upper()))
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print('%s Task Interactive. The sample format is <%s>' % (task_type.upper(), ', '.join(sample_format)))
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case_cnt = 1
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while True:
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print('Case%d:' % case_cnt)
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sample = []
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for single in sample_format:
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temp_ = input('\t%s: ' % single)
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if temp_.lower() == 'exit':
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exit(0)
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sample.append(temp_)
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sample = '\t'.join(sample)
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result = lm.interactive([sample], predict_file_columns, conf.predict_fields, params.predict_mode)
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print('\tInference result: %s' % result)
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case_cnt += 1
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else:
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raise Exception('Predict mode support interactive|batch, get %s' % params.predict_mode)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Prediction')
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parser.add_argument("--conf_path", type=str, help="configuration path")
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parser.add_argument("--predict_mode", type=str, default='batch', help='interactive|batch')
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parser.add_argument("--predict_data_path", type=str, help='specify another predict data path, instead of the one defined in configuration file')
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parser.add_argument("--previous_model_path", type=str, help='load model trained previously.')
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parser.add_argument("--predict_output_path", type=str, help='specify another prediction output path, instead of conf[outputs][save_base_dir] + conf[outputs][predict_output_name] defined in configuration file')
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parser.add_argument("--log_dir", type=str)
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parser.add_argument("--batch_size", type=int, help='batch_size of each gpu')
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parser.add_argument("--mode", type=str, default='normal', help='normal|philly')
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parser.add_argument("--force", type=bool, default=False, help='Allow overwriting if some files or directories already exist.')
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parser.add_argument("--disable_log_file", type=bool, default=False, help='If True, disable log file')
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parser.add_argument("--debug", type=bool, default=False)
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params, _ = parser.parse_known_args()
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assert params.conf_path, 'Please specify a configuration path via --conf_path'
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if params.debug is True:
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import debugger
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main(params) |