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| import os | |
| import json | |
| import re | |
| import random | |
| from collections import defaultdict | |
| import argparse | |
| import transformers | |
| from transformers import BartTokenizer | |
| import torch | |
| from torch.utils.data import DataLoader | |
| import pytorch_lightning as pl | |
| from .data import IEDataset, my_collate | |
| MAX_LENGTH=424 | |
| MAX_TGT_LENGTH=72 | |
| DOC_STRIDE=256 | |
| print("data_module.py") | |
| class RAMSDataModule(pl.LightningDataModule): | |
| def __init__(self, args): | |
| super().__init__() | |
| self.hparams = args | |
| self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-large') | |
| self.tokenizer.add_tokens([' <arg>',' <tgr>']) | |
| def get_event_type(self,ex): | |
| evt_type = [] | |
| for evt in ex['evt_triggers']: | |
| for t in evt[2]: | |
| evt_type.append( t[0]) | |
| return evt_type | |
| def create_gold_gen(self, ex, ontology_dict,mark_trigger=True): | |
| '''assumes that each line only contains 1 event. | |
| Input: <s> Template with special <arg> placeholders </s> </s> Passage </s> | |
| Output: <s> Template with arguments and <arg> when no argument is found. | |
| ''' | |
| evt_type = self.get_event_type(ex)[0] | |
| context_words = [w for sent in ex['sentences'] for w in sent ] | |
| template = ontology_dict[evt_type.replace('n/a','unspecified')]['template'] | |
| input_template = re.sub(r'<arg\d>', '<arg>', template) | |
| space_tokenized_input_template = input_template.split(' ') | |
| tokenized_input_template = [] | |
| for w in space_tokenized_input_template: | |
| tokenized_input_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True)) | |
| for triple in ex['gold_evt_links']: | |
| trigger_span, argument_span, arg_name = triple | |
| arg_num = ontology_dict[evt_type.replace('n/a','unspecified')][arg_name] | |
| arg_text = ' '.join(context_words[argument_span[0]:argument_span[1]+1]) | |
| template = re.sub('<{}>'.format(arg_num),arg_text , template) | |
| trigger = ex['evt_triggers'][0] | |
| if mark_trigger: | |
| trigger_span_start = trigger[0] | |
| trigger_span_end = trigger[1] +2 # one for inclusion, one for extra start marker | |
| prefix = self.tokenizer.tokenize(' '.join(context_words[:trigger[0]]), add_prefix_space=True) | |
| tgt = self.tokenizer.tokenize(' '.join(context_words[trigger[0]: trigger[1]+1]), add_prefix_space=True) | |
| suffix = self.tokenizer.tokenize(' '.join(context_words[trigger[1]+1:]), add_prefix_space=True) | |
| context = prefix + [' <tgr>', ] + tgt + [' <tgr>', ] + suffix | |
| else: | |
| context = self.tokenizer.tokenize(' '.join(context_words), add_prefix_space=True) | |
| output_template = re.sub(r'<arg\d>','<arg>', template ) | |
| space_tokenized_template = output_template.split(' ') | |
| tokenized_template = [] | |
| for w in space_tokenized_template: | |
| tokenized_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True)) | |
| return tokenized_input_template, tokenized_template, context | |
| def load_ontology(self): | |
| # read ontology | |
| ontology_dict ={} | |
| with open('aida_ontology_cleaned.csv','r') as f: | |
| for lidx, line in enumerate(f): | |
| if lidx == 0:# header | |
| continue | |
| fields = line.strip().split(',') | |
| if len(fields) < 2: | |
| break | |
| evt_type = fields[0] | |
| args = fields[2:] | |
| ontology_dict[evt_type] = { | |
| 'template': fields[1] | |
| } | |
| for i, arg in enumerate(args): | |
| if arg !='': | |
| ontology_dict[evt_type]['arg{}'.format(i+1)] = arg | |
| ontology_dict[evt_type][arg] = 'arg{}'.format(i+1) | |
| x = 1 | |
| while(x > 0): | |
| #print(ontology_dict) | |
| x = x - 1 | |
| return ontology_dict | |
| def prepare_data(self): | |
| if not os.path.exists('span_preprocessed_data'): | |
| os.makedirs('span_preprocessed_data') | |
| ontology_dict = self.load_ontology() | |
| for split,f in [('train',self.hparams.train_file), ('val',self.hparams.val_file), ('test',self.hparams.test_file)]: | |
| with open(f,'r') as reader, open('span_preprocessed_data/{}.jsonl'.format(split), 'w') as writer: | |
| for lidx, line in enumerate(reader): | |
| ex = json.loads(line.strip()) | |
| input_template, output_template, context= self.create_gold_gen(ex, ontology_dict, self.hparams.mark_trigger) | |
| input_tokens = self.tokenizer.encode_plus(input_template, context, | |
| add_special_tokens=True, | |
| add_prefix_space=True, | |
| max_length=MAX_LENGTH, | |
| truncation='only_second', | |
| padding='max_length') | |
| tgt_tokens = self.tokenizer.encode_plus(output_template, | |
| add_special_tokens=True, | |
| add_prefix_space=True, | |
| max_length=MAX_TGT_LENGTH, | |
| truncation=True, | |
| padding='max_length') | |
| processed_ex = { | |
| # 'idx': lidx, | |
| 'doc_key': ex['doc_key'], | |
| 'input_token_ids':input_tokens['input_ids'], | |
| 'input_attn_mask': input_tokens['attention_mask'], | |
| 'tgt_token_ids': tgt_tokens['input_ids'], | |
| 'tgt_attn_mask': tgt_tokens['attention_mask'], | |
| } | |
| writer.write(json.dumps(processed_ex) + '\n') | |
| def train_dataloader(self): | |
| dataset = IEDataset('span_preprocessed_data/train.jsonl') | |
| dataloader = DataLoader(dataset, | |
| pin_memory=True, num_workers=2, | |
| collate_fn=my_collate, | |
| batch_size=self.hparams.train_batch_size, | |
| shuffle=True) | |
| return dataloader | |
| def val_dataloader(self): | |
| dataset = IEDataset('span_preprocessed_data/val.jsonl') | |
| dataloader = DataLoader(dataset, pin_memory=True, num_workers=2, | |
| collate_fn=my_collate, | |
| batch_size=self.hparams.eval_batch_size, shuffle=False) | |
| return dataloader | |
| def test_dataloader(self): | |
| dataset = IEDataset('span_preprocessed_data/test.jsonl') | |
| dataloader = DataLoader(dataset, pin_memory=True, num_workers=2, | |
| collate_fn=my_collate, | |
| batch_size=self.hparams.eval_batch_size, shuffle=False) | |
| return dataloader | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--train-file',type=str,default='data/RAMS_1.0/data/train.jsonlines') | |
| parser.add_argument('--val-file', type=str, default='data/RAMS_1.0/data/dev.jsonlines') | |
| parser.add_argument('--test-file', type=str, default='data/RAMS_1.0/data/test.jsonlines') | |
| parser.add_argument('--train_batch_size', type=int, default=2) | |
| parser.add_argument('--eval_batch_size', type=int, default=4) | |
| parser.add_argument('--mark-trigger', action='store_true', default=True) | |
| args = parser.parse_args() | |
| dm = RAMSDataModule(args=args) | |
| dm.prepare_data() | |
| # training dataloader | |
| dataloader = dm.train_dataloader() | |
| for idx, batch in enumerate(dataloader): | |
| print(batch) | |
| break | |
| # val dataloader | |