def validate_unofficial(args, data_loader, model, global_stats, mode): """Run one full unofficial validation. Unofficial = doesn't use SQuAD script. """ from sklearn.metrics import roc_auc_score, f1_score eval_time = utils.Timer() trigger_acc = utils.AverageMeter() eval_time = utils.Timer() start_acc = utils.AverageMeter() end_acc = utils.AverageMeter() exact_match = utils.AverageMeter() # Make predictions all_pred = [] all_pred_label = [] all_gt = [] examples = 0 for ex in data_loader: batch_size = ex[0].size(0) pred_score, pred_label, pred_s, pred_e = model.predict(ex) target_s, target_e = ex[-4:-2] accuracies = eval_accuracies_rc(pred_s, target_s, pred_e, target_e) start_acc.update(accuracies[0], batch_size) end_acc.update(accuracies[1], batch_size) exact_match.update(accuracies[2], batch_size) gt_label = ex[-1] all_pred.extend([x[1] for x in pred_score]) all_gt.extend(gt_label) all_pred_label.extend(pred_label) # We get metrics for independent start/end and joint start/end accuracies = eval_accuracies(pred_label, gt_label) trigger_acc.update(accuracies, batch_size) # If getting train accuracies, sample max 10k examples += batch_size # only test train top 10000 if mode == 'train' and examples >= 1e4: break auc_score = roc_auc_score(all_gt, all_pred) f1_scores = f1_score(all_gt, all_pred_label,average=None) logger.info('%s valid unofficial: Epoch = %d | ' % (mode, global_stats['epoch'], ) + 'neg_f1 = %.2f | pos_f1 = %.2f | trigger_auc = %.2f | trigger_acc = %.2f | examples = %d | ' % (f1_scores[0], f1_scores[1], auc_score, trigger_acc.avg, examples) + 'valid time = %.2f (s)' % eval_time.time()) return {'auc': auc_score, 'trigger_acc': trigger_acc.avg}
def main(args): dev_exs = utils.load_data(args, args.dev_file) dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs} dev_answers = utils.load_answers(args.dev_json) model = DocReader.load(args.pretrained, args) model.init_optimizer() if args.cuda: model.cuda() if args.parallel: model.parallelize() dev_dataset = reader_data.ReaderDataset(dev_exs, model, single_answer=False) if args.sort_by_len: dev_sampler = reader_data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=reader_vector.batchify, pin_memory=args.cuda, ) stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0} scores, pred_objs = compute_expected_metric(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers) return scores, pred_objs
def train(args, data_loader, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): train_loss.update(*model.update(ex)) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() if args.indexcheckpoint != -1 and idx != 0 and idx % args.indexcheckpoint == 0: checkpointName = args.model_file + str( idx / args.indexcheckpoint) + ':' + str( global_stats['epoch']) + '.checkpoint' model.checkpoint(checkpointName, global_stats['epoch']) print(['new checkpoint at : %s' % checkpointName]) logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' % (global_stats['epoch'], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + '.checkpoint', global_stats['epoch'] + 1)
def validate_unofficial(args, data_loader, model, global_stats, mode): """Run one full unofficial validation. Unofficial = doesn't use SQuAD script. """ eval_time = utils.Timer() start_acc = utils.AverageMeter() end_acc = utils.AverageMeter() exact_match = utils.AverageMeter() # Make predictions examples = 0 for ex in data_loader: batch_size = ex[0].size(0) pred_s, pred_e, _ = model.predict(ex) target_s, target_e = ex[-3:-1] # We get metrics for independent start/end and joint start/end accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e) start_acc.update(accuracies[0], batch_size) end_acc.update(accuracies[1], batch_size) exact_match.update(accuracies[2], batch_size) # If getting train accuracies, sample max 10k examples += batch_size if mode == 'train' and examples >= 1e4: break logger.info('%s valid unofficial: Epoch = %d | start = %.2f | ' % (mode, global_stats['epoch'], start_acc.avg) + 'end = %.2f | exact = %.2f | examples = %d | ' % (end_acc.avg, exact_match.avg, examples) + 'valid time = %.2f (s)' % eval_time.time()) return {'exact_match': exact_match.avg}
def train(args, data_loader, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): ''' print('ex:::: ' + str(ex)) sys.exit() ''' train_loss.update(*model.update(ex)) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' % (global_stats['epoch'], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + '.checkpoint', global_stats['epoch'] + 1)
def validate_official(args, data_loader, model, global_stats, offsets, texts, answers): """Run one full official validation. Uses exact spans and same exact match/F1 score computation as in the SQuAD script. Extra arguments: offsets: The character start/end indices for the tokens in each context. texts: Map of qid --> raw text of examples context (matches offsets). answers: Map of qid --> list of accepted answers. """ clean_id_file = open(os.path.join(DATA_DIR, "clean_qids.txt"), "w+") eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() # Run through examples examples = 0 bad_examples = 0 for ex in data_loader: ex_id, batch_size = ex[-1], ex[0].size(0) chosen_offset = ex[-2] pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): if pred_s[i][0] >= len(offsets[ex_id[i]]) or pred_e[i][0] >= len( offsets[ex_id[i]]): bad_examples += 1 continue if args.use_sentence_selector: s_offset = chosen_offset[i][pred_s[i][0]][0] e_offset = chosen_offset[i][pred_e[i][0]][1] else: s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][s_offset:e_offset] # Compute metrics ground_truths = answers[ex_id[i]] exact_match.update( utils.metric_max_over_ground_truths(utils.exact_match_score, prediction, ground_truths)) f1.update( utils.metric_max_over_ground_truths(utils.f1_score, prediction, ground_truths)) f1_example = utils.metric_max_over_ground_truths( utils.f1_score, prediction, ground_truths) if f1_example != 0: clean_id_file.write(ex_id + "\n") examples += batch_size clean_id_file.close() logger.info('dev valid official: Epoch = %d | EM = %.2f | ' % (global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) logger.info('Bad Offset Examples during official eval: %d' % bad_examples) return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def train(args, data_loader, data_loader_source, data_loader_target, train_loader_source_Q, train_loader_target_Q, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader_source): # Calculate n_critic epoch = global_stats['epoch'] n_critic = args.n_critic if n_critic > 0 and ((epoch == 0 and idx <= 25) or (idx % 500 == 0)): n_critic = 10 train_loss.update(*model.update(ex, n_critic, epoch)) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' % (global_stats['epoch'], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + '.checkpoint', global_stats['epoch'] + 1)
def validate_official(args, data_loader, model, global_stats, offsets, texts, answers, mode="dev"): """Run one full official validation. Uses exact spans and same exact match/F1 score computation as in the SQuAD script. Extra arguments: offsets: The character start/end indices for the tokens in each context. texts: Map of qid --> raw text of examples context (matches offsets). answers: Map of qid --> list of accepted answers. """ eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() # Run through examples examples = 0 for ex in data_loader: ex_id, batch_size = ex[-1], ex[0].size(0) pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][s_offset:e_offset] # Compute metrics ground_truths = answers[ex_id[i]] exact_match.update( utils.metric_max_over_ground_truths(utils.exact_match_score, prediction, ground_truths)) f1.update( utils.metric_max_over_ground_truths(utils.f1_score, prediction, ground_truths)) examples += batch_size logger.info(mode + ' valid official: Epoch = %d | EM = %.2f | ' % (global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def train(args, data_loader, model, global_stats, dev_loader): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): train_loss.update(*model.update(ex)) global_stats['Loss_Train'] = float(train_loss.avg) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() ####### Fix this later if args.show_dev_loss: dev_loss = utils.AverageMeter() for idx, ex in enumerate(dev_loader): if args.cuda: inputs = [ e if e is None else Variable(e.cuda(async=True)) for e in ex[:5] ] target_s = Variable(ex[5].cuda(async=True)) target_e = Variable(ex[6].cuda(async=True)) else: print("No cudaaa") inputs = [e if e is None else e for e in ex[:5]] target_s = ex[5] target_e = ex[6] score_s, score_e = model.network(*inputs) loss = F.nll_loss(score_s, target_s) + F.nll_loss( score_e, target_e) dev_loss.update(loss.data[0], ex[0].size(0))
def train(args, data_loader, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): train_loss.update(*model.update(ex)) if idx % args.display_iter == 0: logger.info("train: Epoch = %d | iter = %d/%d | " % (global_stats["epoch"], idx, len(data_loader)) + "loss = %.2f | elapsed time = %.2f (s)" % (train_loss.avg, global_stats["timer"].time())) train_loss.reset() logger.info("train: Epoch %d done. Time for epoch = %.2f (s)" % (global_stats["epoch"], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + ".checkpoint", global_stats["epoch"] + 1)
def main(args): # -------------------------------------------------------------------------- # DATA logger.info('-' * 100) logger.info('Load data files') train_exs = utils.load_data(args, args.train_file, skip_no_answer=True, trainset=True) logger.info('Num train examples = %d' % len(train_exs)) dev_exs = utils.load_data(args, args.dev_file, trainset=False) logger.info('Num dev examples = %d' % len(dev_exs)) # If we are doing offician evals then we need to: # 1) Load the original text to retrieve spans from offsets. # 2) Load the (multiple) text answers for each question. if args.official_eval: dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs} dev_answers = utils.load_answers(args.dev_json) # -------------------------------------------------------------------------- # MODEL logger.info('-' * 100) start_epoch = 0 if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'): # Just resume training, no modifications. logger.info('Found a checkpoint...') checkpoint_file = args.model_file + '.checkpoint' model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args) else: # Training starts fresh. But the model state is either pretrained or # newly (randomly) initialized. if args.pretrained: logger.info('Using pretrained model...') model = DocReader.load(args.pretrained, args) if args.expand_dictionary: logger.info('Expanding dictionary for new data...') # Add words in training + dev examples words = utils.load_words(args, train_exs + dev_exs) added = model.expand_dictionary(words) # Load pretrained embeddings for added words if args.embedding_file: model.load_embeddings(added, args.embedding_file) else: logger.info('Training model from scratch...') model = init_from_scratch(args, train_exs, dev_exs) # COMMENTED OUT QUANTIZATION/TT SUPPORT # if args.embed_type != 'plain': # # Jian: replace embeddings if specified by args # replace_embeddings(model.network, args, logger) # Set up partial tuning of embeddings if args.tune_partial > 0: logger.info('-' * 100) logger.info('Counting %d most frequent question words' % args.tune_partial) top_words = utils.top_question_words(args, train_exs, model.word_dict) for word in top_words[:5]: logger.info(word) logger.info('...') for word in top_words[-6:-1]: logger.info(word) model.tune_embeddings([w[0] for w in top_words]) # Set up optimizer model.init_optimizer() # Use the GPU? if args.cuda: model.cuda() # Use multiple GPUs? if args.parallel: model.parallelize() # -------------------------------------------------------------------------- # DATA ITERATORS # Two datasets: train and dev. If we sort by length it's faster. logger.info('-' * 100) logger.info('Make data loaders') bert_tokenizer = (None if not args.use_bert_embeddings else BertTokenizer.from_pretrained(args.bert_model_name, do_lower_case='uncased' in args.bert_model_name)) train_dataset = data.ReaderDataset(train_exs, model, single_answer=True, bert_tokenizer=bert_tokenizer) if args.sort_by_len: train_sampler = data.SortedBatchSampler(train_dataset.lengths(), args.batch_size, shuffle=True) else: train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) dev_dataset = data.ReaderDataset(dev_exs, model, single_answer=False, bert_tokenizer=bert_tokenizer) if args.sort_by_len: dev_sampler = data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) # ------------------------------------------------------------------------- # PRINT CONFIG logger.info('-' * 100) logger.info('CONFIG:\n%s' % json.dumps(vars(args), indent=4, sort_keys=True)) # -------------------------------------------------------------------------- # TRAIN/VALID LOOP logger.info('-' * 100) logger.info('Starting training...') stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0} f1_scores = [] exact_match_scores = [] for epoch in range(start_epoch, args.num_epochs): stats['epoch'] = epoch # COMMENTED OUT QUANTIZATION/TT SUPPORT # Log model parameter status # log_param_list(model.network, logger) # Train train(args, train_loader, model, stats) # Validate unofficial (train) validate_unofficial(args, train_loader, model, stats, mode='train') # Validate unofficial (dev) result = validate_unofficial(args, dev_loader, model, stats, mode='dev') # Validate official if args.official_eval: result = validate_official(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers) # Save best valid if result[args.valid_metric] > stats['best_valid']: logger.info('Best valid: %s = %.2f (epoch %d, %d updates)' % (args.valid_metric, result[args.valid_metric], stats['epoch'], model.updates)) model.save(args.model_file) stats['best_valid'] = result[args.valid_metric] f1_scores.append(result['f1']) exact_match_scores.append(result['exact_match']) return f1_scores, exact_match_scores
def main(args): # -------------------------------------------------------------------------- # DATA logger.info('-' * 100) logger.info('Load data files') train_exs = utils.load_data(args, args.train_file, skip_no_answer=True) logger.info('Num train examples = %d' % len(train_exs)) dev_exs = utils.load_data(args, args.dev_file) logger.info('Num dev examples = %d' % len(dev_exs)) # If we are doing offician evals then we need to: # 1) Load the original text to retrieve spans from offsets. # 2) Load the (multiple) text answers for each question. if args.official_eval: if args.standard: dev_texts = utils.load_text_standard(args.dev_json) else: dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs} if args.standard: dev_answers = utils.load_answers_standard(args.dev_json) else: dev_answers = utils.load_answers(args.dev_json) # -------------------------------------------------------------------------- # MODEL logger.info('-' * 100) start_epoch = 0 if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'): # Just resume training, no modifications. logger.info('Found a checkpoint...') checkpoint_file = args.model_file + '.checkpoint' model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args) else: # Training starts fresh. But the model state is either pretrained or # newly (randomly) initialized. if args.pretrained: logger.info('Using pretrained model...') model = DocReader.load(args.pretrained, args) if args.expand_dictionary: logger.info('Expanding dictionary for new data...') # Add words in training + dev examples words = utils.load_words(args, train_exs + dev_exs) added = model.expand_dictionary(words) # Load pretrained embeddings for added words if args.embedding_file: model.load_embeddings(added, args.embedding_file) else: logger.info('Training model from scratch...') model = init_from_scratch(args, train_exs, dev_exs) # Set up partial tuning of embeddings if args.tune_partial > 0: logger.info('-' * 100) logger.info('Counting %d most frequent question words' % args.tune_partial) top_words = utils.top_question_words( args, train_exs, model.word_dict ) for word in top_words[:5]: logger.info(word) logger.info('...') for word in top_words[-6:-1]: logger.info(word) model.tune_embeddings([w[0] for w in top_words]) # Set up optimizer model.init_optimizer() # Use the GPU? if args.cuda: model.cuda() # Use multiple GPUs? if args.parallel: model.parallelize() # -------------------------------------------------------------------------- # DATA ITERATORS # Two datasets: train and dev. If we sort by length it's faster. logger.info('-' * 100) logger.info('Make data loaders') train_dataset = data.ReaderDataset(train_exs, model, single_answer=True) if args.sort_by_len: train_sampler = data.SortedBatchSampler(train_dataset.lengths(), train_dataset.labels(), args.batch_size, shuffle=True) else: train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) dev_dataset = data.ReaderDataset(dev_exs, model, single_answer=False) if args.sort_by_len: dev_sampler = data.SortedBatchSampler(dev_dataset.lengths(), dev_dataset.labels(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) # ------------------------------------------------------------------------- # PRINT CONFIG logger.info('-' * 100) logger.info('CONFIG:\n%s' % json.dumps(vars(args), indent=4, sort_keys=True)) # -------------------------------------------------------------------------- # TRAIN/VALID LOOP logger.info('-' * 100) logger.info('Starting training...') stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0} for epoch in range(start_epoch, args.num_epochs): stats['epoch'] = epoch # Train train(args, train_loader, model, stats) # Validate unofficial (train) validate_unofficial(args, train_loader, model, stats, mode='train') # Validate unofficial (dev) result = validate_unofficial(args, dev_loader, model, stats, mode='dev') # Validate official #if args.official_eval: # result = validate_official(args, dev_loader, model, stats, # dev_offsets, dev_texts, dev_answers) # Save best valid if result['auc'] > stats['best_valid']: logger.info('Best valid: %s = %.2f (epoch %d, %d updates)' % ('auc_score', result['auc'], stats['epoch'], model.updates)) logger.info('save model %s' % args.model_file) model.save(args.model_file) stats['best_valid'] = result['auc'] model.save(os.path.join(os.path.dirname(args.model_file), str(epoch) + '.' + os.path.basename(args.model_file)))
def validate_official(args, data_loader, model, global_stats, offsets, texts, questions, answers): """Run one full official validation. Uses exact spans and same exact match/F1 score computation as in the SQuAD script. Extra arguments: offsets: The character start/end indices for the tokens in each context. texts: Map of qid --> raw text of examples context (matches offsets). answers: Map of qid --> list of accepted answers. """ eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() # Run through examples examples = 0 em_false = {} # cid -> (context, [(qid, question, answer)...]) predictions = {} # qid -> prediction for ex in data_loader: ex_id, batch_size = ex[-1], ex[0].size(0) pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][1][s_offset:e_offset] cid = texts[ex_id[i]][0] predictions[ex_id[i]] = prediction # Compute metrics ground_truths = answers[ex_id[i]] em_score = utils.metric_max_over_ground_truths( utils.exact_match_score, prediction, ground_truths) if em_score < 1: if cid not in em_false: em_false[cid] = { 'text': texts[ex_id[i]][1], 'qa': [{ 'qid': ex_id[i], 'question': questions[ex_id[i]], 'answers': answers[ex_id[i]], 'prediction': prediction }] } else: em_false[cid]['qa'].append({ 'qid': ex_id[i], 'question': questions[ex_id[i]], 'answers': answers[ex_id[i]], 'prediction': prediction }) exact_match.update(em_score) f1.update( utils.metric_max_over_ground_truths(utils.f1_score, prediction, ground_truths)) examples += batch_size logger.info('dev valid official: Epoch = %d | EM = %.2f | ' % (global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) return { 'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100 }, em_false, predictions
def main(args): # -------------------------------------------------------------------------- # DATA logger.info('-' * 100) logger.info('Load data files') train_exs = utils.load_data(args, args.train_file, skip_no_answer=True) logger.info('Num train examples = %d' % len(train_exs)) dev_exs = utils.load_data(args, args.dev_file, skip_no_answer=True) logger.info('Num dev examples = %d' % len(dev_exs)) # If we are doing offician evals then we need to: # 1) Load the original text to retrieve spans from offsets. # 2) Load the (multiple) text answers for each question. if args.official_eval: dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs} dev_answers = utils.load_answers(args.dev_json) train_texts = utils.load_text(args.train_json) train_offsets = {ex['id']: ex['offsets'] for ex in train_exs} train_answers = utils.load_answers(args.train_json) # -------------------------------------------------------------------------- # MODEL logger.info('-' * 100) start_epoch = 0 if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'): # Just resume training, no modifications. logger.info('Found a checkpoint...') checkpoint_file = args.model_file + '.checkpoint' model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args) else: # Training starts fresh. But the model state is either pretrained or # newly (randomly) initialized. if args.pretrained: logger.info('Using pretrained model...') model = DocReader.load(args.pretrained, args) if args.expand_dictionary: logger.info('Expanding dictionary for new data...') # Add words in training + dev examples words = utils.load_words(args, train_exs + dev_exs) added = model.expand_dictionary(words) # Load pretrained embeddings for added words if args.embedding_file: model.load_embeddings(added, args.embedding_file) else: logger.info('Training model from scratch...') model = init_from_scratch(args, train_exs, dev_exs) # Set up partial tuning of embeddings if args.tune_partial > 0: logger.info('-' * 100) logger.info('Counting %d most frequent question words' % args.tune_partial) top_words = utils.top_question_words(args, train_exs, model.word_dict) for word in top_words[:5]: logger.info(word) logger.info('...') for word in top_words[-6:-1]: logger.info(word) model.tune_embeddings([w[0] for w in top_words]) # Set up optimizer model.init_optimizer() # Use the GPU? if args.cuda: model.cuda() # Use multiple GPUs? if args.parallel: model.parallelize() # -------------------------------------------------------------------------- # DATA ITERATORS # Two datasets: train and dev. If we sort by length it's faster. logger.info('-' * 100) logger.info('Make data loaders') train_dataset = data.ReaderDataset(train_exs, model, single_answer=True) if args.sort_by_len: train_sampler = data.SortedBatchSampler(train_dataset.lengths(), args.batch_size, shuffle=True) else: train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) dev_dataset = data.ReaderDataset(dev_exs, model, single_answer=True) if args.sort_by_len: dev_sampler = data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) # ------------------------------------------------------------------------- # PRINT CONFIG logger.info('-' * 100) logger.info('CONFIG:\n%s' % json.dumps(vars(args), indent=4, sort_keys=True)) # -------------------------------------------------------------------------- # TRAIN/VALID LOOP logger.info('-' * 100) logger.info('Starting training...') stats = { 'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0, 'epoch_best': 0, 'F1_Dev_best': 0, 'EM_Dev_best': 0, 'S_Dev_best': 0, 'E_Dev_best': 0, 'Exact_Dev_best': 0, 'F1_Dev': 0, 'EM_Dev': 0, 'S_Dev': 0, 'E_Dev': 0, 'Exact_Dev': 0, "Loss_Dev": 0, 'F1_Train': 0, 'EM_Train': 0, 'S_Train': 0, 'E_Train': 0, 'Exact_Train': 0, "Loss_Train": 0 } loss_file = open(args.loss_file, 'w') header = [ 'epoch', 'F1_Dev', 'EM_Dev', 'S_Dev', 'E_Dev', 'Exact_Dev', "Loss_Dev", 'F1_Train', 'EM_Train', 'S_Train', 'E_Train', 'Exact_Train', "Loss_Train" ] loss_file.write(",".join(header) + "\n") for epoch in range(start_epoch, args.num_epochs): stats['epoch'] = epoch # Train train(args, train_loader, model, stats, dev_loader) # Validate unofficial (train) Train_result = validate_unofficial(args, train_loader, model, stats, mode='train') # Validate unofficial (dev) result = validate_unofficial(args, dev_loader, model, stats, mode='dev') # Validate official if args.official_eval: result = validate_official(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers, mode='dev') stats['F1_Dev'] = result["f1"] stats['EM_Dev'] = result["exact_match"] if args.test_official_train: result_train_official = validate_official(args, train_loader, model, stats, train_offsets, train_texts, train_answers, mode='train') stats['F1_Train'] = result_train_official["f1"] stats['EM_Train'] = result_train_official["exact_match"] # Save best valid toWrite = [] for key, value in stats.items(): if (key != 'timer' and key != 'best_valid' and key[-4:] != 'best'): toWrite.append(str(round(value, 3))) toWrite = ",".join(toWrite) + "\n" loss_file.write(toWrite) if result[args.valid_metric] > stats['best_valid']: logger.info('Best valid: %s = %.2f (epoch %d, %d updates)' % (args.valid_metric, result[args.valid_metric], stats['epoch'], model.updates)) model.save(args.model_file) stats['best_valid'] = result[args.valid_metric] stats['F1_Dev_best'] = result["f1"] stats['EM_Dev_best'] = result["exact_match"] stats['S_Dev_best'] = stats['S_Dev'] stats['E_Dev_best'] = stats['E_Dev'] stats['Exact_Dev_best'] = stats['Exact_Dev'] stats['epoch_best'] = stats['epoch'] loss_file.close() with open(args.best_loss_file, 'w+') as logFile: #head = ['epoch_best','F1_Dev_best','EM_Dev_best','S_Dev_best','E_Dev_best','Exact_Dev_best','F1_Train','EM_train','S_Train','E_Train','Exact_Train','Loss_Train'] toWrite = [] for key, value in stats.items(): if key[-4:] == 'best' or key[-5:] == "Train": toWrite.append(str(round(value, 3))) toWrite = ",".join(toWrite) logFile.write(toWrite)
def main(args): # -------------------------------------------------------------------------- # DATA logger.info('-' * 100) logger.info('Load data files') train_exs = utils.load_data(args, args.train_file, skip_no_answer=True) logger.info('Num train examples = %d' % len(train_exs)) dev_exs = utils.load_data(args, args.dev_file) logger.info('Num dev examples = %d' % len(dev_exs)) # If we are doing offician evals then we need to: # 1) Load the original text to retrieve spans from offsets. # 2) Load the (multiple) text answers for each question. if args.official_eval: dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs} dev_answers = utils.load_answers(args.dev_json) else: dev_texts = None dev_offsets = None dev_answers = None # -------------------------------------------------------------------------- # MODEL logger.info('-' * 100) start_epoch = 0 if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'): # Just resume training, no modifications. logger.info('Found a checkpoint...') checkpoint_file = args.model_file + '.checkpoint' model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args) else: # Training starts fresh. But the model state is either pretrained or # newly (randomly) initialized. if args.pretrained: logger.info('Using pretrained model...') model = DocReader.load(args.pretrained, args) if args.expand_dictionary: logger.info('Expanding dictionary for new data...') # Add words in training + dev examples words = utils.load_words(args, train_exs + dev_exs) added_words = model.expand_dictionary(words) # Load pretrained embeddings for added words if args.embedding_file: model.load_embeddings(added_words, args.embedding_file) logger.info('Expanding char dictionary for new data...') # Add words in training + dev examples chars = utils.load_chars(args, train_exs + dev_exs) added_chars = model.expand_char_dictionary(chars) # Load pretrained embeddings for added words if args.char_embedding_file: model.load_char_embeddings(added_chars, args.char_embedding_file) else: logger.info('Training model from scratch...') model = init_from_scratch(args, train_exs, dev_exs) # Set up partial tuning of embeddings if args.tune_partial > 0: logger.info('-' * 100) logger.info('Counting %d most frequent question words' % args.tune_partial) top_words = utils.top_question_words( args, train_exs, model.word_dict ) for word in top_words[:5]: logger.info(word) logger.info('...') for word in top_words[-6:-1]: logger.info(word) model.tune_embeddings([w[0] for w in top_words]) # Set up optimizer model.init_optimizer() # Use the GPU? if args.cuda: model.cuda() # Use multiple GPUs? if args.parallel: model.parallelize() if args.use_ema: ema = EMA(args.decay) model.ema = ema for name, param in model.network.named_parameters(): if param.requires_grad: ema.register(name, param.data) # -------------------------------------------------------------------------- # DATA ITERATORS # Two datasets: train and dev. If we sort by length it's faster. logger.info('-' * 100) logger.info('Make data loaders') train_dataset = data.ReaderDataset(train_exs, model, single_answer=True) if args.sort_by_len: train_sampler = data.SortedBatchSampler(train_dataset.lengths(), args.batch_size, shuffle=True) else: train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) dev_dataset = data.ReaderDataset(dev_exs, model, single_answer=False) if args.sort_by_len: dev_sampler = data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) # ------------------------------------------------------------------------- # PRINT CONFIG logger.info('-' * 100) logger.info('CONFIG:\n%s' % json.dumps(vars(args), indent=4, sort_keys=True)) # -------------------------------------------------------------------------- # TRAIN/VALID LOOP logger.info('-' * 100) logger.info('Starting training...') stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0} model_prefix = os.path.join(args.model_dir, args.model_name) kept_models = [] best_model_path = '' for epoch in range(start_epoch, args.num_epochs): stats['epoch'] = epoch # Train train(args, train_loader, model, stats) # Validate unofficial (train) logger.info('eval: train split unofficially...') validate_unofficial(args, train_loader, model, stats, mode='train') if args.official_eval: # Validate official (dev) logger.info('eval: dev split unofficially..') result = validate_official(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers) else: # Validate unofficial (dev) logger.info('train: evaluating dev split evaluating dev official...') result = validate_unofficial(args, dev_loader, model, stats, mode='dev') em = result['exact_match'] f1 = result['f1'] suffix = 'em_{:4.2f}-f1_{:4.2f}.mdl'.format(em, f1) # Save best valid model_file = '{}-epoch_{}-{}'.format(model_prefix, epoch, suffix) if args.valid_metric: if result[args.valid_metric] > stats['best_valid']: for f in glob.glob('{}-best*'.format(model_prefix)): os.remove(f) logger.info('eval: dev best %s = %.2f (epoch %d, %d updates)' % (args.valid_metric, result[args.valid_metric], stats['epoch'], model.updates)) model_file = '{}-best-epoch_{}-{}'.format(model_prefix, epoch, suffix) best_model_path = model_file model.save(model_file) stats['best_valid'] = result[args.valid_metric] # for f in kept_models: # os.remove(f) kept_models.clear() else: # model.save(model_file) kept_models.append(model_file) if len(kept_models) >= args.early_stop: logger.info('Finished training due to %s not improved for %d epochs, best model is at: %s' % (args.valid_metric, args.early_stop, best_model_path)) return else: # just save model every epoch since no validation metric is given model.save(model_file)
def main(args): # -------------------------------------------------------------------------- # DATA logger.info("-" * 100) logger.info("Load data files") train_exs = utils.load_data(args, args.train_file, skip_no_answer=True) logger.info("Num train examples = %d" % len(train_exs)) dev_exs = utils.load_data(args, args.dev_file) logger.info("Num dev examples = %d" % len(dev_exs)) # If we are doing offician evals then we need to: # 1) Load the original text to retrieve spans from offsets. # 2) Load the (multiple) text answers for each question. if args.official_eval: dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex["id"]: ex["offsets"] for ex in dev_exs} dev_answers = utils.load_answers(args.dev_json) # -------------------------------------------------------------------------- # MODEL logger.info("-" * 100) start_epoch = 0 if args.checkpoint and os.path.isfile(args.model_file + ".checkpoint"): # Just resume training, no modifications. logger.info("Found a checkpoint...") checkpoint_file = args.model_file + ".checkpoint" model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args) else: # Training starts fresh. But the model state is either pretrained or # newly (randomly) initialized. if args.pretrained: logger.info("Using pretrained model...") model = DocReader.load(args.pretrained, args) if args.expand_dictionary: logger.info("Expanding dictionary for new data...") # Add words in training + dev examples words = utils.load_words(args, train_exs + dev_exs) added = model.expand_dictionary(words) # Load pretrained embeddings for added words if args.embedding_file: model.load_embeddings(added, args.embedding_file) else: logger.info("Training model from scratch...") model = init_from_scratch(args, train_exs, dev_exs) # Set up partial tuning of embeddings if args.tune_partial > 0: logger.info("-" * 100) logger.info("Counting %d most frequent question words" % args.tune_partial) top_words = utils.top_question_words(args, train_exs, model.word_dict) for word in top_words[:5]: logger.info(word) logger.info("...") for word in top_words[-6:-1]: logger.info(word) model.tune_embeddings([w[0] for w in top_words]) # Set up optimizer model.init_optimizer() # Use the GPU? if args.cuda: model.cuda() # Use multiple GPUs? if args.parallel: model.parallelize() # -------------------------------------------------------------------------- # DATA ITERATORS # Two datasets: train and dev. If we sort by length it's faster. logger.info("-" * 100) logger.info("Make data loaders") train_dataset = data.ReaderDataset(train_exs, model, single_answer=True) if args.sort_by_len: train_sampler = data.SortedBatchSampler(train_dataset.lengths(), args.batch_size, shuffle=True) else: train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) dev_dataset = data.ReaderDataset(dev_exs, model, single_answer=False) if args.sort_by_len: dev_sampler = data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=vector.batchify, pin_memory=args.cuda, ) # ------------------------------------------------------------------------- # PRINT CONFIG logger.info("-" * 100) logger.info("CONFIG:\n%s" % json.dumps(vars(args), indent=4, sort_keys=True)) # -------------------------------------------------------------------------- # TRAIN/VALID LOOP logger.info("-" * 100) logger.info("Starting training...") stats = {"timer": utils.Timer(), "epoch": 0, "best_valid": 0} for epoch in range(start_epoch, args.num_epochs): stats["epoch"] = epoch # Train train(args, train_loader, model, stats) # Validate unofficial (train) # validate_unofficial(args, train_loader, model, stats, mode='train') # Validate official if args.official_eval: result = validate_official(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers) else: # Validate unofficial (dev) result = validate_unofficial(args, dev_loader, model, stats, mode="dev") # Save best valid if result[args.valid_metric] > stats["best_valid"]: logger.info("Best valid: %s = %.2f (epoch %d, %d updates)" % ( args.valid_metric, result[args.valid_metric], stats["epoch"], model.updates, )) model.save(args.model_file) stats["best_valid"] = result[args.valid_metric]
def main(args): # -------------------------------------------------------------------------- # DATA logger.info('-' * 100) logger.info('Load data files') train_exs = [] for t_file in args.train_file: train_exs += utils.load_data(args, t_file, skip_no_answer=True) np.random.shuffle(train_exs) logger.info('Num train examples = %d' % len(train_exs)) dev_exs = utils.load_data(args, args.dev_file) logger.info('Num dev examples = %d' % len(dev_exs)) # If we are doing offician evals then we need to: # 1) Load the original text to retrieve spans from offsets. # 2) Load the (multiple) text answers for each question. if args.official_eval: dev_texts = utils.load_text(args.dev_json) dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs} dev_answers = utils.load_answers(args.dev_json) ## OFFSET comes from the gold sentence; the predicted sentence value shoule be maintained and sent to official validation set # -------------------------------------------------------------------------- # MODEL logger.info('-' * 100) start_epoch = 0 if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'): # Just resume training, no modifications. logger.info('Found a checkpoint...') checkpoint_file = args.model_file + '.checkpoint' model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args) else: # Training starts fresh. But the model state is either pretrained or # newly (randomly) initialized. if args.pretrained: logger.info('Using pretrained model...') model = DocReader.load(args.pretrained, args) if args.expand_dictionary: logger.info('Expanding dictionary for new data...') # Add words in training + dev examples words = utils.load_words(args, train_exs + dev_exs) added = model.expand_dictionary(words) # Load pretrained embeddings for added words if args.embedding_file: model.load_embeddings(added, args.embedding_file) else: logger.info('Training model from scratch...') model = init_from_scratch(args, train_exs, dev_exs) # Set up partial tuning of embeddings if args.tune_partial > 0: logger.info('-' * 100) logger.info('Counting %d most frequent question words' % args.tune_partial) top_words = utils.top_question_words(args, train_exs, model.word_dict) for word in top_words[:5]: logger.info(word) logger.info('...') for word in top_words[-6:-1]: logger.info(word) model.tune_embeddings([w[0] for w in top_words]) # Set up optimizer model.init_optimizer() # Use the GPU? if args.cuda: model.cuda() # Use multiple GPUs? if args.parallel: model.parallelize() # -------------------------------------------------------------------------- # DATA ITERATORS # Two datasets: train and dev. If we sort by length it's faster. # Sentence selection objective : run the sentence selector as a submodule logger.info('-' * 100) logger.info('Make data loaders') train_dataset = reader_data.ReaderDataset(train_exs, model, single_answer=True) # Filter out None examples in training dataset (where sentence selection fails) #train_dataset.examples = [t for t in train_dataset.examples if t is not None] if args.sort_by_len: train_sampler = reader_data.SortedBatchSampler(train_dataset.lengths(), args.batch_size, shuffle=True) else: train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset) if args.use_sentence_selector: train_batcher = reader_vector.sentence_batchifier(model, single_answer=True) # batching_function = train_batcher.batchify batching_function = reader_vector.batchify else: batching_function = reader_vector.batchify train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=args.data_workers, collate_fn=batching_function, pin_memory=args.cuda, ) dev_dataset = reader_data.ReaderDataset(dev_exs, model, single_answer=False) #dev_dataset.examples = [t for t in dev_dataset.examples if t is not None] if args.sort_by_len: dev_sampler = reader_data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset) if args.use_sentence_selector: dev_batcher = reader_vector.sentence_batchifier(model, single_answer=False) # batching_function = dev_batcher.batchify batching_function = reader_vector.batchify else: batching_function = reader_vector.batchify dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=batching_function, pin_memory=args.cuda, ) ## Dev dataset for measuring performance of the trained sentence selector if args.use_sentence_selector: dev_dataset1 = selector_data.SentenceSelectorDataset( dev_exs, model.sentence_selector, single_answer=False) #dev_dataset1.examples = [t for t in dev_dataset.examples if t is not None] if args.sort_by_len: dev_sampler1 = selector_data.SortedBatchSampler( dev_dataset1.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler1 = torch.utils.data.sampler.SequentialSampler( dev_dataset1) dev_loader1 = torch.utils.data.DataLoader( dev_dataset1, #batch_size=args.test_batch_size, #sampler=dev_sampler1, batch_sampler=dev_sampler1, num_workers=args.data_workers, collate_fn=selector_vector.batchify, pin_memory=args.cuda, ) # ------------------------------------------------------------------------- # PRINT CONFIG logger.info('-' * 100) logger.info('CONFIG:\n%s' % json.dumps(vars(args), indent=4, sort_keys=True)) # -------------------------------------------------------------------------- # TRAIN/VALID LOOP logger.info('-' * 100) logger.info('Starting training...') stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0} # -------------------------------------------------------------------------- # QUICKLY VALIDATE ON PRETRAINED MODEL if args.global_mode == "test": result1 = validate_unofficial(args, dev_loader, model, stats, mode='dev') result2 = validate_official(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers) print(result2[args.valid_metric]) print(result1["exact_match"]) if args.use_sentence_selector: sent_stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0} #sent_selector_results = validate_selector(model.sentence_selector.args, dev_loader1, model.sentence_selector, sent_stats, mode="dev") #print("Sentence Selector model acheives:") #print(sent_selector_results["accuracy"]) if len(args.adv_dev_json) > 0: validate_adversarial(args, model, stats, mode="dev") exit(0) valid_history = [] bad_counter = 0 for epoch in range(start_epoch, args.num_epochs): stats['epoch'] = epoch # Train train(args, train_loader, model, stats) # Validate unofficial (train) validate_unofficial(args, train_loader, model, stats, mode='train') # Validate unofficial (dev) result = validate_unofficial(args, dev_loader, model, stats, mode='dev') # Validate official if args.official_eval: result = validate_official(args, dev_loader, model, stats, dev_offsets, dev_texts, dev_answers) # Save best valid if result[args.valid_metric] >= stats['best_valid']: logger.info('Best valid: %s = %.2f (epoch %d, %d updates)' % (args.valid_metric, result[args.valid_metric], stats['epoch'], model.updates)) model.save(args.model_file) stats['best_valid'] = result[args.valid_metric] bad_counter = 0 else: bad_counter += 1 if bad_counter > args.patience: logger.info("Early Stopping at epoch: %d" % epoch) exit(0)
def validate_adversarial(args, model, global_stats, mode="dev"): # create dataloader for dev sets, load thier jsons, integrate the function for idx, dataset_file in enumerate(args.adv_dev_json): predictions = {} logger.info("Validating Adversarial Dataset %s" % dataset_file) exs = utils.load_data(args, args.adv_dev_file[idx]) logger.info('Num dev examples = %d' % len(exs)) ## Create dataloader dev_dataset = reader_data.ReaderDataset(exs, model, single_answer=False) if args.sort_by_len: dev_sampler = reader_data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler( dev_dataset) if args.use_sentence_selector: dev_batcher = reader_vector.sentence_batchifier( model, single_answer=False) #batching_function = dev_batcher.batchify batching_function = reader_vector.batchify else: batching_function = reader_vector.batchify dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=batching_function, pin_memory=args.cuda, ) texts = utils.load_text(dataset_file) offsets = {ex['id']: ex['offsets'] for ex in exs} answers = utils.load_answers(dataset_file) eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() examples = 0 bad_examples = 0 for ex in dev_loader: ex_id, batch_size = ex[-1], ex[0].size(0) chosen_offset = ex[-2] pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): if pred_s[i][0] >= len( offsets[ex_id[i]]) or pred_e[i][0] >= len( offsets[ex_id[i]]): bad_examples += 1 continue if args.use_sentence_selector: s_offset = chosen_offset[i][pred_s[i][0]][0] e_offset = chosen_offset[i][pred_e[i][0]][1] else: s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][s_offset:e_offset] if args.select_k > 1: prediction = "" offset_subset = chosen_offset[i][pred_s[i][0]:pred_e[i][0]] for enum_, o in enumerate(offset_subset): prediction += texts[ex_id[i]][o[0]:o[1]] + " " prediction = prediction.strip() predictions[ex_id[i]] = prediction ground_truths = answers[ex_id[i]] exact_match.update( utils.metric_max_over_ground_truths( utils.exact_match_score, prediction, ground_truths)) f1.update( utils.metric_max_over_ground_truths( utils.f1_score, prediction, ground_truths)) examples += batch_size logger.info( 'dev valid official for dev file %s : Epoch = %d | EM = %.2f | ' % (dataset_file, global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) orig_f1_score = 0.0 orig_exact_match_score = 0.0 adv_f1_scores = {} # Map from original ID to F1 score adv_exact_match_scores = { } # Map from original ID to exact match score adv_ids = {} all_ids = set() # Set of all original IDs f1 = exact_match = 0 dataset = json.load(open(dataset_file))['data'] for article in dataset: for paragraph in article['paragraphs']: for qa in paragraph['qas']: orig_id = qa['id'].split('-')[0] all_ids.add(orig_id) if qa['id'] not in predictions: message = 'Unanswered question ' + qa[ 'id'] + ' will receive score 0.' # logger.info(message) continue ground_truths = list( map(lambda x: x['text'], qa['answers'])) prediction = predictions[qa['id']] cur_exact_match = utils.metric_max_over_ground_truths( utils.exact_match_score, prediction, ground_truths) cur_f1 = utils.metric_max_over_ground_truths( utils.f1_score, prediction, ground_truths) if orig_id == qa['id']: # This is an original example orig_f1_score += cur_f1 orig_exact_match_score += cur_exact_match if orig_id not in adv_f1_scores: # Haven't seen adversarial example yet, so use original for adversary adv_ids[orig_id] = orig_id adv_f1_scores[orig_id] = cur_f1 adv_exact_match_scores[orig_id] = cur_exact_match else: # This is an adversarial example if (orig_id not in adv_f1_scores or adv_ids[orig_id] == orig_id or adv_f1_scores[orig_id] > cur_f1): # Always override if currently adversary currently using orig_id adv_ids[orig_id] = qa['id'] adv_f1_scores[orig_id] = cur_f1 adv_exact_match_scores[orig_id] = cur_exact_match orig_f1 = 100.0 * orig_f1_score / len(all_ids) orig_exact_match = 100.0 * orig_exact_match_score / len(all_ids) adv_exact_match = 100.0 * sum( adv_exact_match_scores.values()) / len(all_ids) adv_f1 = 100.0 * sum(adv_f1_scores.values()) / len(all_ids) logger.info( "For the file %s Original Exact Match : %.4f ; Original F1 : : %.4f | " % (dataset_file, orig_exact_match, orig_f1) + "Adversarial Exact Match : %.4f ; Adversarial F1 : : %.4f " % (adv_exact_match, adv_f1))