| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| import os | |
| import copy | |
| import logging | |
| from torch import nn | |
| from sklearn.metrics import confusion_matrix, f1_score, accuracy_score | |
| from tqdm import trange, tqdm | |
| from losses import loss_map | |
| from utils.functions import save_model, restore_model | |
| class PretrainManager: | |
| def __init__(self, args, data, model, logger_name = 'Detection'): | |
| self.logger = logging.getLogger(logger_name) | |
| self.set_model_optimizer(args, data, model) | |
| self.train_dataloader = data.dataloader.train_labeled_loader | |
| self.eval_dataloader = data.dataloader.eval_loader | |
| self.test_dataloader = data.dataloader.test_loader | |
| self.loss_fct = loss_map['CrossEntropyLoss'] | |
| self.best_eval_score = None | |
| if args.pretrain or (not os.path.exists(args.model_output_dir)): | |
| self.logger.info('Pre-training Begin...') | |
| self.train_plain(args, data) | |
| self.logger.info('Pre-training finished...') | |
| else: | |
| self.model = restore_model(self.model, args.model_output_dir) | |
| def set_model_optimizer(self, args, data, model): | |
| self.model = model.set_model(args, 'bert') | |
| self.optimizer, self.scheduler = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \ | |
| args.num_train_epochs, args.lr, args.warmup_proportion) | |
| self.device = model.device | |
| def train_plain(self, args, data): | |
| wait = 0 | |
| best_model = None | |
| best_eval_score = 0 | |
| for epoch in trange(int(args.num_train_epochs), desc="Epoch"): | |
| self.model.train() | |
| tr_loss = 0 | |
| nb_tr_examples, nb_tr_steps = 0, 0 | |
| for step, batch in enumerate(tqdm(self.train_dataloader, desc="Iteration")): | |
| batch = tuple(t.to(self.device) for t in batch) | |
| input_ids, input_mask, segment_ids, label_ids = batch | |
| with torch.set_grad_enabled(True): | |
| loss = self.model(input_ids, segment_ids, input_mask, label_ids, mode = "train", loss_fct = self.loss_fct) | |
| self.optimizer.zero_grad() | |
| loss.backward() | |
| self.optimizer.step() | |
| self.scheduler.step() | |
| tr_loss += loss.item() | |
| nb_tr_examples += input_ids.size(0) | |
| nb_tr_steps += 1 | |
| loss = tr_loss / nb_tr_steps | |
| y_true, y_pred = self.get_outputs(args, data, mode = 'eval') | |
| eval_score = round(accuracy_score(y_true, y_pred) * 100, 2) | |
| eval_results = { | |
| 'train_loss': loss, | |
| 'eval_score': eval_score, | |
| 'best_eval_score':best_eval_score, | |
| } | |
| self.logger.info("***** Epoch: %s: Eval results *****", str(epoch + 1)) | |
| for key in sorted(eval_results.keys()): | |
| self.logger.info(" %s = %s", key, str(eval_results[key])) | |
| if eval_score > best_eval_score: | |
| best_model = copy.deepcopy(self.model) | |
| wait = 0 | |
| best_eval_score = eval_score | |
| elif eval_score > 0: | |
| wait += 1 | |
| if wait >= args.wait_patient: | |
| break | |
| self.model = best_model | |
| if args.save_model: | |
| self.logger.info('Trained models are saved in %s', args.model_output_dir) | |
| save_model(self.model, args.model_output_dir) | |
| def get_outputs(self, args, data, mode = 'eval', get_feats = False): | |
| if mode == 'eval': | |
| dataloader = self.eval_dataloader | |
| elif mode == 'test': | |
| dataloader = self.test_dataloader | |
| self.model.eval() | |
| total_labels = torch.empty(0,dtype=torch.long).to(self.device) | |
| total_preds = torch.empty(0,dtype=torch.long).to(self.device) | |
| total_features = torch.empty((0,args.feat_dim)).to(self.device) | |
| total_logits = torch.empty((0, data.num_labels)).to(self.device) | |
| for batch in tqdm(dataloader, desc="Iteration"): | |
| batch = tuple(t.to(self.device) for t in batch) | |
| input_ids, input_mask, segment_ids, label_ids = batch | |
| with torch.set_grad_enabled(False): | |
| pooled_output, logits = self.model(input_ids, segment_ids, input_mask, mode = mode) | |
| total_labels = torch.cat((total_labels,label_ids)) | |
| total_features = torch.cat((total_features, pooled_output)) | |
| total_logits = torch.cat((total_logits, logits)) | |
| if get_feats: | |
| feats = total_features.cpu().numpy() | |
| return feats | |
| else: | |
| total_probs = F.softmax(total_logits.detach(), dim=1) | |
| total_maxprobs, total_preds = total_probs.max(dim = 1) | |
| y_pred = total_preds.cpu().numpy() | |
| y_true = total_labels.cpu().numpy() | |
| return y_true, y_pred |