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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, centroids_cal
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[args.loss_fct]
self.centroids = None
self.best_eval_score = None
if args.pretrain or (not os.path.exists(args.model_output_dir)):
self.logger.info('Pre-training Begin...')
if args.backbone == 'bert_disaware':
self.train_disaware(args, data)
else:
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 train_disaware(self, args, data):
wait = 0
best_model = None
best_centroids = None
best_eval_score = 0
args.device = self.device
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
self.centroids = centroids_cal(self.model, args, data, self.train_dataloader, self.device)
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, centroids = self.centroids)
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(f1_score(y_true, y_pred, average = 'macro') * 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)
best_centroids = copy.copy(self.centroids)
wait = 0
best_eval_score = eval_score
elif eval_score > 0:
wait += 1
if wait >= args.wait_patient:
break
self.model = best_model
self.centroids = best_centroids
self.best_eval_score = best_eval_score
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):
if args.backbone == 'bert_disaware':
pooled_output, logits = self.model(input_ids, segment_ids, input_mask, centroids = self.centroids, labels = label_ids, mode = mode)
else:
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
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