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import torch
import torch.nn.functional as F
import os
import copy
import logging
import torch.nn as nn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from torch.utils.data import RandomSampler, DataLoader
from utils.functions import save_model, mask_tokens, restore_model
from transformers import AutoTokenizer
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.best_eval_score = None
if args.pretrain or (not os.path.exists(args.model_output_dir)):
self.logger.info('Pre-raining start...')
self.train(args, data)
self.logger.info('Pre-training finished...')
else:
pretrained_model_dir = os.path.join(args.method_output_dir, 'pretrain')
self.model = restore_model(self.model, pretrained_model_dir)
def set_model_optimizer(self, args, data, model):
args.backbone = 'bert_mdf_pretrain'
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(self, args, data):
self.model.train()
wait = 0
best_model = None
best_eval_score = 0
tokenizer = AutoTokenizer.from_pretrained(args.pretrained_bert_model)
for epoch in trange(int(args.num_pretrain_epochs), desc="Epoch"):
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
X = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
mask_ids, mask_lb = mask_tokens(input_ids.cpu(), tokenizer)
mask_input_ids = mask_ids.to(self.device)
X_mlm = {"input_ids": mask_input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
with torch.set_grad_enabled(True):
labeled_logits = self.model(X)["logits"]
loss_src = self.model.loss_ce(labeled_logits, label_ids)
loss_mlm = self.model.mlmForward(X_mlm, mask_lb.to(self.device))
lossTOT = loss_src + loss_mlm
lossTOT.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
tr_loss += lossTOT.item()
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
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, mode = 'eval')
eval_score = round(accuracy_score(y_true, y_pred) * 100, 2)
eval_results = {
'train_loss': loss,
'eval_score': eval_score,
'best_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:
pretrained_model_dir = os.path.join(args.method_output_dir, 'pretrain')
if not os.path.exists(pretrained_model_dir):
os.makedirs(pretrained_model_dir)
save_model(self.model, pretrained_model_dir)
def get_outputs(self, args, 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, args.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
X = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
with torch.set_grad_enabled(False):
outputs = self.model(X)
pooled_output = outputs["hidden_states"]
logits = outputs["logits"]
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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