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import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
from sklearn.metrics import accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model
from sklearn.cluster import KMeans
class PretrainDeepAlignedManager:
def __init__(self, args, data, model, logger_name = 'Discovery'):
self.logger = logging.getLogger(logger_name)
args.num_labels = data.n_known_cls
self.set_model_optimizer(args, data, model)
loader = data.dataloader
self.train_labeled_dataloader = loader.train_labeled_outputs['loader']
self.train_dataloader = loader.train_outputs['loader']
self.eval_dataloader = loader.eval_outputs['loader']
self.test_dataloader = loader.test_outputs['loader']
self.loss_fct = loss_map[args.loss_fct]
if args.pretrain:
self.logger.info('Pre-raining start...')
self.train(args, data)
self.logger.info('Pre-training finished...')
else:
self.model = restore_model(self.model, os.path.join(args.method_output_dir, 'pretrain'))
if args.cluster_num_factor > 1:
self.num_labels = data.num_labels
self.num_labels = self.predict_k(args, data)
self.model.to(torch.device('cpu'))
torch.cuda.empty_cache()
def set_model_optimizer(self, args, data, model):
self.model = model.set_model(args, data, 'bert')
self.optimizer , self.scheduler = model.set_optimizer(self.model, len(data.dataloader.train_labeled_examples), args.train_batch_size, \
args.num_pretrain_epochs, args.lr_pre, args.warmup_proportion)
self.device = model.device
def train(self, args, data):
wait = 0
best_model = None
best_eval_score = 0
for epoch in trange(int(args.num_pretrain_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_labeled_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()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
self.optimizer.step()
self.scheduler.step()
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 == 'train':
dataloader = self.train_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
with torch.set_grad_enabled(False):
pooled_output, logits = self.model(input_ids, segment_ids, input_mask)
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
def predict_k(self, args, data):
self.logger.info('Predict number of clusters start...')
self.num_labels = data.num_labels
feats = self.get_outputs(args, mode = 'train', get_feats = True)
km = KMeans(n_clusters = self.num_labels, random_state =args.seed).fit(feats)
y_pred = km.labels_
pred_label_list = np.unique(y_pred)
drop_out = len(feats) / data.num_labels
cnt = 0
for label in pred_label_list:
num = len(y_pred[y_pred == label])
if num < drop_out:
cnt += 1
K = len(pred_label_list) - cnt
self.logger.info('Predict number of clusters finish...')
outputs = {'K': K, 'mean_cluster_size': drop_out}
for key in outputs.keys():
self.logger.info(" %s = %s", key, str(outputs[key]))
return K |