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import torch
import torch.nn.functional as F
import numpy as np
import os
import logging
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from .boundary import BoundaryLoss
from losses import loss_map
from utils.functions import save_model, euclidean_metric
from utils.metrics import F_measure
from utils.functions import restore_model, centroids_cal
from .pretrain import PretrainManager
class ADBManager:
def __init__(self, args, data, model, logger_name = 'Detection'):
self.logger = logging.getLogger(logger_name)
pretrain_model = PretrainManager(args, data, model)
self.model = pretrain_model.model
self.centroids = pretrain_model.centroids
self.pretrain_best_eval_score = pretrain_model.best_eval_score
self.device = model.device
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.train:
self.delta = None
self.delta_points = []
else:
self.model = restore_model(self.model, args.model_output_dir)
self.delta = np.load(os.path.join(args.method_output_dir, 'deltas.npy'))
self.delta = torch.from_numpy(self.delta).to(self.device)
self.centroids = np.load(os.path.join(args.method_output_dir, 'centroids.npy'))
self.centroids = torch.from_numpy(self.centroids).to(self.device)
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(self, args, data):
criterion_boundary = BoundaryLoss(num_labels = data.num_labels, feat_dim = args.feat_dim, device = self.device)
self.delta = F.softplus(criterion_boundary.delta)
self.delta_points.append(self.delta)
optimizer = torch.optim.Adam(criterion_boundary.parameters(), lr = args.lr_boundary)
if self.centroids is None:
self.centroids = centroids_cal(self.model, args, data, self.train_dataloader, self.device)
best_eval_score, best_delta, best_centroids = 0, None, None
wait = 0
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
self.model.train()
# self.model.eval()
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):
features = self.model(input_ids, segment_ids, input_mask, feature_ext=True)
loss, self.delta = criterion_boundary(features, self.centroids, label_ids)
loss.backward()
optimizer.step()
optimizer.zero_grad()
tr_loss += loss.item()
nb_tr_examples += features.shape[0]
nb_tr_steps += 1
print(self.delta)
self.delta_points.append(self.delta)
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:
wait = 0
best_delta = self.delta
best_eval_score = eval_score
else:
if best_eval_score > 0:
wait += 1
if wait >= args.wait_patient:
break
if best_eval_score > 0:
self.delta = best_delta
self.best_eval_score = best_eval_score
if args.save_model:
np.save(os.path.join(args.method_output_dir, 'centroids.npy'), self.centroids.detach().cpu().numpy())
np.save(os.path.join(args.method_output_dir, 'deltas.npy'), self.delta.detach().cpu().numpy())
np.save(os.path.join(args.method_output_dir, 'all_deltas.npy'), self.delta_points)
def get_outputs(self, args, data, mode = 'eval', get_feats = False, pre_train= False, delta = None):
if mode == 'eval':
dataloader = self.eval_dataloader
elif mode == 'test':
dataloader = self.test_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, 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 = self.model(input_ids, segment_ids, input_mask, feature_ext=True)
preds = self.open_classify(data, pooled_output)
total_preds = torch.cat((total_preds, preds))
total_labels = torch.cat((total_labels, label_ids))
total_features = torch.cat((total_features, pooled_output))
if get_feats:
feats = total_features.cpu().numpy()
return total_features, total_labels
else:
y_pred = total_preds.cpu().numpy()
y_true = total_labels.cpu().numpy()
return y_true, y_pred
def open_classify(self, data, features):
logits = euclidean_metric(features, self.centroids)
probs, preds = F.softmax(logits.detach(), dim = 1).max(dim = 1)
euc_dis = torch.norm(features - self.centroids[preds], 2, 1).view(-1)
preds[euc_dis >= self.delta[preds]] = data.unseen_label_id
return preds
def test(self, args, data, show=True):
y_true, y_pred = self.get_outputs(args, data, mode = 'test')
cm = confusion_matrix(y_true, y_pred)
test_results = F_measure(cm)
acc = round(accuracy_score(y_true, y_pred) * 100, 2)
test_results['Acc'] = acc
if show:
self.logger.info("***** Test: Confusion Matrix *****")
self.logger.info("%s", str(cm))
self.logger.info("***** Test results *****")
for key in sorted(test_results.keys()):
self.logger.info(" %s = %s", key, str(test_results[key]))
test_results['y_true'] = y_true
test_results['y_pred'] = y_pred
if args.method == 'DA-ADB:':
test_results['scale'] = args.scale
return test_results
def load_pretrained_model(self, pretrained_model):
pretrained_dict = pretrained_model.state_dict()
self.model.load_state_dict(pretrained_dict, strict=False)
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