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import torch
import torch.nn.functional as F
import logging
import os
import torch.nn as nn
import numpy as np
import copy
import json
from sklearn import svm
import sklearn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, roc_curve, auc
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model
from utils.metrics import F_measure
from torch.utils.data import DataLoader
from .pretrain import PretrainManager
from transformers import AutoTokenizer
class MDFManager:
def __init__(self, args, data, model, logger_name = 'Detection'):
self.logger = logging.getLogger(logger_name)
self.set_model_optimizer(args, data, model)
pretrain_manager = PretrainManager(args, data, model)
self.pretrained_model = pretrain_manager.model
self.load_pretrained_model(self.pretrained_model.bert)
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
def set_model_optimizer(self, args, data, model):
args.backbone = 'bert_mdf'
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 get_hidden_features(self, input_ids=None, attention_mask=None, token_type_ids=None, labels=None,
position_ids=None, head_mask=None, use_cls=False):
outputs = self.model(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask
)
all_hidden_feats = outputs[1] # list (13) of bs x length x hidden
all_feature_list = []
for i in range(len(all_hidden_feats)):
if use_cls:
pooled_feats = self.model.bert.pooler(all_hidden_feats[i]).detach() # bs x max_len x 768 -> bs x 768
# pooled_feats = all_hidden_feats[i][:,0].detach().data.cpu() # bs x max_len x 768 -> bs x 768
# print (pooled_feats.shape)
else:
pooled_feats = torch.mean(all_hidden_feats[i], dim=1, keepdim=False).detach() # bs x max_len x 768 -> bs x 768
all_feature_list.append(pooled_feats.data) # 13 list of bs x 768
return all_feature_list
def sample_X_estimator(self, use_cls=False):
device = self.device
model = self.model
import sklearn.covariance
group_lasso = sklearn.covariance.EmpiricalCovariance(assume_centered=False)
model.eval()
all_layer_features = []
num_layers = 13
for i in range(num_layers):
all_layer_features.append([])
for batch in tqdm(self.train_dataloader, desc="Iteration"):
inputs = tuple(t.to(self.device) for t in batch)
with torch.no_grad():
batch_all_features = self.get_hidden_features(*inputs, use_cls=use_cls)
for i in range(num_layers):
all_layer_features[i].append(batch_all_features[i].cpu()) # save gpu memory
mean_list = []
precision_list = []
for i in range(num_layers):
all_layer_features[i] = torch.cat(all_layer_features[i], axis=0)
sample_mean = torch.mean(all_layer_features[i], axis=0)
X = all_layer_features[i] - sample_mean
group_lasso.fit(X.numpy())
temp_precision = group_lasso.precision_
temp_precision = torch.from_numpy(temp_precision).float()
mean_list.append(sample_mean.to(device))
precision_list.append(temp_precision.to(device))
return mean_list, precision_list
def get_unsup_Mah_score(self, mode, sample_mean, precision, use_cls=False):
device = self.device
model = self.model
model.eval()
num_layers = 13
total_mah_scores = []
for i in range(num_layers):
total_mah_scores.append([])
if mode == 'train_labeled':
dataloader = self.train_dataloader
elif mode == 'test':
dataloader = self.test_dataloader
else:
print('get_unsup_Mah_score error: unexpected mode')
for batch in tqdm(dataloader, desc="Iteration"):
inputs = tuple(t.to(device) for t in batch)
with torch.no_grad():
batch_all_features = self.get_hidden_features(*inputs, use_cls=use_cls)
for i in range(len(batch_all_features)):
batch_sample_mean = sample_mean[i]
out_features = batch_all_features[i]
zero_f = out_features - batch_sample_mean
gaussian_score = -0.5 * ((zero_f @ precision[i]) @ zero_f.t()).diag()
total_mah_scores[i].extend(gaussian_score.cpu().numpy())
for i in range(len(total_mah_scores)):
total_mah_scores[i] = np.expand_dims(np.array(total_mah_scores[i]), axis=1)
return np.concatenate(total_mah_scores, axis=1)
def train(self, args, data):
pass
def test(self, args, data, show=True):
mean_list, precision_list = self.sample_X_estimator(args.use_cls)
train_mah_vanlia = self.get_unsup_Mah_score('train_labeled', mean_list, precision_list, args.use_cls)[:, 1:]
train_mah_scores = train_mah_vanlia
c_lr = svm.OneClassSVM(nu=args.nuu, kernel=args.k)
c_lr.fit(train_mah_scores)
y_true, y_pred_ind = self.get_outputs(args, mode = 'test', model = self.pretrained_model, get_feats = False)
test_total_mah_vanlia = self.get_unsup_Mah_score('test', mean_list, precision_list, args.use_cls)[:, 1:]
y_pred_ood = c_lr.predict(test_total_mah_vanlia)
y_pred = [args.unseen_label_id if y == -1 else y_pred_ind[i] for i, y in enumerate(y_pred_ood)]
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
return test_results
def get_outputs(self, args, mode, model, get_feats = False):
if mode == 'eval':
dataloader = self.eval_dataloader
elif mode == 'test':
dataloader = self.test_dataloader
elif mode == 'train':
dataloader = self.train_dataloader
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 = 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
def load_pretrained_model(self, pretrained_model):
pretrained_dict = pretrained_model.state_dict()
self.model.bert.load_state_dict(pretrained_dict, strict=False)
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