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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.metrics import F_measure
from utils.functions import restore_model
from .pretrain import PretrainManager
from losses.ARPLoss import ARPLoss
class ARPLManager:
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.pretrain_best_eval_score = pretrain_model.best_eval_score
self.device = pretrain_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 not args.train:
self.model = restore_model(self.model, args.model_output_dir)
def train(self, args, data):
self.arpl_criterion = ARPLoss(args)
self.arpl_criterion.to(self.device)
best_eval_score = 0
wait = 0
params_list = [{'params': self.arpl_criterion.parameters()}]
optimizer = torch.optim.Adam(params_list, lr=args.lr_2)
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):
features = self.model(input_ids, segment_ids, input_mask, feature_ext=True)
logits, loss = self.arpl_criterion(features, labels=label_ids)
loss.backward()
optimizer.step()
optimizer.zero_grad()
tr_loss += loss.item()
nb_tr_examples += features.shape[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:
wait = 0
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.best_eval_score = best_eval_score
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_logits = torch.empty((0, data.num_labels)).to(self.device)
total_features = torch.empty((0,args.feat_dim)).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)
logits, loss = self.arpl_criterion(pooled_output)
total_labels = torch.cat((total_labels,label_ids))
total_logits = torch.cat((total_logits, logits))
total_features = torch.cat((total_features, pooled_output))
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)
total_maxprobs_, total_preds_ = total_logits.max(dim=1)
y_prob = total_maxprobs.cpu().numpy()
y_true = total_labels.cpu().numpy()
y_pred = total_preds.cpu().numpy()
if mode == 'test':
in_logits = []
out_logits = []
for ind, logit in enumerate(total_logits.detach().cpu().numpy()):
if y_true[ind] == data.unseen_label_id:
in_logits.append(logit)
else:
out_logits.append(logit)
y_pred[y_prob < args.threshold] = data.unseen_label_id
np.save(os.path.join(args.method_output_dir, 'y_prob.npy'), y_prob)
return y_true, y_pred, in_logits, out_logits
return y_true, y_pred
def test(self, args, data, show=True):
y_true, y_pred, in_logits, out_logits = self.get_outputs(args, data, mode = 'test')
x1, x2 = np.max(in_logits, axis=1), np.max(out_logits, axis=1)
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
test_results['lr_2'] = args.lr_2
test_results['temp'] = args.temp
self.logger.info
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
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