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import torch |
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import torch.nn.functional as F |
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import numpy as np |
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import copy |
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import logging |
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from sklearn.metrics import confusion_matrix |
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from sklearn.cluster import KMeans |
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from tqdm import trange, tqdm |
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from utils.functions import set_seed |
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from utils.metrics import clustering_score |
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from utils.functions import restore_model, save_model |
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def target_distribution(q): |
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weight = q ** 2 / q.sum(0) |
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return (weight.T / weight.sum(1)).T |
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class CDACPlusManager: |
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def __init__(self, args, data, model, logger_name = 'Discovery'): |
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self.logger = logging.getLogger(logger_name) |
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set_seed(args.seed) |
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loader = data.dataloader |
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self.train_dataloader, self.eval_dataloader, self.test_dataloader = \ |
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loader.train_outputs['loader'], loader.eval_outputs['loader'], loader.test_outputs['loader'] |
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self.train_labeled_dataloader = loader.train_labeled_outputs['loader'] |
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self.train_unlabeled_dataloader = loader.train_unlabeled_outputs['loader'] |
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self.model = model.set_model(args, data, 'bert') |
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self.optimizer1 , self.scheduler1 = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \ |
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args.num_train_epochs, args.lr, args.warmup_proportion) |
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self.optimizer2 , self.scheduler2 = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \ |
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args.num_refine_epochs, args.lr, args.warmup_proportion) |
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self.device = model.device |
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if not args.train: |
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self.model = restore_model(self.model, args.model_output_dir) |
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def initialize_centroids(self, args, data): |
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self.logger.info("Initialize centroids...") |
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feats = self.get_outputs(args, mode = 'train_unlabeled', get_feats = True) |
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km = KMeans(n_clusters=data.num_labels, n_jobs=-1, random_state=args.seed) |
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km.fit(feats) |
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self.logger.info("Initialization finished...") |
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self.model.cluster_layer.data = torch.tensor(km.cluster_centers_).to(self.device) |
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def train(self, args, data): |
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self.logger.info('Pairwise-similarity Learning begin...') |
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u = args.u |
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l = args.l |
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eta = 0 |
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eval_pred_last = np.zeros_like(data.dataloader.eval_examples) |
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for epoch in trange(int(args.num_train_epochs), desc="Epoch"): |
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tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0 |
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self.model.train() |
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for step, batch in enumerate(tqdm(self.train_labeled_dataloader, desc="Iteration (labeled)")): |
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batch = tuple(t.to(self.device) for t in batch) |
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input_ids, input_mask, segment_ids, label_ids = batch |
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loss = self.model(input_ids, segment_ids, input_mask, label_ids, u_threshold = u, l_threshold = l, mode = 'train') |
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loss.backward() |
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tr_loss += loss.item() |
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nb_tr_examples += input_ids.size(0) |
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nb_tr_steps += 1 |
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self.optimizer1.step() |
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self.scheduler1.step() |
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self.optimizer1.zero_grad() |
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train_labeled_loss = tr_loss / nb_tr_steps |
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tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0 |
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for step, batch in enumerate(tqdm(self.train_dataloader, desc="Iteration (all train)")): |
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batch = tuple(t.to(self.device) for t in batch) |
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input_ids, input_mask, segment_ids, label_ids = batch |
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loss = self.model(input_ids, segment_ids, input_mask, label_ids, u_threshold = u, l_threshold = l, mode = 'train', semi = True) |
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loss.backward() |
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tr_loss += loss.item() |
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nb_tr_examples += input_ids.size(0) |
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nb_tr_steps += 1 |
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self.optimizer1.step() |
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self.scheduler1.step() |
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self.optimizer1.zero_grad() |
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train_loss = tr_loss / nb_tr_steps |
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eval_true, eval_pred = self.get_outputs(args, mode = 'eval') |
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eval_score = clustering_score(eval_true, eval_pred)['NMI'] |
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delta_label = np.sum(eval_pred != eval_pred_last).astype(np.float32) / eval_pred.shape[0] |
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eval_pred_last = np.copy(eval_pred) |
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train_results = { |
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'u_threshold': round(u, 4), |
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'l_threshold': round(l, 4), |
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'train_labeled_loss': train_labeled_loss, |
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'train_loss': train_loss, |
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'delta_label': delta_label, |
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'eval_score': eval_score |
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} |
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self.logger.info("***** Epoch: %s: Eval results *****", str(epoch)) |
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for key in sorted(train_results.keys()): |
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self.logger.info(" %s = %s", key, str(train_results[key])) |
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eta += 1.1 * 0.009 |
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u = 0.95 - eta |
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l = 0.455 + eta * 0.1 |
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if u < l: |
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break |
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self.logger.info('Pairwise-similarity Learning finished...') |
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self.refine(args, data) |
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def refine(self, args, data): |
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self.logger.info('Cluster refining begin...') |
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self.initialize_centroids(args, data) |
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best_model = None |
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wait = 0 |
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train_preds_last = None |
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best_eval_score = 0 |
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for epoch in range(args.num_refine_epochs): |
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eval_true, eval_pred = self.get_outputs(args, mode = 'eval') |
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eval_score = clustering_score(eval_true, eval_pred)['NMI'] |
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if eval_score > best_eval_score: |
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best_model = copy.deepcopy(self.model) |
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wait = 0 |
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best_eval_score = eval_score |
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self.model = best_model |
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else: |
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wait += 1 |
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if wait > args.wait_patient: |
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break |
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train_pred_logits = self.get_outputs(args, mode = 'train', get_logits = True) |
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p_target = target_distribution(train_pred_logits) |
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train_preds = train_pred_logits.argmax(1) |
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delta_label = np.sum(train_preds != train_preds_last).astype(np.float32) / train_preds.shape[0] |
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train_preds_last = np.copy(train_preds) |
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if epoch > 0 and delta_label < 0.001: |
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self.logger.info('Break at epoch: %s and delta_label: %f.', str(epoch + 1), round(delta_label, 2)) |
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break |
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self.model.train() |
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tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0 |
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for step, batch in enumerate(self.train_dataloader): |
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batch = tuple(t.to(self.device) for t in batch) |
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input_ids, input_mask, segment_ids, label_ids = batch |
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feats, logits = self.model(input_ids, segment_ids, input_mask, mode='finetune') |
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kl_loss = F.kl_div(logits.log(), torch.Tensor(p_target[step * args.train_batch_size: (step + 1) * args.train_batch_size]).to(self.device)) |
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kl_loss.backward() |
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tr_loss += kl_loss.item() |
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nb_tr_examples += input_ids.size(0) |
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nb_tr_steps += 1 |
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self.optimizer2.step() |
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self.scheduler2.step() |
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self.optimizer2.zero_grad() |
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train_loss = tr_loss / nb_tr_steps |
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eval_results = { |
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'kl_loss': round(train_loss, 4), |
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'delta_label': delta_label.round(4), |
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'eval_score': round(eval_score, 2), |
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'best_eval_score': round(best_eval_score, 2) |
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} |
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self.logger.info("***** Epoch: %s: Eval results *****", str(epoch)) |
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for key in sorted(eval_results.keys()): |
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self.logger.info(" %s = %s", key, str(eval_results[key])) |
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self.logger.info('Cluster refining finished...') |
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if args.save_model: |
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save_model(self.model, args.model_output_dir) |
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def get_outputs(self, args, mode = 'eval', get_feats = False, get_logits = False): |
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if mode == 'eval': |
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dataloader = self.eval_dataloader |
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elif mode == 'test': |
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dataloader = self.test_dataloader |
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elif mode == 'train_unlabeled': |
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dataloader = self.train_unlabeled_dataloader |
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elif mode == 'train': |
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dataloader = self.train_dataloader |
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self.model.eval() |
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total_labels = torch.empty(0,dtype=torch.long).to(self.device) |
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total_preds = torch.empty(0,dtype=torch.long).to(self.device) |
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total_features = torch.empty((0, args.num_labels)).to(self.device) |
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total_logits = torch.empty((0, args.num_labels)).to(self.device) |
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for batch in tqdm(dataloader, desc="Iteration"): |
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batch = tuple(t.to(self.device) for t in batch) |
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input_ids, input_mask, segment_ids, label_ids = batch |
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with torch.set_grad_enabled(False): |
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pooled_output, logits = self.model(input_ids, segment_ids, input_mask) |
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total_labels = torch.cat((total_labels, label_ids)) |
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total_features = torch.cat((total_features, pooled_output)) |
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total_logits = torch.cat((total_logits, logits)) |
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if get_feats: |
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feats = total_features.cpu().numpy() |
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return feats |
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elif get_logits: |
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logits = total_logits.cpu().numpy() |
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return logits |
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else: |
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total_preds = total_logits.argmax(1) |
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y_pred = total_preds.cpu().numpy() |
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y_true = total_labels.cpu().numpy() |
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return y_true, y_pred |
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def test(self, args, data): |
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y_true, y_pred = self.get_outputs(args, mode = 'test') |
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test_results = clustering_score(y_true, y_pred) |
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cm = confusion_matrix(y_true,y_pred) |
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self.logger.info |
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self.logger.info("***** Test: Confusion Matrix *****") |
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self.logger.info("%s", str(cm)) |
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self.logger.info("***** Test results *****") |
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for key in sorted(test_results.keys()): |
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self.logger.info(" %s = %s", key, str(test_results[key])) |
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test_results['y_true'] = y_true |
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test_results['y_pred'] = y_pred |
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return test_results |
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