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import numpy as np
import copy
import torch
import torch.nn as nn
from utils.metrics import clustering_score
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from sklearn.cluster import KMeans
from torch.utils.data import (DataLoader, RandomSampler, TensorDataset)
from sklearn.metrics import silhouette_score
from utils.functions import set_seed
from utils.functions import save_model
from sentence_transformers import SentenceTransformer
class SCCLmanager:
def __init__(self, args, data, model, logger_name = 'Discovery'):
self.logger = logging.getLogger(logger_name)
loader = data.dataloader
self.num_labels = data.num_labels
self.train_dataloader, self.test_dataloader = \
loader.train_outputs['loader'], loader.test_outputs['loader']
self.train_input_ids, self.train_input_mask, self.train_segment_ids = \
loader.train_outputs['input_ids'], loader.train_outputs['input_mask'], loader.train_outputs['segment_ids']
self.augdataloader = self.get_augment_dataloader(args)
self.tokenizer = loader.tokenizer
self.set_model_optimizer(args, data, model)
self.cluster_loss = nn.KLDivLoss(size_average=False)
self.contrast_loss = PairConLoss(temperature=args.temperature)
def set_model_optimizer(self, args, data, model):
self.model = model.set_model(args, data, 'bert', args.freeze_bert_parameters)
self.model.bert = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')[0].auto_model
self.device = model.device
self.model.bert.to(self.device)
cluster_centers = self.get_kmeans_centers(self.train_dataloader, args)
self.model.init_model(cluster_centers=cluster_centers, alpha=args.alpha)
self.optimizer = self.get_optimizer(self.model, args)
self.model.to(self.device)
def target_distribution(self, batch: torch.Tensor) -> torch.Tensor:
weight = (batch ** 2) / (torch.sum(batch, 0) + 1e-9)
return (weight.t() / torch.sum(weight, 1)).t()
def get_optimizer(self, model, args):
optimizer = torch.optim.Adam([
{'params':model.bert.parameters()},
{'params':model.contrast_head.parameters(), 'lr': args.lr * args.lr_scale},
{'params':model.cluster_centers, 'lr': args.lr * args.lr_scale}
], lr = args.lr)
return optimizer
def get_kmeans_centers(self, train_loader, args):
for i, batch in enumerate(tqdm(train_loader)):
batch = tuple(t.to(self.device) for t in batch)
train_input_ids, train_input_mask, _, _= batch
corpus_embeddings = self.model.get_mean_embeddings(train_input_ids, train_input_mask)
if i == 0:
all_embeddings = corpus_embeddings.cpu().detach().numpy()
else:
all_embeddings = np.concatenate((all_embeddings, corpus_embeddings.cpu().detach().numpy()), axis=0)
print('embedding shape', all_embeddings.shape)
clustering_model = KMeans(n_clusters=self.num_labels, random_state=args.seed)
clustering_model.fit(all_embeddings)
print("Iterations:{}, centers:{}".format(clustering_model.n_iter_, clustering_model.cluster_centers_.shape))
return clustering_model.cluster_centers_
def train(self, args, data):
self.logger.info('SCCL training starts...')
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
self.model.train()
tr_loss, nb_tr_steps = 0, 0
for batch in tqdm(self.augdataloader, desc="Training(All)"):
with torch.set_grad_enabled(True):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids = batch
embd1, embd2, embd3 = self.model(input_ids, input_mask, task_type="explicit")
# Instance-CL loss
feat1, feat2 = self.model.contrast_logits(embd2, embd3)
losses = self.contrast_loss(feat1, feat2)
loss = args.eta * losses["loss"]
output = self.model.get_cluster_prob(embd1)
target = self.target_distribution(output).detach()
cluster_loss = self.cluster_loss((output+1e-08).log(), target)/output.shape[0]
loss += cluster_loss
losses["cluster_loss"] = cluster_loss.item()
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
tr_loss += loss.item()
nb_tr_steps += 1
train_loss = tr_loss / nb_tr_steps
self.logger.info("***** Epoch: %s: train results *****", str(epoch))
self.logger.info(" train_loss = %s", str(train_loss))
self.logger.info('SCCL training finished...')
if args.save_model:
save_model(self.model, args.model_output_dir)
def test(self, args, data):
feats, y_true = self.get_outputs(args, mode = 'test', model = self.model)
km = KMeans(n_clusters = self.num_labels, random_state=args.seed).fit(feats)
y_pred = km.labels_
test_results = clustering_score(y_true, y_pred)
cm = confusion_matrix(y_true, y_pred)
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
def get_outputs(self, args, mode, model):
if mode == 'eval':
dataloader = self.train_dataloader
elif mode == 'test':
dataloader = self.test_dataloader
elif mode == 'train':
dataloader = self.augdataloader
model.eval()
total_labels = torch.empty(0,dtype=torch.long).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 = model(input_ids, input_mask, task_type = 'evaluate')
total_labels = torch.cat((total_labels,label_ids))
total_features = torch.cat((total_features, pooled_output))
feats = total_features.cpu().numpy()
y_true = total_labels.cpu().numpy()
return feats, y_true
def get_augment_dataloader(self, args):
train_input_ids = self.train_input_ids.unsqueeze(1)
train_input_mask = self.train_input_mask.unsqueeze(1)
train_segment_ids = self.train_segment_ids.unsqueeze(1)
train_input_ids = torch.cat(([train_input_ids, train_input_ids, train_input_ids]), dim = 1)
train_input_mask = torch.cat(([train_input_mask, train_input_mask, train_input_mask]), dim = 1)
train_segment_ids = torch.cat(([train_segment_ids, train_segment_ids, train_segment_ids]), dim = 1)
train_data = TensorDataset(train_input_ids, train_input_mask, train_segment_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler = train_sampler, batch_size = args.train_batch_size )
return train_dataloader
class PairConLoss(nn.Module):
def __init__(self, temperature=0.05):
super(PairConLoss, self).__init__()
self.temperature = temperature
self.eps = 1e-08
print(f"\n Initializing PairConLoss \n")
def forward(self, features_1, features_2):
device = features_1.device
batch_size = features_1.shape[0]
features= torch.cat([features_1, features_2], dim=0)
mask = torch.eye(batch_size, dtype=torch.bool).to(device)
mask = mask.repeat(2, 2)
mask = ~mask
pos = torch.exp(torch.sum(features_1*features_2, dim=-1) / self.temperature)
pos = torch.cat([pos, pos], dim=0)
neg = torch.exp(torch.mm(features, features.t().contiguous()) / self.temperature)
neg = neg.masked_select(mask).view(2*batch_size, -1)
neg_mean = torch.mean(neg)
pos_n = torch.mean(pos)
Ng = neg.sum(dim=-1)
loss_pos = (- torch.log(pos / (Ng+pos))).mean()
return {"loss":loss_pos, "pos_mean":pos_n.detach().cpu().numpy(), "neg_mean":neg_mean.detach().cpu().numpy(), "pos":pos.detach().cpu().numpy(), "neg":neg.detach().cpu().numpy()}
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