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2d06dcc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | import torch
import torch.nn.functional as F
import logging
import os
import torch.nn as nn
import numpy as np
import copy
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model, MemoryBank, fill_memory_bank, view_generator, set_seed
from utils.neighbor_dataset import NeighborsDataset
from torch.utils.data import DataLoader
from .pretrain import PretrainMTP_CLNNManager
from utils.metrics import clustering_score
from transformers import AutoTokenizer
class MTP_CLNNManager:
def __init__(self, args, data, model, logger_name = 'Discovery'):
self.logger = logging.getLogger(logger_name)
pretrain_manager = PretrainMTP_CLNNManager(args, data, model)
set_seed(args.seed)
self.logger = logging.getLogger(logger_name)
loader = data.dataloader
self.train_dataloader, self.eval_dataloader, self.test_dataloader = \
loader.train_outputs['loader'], loader.eval_outputs['loader'], loader.test_outputs['loader']
self.train_dataset = loader.train_outputs['semi_data']
self.tokenizer = AutoTokenizer.from_pretrained(args.pretrained_bert_model)
self.generator = view_generator(self.tokenizer, args)
self.temp=0.07
if args.pretrain:
self.pretrained_model = pretrain_manager.model
self.set_model_optimizer(args, data, model)
self.num_labels = data.num_labels
self.load_pretrained_model(self.pretrained_model)
else:
self.pretrained_model = restore_model(pretrain_manager.model, os.path.join(args.method_output_dir, 'pretrain'))
self.set_model_optimizer(args, data, model)
self.num_labels = data.num_labels
self.model = restore_model(self.model, args.model_output_dir)
topk = {'banking': 50, 'clinc': 60, 'stackoverflow': 300}
if args.cluster_num_factor > 1:
self.logger.info('num_labels is %s, Length of train_dataset is %s', str(self.num_labels), str(len(self.train_dataset)))
args.topk = int((len(self.train_dataset) * 0.5) / self.num_labels)
else:
args.topk = topk[args.dataset]
self.logger.info('Topk for %s is %s', str(args.dataset), str(args.topk))
def set_model_optimizer(self, args, data, model):
if args.dataset == 'stackoverflow':
args.lr = 1e-6
args.backbone = 'bert_MTP'
self.model = model.set_model(args, data, 'bert', args.freeze_train_bert_parameters)
self.optimizer , self.scheduler = model.set_optimizer(self.model, len(data.dataloader.train_examples), args.train_batch_size, \
args.num_train_epochs, args.lr, args.warmup_proportion)
self.device = model.device
self.criterion = self.model.loss_cl
def train(self, args, data):
indices = self.get_neighbor_inds(args, data)
self.get_neighbor_dataset(args, data, indices)
best_eval_score = 0
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 batch in tqdm(self.train_dataloader_neighbor, desc="Iteration"):
anchor = tuple(t.to(self.device) for t in batch["anchor"])
neighbor = tuple(t.to(self.device) for t in batch["neighbor"])
pos_neighbors = batch["possible_neighbors"]
data_inds = batch["index"]
adjacency = self.get_adjacency(args, data_inds, pos_neighbors, batch["target"]) # (bz,bz)
X_an = {"input_ids":self.generator.random_token_replace(anchor[0].cpu()).to(self.device), "attention_mask":anchor[1], "token_type_ids":anchor[2]}
X_ng = {"input_ids":self.generator.random_token_replace(neighbor[0].cpu()).to(self.device), "attention_mask":neighbor[1], "token_type_ids":neighbor[2]}
with torch.set_grad_enabled(True):
f_pos = torch.stack([self.model(X_an)["features"], self.model(X_ng)["features"]], dim=1)
loss = self.criterion(f_pos, mask=adjacency, temperature=self.temp, device = self.device)
tr_loss += loss.item()
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), args.grad_clip)
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
nb_tr_examples += anchor[0].size(0)
nb_tr_steps += 1
loss = tr_loss / nb_tr_steps
self.logger.info("***** Epoch: %s *****", str(epoch))
self.logger.info('Training Loss: %f', np.round(loss, 5))
if ((epoch + 1) % args.update_per_epoch) == 0:
self.logger.info("Update neighbors...")
indices = self.get_neighbor_inds(args, data)
self.get_neighbor_dataset(args, data, indices)
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, get_feats = True)
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, 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_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
X = {"input_ids":input_ids, "attention_mask": input_mask, "token_type_ids": segment_ids}
with torch.set_grad_enabled(False):
pooled_output = model(X)["hidden_states"]
total_labels = torch.cat((total_labels,label_ids))
total_features = torch.cat((total_features, pooled_output))
if get_feats:
feats = total_features.cpu().numpy()
y_true = total_labels.cpu().numpy()
return feats, y_true
def load_pretrained_model(self, pretrained_model):
pretrained_dict = pretrained_model.state_dict()
self.model.load_state_dict(pretrained_dict, strict=False)
def get_neighbor_dataset(self, args, data, indices):
"""convert indices to dataset"""
dataset = NeighborsDataset(self.train_dataset, indices)
self.train_dataloader_neighbor = DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True)
def get_neighbor_inds(self, args, data):
"""get indices of neighbors"""
memory_bank = MemoryBank(len(self.train_dataset), args.feat_dim, self.num_labels, 0.1)
fill_memory_bank(self, self.train_dataloader, self.model, memory_bank)
indices = memory_bank.mine_nearest_neighbors(args.topk, args.gpu_id ,calculate_accuracy=False)
return indices
def get_adjacency(self, args, inds, neighbors, targets):
"""get adjacency matrix"""
adj = torch.zeros(inds.shape[0], inds.shape[0])
for b1, n in enumerate(neighbors):
adj[b1][b1] = 1
for b2, j in enumerate(inds):
if j in n:
adj[b1][b2] = 1
if (targets[b1] == targets[b2]) and (targets[b1]>0) and (targets[b2]>0):
adj[b1][b2] = 1
return adj
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