Spaces:
Runtime error
Runtime error
File size: 14,031 Bytes
dc07399 45ec59e dc07399 45ec59e dc07399 45ec59e dc07399 494b4c7 dc07399 494b4c7 dc07399 494b4c7 dc07399 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
from glob import glob
from tqdm import tqdm
import numpy as np
import pickle
#from sklearn.model_selection import train_test_split
import torch
import os
import ast
#from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
from transformers import EarlyStoppingCallback
from transformers import AutoConfig, AutoModel, AutoTokenizer, AutoModelForSequenceClassification
from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
#from sklearn.utils import shuffle
from transformers import get_cosine_schedule_with_warmup
from torch.nn import functional as F
import random
import pandas as pd
from .datas import make_dataset, make_extract_dataset
from .utils import set_seed, accuracy_per_class, compute_metrics, model_eval, checkpoint_save, EarlyStopping, model_freeze, get_hidden
from .model import classification_model
from transformers import BigBirdTokenizer
import transformers
class NLP_classification():
def __init__(self, model_name=None, data_file=None, max_length=None, random_state=1000, task_type='onehot', freeze_layers=None, num_classifier=1, num_pos_emb_layer=1, gpu_num=0, sentence_piece=True, bertsum=False):
self.model_name = model_name
self.data_file = data_file
self.max_length = max_length
self.random_state = random_state
self.task_type = task_type
self.tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False)
if model_name == 'google/bigbird-roberta-base':
self.tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
self.config = AutoConfig.from_pretrained(model_name, num_labels=6)
#self.pretrained_model = AutoModelForSequenceClassification.from_config(self.config)
self.pretrained_model = AutoModel.from_config(self.config)
self.freeze_layers=freeze_layers
self.num_classifier=num_classifier
self.num_pos_emb_layer=num_pos_emb_layer
self.gpu_num=gpu_num
self.sentence_piece=sentence_piece
self.bertsum=bertsum
if self.max_length is None:
self.padding='longest'
else:
self.padding='max_length'
def training(self, epochs=50, batch_size=4, lr=1e-5, dropout=0.1, data_cut=None, early_stop_count=10,
wandb_log=False, wandb_project=None, wandb_group=None, wandb_name=None, wandb_memo=None):
#os.environ["CUDA_VISIBLE_DEVICES"]= "{0}".format(int(self.gpu_num))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#device = torch.device('cuda:{0}'.format(int(self.gpu_num)))
#torch.cuda.set_device(device)
set_seed(self.random_state)
torch.set_num_threads(10)
if wandb_log is True:
import wandb
wandb.init(project=wandb_project, reinit=True, group=wandb_group, notes=wandb_memo)
wandb.run.name = wandb_name
wandb.run.save()
parameters = wandb.config
parameters.lr = lr
parameters.batch_size = batch_size
parameters.dropout = dropout
parameters.train_num = data_cut
parameters.max_length = self.max_length
parameters.model_name = self.model_name
parameters.task_type = self.task_type
'''data loading'''
train_dataset, val_dataset = make_dataset(csv_file=self.data_file, tokenizer=self.tokenizer, max_length=self.max_length, padding=self.padding, random_state=self.random_state, data_cut=data_cut, sentence_piece=self.sentence_piece)
'''loader making'''
train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=RandomSampler(train_dataset))
val_loader = DataLoader(val_dataset, batch_size=batch_size, sampler=SequentialSampler(val_dataset))
''' model load '''
model=classification_model(self.pretrained_model, self.config, num_classifier=self.num_classifier, num_pos_emb_layer=self.num_pos_emb_layer, bertsum=self.bertsum, device=device)
model=model_freeze(model, self.freeze_layers)
model.to(device)
''' running setting '''
loss_fn = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr, eps=1e-8)
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=(len(train_loader)*epochs))
early_stopping = EarlyStopping(patience = early_stop_count, verbose = True)
''' running '''
best_epoch = None
best_val_f1 = None
for epoch in range(epochs):
model.train()
loss_all = 0
step = 0
for data in tqdm(train_loader):
input_ids=data['input_ids'].to(device, dtype=torch.long)
mask = data['attention_mask'].to(device, dtype=torch.long)
token_type_ids = data['token_type_ids'].to(device, dtype=torch.long)
if self.task_type=='onehot':
targets=data['label_onehot'].to(device, dtype=torch.float)
elif self.task_type=='scalar':
targets=data['label'].to(device, dtype=torch.long)
position = data['position']
inputs = {'input_ids': input_ids, 'attention_mask': mask, 'token_type_ids': token_type_ids,
'labels': targets, 'position': position}
if self.sentence_piece:
sentence_batch = data['sentence_batch'].to(device, dtype=torch.long)
inputs = {'input_ids': input_ids, 'attention_mask': mask, 'token_type_ids': token_type_ids,
'labels': targets, 'sentence_batch': sentence_batch, 'position': position}
outputs = model(inputs)
output = outputs[1]
loss = outputs[0]
optimizer.zero_grad()
#loss=loss_fn(output, targets)
loss_all += loss.item()
loss.backward()
optimizer.step()
scheduler.step()
#print(optimizer.param_groups[0]['lr'])
train_loss = loss_all/len(train_loader)
val_loss, val_acc, val_precision, val_recall, val_f1 = model_eval(model, device, val_loader, task_type=self.task_type, sentence_piece=self.sentence_piece)
if wandb_log is True:
wandb.log({'train_loss':train_loss, 'val_loss':val_loss, 'val_acc':val_acc,
'val_precision':val_precision, 'val_recall':val_recall, 'val_f1':val_f1})
if best_val_f1 is None or val_f1 >= best_val_f1:
best_epoch = epoch+1
best_val_f1 = val_f1
checkpoint_save(model, val_f1, wandb_name=wandb_name)
print('Epoch: {:03d}, Train Loss: {:.7f}, Val Loss: {:.7f}, Val Acc: {:.7f}, Val Precision: {:.7f}, Val Recall: {:.7f}, Val F1: {:.7f} '.format(epoch+1, train_loss, val_loss, val_acc, val_precision, val_recall, val_f1))
early_stopping(val_f1)
if early_stopping.early_stop:
print("Early stopping")
break
wandb.finish()
def prediction(self, selected_model=None, batch_size=8):
#os.environ["CUDA_VISIBLE_DEVICES"]= "{0}".format(int(self.gpu_num))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
set_seed(self.random_state)
torch.set_num_threads(10)
task_type=self.task_type
'''data loading'''
train_dataset, val_dataset = make_dataset(csv_file=self.data_file, tokenizer=self.tokenizer, max_length=self.max_length, padding=self.padding, random_state=self.random_state, data_cut=None, sentence_piece=self.sentence_piece)
'''loader making'''
train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=RandomSampler(train_dataset))
val_loader = DataLoader(val_dataset, batch_size=batch_size, sampler=SequentialSampler(val_dataset))
''' model load '''
model=classification_model(self.pretrained_model, self.config, num_classifier=self.num_classifier, num_pos_emb_layer=self.num_pos_emb_layer, bertsum=self.bertsum, device=device)
model.load_state_dict(torch.load(selected_model))
model.to(device)
''' prediction '''
print('start trainset prediction')
train_results = model_eval(model, device, train_loader, task_type=self.task_type, return_values=True, sentence_piece=self.sentence_piece)
print('start evalset prediction')
eval_results = model_eval(model, device, val_loader, task_type=self.task_type, return_values=True, sentence_piece=self.sentence_piece)
print('train result: acc:{0} | precision:{1} | recall:{2} | f1:{3}'.format(train_results[1], train_results[2], train_results[3], train_results[4]))
print('eval result: acc:{0} | precision:{1} | recall:{2} | f1:{3}'.format(eval_results[1], eval_results[2], eval_results[3], eval_results[4]))
total_text = train_results[7] + eval_results[7]
total_out = train_results[6] + eval_results[6]
total_target = train_results[5] + eval_results[5]
if self.task_type == 'onehot':
total_out = [i.argmax() for i in total_out]
total_target = [i.argmax() for i in total_target]
total_data = {'text':total_text, 'label':total_target, 'predict':total_out}
total_df = pd.DataFrame(total_data)
''' result return '''
return total_df
def get_embedding(self, selected_model=None, batch_size=8, return_hidden=True, return_hidden_pretrained=False):
#os.environ["CUDA_VISIBLE_DEVICES"]= "{0}".format(int(self.gpu_num))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#device = torch.device('cuda:{0}'.format(int(self.gpu_num)))
#torch.cuda.set_device(device)
set_seed(self.random_state)
torch.set_num_threads(10)
task_type=self.task_type
'''data loading'''
train_dataset, val_dataset = make_dataset(csv_file=self.data_file, tokenizer=self.tokenizer, max_length=self.max_length, padding=self.padding, random_state=self.random_state, data_cut=None, sentence_piece=self.sentence_piece)
'''loader making'''
train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=RandomSampler(train_dataset))
val_loader = DataLoader(val_dataset, batch_size=batch_size, sampler=SequentialSampler(val_dataset))
''' model load '''
model=classification_model(self.pretrained_model, self.config, num_classifier=self.num_classifier, num_pos_emb_layer=self.num_pos_emb_layer, bertsum=self.bertsum, device=device)
model.return_hidden = return_hidden
model.return_hidden_pretrained = return_hidden_pretrained
if selected_model is not None:
model.load_state_dict(torch.load(selected_model))
model.to(device)
''' get hidden '''
print('start make hidden states (trainset)')
train_hiddens, train_targets = get_hidden(model, device, train_loader, task_type=self.task_type, sentence_piece=self.sentence_piece)
print('start evalset prediction (eval set)')
eval_hiddens, eval_targets = get_hidden(model, device, val_loader, task_type=self.task_type, sentence_piece=self.sentence_piece)
total_hiddens = np.array(train_hiddens + eval_hiddens)
total_targets = np.array(train_targets + eval_targets)
return total_hiddens, total_targets
def label_extraction(self, paragraphs, positions, selected_model=None, batch_size=16):
label_dict = {'Abstract':0, 'Introduction':1, 'Main':2, 'Methods':3, 'Summary':4, 'Captions':5}
#os.environ["CUDA_VISIBLE_DEVICES"]= "{0}".format(int(self.gpu_num))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
set_seed(self.random_state)
torch.set_num_threads(10)
''' data to list '''
is_list = True
if not isinstance(paragraphs, list):
paragraphs = [paragraphs]
is_list = False
if not isinstance(positions, list):
positions = [positions]
is_list = False
'''data encoding'''
dataset = make_extract_dataset(paragraphs, positions, tokenizer=self.tokenizer, max_length=self.max_length)
'''loader making'''
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
''' model load '''
model=classification_model(self.pretrained_model, self.config, num_classifier=self.num_classifier, num_pos_emb_layer=self.num_pos_emb_layer, bertsum=self.bertsum, device=device)
model.load_state_dict(torch.load(selected_model))
model.to(device)
''' prediction '''
model.eval()
predicts = []
with torch.no_grad():
for batch in tqdm(data_loader):
inputs = {}
inputs['input_ids'] = batch['input_ids'].to(device)
inputs['attention_mask'] = batch['attention_mask'].to(device)
inputs['token_type_ids'] = batch['token_type_ids'].to(device)
inputs['position'] = batch['position']
outputs = model(inputs)
logits = outputs[1]
logits = logits.detach().cpu().numpy()
logits = logits.argmax(axis=1).flatten()
logits = logits.tolist()
predicts.extend(logits)
predicts = [list(label_dict.keys())[list(label_dict.values()).index(i)] for i in predicts]
if not is_list:
predicts = predicts[0]
return predicts
|