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run_classifier.py
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import argparse
import os
import random
import numpy as np
import torch
from models import Bert, KLBert, Bert_concat
from optimizer import BertAdam
from sklearn.metrics import precision_recall_fscore_support
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from tqdm import tqdm, trange
from transformers import BertTokenizer
from utils import Processer
def accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def macro_f1(y_true, y_pred):
preds = np.argmax(y_pred, axis=-1)
true = y_true
p_macro, r_macro, f_macro, support_macro \
= precision_recall_fscore_support(true, preds, average='macro')
return p_macro, r_macro, f_macro
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir",
default='../data/Ptacek',
type=str)
parser.add_argument("--bert_model", default="bert-base-uncased", type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--max_seq_length",
default=40,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--max_know_length",
default=20,
type=int,
help="The maximum total knowledge input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_test",
action='store_true',
help="Whether to run on the test set.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=16,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=8.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--model_select', default='KL-Bert',
help='model select') # 'Bert-Base', 'KL-Bert', 'Bert_concat'
parser.add_argument('--know_strategy', default='common_know.txt',
help='know strategy') # common_know.txt, major_sent_know.txt, minor_sent_know.txt
parser.add_argument('--know_num', default="5",
help='know num to use') # 1 2 3 4 5
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print("device: {} n_gpu: {}".format(device, n_gpu))
print("************** Using: " + torch.cuda.get_device_name(0) + " ******************")
# set seed val
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_test:
raise ValueError("At least one of 'do_train' or 'do_test' must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
processor = Processer(args.data_dir, args.model_select, args.know_strategy, args.max_seq_length,
args.max_know_length, int(args.know_num))
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=True)
train_examples = None
num_train_steps = None
eval_examples = None
if args.do_train:
train_examples = processor.get_train_examples()
eval_examples = processor.get_eval_examples()
num_train_steps = int((len(train_examples) * args.num_train_epochs) / args.train_batch_size)
if args.model_select == 'Bert-Base':
model = Bert()
elif args.model_select == 'KL-Bert':
model = KLBert()
elif args.model_select == 'Bert_concat':
model = Bert_concat()
else:
raise ValueError("A model must be given.")
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
]
t_total = num_train_steps
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
train_loss = 0
if args.do_train:
train_features = processor.convert_examples_to_features(train_examples, label_list, tokenizer)
eval_features = processor.convert_examples_to_features(eval_examples, label_list, tokenizer)
train_input_ids, train_input_mask, train_know_ids, train_know_mask, train_label_ids = train_features
train_data = TensorDataset(train_input_ids, train_input_mask, train_know_ids, train_know_mask,
train_label_ids)
train_dataloader = DataLoader(train_data, sampler=RandomSampler(train_data),
batch_size=args.train_batch_size)
eval_input_ids, eval_input_mask, eval_know_ids, eval_know_mask, eval_label_ids = eval_features
eval_data = TensorDataset(eval_input_ids, eval_input_mask, eval_know_ids, eval_know_mask,
eval_label_ids)
eval_dataloader = DataLoader(eval_data, sampler=SequentialSampler(eval_data),
batch_size=args.eval_batch_size)
max_acc = 0.0
print("*************** Running training ***************")
for train_idx in trange(int(args.num_train_epochs), desc="Epoch"):
print("********** Epoch: " + str(train_idx + 1) + " **********")
print(" Num examples = %d", len(train_examples))
print(" Batch size = %d", args.train_batch_size)
print(" Num steps = %d", num_train_steps)
model.train()
tr_loss = 0
nb_tr_steps = 0
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
train_input_ids, train_input_mask, train_know_ids, train_know_mask, train_label_ids = batch
loss = model(train_input_ids, train_input_mask, train_know_ids, train_know_mask, train_label_ids)
if n_gpu > 1:
loss = loss.mean()
loss.backward()
tr_loss += loss.item()
nb_tr_steps += 1
optimizer.step()
optimizer.zero_grad()
print("***** Running evaluation on Dev Set*****")
print(" Num examples = %d", len(eval_examples))
print(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
true_label_list = []
pred_label_list = []
for batch in eval_dataloader:
batch = tuple(t.to(device) for t in batch)
eval_input_ids, eval_input_mask, eval_know_ids, eval_know_mask, eval_label_ids = batch
with torch.no_grad():
tmp_eval_loss = model(eval_input_ids, eval_input_mask, eval_know_ids, eval_know_mask,
eval_label_ids)
logits = model(eval_input_ids, eval_input_mask, eval_know_ids, eval_know_mask)
logits = logits.detach().cpu().numpy()
label_ids = eval_label_ids.to('cpu').numpy()
true_label_list.append(label_ids)
pred_label_list.append(logits)
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += eval_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
train_loss = tr_loss / nb_tr_steps if args.do_train else None
true_label = np.concatenate(true_label_list)
pred_outputs = np.concatenate(pred_label_list)
precision, recall, F_score = macro_f1(true_label, pred_outputs)
print("***** Dev Eval results *****")
print(f'train_loss: {train_loss}, eval_loss: {eval_loss}, accuracy: {eval_accuracy}, precision: {precision}, recall: {recall}, f_score: {f_score}')
if eval_accuracy > max_acc:
torch.save(model.state_dict(), output_model_file)
max_acc = eval_accuracy
if args.do_test:
model.load_state_dict(torch.load(output_model_file))
model.to(device)
test_examples = processor.get_test_examples()
print("***** Running evaluation on Test Set*****")
print(" Num examples = %d", len(test_examples))
print(" Batch size = %d", args.eval_batch_size)
test_features = processor.convert_examples_to_features(test_examples, label_list, tokenizer)
test_input_ids, test_input_mask, test_know_ids, test_know_mask, test_label_ids = test_features
test_data = TensorDataset(test_input_ids, test_input_mask, test_know_ids, test_know_mask, test_label_ids)
test_dataloader = DataLoader(test_data, sampler=SequentialSampler(test_data),
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
true_label_list = []
pred_label_list = []
for batch in test_dataloader:
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
test_input_ids, test_input_mask, test_know_ids, test_know_mask, test_label_ids = batch
tmp_eval_loss = model(test_input_ids, test_input_mask, test_know_ids, test_know_mask,
test_label_ids)
logits = model(test_input_ids, test_input_mask, test_know_ids, test_know_mask)
logits = logits.detach().cpu().numpy()
label_ids = test_label_ids.to('cpu').numpy()
true_label_list.append(label_ids)
pred_label_list.append(logits)
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += test_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = train_loss if args.do_train else None
true_label = np.concatenate(true_label_list)
pred_outputs = np.concatenate(pred_label_list)
precision, recall, F_score = macro_f1(true_label, pred_outputs)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
print("***** Test Eval results *****")
print(f"train_loss: {loss}, test_loss: {eval_loss}, accucary: {eval_accuracy}, precision: {precision}, recall: {recall}, f_score: {F_score}")
writer.write(f"train_loss: {loss}, test_loss: {eval_loss}, accucary: {eval_accuracy}, precision: {precision}, recall: {recall}, f_score: {F_score}\n")
if __name__ == "__main__":
main()