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main_vua.py
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main_vua.py
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import torch
import torch.nn as nn
import torch.optim as optim
import os
import json
import time
import h5py
import random
import argparse
from sklearn import metrics
from util import Vocab
from util import mkdir
from util import set_seed
from util import read_dataset
from util import saveSenResult
from util import read_cataloge
from util import batch_loader
from util import get_time_dif
from util import get_query_matrix
from util import get_embedding_matrix
from model import RNNSequenceClassifier
print("PyTorch version:")
print(torch.__version__)
print("GPU Detected:")
print(torch.cuda.is_available())
using_GPU = True
# Training phase.
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Path options.
parser.add_argument("--pretrained_w2v_model_path", required=True, type=str,
help="Path of the tence w2v pretrained model.")
parser.add_argument("--query_matrix_path", required=True, type=str,
help="Path of the query matrix.")
parser.add_argument("--summary_result_path", required=True, type=str,
help="Path of the output model.")
parser.add_argument("--output_result_path", required=True, type=str,
help="Path of the output result.")
parser.add_argument("--train_path", type=str, required=True,
help="Path of the trainset.")
parser.add_argument("--dev_path", type=str, required=True,
help="Path of the devset.")
parser.add_argument("--test_path", type=str, required=True,
help="Path of the testset.")
parser.add_argument("--vocab_path", type=str, required=True,
help="Path of the vocab.")
parser.add_argument("--elmo_path", type=str, required=True,
help="Path of the elmo features.")
# Model options.
parser.add_argument("--language_type", type=str, choices=["en", "zh"], required=True,
help="Num of the classes.")
parser.add_argument("--num_classes", type=int, default=3,
help="Num of the classes.")
parser.add_argument("--batch_size", type=int, default=64,
help="Batch size.")
parser.add_argument("--require_improvement", type=int, default=5,
help="Require improvement.")
parser.add_argument("--epochs_num", type=int, default=100,
help="Number of epochs.")
parser.add_argument("--w2v_embedding_dim", type=int, required=True,
help="w2v embedding dim.")
parser.add_argument("--elmo_embedding_dim", type=int, default=1024,
help="elmo embedding dim.")
parser.add_argument("--input_dim", type=int, required=True,
help="input embedding dim.")
parser.add_argument("--seq_length", type=int, default=128,
help="Sequence length.")
parser.add_argument("--hidden_size", type=int, default=200,
help="hidden size.")
parser.add_argument("--layers_num", type=int, default=2,
help="Number of layers.")
parser.add_argument("--attention_query_size", type=int, default=200,
help="Size of attention query matrix.")
parser.add_argument("--attention_layer", choices=["att", "m_a", "m_pre_orl_a", "m_pre_orl_pun_a", "m_pol_untrain_a", "mpa", "mpoa"], required=True,
help="attention type.")
parser.add_argument("--pretrain_model_type",
choices=["w2v", "elmo", "none"],
required=True,
help="pretrain model type.")
# Optimizer options.
parser.add_argument("--learning_rate", type=float, default=0.1,
help="Learning rate.")
parser.add_argument("--momentum", type=float, default=0.9,
help="momentum.")
# Training options.
parser.add_argument("--dropout", type=float, default=0.2,
help="Dropout.")
parser.add_argument("--is_bidir", type=int, default=2,
help="bidir or only one.")
parser.add_argument("--report_steps", type=int, default=100,
help="Specific steps to print prompt.")
parser.add_argument("--seed", type=int, default=7,
help="Random seed.")
parser.add_argument("--run_type", type=str, required=True,
help="usage: python main_vua.py [train / test]")
args = parser.parse_args()
#set numpy、random、etc seeds
set_seed(args.seed)
#set vocab
vocab = Vocab()
vocab.load(args.vocab_path)
label_columns = read_cataloge(args.dev_path)
#set embedding
embeddings = get_embedding_matrix(args, vocab, normalization=False)
elmo_embedding = h5py.File(args.elmo_path, 'r')
query_matrix = get_query_matrix(args)
# For simplicity, we use DataParallel wrapper to use multiple GPUs.
model = RNNSequenceClassifier(args, embeddings, query_matrix)
model = model.cuda()
best_josn = {'F_macro': 0, 'P_macro': 0, 'R_macro': 0, 'Best_F_macro': 0, 'ACC': 0, 'F_negative': 0,
'F_positive': 0, 'Predict': [], 'Label': [], 'Weights': [], 'Last_up_epoch': 0, 'Total_batch_loss': 0,
'F_nuetral': 0, 'Time':0, 'Total_orthogonal_loss': 0, 'train_num': 0, 'test_num': 0, 'dev_num':0}
def evaluate(args, is_test):
model.eval()
if is_test:
print("Start testing.")
dataset = read_dataset(args, args.test_path, label_columns, vocab)
best_josn['test_num'] = len(dataset)
writer_result = open(os.path.join(args.output_result_path, 'result.txt'), encoding='utf-8', mode='w')
writer_summary_result = open(os.path.join(args.summary_result_path, 'summary_result.txt'), mode='a')
else:
dataset = read_dataset(args, args.dev_path, label_columns, vocab)
best_josn['dev_num'] = len(dataset)
random.shuffle(dataset)
input_ids = torch.LongTensor([example[0] for example in dataset])
label_ids = torch.LongTensor([example[1] for example in dataset])
length_ids = torch.LongTensor([example[2] for example in dataset])
input = [example[3] for example in dataset]
if is_test:
batch_size = 1
else:
batch_size = args.batch_size
for i, (input_ids_batch, label_ids_batch, length_ids_batch) in enumerate(
batch_loader(batch_size, input_ids, label_ids, length_ids)):
model.zero_grad()
input_ids_batch = input_ids_batch.cuda()
label_ids_batch = label_ids_batch.cuda()
length_ids_batch = length_ids_batch.cuda()
if args.attention_layer == 'att':
predicted, weight = model(input_ids_batch, length_ids_batch, elmo_embedding)
else:
predicted, weight,_ = model(input_ids_batch, length_ids_batch, elmo_embedding)
best_josn['Weights'] += weight.squeeze(dim=1).cpu().detach().numpy().tolist()
_, predicted_labels = torch.max(predicted.data, 1)
best_josn['Predict'] += predicted_labels.cpu().numpy().tolist()
best_josn['Label'] += label_ids_batch.data.cpu().numpy().tolist()
if is_test:
details_result = metrics.classification_report(best_josn['Label'], best_josn['Predict'])
best_josn['P_macro'], best_josn['R_macro'], best_josn['F_macro'], _ = metrics.precision_recall_fscore_support(best_josn['Label'], best_josn['Predict'], average="macro")
best_josn['ACC'] = metrics.classification.accuracy_score(best_josn['Label'], best_josn['Predict'])
saveSenResult(input, best_josn['Label'], best_josn['Predict'], args, best_josn['Weights'])
writer_result.writelines(details_result)
print("Testing Acc: {:.4f}, F_macro: {:.4f}, P_macro: {:.4f}, R_macro: {:.4f}".format(best_josn['ACC'],
best_josn['F_macro'],
best_josn['P_macro'],
best_josn['R_macro']))
writer_result.writelines(
"Testing Acc: {:.4f}, F_macro: {:.4f}, P_macro: {:.4f}, R_macro: {:.4f}".format(best_josn['ACC'],
best_josn['F_macro'],
best_josn['P_macro'],
best_josn['R_macro']))
writer_summary_result.writelines('保存路径'+args.output_result_path+'\n')
writer_summary_result.writelines(
"Testing Acc: {:.4f}, F_macro: {:.4f}, P_macro: {:.4f}, R_macro: {:.4f}\n\n".format(best_josn['ACC'],
best_josn['F_macro'],
best_josn['P_macro'],
best_josn['R_macro']))
writer_summary_result.writelines(details_result)
else:
best_josn['P_macro'], best_josn['R_macro'], best_josn['F_macro'], _ = metrics.precision_recall_fscore_support(best_josn['Label'], best_josn['Predict'],average="macro")
best_josn['ACC'] = metrics.classification.accuracy_score(best_josn['Label'], best_josn['Predict'])
def train():
print("Start training.")
mkdir(args.output_result_path)
writer_process = open(os.path.join(args.output_result_path, 'process.txt'), mode='w')
writer_process.writelines("Start training.")
trainset = read_dataset(args, args.train_path, label_columns, vocab)
random.shuffle(trainset)
best_josn['train_num'] = len(trainset)
input_ids = torch.LongTensor([example[0] for example in trainset])
label_ids = torch.LongTensor([example[1] for example in trainset])
length_ids = torch.LongTensor([example[2] for example in trainset])
print("Batch size: ", args.batch_size)
print("The number of training instances:", best_josn['train_num'])
start_time = time.time()
best_josn['Time'] = get_time_dif(start_time)
print("Time usage:", best_josn['Time'])
param_optimizer = list(model.named_parameters())
nll_criterion = nn.NLLLoss()
if args.attention_layer == 'm_pol_untrain_a':
optimizer_grouped_parameters = [{'params': [p for n, p in param_optimizer if ('query_embedding.weight' not in n)], 'weight_decay_rate': 0.01}]
else:
optimizer_grouped_parameters = [{'params': [p for n, p in param_optimizer], 'weight_decay_rate': 0.01}]
optimizer = optim.SGD(optimizer_grouped_parameters, lr = args.learning_rate, momentum = args.momentum)
for epoch in range(1, args.epochs_num + 1):
model.train()
for i, (input_ids_batch, label_ids_batch, length_ids_batch) in enumerate(
batch_loader(args.batch_size, input_ids, label_ids,length_ids)):
model.zero_grad()
input_ids_batch = input_ids_batch.cuda()
label_ids_batch = label_ids_batch.cuda()
length_ids_batch = length_ids_batch.cuda()
if args.attention_layer == 'att':
predicted_ids_batch,_ = model(input_ids_batch, length_ids_batch, elmo_embedding)
else:
predicted_ids_batch, _, orthogonal_loss = model(input_ids_batch, length_ids_batch, elmo_embedding)
best_josn['Total_orthogonal_loss'] += orthogonal_loss
batch_loss = nll_criterion(predicted_ids_batch, label_ids_batch)
best_josn['Total_batch_loss'] += batch_loss
if args.attention_layer != 'm_pre_orl_pun_a' and args.attention_layer != 'mpoa':
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
else:
optimizer.zero_grad()
(0.1*orthogonal_loss).backward(retain_graph=True)
(0.9*batch_loss).backward()
optimizer.step()
best_josn['Time'] = get_time_dif(start_time)
if (i + 1) % args.report_steps == 0:
if args.attention_layer == 'att':
print("Epoch id: {}, Training steps: {}, Avg batch loss: {:.4f}, Time: {}".format(epoch, i + 1,
best_josn['Total_batch_loss'] / args.report_steps, best_josn['Time']))
writer_process.writelines("Epoch id: {}, Training steps: {}, Avg batch loss: {:.4f}, Time: {}".format(epoch, i + 1,
best_josn['Total_batch_loss'] / args.report_steps, best_josn['Time']))
else:
print("Epoch id: {}, Training steps: {}, Avg batch loss: {:.4f}, Avg orthogonal loss: {:.4f}, Time: {}".format(epoch, i + 1,
best_josn['Total_batch_loss'] / args.report_steps, best_josn['Total_orthogonal_loss'] / args.report_steps, best_josn['Time']))
writer_process.writelines("Epoch id: {}, Training steps: {}, Avg batch loss: {:.4f}, Avg orthogonal loss: {:.4f}, Time: {}".format(epoch, i + 1,
best_josn['Total_batch_loss'] / args.report_steps, best_josn['Total_orthogonal_loss'] / args.report_steps, best_josn['Time']))
best_josn['Total_batch_loss'] = 0
best_josn['Total_orthogonal_loss'] = 0
# 读取验证集
evaluate(args, False)
best_josn['Time'] = get_time_dif(start_time)
if best_josn['F_macro'] > best_josn['Best_F_macro'] + 0.001:
best_josn['Best_F_macro'] = best_josn['F_macro']
best_josn['Last_up_epoch'] = epoch
torch.save(model, os.path.join(args.output_result_path, 'result.pkl'))
print("Deving Acc: {:.4f}, F_macro: {:.4f}, Time: {} *".format(best_josn['ACC'], best_josn['F_macro'], best_josn['Time']))
writer_process.writelines("Deving Acc: {:.4f}, F_macro: {:.4f}, Time: {} *".format(best_josn['ACC'], best_josn['F_macro'], best_josn['Time']))
elif epoch - best_josn['Last_up_epoch'] == args.require_improvement:
print("No optimization for a long time, auto-stopping...")
writer_process.writelines("No optimization for a long time, auto-stopping...")
break
else:
print("Deving Acc: {:.4f}, F_macro: {:.4f}, Time: {} ".format(best_josn['ACC'], best_josn['F_macro'], best_josn['Time']))
writer_process.writelines("Deving Acc: {:.4f}, F_macro: {:.4f}, Time: {} ".format(best_josn['ACC'], best_josn['F_macro'], best_josn['Time']))
if args.run_type == 'train':
train()
else:
model = torch.load(os.path.join(args.output_result_path, 'result.pkl'))
evaluate(args, True)
if __name__ == "__main__":
main()