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main.py
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# -*- coding: utf-8 -*-
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim
import torch.utils.data as data
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from config import Config
from TextCNN import TextCNN
from RCNN import RCNN
from RNN import myRNN, LSTMClassifier
from dataset import SSTDataset, loadGloveModel
import argparse
import time
def collate_fn(data):
"""Pad data in a batch.
处理batch，把batch里面的tensor补到这个batch中最长的。
Parameters
----------
data : list((tensor, int), )
data and label in a batch
Returns
-------
tuple(tensor, tensor)
"""
# data: [(tensor, label), ...]
max_len = max([i[0].shape[0] for i in data])
labels = torch.tensor([i[1] for i in data], dtype=torch.long) # labels in this batch
# print('labels', labels)
padded = torch.zeros((len(data), max_len), dtype=torch.long) # padded tensor
# randomizing might be better
# print('pad', padded.size())
for i, _ in enumerate(padded):
padded[i][:data[i][0].shape[0]] = data[i][0]
return padded, labels
def evaluation(data_iter, model, args):
# Evaluating the given model
model.eval()
with torch.no_grad():
corrects = 0
avg_loss = 0
# total = 0
for data, label in data_iter:
sentences = data.to(args.device, non_blocking=True)
labels = label.to(args.device, non_blocking=True)
logit = model(sentences)
# torch.max(logit, 1)[1]: index
corrects += (torch.max(logit, 1)[1].view(labels.size()).data == labels.data).sum().item()
size = len(data_iter.dataset)
model.train()
return 100.0 * corrects / size
def main():
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--epoch', type=int, default=10)
parser.add_argument('--kernel_num', type=int, default=100, help='Number of each size of kernels used in CNN')
parser.add_argument('--label_num', type=int, default=2, help='Target label numbers')
parser.add_argument('--log_interval', type=int, default=100)
parser.add_argument('--wordvec_dim', type=int, default=50, help='Dimension of GloVe vectors')
parser.add_argument('--model_name', type=str, default='rcnn', help='Which model to use')
parser.add_argument('--kernel_sizes', type=str, default='3,4,5', help='Sizes of kernels used in CNN')
parser.add_argument('--dataset_path', type=str, default='data/dataset/', help='PATH to dataset')
args = parser.parse_args()
# torch.manual_seed(args.seed)[]
start = time.time()
wordvec = loadGloveModel('data/glove/glove.6B.'+ str(args.wordvec_dim) +'d.txt')
args.device = device
args.weight = torch.tensor(wordvec.values, dtype=torch.float) # word embedding for the embedding layer
args.kernel_sizes = [int(k) for k in args.kernel_sizes.split(',')]
# Datasets
training_set = SSTDataset(args.dataset_path, 'train', args.label_num, args.wordvec_dim, wordvec)
testing_set = SSTDataset(args.dataset_path, 'test', args.label_num, args.wordvec_dim, wordvec)
validation_set = SSTDataset(args.dataset_path, 'dev', args.label_num, args.wordvec_dim, wordvec)
training_iter = DataLoader(dataset=training_set,
batch_size=args.batch_size,
num_workers=0, shuffle=True, collate_fn=collate_fn, pin_memory=True)
testing_iter = DataLoader(dataset=testing_set,
batch_size=args.batch_size,
num_workers=0, collate_fn=collate_fn, pin_memory=True)
validation_iter = DataLoader(dataset=validation_set,
batch_size=args.batch_size,
num_workers=0, collate_fn=collate_fn, pin_memory=True)
print(time.time() - start)
model_name = args.model_name.lower()
print(model_name)
# Select model
if model_name == 'cnn':
model = TextCNN(args).to(device)
elif model_name == 'lstm':
model = LSTMClassifier(args).to(device)
elif model_name == 'rcnn':
model = RCNN(args).to(device)
elif model_name == 'rnn':
model = myRNN(args).to(device)
else:
print('Unrecognized model name!')
exit(1)
del wordvec # Save some memory
criterion = nn.CrossEntropyLoss()
# optimizer = optim.SGD(model.parameters(), lr=config.lr)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) # Adam优化器
step = 0
loss_sum = 0
# Train
test_acc = []
best_acc = 0
for epoch in range(1, args.epoch+1):
for data, label in training_iter:
sentences = data.to(device, non_blocking=True) # Asynchronous loading
# sentences = data.flip(dims=(-1,)).to(device, dtype=torch.long)
labels = label.to(device, non_blocking=True)
optimizer.zero_grad()
logits = model(sentences) # 训练
loss = criterion(logits, labels) # 损失
loss_sum += loss.data # 求和loss
step += 1
if step % args.log_interval == 0:
print("epoch", epoch, end=' ')
print("avg loss: %.5f" % (loss_sum/args.log_interval))
loss_sum = 0
step = 0
loss.backward()
optimizer.step()
# test
acc = evaluation(testing_iter, model, args)
if acc > best_acc:
best_acc = acc
# torch.save(model.state_dict(), 'model_{}_{}_{}.ckpt'.format(args.model_name, args.wordvec_dim, args.label_num))
test_acc.append(acc)
print('test acc {:.4f}'.format(acc))
print('train acc {:.4f}'.format(evaluation(training_iter, model, args)))
best = 0
best_acc = 0
for i, a in enumerate(test_acc):
if a > best_acc:
best_acc = a
best = i + 1
print('best: epoch {}, acc {:.4f}'.format(best, best_acc))
print("Parameters:")
delattr(args, 'weight')
for attr, value in sorted(args.__dict__.items()):
print("\t{}={}".format(attr.upper(), value))
if __name__ == "__main__":
main()