/
main_att.py
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/
main_att.py
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# -*- coding: utf-8 -*-
from config import opt
import models
import dataset
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from utils import save_pr, now, eval_metric
def collate_fn(batch):
'''
custom for DataLoader
'''
data, label = zip(*batch)
return data, label
def test(**kwargs):
pass
def train(**kwargs):
kwargs.update({'model': 'PCNN_ATT'})
opt.parse(kwargs)
if opt.use_gpu:
torch.cuda.set_device(opt.gpu_id)
model = getattr(models, 'PCNN_ATT')(opt)
if opt.use_gpu:
model.cuda()
# loading data
DataModel = getattr(dataset, opt.data + 'Data')
train_data = DataModel(opt.data_root, train=True)
train_data_loader = DataLoader(train_data, opt.batch_size, shuffle=True, num_workers=opt.num_workers, collate_fn=collate_fn)
test_data = DataModel(opt.data_root, train=False)
test_data_loader = DataLoader(test_data, batch_size=opt.batch_size, shuffle=False, num_workers=opt.num_workers, collate_fn=collate_fn)
print('{} train data: {}; test data: {}'.format(now(), len(train_data), len(test_data)))
# criterion and optimizer
# criterion = nn.CrossEntropyLoss()
optimizer = optim.Adadelta(model.parameters(), rho=0.95, eps=1e-6)
# train
# max_pre = -1.0
# max_rec = -1.0
for epoch in range(opt.num_epochs):
total_loss = 0
for idx, (data, label_set) in enumerate(train_data_loader):
label = [l[0] for l in label_set]
optimizer.zero_grad()
model.batch_size = opt.batch_size
loss = model(data, label)
if opt.use_gpu:
label = torch.LongTensor(label).cuda()
else:
label = torch.LongTensor(label)
loss.backward()
optimizer.step()
total_loss += loss.item()
# if idx % 100 == 99:
# print('{}: Train iter: {} finish'.format(now(), idx))
if epoch > 2:
# true_y, pred_y, pred_p= predict(model, test_data_loader)
# all_pre, all_rec = eval_metric(true_y, pred_y, pred_p)
pred_res, p_num = predict_var(model, test_data_loader)
all_pre, all_rec = eval_metric_var(pred_res, p_num)
last_pre, last_rec = all_pre[-1], all_rec[-1]
if last_pre > 0.24 and last_rec > 0.24:
save_pr(opt.result_dir, model.model_name, epoch, all_pre, all_rec, opt=opt.print_opt)
print('{} Epoch {} save pr'.format(now(), epoch + 1))
print('{} Epoch {}/{}: train loss: {}; test precision: {}, test recall {}'.format(now(), epoch + 1, opt.num_epochs, total_loss, last_pre, last_rec))
else:
print('{} Epoch {}/{}: train loss: {};'.format(now(), epoch + 1, opt.num_epochs, total_loss))
def predict_var(model, test_data_loader):
'''
Apply the prediction method in Lin 2016
'''
model.eval()
res = []
true_y = []
for idx, (data, labels) in enumerate(test_data_loader):
out = model(data)
true_y.extend(labels)
if opt.use_gpu:
# out = map(lambda o: o.data.cpu().numpy().tolist(), out)
out = out.data.cpu().numpy().tolist()
else:
# out = map(lambda o: o.data.numpy().tolist(), out)
out = out.data.numpy().tolist()
for r in range(1, opt.rel_num):
for j in range(len(out[0])):
res.append([labels[j], r, out[r][j]])
# if idx % 100 == 99:
# print('{} Eval: iter {}'.format(now(), idx))
model.train()
positive_num = len([i for i in true_y if i[0] > 0])
return res, positive_num
def eval_metric_var(pred_res, p_num):
'''
Apply the evalation method in Lin 2016
'''
pred_res_sort = sorted(pred_res, key=lambda x: -x[2])
correct = 0.0
all_pre = []
all_rec = []
for i in range(2000):
true_y = pred_res_sort[i][0]
pred_y = pred_res_sort[i][1]
for j in true_y:
if pred_y == j:
correct += 1
break
precision = correct / (i + 1)
recall = correct / p_num
all_pre.append(precision)
all_rec.append(recall)
print("positive_num: {}; correct: {}".format(p_num, correct))
return all_pre, all_rec
def predict(model, test_data_loader):
'''
Apply the prediction method in Zeng 2015
'''
model.eval()
pred_y = []
true_y = []
pred_p = []
for idx, (data, labels) in enumerate(test_data_loader):
true_y.extend(labels)
out = model(data)
res = torch.max(out, 1)
if model.opt.use_gpu:
pred_y.extend(res[1].data.cpu().numpy().tolist())
pred_p.extend(res[0].data.cpu().numpy().tolist())
else:
pred_y.extend(res[1].data.numpy().tolist())
pred_p.extend(res[0].data.numpy().tolist())
# if idx % 100 == 99:
# print('{} Eval: iter {}'.format(now(), idx))
size = len(test_data_loader.dataset)
assert len(pred_y) == size and len(true_y) == size
assert len(pred_y) == len(pred_p)
model.train()
return true_y, pred_y, pred_p
if __name__ == "__main__":
import fire
fire.Fire()