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main.py
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main.py
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#coding:utf8
from config import opt
import models
import os
import tqdm
from data.dataset import ZhihuData
import torch as t
import time
import fire
import torchnet as tnt
from torch.utils import data
from torch.autograd import Variable
from utils.visualize import Visualizer
from utils import get_score
vis = Visualizer(opt.env)
def hook():pass
def val(model,dataset):
'''
计算模型在验证集上的分数
'''
dataset.train(False)
model.eval()
dataloader = data.DataLoader(dataset,
batch_size = opt.batch_size,
shuffle = False,
num_workers = opt.num_workers,
pin_memory = True
)
predict_label_and_marked_label_list=[]
for ii,((title,content),label) in tqdm.tqdm(enumerate(dataloader)):
title,content,label = Variable(title.cuda(),volatile=True),\
Variable(content.cuda(),volatile=True),\
Variable(label.cuda(),volatile=True)
score = model(title,content)
# !TODO: 优化此处代码
# 1. append
# 2. for循环
# 3. topk 代替sort
predict = score.data.topk(5,dim=1)[1].cpu().tolist()
true_target = label.data.float().topk(5,dim=1)
true_index=true_target[1][:,:5]
true_label=true_target[0][:,:5]
tmp= []
for jj in range(label.size(0)):
true_index_=true_index[jj]
true_label_=true_label[jj]
true=true_index_[true_label_>0]
tmp.append((predict[jj],true.tolist()))
predict_label_and_marked_label_list.extend(tmp)
del score
dataset.train(True)
model.train()
scores,prec_,recall_,_ss=get_score(predict_label_and_marked_label_list)
return (scores,prec_,recall_,_ss)
def main(**kwargs):
'''
训练入口
'''
opt.parse(kwargs,print_=False)
if opt.debug:import ipdb;ipdb.set_trace()
model = getattr(models,opt.model)(opt).cuda() # 360M -> 916M
if opt.model_path:
model.load(opt.model_path)
print(model)
opt.parse(kwargs,print_=True) # 打印配置信息
vis.reinit(opt.env)
pre_loss=1.0
lr,lr2=opt.lr,opt.lr2
loss_function = getattr(models,opt.loss)()
dataset = ZhihuData(opt.train_data_path,opt.labels_path,type_=opt.type_,augument=opt.augument)
dataloader = data.DataLoader(dataset,
batch_size = opt.batch_size,
shuffle = opt.shuffle,
num_workers = opt.num_workers,
pin_memory = True
)
optimizer = model.get_optimizer(lr,opt.lr2,opt.weight_decay)
loss_meter = tnt.meter.AverageValueMeter() # 平均损失
score_meter=tnt.meter.AverageValueMeter()
best_score = 0
for epoch in range(opt.max_epoch):
loss_meter.reset()
score_meter.reset()
for ii,((title,content),label) in tqdm.tqdm(enumerate(dataloader)):
# 训练 更新参数
title,content,label = Variable(title.cuda()),Variable(content.cuda()),Variable(label.cuda())
optimizer.zero_grad()
score = model(title,content) # 916M -> 969M
loss = loss_function(score,opt.weight*label.float()) # 计算损失
loss_meter.add(loss.data[0])
#print '######################## 1 ####################'
#print os.popen('nvidia-smi').read()
loss.backward() # 969M -> 1528M
#print '######################## 2 ####################'
#print os.popen('nvidia-smi').read()
optimizer.step() # 更新模型参数
if ii%opt.plot_every ==opt.plot_every-1:
### 可视化
if os.path.exists(opt.debug_file):
import ipdb
ipdb.set_trace()
predict = score.data.topk(5,dim=1)[1].cpu().tolist()
true_target = label.data.float().cpu().topk(5,dim=1)
true_index=true_target[1][:,:5]
true_label=true_target[0][:,:5]
predict_label_and_marked_label_list=[]
for jj in range(label.size(0)):
true_index_=true_index[jj]
true_label_=true_label[jj]
true=true_index_[true_label_>0]
predict_label_and_marked_label_list.append((predict[jj],true.tolist()))
score_,prec_,recall_,_ss=get_score(predict_label_and_marked_label_list)
score_meter.add(score_)
# vis.vis.text('predict_label_and_marked_label_list: %s' %(predict_label_and_marked_label_list),win='tmp')
vis.vis.text('prec:%s,recall:%s,score:%s,a:%s,predict_label_and_marked_label_list:%s' %(prec_,recall_,score_,_ss,predict_label_and_marked_label_list),win='tmp')
vis.plot('scores', score_meter.value()[0])
#eval()
vis.plot('loss', loss_meter.value()[0])
k = t.randperm(label.size(0))[0]
if ii%opt.decay_every == opt.decay_every-1:
# 计算在验证集上的分数,并相对应的调整学习率
del loss
scores,prec_,recall_ ,_ss= val(model,dataset)
vis.log({' epoch:':epoch,' lr: ':lr,'scores':scores,'prec':prec_,'recall':recall_,'ss':_ss,'scores_train':score_meter.value()[0],'loss':loss_meter.value()[0]})
if scores>best_score:
best_score = scores
best_path = model.save(name = str(scores),new=True)
if scores < best_score:
model.load(best_path,change_opt=False)
lr = lr * opt.lr_decay
lr2= 2e-4 if lr2==0 else lr2*0.8
optimizer = model.get_optimizer(lr,lr2,0)
pre_loss = loss_meter.value()[0]
loss_meter.reset()
score_meter.reset()
if __name__=="__main__":
fire.Fire()