#coding:utf8
from config import opt
import models
import os
import tqdm
from data.dataset import ZhihuData,ZhihuALLData
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#,get_optimizer
vis = Visualizer(opt.env)
'''
训练 stack-attention 不过这些模型比较差 没用过拟合
'''
def hook():pass
def val(model,dataset):
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[0].cuda()),Variable(title[1].cuda())),(Variable(content[0].cuda()),Variable(content[1].cuda())),Variable(label.cuda())
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)#[1].cpu().tolist()#sort(dim=1,descending=True)
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):
#动态加全职衰减
origin_weight_decay=1e-5
opt.parse(kwargs,print_=False)
if opt.debug:import ipdb;ipdb.set_trace()
###################################
# opt.model_names=['MultiCNNTextBNDeep','CNNText_inception',
# #'RCNN',
# 'LSTMText','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410330780091','checkpoints/CNNText_tmp_word_0.41096749885',
# #'checkpoints/RCNN_word_0.411511574999',
# 'checkpoints/LSTMText_word_0.411994005382','checkpoints/CNNText_tmp_char_0.402429167301']
######################################
# opt.model_names=['MultiCNNTextBNDeep',
# #'CNNText_inception',
# #'RCNN',
# 'LSTMText_boost',
# #'CNNText_inception_boost'
# ]
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410330780091',
# # 'checkpoints/CNNText_tmp_word_0.41096749885',
# #'checkpoints/RCNN_word_0.411511574999',
# 'checkpoints/LSTMText_word_0.381833388089',
# #'checkpoints/CNNText_tmp_0.376364647145'
# ]
#####################################################3
opt.model_names=['MultiCNNTextBNDeep',
#'RCNN',
'LSTMText',
'CNNText_inception',
'CNNText_inception-boost'
]
opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410330780091',
'checkpoints/LSTMText_word_0.381833388089',
'checkpoints/CNNText_tmp_0.380390420742',
#'checkpoints/RCNN_word_0.411511574999',
'checkpoints/CNNText_tmp_0.376364647145'
]
# opt.model_path='checkpoints/BoostModel_word_0.412524727048'
#**************************************
#############################################3
opt.model_names=['MultiCNNTextBNDeep',
'LSTMText',
'MultiCNNTextBNDeep-boost'
]
opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410330780091',
'checkpoints/LSTMText_word_0.411994005382',
None
]
opt.model_path='checkpoints/BoostModel2_word_0.410618920827'
#*********************************************
# opt.model_names=['MultiCNNTextBNDeep','LSTMText','CNNText_inception','RCNN']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/LSTMText_word_0.381833388089','checkpoints/CNNText_tmp_0.376364647145','checkpoints/RCNN_char_0.3456599248']
model = getattr(models,opt.model)(opt).cuda()
# if opt.model_path:
# model.load(opt.model_path)
print(model)
opt.parse(kwargs,print_=True)
vis.reinit(opt.env)
pre_loss=1.0
lr,lr2=opt.lr,opt.lr2
loss_function = getattr(models,opt.loss)()
if opt.all:dataset = ZhihuALLData(opt.train_data_path,opt.labels_path,type_=opt.type_)
else :dataset = ZhihuData(opt.train_data_path,opt.labels_path,type_=opt.type_)
dataloader = data.DataLoader(dataset,
batch_size = opt.batch_size,
shuffle = opt.shuffle,
num_workers = opt.num_workers,
pin_memory = True
)
optimizer = model.get_optimizer(opt.lr,opt.lr2,0)
loss_meter = tnt.meter.AverageValueMeter()
score_meter=tnt.meter.AverageValueMeter()
best_score = 0
# pre_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)):
title,content,label = (Variable(title[0].cuda()),Variable(title[1].cuda())),(Variable(content[0].cuda()),Variable(content[1].cuda())),Variable(label.cuda())
optimizer.zero_grad()
score = model(title,content)
loss = loss_function(score,opt.weight*label.float())
loss_meter.add(loss.data[0])
loss.backward()
optimizer.step()
if ii%opt.plot_every ==opt.plot_every-1:
if os.path.exists(opt.debug_file):
import ipdb
ipdb.set_trace()
predict = score.data.topk(5,dim=1)[1].cpu().tolist()#(dim=1,descending=True)[1][:,:5].tolist()
true_target = label.data.float().topk(5,dim=1)#[1].cpu().tolist()#sort(dim=1,descending=True)
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('prec:%s,recall:%s,score:%s,a:%s' %(prec_,recall_,score_,_ss),win='tmp')
vis.plot('scores', score_meter.value()[0])
#eval()
vis.plot('loss', loss_meter.value()[0])
# 随机展示一个输出的分布
k = t.randperm(label.size(0))[0]
output=t.nn.functional.sigmoid(score)
# vis.vis.histogram(
# output.data[k].view(-1).cpu(), win=u'output_hist', opts=dict
# (title='output_hist'))
# print "epoch:%4d/%4d,time: %.8f,loss: %.8f " %(epoch,ii,time.time()-start,loss_meter.value()[0])
if ii%opt.decay_every == opt.decay_every-1:
del loss
scores,prec_,recall_ ,_ss= val(model,dataset)
if scores>best_score:
best_score = scores
best_path = model.save(name = str(scores),new=True)
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:
model.load(best_path,change_opt=False)
#lr = lr*opt.lr_decay
#optimizer = model.get_optimizer(lr)
lr = lr * opt.lr_decay
# 第二种降低学习率的方法:不会有moment等的丢失
if lr2==0:lr2=2e-4
else : lr2 = lr2*opt.lr_decay
optimizer = model.get_optimizer(lr,lr2,0)
origin_weight_decay=5*origin_weight_decay
# optimizer = model.get_optimizer(lr,lr2,0,weight_decay=origin_weight_decay)
# origin_weight_decay=5*origin_weight_decay
# for param_group in optimizer.param_groups:
# param_group['lr'] *= opt.lr_decay
# if param_group['lr'] ==0:
# param_group['lr'] = 1e-4
pre_loss = loss_meter.value()[0]
# pre_score = score_meter.value()[0]
# pre_score = scores
loss_meter.reset()
score_meter.reset()
if lr < opt.min_lr:
break
# model.save()
if __name__=="__main__":
fire.Fire()
