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main-all.1.py
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main-all.1.py
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#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)
'''
直接训练最好模型的attention stack
'''
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
opt.parse(kwargs,print_=False)
if opt.debug:import ipdb;ipdb.set_trace()
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','CNNText_inception','RCNN','CNNText_inception','LSTMText']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.41124002492','checkpoints/RCNN_word_0.411511574999','checkpoints/LSTMText_word_0.411994005382','checkpoints/CNNText_tmp_char_0.402429167301','checkpoints/RCNN_char_0.403710422571','checkpoints/CNNText_tmp_word_0.41096749885','checkpoints/LSTMText_char_0.403192339135',]#'checkpoints/FastText_word_0.400391584867']
##################iMultiModelAll2_word_0.425600838271################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.41124002492','checkpoints/RCNN_word_0.411511574999','checkpoints/LSTMText_word_0.411994005382','checkpoints/RCNN_char_0.403710422571','checkpoints/CNNText_tmp_char_0.402429167301']
#####-------------------------------------------------------#####
#############################################################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/RCNN_word_0.373609030286','checkpoints/LSTMText_word_0.381833388089','checkpoints/RCNN_char_0.3456599248','checkpoints/CNNText_tmp_0.352036505041']
#####-------------------------------------------------------#####
# opt.model_names=['LSTMText','MultiCNNTextBNDeep']
# opt.model_paths=['checkpoints/LSTMText_word_0.396765494482','checkpoints/MultiCNNTextBNDeep_word_0.391018392216']
# opt.fold=1
# from data.dataset import ALLFoldData as ZhihuALLData
########################################################################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','CNNText_inception']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/RCNN_word_0.373609030286','checkpoints/LSTMText_word_0.381833388089','checkpoints/RCNN_char_0.3456599248','checkpoints/CNNText_tmp_0.352036505041']
#######################################0.41884129858126845-force#####################
# opt.model_names=['MultiCNNTextBNDeep','RCNN','LSTMText','RCNN','MultiCNNTextBNDeep']
# opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.410011182415','checkpoints/RCNN_word_0.413446202556','checkpoints/LSTMText_word_0.413681107036','checkpoints/RCNN_char_0.398655349075','checkpoints/MultiCNNTextBNDeep_char_0.38666657051']
#######################################################################
#############################################MultiModelallfast_0.419088#####################################
opt.model_names=['MultiCNNTextBNDeep','FastText3','LSTMText','CNNText_inception']
opt.model_paths = ['checkpoints/MultiCNNTextBNDeep_word_0.41124002492','checkpoints/FastText3_word_0.40810787337','checkpoints/LSTMText_word_0.413681107036','checkpoints/CNNText_tmp_char_0.402429167301'
########################################################################################3
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_,augument=opt.augument)
# 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)
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,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])
vis.plot('loss', loss_meter.value()[0])
if ii%opt.decay_every == opt.decay_every-1:
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
if lr2==0:lr2=1e-4
else : lr2 = lr2*0.5
optimizer = model.get_optimizer(lr,lr2,0)
pre_loss = loss_meter.value()[0]
loss_meter.reset()
score_meter.reset()
if __name__=="__main__":
fire.Fire()