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train.py
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train.py
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import random
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from misc.MI_losses import Triplet_MI_loss
from model.WideResnet import WideResnet
from dataloader.loading_data import loading_data,update_loading
from label_guessor import LabelGuessor
from misc.lr_scheduler import WarmupCosineLrScheduler
from misc.ema import EMA
from config import cfg
from misc.utils import *
import copy
class Trainer():
def __init__(self,pwd):
self.model = WideResnet(cfg.n_classes, k=cfg.wresnet_k, n=cfg.wresnet_n, batchsize= cfg.batch_size)
self.model= self.model.cuda()
self.unlabeled_trainloader, self.val_loader = loading_data()
self.labeled_trainloader = update_loading(epoch = 0)
wd_params, non_wd_params = [], []
for param in self.model.parameters():
if len(param.size()) == 1:
non_wd_params.append(param)
else:
wd_params.append(param)
param_list = [{'params': wd_params}, {'params': non_wd_params, 'weight_decay': 0}]
self.optimizer = torch.optim.SGD(param_list, lr=cfg.lr, weight_decay=cfg.weight_decay,momentum=cfg.momentum, nesterov=True)
self.n_iters_per_epoch = cfg.n_imgs_per_epoch // cfg.batch_size
self.lr_schdlr = WarmupCosineLrScheduler(self.optimizer, max_iter=self.n_iters_per_epoch * cfg.n_epoches, warmup_iter=0)
self.lb_guessor = LabelGuessor(args=cfg)
self.train_record = {'best_acc1': 0, 'best_model_name': '','last_model_name': ''}
self.cross_entropy = nn.CrossEntropyLoss().cuda()
self.i_tb = 0
self.epoch = 0
self.exp_name = cfg.exp_name
self.exp_path = cfg.exp_path
if cfg.resume:
print('Loaded resume weights for WideResnet')
latest_state = torch.load(cfg.resume_model)
self.model.load_state_dict(latest_state['net'])
self.optimizer.load_state_dict(latest_state['optimizer'])
self.lr_schdlr.load_state_dict(latest_state['scheduler'])
self.epoch = latest_state['epoch'] + 1
self.i_tb = latest_state['i_tb']
self.train_record = latest_state['train_record']
self.exp_path = latest_state['exp_path']
self.exp_name = latest_state['exp_name']
self.ema = EMA(self.model, cfg.ema_alpha)
self.writer,self.log_txt = logger(cfg.exp_path, cfg.exp_name, pwd, ['exp','dataset','pretrained','pre_trained'])
def forward(self):
print('start to train')
for epoch in range(self.epoch,cfg.n_epoches):
self.epoch=epoch
print(('='*50+'epoch: {}'+'='*50).format(self.epoch+1))
self.train()
torch.cuda.empty_cache()
self.evaluate(self.ema)
def train(self):
if self.epoch > cfg.start_add_samples_epoch:
indexs, pre_targets = self.lb_guessor.label_generator.new_data()
self.labeled_trainloader = update_loading(copy.deepcopy(indexs), copy.deepcopy(pre_targets),self.epoch)
self.lb_guessor.init_for_add_sample(self.epoch, cfg.start_add_samples_epoch)
self.model.train()
Loss,Loss_L, Loss_U, Loss_U_Real, Loss_MI=AverageMeter(),AverageMeter(),AverageMeter(),\
AverageMeter(),AverageMeter()
Correct_Num,Valid_Num =AverageMeter(), AverageMeter()
st = time.time()
l_set, u_set = iter(self.labeled_trainloader), iter(self.unlabeled_trainloader)
for it in range(self.n_iters_per_epoch):
(img, img_l_weak, img_l_strong), lbs_l = next(l_set)
(img_u, img_u_weak, img_u_strong), lbs_u_real, index_u = next(u_set)
img_l_weak,img_l_strong,lbs_l = img_l_weak.cuda(), img_l_strong.cuda(),lbs_l.cuda()
img_u,img_u_weak,img_u_strong = img_u.cuda(),img_u_weak.cuda(),img_u_strong.cuda()
lbs_u, valid_u = self.lb_guessor(self.model, img_l_weak, img_u_weak, lbs_l,index_u)
n_u = img_u_strong.size(0)
img_cat = torch.cat([img_l_weak,img_u,img_u_weak, img_u_strong], dim=0).detach()
_, __, pred_l, pred_u = self.model(img_cat)
pred_u_o, pred_u_w, pred_u_s = pred_u[:n_u], pred_u[n_u:2 * n_u], pred_u[2 * n_u:]
#=====================cross-entropy loss for labeled data==============
loss_l = self.cross_entropy(pred_l, lbs_l)
# =====================T-MI loss for unlabeled data==============
if self.epoch>=20:
T_MI_loss = Triplet_MI_loss(pred_u_o,pred_u_w,pred_u_s)
else:
T_MI_loss = torch.tensor(0)
# =====================cross-entropy loss for unlabeled data==============
if lbs_u.size(0)>0 and self.epoch>=2:
pred_u_s = pred_u_s[valid_u]
loss_u = self.cross_entropy(pred_u_s, lbs_u)
with torch.no_grad():
lbs_u_real = lbs_u_real[valid_u].cuda()
valid_num = lbs_u_real.size(0)
corr_lb = (lbs_u_real == lbs_u)
loss_u_real = F.cross_entropy(pred_u_s, lbs_u_real)
else:
loss_u = torch.tensor(0)
loss_u_real = torch.tensor(0)
corr_lb = torch.tensor(0)
valid_num = 0
loss = loss_l + cfg.lam_u * loss_u + 0.1 * T_MI_loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.ema.update_params()
self.lr_schdlr.step()
Loss.update(loss.item())
Loss_L.update(loss_l.item())
Loss_U.update(loss_u.item())
Loss_U_Real.update(loss_u_real.item())
Loss_MI.update(T_MI_loss.item())
Correct_Num.update(corr_lb.sum().item())
Valid_Num.update(valid_num)
if (it+1) % 256 == 0:
self.i_tb += 1
self.writer.add_scalar('loss_u', Loss_U.avg, self.i_tb)
self.writer.add_scalar('loss_MI', Loss_MI.avg, self.i_tb)
ed = time.time()
t = ed -st
lr_log = [pg['lr'] for pg in self.optimizer.param_groups]
lr_log = sum(lr_log) / len(lr_log)
msg = ', '.join([
' [iter: {}',
'loss: {:.3f}',
'loss_l: {:.4f}',
'loss_u: {:.4f}',
'loss_u_real: {:.4f}',
'loss_MI: {:.4f}',
'correct: {}/{}',
'lr: {:.4f}',
'time: {:.2f}]',
]).format(
it+1, Loss.avg, Loss_L.avg, Loss_U.avg,Loss_U_Real.avg, Loss_MI.avg,
int(Correct_Num.avg), int(Valid_Num.avg), lr_log, t
)
st = ed
print(msg)
self.ema.update_buffer()
self.writer.add_scalar('acc_overall', Correct_Num.sum/(cfg.n_imgs_per_epoch*cfg.mu), self.epoch+1)
self.writer.add_scalar('acc_in_labeled', Correct_Num.sum/(Valid_Num.sum+1e-10), self.epoch+1)
def evaluate(self,ema):
ema.apply_shadow()
ema.model.eval()
ema.model.cuda()
matches = []
for ims, lbs in self.val_loader:
ims = ims.cuda()
lbs = lbs.cuda()
with torch.no_grad():
__,preds = ema.model(ims, mode='val')
scores = torch.softmax(preds, dim=1)
_, preds = torch.max(scores, dim=1)
match = lbs == preds
matches.append(match)
matches = torch.cat(matches, dim=0).float()
acc = torch.mean(matches)
self.writer.add_scalar('val_acc', acc, self.epoch)
self.train_record = update_model(ema.model, self.optimizer,self.lr_schdlr,self.epoch, self.i_tb,self.exp_path,self.exp_name,acc, self.train_record)
print_summary(cfg.exp_name,acc, self.train_record)
ema.restore()
if __name__ == '__main__':
import os
# ------------prepare enviroment------------
seed = cfg.seed
if seed is not None:
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.gpu_id
pwd = os.path.split(os.path.realpath(__file__))[0]
trainer = Trainer(pwd)
trainer.forward()