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train.py
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# -*- coding:utf-8 _*-
"""
@author:fxw
@file: train.py.py
@time: 2020/04/28
"""
import sys
sys.path.append('/home/aistudio/external-libraries')
import os
import torch
import torch.nn as nn
import yaml
import argparse
import numpy as np
olderr = np.seterr(all='ignore')
from models.DBNet import DBNet
from torch.autograd import Variable
from loss.loss import L1BalanceCELoss
from dataloader.dataload import DataLoader
from utils.Logger import Logger
from utils.metrics import runningScore
from utils.model_eval import val
from utils.tools import *
from utils.set_optimizer import *
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def updateBN(model,config):
tag = 0
for m in model.modules():
if(tag>69):
break
if isinstance(m, nn.BatchNorm2d):
if hasattr(m.weight, 'data'):
m.weight.grad.data.add_(config['train']['sr_lr']*torch.sign(m.weight.data)) #L1正则
tag+=1
def set_seed(seed):
import numpy as np
import random
import torch
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
GLOBAL_WORKER_ID = None
GLOBAL_SEED = 2020
def worker_init_fn(worker_id):
global GLOBAL_WORKER_ID
GLOBAL_WORKER_ID = worker_id
set_seed(GLOBAL_SEED + worker_id)
def train_net(config):
os.environ["CUDA_VISIBLE_DEVICES"] = config['train']['gpu_id']
data_loader = DataLoader(config)
train_loader = torch.utils.data.DataLoader(
data_loader,
batch_size=config['train']['batch_size'],
shuffle=True,
num_workers=config['train']['num_workers'],
worker_init_fn = worker_init_fn,
drop_last=True,
pin_memory=False)
start_epoch = 0
running_metric_binary = runningScore(2)
if not (os.path.exists(config['train']['checkpoints'])):
os.mkdir(config['train']['checkpoints'])
checkpoints = os.path.join(config['train']['checkpoints'],"DB_%s_bs_%d_ep_%d" % (config['train']['backbone'],
config['train']['batch_size'], config['train']['n_epoch']))
if not (os.path.exists(checkpoints)):
os.mkdir(checkpoints)
model = DBNet(config).cuda()
criterion = L1BalanceCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=config['train']['base_lr'], momentum=0.99, weight_decay=5e-4)
if config['train']['restore']:
print('Resuming from checkpoint.')
assert os.path.isfile(config['train']['resume']), 'Error: no checkpoint directory found!'
checkpoint = torch.load(config['train']['resume'])
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
log_write = Logger(os.path.join(checkpoints, 'log.txt'), title=config['train']['backbone'], resume=True)
else:
print('Training from scratch.')
log_write = Logger(os.path.join(checkpoints,'log.txt'), title=config['train']['backbone'])
log_write.set_names([' epoch', 'Total loss', ' Bce loss', 'Thresh loss', ' L1 loss', 'Binary Acc', 'Binary IoU', ' rescall',' precision',' hmean'])
max_hmean = -1
for epoch in range(start_epoch,config['train']['n_epoch']):
model.train()
bce_loss_list = []
thresh_loss_list = []
l1_loss_list = []
total_loss_list = []
if(config['train']['decay_method']=='e_decay'):
adjust_learning_rate_poly(config['train']['base_lr'], optimizer, epoch, max_epoch=config['train']['n_epoch'], factor=0.9)
else:
adjust_learning_rate(config, optimizer, epoch,config['train']['gama'])
for batch_idx, (imgs, gts, gt_masks, thresh_maps, thresh_masks) in enumerate(train_loader):
imgs = Variable(imgs.cuda())
gts = Variable(gts.cuda())
gt_masks = Variable(gt_masks.cuda())
thresh_maps = Variable(thresh_maps.cuda())
thresh_masks = Variable(thresh_masks.cuda())
batch = {}
batch['gt'] = gts
batch['mask'] = gt_masks
batch['thresh_map'] = thresh_maps
batch['thresh_mask'] = thresh_masks
pre = model(imgs)
loss, metrics = criterion(pre, batch)
optimizer.zero_grad()
loss.backward()
if(config['train']['use_sr']):
updateBN(model,config)
optimizer.step()
score_binary = cal_binary_score(pre['binary'], gts, gt_masks.unsqueeze(1), running_metric_binary)
bce_loss_list.append(metrics['bce_loss'].item())
thresh_loss_list.append(metrics['thresh_loss'].item())
l1_loss_list.append(metrics['l1_loss'].item())
total_loss_list.append(loss.item())
if batch_idx % config['train']['show_step'] == 0:
if(config['train']['print_format']=='linux'):
headers = ['epoch/epochs','batch/batchs' ,'TotalLoss' ,'BceLoss',' ThreshLoss','L1Loss', 'Binary Acc','Binary IoU', 'Lr Rate']
show_item = [[str(epoch)+'/'+str(config['train']['n_epoch']),
str(batch_idx + 1)+'/'+str(len(train_loader)),
get_str(np.mean(total_loss_list)),
get_str(np.mean(bce_loss_list)),
get_str(np.mean(thresh_loss_list)),
get_str(np.mean(l1_loss_list)),
get_str(score_binary['Mean Acc']),
get_str(score_binary['Mean IoU']),
get_str(optimizer.param_groups[0]['lr'])
]]
print_table(headers,show_item,type_str='train')
else:
output_log = '({epoch}/{epochs}/{batch}/{size}) | TotalLoss: {total_loss:.4f} | BceLoss: {bce_loss:.4f} | ThreshLoss: {thresh_loss: .4f} | L1Loss: {l1_loss: .4f} | Binary Acc: {bin_acc: .4f} | Binary IoU: {bin_iou: .4f} | Lr: {lr: .4f}'.format(
epoch=epoch,
epochs=config['train']['n_epoch'] ,
batch=batch_idx + 1,
size=len(train_loader),
total_loss=np.mean(total_loss_list),
bce_loss=np.mean(bce_loss_list),
thresh_loss=np.mean(thresh_loss_list),
l1_loss=np.mean(l1_loss_list),
bin_acc=score_binary['Mean Acc'],
bin_iou=score_binary['Mean IoU'],
lr=optimizer.param_groups[0]['lr']
)
print(output_log)
sys.stdout.flush()
if( epoch > config['train']['start_val_epoch']):
result_dict = val(model,config)
rescall,precision,hmean = result_dict['recall'],result_dict['precision'],result_dict['hmean']
print('epoch:',epoch,'rescall:',rescall,'precision:',precision,'hmean:',hmean)
else:
rescall = 0
precision = 0
hmean = 0
log_write.append([epoch, np.mean(total_loss_list), np.mean(bce_loss_list), np.mean(thresh_loss_list),
np.mean(l1_loss_list), score_binary['Mean Acc'], score_binary['Mean IoU'],
rescall,precision,hmean])
if(hmean > max_hmean and config['train']['start_val_epoch'] < config['train']['n_epoch']):
max_hmean = hmean
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'lr': config['train']['base_lr'],
'optimizer': optimizer.state_dict(),
}, checkpoint=checkpoints,filename='best_model.pth.tar')
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'lr': config['train']['base_lr'],
'optimizer': optimizer.state_dict(),
}, checkpoint=checkpoints)
if __name__ == '__main__':
stream = open('config.yaml', 'r', encoding='utf-8')
config = yaml.load(stream,Loader=yaml.FullLoader)
train_net(config)