PytorchCNN项目搭建 6--- 训练、验证CNN
整体的代码在我的github上面可以查阅
上几次的实验已经下载了数据集，并且写好了models，并进行了一些基础的配置，这次的主要目标是写好训练过程。主要的流程如下：
- 配置args, cfg, log等
- 将之前的数据集datasetset经过DataLoader变成data_loader
- 加载网络net
- 选择损失函数和优化器
- 训练网络，得到损失值loss
import os
import pdb
import argparse
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
from mmcv import Config
import numpy as np
from log.logger import Logger
from dataset.dataset import Cifar10Dataset
from utils.get_net import get_network
from utils.visualization import plot_acc_loss
from loss import MyCrossEntropy
1. 配置args，cfg，log，等
def parser():
parse = argparse.ArgumentParser(description='Pytorch Cifar10 Training')
parse.add_argument('--config','-c',default='./config/config.py',help='config file path') # 配置config
parse.add_argument('--net','-n',type=str,required=True,help='input which model to use') # 配置网络net
parse.add_argument('--pretrain','-p',action='store_true',help='Location pretrain data') # 网络是否进行预训练？默认为否
parse.add_argument('--resume','-r',action='store_true',help='resume from checkpoint') # 是否进行断点续训？默认为否
parse.add_argument('--epoch','-e',default=None,help='resume from epoch') # 断点续训从哪个epoch开始
parse.add_argument('--gpuid','-g',type=int,default=0,help='GPU ID') # 是否指定进行训练的 GPU_ID
args = parse.parse_args()
return args
2. 将数据集dataset经DataLoader变成dataloader
def DataLoad(cfg):
trainset = Cifar10Dataset(txt=cfg.PARA.cifar10_paths.train_data_txt, transform='for_train')
validset = Cifar10Dataset(txt=cfg.PARA.cifar10_paths.valid_data_txt, transform='for_valid')
train_loader = DataLoader(dataset=trainset, batch_size=cfg.PARA.train.batch_size, drop_last=True, shuffle=True, num_workers=cfg.PARA.train.num_workers)
valid_loader = DataLoader(dataset=validset, batch_size=cfg.PARA.train.batch_size, drop_last=True, shuffle=True, num_workers=cfg.PARA.train.num_workers)
return train_loader, valid_loader
3. 加载网络net
4. 选择损失函数和优化器
net = get_network(args, cfg).cuda(args.gpuid)
criterion = MyCrossEntropy().cuda(args.gpuid)
optimizer = optim.SGD(net.parameters(), lr=cfg.PARA.train.lr, momentum=cfg.PARA.train.momentum)
5. 训练网络，得到损失值
def train(net,criterion,optimizer, train_loader, valid_loader, args, log, cfg):
for epoch in range(cfg.PARA.train.epochs): #在一个epoch下，既进行训练，也进行验证
net.train()
train_loss = 0.0
train_total = 0.0
for i, data in enumerate(train_loader, 0):
length = len(train_loader) #length = 47500 / batch_size
inputs, labels = data
inputs, labels = Variable(inputs.cuda(args.gpuid)), Variable(labels.cuda(args.gpuid))
# pdb.set_trace()
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs,labels)
# pdb.set_trace()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
train_total += labels.size(0)
if (i+1+epoch*length)%100==0:
log.logger.info('[Epoch:%d, iter:%d] Loss: %.5f '
%(epoch+1, (i+1+epoch*length), train_loss/ (i+1)))
with open(cfg.PARA.utils_paths.visual_path + args.net + '_train.txt', 'a') as f:
f.write('epoch=%d,loss=%.5f\n' % (epoch + 1, train_loss / length))
net.eval()
valid_loss = 0.0
valid_total = 0.0
with torch.no_grad(): # 强制之后的内容不进行计算图的构建，不使用梯度反传
for i, data in enumerate(valid_loader, 0):
length = len(valid_loader)
inputs, labels = data
inputs, labels = Variable(inputs.cuda(args.gpuid)), Variable(labels.cuda(args.gpuid))
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
valid_total += labels.size(0)
# correct += (predicted == labels).sum()
loss = criterion(outputs, labels)
valid_loss += loss.item()
log.logger.info('Validation | Loss: %.5f' % (valid_loss / length))
with open(cfg.PARA.utils_paths.visual_path+args.net+'_valid.txt','a') as f:
f.write('epoch=%d,loss=%.5f\n' %(epoch+1, valid_loss/length))
'''save model's net & epoch to checkpoint'''
log.logger.info('Save model to checkpoint ' )
checkpoint = {
'net': net.state_dict(),'epoch':epoch}
if not os.path.exists(cfg.PARA.utils_paths.checkpoint_path+args.net):os.makedirs(cfg.PARA.utils_paths.checkpoint_path+args.net)
torch.save(checkpoint, cfg.PARA.utils_paths.checkpoint_path+args.net+'/'+str(epoch+1)+'ckpt.pth')
主函数
def main():
args = parser()
cfg = Config.fromfile(args.config)
log = Logger(cfg.PARA.utils_paths.log_path+ args.net + '_trainlog.txt',level='info')
start_epoch = 0
log.logger.info('==> Preparing dataset <==')
train_loader, valid_loader = DataLoad(cfg)
log.logger.info('==> Loading model <==')
if args.pretrain:
log.logger.info('Loading Pretrain Data')
net = get_network(args, cfg).cuda(args.gpuid)
criterion = MyCrossEntropy().cuda(args.gpuid)
optimizer = optim.SGD(net.parameters(), lr=cfg.PARA.train.lr, momentum=cfg.PARA.train.momentum)
# net = resnet18().cuda(args.gpuid)
log.logger.info('==> SUM NET Params <==')
get_model_params(net,args,cfg)
# if torch.cuda.device_count()>1:#DataParallel is based on Parameter server
# net = nn.DataParallel(net, device_ids=cfg.PARA.train.device_ids)
torch.backends.cudnn.benchmark = True
'''断点续训否'''
if args.resume:
log.logger.info('Resuming from checkpoint')
checkpoint = torch.load(cfg.PARA.utils_paths.checkpoint_path+args.net+'/'+args.epoch + 'ckpt.pth')
net.load_state_dict(checkpoint['net'])
start_epoch = checkpoint['epoch']
log.logger.info('==> Waiting Train <==')
train(net=net,criterion=criterion,optimizer=optimizer,
train_loader=train_loader,valid_loader=valid_loader,args=args,log=log,cfg=cfg)
log.logger.info('==> Finish Train <==')
log.logger.info('==> Plot Train_Vilid Loss & Save to Visual <==')
plot_acc_loss(args, cfg=cfg)
log.logger.info('*'*25)
if __name__ == '__main__':
main()
说明：
主函数在后台运行，并指定net
参考文献
最后感谢我的师兄，是他手把手教我搭建了整个项目，还有实验室一起学习的小伙伴~ 希望他们万事胜意，鹏程万里！