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main.py
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main.py
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import argparse
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torch.nn.functional as F
import time
from torch.autograd import Variable
from dataloader import listflowfile as lt
from dataloader import SecenFlowLoader as DA
import utils.logger as logger
from utils.flops_hook import profile
from models.LWANet import *
parser = argparse.ArgumentParser(description='LWANet with Sceneflow dataset')
parser.add_argument('--maxdisp', type=int, default=192, help='maxium disparity')
parser.add_argument('--loss_weights', type=float, nargs='+', default=[1., 1.])
parser.add_argument('--maxdisplist', type=int, nargs='+', default=[24, 3, 3])
parser.add_argument('--lr', type=float, default=5e-4, help='learning rate')
parser.add_argument('--with_cspn', type =bool, default= True, help='with cspn network or not')
parser.add_argument('--datapath', default='/data6/wsgan/SenceFlow/train/', help='datapath')
parser.add_argument('--epochs', type=int, default=50, help='number of epochs to train')
parser.add_argument('--train_bsize', type=int, default=16, help='batch size for training (default: 12)')
parser.add_argument('--test_bsize', type=int, default=8, help='batch size for testing (default: 8)')
parser.add_argument('--save_path', type=str, default='./results/sceneflow/', help='the path of saving checkpoints and log')
parser.add_argument('--resume', type=str, default=None, help='resume path')
parser.add_argument('--print_freq', type=int, default=400, help='print frequence')
parser.add_argument('--model_types', type=str, default='LWANet', help='model_types : 3D, P3D')
parser.add_argument('--conv_3d_types1', type=str, default='P3D', help='model_types : 3D, P3D ')
parser.add_argument('--conv_3d_types2', type=str, default='P3D', help='model_types : 3D, P3D')
parser.add_argument('--cost_volume', type=str, default='Difference', help='cost_volume type : "Concat" , "Difference" or "Distance_based" ')
parser.add_argument('--train', type =bool, default=True, help='train or test ')
args = parser.parse_args()
#CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py
def main():
global args
train_left_img, train_right_img, train_left_disp, test_left_img, test_right_img, test_left_disp = lt.dataloader(
args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(train_left_img, train_right_img, train_left_disp, True),
batch_size=args.train_bsize, shuffle=True, num_workers=4, drop_last=False)
TestImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(test_left_img, test_right_img, test_left_disp, False),
batch_size=args.test_bsize, shuffle=False, num_workers=4, drop_last=False)
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
log = logger.setup_logger(args.save_path + '/training.log')
for key, value in sorted(vars(args).items()):
log.info(str(key) + ': ' + str(value))
model = LWANet(args)
# FLOPs, params = count_flops(model.cuda())
# log.info('Number of model parameters: {}'.format(params))
# log.info('Number of model FLOPs: {}'.format(FLOPs))
model = nn.DataParallel(model).cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))
args.start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
log.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
log.info("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
log.info("=> no checkpoint found at '{}'".format(args.resume))
log.info("=> Will start from scratch.")
else:
log.info('Not Resume')
start_full_time = time.time()
if args.train:
for epoch in range(args.start_epoch, args.epochs):
log.info('This is {}-th epoch'.format(epoch))
train(TrainImgLoader, model, optimizer, log, epoch)
savefilename = args.save_path + '/checkpoint_' + str(epoch) + '.tar'
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, savefilename)
if not epoch % 10:
test(TestImgLoader, model, log)
test(TestImgLoader, model, log)
log.info('full training time = {:.2f} Hours'.format((time.time() - start_full_time) / 3600))
def train(dataloader, model, optimizer, log, epoch=0):
stages = 2
losses = [AverageMeter() for _ in range(stages)]
length_loader = len(dataloader)
model.train()
for batch_idx, (imgL, imgR, disp_L) in enumerate(dataloader):
imgL = imgL.float().cuda()
imgR = imgR.float().cuda()
disp_L = disp_L.float().cuda()
optimizer.zero_grad()
mask = (disp_L < args.maxdisp) & (disp_L > 0)
if mask.float().sum() == 0:
continue
mask.detach_()
outputs, self_supervised_loss = model(imgL, imgR)
stages = len(outputs)
outputs = [torch.squeeze(output, 1) for output in outputs]
loss = [args.loss_weights[x] * F.smooth_l1_loss(outputs[x][mask], disp_L[mask], size_average=True)
for x in range(stages)]
sum(loss).backward()
optimizer.step()
for idx in range(stages):
losses[idx].update(loss[idx].item()/args.loss_weights[idx])
if batch_idx % args.print_freq ==0:
info_str = ['Stage {} = {:.2f}({:.2f})'.format(x, losses[x].val, losses[x].avg) for x in range(stages)]
info_str = '\t'.join(info_str)
log.info('Epoch{} [{}/{}] {}'.format(
epoch, batch_idx, length_loader, info_str))
info_str = '\t'.join(['Stage {} = {:.2f}'.format(x, losses[x].avg) for x in range(stages)])
log.info('Average train loss = ' + info_str)
def test(dataloader, model, log):
stages = 2
EPEs = [AverageMeter() for _ in range(stages)]
length_loader = len(dataloader)
model.eval()
inference_time = 0
for batch_idx, (imgL, imgR, disp_L) in enumerate(dataloader):
imgL = imgL.float().cuda()
imgR = imgR.float().cuda()
disp_L = disp_L.float().cuda()
mask = disp_L < args.maxdisp
with torch.no_grad():
time_start = time.perf_counter()
outputs, monoloss = model(imgL, imgR)
single_inference_time = time.perf_counter() - time_start
inference_time += single_inference_time
stages = len(outputs)
for x in range(stages):
if len(disp_L[mask]) == 0:
EPEs[x].update(0)
continue
output = torch.squeeze(outputs[x], 1)
output = output[:, 4:, :]
EPEs[x].update((output[mask] - disp_L[mask]).abs().mean())
if batch_idx % args.print_freq == 0:
info_str = '\t'.join(['Stage {} = {:.2f}({:.2f})'.format(x, EPEs[x].val, EPEs[x].avg) for x in range(stages)])
log.info('[{}/{}] {}'.format(
batch_idx, length_loader, info_str))
log.info(('=> Mean inference time for %d images: %.3fs' % (
length_loader, inference_time / length_loader)))
info_str = ', '.join(['Stage {}={:.2f}'.format(x, EPEs[x].avg) for x in range(stages)])
log.info('Average test EPE = ' + info_str)
def adjust_learning_rate(optimizer, epoch):
if epoch <= 20:
lr = args.lr
elif 20 <epoch <= 30:
lr = args.lr * 0.5
elif 30< epoch <= 40:
lr = args.lr * 0.25
else:
lr = args.lr * 0.125
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def count_flops(model):
input = Variable(torch.randn(1, 3, 544, 960)).cuda()
FLOPs, params = profile(model, inputs = (input,input))
return FLOPs, params
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
if __name__ == '__main__':
main()
# 快捷指令
# https://www.cnblogs.com/kaiye/p/6275207.html
# tmux new -s name 创建session
# tmux a -t name 进入session
# tmux ls 查看session
# tmux kill-session -t name 杀掉session 进程
# ctrl + B 松开 D 退出当前session的全部窗口
# ctrl + B 松开+ C 基于当前session新建窗口
# exit 退出当前bash的窗口
# ctrl +B 松开 + x 删除当前bash
# ctrl + B 松开 - 水平分屏
# ctrl + B 松开 shift + - 垂直分屏
# tmux kill-server # 删除所有的会话