parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed_all(args.seed)
test_left_img1, test_right_img1, test_left_disp1, test_left_img, test_right_img, test_left_disp = ls.dataloader(
args.datapath)
TestImgLoader = torch.utils.data.DataLoader(DA.myImageFloder(
test_left_img, test_right_img, test_left_disp, False),
batch_size=1,
shuffle=False,
num_workers=4,
drop_last=False)
if args.model == 'ShuffleStereo8':
model = MABNet_origin(args.maxdisp)
elif args.model == 'ShuffleStereo16':
model = ShuffleStereo16(args.maxdisp)
else:
print('no model')
if args.cuda:
model = nn.DataParallel(model)
model.cuda()
from dataloader import KITTIloader_VirtualKT2 as ls
train_file_list = args.vkt2_train_list
val_file_list = args.vkt2_val_list
virtual_kitti2 = True
else:
raise Exception("No suitable KITTI found ...")
print('[??] args.datapath = ', args.datapath)
all_left_img, all_right_img, all_left_disp, test_left_img, \
test_right_img, test_left_disp = ls.dataloader(
args.datapath, train_file_list, val_file_list)
TrainImgLoader = torch.utils.data.DataLoader(DA.myImageFloder(
all_left_img,
all_right_img,
all_left_disp,
training=True,
virtual_kitti2=virtual_kitti2),
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
drop_last=False)
TestImgLoader = torch.utils.data.DataLoader(DA.myImageFloder(
test_left_img,
test_right_img,
test_left_disp,
training=False,
virtual_kitti2=virtual_kitti2),
batch_size=args.batch_size,
shuffle=False,
def main(log):
global args
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
## init dist ##
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
args.world_size = 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."
if args.model_types == "PSMNet":
model = PSMNet(args)
args.loss_weights = [0.5, 0.7, 1.]
elif args.model_types == "PSMNet_DSM":
model = PSMNet_DSM(args)
args.loss_weights = [0.5, 0.7, 1.]
elif args.model_types == "Hybrid_Net_DSM" or "Hybrid_Net":
model = Hybrid_Net(args)
args.loss_weights = [0.5, 0.7, 1., 1., 1.]
else:
AssertionError("model error")
if args.datatype == '2015':
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader2015(
args.datapath2015, split=args.split_for_val)
elif args.datatype == '2012':
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader2012(
args.datapath2012, split=False)
elif args.datatype == 'mix':
all_left_img_2015, all_right_img_2015, all_left_disp_2015, test_left_img_2015, test_right_img_2015, test_left_disp_2015 = ls.dataloader2015(
args.datapath2015, split=False)
all_left_img_2012, all_right_img_2012, all_left_disp_2012, test_left_img_2012, test_right_img_2012, test_left_disp_2012 = ls.dataloader2012(
args.datapath2012, split=False)
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = \
all_left_img_2015 + all_left_img_2012, all_right_img_2015 + all_right_img_2012, \
all_left_disp_2015 + all_left_disp_2012, test_left_img_2015 + test_left_img_2012, \
test_right_img_2015 + test_right_img_2012, test_left_disp_2015 + test_left_disp_2012
else:
AssertionError("please define the finetune dataset")
train_set = DA.myImageFloder(all_left_img, all_right_img, all_left_disp,
True)
val_set = DA.myImageFloder(test_left_img, test_right_img, test_left_disp,
False)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_set)
else:
train_sampler = None
val_sampler = None
TrainImgLoader = torch.utils.data.DataLoader(train_set,
batch_size=args.train_bsize,
shuffle=False,
num_workers=4,
pin_memory=True,
sampler=train_sampler,
drop_last=False)
TestImgLoader = torch.utils.data.DataLoader(val_set,
batch_size=args.test_bsize,
shuffle=False,
num_workers=4,
pin_memory=True,
sampler=None,
drop_last=False)
num_train = len(TrainImgLoader)
num_test = len(TestImgLoader)
if args.local_rank == 0:
for key, value in sorted(vars(args).items()):
log.info(str(key) + ': ' + str(value))
stages = len(args.loss_weights)
# note
if args.sync_bn:
import apex
model = apex.parallel.convert_syncbn_model(model)
if args.local_rank == 0:
log.info(
"using apex synced BN-----------------------------------------------------"
)
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=0.1, betas=(0.9, 0.999))
model, optimizer = amp.initialize(
model,
optimizer,
opt_level=args.opt_level,
keep_batchnorm_fp32=args.keep_batchnorm_fp32,
loss_scale=args.loss_scale)
if args.distributed:
model = DDP(model, delay_allreduce=True)
if args.local_rank == 0:
log.info(
"using distributed-----------------------------------------------------"
)
if args.pretrained:
if os.path.isfile(args.pretrained):
checkpoint = torch.load(args.pretrained, map_location='cpu')
model.load_state_dict(checkpoint['state_dict'], strict=True)
if args.local_rank == 0:
log.info("=> loaded pretrained model '{}'".format(
args.pretrained))
else:
if args.local_rank == 0:
log.info("=> no pretrained model found at '{}'".format(
args.pretrained))
log.info("=> Will start from scratch.")
args.start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
if args.local_rank == 0:
log.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location='cpu')
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch'] + 1
if args.local_rank == 0:
log.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
if args.local_rank == 0:
log.info("=> no checkpoint found at '{}'".format(args.resume))
log.info("=> Will start from scratch.")
else:
if args.local_rank == 0:
log.info('Not Resume')
if args.local_rank == 0:
log.info('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
start_full_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
total_train_loss = 0
total_d1 = 0
total_epe = 0
adjust_learning_rate(optimizer, epoch)
losses = [AverageMeter() for _ in range(stages)]
## training ##
for batch_idx, (imgL_crop, imgR_crop,
disp_crop_L) in enumerate(TrainImgLoader):
loss = train(imgL_crop, imgR_crop, disp_crop_L, model, optimizer)
for idx in range(stages):
losses[idx].update(loss[idx].item() / args.loss_weights[idx])
# # record loss
info_str = [
'Stage {} = {:.2f}({:.2f})'.format(x, losses[x].val,
losses[x].avg)
for x in range(stages)
]
info_str = '\t'.join(info_str)
if args.local_rank == 0:
log.info('losses Epoch{} [{}/{}] {}'.format(
epoch, batch_idx, len(TrainImgLoader), info_str))
total_train_loss += loss[-1]
if args.local_rank == 0:
log.info('epoch %d total training loss = %.3f' %
(epoch, total_train_loss / num_train))
if epoch % 50 == 0:
## Test ##
inference_time = 0
for batch_idx, (imgL, imgR, disp_L) in enumerate(TestImgLoader):
epe, d1, single_inference_time = test(imgL, imgR, disp_L,
model)
inference_time += single_inference_time
total_d1 += d1
total_epe += epe
if args.distributed:
total_epe = reduce_tensor(total_epe.data)
total_d1 = reduce_tensor(total_d1.data)
else:
total_epe = total_epe
total_d1 = total_d1
if args.local_rank == 0:
log.info('epoch %d avg_3-px error in val = %.3f' %
(epoch, total_d1 / num_test * 100))
log.info('epoch %d avg_epe in val = %.3f' %
(epoch, total_epe / num_test))
log.info(('=> Mean inference time for %d images: %.3fs' %
(num_test, inference_time / num_test)))
if args.local_rank == 0:
if epoch % 100 == 0:
savefilename = args.save_path + '/finetune_' + str(
epoch) + '.tar'
torch.save(
{
'epoch': epoch,
'state_dict': model.state_dict(),
'train_loss':
total_train_loss / len(TrainImgLoader),
'test_loss': total_d1 / len(TestImgLoader) * 100,
}, savefilename)
if args.local_rank == 0:
log.info('full finetune time = %.2f HR' %
((time.time() - start_full_time) / 3600))
def main():
epoch_start = 0
max_acc = 0
max_epo = 0
# 数据集部分
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(
args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(DA.myImageFloder(
all_left_img, all_right_img, all_left_disp, True),
batch_size=args.batch_size,
shuffle=True,
num_workers=12,
drop_last=True)
TestImgLoader = torch.utils.data.DataLoader(DA.myImageFloder(
test_left_img, test_right_img, test_left_disp, False),
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
drop_last=False)
start_full_time = time.time()
for epoch in range(epoch_start, args.epochs + 1):
print("epoch:", epoch)
# training
total_train_loss = 0
for batch_idx, (imgL_crop, imgR_crop,
disp_crop_L) in enumerate(TrainImgLoader):
loss = train(imgL_crop, imgR_crop, disp_crop_L)
print(loss)
exit()
total_train_loss += loss
print('epoch:{}, step:{}, loss:{}'.format(epoch, batch_idx, loss))
print('epoch %d average training loss = %.3f' %
(epoch, total_train_loss / len(TrainImgLoader)))
# test
total_test_three_pixel_error_rate = 0
for batch_idx, (imgL, imgR, disp_L) in enumerate(TestImgLoader):
test_three_pixel_error_rate = test(imgL, imgR, disp_L)
total_test_three_pixel_error_rate += test_three_pixel_error_rate
print('epoch %d total 3-px error in val = %.3f' %
(epoch,
total_test_three_pixel_error_rate / len(TestImgLoader) * 100))
acc = (1 -
total_test_three_pixel_error_rate / len(TestImgLoader)) * 100
if acc > max_acc:
max_acc = acc
max_epo = epoch
savefilename = './kitti15.tar'
#
# savefilename = root_path + '/checkpoints/checkpoint_finetune_kitti15.tar'
torch.save(
{
'state_dict': model.state_dict(),
'total_train_loss': total_train_loss,
'epoch': epoch + 1,
'optimizer_state_dict': optimizer.state_dict(),
'max_acc': max_acc,
'max_epoch': max_epo
}, savefilename)
print("-- max acc checkpoint saved --")
print('MAX epoch %d test 3 pixel correct rate = %.3f' %
(max_epo, max_acc))
print('full finetune time = %.2f HR' %
((time.time() - start_full_time) / 3600))
print(max_epo)
print(max_acc)
def main():
global args
log = logger.setup_logger(args.save_path + '/training.log')
# train_left_img, train_right_img, train_left_disp, test_left_img, test_right_img, test_left_disp, test_fn = ls.dataloader(
# args.datapath,log, args.split_file)
#
# 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, test_fn),
# batch_size=args.test_bsize, shuffle=False, num_workers=4, drop_last=False)
train_left_img, train_right_img, train_left_disp, test_left_img, test_right_img, left_val_disp, val_fn, left_train_semantic, left_val_semantic = ls.dataloader(
args.datapath, log, args.split_file)
TrainImgLoader = torch.utils.data.DataLoader(DA.myImageFloder(
train_left_img, train_right_img, train_left_disp, left_train_semantic,
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, left_val_disp, left_val_semantic, False,
val_fn),
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)
for key, value in sorted(vars(args).items()):
log.info(str(key) + ': ' + str(value))
model = models.anynet.AnyNet(args)
model = nn.DataParallel(model).cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))
log.info('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
if args.pretrained:
if os.path.isfile(args.pretrained):
checkpoint = torch.load(args.pretrained)
model.load_state_dict(checkpoint['state_dict'], strict=False)
log.info("=> loaded pretrained model '{}'".format(args.pretrained))
else:
log.info("=> no pretrained model found at '{}'".format(
args.pretrained))
log.info("=> Will start from scratch.")
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)
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')
cudnn.benchmark = True
test(TestImgLoader, model, log)
return
help='save model')
parser.add_argument('--logdir', default='./snapshots/',
help='save log')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
t_loader = KL.KITTIDataset(args.data_path,phase='train',n_class=args.n_class,flip_rate=0.5)#CL.CityScapesDataset
v_loader =KL.KITTIDataset(args.data_path,phase='val',n_class=args.n_class,flip_rate=0.)#CL.CityScapesDataset
trainloader = data.DataLoader(t_loader,batch_size=1, shuffle=True, num_workers=4,drop_last=False)
valloader = data.DataLoader(v_loader,batch_size=1, shuffle=False, num_workers=4, drop_last=False)
model = basic(args.maxdisp,args.n_class)
if args.cuda:
model.cuda()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if args.loadmodel is not None:
state_dict = torch.load(args.loadmodel)
model.load_state_dict(state_dict['state_dict'])
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
def main():
global args
log = logger.setup_logger(args.save_path + '/training.log')
train_left_img, train_right_img, train_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(
args.datapath, log, args.split_file)
n_train = int(len(train_left_img))
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)
for key, value in sorted(vars(args).items()):
log.info(str(key) + ': ' + str(value))
model = models.anynet.AnyNet(args)
torch.save(model, './model_para.pth')
model = nn.DataParallel(model).cuda()
torch.manual_seed(2.0)
left = torch.randn(1, 3, 256, 512)
right = torch.randn(1, 3, 256, 512)
with SummaryWriter(comment='AnyNet_model_stracture') as w:
w.add_graph(model, (
left,
right,
))
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))
log.info('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
args.start_epoch = 0
if args.resume: #训练中断后，继续加载
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']) #模型加载,
optimizer.load_state_dict(checkpoint['optimizer']) #优化器加载
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')
cudnn.benchmark = True
start_full_time = time.time()
train(TrainImgLoader, model, optimizer, log, n_train,
TestImgLoader) #开始进行模型训练
test(TestImgLoader, model, log)
log.info('full training time = {:.2f} Hours'.format(
(time.time() - start_full_time) / 3600))
args = parser.parse_args()
if args.KITTI == '2015':
from dataloader import KITTI_submission_loader as DA
else:
from dataloader import KITTI_submission_loader2012 as DA
test_left_img, test_right_img = DA.dataloader(args.datapath)
from dataloader import KITTIloader2015 as lt
from dataloader import KITTILoader as DA_tmp
all_left_img, all_right_img, all_left_disp, test_left_img_tmp, test_right_img_tmp, test_left_disp = lt.dataloader(args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(
DA_tmp.myImageFloder(all_left_img,all_right_img,all_left_disp, True),
batch_size= 1, shuffle= True, num_workers= 8, drop_last=False)
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.model == 'stackhourglass':
model = psm_net.PSMNet(args.maxdisp)
elif args.model == 'basic':
model = basic_net.PSMNet(args.maxdisp)
elif args.model == 'concatNet':
model = concatNet.PSMNet(args.maxdisp)
if args.datatype == '2015':
from dataloader import KITTIloader2015 as ls
elif args.datatype == '2012':
from dataloader import KITTIloader2012 as ls
#读取数据路径（默认读取2012的数据，返回图片的路径）
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(
args.datapath)
#读数据，并且讲数据以12个batch进行封装，并且图片全部shuffle
#TrainImgLoader是一个list包括了imgL_crop, imgR_crop, disp_crop_L，list[i]表示一个batch_size
#kitti2012_data = DA.myImageFloder(all_left_img,all_right_img,all_left_disp, True)
TrainImgLoader = DA.ImgLoader(DA.myImageFloder(all_left_img, all_right_img,
all_left_disp, True),
BATCH_SIZE=1)
#TestImgLoader = DA.ImgLoader(
# DA.myImageFloder(test_left_img,test_right_img,test_left_disp, False),
# BATCH_SIZE= 8)
"""
#读取已有的模型（后续在写）
if args.loadmodel is not None:
state_dict = torch.load(args.loadmodel)
model.load_state_dict(state_dict['state_dict'])
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
"""
"""
def test(imgL,imgR,disp_true):
help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.datatype == '2015':
from dataloader import KITTIloader2015 as ls
elif args.datatype == '2012':
from dataloader import KITTIloader2012 as ls
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(all_left_img,all_right_img,all_left_disp, True),
batch_size= 2, 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= 2, shuffle= False, num_workers= 4, drop_last=False)
if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp)
elif args.model == 'basic':
model = basic(args.maxdisp)
else:
print('no model')
if args.cuda:
model = nn.DataParallel(model)
from dataloader import KITTILoader as DA
elif args.datatype == 'sceneflow':
from dataloader import listflowfile as ls
from dataloader import SecenFlowLoader as DA
elif args.datatype == 'kitti_object':
from dataloader.KITTIObjectLoader import KITTIObjectLoader
else:
print('unknown datatype: ', args.datatype)
sys.exit()
if args.datatype == 'kitti_object':
dataloader = KITTIObjectLoader(args.datapath, 'trainval')
else:
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(
args.datapath)
dataloader = DA.myImageFloder(test_left_img, test_right_img,
test_left_disp, False)
if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp)
elif args.model == 'basic':
model = basic(args.maxdisp)
else:
print('no model')
refine_model = unet_refine.resnet34(pretrained=True, rgbd=True)
#refine_model = unet_refine.resnet18(pretrained=True)
model = nn.DataParallel(model, device_ids=[0])
model.cuda()
refine_model = nn.DataParallel(refine_model)
refine_model.cuda()
help='Display output')
parser.add_argument('--save',
action='store_true',
default=False,
help='Save output')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='Print stats for each single image')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
all_left, all_right, all_guide, all_disp = listLoader.dataloader(args.datapath)
TestImgLoader = torch.utils.data.DataLoader(dataLoader.imageLoader(
all_left, all_right, all_guide, all_disp),
batch_size=1,
shuffle=False,
num_workers=4,
drop_last=False)
# build model
model = psmnet(192, args.guided)
if args.cuda:
model = nn.DataParallel(model)
model.cuda()
if args.loadmodel is not None:
state_dict = torch.load(args.loadmodel)
model.load_state_dict(state_dict['state_dict'])
help='load image as RGB or gray')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.datatype == '2015':
from dataloader import KITTIloader2015 as ls
elif args.datatype == '2012':
from dataloader import KITTIloader2012 as ls
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(all_left_img,all_right_img,all_left_disp, True, colormode=args.colormode),
batch_size= 12, shuffle= True, num_workers= 8, drop_last=False)
TestImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(test_left_img,test_right_img,test_left_disp, False, colormode=args.colormode),
batch_size= 3, shuffle= False, num_workers= 4, drop_last=False)
if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp, colormode=args.colormode)
elif args.model == 'basic':
model = basic(args.maxdisp, colormode=args.colormode)
else:
print('no model')
if args.cuda:
model = nn.DataParallel(model)
def main():
global args
log = logger.setup_logger(args.save_path + '/training.log')
train_left_img, train_right_img, train_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(
args.datapath, log)
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)
for key, value in sorted(vars(args).items()):
log.info(str(key) + ': ' + str(value))
model = models.anynet.AnyNet(args)
model = nn.DataParallel(model).cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))
log.info('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
if args.pretrained:
if os.path.isfile(args.pretrained):
checkpoint = torch.load(args.pretrained)
model.load_state_dict(checkpoint['state_dict'])
log.info("=> loaded pretrained model '{}'".format(args.pretrained))
else:
log.info("=> no pretrained model found at '{}'".format(
args.pretrained))
log.info("=> Will start from scratch.")
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)
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')
cudnn.benchmark = True
start_full_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
log.info('This is {}-th epoch'.format(epoch))
adjust_learning_rate(optimizer, epoch)
train(TrainImgLoader, model, optimizer, log, epoch)
savefilename = args.save_path + '/checkpoint.tar'
torch.save(
{
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, savefilename)
if epoch % 1 == 0:
test(TestImgLoader, model, log)
test(TestImgLoader, model, log)
log.info('full training time = {:.2f} Hours'.format(
(time.time() - start_full_time) / 3600))
def main():
global args
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
train_left_img, train_right_img, train_left_disp, test_left_img, test_right_img, test_left_disp = ls.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=1,
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 = StereoNet(maxdisp=args.maxdisp)
model = nn.DataParallel(model).cuda()
model.apply(weights_init)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[200],
gamma=args.gamma)
log.info('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
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)
model.load_state_dict(checkpoint['state_dict'])
log.info("=> loaded checkpoint '{}' (epeoch {})".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")
min_erro = 100000
max_epo = 0
start_full_time = time.time()
for epoch in range(args.start_epoch, args.epoch):
log.info('This is {}-th epoch'.format(epoch))
train(TrainImgLoader, model, optimizer, log, epoch)
scheduler.step()
erro = test(TestImgLoader, model, log)
if erro < min_erro:
max_epo = epoch
min_erro = erro
savefilename = args.save_path + 'finetune_checkpoint_{}.pth'.format(
max_epo)
torch.save({
'epoch': epoch,
'state_dict': model.state_dict()
}, savefilename)
log.info('MIN epoch %d total test erro = %.3f' % (max_epo, min_erro))
log.info('full training time = {: 2f} Hours'.format(
(time.time() - start_full_time) / 3600))
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = kl2012.dataloader(
args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(kl.myImageFloder(
all_left_img, all_right_img, all_left_disp, True),
batch_size=3,
shuffle=True,
num_workers=8,
drop_last=False)
TestImgLoader = torch.utils.data.DataLoader(kl.myImageFloder(
test_left_img, test_right_img, test_left_disp, False),
batch_size=3,
shuffle=False,
num_workers=4,
drop_last=False)
if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp)
elif args.model == 'basic':
# seed
deterministic_training(args.seed)
(
all_left_img,
all_right_img,
all_left_disp,
test_left_img,
test_right_img,
test_left_disp,
) = lt.dataloader(args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(all_left_img, all_right_img, all_left_disp, True),
batch_size=8,
shuffle=True,
num_workers=8,
drop_last=False,
)
TestImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(test_left_img, test_right_img, test_left_disp, False),
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=False,
)
from dataloader import KITTI_submission_loader as DA
