def data_and_model_loader(config,
pretrained_depth_path,
pretrained_pose_path,
seq=None,
load_depth=True):
if seq == None:
seq = config['test_seq']
else:
seq = [seq]
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
test_dset = KittiLoaderPytorch(
config, [seq, seq, seq],
mode='test',
transform_img=get_data_transforms(config)['test'])
test_dset_loaders = torch.utils.data.DataLoader(
test_dset,
batch_size=config['minibatch'],
shuffle=False,
num_workers=6)
eval_dsets = {'test': test_dset_loaders}
if load_depth:
depth_model = models.depth_model(config).to(device)
pose_model = models.pose_model(config).to(device)
if pretrained_depth_path is not None and load_depth == True:
depth_model.load_state_dict(torch.load(pretrained_depth_path))
if pretrained_pose_path is not None:
pose_model.load_state_dict(torch.load(pretrained_pose_path))
pose_model.train(False)
pose_model.eval()
if load_depth:
depth_model.train(False)
depth_model.eval()
else:
depth_model = None
mmodels = [depth_model, pose_model]
return test_dset_loaders, mmodels, device
args = parser.parse_args()
config={
'num_frames': None,
'skip':1, ### if not one, we skip every 'skip' samples that are generated ({1,2}, {2,3}, {3,4} becomes {1,2}, {3,4})
'correction_rate': 1, ### if not one, only perform corrections every 'correction_rate' frames (samples become {1,3},{3,5},{5,7} when 2)
'minibatch':15, ##minibatch size
'load_pretrained_depth': True,
'freeze_depthnet': True,
}
for k in args.__dict__:
config[k] = args.__dict__[k]
print(config)
print(args.train_seq, args.test_seq, args.val_seq)
args.data_dir = '{}/{}_res'.format(args.data_dir, config['img_resolution'])
config['data_dir'] = '{}/{}_res'.format(config['data_dir'], config['img_resolution'])
dsets = {x: KittiLoaderPytorch(config, [args.train_seq, args.val_seq, args.test_seq], mode=x, transform_img=get_data_transforms(config)[x], \
augment=config['augment_motion'], skip=config['skip']) for x in ['train', 'val']}
dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=config['minibatch'], shuffle=True, num_workers=8) for x in ['train', 'val']}
val_dset = KittiLoaderPytorch(config, [args.train_seq, args.val_seq, args.test_seq], mode='val', transform_img=get_data_transforms(config)['val'])
val_dset_loaders = torch.utils.data.DataLoader(val_dset, batch_size=config['minibatch'], shuffle=False, num_workers=8)
test_dset = KittiLoaderPytorch(config, [args.train_seq, args.val_seq, args.test_seq], mode='test', transform_img=get_data_transforms(config)['test'])
test_dset_loaders = torch.utils.data.DataLoader(test_dset, batch_size=config['minibatch'], shuffle=False, num_workers=8)
eval_dsets = {'val': val_dset_loaders, 'test':test_dset_loaders}
def main():
results = {}
results['pose_output_type'] = config['pose_output_type']
results['estimator'] = config['estimator_type']
config['device'] = device
start = time.time()
'{}/**depth**best-loss-val_seq-**-test_seq-**.pth'.format(dir))[0]
pretrained_pose_path = glob.glob(
'{}/**pose**best-loss-val_seq-**-test_seq-**.pth'.format(dir))[0]
config['augment_motion'] = False
config['augment_backwards'] = False
config['test_seq'] = [seq]
config['minibatch'] = 5
device = config['device']
config['data_dir'] = path_to_dset_downsized + config[
'img_resolution'] + '_res/'
### dataset and model loading
from data.kitti_loader_stereo import KittiLoaderPytorch
test_dset = KittiLoaderPytorch(
config, [[seq], [seq], [seq]],
mode='test',
transform_img=get_data_transforms(config)['test'])
test_dset_loaders = torch.utils.data.DataLoader(
test_dset,
batch_size=config['minibatch'],
shuffle=False,
num_workers=6)
import models.packetnet_depth_and_egomotion as models_packetnet
import models.depth_and_egomotion as models
depth_model = models.depth_model(config).to(device)
pose_model = models_packetnet.pose_model(config).to(device)
pretrained_depth_path = glob.glob(
'{}/**depth**best-loss-val_seq-**-test_seq-{}**.pth'.format(dir,
''))[0]
date = 'best_stereo'
pretrained_path = '{}/{}/2019-6-24-13-4-most_loop_closures-val_seq-00-test_seq-05.pth'.format(
model_dirs, date)
output_dir = '{}{}/'.format(model_dirs, date)
args.data_dir = '{}/{}'.format(args.data_dir, args.mode)
seq = [args.val_seq] #model.replace(output_dir,'').replace('/','').replace
figures_output_dir = '{}figs'.format(output_dir)
os.makedirs(figures_output_dir, exist_ok=True)
os.makedirs(figures_output_dir + '/imgs', exist_ok=True)
os.makedirs(figures_output_dir + '/depth', exist_ok=True)
os.makedirs(figures_output_dir + '/exp_mask', exist_ok=True)
test_dset = KittiLoaderPytorch(
args.data_dir,
config, [seq, seq, seq],
mode='test',
transform_img=get_data_transforms(config)['val'])
test_dset_loaders = torch.utils.data.DataLoader(test_dset,
batch_size=config['minibatch'],
shuffle=False,
num_workers=4)
eval_dsets = {'test': test_dset_loaders}
Reconstructor = stn.Reconstructor().to(device)
model = mono_model_joint.joint_model(
num_img_channels=(6 + 2 * config['use_flow']),
output_exp=args.exploss,
dropout_prob=config['dropout_prob']).to(device)
model.load_state_dict(torch.load(pretrained_path))
_, _, _, _, _, corr_traj, corr_traj_rot, est_traj, gt_traj, _, _, _ = test_trajectory(