def build_network(self):
# model
feature_extractor = models.resnet34(pretrained=False)
posenet = PoseNet(feature_extractor, droprate=self.dropout, pretrained=False)
if self.model.find('mapnet') >= 0:
model = MapNet(mapnet=posenet)
else:
model = posenet
return model.eval()
srq = section.getfloat('gamma')
section = settings['training']
seed = section.getint('seed')
# model
feature_extractor = models.resnet34(pretrained=True)
posenet = PoseNet(feature_extractor,
droprate=dropout,
pretrained=True,
filter_nans=False)
if args.model == 'posenet':
model = posenet
elif args.model.find('mapnet') >= 0:
model = MapNet(mapnet=posenet)
else:
raise NotImplementedError
# loss function
if args.model == 'posenet':
train_criterion = PoseNetCriterion(sax=sax, saq=saq, learn_beta=True)
val_criterion = PoseNetCriterion()
elif args.model.find('mapnet') >= 0:
kwargs = dict(sax=sax,
saq=saq,
srx=srx,
srq=srq,
learn_beta=True,
learn_gamma=True)
train_criterion = MapNetCriterion(**kwargs)
skip = section.getint('skip')
real = section.getboolean('real')
variable_skip = section.getboolean('variable_skip')
fc_vos = args.dataset == 'RobotCar'
if args.pose_graph:
vo_lib = section.get('vo_lib')
sax = section.getfloat('s_abs_trans', 1)
saq = section.getfloat('s_abs_rot', 1)
srx = section.getfloat('s_rel_trans', 20)
srq = section.getfloat('s_rel_rot', 20)
# model
feature_extractor = models.resnet34(pretrained=False)
posenet = PoseNet(feature_extractor, droprate=dropout, pretrained=False)
if (args.model.find('mapnet') >= 0) or args.pose_graph:
model = MapNet(mapnet=posenet)
else:
model = posenet
model.eval()
# loss functions
t_criterion = lambda t_pred, t_gt: np.linalg.norm(t_pred - t_gt)
q_criterion = quaternion_angular_error
# load weights
weights_filename = osp.expanduser(args.weights)
if osp.isfile(weights_filename):
loc_func = lambda storage, loc: storage
checkpoint = torch.load(weights_filename, map_location=loc_func)
load_state_dict(model, checkpoint['model_state_dict'])
print 'Loaded weights from {:s}'.format(weights_filename)
skip = section.getint('skip')
real = section.getboolean('real')
variable_skip = section.getboolean('variable_skip')
fc_vos = args.dataset == 'RobotCar'
if args.pose_graph:
vo_lib = section.get('vo_lib')
sax = section.getfloat('s_abs_trans', 1)
saq = section.getfloat('s_abs_rot', 1)
srx = section.getfloat('s_rel_trans', 20)
srq = section.getfloat('s_rel_rot', 20)
# model
feature_extractor = models.resnet34(pretrained=False)
posenet = PoseNet(feature_extractor, droprate=dropout, pretrained=False)
if (args.model.find('mapnet') >= 0) or args.pose_graph:
model = MapNet(mapnet=posenet)
else:
model = posenet
model.eval()
# loss functions
t_criterion = lambda t_pred, t_gt: np.linalg.norm(t_pred - t_gt)
q_criterion = quaternion_angular_error
# load weights
weights_filename = osp.expanduser(args.weights)
if osp.isfile(weights_filename):
loc_func = lambda storage, loc: storage
checkpoint = torch.load(weights_filename, map_location=loc_func)
load_state_dict(model, checkpoint['model_state_dict'])
print 'Loaded weights from {:s}'.format(weights_filename)