def __init__(self, params, model_path=None, **kargs):
super(TrackerSiamRPNBIG, self).__init__(name='SiamRPN',
is_deterministic=True)
self.model = SiameseAlexNet()
self.cuda = torch.cuda.is_available()
self.device = torch.device('cuda:0' if self.cuda else 'cpu')
checkpoint = torch.load(model_path, map_location=self.device)
#print("1")
if 'model' in checkpoint.keys():
self.model.load_state_dict(
torch.load(model_path, map_location=self.device)['model'])
else:
self.model.load_state_dict(
torch.load(model_path, map_location=self.device))
if self.cuda:
self.model = self.model.cuda()
self.model.eval()
self.transforms = transforms.Compose([ToTensor()])
valid_scope = 2 * config.valid_scope + 1
self.anchors = util.generate_anchors(config.total_stride,
config.anchor_base_size,
config.anchor_scales,
config.anchor_ratios, valid_scope)
self.window = np.tile(
np.outer(np.hanning(config.score_size),
np.hanning(config.score_size))[None, :],
[config.anchor_num, 1, 1]).flatten()
self.data_loader = TrackerDataLoader()
def __init__(self, net_path=None, **kargs):
super(TrackerSiamRPN, self).__init__(name='SiamRPN',
is_deterministic=True)
'''setup GPU device if available'''
self.cuda = torch.cuda.is_available()
self.device = torch.device('cuda:0' if self.cuda else 'cpu')
'''setup model'''
self.net = SiameseAlexNet()
if net_path is not None:
self.net.load_state_dict(
torch.load(net_path,
map_location=lambda storage, loc: storage))
if self.cuda:
self.net = self.net.to(self.device)
'''setup optimizer'''
self.optimizer = torch.optim.SGD(self.net.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
self.anchors = util.generate_anchors(config.anchor_total_stride,
config.anchor_base_size,
config.anchor_scales,
config.anchor_ratios,
config.anchor_valid_scope)
def __init__(self, net_path=None, **kargs):
super(TrackerSiamRPN, self).__init__(name='SiamRPN',
is_deterministic=True)
'''setup GPU device if available'''
self.cuda = torch.cuda.is_available()
self.device = torch.device('cuda' if self.cuda else 'cpu')
'''setup model'''
self.net = SiameseAlexNet()
self.net.init_weights()
if net_path is not None:
self.net.load_state_dict(
torch.load(net_path,
map_location=lambda storage, loc: storage))
if self.cuda:
print('Training with GPU.')
self.net = self.net.to(self.device)
'''setup optimizer'''
print("Learning rate: {}".format(config.lr))
self.optimizer = torch.optim.SGD(self.net.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)