def __init__(self, params, model_path = None, name='SiamRPN', **kargs):
super(TrackerSiamRPNLate, self).__init__(name=name, is_deterministic=True)
self.model = SiameseAlexNetLate()
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 = TrackerRGBTDataLoader()
self.old_loader = TrackerDataLoader()
def __init__(self,
data_roots=('/media/nlpr/zwzhou/数据资源/MOT16/train', ),
subsets=(('MOT16-02', 'MOT16-04', 'MOT16-05', 'MOT16-09',
'MOT16-10', 'MOT16-11', 'MOT16-13'), ),
transform=True):
self.visible_th = 0.4
self.img_files, self.labels = [], []
for i, dr in enumerate(data_roots):
subset = subsets[i]
for ss in subset:
self.img_files.append([
osp.join(dr, ss, 'img1', k)
for k in os.listdir(osp.join(dr, ss, 'img1'))
])
self.labels.append(
self.read_labels(osp.join(dr, ss, 'gt/gt.txt')))
self.index_split = [len(t) for t in self.img_files]
self.acc_indices = [
sum(self.index_split[:i]) for i in range(len(self.index_split))
]
self.transform = transform
self.anchors = util.generate_anchors(config.stride, config.stride,
config.anchor_scales,
config.anchor_ratios,
config.score_size)
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, seq_dataset, name='GOT-10k'):
self.max_inter = config.max_inter
self.sub_class_dir = seq_dataset
self.ret = {}
self.count = 0
self.name = name
self.anchors = util.generate_anchors(config.anchor_total_stride,
config.anchor_base_size,
config.anchor_scales,
config.anchor_ratios,
config.score_size)
def __init__(self, seq_dataset_rgb, seq_dataset_i, z_transforms, x_transforms, name = 'RGBT-234'):
self.max_inter = config.max_inter
self.z_transforms = z_transforms
self.x_transforms = x_transforms
self.seq_dataset_rgb = seq_dataset_rgb
self.seq_dataset_i = seq_dataset_i
self.ret = {}
self.count = 0
self.index = 3000
self.name = name
self.anchors = util.generate_anchors( config.anchor_total_stride,
config.anchor_base_size,
config.anchor_scales,
config.anchor_ratios,
config.score_size)
