def initialize(self):
if os.path.isabs(self.net_path):
net_path_full = self.net_path
else:
net_path_full = os.path.join(env_settings().network_path,
self.net_path)
self.net, _ = load_network(net_path_full, backbone_pretrained=False)
if self.use_gpu:
self.net.cuda()
self.net.eval()
# self.iou_predictor = self.net.bb_regressor # original
self.rgb_bb_regressor = self.net.rgb_bb_regressor
self.t_bb_regressor = self.net.t_bb_regressor
self.iou_guess = self.net.iou_guess
self.layer_stride = {
'conv1': 2,
'layer1': 4,
'layer2': 8,
'layer3': 16,
'layer4': 32,
'classification': 16,
'fc': None
}
self.layer_dim = {
'conv1': 64,
'layer1': 64,
'layer2': 128,
'layer3': 256,
'layer4': 512,
'classification': 256,
'fc': None
}
self.iounet_feature_layers = self.net.bb_regressor_layer
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.feature_layers = sorted(
list(set(self.output_layers + self.iounet_feature_layers)))
self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1)
self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1)
def __init__(self, name: str, parameter_name: str, exp_name: str, run_id: int = None, checkpoint_id = None, flag: str = None):
self.name = name
self.exp_name = exp_name
self.parameter_name = parameter_name
self.run_id = run_id
self.flag = flag
self.checkpoint_id = checkpoint_id
env = env_settings()
tracker_module = importlib.import_module('core.tracker.{}'.format(self.name))
self.parameters = self.get_parameters()
self.tracker_class = tracker_module.get_tracker_class()
self.default_visualization = getattr(self.parameters, 'visualization', False)
self.default_debug = getattr(self.parameters, 'debug', 0)
def initialize(self):
if os.path.isabs(self.net_path):
net_path_full = self.net_path
else:
net_path_full = os.path.join(env_settings().network_path,
self.net_path)
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.layer_stride = {
'vggconv1': 2,
'conv1': 2,
'layer1': 4,
'layer2': 8,
'layer3': 16,
'layer4': 32,
'fc': None
}
self.layer_dim = {
'vggconv1': 96,
'conv1': 64,
'layer1': 64,
'layer2': 128,
'layer3': 256,
'layer4': 512,
'fc': None
}
self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1)
self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1)
self.net = resnet18_vggmconv1(self.output_layers, path=net_path_full)
if self.use_gpu:
self.net.cuda()
self.net.eval()
def __init__(self):
self.env_settings = env_settings()