def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.model_manager = ModelManager(configer)
self.test_loader = TestDataLoader(configer)
self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')
self.gan_net = None
self._init_model()
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.pose_model_manager = ModelManager(configer)
self.device = torch.device(
'cpu' if self.configer.get('gpu') is None else 'cuda')
self.pose_net = None
self._init_model()
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.pose_visualizer = PoseVisualizer(configer)
self.pose_parser = PoseParser(configer)
self.pose_model_manager = ModelManager(configer)
self.pose_data_loader = DataLoader(configer)
self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')
self.pose_net = None
self._init_model()
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.det_visualizer = DetVisualizer(configer)
self.det_parser = DetParser(configer)
self.det_model_manager = ModelManager(configer)
self.test_loader = TestDataLoader(configer)
self.roi_sampler = FRROISampler(configer)
self.rpn_target_generator = RPNTargetAssigner(configer)
self.fr_priorbox_layer = FRPriorBoxLayer(configer)
self.fr_roi_generator = FRROIGenerator(configer)
self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')
self.det_net = None
self._init_model()
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.cls_parser = ClsParser(configer)
self.device = torch.device(
'cpu' if self.configer.get('gpu') is None else 'cuda')
self.cls_net = None
if self.configer.get('dataset') == 'imagenet':
with open(
os.path.join(
self.configer.get('project_dir'),
'datasets/cls/imagenet/imagenet_class_index.json')
) as json_stream:
name_dict = json.load(json_stream)
name_seq = [
name_dict[str(i)][1]
for i in range(self.configer.get('data', 'num_classes'))
]
self.configer.add(['details', 'name_seq'], name_seq)
self._init_model()