def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.cls_running_score = ClsRunningScore(configer)
self.cls_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.runner_state = dict()
self._init_model()
def __init__(self, configer):
self.configer = configer
self.runner_state = dict()
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = DictAverageMeter()
self.val_losses = DictAverageMeter()
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.running_score = ClsRunningScore(configer)
self.cls_net = self.cls_model_manager.get_cls_model()
self.solver_dict = self.configer.get('solver')
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.solver_dict)
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.loss = self.cls_model_manager.get_cls_loss()
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.cls_model_manager = ClsModelManager(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()