def __init__(self):
self.train_frames = args.train_frames
print("===> Building model")
# Model Setting
# prtrained model setting
self.extractor = resnet101(num_classes=400,
shortcut_type='B',
cardinality=32,
sample_size=args.crop_size,
sample_duration=args.train_frames)
# load pretrained model
# eval 모드를 무조건 해야됨!
weight = get_pretrain_weight(pretrain_path, self.extractor)
self.extractor.load_state_dict(weight)
self.extractor.eval()
self.G = BaseNet(16384 * 4 * 4 * 4, 10)
# optimizer
self.optimizerG = optim.Adam(self.G.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-8)
# loss
self.BCE_loss = nn.BCELoss()
self.L1_loss = nn.L1Loss()
self.L2_loss = nn.MSELoss()
# data
self.train_data = train_data_loader
# self.test_data = test_data_loader
# training
self.device = device
self.epochs = args.epochs
self.avg_G_loss_arr = []
self.checkpoint = args.checkpoint
# cuda
if torch.cuda.is_available():
self.extractor.cuda()
self.G.cuda()
# model save
self.save_mname = args.save_model_name
# checkpoint root
make_dirs(log_path)
self.log_dir = log_path + f'/{self.save_mname}'
# self.load_check_dir =
self.save_check_dir = make_dirs(self.log_dir) + '/' + 'checkpoint.pkl'
# CSV logging system
self.CSVlogger = LogCSV(log_dir=self.log_dir +
f"/{self.save_mname}_log.csv",
header=['epoch', 'avg_G_Loss', 'accuracy'])
def checkpoint_set(self):
"""
Todo setting checkpoint argument
:param self.save_mname: saved model name in each checkpoint
:param self.log_dir: location of checkpoint saving folder
:param self.save_check_dir: filename of checkpoint
:param self.CSVlogger: CSVlogger setting for check a loss
"""
# model save
self.save_mname = args.save_model_name
# checkpoint root
make_dirs(log_path)
self.log_dir = log_path + f'/{self.save_mname}'
self.save_check_dir = make_dirs(self.log_dir)
self.graph_dir = self.log_dir
# CSV logging system
self.CSVlogger = LogCSV(log_dir=self.log_dir + f"/{self.save_mname}_log.csv",
header=['epoch', 'avg_G_Loss', 'accuracy'])