args = parser.parse_args()
print(f"{gct()} : start time")
random.seed(cfg.PROJ.SEED)
torch.manual_seed(cfg.PROJ.SEED)
np.random.seed(cfg.PROJ.SEED)
print(f"{gct()} : model init")
det = RFDet(
cfg.TRAIN.score_com_strength,
cfg.TRAIN.scale_com_strength,
cfg.TRAIN.NMS_THRESH,
cfg.TRAIN.NMS_KSIZE,
cfg.TRAIN.TOPK,
cfg.MODEL.GAUSSIAN_KSIZE,
cfg.MODEL.GAUSSIAN_SIGMA,
cfg.MODEL.KSIZE,
cfg.MODEL.padding,
cfg.MODEL.dilation,
cfg.MODEL.scale_list,
)
des = HardNetNeiMask(cfg.HARDNET.MARGIN, cfg.MODEL.COO_THRSH)
model = Network(det, des, cfg.LOSS.SCORE, cfg.LOSS.PAIR, cfg.PATCH.SIZE,
cfg.TRAIN.TOPK)
print(f"{gct()} : to device")
device = torch.device("cuda")
model = model.to(device)
resume = args.resume
print(f"{gct()} : in {resume}")
if mgpu:
print(f"{gct()} : Train with {torch.cuda.device_count()} GPUs")
torch.backends.cudnn.deterministic = True
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
###############################################################################
# Build the model
###############################################################################
print(f"{gct()} : Build the model")
det = RFDet(
cfg.TRAIN.score_com_strength,
cfg.TRAIN.scale_com_strength,
cfg.TRAIN.NMS_THRESH,
cfg.TRAIN.NMS_KSIZE,
cfg.TRAIN.TOPK,
cfg.MODEL.GAUSSIAN_KSIZE,
cfg.MODEL.GAUSSIAN_SIGMA,
)
des = HardNetNeiMask(cfg.HARDNET.MARGIN, cfg.MODEL.COO_THRSH)
model = Network(
det, des, cfg.LOSS.SCORE, cfg.LOSS.PAIR, cfg.PATCH.SIZE, cfg.TRAIN.TOPK
)
if mgpu:
model = torch.nn.DataParallel(model)
model = model.to(device=device)
###############################################################################
# Load train data
###############################################################################
