Пример #1
0
    def __init__(self,netPath = cfg.MODEL_PATH):
        super().__init__()

        self.net = NET.MainNet(cfg.CLASS_NUM).to(cfg.device)
        self.net.load_state_dict(torch.load(netPath))
        self.net.eval()
Пример #2
0
    loss_obj_cls = cls_loss_fn(output_obj[:, 5:], target_obj[:, 5].long())

    loss_obj = loss_obj_conf + loss_obj_center + loss_obj_wh + loss_obj_cls

    #负样本
    los_noobj = con_loss_fn(output_noobj[:, 4], target_noobj[:, 4])

    return alpaha * loss_obj + (1 - alpaha) * los_noobj


if __name__ == '__main__':
    data_sets = dateset.MyDataset()
    train_loader = DataLoader(data_sets, batch_size=4, shuffle=True)
    writer = SummaryWriter()

    net = NET.MainNet(cfg.CLASS_NUM).to(cfg.device)

    if os.path.exists(cfg.MODEL_PATH):
        print("netLoad")
        net.load_state_dict(torch.load(cfg.MODEL_PATH))

    # for name, value in net.named_parameters():
    #     if name[:8] == "trunk_13":
    #         print(value)

    opt = torch.optim.Adam(net.parameters())

    loss = None
    for epoch in range(100):
        for target_13, target_26, target_52, imgData in train_loader:
            ouput_13, ouput_26, ouput_52 = net(imgData)