Method load_model

inference/gradio_composite_demo/rife/RIFE.py:37–42

(self, path, rank=0)

        self.flownet.to(device)

    def load_model(self, path, rank=0):
        def convert(param):
            return {k.replace("module.", ""): v for k, v in param.items() if "module." in k}

        if rank <= 0:
            self.flownet.load_state_dict(convert(torch.load("{}/flownet.pkl".format(path))))

    def save_model(self, path, rank=0):
        if rank == 0: