Method inference

model/RIFE.py:56–67

(self, img0, img1, scale=1, scale_list=None, TTA=False, timestep=0.5)

            torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))

    def inference(self, img0, img1, scale=1, scale_list=None, TTA=False, timestep=0.5):
        if scale_list is None:
            scale_list = [4, 2, 1]
        for i in range(3):
            scale_list[i] = scale_list[i] * 1.0 / scale
        imgs = torch.cat((img0, img1), 1)
        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(imgs, scale_list, timestep=timestep)
        if TTA == False:
            return merged[2]
        else:
            flow2, mask2, merged2, flow_teacher2, merged_teacher2, loss_distill2 = self.flownet(imgs.flip(2).flip(3), scale_list, timestep=timestep)
            return (merged[2] + merged2[2].flip(2).flip(3)) / 2
    
    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
        for param_group in self.optimG.param_groups: