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https://github.com/hzwer/ECCV2022-RIFE.git
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Update RIFE.py
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@@ -53,7 +53,9 @@ class Model:
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if rank == 0:
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torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))
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def inference(self, img0, img1, scale=1, scale_list=[4, 2, 1], TTA=False, timestep=0.5):
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def inference(self, img0, img1, scale=1, scale_list=None, TTA=False, timestep=0.5):
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if scale_list is None:
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scale_list = [4, 2, 1]
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for i in range(3):
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scale_list[i] = scale_list[i] * 1.0 / scale
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imgs = torch.cat((img0, img1), 1)
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