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@ -818,6 +818,10 @@ class SemanticKittiSampler(Sampler):
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_, gen_indices = torch.topk(self.dataset.potentials, num_centers, largest=False, sorted=True)
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else:
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gen_indices = torch.randperm(self.dataset.potentials.shape[0])
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while gen_indices.shape[0] < num_centers:
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new_gen_indices = torch.randperm(self.dataset.potentials.shape[0]).type(torch.int32)
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gen_indices = torch.cat((gen_indices, new_gen_indices), dim=0)
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gen_indices = gen_indices[:num_centers]
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# Update potentials (Change the order for the next epoch)
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self.dataset.potentials[gen_indices] = torch.ceil(self.dataset.potentials[gen_indices])
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