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@ -772,9 +772,9 @@ class SemanticKittiSampler(Sampler):
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if class_n < class_potentials.shape[0]:
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_, class_indices = torch.topk(class_potentials, class_n, largest=False)
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else:
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class_indices = torch.zeros((0,), dtype=torch.int32)
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class_indices = torch.zeros((0,), dtype=torch.int64)
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while class_indices.shape[0] < class_n:
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new_class_inds = torch.randperm(class_potentials.shape[0]).type(torch.int32)
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new_class_inds = torch.randperm(class_potentials.shape[0]).type(torch.int64)
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class_indices = torch.cat((class_indices, new_class_inds), dim=0)
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class_indices = class_indices[:class_n]
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class_indices = self.dataset.class_frames[i][class_indices]
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