Corrections
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@ -776,8 +776,8 @@ class SemanticKittiSampler(Sampler):
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gen_classes.append(class_indices * 0 + c)
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# Update potentials
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self.dataset.potentials[class_indices] = np.ceil(self.dataset.potentials[class_indices])
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self.dataset.potentials[class_indices] += np.random.rand(class_indices.shape[0]) * 0.1 + 0.1
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self.dataset.potentials[class_indices] = torch.ceil(self.dataset.potentials[class_indices])
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self.dataset.potentials[class_indices] += torch.from_numpy(np.random.rand(class_indices.shape[0]) * 0.1 + 0.1)
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# Stack the chosen indices of all classes
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gen_indices = torch.cat(gen_indices, dim=0)
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@ -789,8 +789,8 @@ class SemanticKittiSampler(Sampler):
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gen_classes = gen_classes[rand_order]
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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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self.dataset.potentials[gen_indices] += torch.from_numpy(np.random.rand(gen_indices.shape[0]) * 0.1 + 0.1)
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#self.dataset.potentials[gen_indices] = torch.ceil(self.dataset.potentials[gen_indices])
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#self.dataset.potentials[gen_indices] += torch.from_numpy(np.random.rand(gen_indices.shape[0]) * 0.1 + 0.1)
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# Update epoch inds
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self.dataset.epoch_inds += gen_indices
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