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Hi,
Thank you for your interest!
The ImageNet class has for attribute a list imgs of (image path, class_index) tuples. On the other hand, the default CIFAR100 class has for attributes a list of paths (data) and a list of targets (targets).
Therefore, you need to change the class defining CIFAR100 dataset to make it similar to the ImageNet one. To do so, you can write your own class inheriting from torch.utils.data.Dataset and containing a list imgs of (image path, class_index) tuples. For example you could do:
self.imgs = [(self.data[i], self.targets[i]) for i in range(len(self.data))]

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Thanks a lot!

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Hello, have you successfully reproduced this code with cifar data set? What is the clustering result? Looking forward to your answer, thank you very much！

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