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April 17, 2024 01:35 am GMT

unique() and unique_consecutive() in PyTorch

unique() can get the zero or more unique elements of a 0D or more D tensor as shown below:

*Memos:

  • unique() can be called both from torch and a tensor.
  • The 2nd argument with torch or the 1st argument with a tensor is sorted(Default:True). *Currently setting Falseis also sorted.
  • The 3rd argument with torch or the 2nd argument with a tensor is return_inverse(Default:False).
  • The 4th argument with torch or the 3rd argument with a tensor is return_counts(Default:False) which returns the number of each element of the original tensor.
  • The 5th argument with torch or the 4th argument with a tensor is dim(Default:None).
  • Only zero or more integers, floating-point numbers or boolean values can be used so zero or more complex numbers cannot be used.
import torchmy_tensor = torch.tensor([[[2, 2, 0,], [0, 1, 1]],                          [[1, 3, 0], [0, 0, 2]]])torch.unique(my_tensor)my_tensor.unique()# tensor([0, 1, 2, 3])torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True)# (tensor([0, 1, 2, 3]),#  tensor([[[2, 2, 0], [0, 1, 1]],#          [[1, 3, 0], [0, 0, 2]]]),#  tensor([5, 3, 3, 1]))torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True,             dim=0)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True,                 dim=0)torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True,             dim=-3)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True,                 dim=-3)# (tensor([[[1, 3, 0], [0, 0, 2]],#           [[2, 2, 0], [0, 1, 1]]]),#  tensor([1, 0]),#  tensor([1, 1]))torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True,             dim=1)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True,                 dim=1)torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True,             dim=-2)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True,                 dim=-2)# (tensor([[[0, 1, 1], [2, 2, 0]],#          [[0, 0, 2], [1, 3, 0]]]),#  tensor([1, 0]),#  tensor([1, 1]))torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True,             dim=2)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True,                 dim=2)torch.unique(my_tensor,             sorted=False,             return_inverse=True,             return_counts=True,             dim=-1)my_tensor.unique(sorted=False,                 return_inverse=True,                 return_counts=True,                 dim=-1)# (tensor([[[0, 2, 2], [1, 0, 1]],#          [[0, 1, 3], [2, 0, 0]]]),#  tensor([1, 2, 0]),#  tensor([1, 1, 1]))my_tensor = torch.tensor([[[2., 2., 0.,], [False, 1., True]],                          [[1., 3., 0.], [False, 0., 2.]]])torch.unique(my_tensor)my_tensor.unique()# tensor([0., 1., 2., 3.])

unique_consecutive() can get the zero or more unique elements of a 0D or more D tensor by consecutiveness as shown below:

*Memos:

  • unique_consecutive() can be called both from torch and a tensor.
  • The 2nd argument with torch or the 1st argument with a tensor is return_inverse(Default:False) which returns the original tensor.
  • The 3rd argument with torch or the 2nd argument with a tensor is return_counts(Default:False) which returns the number of each element of the original tensor.
  • The 4th argument with torch or the 3rd argument with a tensor is dim(Default:None).
  • unique_consecutive() doesn't have sorted argument.
  • Only zero or more integers, floating-point numbers or boolean values can be used so zero or more complex numbers cannot be used.
import torchmy_tensor = torch.tensor([[[2, 2, 0,], [0, 1, 1]],                          [[1, 3, 0], [0, 0, 2]]])torch.unique_consecutive(my_tensor)my_tensor.unique_consecutive()# tensor([2, 0, 1, 3, 0, 2])torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True)# (tensor([2, 0, 1, 3, 0, 2]),#  tensor([[[0, 0, 1], [1, 2, 2]],#          [[2, 3, 4], [4, 4, 5]]]),#  tensor([2, 2, 3, 1, 3, 1]))torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True,                         dim=0)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True,                             dim=0)torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True,                         dim=-3)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True,                             dim=-3)# (tensor([[[2, 2, 0], [0, 1, 1]],#          [[1, 3, 0], [0, 0, 2]]]),#  tensor([0, 1]),#  tensor([1, 1]))torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True,                         dim=1)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True,                             dim=1)torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True,                         dim=-2)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True,                             dim=-2)# (tensor([[[2, 2, 0], [0, 1, 1]],#          [[1, 3, 0], [0, 0, 2]]]),#  tensor([0, 1]),#  tensor([1, 1]))torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True,                         dim=2)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True,                             dim=2)torch.unique_consecutive(my_tensor,                         return_inverse=True,                         return_counts=True,                         dim=-1)my_tensor.unique_consecutive(return_inverse=True,                             return_counts=True,                             dim=-1)# (tensor([[[2, 2, 0], [0, 1, 1]],#         [[1, 3, 0], [0, 0, 2]]]),#  tensor([0, 1, 2]),#  tensor([1, 1, 1]))my_tensor = torch.tensor([[[2., 2., 0.,], [False, 1., True]],                          [[1., 3., 0.], [False, 0., 2.]]])torch.unique_consecutive(my_tensor)my_tensor.unique_consecutive()# tensor([2., 0., 1., 3., 0., 2.])

Original Link: https://dev.to/hyperkai/unique-and-uniqueconsecutive-in-pytorch-hji

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