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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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unique and unique_consecutive in PyTorch

#python #pytorch #unique #uniqueconsecutive

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*[Warning]-unique() and unique_consecutive() are really tricky.

unique() can get the one or more 1D or more D tensors of zero or more unique elements, their indices and their counts as shown below:

*Memos:

  • unique() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or bool).
  • The 2nd argument with torch or the 1st argument with a tensor is sorted(Optional-Default:True-Type:bool). *Currently, setting False is also sorted.
  • The 3rd argument with torch or the 2nd argument with a tensor is return_inverse(Optional-Default:False-Type:bool). *It returns the indices for where elements in the original input ended up in the returned unique list.
  • The 4th argument with torch or the 3rd argument with a tensor is return_counts(Optional-Default:False-Type:bool). *It returns the counts for each unique element.
  • The 5th argument with torch or the 4th argument with a tensor is dim(Optional-Type:int). 
import torch my_tensor = torch.tensor([[[2, 2, 0], [0, 1, 1]], [[1, 3, 0], [0, 0, 2]]]) torch.unique(input=my_tensor) my_tensor.unique() # tensor([0, 1, 2, 3])  torch.unique(input=my_tensor, 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(input=my_tensor, sorted=False, return_inverse=True, return_counts=True, dim=0) torch.unique(input=my_tensor, 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(input=my_tensor, sorted=False, return_inverse=True, return_counts=True, dim=1) torch.unique(input=my_tensor, 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(input=my_tensor, sorted=False, return_inverse=True, return_counts=True, dim=2) torch.unique(input=my_tensor, 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.], [0., 1., 1.]], [[1., 3., 0.], [0., 0., 2.]]]) torch.unique(input=my_tensor) # tensor([0., 1., 2., 3.])  my_tensor = torch.tensor([[[True, False, True], [True, False, True]], [[False, True, False], [False, True, False]]]) torch.unique(input=my_tensor) # tensor([False, True])
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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 used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or bool).
  • The 2nd argument with torch or the 1st argument with a tensor is return_inverse(Optional-Default:False-Type:bool). *It returns the indices for where elements in the original input ended up in the returned unique list.
  • The 3rd argument with torch or the 2nd argument with a tensor is return_counts(Optional-Default:False-Type:bool). *It returns the counts for each unique element.
  • The 4th argument with torch or the 3rd argument with a tensor is dim(Optional-Type:int).
  • unique_consecutive() doesn't have sorted argument. 
import torch my_tensor = torch.tensor([[[2, 2, 0], [0, 1, 1]], [[1, 3, 0], [0, 0, 2]]]) torch.unique_consecutive(input=my_tensor) my_tensor.unique_consecutive() # tensor([2, 0, 1, 3, 0, 2])  torch.unique_consecutive(input=my_tensor, 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(input=my_tensor, return_inverse=True, return_counts=True, dim=0) torch.unique_consecutive(input=my_tensor, 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(input=my_tensor, return_inverse=True, return_counts=True, dim=1) torch.unique_consecutive(my_tensor, 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(input=my_tensor, return_inverse=True, return_counts=True, dim=2) torch.unique_consecutive(input=my_tensor, 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.], [0., 1., 1.]], [[1., 3., 0.], [0., 0., 2.]]]) torch.unique_consecutive(input=my_tensor) # tensor([2., 0., 1., 3., 0., 2.])  my_tensor = torch.tensor([[[True, False, True], [True, False, True]], [[False, True, False], [False, True, False]]]) torch.unique_consecutive(input=my_tensor) # tensor([True, False, True, False, True, False, True, False, True, False])
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