unique and unique_consecutive in PyTorch

#python #pytorch #unique #uniqueconsecutive

*[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])

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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unique and unique_consecutive in PyTorch

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