With python lists, we can do:

``````a = [1, 2, 3]
assert a.index(2) == 1
``````

How can a pytorch tensor find the `.index()` directly?

I think there is no direct translation from `list.index()` to a pytorch function. However, you can achieve similar results using `tensor==number` and then the `nonzero()` function. For example:

``````t = torch.Tensor([1, 2, 3])
print ((t == 2).nonzero(as_tuple=True))
``````

This piece of code returns

1

[torch.LongTensor of size 1×1]

For multidimensional tensors you can do:

``````(tensor == target_value).nonzero(as_tuple=True)
``````

The resulting tensor will be of shape `number_of_matches x tensor_dimension`. For example, say `tensor` is a `3 x 4` tensor (that means the dimension is 2), the result will be a 2D-tensor with the indexes for the matches in the rows.

``````tensor = torch.Tensor([[1, 2, 2, 7], [3, 1, 2, 4], [3, 1, 9, 4]])
(tensor == 2).nonzero(as_tuple=False)
>>> tensor([[0, 1],
[0, 2],
[1, 2]])
``````

Based on others’ answers:

``````t = torch.Tensor([1, 2, 3])
print((t==1).nonzero().item())
``````

``````x = torch.Tensor([11, 22, 33, 22])
print((x==22).nonzero().squeeze())
``````

tensor([1, 3])

Can be done by converting to numpy as follows

``````import torch
x = torch.range(1,4)
print(x)
===> tensor([ 1.,  2.,  3.,  4.])
nx = x.numpy()
np.where(nx == 3)
===> 2
``````

The answers already given are great but they don’t handle when I tried it when there is no match. For that see this:

``````def index(tensor: Tensor, value, ith_match:int =0) -> Tensor:
"""
Returns generalized index (i.e. location/coordinate) of the first occurence of value
in Tensor. For flat tensors (i.e. arrays/lists) it returns the indices of the occurrences
of the value you are looking for. Otherwise, it returns the "index" as a coordinate.
If there are multiple occurences then you need to choose which one you want with ith_index.
e.g. ith_index=0 gives first occurence.

Reference: https://stackoverflow.com/a/67175757/1601580
:return:
"""
# bool tensor of where value occurred
places_where_value_occurs = (tensor == value)
# get matches as a "coordinate list" where occurence happened
matches = (tensor == value).nonzero()  # [number_of_matches, tensor_dimension]
if matches.size(0) == 0:  # no matches
return -1
else:
# get index/coordinate of the occurence you want (e.g. 1st occurence ith_match=0)
index = matches[ith_match]
return index
``````

credit to this great answer: https://stackoverflow.com/a/67175757/1601580

for finding index of an element in 1d tensor/array
Example

``````mat=torch.tensor([1,8,5,3])
``````

to find index of 5

``````five=5

numb_of_col=4
for o in range(numb_of_col):
if mat[o]==five:
print(torch.tensor([o]))
``````

To find element index of a 2d/3d tensor covert it into 1d
#ie example.view(number of elements)

Example

``````mat=torch.tensor([[1,2],[4,3])
#to find index of 2

five = 2
mat=mat.view(4)
numb_of_col = 4
for o in range(numb_of_col):
if mat[o] == five:
print(torch.tensor([o]))
``````

For floating point tensors, I use this to get the index of the element in the tensor.

``````print((torch.abs((torch.max(your_tensor).item()-your_tensor))<0.0001).nonzero())
``````

Here I want to get the index of max_value in the float tensor, you can also put your value like this to get the index of any elements in tensor.

``````print((torch.abs((YOUR_VALUE-your_tensor))<0.0001).nonzero())
``````

``````    import torch
x_data = variable(torch.Tensor([[1.0], [2.0], [3.0]]))
print(x_data.data)
>>tensor([1.])
``````