How does dim argument of "Tensor.scatter_" method in PyTorch work?

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Could anyone teach me why the below code uses dim=1 in the scatter_ method? The meaning of the attached codes is for one-hot encoding. I tried to read the PyTorch document example and thought I should use dim=0 for the desired result. However, the result has shown that dim=1 is correct instead.

>>> target = torch.tensor([3, 5, 0, 2, 7, 5])
>>> target
tensor([3, 5, 0, 2, 7, 5])

>>> onehot = torch.zeros(target.shape[0], 8)
>>> onehot.scatter_(1, target.unsqueeze(1), 1.0)
tensor([[0., 0., 0., 1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 1., 0., 0.],
        [1., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 1., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 1.],
        [0., 0., 0., 0., 0., 1., 0., 0.]])
pytorch arguments python
1 Answers

You are applying scatter on a zero tensor onehot shaped (len(target), 8) on dim=1 using target as input and 1. as value. This will have the following effect on onehot:

onehot[i][target[i][j]] = 1.

This means for every row in target it will look at the unique value since j is always equal to 1 and use it to index the 2nd axis of onehot. In other words, for every row, it takes the value from target to position the 1. among the columns of onehot.

Step by step illustration would be:

>>> for i in range(len(target)):
...     k = target[i] # k, depends on values of target i.e. dim=1
...     onehot[i, k] = 1
...     print(onehot)

tensor([[0., 0., 0., 1., 0., 0., 0., 0.],  # i=0; k=3
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.]])

tensor([[0., 0., 0., 1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 1., 0., 0.],  # i=1; k=5
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.]])

tensor([[0., 0., 0., 1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 1., 0., 0.],
        [1., 0., 0., 0., 0., 0., 0., 0.],  # i=2; k=0
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.]])

tensor([[0., 0., 0., 1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 1., 0., 0.],
        [1., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 1., 0., 0., 0., 0., 0.],  # i=3; k=2
        [0., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 0.]])

tensor([[0., 0., 0., 1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 1., 0., 0.],
        [1., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 1., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 1.],  # i=4; k=7
        [0., 0., 0., 0., 0., 0., 0., 0.]])

tensor([[0., 0., 0., 1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 1., 0., 0.],
        [1., 0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 1., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 0., 1.],
        [0., 0., 0., 0., 0., 1., 0., 0.]]) # i=5; k=5

Notice that onehot.scatter_(0, target.unsqueeze(1), 1.0) would have produced:

onehot[target[i][j]][j] = 1.

Which is a valid operation only if you initialize onehot the other way around:

>>> onehot = torch.zeros(8, len(target))
>>> onehot.scatter_(0, target.unsqueeze(1), 1.)
tensor([[1., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0.],
        [1., 0., 0., 0., 0., 0.],
        [1., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0.],
        [1., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0.],
        [1., 0., 0., 0., 0., 0.]])

And you get the transpose of the other matrix.

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