test_mean.py

import pytest
import torch
from torch.autograd import Variable
from torch_scatter import scatter_mean_, scatter_mean

from .utils import tensor_strs, Tensor


# @pytest.mark.parametrize('str', tensor_strs)
# def test_scatter_add(str):
def test_scatter_mean():
    input = [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]]
    index = [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]]
    input = torch.FloatTensor(input)
    index = torch.LongTensor(index)
    output = input.new(2, 6).fill_(0)
    # expected_output = [[0, 0, 4, 3, 3, 0], [2, 4, 4, 0, 0, 0]]

    scatter_mean_(output, index, input, dim=1)
    print(output)
    # assert output.tolist() == expected_output

    # output = scatter_add(index, input, dim=1)
    # assert output.tolist(), expected_output

    # output = Variable(output).fill_(0)
    # index = Variable(index)
    # input = Variable(input, requires_grad=True)
    # scatter_add_(output, index, input, dim=1)

    # grad_output = [[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]
    # grad_output = Tensor(str, grad_output)

    # output.backward(grad_output)
    # assert index.data.tolist() == input.grad.data.tolist()