Hey guys,
I’m currently working with word embeddings and I need to perform a pointwise division operations over 5 dimensional tensors and I’m not being very successful so far…
Maybe someone can tell me why this is not working:
    def forward(self, x, mask):

        boolean_mask = torch.from_numpy(np.not_equal(mask.data.numpy(), self.mask_value).astype('int32'))
        count = boolean_mask.sum(self.axis)
        count.squeeze_(self.axis)
        count.unsqueeze_(len(count.size()))
        total = x.sum(self.axis)
        return total / count.float()

To give you a specific example:
x.size(): (10, 5, 6, 3, 64)
mask: (10, 5, 6, 3)
It goes well right before the return, where:
total.size(): (10, 5, 3, 64)
count.size(): (10, 5, 3, 1)
On one hand, if I don’t cast count to a Variable the division operator I get this:
assert not torch.is_tensor(other) AssertionError
On the other hand, If I do Variable(count) I get:
RuntimeError: inconsistent tensor size at /Users/soumith/code/builder/wheel/pytorch-src/torch/lib/TH/generic/THTensorMath.c:869
Is this a broadcasting problem, or something like that?