Advanced Indexing

Hi all,

I use PyTorch version 0.2.0_4 and get an IndexError which I cannot explain:

print("X:", x.size())    
print("TYPE:", type(self.neuron_map[k]))


X: torch.Size([25, 8])
TYPE: <class 'list'>


 x[:, self.neuron_map[k]]

results in

 IndexError: When performing advanced indexing the indexing objects must be LongTensors or convertible to LongTensors

I cannot understand why this happens and I have no idea how to fix this. Any help appreciated.

can you do:

print(self.neuron_map[k]), I’m curious of it’s contents.

Also try:

 x[:, torch.LongTensor(self.neuron_map[k])]
print("INDS:", self.neuron_map[k])

results in:

INDS: [0, 1]


inds = torch.LongTensor(self.neuron_map[k])

runs into

RuntimeError: tried to construct a tensor from a int sequence, but found an item of type numpy.int64 at index (0)

I actually found a workaround:

inds = np.array(self.neuron_map[k], dtype=np.int64)
inds = torch.LongTensor(inds)
nn_list.append(self.linears[k](x[:, inds]))

I actually have an additional question. The reason, I am splitting the tensor is to apply linear units (like in last posted code line). For the result, i use:

x_out =, 1)

How efficient is this, as compared to manually implement an autograd.Function (forward and backward)?

it should be pretty efficient if x[:, inds] is large enough. the matrix multiply will prob dominate the cost.

Writing a batched matrix multiply by hand is not easy to do efficiently.