Simplify `1DConv` to `RNN` in `Sequential` Block

[thomelane]

As far as I'm aware it's not possible to create a Sequential Block going from 1DConv to RNN without implementing a custom transpose block. Although simple for the user to implement, I think this network configuration is common enough for us to think about finding an easier way.

Issue

One potential solution would be to select compatible layouts, but 1DConv only supports 'NCW' (a.k.a. 'NCT'), and RNN only supports 'TNC' and 'NTC'. So they are incompatible (without a transpose).

Although the following code runs without error, the final shape is incorrect since incompatible layouts have been used: i.e. 'NCT' with 'TNC'. Would intuitively expect the output shape to be (8, 1, 3) where T=8, N=1, C=3. But we get (1, 2, 3).

import mxnet as mx

net = mx.gluon.nn.Sequential()
net.add(mx.gluon.nn.Conv1D(channels=2, kernel_size=3))
net.add(mx.gluon.rnn.RNN(hidden_size=3))
net.initialize()

data = mx.ndarray.random.uniform(shape=(1,1,10))
output = net(data)
print(output.shape)

Options/Ideas

1) Support more layout types in 1DConv or RNN. Which could perform the transposes. 2) Create a 'Transpose' Block, so can add between the Conv1D and RNN. e.g.

net = mx.gluon.nn.Sequential()
net.add(mx.gluon.nn.Conv1D(channels=2, kernel_size=3))
net.add(mx.gluon.nn.Transpose(axes=(2,0,1))
net.add(mx.gluon.rnn.RNN(hidden_size=3))

3) Create a generic 'Operator' Block, so can add arbitrary operator in Sequential Blocks. e.g.

net = mx.gluon.nn.Sequential()
net.add(mx.gluon.nn.Conv1D(channels=2, kernel_size=3))
net.add(mx.gluon.nn.Operator('transpose', axes=(2,0,1))
net.add(mx.gluon.rnn.RNN(hidden_size=3))

[thomelane]

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