allow other embedding-size besides 1024

We’re using this implementation for a research project, and we’ve seen problems when trying to use other values of embedding-size. Can you give us some hints about what could be causing this situation?

pybullet build time: Jun 20 2019 15:31:37
Traceback (most recent call last):
  File "/current_project/robo-planet/torch_planet/main.py", line 233, in <module>
    beliefs, prior_states, prior_means, prior_std_devs, posterior_states, posterior_means, posterior_std_devs = transition_model(init_state, actions[:-1], init_belief, bottle(encoder, (observations[1:], )), nonterminals[:-1])
  File "/current_project/robo-planet/torch_planet/models.py", line 10, in bottle
    y = f(*map(lambda x: x[0].view(x[1][0] * x[1][1], *x[1][2:]), zip(x_tuple, x_sizes)))
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
RuntimeError: 
shape '[-1, 4096]' is invalid for input of size 2508800:
operation failed in interpreter:

            return torch.transpose(self, dim0, dim1), backward

        def view(self,
                 size: List[int]):
            self_size = self.size()
            def backward(grad_output):
                return grad_output.reshape(self_size), None

            return torch.view(self, size), backward
                   ~~~~~~~~~~ <--- HERE

[maximecb]

The encoder and decoder architectures seem to have been taken directly from World Models.

That is what they claimed in the article. David Ha was also on both papers.

[Kaixhin]

Nope I haven’t played around with this (agreed that it could probably be smaller). The encoder and decoder architectures seem to have been taken directly from World Models.