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Hi!
I'm developing my learning framework based on SPiRL, using my custom dataset.
In skill learning phase, when I set "n_input_frames=4", I got the following error message at the _get_seq_enc() of ImageClSPiRLMdl:
Code:
stacked_imgs = torch.cat([inputs.images[:, t:t+inputs.actions.shape[1]]
for t in range(self._hp.n_input_frames)], dim=2)
Error Message:
RuntimeError: Sizes of tensors must match except in dimension 2. Got 13 and 12 in dimension 1 (The offending index is 2)
In this case, the shape of each element is as following
'actions'={Tensor: (128, 13, 7)}
'pad_mask'={Tensor: (128, 14)}
'states'={Tensor: (128, 14, 34)}
'images'={Tensor: (128, 14, 3, 128, 128)}
'observations'={Tensor: (128, 13, 7)}
It works well if the n_input_frames is less than 3, but occurs error if the value is greater than 2.
I think the shape of action should be (128, 11, 7) to run the code line correctly.
How can I solve this problem?
The text was updated successfully, but these errors were encountered:
Hi!
I'm developing my learning framework based on SPiRL, using my custom dataset.
In skill learning phase, when I set "n_input_frames=4", I got the following error message at the _get_seq_enc() of ImageClSPiRLMdl:
Code:
stacked_imgs = torch.cat([inputs.images[:, t:t+inputs.actions.shape[1]]
for t in range(self._hp.n_input_frames)], dim=2)
Error Message:
RuntimeError: Sizes of tensors must match except in dimension 2. Got 13 and 12 in dimension 1 (The offending index is 2)
In this case, the shape of each element is as following
'actions'={Tensor: (128, 13, 7)}
'pad_mask'={Tensor: (128, 14)}
'states'={Tensor: (128, 14, 34)}
'images'={Tensor: (128, 14, 3, 128, 128)}
'observations'={Tensor: (128, 13, 7)}
It works well if the n_input_frames is less than 3, but occurs error if the value is greater than 2.
I think the shape of action should be (128, 11, 7) to run the code line correctly.
How can I solve this problem?
The text was updated successfully, but these errors were encountered: