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filangelos
reviewed
Aug 25, 2020
filangelos
reviewed
Aug 25, 2020
filangelos
reviewed
Aug 25, 2020
filangelos
reviewed
Aug 25, 2020
| y = torch.normal( # pylint: disable=no-member | ||
| mean=batch["player_future"][..., :2], | ||
| mean=target_mean, | ||
| std=torch.ones_like(batch["player_future"][..., :2]) * noise_level, # pylint: disable=no-member |
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use target_mean instead of batch["player_future"][..., :2]
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@Filangel it looks like I'm already using target_mean. Can you clarify?
filangelos
reviewed
Aug 25, 2020
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| # Calculates loss (NLL). | ||
| loss = -torch.mean(log_prob - logabsdet, dim=0) # pylint: disable=no-member | ||
| if use_tcn: |
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the API for the decoder should be the same, regardless if it's an RNN or a TCN
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Can you clarify what you mean by this? By API, do you want the TCN decoder to similarly have a .inverse method, as the Autoregressive decoder does?
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