So I load this checkpoint path from the trained latent ODE model:
ckpt_path = '/experiments/experiment_11387.ckpt'
checkpt = torch.load(ckpt_path)
If I want to feed a new unseen time-series into the model and visualize the forecast, should I use LatentODE().get_reconstruct? If so, how do I initialize the model from the weights from the checkpoint path (i.e. the model I just trained)?
I'm trying to use the Visualize functions but they seem to need a data_dict and I can't find where you make this or what components it should have.
So I load this checkpoint path from the trained latent ODE model: ckpt_path = '/experiments/experiment_11387.ckpt' checkpt = torch.load(ckpt_path)
If I want to feed a new unseen time-series into the model and visualize the forecast, should I use LatentODE().get_reconstruct? If so, how do I initialize the model from the weights from the checkpoint path (i.e. the model I just trained)?
I'm trying to use the Visualize functions but they seem to need a data_dict and I can't find where you make this or what components it should have.