# TODO: This should be a single reused function
# The size of self needs to grow to match other along dim=0.
if self_shape[0] < other_shape[0]:
pad_dim = required_first_dim - self_shape[0]
pad_dim_full = (pad_dim,) + tuple(self_shape[1:])
output_padding = torch.full(
pad_dim_full,
fill_value=-float("inf"),
dtype=self.estimator_outputs.dtype, # TODO: This isn't working! Hence the cast below...
device=self.estimator_outputs.device,
)
self.estimator_outputs = torch.cat(
(self.estimator_outputs, output_padding),
dim=0,
)
This logic appears multiple times in the library and could be abstracted into a utility function.
In
trajectories.py
we have the following:This logic appears multiple times in the library and could be abstracted into a utility function.