mirror of
https://github.com/vale981/ray
synced 2025-03-07 02:51:39 -05:00
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
from ray.rllib.utils.framework import try_import_torch
|
|
|
|
torch, _ = try_import_torch()
|
|
|
|
|
|
def sequence_mask(lengths, maxlen, dtype=None):
|
|
"""
|
|
Exact same behavior as tf.sequence_mask.
|
|
Thanks to Dimitris Papatheodorou
|
|
(https://discuss.pytorch.org/t/pytorch-equivalent-for-tf-sequence-mask/
|
|
39036).
|
|
"""
|
|
if maxlen is None:
|
|
maxlen = lengths.max()
|
|
|
|
mask = ~(torch.ones((len(lengths), maxlen)).cumsum(dim=1).t() > lengths). \
|
|
t()
|
|
mask.type(dtype or torch.bool)
|
|
|
|
return mask
|
|
|
|
|
|
def convert_to_non_torch_type(stats_dict):
|
|
"""Converts values in stats_dict to non-Tensor numpy or python types.
|
|
|
|
Args:
|
|
stats_dict (dict): A flat key, value dict, the values of which will be
|
|
converted and returned as a new dict.
|
|
|
|
Returns:
|
|
dict: A new dict with the same structure as stats_dict, but with all
|
|
values converted to non-torch Tensor types.
|
|
"""
|
|
ret = {}
|
|
for k, v in stats_dict.items():
|
|
if isinstance(v, torch.Tensor):
|
|
ret[k] = v.cpu().item() if len(v.size()) == 0 else v.cpu().numpy()
|
|
else:
|
|
ret[k] = v
|
|
return ret
|