mirror of
https://github.com/vale981/ray
synced 2025-03-08 11:31:40 -05:00
2fb219a658
* Rollback. * Fix import tree error by adding meaningful error and replacing by tf.nest wherever possible. * LINT. * LINT. * Fix. * Fix log-likelihood test case failing on travis.
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import logging
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from ray.rllib.utils.framework import try_import_torch
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torch, _ = try_import_torch()
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logger = logging.getLogger(__name__)
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try:
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import tree
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except (ImportError, ModuleNotFoundError) as e:
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logger.warning("`dm-tree` is not installed! Run `pip install dm-tree`.")
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raise e
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def sequence_mask(lengths, maxlen, dtype=None):
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"""
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Exact same behavior as tf.sequence_mask.
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Thanks to Dimitris Papatheodorou
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(https://discuss.pytorch.org/t/pytorch-equivalent-for-tf-sequence-mask/
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39036).
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"""
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if maxlen is None:
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maxlen = lengths.max()
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mask = ~(torch.ones((len(lengths), maxlen)).cumsum(dim=1).t() > lengths). \
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t()
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mask.type(dtype or torch.bool)
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return mask
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def convert_to_non_torch_type(stats):
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"""Converts values in stats_dict to non-Tensor numpy or python types.
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Args:
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stats (any): Any (possibly nested) struct, the values in which will be
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converted and returned as a new struct with all torch tensors
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being converted to numpy types.
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Returns:
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dict: A new dict with the same structure as stats_dict, but with all
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values converted to non-torch Tensor types.
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"""
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# The mapping function used to numpyize torch Tensors.
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def mapping(item):
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if isinstance(item, torch.Tensor):
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return item.cpu().item() if len(item.size()) == 0 else \
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item.cpu().numpy()
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else:
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return item
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return tree.map_structure(mapping, stats)
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