# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import torch
from .messenger import Messenger
[docs]class MaskMessenger(Messenger):
"""
Given a stochastic function with some batched sample statements and
masking tensor, mask out some of the sample statements elementwise.
:param fn: a stochastic function (callable containing Pyro primitive calls)
:param torch.BoolTensor mask: a ``{0,1}``-valued masking tensor
(1 includes a site, 0 excludes a site)
:returns: stochastic function decorated with a :class:`~pyro.poutine.scale_messenger.MaskMessenger`
"""
def __init__(self, mask):
if isinstance(mask, torch.Tensor):
if mask.dtype != torch.bool:
raise ValueError(
"Expected mask to be a BoolTensor but got {}".format(type(mask))
)
elif mask not in (True, False):
raise ValueError(
"Expected mask to be a boolean but got {}".format(type(mask))
)
super().__init__()
self.mask = mask
def _process_message(self, msg):
msg["mask"] = self.mask if msg["mask"] is None else msg["mask"] & self.mask
return None