Source code for pyro.distributions.torch_transform

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import torch

[docs]class TransformModule(torch.distributions.Transform, torch.nn.Module): """ Transforms with learnable parameters such as normalizing flows should inherit from this class rather than `Transform` so they are also a subclass of `nn.Module` and inherit all the useful methods of that class. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def __hash__(self): return super(torch.nn.Module, self).__hash__()
[docs]class ComposeTransformModule(torch.distributions.ComposeTransform, torch.nn.ModuleList): """ This allows us to use a list of `TransformModule` in the same way as :class:`~torch.distributions.transform.ComposeTransform`. This is needed so that transform parameters are automatically registered by Pyro's param store when used in :class:`~pyro.nn.module.PyroModule` instances. """ def __init__(self, parts, cache_size=0): super().__init__(parts, cache_size=cache_size) for part in parts: if isinstance(part, torch.nn.Module): self.append(part) def __hash__(self): return super(torch.nn.Module, self).__hash__()
[docs] def with_cache(self, cache_size=1): if cache_size == self._cache_size: return self return ComposeTransformModule(, cache_size=cache_size)