Source code for texar.torch.module_base

# Copyright 2019 The Texar Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
Base class for modules.
from abc import ABC
from typing import Any, Dict, List, Optional, Union

from torch import nn

from texar.torch.hyperparams import HParams

__all__ = [

[docs]class ModuleBase(nn.Module, ABC): r"""Base class inherited by modules that are configurable through hyperparameters. This is a subclass of :torch_nn:`Module`. A Texar module inheriting :class:`~texar.torch.ModuleBase` is **configurable through hyperparameters**. That is, each module defines allowed hyperparameters and default values. Hyperparameters not specified by users will take default values. Args: hparams (dict, optional): Hyperparameters of the module. See :meth:`default_hparams` for the structure and default values. """ def __init__(self, hparams: Optional[Union[HParams, Dict[str, Any]]] = None): super().__init__() if not hasattr(self, '_hparams'): self._hparams = HParams(hparams, self.default_hparams()) else: # Probably already parsed by subclasses. We rely on subclass # implementations to get this right. # As a sanity check, we require `hparams` to be `None` in this case. if hparams is not None: raise ValueError( "`self._hparams` is already assigned, but `hparams` " "argument is not None.")
[docs] @staticmethod def default_hparams() -> Dict[str, Any]: r"""Returns a `dict` of hyperparameters of the module with default values. Used to replace the missing values of input `hparams` during module construction. .. code-block:: python { "name": "module" } """ return { 'name': 'module' }
@property def trainable_variables(self) -> List[nn.Parameter]: r"""The list of trainable variables (parameters) of the module. Parameters of this module and all its submodules are included. .. note:: The list returned may contain duplicate parameters (e.g. output layer shares parameters with embeddings). For most usages, it's not necessary to ensure uniqueness. """ return [x for x in self.parameters() if x.requires_grad] @property def hparams(self) -> HParams: r"""An :class:`~texar.torch.HParams` instance. The hyperparameters of the module. """ return self._hparams @property def output_size(self): r"""The feature size of :meth:`forward` output tensor(s), usually it is equal to the last dimension value of the output tensor size. """ raise NotImplementedError