Source code for texar.torch.modules.pretrained.pretrained_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 Pre-trained Modules.
import os
import sys
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

from torch import nn

from import maybe_download, get_filename
from texar.torch.hyperparams import HParams
from texar.torch.module_base import ModuleBase
from texar.torch.utils.types import MaybeList

__all__ = [

_default_texar_download_dir: Optional[Path] = None

def default_download_dir(name: str) -> Path:
    r"""Return the directory to which packages will be downloaded by default.
    global _default_texar_download_dir  # pylint: disable=global-statement
    if _default_texar_download_dir is None:
        if sys.platform == 'win32' and 'APPDATA' in os.environ:
            # On Windows, use %APPDATA%
            home_dir = Path(os.environ['APPDATA'])
            # Otherwise, install in the user's home directory.
            home_dir = Path.home()

        if os.access(home_dir, os.W_OK):
            _default_texar_download_dir = home_dir / 'texar_data'
            raise ValueError(f"The path {home_dir} is not writable. Please "
                             f"manually specify the download directory")

    if not _default_texar_download_dir.exists():

    return _default_texar_download_dir / name

def set_default_download_dir(path: Union[str, Path]) -> None:
    if isinstance(path, str):
        path = Path(path)
    elif not isinstance(path, Path):
        raise ValueError("`path` must be a string or a pathlib.Path object")

    if not os.access(path, os.W_OK):
        raise ValueError(
            f"The specified download directory {path} is not writable")

    global _default_texar_download_dir  # pylint: disable=global-statement
    _default_texar_download_dir = path

[docs]class PretrainedMixin(ModuleBase, ABC): r"""A mixin class for all pre-trained classes to inherit. """ _MODEL_NAME: str _MODEL2URL: Dict[str, MaybeList[str]] pretrained_model_dir: Optional[str] @classmethod def available_checkpoints(cls) -> List[str]: return list(cls._MODEL2URL.keys()) def _name_to_variable(self, name: str) -> nn.Parameter: r"""Find the corresponding variable given the specified name. """ pointer = self for m_name in name.split("."): if m_name.isdigit(): num = int(m_name) pointer = pointer[num] # type: ignore else: pointer = getattr(pointer, m_name) return pointer # type: ignore
[docs] def load_pretrained_config(self, pretrained_model_name: Optional[str] = None, cache_dir: Optional[str] = None, hparams=None): r"""Load paths and configurations of the pre-trained model. Args: pretrained_model_name (optional): A str with the name of a pre-trained model to load. If `None`, will use the model name in :attr:`hparams`. cache_dir (optional): The path to a folder in which the pre-trained models will be cached. If `None` (default), a default directory will be used. hparams (dict or HParams, optional): Hyperparameters. Missing hyperparameter will be set to default values. See :meth:`default_hparams` for the hyperparameter structure and default values. """ 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.") self.pretrained_model_dir = None self.pretrained_model_name = pretrained_model_name if self.pretrained_model_name is None: self.pretrained_model_name = self._hparams.pretrained_model_name if self.pretrained_model_name is not None: self.pretrained_model_dir = self.download_checkpoint( self.pretrained_model_name, cache_dir) pretrained_model_hparams = self._transform_config( self.pretrained_model_name, self.pretrained_model_dir) self._hparams = HParams( pretrained_model_hparams, self._hparams.todict())
def init_pretrained_weights(self, *args, **kwargs): if self.pretrained_model_dir: self._init_from_checkpoint( self.pretrained_model_name, self.pretrained_model_dir, *args, **kwargs) else: self.reset_parameters()
[docs] def reset_parameters(self): r"""Initialize parameters of the pre-trained model. This method is only called if pre-trained checkpoints are not loaded. """ pass
[docs] @staticmethod def default_hparams(): r"""Returns a dictionary of hyperparameters with default values. .. code-block:: python { "pretrained_model_name": None, "name": "pretrained_base" } """ return { 'pretrained_model_name': None, 'name': "pretrained_base", '@no_typecheck': ['pretrained_model_name'] }
[docs] @classmethod def download_checkpoint(cls, pretrained_model_name: str, cache_dir: Optional[str] = None) -> str: r"""Download the specified pre-trained checkpoint, and return the directory in which the checkpoint is cached. Args: pretrained_model_name (str): Name of the model checkpoint. cache_dir (str, optional): Path to the cache directory. If `None`, uses the default directory (user's home directory). Returns: Path to the cache directory. """ if pretrained_model_name in cls._MODEL2URL: download_path = cls._MODEL2URL[pretrained_model_name] else: raise ValueError( f"Pre-trained model not found: {pretrained_model_name}") if cache_dir is None: cache_path = default_download_dir(cls._MODEL_NAME) else: cache_path = Path(cache_dir) cache_path = cache_path / pretrained_model_name if not cache_path.exists(): if isinstance(download_path, str): filename = get_filename(download_path) maybe_download(download_path, cache_path, extract=True) # removing the compressed file (cache_path / filename).unlink() folder = None # if extracted into a new directory for file in cache_path.iterdir(): if file.is_dir(): folder = file if folder is not None: for file in folder.iterdir(): file.rename(file.parents[1] / folder.rmdir() else: for path in download_path: maybe_download(path, cache_path) print(f"Pre-trained {cls._MODEL_NAME} checkpoint " f"{pretrained_model_name} cached to {cache_path}") else: print(f"Using cached pre-trained {cls._MODEL_NAME} checkpoint " f"from {cache_path}.") return str(cache_path)
[docs] @classmethod @abstractmethod def _transform_config(cls, pretrained_model_name: str, cache_dir: str) -> Dict[str, Any]: r"""Load the official configuration file and transform it into Texar-style hyperparameters. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. Returns: dict: Texar module hyperparameters. """ raise NotImplementedError
[docs] @abstractmethod def _init_from_checkpoint(self, pretrained_model_name: str, cache_dir: str, **kwargs): r"""Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. **kwargs: Additional arguments for specific models. """ raise NotImplementedError