add __all__ and __doc__ for all files in module 'core', using 'undocumented' tags

fastNLP/core/__init__.py

@@ -10,8 +10,72 @@ core 模块里实现了 fastNLP 的核心框架，常用的功能都可以从 fa
 
 对于常用的功能，你只需要在 :doc:`fastNLP` 中查看即可。如果想了解各个子模块的具体作用，您可以在下面找到每个子模块的具体文档。
 
-    
 """
+__all__ = [
+    "DataSet",
+    
+    "Instance",
+    
+    "FieldArray",
+    "Padder",
+    "AutoPadder",
+    "EngChar2DPadder",
+    
+    "Vocabulary",
+    
+    "DataSetIter",
+    "BatchIter",
+    "TorchLoaderIter",
+    
+    "Const",
+    
+    "Tester",
+    "Trainer",
+    
+    "cache_results",
+    "seq_len_to_mask",
+    "get_seq_len",
+    "logger",
+    
+    "Callback",
+    "GradientClipCallback",
+    "EarlyStopCallback",
+    "FitlogCallback",
+    "EvaluateCallback",
+    "LRScheduler",
+    "ControlC",
+    "LRFinder",
+    "TensorboardCallback",
+    "WarmupCallback",
+    'SaveModelCallback',
+    "EchoCallback",
+    "TesterCallback",
+    "CallbackException",
+    "EarlyStopError",
+    
+    "LossFunc",
+    "CrossEntropyLoss",
+    "L1Loss",
+    "BCELoss",
+    "NLLLoss",
+    "LossInForward",
+    
+    "AccuracyMetric",
+    "SpanFPreRecMetric",
+    "ExtractiveQAMetric",
+    
+    "Optimizer",
+    "SGD",
+    "Adam",
+    "AdamW",
+    
+    "SequentialSampler",
+    "BucketSampler",
+    "RandomSampler",
+    "Sampler",
+]
+
+from ._logger import logger
 from .batch import DataSetIter, BatchIter, TorchLoaderIter
 from .callback import Callback, GradientClipCallback, EarlyStopCallback, FitlogCallback, EvaluateCallback, \
     LRScheduler, ControlC, LRFinder, TensorboardCallback, WarmupCallback, SaveModelCallback, EchoCallback, \
@@ -28,4 +92,3 @@ from .tester import Tester
 from .trainer import Trainer
 from .utils import cache_results, seq_len_to_mask, get_seq_len
 from .vocabulary import Vocabulary
-from ._logger import logger

fastNLP/core/_logger.py

@@ -1,15 +1,15 @@
-import logging
-import logging.config
-import torch
-import _pickle as pickle
-import os
-import sys
-import warnings
+"""undocumented"""
 
 __all__ = [
     'logger',
 ]
 
+import logging
+import logging.config
+import os
+import sys
+import warnings
+
 ROOT_NAME = 'fastNLP'
 
 try:
@@ -103,7 +103,6 @@ def _set_stdout_handler(logger, stdout='tqdm', level='INFO'):
         logger.addHandler(stream_handler)
 
 
-
 class FastNLPLogger(logging.getLoggerClass()):
     def __init__(self, name):
         super().__init__(name)
@@ -116,7 +115,10 @@ class FastNLPLogger(logging.getLoggerClass()):
         """set stdout format and level"""
         _set_stdout_handler(self, stdout, level)
 
+
 logging.setLoggerClass(FastNLPLogger)
+
+
 # print(logging.getLoggerClass())
 # print(logging.getLogger())
 

fastNLP/core/_parallel_utils.py

@@ -1,11 +1,14 @@
+"""undocumented"""
+
+__all__ = []
 
 import threading
+
 import torch
 from torch import nn
 from torch.nn.parallel.parallel_apply import get_a_var
-
-from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
 from torch.nn.parallel.replicate import replicate
+from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
 
 
 def parallel_apply(modules, func_name, inputs, kwargs_tup=None, devices=None):
@@ -79,6 +82,7 @@ def _data_parallel_wrapper(func_name, device_ids, output_device):
     :param output_device: nn.DataParallel中的output_device
     :return:
     """
+    
     def wrapper(network, *inputs, **kwargs):
         inputs, kwargs = scatter_kwargs(inputs, kwargs, device_ids, dim=0)
         if len(device_ids) == 1:
@@ -86,6 +90,7 @@ def _data_parallel_wrapper(func_name, device_ids, output_device):
         replicas = replicate(network, device_ids[:len(inputs)])
         outputs = parallel_apply(replicas, func_name, inputs, kwargs, device_ids[:len(replicas)])
         return gather(outputs, output_device)
+    
     return wrapper
 
 

fastNLP/core/const.py

@@ -1,3 +1,13 @@
+"""
+.. todo::
+    doc
+"""
+
+__all__ = [
+    "Const"
+]
+
+
 class Const:
     """
     fastNLP中field命名常量。

fastNLP/core/dist_trainer.py

@@ -1,29 +1,29 @@
-"""
+"""undocumented
 正在开发中的分布式训练代码
 """
+import logging
+import os
+import time
+from datetime import datetime
+
 import torch
 import torch.cuda
-import torch.optim
 import torch.distributed as dist
-from torch.utils.data.distributed import DistributedSampler
+import torch.optim
+from pkg_resources import parse_version
 from torch.nn.parallel import DistributedDataParallel as DDP
-import os
+from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm
-import time
-from datetime import datetime, timedelta
-from functools import partial
 
+from ._logger import logger
 from .batch import DataSetIter, BatchIter
 from .callback import DistCallbackManager, CallbackException, TesterCallback
 from .dataset import DataSet
 from .losses import _prepare_losser
 from .optimizer import Optimizer
 from .utils import _build_args
-from .utils import _move_dict_value_to_device
 from .utils import _get_func_signature
-from  ._logger import logger
-import logging
-from pkg_resources import parse_version
+from .utils import _move_dict_value_to_device
 
 __all__ = [
     'get_local_rank',

fastNLP/core/field.py

@@ -1,18 +1,25 @@
+"""
+.. todo::
+    doc
+"""
+
 __all__ = [
     "Padder",
     "AutoPadder",
     "EngChar2DPadder",
 ]
 
-from numbers import Number
-import torch
-import numpy as np
-from typing import Any
 from abc import abstractmethod
-from copy import deepcopy
 from collections import Counter
-from .utils import _is_iterable
+from copy import deepcopy
+from numbers import Number
+from typing import Any
+
+import numpy as np
+import torch
+
 from ._logger import logger
+from .utils import _is_iterable
 
 
 class SetInputOrTargetException(Exception):

fastNLP/core/predictor.py

@@ -1,13 +1,15 @@
-"""
-    ..todo::
-        检查这个类是否需要
-"""
+"""undocumented"""
+
+__all__ = [
+    "Predictor"
+]
+
 from collections import defaultdict
 
 import torch
 
-from . import DataSetIter
 from . import DataSet
+from . import DataSetIter
 from . import SequentialSampler
 from .utils import _build_args, _move_dict_value_to_device, _get_model_device
 

fastNLP/core/vocabulary.py

@@ -1,16 +1,22 @@
+"""
+.. todo::
+    doc
+"""
+
 __all__ = [
     "Vocabulary",
     "VocabularyOption",
 ]
 
-from functools import wraps
 from collections import Counter
+from functools import partial
+from functools import wraps
+
+from ._logger import logger
 from .dataset import DataSet
 from .utils import Option
-from functools import partial
-import numpy as np
 from .utils import _is_iterable
-from ._logger import logger
+
 
 class VocabularyOption(Option):
     def __init__(self,

fastNLP/embeddings/contextual_embedding.py

@@ -8,15 +8,17 @@ __all__ = [
 ]
 
 from abc import abstractmethod
+
 import torch
 
-from ..core.vocabulary import Vocabulary
-from ..core.dataset import DataSet
-from ..core.batch import DataSetIter
-from ..core.sampler import SequentialSampler
-from ..core.utils import _move_model_to_device, _get_model_device
 from .embedding import TokenEmbedding
 from ..core import logger
+from ..core.batch import DataSetIter
+from ..core.dataset import DataSet
+from ..core.sampler import SequentialSampler
+from ..core.utils import _move_model_to_device, _get_model_device
+from ..core.vocabulary import Vocabulary
+
 
 class ContextualEmbedding(TokenEmbedding):
     def __init__(self, vocab: Vocabulary, word_dropout: float = 0.0, dropout: float = 0.0):