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

fastNLP/modules/decoder/__init__.py

@@ -1,3 +1,7 @@
+"""
+.. todo::
+    doc
+"""
 __all__ = [
     "MLP",
     "ConditionalRandomField",
@@ -6,6 +10,6 @@ __all__ = [
 ]
 
 from .crf import ConditionalRandomField
+from .crf import allowed_transitions
 from .mlp import MLP
 from .utils import viterbi_decode
-from .crf import allowed_transitions

fastNLP/modules/decoder/crf.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "ConditionalRandomField",
     "allowed_transitions"
@@ -9,13 +11,14 @@ from torch import nn
 from ..utils import initial_parameter
 from ...core import Vocabulary
 
+
 def allowed_transitions(id2target, encoding_type='bio', include_start_end=False):
     """
     别名：:class:`fastNLP.modules.allowed_transitions`  :class:`fastNLP.modules.decoder.allowed_transitions`
 
     给定一个id到label的映射表，返回所有可以跳转的(from_tag_id, to_tag_id)列表。
 
-    :param dict,Vocabulary id2target: key是label的indices，value是str类型的tag或tag-label。value可以是只有tag的, 比如"B", "M"; 也可以是
+    :param dict, ~fastNLP.Vocabulary id2target: key是label的indices，value是str类型的tag或tag-label。value可以是只有tag的, 比如"B", "M"; 也可以是
         "B-NN", "M-NN", tag和label之间一定要用"-"隔开。一般可以通过Vocabulary.idx2word得到id2label。
     :param str encoding_type: 支持"bio", "bmes", "bmeso", "bioes"。
     :param bool include_start_end: 是否包含开始与结尾的转换。比如在bio中，b/o可以在开头，但是i不能在开头；

fastNLP/modules/decoder/mlp.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "MLP"
 ]

fastNLP/modules/decoder/utils.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "viterbi_decode"
 ]

fastNLP/modules/dropout.py

@@ -1,4 +1,8 @@
-__all__ = []
+"""undocumented"""
+
+__all__ = [
+    "TimestepDropout"
+]
 
 import torch
 

fastNLP/modules/encoder/__init__.py

@@ -1,3 +1,8 @@
+"""
+.. todo::
+    doc
+"""
+
 __all__ = [
     # "BertModel",
 
@@ -24,13 +29,12 @@ __all__ = [
     "MultiHeadAttention",
 ]
 
+from .attention import MultiHeadAttention
 from .bert import BertModel
 from .char_encoder import ConvolutionCharEncoder, LSTMCharEncoder
 from .conv_maxpool import ConvMaxpool
 from .lstm import LSTM
+from .pooling import MaxPool, MaxPoolWithMask, AvgPool, AvgPoolWithMask
 from .star_transformer import StarTransformer
 from .transformer import TransformerEncoder
 from .variational_rnn import VarRNN, VarLSTM, VarGRU
-
-from .pooling import MaxPool, MaxPoolWithMask, AvgPool, AvgPoolWithMask
-from .attention import MultiHeadAttention

fastNLP/modules/encoder/_elmo.py

@@ -1,7 +1,9 @@
-"""
+"""undocumented
 这个页面的代码大量参考了 allenNLP
 """
 
+__all__ = []
+
 from typing import Optional, Tuple, List, Callable
 
 import torch

fastNLP/modules/encoder/attention.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "MultiHeadAttention"
 ]

fastNLP/modules/encoder/bert.py

@@ -1,4 +1,4 @@
-"""
+"""undocumented
 这个页面的代码很大程度上参考(复制粘贴)了https://github.com/huggingface/pytorch-pretrained-BERT的代码， 如果你发现该代码对你
     有用，也请引用一下他们。
 """
@@ -8,17 +8,17 @@ __all__ = [
 ]
 
 import collections
-
-import unicodedata
 import copy
 import json
 import math
 import os
+import unicodedata
 
 import torch
 from torch import nn
-from ...core import logger
+
 from ..utils import _get_file_name_base_on_postfix
+from ...core import logger
 
 CONFIG_FILE = 'bert_config.json'
 VOCAB_NAME = 'vocab.txt'

fastNLP/modules/encoder/char_encoder.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "ConvolutionCharEncoder",
     "LSTMCharEncoder"

fastNLP/modules/encoder/conv_maxpool.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "ConvMaxpool"
 ]

fastNLP/modules/encoder/lstm.py

@@ -1,7 +1,8 @@
-"""
+"""undocumented
 轻量封装的 Pytorch LSTM 模块.
 可在 forward 时传入序列的长度, 自动对padding做合适的处理.
 """
+
 __all__ = [
     "LSTM"
 ]

fastNLP/modules/encoder/pooling.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "MaxPool",
     "MaxPoolWithMask",

fastNLP/modules/encoder/star_transformer.py

@@ -1,6 +1,7 @@
-"""
+"""undocumented
 Star-Transformer 的encoder部分的 Pytorch 实现
 """
+
 __all__ = [
     "StarTransformer"
 ]

fastNLP/modules/encoder/transformer.py

@@ -1,3 +1,5 @@
+"""undocumented"""
+
 __all__ = [
     "TransformerEncoder"
 ]

fastNLP/modules/encoder/variational_rnn.py

@@ -1,6 +1,7 @@
-"""
+"""undocumented
 Variational RNN 的 Pytorch 实现
 """
+
 __all__ = [
     "VarRNN",
     "VarLSTM",

fastNLP/modules/utils.py

@@ -1,10 +1,20 @@
+"""
+.. todo::
+    doc
+"""
+
+__all__ = [
+    "initial_parameter",
+    "summary"
+]
+
+import os
 from functools import reduce
 
 import torch
 import torch.nn as nn
 import torch.nn.init as init
-import glob
-import os
+
 
 def initial_parameter(net, initial_method=None):
     """A method used to initialize the weights of PyTorch models.
@@ -40,7 +50,7 @@ def initial_parameter(net, initial_method=None):
         init_method = init.uniform_
     else:
         init_method = init.xavier_normal_
-
+    
     def weights_init(m):
         # classname = m.__class__.__name__
         if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d) or isinstance(m, nn.Conv3d):  # for all the cnn
@@ -66,7 +76,7 @@ def initial_parameter(net, initial_method=None):
                     else:
                         init.normal_(w.data)  # bias
                 # print("init else")
-
+    
     net.apply(weights_init)
 
 
@@ -79,11 +89,11 @@ def summary(model: nn.Module):
     """
     train = []
     nontrain = []
-
+    
     def layer_summary(module: nn.Module):
         def count_size(sizes):
-            return reduce(lambda x, y: x*y, sizes)
-
+            return reduce(lambda x, y: x * y, sizes)
+        
         for p in module.parameters(recurse=False):
             if p.requires_grad:
                 train.append(count_size(p.shape))
@@ -91,7 +101,7 @@ def summary(model: nn.Module):
                 nontrain.append(count_size(p.shape))
         for subm in module.children():
             layer_summary(subm)
-
+    
     layer_summary(model)
     total_train = sum(train)
     total_nontrain = sum(nontrain)
@@ -101,7 +111,7 @@ def summary(model: nn.Module):
     strings.append('Trainable params: {:,}'.format(total_train))
     strings.append('Non-trainable params: {:,}'.format(total_nontrain))
     max_len = len(max(strings, key=len))
-    bar = '-'*(max_len + 3)
+    bar = '-' * (max_len + 3)
     strings = [bar] + strings + [bar]
     print('\n'.join(strings))
     return total, total_train, total_nontrain
@@ -128,9 +138,9 @@ def _get_file_name_base_on_postfix(dir_path, postfix):
     :param postfix: 形如".bin", ".json"等
     :return: str，文件的路径
     """
-    files = list(filter(lambda filename:filename.endswith(postfix), os.listdir(os.path.join(dir_path))))
+    files = list(filter(lambda filename: filename.endswith(postfix), os.listdir(os.path.join(dir_path))))
     if len(files) == 0:
         raise FileNotFoundError(f"There is no file endswith *{postfix} file in {dir_path}")
     elif len(files) > 1:
         raise FileExistsError(f"There are multiple *{postfix} files in {dir_path}")
-    return os.path.join(dir_path, files[0])
+    return os.path.join(dir_path, files[0])