From 3651d61f41c267ef4801dc53e5ac359f8b71606f Mon Sep 17 00:00:00 2001
From: ChenXin <will131@foxmail.com>
Date: Wed, 4 Sep 2019 14:47:45 +0800
Subject: [PATCH] delete the alias in files.

---
 fastNLP/embeddings/bert_embedding.py                     | 2 --
 fastNLP/embeddings/char_embedding.py                     | 4 ----
 fastNLP/embeddings/elmo_embedding.py                     | 2 --
 fastNLP/embeddings/embedding.py                          | 2 --
 fastNLP/embeddings/stack_embedding.py                    | 2 --
 fastNLP/embeddings/static_embedding.py                   | 2 --
 fastNLP/modules/decoder/crf.py                           | 2 --
 fastNLP/modules/decoder/mlp.py                           | 2 --
 fastNLP/modules/decoder/utils.py                         | 2 --
 fastNLP/modules/encoder/attention.py                     | 1 -
 fastNLP/modules/encoder/bert.py                          | 2 --
 fastNLP/modules/encoder/char_encoder.py                  | 6 ------
 fastNLP/modules/encoder/conv_maxpool.py                  | 2 --
 fastNLP/modules/encoder/lstm.py                          | 2 --
 fastNLP/modules/encoder/pooling.py                       | 8 --------
 fastNLP/modules/encoder/star_transformer.py              | 3 ---
 fastNLP/modules/encoder/transformer.py                   | 3 ---
 fastNLP/modules/encoder/variational_rnn.py               | 6 ------
 reproduction/text_classification/data/sstloader.py       | 8 ++++----
 reproduction/text_classification/model/awdlstm_module.py | 2 --
 20 files changed, 4 insertions(+), 59 deletions(-)

diff --git a/fastNLP/embeddings/bert_embedding.py b/fastNLP/embeddings/bert_embedding.py
index 08615fe0..17f6769d 100644
--- a/fastNLP/embeddings/bert_embedding.py
+++ b/fastNLP/embeddings/bert_embedding.py
@@ -26,8 +26,6 @@ from ..core import logger
 
 class BertEmbedding(ContextualEmbedding):
     """
-    别名：:class:`fastNLP.embeddings.BertEmbedding`   :class:`fastNLP.embeddings.bert_embedding.BertEmbedding`
-
     使用BERT对words进行编码的Embedding。建议将输入的words长度限制在430以内，而不要使用512(根据预训练模型参数，可能有变化)。这是由于
     预训练的bert模型长度限制为512个token，而因为输入的word是未进行word piece分割的(word piece的分割有BertEmbedding在输入word
     时切分)，在分割之后长度可能会超过最大长度限制。
diff --git a/fastNLP/embeddings/char_embedding.py b/fastNLP/embeddings/char_embedding.py
index 379d4eee..59109206 100644
--- a/fastNLP/embeddings/char_embedding.py
+++ b/fastNLP/embeddings/char_embedding.py
@@ -24,8 +24,6 @@ from ..core import logger
 
 class CNNCharEmbedding(TokenEmbedding):
     """
-    别名：:class:`fastNLP.embeddings.CNNCharEmbedding`   :class:`fastNLP.embeddings.char_embedding.CNNCharEmbedding`
-
     使用CNN生成character embedding。CNN的结构为, embed(x) -> Dropout(x) -> CNN(x) -> activation(x) -> pool -> fc -> Dropout.
     不同的kernel大小的fitler结果是concat起来然后通过一层fully connected layer, 然后输出word的表示。
 
@@ -179,8 +177,6 @@ class CNNCharEmbedding(TokenEmbedding):
 
 class LSTMCharEmbedding(TokenEmbedding):
     """
-    别名：:class:`fastNLP.embeddings.LSTMCharEmbedding`   :class:`fastNLP.embeddings.char_embedding.LSTMCharEmbedding`
-
     使用LSTM的方式对character进行encode. embed(x) -> Dropout(x) -> LSTM(x) -> activation(x) -> pool -> Dropout
 
     Example::
diff --git a/fastNLP/embeddings/elmo_embedding.py b/fastNLP/embeddings/elmo_embedding.py
index d82344e4..0ec0caa0 100644
--- a/fastNLP/embeddings/elmo_embedding.py
+++ b/fastNLP/embeddings/elmo_embedding.py
@@ -22,8 +22,6 @@ from ..core import logger
 
 class ElmoEmbedding(ContextualEmbedding):
     """
-    别名：:class:`fastNLP.embeddings.ElmoEmbedding`   :class:`fastNLP.embeddings.elmo_embedding.ElmoEmbedding`
-
     使用ELMo的embedding。初始化之后，只需要传入words就可以得到对应的embedding。当前支持的使用名称初始化的模型有以下的这些(待补充)
 
     Example::
diff --git a/fastNLP/embeddings/embedding.py b/fastNLP/embeddings/embedding.py
index 5e7b9803..255b0823 100644
--- a/fastNLP/embeddings/embedding.py
+++ b/fastNLP/embeddings/embedding.py
@@ -17,8 +17,6 @@ from .utils import get_embeddings
 
 class Embedding(nn.Module):
     """
-    别名：:class:`fastNLP.embeddings.Embedding`   :class:`fastNLP.embeddings.embedding.Embedding`
-
     词向量嵌入，支持输入多种方式初始化. 可以通过self.num_embeddings获取词表大小; self.embedding_dim获取embedding的维度.
 
     Example::
diff --git a/fastNLP/embeddings/stack_embedding.py b/fastNLP/embeddings/stack_embedding.py
index 14781945..e83a275c 100644
--- a/fastNLP/embeddings/stack_embedding.py
+++ b/fastNLP/embeddings/stack_embedding.py
@@ -17,8 +17,6 @@ from .embedding import TokenEmbedding
 
 class StackEmbedding(TokenEmbedding):
     """
-    别名：:class:`fastNLP.embeddings.StackEmbedding`   :class:`fastNLP.embeddings.stack_embedding.StackEmbedding`
-
     支持将多个embedding集合成一个embedding。
 
     Example::
diff --git a/fastNLP/embeddings/static_embedding.py b/fastNLP/embeddings/static_embedding.py
index c768f32f..8249aa11 100644
--- a/fastNLP/embeddings/static_embedding.py
+++ b/fastNLP/embeddings/static_embedding.py
@@ -24,8 +24,6 @@ from ..core import logger
 
 class StaticEmbedding(TokenEmbedding):
     """
-    别名：:class:`fastNLP.embeddings.StaticEmbedding`   :class:`fastNLP.embeddings.static_embedding.StaticEmbedding`
-
     StaticEmbedding组件. 给定预训练embedding的名称或路径，根据vocab从embedding中抽取相应的数据(只会将出现在vocab中的词抽取出来，
     如果没有找到，则会随机初始化一个值(但如果该word是被标记为no_create_entry的话，则不会单独创建一个值，而是会被指向unk的index))。
     当前支持自动下载的预训练vector有以下的几种(待补充);
diff --git a/fastNLP/modules/decoder/crf.py b/fastNLP/modules/decoder/crf.py
index c13ea50c..e2a751f8 100644
--- a/fastNLP/modules/decoder/crf.py
+++ b/fastNLP/modules/decoder/crf.py
@@ -15,8 +15,6 @@ from typing import Union
 
 def allowed_transitions(tag_vocab:Union[Vocabulary, dict], encoding_type=None, 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 ~fastNLP.Vocabulary,dict tag_vocab: 支持类型为tag或tag-label。只有tag的,比如"B", "M"; 也可以是"B-NN", "M-NN",
diff --git a/fastNLP/modules/decoder/mlp.py b/fastNLP/modules/decoder/mlp.py
index f6e687a7..3e594de1 100644
--- a/fastNLP/modules/decoder/mlp.py
+++ b/fastNLP/modules/decoder/mlp.py
@@ -12,8 +12,6 @@ from ..utils import initial_parameter
 
 class MLP(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MLP`  :class:`fastNLP.modules.decoder.MLP`
-
     多层感知器
 
     :param List[int] size_layer: 一个int的列表，用来定义MLP的层数，列表中的数字为每一层是hidden数目。MLP的层数为 len(size_layer) - 1
diff --git a/fastNLP/modules/decoder/utils.py b/fastNLP/modules/decoder/utils.py
index 118b1414..e0d2af68 100644
--- a/fastNLP/modules/decoder/utils.py
+++ b/fastNLP/modules/decoder/utils.py
@@ -8,8 +8,6 @@ import torch
 
 def viterbi_decode(logits, transitions, mask=None, unpad=False):
     r"""
-    别名：:class:`fastNLP.modules.viterbi_decode`  :class:`fastNLP.modules.decoder.viterbi_decode`
-
     给定一个特征矩阵以及转移分数矩阵，计算出最佳的路径以及对应的分数
 
     :param torch.FloatTensor logits: batch_size x max_len x num_tags，特征矩阵。
diff --git a/fastNLP/modules/encoder/attention.py b/fastNLP/modules/encoder/attention.py
index 6a973864..0d832653 100644
--- a/fastNLP/modules/encoder/attention.py
+++ b/fastNLP/modules/encoder/attention.py
@@ -45,7 +45,6 @@ class DotAttention(nn.Module):
 
 class MultiHeadAttention(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MultiHeadAttention`   :class:`fastNLP.modules.encoder.MultiHeadAttention`
 
     :param input_size: int, 输入维度的大小。同时也是输出维度的大小。
     :param key_size: int, 每个head的维度大小。
diff --git a/fastNLP/modules/encoder/bert.py b/fastNLP/modules/encoder/bert.py
index 6f6c4291..12379718 100644
--- a/fastNLP/modules/encoder/bert.py
+++ b/fastNLP/modules/encoder/bert.py
@@ -348,8 +348,6 @@ class BertPooler(nn.Module):
 
 class BertModel(nn.Module):
     """
-    别名：:class:`fastNLP.modules.BertModel`   :class:`fastNLP.modules.encoder.BertModel`
-
     BERT(Bidirectional Embedding Representations from Transformers).
 
     用预训练权重矩阵来建立BERT模型::
diff --git a/fastNLP/modules/encoder/char_encoder.py b/fastNLP/modules/encoder/char_encoder.py
index e40bd0dd..dc73f447 100644
--- a/fastNLP/modules/encoder/char_encoder.py
+++ b/fastNLP/modules/encoder/char_encoder.py
@@ -13,8 +13,6 @@ from ..utils import initial_parameter
 # from torch.nn.init import xavier_uniform
 class ConvolutionCharEncoder(nn.Module):
     """
-    别名：:class:`fastNLP.modules.ConvolutionCharEncoder`   :class:`fastNLP.modules.encoder.ConvolutionCharEncoder`
-
     char级别的卷积编码器.
     
     :param int char_emb_size: char级别embedding的维度. Default: 50
@@ -60,11 +58,7 @@ class ConvolutionCharEncoder(nn.Module):
 
 class LSTMCharEncoder(nn.Module):
     """
-    别名：:class:`fastNLP.modules.LSTMCharEncoder`   :class:`fastNLP.modules.encoder.LSTMCharEncoder`
-
     char级别基于LSTM的encoder.
-    
-    
     """
 
     def __init__(self, char_emb_size=50, hidden_size=None, initial_method=None):
diff --git a/fastNLP/modules/encoder/conv_maxpool.py b/fastNLP/modules/encoder/conv_maxpool.py
index 68415189..bf629eba 100644
--- a/fastNLP/modules/encoder/conv_maxpool.py
+++ b/fastNLP/modules/encoder/conv_maxpool.py
@@ -10,8 +10,6 @@ import torch.nn.functional as F
 
 class ConvMaxpool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.ConvMaxpool`   :class:`fastNLP.modules.encoder.ConvMaxpool`
-
     集合了Convolution和Max-Pooling于一体的层。给定一个batch_size x max_len x input_size的输入，返回batch_size x
     sum(output_channels) 大小的matrix。在内部，是先使用CNN给输入做卷积，然后经过activation激活层，在通过在长度(max_len)
     这一维进行max_pooling。最后得到每个sample的一个向量表示。
diff --git a/fastNLP/modules/encoder/lstm.py b/fastNLP/modules/encoder/lstm.py
index 1f3eae6d..1dd1f0df 100644
--- a/fastNLP/modules/encoder/lstm.py
+++ b/fastNLP/modules/encoder/lstm.py
@@ -14,8 +14,6 @@ import torch.nn.utils.rnn as rnn
 
 class LSTM(nn.Module):
     """
-    别名：:class:`fastNLP.modules.LSTM`  :class:`fastNLP.modules.encoder.LSTM`
-
     LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下，将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化
         为1; 且可以应对DataParallel中LSTM的使用问题。
 
diff --git a/fastNLP/modules/encoder/pooling.py b/fastNLP/modules/encoder/pooling.py
index b1272284..c248601d 100644
--- a/fastNLP/modules/encoder/pooling.py
+++ b/fastNLP/modules/encoder/pooling.py
@@ -12,8 +12,6 @@ import torch.nn as nn
 
 class MaxPool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MaxPool`  :class:`fastNLP.modules.encoder.MaxPool`
-
     Max-pooling模块。
     
     :param stride: 窗口移动大小，默认为kernel_size
@@ -61,8 +59,6 @@ class MaxPool(nn.Module):
 
 class MaxPoolWithMask(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MaxPoolWithMask`  :class:`fastNLP.modules.encoder.MaxPoolWithMask`
-
     带mask矩阵的max pooling。在做max-pooling的时候不会考虑mask值为0的位置。
     """
 
@@ -101,8 +97,6 @@ class KMaxPool(nn.Module):
 
 class AvgPool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.AvgPool`  :class:`fastNLP.modules.encoder.AvgPool`
-
     给定形如[batch_size, max_len, hidden_size]的输入，在最后一维进行avg pooling. 输出为[batch_size, hidden_size]
     """
 
@@ -128,8 +122,6 @@ class AvgPool(nn.Module):
 
 class AvgPoolWithMask(nn.Module):
     """
-    别名：:class:`fastNLP.modules.AvgPoolWithMask`  :class:`fastNLP.modules.encoder.AvgPoolWithMask`
-
     给定形如[batch_size, max_len, hidden_size]的输入，在最后一维进行avg pooling. 输出为[batch_size, hidden_size], pooling
     的时候只会考虑mask为1的位置
     """
diff --git a/fastNLP/modules/encoder/star_transformer.py b/fastNLP/modules/encoder/star_transformer.py
index 02d7a6a0..bb47d9b5 100644
--- a/fastNLP/modules/encoder/star_transformer.py
+++ b/fastNLP/modules/encoder/star_transformer.py
@@ -14,9 +14,6 @@ from torch.nn import functional as F
 
 class StarTransformer(nn.Module):
     """
-    别名：:class:`fastNLP.modules.StarTransformer`  :class:`fastNLP.modules.encoder.StarTransformer`
-
-
     Star-Transformer 的encoder部分。 输入3d的文本输入, 返回相同长度的文本编码
 
     paper: https://arxiv.org/abs/1902.09113
diff --git a/fastNLP/modules/encoder/transformer.py b/fastNLP/modules/encoder/transformer.py
index d8a612a0..d29a10c3 100644
--- a/fastNLP/modules/encoder/transformer.py
+++ b/fastNLP/modules/encoder/transformer.py
@@ -10,9 +10,6 @@ from .attention import MultiHeadAttention
 
 class TransformerEncoder(nn.Module):
     """
-    别名：:class:`fastNLP.modules.TransformerEncoder`  :class:`fastNLP.modules.encoder.TransformerEncoder`
-
-
     transformer的encoder模块，不包含embedding层
 
     :param int num_layers: transformer的层数
diff --git a/fastNLP/modules/encoder/variational_rnn.py b/fastNLP/modules/encoder/variational_rnn.py
index 933555c8..17e2ad23 100644
--- a/fastNLP/modules/encoder/variational_rnn.py
+++ b/fastNLP/modules/encoder/variational_rnn.py
@@ -223,8 +223,6 @@ class VarRNNBase(nn.Module):
 
 class VarLSTM(VarRNNBase):
     """
-    别名：:class:`fastNLP.modules.VarLSTM`  :class:`fastNLP.modules.encoder.VarLSTM`
-
     Variational Dropout LSTM.
 
     :param input_size:  输入 `x` 的特征维度
@@ -248,8 +246,6 @@ class VarLSTM(VarRNNBase):
 
 class VarRNN(VarRNNBase):
     """
-    别名：:class:`fastNLP.modules.VarRNN`  :class:`fastNLP.modules.encoder.VarRNN`
-
     Variational Dropout RNN.
 
     :param input_size:  输入 `x` 的特征维度
@@ -273,8 +269,6 @@ class VarRNN(VarRNNBase):
 
 class VarGRU(VarRNNBase):
     """
-    别名：:class:`fastNLP.modules.VarGRU`  :class:`fastNLP.modules.encoder.VarGRU`
-
     Variational Dropout GRU.
 
     :param input_size:  输入 `x` 的特征维度
diff --git a/reproduction/text_classification/data/sstloader.py b/reproduction/text_classification/data/sstloader.py
index b635a14a..4e860279 100644
--- a/reproduction/text_classification/data/sstloader.py
+++ b/reproduction/text_classification/data/sstloader.py
@@ -11,11 +11,7 @@ from reproduction.utils import check_dataloader_paths, get_tokenizer
 
 
 class SSTLoader(DataSetLoader):
-    URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'
-    DATA_DIR = 'sst/'
-
     """
-    别名：:class:`fastNLP.io.SSTLoader` :class:`fastNLP.io.dataset_loader.SSTLoader`
     读取SST数据集, DataSet包含fields::
         words: list(str) 需要分类的文本
         target: str 文本的标签
@@ -23,6 +19,10 @@ class SSTLoader(DataSetLoader):
     :param subtree: 是否将数据展开为子树，扩充数据量. Default: ``False``
     :param fine_grained: 是否使用SST-5标准，若 ``False`` , 使用SST-2。Default: ``False``
     """
+
+    URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'
+    DATA_DIR = 'sst/'
+
     def __init__(self, subtree=False, fine_grained=False):
         self.subtree = subtree
         tag_v = {'0': 'very negative', '1': 'negative', '2': 'neutral',
diff --git a/reproduction/text_classification/model/awdlstm_module.py b/reproduction/text_classification/model/awdlstm_module.py
index 87bfe730..a586ed2d 100644
--- a/reproduction/text_classification/model/awdlstm_module.py
+++ b/reproduction/text_classification/model/awdlstm_module.py
@@ -17,8 +17,6 @@ from .weight_drop import WeightDrop
 
 class LSTM(nn.Module):
     """
-    别名：:class:`fastNLP.modules.LSTM`  :class:`fastNLP.modules.encoder.lstm.LSTM`
-
     LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下，将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化
         为1; 且可以应对DataParallel中LSTM的使用问题。