删除encoder中的嵌套层

fastNLP/models/cnn_text_classification.py

@@ -10,7 +10,7 @@ from ..modules import encoder
 
 class CNNText(torch.nn.Module):
     """
-    别名：:class:`fastNLP.models.CNNText`  :class:`fastNLP.modules.aggregator.cnn_text_classification.CNNText`
+    别名：:class:`fastNLP.models.CNNText`  :class:`fastNLP.models.cnn_text_classification.CNNText`
 
     使用CNN进行文本分类的模型
     'Yoon Kim. 2014. Convolution Neural Networks for Sentence Classification.'

fastNLP/models/sequence_labeling.py

@@ -10,7 +10,7 @@ from torch import nn
 
 class SeqLabeling(BaseModel):
     """
-    别名：:class:`fastNLP.models.SeqLabeling`  :class:`fastNLP.modules.aggregator.sequence_labeling.SeqLabeling`
+    别名：:class:`fastNLP.models.SeqLabeling`  :class:`fastNLP.models.sequence_labeling.SeqLabeling`
 
     一个基础的Sequence labeling的模型。
     用于做sequence labeling的基础类。结构包含一层Embedding，一层LSTM(单向，一层)，一层FC，以及一层CRF。
@@ -102,7 +102,7 @@ class SeqLabeling(BaseModel):
 
 class AdvSeqLabel(nn.Module):
     """
-    别名：:class:`fastNLP.models.AdvSeqLabel`  :class:`fastNLP.modules.aggregator.sequence_labeling.AdvSeqLabel`
+    别名：:class:`fastNLP.models.AdvSeqLabel`  :class:`fastNLP.models.sequence_labeling.AdvSeqLabel`
 
     更复杂的Sequence Labelling模型。结构为Embedding, LayerNorm, 双向LSTM(两层)，FC，LayerNorm，DropOut，FC，CRF。
     

fastNLP/modules/aggregator/pooling.py

@@ -5,7 +5,7 @@ import torch.nn as nn
 
 class MaxPool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.aggregator.MaxPool`  :class:`fastNLP.modules.aggregator.pooling.MaxPool`
+    别名：:class:`fastNLP.modules.MaxPool`  :class:`fastNLP.modules.aggregator.pooling.MaxPool`
 
     Max-pooling模块。
     
@@ -53,7 +53,7 @@ class MaxPool(nn.Module):
 
 class MaxPoolWithMask(nn.Module):
     """
-    别名：:class:`fastNLP.modules.aggregator.MaxPoolWithMask`  :class:`fastNLP.modules.aggregator.pooling.MaxPoolWithMask`
+    别名：:class:`fastNLP.modules.MaxPoolWithMask`  :class:`fastNLP.modules.aggregator.pooling.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。
     """
@@ -93,7 +93,7 @@ class KMaxPool(nn.Module):
 
 class AvgPool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.aggregator.AvgPool`  :class:`fastNLP.modules.aggregator.pooling.AvgPool`
+    别名：:class:`fastNLP.modules.AvgPool`  :class:`fastNLP.modules.aggregator.pooling.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]
     """
@@ -120,7 +120,7 @@ class AvgPool(nn.Module):
 
 class AvgPoolWithMask(nn.Module):
     """
-    别名：:class:`fastNLP.modules.aggregator.AvgPoolWithMask`  :class:`fastNLP.modules.aggregator.pooling.AvgPoolWithMask`
+    别名：:class:`fastNLP.modules.AvgPoolWithMask`  :class:`fastNLP.modules.aggregator.pooling.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

fastNLP/modules/decoder/CRF.py

@@ -6,7 +6,7 @@ from ..utils import initial_parameter
 
 def allowed_transitions(id2target, encoding_type='bio', include_start_end=True):
     """
-    别名：:class:`fastNLP.modules.decoder.allowed_transitions`  :class:`fastNLP.modules.decoder.CRF.allowed_transitions`
+    别名：:class:`fastNLP.modules.allowed_transitions`  :class:`fastNLP.modules.decoder.CRF.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。
 
@@ -136,7 +136,7 @@ def _is_transition_allowed(encoding_type, from_tag, from_label, to_tag, to_label
 
 class ConditionalRandomField(nn.Module):
     """
-    别名：:class:`fastNLP.modules.decoder.ConditionalRandomField`  :class:`fastNLP.modules.decoder.CRF.ConditionalRandomField`
+    别名：:class:`fastNLP.modules.ConditionalRandomField`  :class:`fastNLP.modules.decoder.CRF.ConditionalRandomField`
 
     条件随机场。
     提供forward()以及viterbi_decode()两个方法，分别用于训练与inference。

fastNLP/modules/decoder/utils.py

@@ -4,7 +4,7 @@ import torch
 
 def viterbi_decode(logits, transitions, mask=None, unpad=False):
     """
-    别名：:class:`fastNLP.modules.decoder.viterbi_decode`  :class:`fastNLP.modules.decoder.utils.viterbi_decode
+    别名：:class:`fastNLP.modules.viterbi_decode`  :class:`fastNLP.modules.decoder.utils.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
 

fastNLP/modules/encoder/char_encoder.py

@@ -7,7 +7,7 @@ from ..utils import initial_parameter
 # from torch.nn.init import xavier_uniform
 class ConvolutionCharEncoder(nn.Module):
     """
-    别名：:class:`fastNLP.modules.encoder.ConvolutionCharEncoder`   :class:`fastNLP.modules.encoder.char_encoder.ConvolutionCharEncoder`
+    别名：:class:`fastNLP.modules.ConvolutionCharEncoder`   :class:`fastNLP.modules.encoder.char_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

fastNLP/modules/encoder/conv_maxpool.py

@@ -10,7 +10,7 @@ from ..utils import initial_parameter
 
 class ConvMaxpool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.encoder.ConvMaxpool`   :class:`fastNLP.modules.encoder.conv_maxpool.ConvMaxpool`
+    别名：:class:`fastNLP.modules.ConvMaxpool`   :class:`fastNLP.modules.encoder.conv_maxpool.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)

fastNLP/modules/encoder/lstm.py

@@ -10,7 +10,7 @@ from ..utils import initial_parameter
 
 class LSTM(nn.Module):
     """
-    别名：:class:`fastNLP.modules.encoder.LSTM`  :class:`fastNLP.modules.encoder.lstm.LSTM`
+    别名：:class:`fastNLP.modules.LSTM`  :class:`fastNLP.modules.encoder.lstm.LSTM`
 
     LSTM 模块, 轻量封装的Pytorch LSTM
 

fastNLP/modules/encoder/star_transformer.py

@@ -8,7 +8,7 @@ import numpy as NP
 
 class StarTransformer(nn.Module):
     """
-    别名：:class:`fastNLP.modules.encoder.StarTransformer`  :class:`fastNLP.modules.encoder.star_transformer.StarTransformer`
+    别名：:class:`fastNLP.modules.StarTransformer`  :class:`fastNLP.modules.encoder.star_transformer.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

fastNLP/modules/encoder/transformer.py

@@ -6,7 +6,7 @@ from ..dropout import TimestepDropout
 
 class TransformerEncoder(nn.Module):
     """
-    别名：:class:`fastNLP.modules.encoder.TransformerEncoder`  :class:`fastNLP.modules.encoder.transformer.TransformerEncoder`
+    别名：:class:`fastNLP.modules.TransformerEncoder`  :class:`fastNLP.modules.encoder.transformer.TransformerEncoder`
 
 
     transformer的encoder模块，不包含embedding层

fastNLP/modules/encoder/variational_rnn.py

@@ -197,7 +197,7 @@ class VarRNNBase(nn.Module):
 
 class VarLSTM(VarRNNBase):
     """
-    别名：:class:`fastNLP.modules.encoder.VarLSTM`  :class:`fastNLP.modules.encoder.variational_rnn.VarLSTM`
+    别名：:class:`fastNLP.modules.VarLSTM`  :class:`fastNLP.modules.encoder.variational_rnn.VarLSTM`
 
     Variational Dropout LSTM.
 
@@ -221,7 +221,7 @@ class VarLSTM(VarRNNBase):
 
 class VarRNN(VarRNNBase):
     """
-    别名：:class:`fastNLP.modules.encoder.VarRNN`  :class:`fastNLP.modules.encoder.variational_rnn.VarRNN`
+    别名：:class:`fastNLP.modules.VarRNN`  :class:`fastNLP.modules.encoder.variational_rnn.VarRNN`
 
     Variational Dropout RNN.
 
@@ -244,7 +244,7 @@ class VarRNN(VarRNNBase):
 
 class VarGRU(VarRNNBase):
     """
-    别名：:class:`fastNLP.modules.encoder.VarGRU`  :class:`fastNLP.modules.encoder.variational_rnn.VarGRU`
+    别名：:class:`fastNLP.modules.VarGRU`  :class:`fastNLP.modules.encoder.variational_rnn.VarGRU`
 
     Variational Dropout GRU.