fix bug in tests

fastNLP/modules/__init__.py

@@ -1,11 +1,11 @@
 """
 大部分用于的 NLP 任务神经网络都可以看做由编码 :mod:`~fastNLP.modules.encoder` 、
-聚合 :mod:`~fastNLP.modules.aggregator` 、解码 :mod:`~fastNLP.modules.decoder` 三种模块组成。
+解码 :mod:`~fastNLP.modules.decoder` 两种模块组成。
 
 .. image:: figures/text_classification.png
 
 :mod:`~fastNLP.modules` 中实现了 fastNLP 提供的诸多模块组件，可以帮助用户快速搭建自己所需的网络。
-三种模块的功能和常见组件如下:
+两种模块的功能和常见组件如下:
 
 +-----------------------+-----------------------+-----------------------+
 | module type           | functionality         | example               |
@@ -13,9 +13,6 @@
 | encoder               | 将输入编码为具有具    | embedding, RNN, CNN,  |
 |                       | 有表示能力的向量      | transformer           |
 +-----------------------+-----------------------+-----------------------+
-| aggregator            | 从多个向量中聚合信息  | self-attention,       |
-|                       |                       | max-pooling           |
-+-----------------------+-----------------------+-----------------------+
 | decoder               | 将具有某种表示意义的  | MLP, CRF              |
 |                       | 向量解码为需要的输出  |                       |
 |                       | 形式                  |                       |
@@ -46,10 +43,8 @@ __all__ = [
     "allowed_transitions",
 ]
 
-from . import aggregator
 from . import decoder
 from . import encoder
-from .aggregator import *
 from .decoder import *
 from .dropout import TimestepDropout
 from .encoder import *

fastNLP/modules/encoder/__init__.py

@@ -22,7 +22,14 @@ __all__ = [
     
     "VarRNN",
     "VarLSTM",
-    "VarGRU"
+    "VarGRU",
+
+    "MaxPool",
+    "MaxPoolWithMask",
+    "AvgPool",
+    "AvgPoolWithMask",
+
+    "MultiHeadAttention",
 ]
 from ._bert import BertModel
 from .bert import BertWordPieceEncoder
@@ -34,3 +41,6 @@ from .lstm import LSTM
 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/attention.py

@@ -45,8 +45,7 @@ class DotAttention(nn.Module):
 
 class MultiHeadAttention(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MultiHeadAttention`   :class:`fastNLP.modules.aggregator.attention.MultiHeadAttention`
-
+    别名：:class:`fastNLP.modules.MultiHeadAttention`   :class:`fastNLP.modules.encoder.attention.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。

fastNLP/modules/encoder/bert.py

@@ -2,35 +2,22 @@
 import os
 from torch import nn
 import torch
-from ...io.file_utils import _get_base_url, cached_path
+from ...io.file_utils import _get_base_url, cached_path, PRETRAINED_BERT_MODEL_DIR
 from ._bert import _WordPieceBertModel, BertModel
 
+
 class BertWordPieceEncoder(nn.Module):
     """
     读取bert模型，读取之后调用index_dataset方法在dataset中生成word_pieces这一列。
 
-    :param fastNLP.Vocabulary vocab: 词表
     :param str model_dir_or_name: 模型所在目录或者模型的名称。默认值为``en-base-uncased``
     :param str layers:最终结果中的表示。以','隔开层数，可以以负数去索引倒数几层
     :param bool requires_grad: 是否需要gradient。
     """
-    def __init__(self, model_dir_or_name:str='en-base-uncased', layers:str='-1',
-                 requires_grad:bool=False):
+    def __init__(self, model_dir_or_name: str='en-base-uncased', layers: str='-1',
+                 requires_grad: bool=False):
         super().__init__()
         PRETRAIN_URL = _get_base_url('bert')
-        PRETRAINED_BERT_MODEL_DIR = {'en': 'bert-base-cased-f89bfe08.zip',
-                                     'en-base-uncased': 'bert-base-uncased-3413b23c.zip',
-                                     'en-base-cased': 'bert-base-cased-f89bfe08.zip',
-                                     'en-large-uncased': 'bert-large-uncased-20939f45.zip',
-                                     'en-large-cased': 'bert-large-cased-e0cf90fc.zip',
-
-                                     'cn': 'bert-base-chinese-29d0a84a.zip',
-                                     'cn-base': 'bert-base-chinese-29d0a84a.zip',
-
-                                     'multilingual': 'bert-base-multilingual-cased-1bd364ee.zip',
-                                     'multilingual-base-uncased': 'bert-base-multilingual-uncased-f8730fe4.zip',
-                                     'multilingual-base-cased': 'bert-base-multilingual-cased-1bd364ee.zip',
-                                     }
 
         if model_dir_or_name in PRETRAINED_BERT_MODEL_DIR:
             model_name = PRETRAINED_BERT_MODEL_DIR[model_dir_or_name]
@@ -89,4 +76,4 @@ class BertWordPieceEncoder(nn.Module):
         outputs = self.model(word_pieces, token_type_ids)
         outputs = torch.cat([*outputs], dim=-1)
 
-        return outputs
+        return outputs

fastNLP/modules/encoder/pooling.py

@@ -1,7 +1,8 @@
 __all__ = [
     "MaxPool",
     "MaxPoolWithMask",
-    "AvgPool"
+    "AvgPool",
+    "AvgPoolWithMask"
 ]
 import torch
 import torch.nn as nn
@@ -9,7 +10,7 @@ import torch.nn as nn
 
 class MaxPool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MaxPool`  :class:`fastNLP.modules.aggregator.pooling.MaxPool`
+    别名：:class:`fastNLP.modules.MaxPool`  :class:`fastNLP.modules.encoder.pooling.MaxPool`
 
     Max-pooling模块。
     
@@ -58,7 +59,7 @@ class MaxPool(nn.Module):
 
 class MaxPoolWithMask(nn.Module):
     """
-    别名：:class:`fastNLP.modules.MaxPoolWithMask`  :class:`fastNLP.modules.aggregator.pooling.MaxPoolWithMask`
+    别名：:class:`fastNLP.modules.MaxPoolWithMask`  :class:`fastNLP.modules.encoder.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。
     """
@@ -98,7 +99,7 @@ class KMaxPool(nn.Module):
 
 class AvgPool(nn.Module):
     """
-    别名：:class:`fastNLP.modules.AvgPool`  :class:`fastNLP.modules.aggregator.pooling.AvgPool`
+    别名：:class:`fastNLP.modules.AvgPool`  :class:`fastNLP.modules.encoder.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]
     """
@@ -125,7 +126,7 @@ class AvgPool(nn.Module):
 
 class AvgPoolWithMask(nn.Module):
     """
-    别名：:class:`fastNLP.modules.AvgPoolWithMask`  :class:`fastNLP.modules.aggregator.pooling.AvgPoolWithMask`
+    别名：:class:`fastNLP.modules.AvgPoolWithMask`  :class:`fastNLP.modules.encoder.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

reproduction/README.md

@@ -9,7 +9,7 @@
 
 # 任务复现
 ## Text Classification (文本分类)
-- still in progress
+- [Text Classification 文本分类任务复现](text_classification)
 
 
 ## Matching (自然语言推理/句子匹配)
@@ -21,11 +21,11 @@
 
 
 ## Coreference resolution (指代消解)
-- still in progress
+- [Coreference resolution 指代消解任务复现](coreference_resolution)
 
 
 ## Summarization (摘要)
-- still in progress
+- [BertSum](Summmarization)
 
 
 ## ...