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

fastNLP/models/base_model.py

@@ -1,3 +1,7 @@
+"""undocumented"""
+
+__all__ = []
+
 import torch
 
 from ..modules.decoder.mlp import MLP

fastNLP/models/bert.py

@@ -1,16 +1,20 @@
-"""
+"""undocumented
 bert.py is modified from huggingface/pytorch-pretrained-BERT, which is licensed under the Apache License 2.0.
 
 """
+
+__all__ = []
+
 import os
+
 import torch
 from torch import nn
 
 from .base_model import BaseModel
 from ..core.const import Const
+from ..core.utils import seq_len_to_mask
 from ..modules.encoder import BertModel
 from ..modules.encoder.bert import BertConfig, CONFIG_FILE
-from ..core.utils import seq_len_to_mask
 
 
 class BertForSequenceClassification(BaseModel):

fastNLP/models/cnn_text_classification.py

@@ -1,3 +1,8 @@
+"""
+.. todo::
+    doc
+"""
+
 __all__ = [
     "CNNText"
 ]
@@ -7,8 +12,8 @@ import torch.nn as nn
 
 from ..core.const import Const as C
 from ..core.utils import seq_len_to_mask
-from ..modules import encoder
 from ..embeddings import embedding
+from ..modules import encoder
 
 
 class CNNText(torch.nn.Module):

fastNLP/models/enas_controller.py

@@ -1,5 +1,10 @@
-# Code Modified from https://github.com/carpedm20/ENAS-pytorch
-"""A module with NAS controller-related code."""
+"""undocumented
+Code Modified from https://github.com/carpedm20/ENAS-pytorch
+A module with NAS controller-related code.
+"""
+
+__all__ = []
+
 import collections
 import os
 

fastNLP/models/enas_model.py

@@ -1,7 +1,10 @@
-"""
+"""undocumented
 Module containing the shared RNN model.
 Code Modified from https://github.com/carpedm20/ENAS-pytorch
 """
+
+__all__ = []
+
 import collections
 
 import numpy as np

fastNLP/models/enas_trainer.py

@@ -1,11 +1,15 @@
-# Code Modified from https://github.com/carpedm20/ENAS-pytorch
+"""undocumented
+Code Modified from https://github.com/carpedm20/ENAS-pytorch
+"""
+
+__all__ = []
+
 import math
-import numpy as np
 import time
-import torch
-
 from datetime import datetime, timedelta
 
+import numpy as np
+import torch
 from torch.optim import Adam
 
 try:
@@ -15,7 +19,7 @@ except:
 
 from ..core.trainer import Trainer
 from ..core.batch import DataSetIter
-from ..core.callback import CallbackManager, CallbackException
+from ..core.callback import CallbackException
 from ..core.dataset import DataSet
 from ..core.utils import _move_dict_value_to_device
 from . import enas_utils as utils

fastNLP/models/enas_utils.py

@@ -1,7 +1,11 @@
-# Code Modified from https://github.com/carpedm20/ENAS-pytorch
+"""undocumented
+Code Modified from https://github.com/carpedm20/ENAS-pytorch
+"""
+
+__all__ = []
 
-from collections import defaultdict
 import collections
+from collections import defaultdict
 
 import numpy as np
 import torch

fastNLP/models/sequence_labeling.py

@@ -1,5 +1,5 @@
 """
-    本模块实现了几种序列标注模型
+本模块实现了几种序列标注模型
 """
 __all__ = [
     "SeqLabeling",
@@ -12,14 +12,14 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from .base_model import BaseModel
-from ..embeddings import embedding
-from ..modules import decoder, encoder
-from ..modules.decoder.crf import allowed_transitions
-from ..core.utils import seq_len_to_mask
 from ..core.const import Const as C
-from ..modules import LSTM
+from ..core.utils import seq_len_to_mask
+from ..embeddings import embedding
 from ..embeddings import get_embeddings
 from ..modules import ConditionalRandomField
+from ..modules import LSTM
+from ..modules import decoder, encoder
+from ..modules.decoder.crf import allowed_transitions
 
 
 class BiLSTMCRF(BaseModel):

fastNLP/models/snli.py

@@ -1,3 +1,7 @@
+"""
+.. todo::
+    doc
+"""
 __all__ = [
     "ESIM"
 ]
@@ -5,13 +9,12 @@ __all__ = [
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
 from torch.nn import CrossEntropyLoss
 
 from .base_model import BaseModel
-from ..embeddings.embedding import TokenEmbedding, Embedding
 from ..core.const import Const
 from ..core.utils import seq_len_to_mask
+from ..embeddings.embedding import TokenEmbedding, Embedding
 
 
 class ESIM(BaseModel):