一些符合 PEP8 的微调

fastNLP/__init__.py

@@ -52,8 +52,8 @@ __all__ = [
     
     "cache_results"
 ]
+__version__ = '0.4.0'
+
 from .core import *
 from . import models
 from . import modules
-
-__version__ = '0.4.0'

fastNLP/core/batch.py

@@ -2,6 +2,10 @@
 batch 模块实现了 fastNLP 所需的 Batch 类。
 
 """
+__all__ = [
+    "Batch"
+]
+
 import atexit
 from queue import Empty, Full
 
@@ -11,10 +15,6 @@ import torch.multiprocessing as mp
 
 from .sampler import RandomSampler
 
-__all__ = [
-    "Batch"
-]
-
 _python_is_exit = False
 
 

fastNLP/core/callback.py

@@ -49,6 +49,18 @@ callback模块实现了 fastNLP 中的许多 callback 类，用于增强 :class:
     trainer.train()
 
 """
+__all__ = [
+    "Callback",
+    "GradientClipCallback",
+    "EarlyStopCallback",
+    "TensorboardCallback",
+    "LRScheduler",
+    "ControlC",
+    
+    "CallbackException",
+    "EarlyStopError"
+]
+
 import os
 
 import torch
@@ -62,18 +74,6 @@ except:
 
 from ..io.model_io import ModelSaver, ModelLoader
 
-__all__ = [
-    "Callback",
-    "GradientClipCallback",
-    "EarlyStopCallback",
-    "TensorboardCallback",
-    "LRScheduler",
-    "ControlC",
-    
-    "CallbackException",
-    "EarlyStopError"
-]
-
 
 class Callback(object):
     """

fastNLP/core/dataset.py

@@ -272,6 +272,10 @@
 
 
 """
+__all__ = [
+    "DataSet"
+]
+
 import _pickle as pickle
 import warnings
 
@@ -282,10 +286,6 @@ from .field import FieldArray
 from .instance import Instance
 from .utils import _get_func_signature
 
-__all__ = [
-    "DataSet"
-]
-
 
 class DataSet(object):
     """

fastNLP/core/field.py

@@ -3,10 +3,6 @@ field模块实现了 FieldArray 和若干 Padder。 FieldArray 是  :class:`~fas
 原理部分请参考 :doc:`fastNLP.core.dataset`
 
 """
-from copy import deepcopy
-
-import numpy as np
-
 __all__ = [
     "FieldArray",
     "Padder",
@@ -14,6 +10,10 @@ __all__ = [
     "EngChar2DPadder"
 ]
 
+from copy import deepcopy
+
+import numpy as np
+
 
 class FieldArray(object):
     """

fastNLP/core/losses.py

@@ -2,6 +2,18 @@
 losses 模块定义了 fastNLP 中所需的各种损失函数，一般做为 :class:`~fastNLP.Trainer` 的参数使用。
 
 """
+__all__ = [
+    "LossBase",
+    
+    "LossFunc",
+    "LossInForward",
+    
+    "CrossEntropyLoss",
+    "BCELoss",
+    "L1Loss",
+    "NLLLoss"
+]
+
 import inspect
 from collections import defaultdict
 
@@ -15,18 +27,6 @@ from .utils import _check_arg_dict_list
 from .utils import _check_function_or_method
 from .utils import _get_func_signature
 
-__all__ = [
-    "LossBase",
-    
-    "LossFunc",
-    "LossInForward",
-    
-    "CrossEntropyLoss",
-    "BCELoss",
-    "L1Loss",
-    "NLLLoss"
-]
-
 
 class LossBase(object):
     """

fastNLP/core/metrics.py

@@ -2,6 +2,13 @@
 metrics 模块实现了 fastNLP 所需的各种常用衡量指标，一般做为 :class:`~fastNLP.Trainer` 的参数使用。
 
 """
+__all__ = [
+    "MetricBase",
+    "AccuracyMetric",
+    "SpanFPreRecMetric",
+    "SQuADMetric"
+]
+
 import inspect
 from collections import defaultdict
 
@@ -16,13 +23,6 @@ from .utils import _get_func_signature
 from .utils import seq_len_to_mask
 from .vocabulary import Vocabulary
 
-__all__ = [
-    "MetricBase",
-    "AccuracyMetric",
-    "SpanFPreRecMetric",
-    "SQuADMetric"
-]
-
 
 class MetricBase(object):
     """

fastNLP/core/optimizer.py

@@ -2,14 +2,14 @@
 optimizer 模块定义了 fastNLP 中所需的各种优化器，一般做为 :class:`~fastNLP.Trainer` 的参数使用。
 
 """
-import torch
-
 __all__ = [
     "Optimizer",
     "SGD",
     "Adam"
 ]
 
+import torch
+
 
 class Optimizer(object):
     """

fastNLP/core/sampler.py

@@ -1,10 +1,6 @@
 """
 sampler 子类实现了 fastNLP 所需的各种采样器。
 """
-from itertools import chain
-
-import numpy as np
-
 __all__ = [
     "Sampler",
     "BucketSampler",
@@ -12,6 +8,10 @@ __all__ = [
     "RandomSampler"
 ]
 
+from itertools import chain
+
+import numpy as np
+
 
 class Sampler(object):
     """

fastNLP/core/trainer.py

@@ -295,6 +295,9 @@ Example2.3
     fastNLP已经自带了很多callback函数供使用，可以参考 :doc:`fastNLP.core.callback` 。
 
 """
+__all__ = [
+    "Trainer"
+]
 
 import os
 import time

fastNLP/core/utils.py

@@ -1,6 +1,11 @@
 """
 utils模块实现了 fastNLP 内部和外部所需的很多工具。其中用户可以使用的是 :func:`cache_results` 修饰器。
 """
+__all__ = [
+    "cache_results",
+    "seq_len_to_mask"
+]
+
 import _pickle
 import inspect
 import os
@@ -11,10 +16,6 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-__all__ = [
-    "cache_results",
-    "seq_len_to_mask"
-]
 
 _CheckRes = namedtuple('_CheckRes', ['missing', 'unused', 'duplicated', 'required', 'all_needed',
                                      'varargs'])

fastNLP/core/vocabulary.py

@@ -1,12 +1,12 @@
+__all__ = [
+    "Vocabulary"
+]
+
 from functools import wraps
 from collections import Counter
 
 from .dataset import DataSet
 
-__all__ = [
-    "Vocabulary"
-]
-
 
 def _check_build_vocab(func):
     """A decorator to make sure the indexing is built before used.
@@ -322,7 +322,7 @@ class Vocabulary(object):
         :return str word: the word
         """
         return self.idx2word[idx]
-
+    
     def clear(self):
         """
         删除Vocabulary中的词表数据。相当于重新初始化一下。
@@ -333,7 +333,7 @@ class Vocabulary(object):
         self.word2idx = None
         self.idx2word = None
         self.rebuild = True
-
+    
     def __getstate__(self):
         """Use to prepare data for pickle.
 

fastNLP/io/__init__.py

@@ -9,11 +9,6 @@
 
 这些类的使用方法如下:
 """
-from .embed_loader import EmbedLoader
-from .dataset_loader import DataSetLoader, CSVLoader, JsonLoader, ConllLoader, SNLILoader, SSTLoader, \
-    PeopleDailyCorpusLoader, Conll2003Loader
-from .model_io import ModelLoader, ModelSaver
-
 __all__ = [
     'EmbedLoader',
     
@@ -29,3 +24,8 @@ __all__ = [
     'ModelLoader',
     'ModelSaver',
 ]
+
+from .embed_loader import EmbedLoader
+from .dataset_loader import DataSetLoader, CSVLoader, JsonLoader, ConllLoader, SNLILoader, SSTLoader, \
+    PeopleDailyCorpusLoader, Conll2003Loader
+from .model_io import ModelLoader, ModelSaver

fastNLP/io/base_loader.py

@@ -1,10 +1,10 @@
-import _pickle as pickle
-import os
-
 __all__ = [
     "BaseLoader"
 ]
 
+import _pickle as pickle
+import os
+
 
 class BaseLoader(object):
     """

fastNLP/io/config_io.py

@@ -3,18 +3,18 @@
  .. todo::
     这个模块中的类可能被抛弃？
 """
-import configparser
-import json
-import os
-
-from .base_loader import BaseLoader
-
 __all__ = [
     "ConfigLoader",
     "ConfigSection",
     "ConfigSaver"
 ]
 
+import configparser
+import json
+import os
+
+from .base_loader import BaseLoader
+
 
 class ConfigLoader(BaseLoader):
     """

fastNLP/io/dataset_loader.py

@@ -10,12 +10,6 @@ dataset_loader模块实现了许多 DataSetLoader, 用于读取不同格式的
 
     # ... do stuff
 """
-from nltk.tree import Tree
-
-from ..core.dataset import DataSet
-from ..core.instance import Instance
-from .file_reader import _read_csv, _read_json, _read_conll
-
 __all__ = [
     'DataSetLoader',
     'CSVLoader',
@@ -27,6 +21,12 @@ __all__ = [
     'Conll2003Loader',
 ]
 
+from nltk.tree import Tree
+
+from ..core.dataset import DataSet
+from ..core.instance import Instance
+from .file_reader import _read_csv, _read_json, _read_conll
+
 
 def _download_from_url(url, path):
     try:

fastNLP/io/embed_loader.py

@@ -1,3 +1,7 @@
+__all__ = [
+    "EmbedLoader"
+]
+
 import os
 import warnings
 
@@ -6,10 +10,6 @@ import numpy as np
 from ..core.vocabulary import Vocabulary
 from .base_loader import BaseLoader
 
-__all__ = [
-    "EmbedLoader"
-]
-
 
 class EmbedLoader(BaseLoader):
     """

fastNLP/io/model_io.py

@@ -1,15 +1,15 @@
 """
 用于载入和保存模型
 """
-import torch
-
-from .base_loader import BaseLoader
-
 __all__ = [
     "ModelLoader",
     "ModelSaver"
 ]
 
+import torch
+
+from .base_loader import BaseLoader
+
 
 class ModelLoader(BaseLoader):
     """

fastNLP/models/__init__.py

@@ -7,15 +7,6 @@ fastNLP 在 :mod:`~fastNLP.models` 模块中内置了如 :class:`~fastNLP.models
 
 
 """
-from .base_model import BaseModel
-from .bert import BertForMultipleChoice, BertForQuestionAnswering, BertForSequenceClassification, \
-    BertForTokenClassification
-from .biaffine_parser import BiaffineParser, GraphParser
-from .cnn_text_classification import CNNText
-from .sequence_labeling import SeqLabeling, AdvSeqLabel
-from .snli import ESIM
-from .star_transformer import StarTransEnc, STSeqCls, STNLICls, STSeqLabel
-
 __all__ = [
     "CNNText",
     
@@ -32,3 +23,12 @@ __all__ = [
     "BiaffineParser",
     "GraphParser"
 ]
+
+from .base_model import BaseModel
+from .bert import BertForMultipleChoice, BertForQuestionAnswering, BertForSequenceClassification, \
+    BertForTokenClassification
+from .biaffine_parser import BiaffineParser, GraphParser
+from .cnn_text_classification import CNNText
+from .sequence_labeling import SeqLabeling, AdvSeqLabel
+from .snli import ESIM
+from .star_transformer import StarTransEnc, STSeqCls, STNLICls, STSeqLabel

fastNLP/models/biaffine_parser.py

@@ -1,6 +1,11 @@
 """
 Biaffine Dependency Parser 的 Pytorch 实现.
 """
+__all__ = [
+    "BiaffineParser",
+    "GraphParser"
+]
+
 import numpy as np
 import torch
 import torch.nn as nn
@@ -19,11 +24,6 @@ from ..modules.utils import get_embeddings
 from .base_model import BaseModel
 from ..core.utils import seq_len_to_mask
 
-__all__ = [
-    "BiaffineParser",
-    "GraphParser"
-]
-
 
 def _mst(scores):
     """

fastNLP/models/cnn_text_classification.py

@@ -1,13 +1,13 @@
+__all__ = [
+    "CNNText"
+]
+
 import torch
 import torch.nn as nn
 
 from ..core.const import Const as C
 from ..modules import encoder
 
-__all__ = [
-    "CNNText"
-]
-
 
 class CNNText(torch.nn.Module):
     """

fastNLP/models/enas_utils.py

@@ -1,6 +1,5 @@
 # Code Modified from https://github.com/carpedm20/ENAS-pytorch
 
-from __future__ import print_function
 from collections import defaultdict
 import collections
 

fastNLP/models/sequence_labeling.py

@@ -1,6 +1,11 @@
 """
     本模块实现了两种序列标注模型
 """
+__all__ = [
+    "SeqLabeling",
+    "AdvSeqLabel"
+]
+
 import torch
 import torch.nn as nn
 
@@ -10,11 +15,6 @@ from ..modules.decoder.crf import allowed_transitions
 from ..core.utils import seq_len_to_mask
 from ..core.const import Const as C
 
-__all__ = [
-    "SeqLabeling",
-    "AdvSeqLabel"
-]
-
 
 class SeqLabeling(BaseModel):
     """

fastNLP/models/snli.py

@@ -1,3 +1,7 @@
+__all__ = [
+    "ESIM"
+]
+
 import torch
 import torch.nn as nn
 
@@ -8,10 +12,6 @@ from ..modules import encoder as Encoder
 from ..modules import aggregator as Aggregator
 from ..core.utils import seq_len_to_mask
 
-__all__ = [
-    "ESIM"
-]
-
 my_inf = 10e12
 
 

fastNLP/models/star_transformer.py

@@ -1,6 +1,13 @@
 """
 Star-Transformer 的 Pytorch 实现。
 """
+__all__ = [
+    "StarTransEnc",
+    "STNLICls",
+    "STSeqCls",
+    "STSeqLabel",
+]
+
 import torch
 from torch import nn
 
@@ -9,13 +16,6 @@ from ..core.utils import seq_len_to_mask
 from ..modules.utils import get_embeddings
 from ..core.const import Const
 
-__all__ = [
-    "StarTransEnc",
-    "STNLICls",
-    "STSeqCls",
-    "STSeqLabel",
-]
-
 
 class StarTransEnc(nn.Module):
     """

fastNLP/modules/__init__.py

@@ -22,15 +22,6 @@
 +-----------------------+-----------------------+-----------------------+
 
 """
-from . import aggregator
-from . import decoder
-from . import encoder
-from .aggregator import *
-from .decoder import *
-from .dropout import TimestepDropout
-from .encoder import *
-from .utils import get_embeddings
-
 __all__ = [
     # "BertModel",
     "ConvolutionCharEncoder",
@@ -54,3 +45,12 @@ __all__ = [
     "viterbi_decode",
     "allowed_transitions",
 ]
+
+from . import aggregator
+from . import decoder
+from . import encoder
+from .aggregator import *
+from .decoder import *
+from .dropout import TimestepDropout
+from .encoder import *
+from .utils import get_embeddings

fastNLP/modules/aggregator/__init__.py

@@ -1,10 +1,3 @@
-from .pooling import MaxPool
-from .pooling import MaxPoolWithMask
-from .pooling import AvgPool
-from .pooling import AvgPoolWithMask
-
-from .attention import MultiHeadAttention
-
 __all__ = [
     "MaxPool",
     "MaxPoolWithMask",
@@ -12,3 +5,10 @@ __all__ = [
     
     "MultiHeadAttention",
 ]
+
+from .pooling import MaxPool
+from .pooling import MaxPoolWithMask
+from .pooling import AvgPool
+from .pooling import AvgPoolWithMask
+
+from .attention import MultiHeadAttention

fastNLP/modules/aggregator/attention.py

@@ -1,3 +1,7 @@
+__all__ = [
+    "MultiHeadAttention"
+]
+
 import math
 
 import torch
@@ -8,10 +12,6 @@ from ..dropout import TimestepDropout
 
 from ..utils import initial_parameter
 
-__all__ = [
-    "MultiHeadAttention"
-]
-
 
 class DotAttention(nn.Module):
     """

fastNLP/modules/aggregator/pooling.py

@@ -1,4 +1,8 @@
-__all__ = ["MaxPool", "MaxPoolWithMask", "AvgPool"]
+__all__ = [
+    "MaxPool",
+    "MaxPoolWithMask",
+    "AvgPool"
+]
 import torch
 import torch.nn as nn
 
@@ -16,6 +20,7 @@ class MaxPool(nn.Module):
     :param kernel_size: max pooling的窗口大小，默认为tensor最后k维，其中k为dimension
     :param ceil_mode:
     """
+    
     def __init__(self, stride=None, padding=0, dilation=1, dimension=1, kernel_size=None, ceil_mode=False):
         
         super(MaxPool, self).__init__()
@@ -125,7 +130,7 @@ class AvgPoolWithMask(nn.Module):
     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
     """
-
+    
     def __init__(self):
         super(AvgPoolWithMask, self).__init__()
         self.inf = 10e12

fastNLP/modules/decoder/__init__.py

@@ -1,11 +1,11 @@
-from .crf import ConditionalRandomField
-from .mlp import MLP
-from .utils import viterbi_decode
-from .crf import allowed_transitions
-
 __all__ = [
     "MLP",
     "ConditionalRandomField",
     "viterbi_decode",
     "allowed_transitions"
 ]
+
+from .crf import ConditionalRandomField
+from .mlp import MLP
+from .utils import viterbi_decode
+from .crf import allowed_transitions

fastNLP/modules/decoder/crf.py

@@ -1,13 +1,13 @@
-import torch
-from torch import nn
-
-from ..utils import initial_parameter
-
 __all__ = [
     "ConditionalRandomField",
     "allowed_transitions"
 ]
 
+import torch
+from torch import nn
+
+from ..utils import initial_parameter
+
 
 def allowed_transitions(id2target, encoding_type='bio', include_start_end=True):
     """

fastNLP/modules/decoder/mlp.py

@@ -1,12 +1,12 @@
+__all__ = [
+    "MLP"
+]
+
 import torch
 import torch.nn as nn
 
 from ..utils import initial_parameter
 
-__all__ = [
-    "MLP"
-]
-
 
 class MLP(nn.Module):
     """

fastNLP/modules/decoder/utils.py

@@ -1,8 +1,7 @@
-import torch
-
 __all__ = [
     "viterbi_decode"
 ]
+import torch
 
 
 def viterbi_decode(logits, transitions, mask=None, unpad=False):

fastNLP/modules/dropout.py

@@ -1,6 +1,8 @@
-import torch
 __all__ = []
 
+import torch
+
+
 class TimestepDropout(torch.nn.Dropout):
     """
     别名：:class:`fastNLP.modules.TimestepDropout`
@@ -8,7 +10,7 @@ class TimestepDropout(torch.nn.Dropout):
     接受的参数shape为``[batch_size, num_timesteps, embedding_dim)]`` 使用同一个mask(shape为``(batch_size, embedding_dim)``)
      在每个timestamp上做dropout。
     """
-
+    
     def forward(self, x):
         dropout_mask = x.new_ones(x.shape[0], x.shape[-1])
         torch.nn.functional.dropout(dropout_mask, self.p, self.training, inplace=True)

fastNLP/modules/encoder/__init__.py

@@ -1,12 +1,3 @@
-from .bert import BertModel
-from .char_encoder import ConvolutionCharEncoder, LSTMCharEncoder
-from .conv_maxpool import ConvMaxpool
-from .embedding import Embedding
-from .lstm import LSTM
-from .star_transformer import StarTransformer
-from .transformer import TransformerEncoder
-from .variational_rnn import VarRNN, VarLSTM, VarGRU
-
 __all__ = [
     # "BertModel",
     
@@ -27,3 +18,11 @@ __all__ = [
     "VarLSTM",
     "VarGRU"
 ]
+from .bert import BertModel
+from .char_encoder import ConvolutionCharEncoder, LSTMCharEncoder
+from .conv_maxpool import ConvMaxpool
+from .embedding import Embedding
+from .lstm import LSTM
+from .star_transformer import StarTransformer
+from .transformer import TransformerEncoder
+from .variational_rnn import VarRNN, VarLSTM, VarGRU

fastNLP/modules/encoder/char_encoder.py

@@ -1,12 +1,11 @@
-import torch
-import torch.nn as nn
-
-from ..utils import initial_parameter
-
 __all__ = [
     "ConvolutionCharEncoder",
     "LSTMCharEncoder"
 ]
+import torch
+import torch.nn as nn
+
+from ..utils import initial_parameter
 
 
 # from torch.nn.init import xavier_uniform

fastNLP/modules/encoder/conv_maxpool.py

@@ -1,13 +1,12 @@
+__all__ = [
+    "ConvMaxpool"
+]
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from ..utils import initial_parameter
 
-__all__ = [
-    "ConvMaxpool"
-]
-
 
 class ConvMaxpool(nn.Module):
     """

fastNLP/modules/encoder/embedding.py

@@ -1,9 +1,8 @@
-import torch.nn as nn
-from ..utils import get_embeddings
-
 __all__ = [
     "Embedding"
 ]
+import torch.nn as nn
+from ..utils import get_embeddings
 
 
 class Embedding(nn.Embedding):

fastNLP/modules/encoder/lstm.py

@@ -2,16 +2,16 @@
 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"
+]
+
 import torch
 import torch.nn as nn
 import torch.nn.utils.rnn as rnn
 
 from ..utils import initial_parameter
 
-__all__ = [
-    "LSTM"
-]
-
 
 class LSTM(nn.Module):
     """

fastNLP/modules/encoder/star_transformer.py

@@ -1,15 +1,15 @@
 """
 Star-Transformer 的encoder部分的 Pytorch 实现
 """
+__all__ = [
+    "StarTransformer"
+]
+
 import numpy as NP
 import torch
 from torch import nn
 from torch.nn import functional as F
 
-__all__ = [
-    "StarTransformer"
-]
-
 
 class StarTransformer(nn.Module):
     """

fastNLP/modules/encoder/transformer.py

@@ -1,12 +1,11 @@
+__all__ = [
+    "TransformerEncoder"
+]
 from torch import nn
 
 from ..aggregator.attention import MultiHeadAttention
 from ..dropout import TimestepDropout
 
-__all__ = [
-    "TransformerEncoder"
-]
-
 
 class TransformerEncoder(nn.Module):
     """

fastNLP/modules/encoder/variational_rnn.py

@@ -1,6 +1,12 @@
 """
 Variational RNN 的 Pytorch 实现
 """
+__all__ = [
+    "VarRNN",
+    "VarLSTM",
+    "VarGRU"
+]
+
 import torch
 import torch.nn as nn
 from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence
@@ -17,25 +23,19 @@ except ImportError:
 
 from ..utils import initial_parameter
 
-__all__ = [
-    "VarRNN",
-    "VarLSTM",
-    "VarGRU"
-]
-
 
 class VarRnnCellWrapper(nn.Module):
     """
     Wrapper for normal RNN Cells, make it support variational dropout
     """
-
+    
     def __init__(self, cell, hidden_size, input_p, hidden_p):
         super(VarRnnCellWrapper, self).__init__()
         self.cell = cell
         self.hidden_size = hidden_size
         self.input_p = input_p
         self.hidden_p = hidden_p
-
+    
     def forward(self, input_x, hidden, mask_x, mask_h, is_reversed=False):
         """
         :param PackedSequence input_x: [seq_len, batch_size, input_size]
@@ -47,13 +47,13 @@ class VarRnnCellWrapper(nn.Module):
                 hidden: for LSTM, tuple of (h_n, c_n), [batch_size, hidden_size]
                         for other RNN, h_n, [batch_size, hidden_size]
         """
-
+        
         def get_hi(hi, h0, size):
             h0_size = size - hi.size(0)
             if h0_size > 0:
                 return torch.cat([hi, h0[:h0_size]], dim=0)
             return hi[:size]
-
+        
         is_lstm = isinstance(hidden, tuple)
         input, batch_sizes = input_x.data, input_x.batch_sizes
         output = []
@@ -64,7 +64,7 @@ class VarRnnCellWrapper(nn.Module):
         else:
             batch_iter = batch_sizes
             idx = 0
-
+        
         if is_lstm:
             hn = (hidden[0].clone(), hidden[1].clone())
         else:
@@ -91,7 +91,7 @@ class VarRnnCellWrapper(nn.Module):
                 hi = cell(input_i, hi)
                 hn[:size] = hi
                 output.append(hi)
-
+        
         if is_reversed:
             output = list(reversed(output))
         output = torch.cat(output, dim=0)
@@ -117,7 +117,7 @@ class VarRNNBase(nn.Module):
     :param hidden_dropout: 对每个隐状态的dropout概率. Default: 0
     :param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False``
     """
-
+    
     def __init__(self, mode, Cell, input_size, hidden_size, num_layers=1,
                  bias=True, batch_first=False,
                  input_dropout=0, hidden_dropout=0, bidirectional=False):
@@ -141,7 +141,7 @@ class VarRNNBase(nn.Module):
                     cell, self.hidden_size, input_dropout, hidden_dropout))
         initial_parameter(self)
         self.is_lstm = (self.mode == "LSTM")
-
+    
     def _forward_one(self, n_layer, n_direction, input, hx, mask_x, mask_h):
         is_lstm = self.is_lstm
         idx = self.num_directions * n_layer + n_direction
@@ -150,7 +150,7 @@ class VarRNNBase(nn.Module):
         output_x, hidden_x = cell(
             input, hi, mask_x, mask_h, is_reversed=(n_direction == 1))
         return output_x, hidden_x
-
+    
     def forward(self, x, hx=None):
         """
 
@@ -170,13 +170,13 @@ class VarRNNBase(nn.Module):
         else:
             max_batch_size = int(x.batch_sizes[0])
         x, batch_sizes = x.data, x.batch_sizes
-
+        
         if hx is None:
             hx = x.new_zeros(self.num_layers * self.num_directions,
                              max_batch_size, self.hidden_size, requires_grad=True)
             if is_lstm:
                 hx = (hx, hx.new_zeros(hx.size(), requires_grad=True))
-
+        
         mask_x = x.new_ones((max_batch_size, self.input_size))
         mask_out = x.new_ones(
             (max_batch_size, self.hidden_size * self.num_directions))
@@ -185,7 +185,7 @@ class VarRNNBase(nn.Module):
                               training=self.training, inplace=True)
         nn.functional.dropout(mask_out, p=self.hidden_dropout,
                               training=self.training, inplace=True)
-
+        
         hidden = x.new_zeros(
             (self.num_layers * self.num_directions, max_batch_size, self.hidden_size))
         if is_lstm:
@@ -207,16 +207,16 @@ class VarRNNBase(nn.Module):
                 else:
                     hidden[idx] = hidden_x
             x = torch.cat(output_list, dim=-1)
-
+        
         if is_lstm:
             hidden = (hidden, cellstate)
-
+        
         if is_packed:
             output = PackedSequence(x, batch_sizes)
         else:
             x = PackedSequence(x, batch_sizes)
             output, _ = pad_packed_sequence(x, batch_first=self.batch_first)
-
+        
         return output, hidden
 
 
@@ -236,11 +236,11 @@ class VarLSTM(VarRNNBase):
     :param hidden_dropout: 对每个隐状态的dropout概率. Default: 0
     :param bidirectional: 若为 ``True``, 使用双向的LSTM. Default: ``False``
     """
-
+    
     def __init__(self, *args, **kwargs):
         super(VarLSTM, self).__init__(
             mode="LSTM", Cell=nn.LSTMCell, *args, **kwargs)
-
+    
     def forward(self, x, hx=None):
         return super(VarLSTM, self).forward(x, hx)
 
@@ -261,11 +261,11 @@ class VarRNN(VarRNNBase):
     :param hidden_dropout: 对每个隐状态的dropout概率. Default: 0
     :param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False``
     """
-
+    
     def __init__(self, *args, **kwargs):
         super(VarRNN, self).__init__(
             mode="RNN", Cell=nn.RNNCell, *args, **kwargs)
-
+    
     def forward(self, x, hx=None):
         return super(VarRNN, self).forward(x, hx)
 
@@ -286,10 +286,10 @@ class VarGRU(VarRNNBase):
     :param hidden_dropout: 对每个隐状态的dropout概率. Default: 0
     :param bidirectional: 若为 ``True``, 使用双向的GRU. Default: ``False``
     """
-
+    
     def __init__(self, *args, **kwargs):
         super(VarGRU, self).__init__(
             mode="GRU", Cell=nn.GRUCell, *args, **kwargs)
-
+    
     def forward(self, x, hx=None):
         return super(VarGRU, self).forward(x, hx)

fastNLP/modules/utils.py

@@ -1,5 +1,5 @@
 from functools import reduce
-from collections import OrderedDict
+
 import numpy as np
 import torch
 import torch.nn as nn