Module存储了模块类的函数

pytorch中模块非常容易使用，只需要派生自Module，重载两个函数就行了，那么Module都做了什么

class Module(object):
  def __init__(self):        self._backend = thnn_backend        self._parameters = OrderedDict()        self._buffers = OrderedDict()        self._backward_hooks = OrderedDict()        self._forward_hooks = OrderedDict()        self._forward_pre_hooks = OrderedDict()        self._modules = OrderedDict()        self.training = True

构造函数生成一堆有序字典，用来存储各种参数，暂且不表，先说第一个结构self._backend是一个全局THNNFunctionBackend()类，存储一个一系列函数指针， 这个类派生类是FunctionBackend

class FunctionBackend(object):
    def __init__(self):        self.function_classes = {}    def register_function(self, name, function_class):        self.function_classes[name] = function_class

其中这个类的function_classes字典的键是名称，值是函数，使用register_function添加注册，注册完毕后约有118个函数，本文的pytorch版本是0.4.1

RNN                                      <function RNN at 0x7f4330534378>
RNNTanhCell                              <function RNNTanhCell at 0x7f4330530d90>
RNNReLUCell                              <function RNNReLUCell at 0x7f43305309d8>
LSTMCell                                 <function LSTMCell at 0x7f4330530e18>
GRUCell                                  <function GRUCell at 0x7f4330530ea0>
Dropout                                  <class 'torch.nn._functions.dropout.Dropout'>Dropout2d                                <class 'torch.nn._functions.dropout.FeatureDropout'>Dropout3d                                <class 'torch.nn._functions.dropout.FeatureDropout'>MarginCriterion                          <class 'torch.nn._functions.thnn.auto.MarginCriterion'>MarginCriterionBackward                  <class 'torch.nn._functions.thnn.auto.MarginCriterionBackward'>GatedLinear                              <class 'torch.nn._functions.thnn.auto.GatedLinear'>GatedLinearBackward                      <class 'torch.nn._functions.thnn.auto.GatedLinearBackward'>SpatialFullConvolutionMap                <class 'torch.nn._functions.thnn.auto.SpatialFullConvolutionMap'>SpatialFullConvolutionMapBackward        <class 'torch.nn._functions.thnn.auto.SpatialFullConvolutionMapBackward'>VolumetricFractionalMaxPooling           <class 'torch.nn._functions.thnn.auto.VolumetricFractionalMaxPooling'>VolumetricFractionalMaxPoolingBackward   <class 'torch.nn._functions.thnn.auto.VolumetricFractionalMaxPoolingBackward'>VolumetricFullDilatedConvolution         <class 'torch.nn._functions.thnn.auto.VolumetricFullDilatedConvolution'>VolumetricFullDilatedConvolutionBackward <class 'torch.nn._functions.thnn.auto.VolumetricFullDilatedConvolutionBackward'>Col2Im                                   <class 'torch.nn._functions.thnn.auto.Col2Im'>Col2ImBackward                           <class 'torch.nn._functions.thnn.auto.Col2ImBackward'>DilatedConv2d                            <class 'torch.nn._functions.thnn.auto.DilatedConv2d'>DilatedConv2dBackward                    <class 'torch.nn._functions.thnn.auto.DilatedConv2dBackward'>SpatialConvolutionLocal                  <class 'torch.nn._functions.thnn.auto.SpatialConvolutionLocal'>SpatialConvolutionLocalBackward          <class 'torch.nn._functions.thnn.auto.SpatialConvolutionLocalBackward'>FeatureLPPooling                         <class 'torch.nn._functions.thnn.auto.FeatureLPPooling'>FeatureLPPoolingBackward                 <class 'torch.nn._functions.thnn.auto.FeatureLPPoolingBackward'>VolumetricGridSamplerBilinear            <class 'torch.nn._functions.thnn.auto.VolumetricGridSamplerBilinear'>VolumetricGridSamplerBilinearBackward    <class 'torch.nn._functions.thnn.auto.VolumetricGridSamplerBilinearBackward'>TemporalUpSamplingNearest                <class 'torch.nn._functions.thnn.auto.TemporalUpSamplingNearest'>TemporalUpSamplingNearestBackward        <class 'torch.nn._functions.thnn.auto.TemporalUpSamplingNearestBackward'>SpatialUpSamplingNearest                 <class 'torch.nn._functions.thnn.auto.SpatialUpSamplingNearest'>SpatialUpSamplingNearestBackward         <class 'torch.nn._functions.thnn.auto.SpatialUpSamplingNearestBackward'>ReflectionPad1d                          <class 'torch.nn._functions.thnn.auto.ReflectionPad1d'>ReflectionPad1dBackward                  <class 'torch.nn._functions.thnn.auto.ReflectionPad1dBackward'>SpatialConvolutionMap                    <class 'torch.nn._functions.thnn.auto.SpatialConvolutionMap'>SpatialConvolutionMapBackward            <class 'torch.nn._functions.thnn.auto.SpatialConvolutionMapBackward'>NLLLoss                                  <class 'torch.nn._functions.thnn.auto.NLLLoss'>NLLLossBackward                          <class 'torch.nn._functions.thnn.auto.NLLLossBackward'>Softplus                                 <class 'torch.nn._functions.thnn.auto.Softplus'>SoftplusBackward                         <class 'torch.nn._functions.thnn.auto.SoftplusBackward'>LogSigmoid                               <class 'torch.nn._functions.thnn.auto.LogSigmoid'>LogSigmoidBackward                       <class 'torch.nn._functions.thnn.auto.LogSigmoidBackward'>SpatialUpSamplingBilinear                <class 'torch.nn._functions.thnn.auto.SpatialUpSamplingBilinear'>SpatialUpSamplingBilinearBackward        <class 'torch.nn._functions.thnn.auto.SpatialUpSamplingBilinearBackward'>ReplicationPad3d                         <class 'torch.nn._functions.thnn.auto.ReplicationPad3d'>ReplicationPad3dBackward                 <class 'torch.nn._functions.thnn.auto.ReplicationPad3dBackward'>MultiMarginLoss                          <class 'torch.nn._functions.thnn.auto.MultiMarginLoss'>MultiMarginLossBackward                  <class 'torch.nn._functions.thnn.auto.MultiMarginLossBackward'>ReplicationPad1d                         <class 'torch.nn._functions.thnn.auto.ReplicationPad1d'>ReplicationPad1dBackward                 <class 'torch.nn._functions.thnn.auto.ReplicationPad1dBackward'>MultiLabelMarginLoss                     <class 'torch.nn._functions.thnn.auto.MultiLabelMarginLoss'>MultiLabelMarginLossBackward             <class 'torch.nn._functions.thnn.auto.MultiLabelMarginLossBackward'>SpatialFullDilatedConvolution            <class 'torch.nn._functions.thnn.auto.SpatialFullDilatedConvolution'>SpatialFullDilatedConvolutionBackward    <class 'torch.nn._functions.thnn.auto.SpatialFullDilatedConvolutionBackward'>SoftMarginLoss                           <class 'torch.nn._functions.thnn.auto.SoftMarginLoss'>SoftMarginLossBackward                   <class 'torch.nn._functions.thnn.auto.SoftMarginLossBackward'>NLLLoss2d                                <class 'torch.nn._functions.thnn.auto.NLLLoss2d'>NLLLoss2dBackward                        <class 'torch.nn._functions.thnn.auto.NLLLoss2dBackward'>MSELoss                                  <class 'torch.nn._functions.thnn.auto.MSELoss'>MSELossBackward                          <class 'torch.nn._functions.thnn.auto.MSELossBackward'>Sigmoid                                  <class 'torch.nn._functions.thnn.auto.Sigmoid'>SigmoidBackward                          <class 'torch.nn._functions.thnn.auto.SigmoidBackward'>VolumetricUpSamplingTrilinear            <class 'torch.nn._functions.thnn.auto.VolumetricUpSamplingTrilinear'>VolumetricUpSamplingTrilinearBackward    <class 'torch.nn._functions.thnn.auto.VolumetricUpSamplingTrilinearBackward'>BCELoss                                  <class 'torch.nn._functions.thnn.auto.BCELoss'>BCELossBackward                          <class 'torch.nn._functions.thnn.auto.BCELossBackward'>Square                                   <class 'torch.nn._functions.thnn.auto.Square'>SquareBackward                           <class 'torch.nn._functions.thnn.auto.SquareBackward'>ReplicationPad2d                         <class 'torch.nn._functions.thnn.auto.ReplicationPad2d'>ReplicationPad2dBackward                 <class 'torch.nn._functions.thnn.auto.ReplicationPad2dBackward'>L1Loss                                   <class 'torch.nn._functions.thnn.auto.L1Loss'>L1LossBackward                           <class 'torch.nn._functions.thnn.auto.L1LossBackward'>SpatialGridSamplerBilinear               <class 'torch.nn._functions.thnn.auto.SpatialGridSamplerBilinear'>SpatialGridSamplerBilinearBackward       <class 'torch.nn._functions.thnn.auto.SpatialGridSamplerBilinearBackward'>Sqrt                                     <class 'torch.nn._functions.thnn.auto.Sqrt'>SqrtBackward                             <class 'torch.nn._functions.thnn.auto.SqrtBackward'>TemporalRowConvolution                   <class 'torch.nn._functions.thnn.auto.TemporalRowConvolution'>TemporalRowConvolutionBackward           <class 'torch.nn._functions.thnn.auto.TemporalRowConvolutionBackward'>SpatialFractionalMaxPooling              <class 'torch.nn._functions.thnn.auto.SpatialFractionalMaxPooling'>SpatialFractionalMaxPoolingBackward      <class 'torch.nn._functions.thnn.auto.SpatialFractionalMaxPoolingBackward'>TemporalUpSamplingLinear                 <class 'torch.nn._functions.thnn.auto.TemporalUpSamplingLinear'>TemporalUpSamplingLinearBackward         <class 'torch.nn._functions.thnn.auto.TemporalUpSamplingLinearBackward'>VolumetricDilatedMaxPooling              <class 'torch.nn._functions.thnn.auto.VolumetricDilatedMaxPooling'>VolumetricDilatedMaxPoolingBackward      <class 'torch.nn._functions.thnn.auto.VolumetricDilatedMaxPoolingBackward'>Threshold                                <class 'torch.nn._functions.thnn.auto.Threshold'>ThresholdBackward                        <class 'torch.nn._functions.thnn.auto.ThresholdBackward'>Abs                                      <class 'torch.nn._functions.thnn.auto.Abs'>AbsBackward                              <class 'torch.nn._functions.thnn.auto.AbsBackward'>Softshrink                               <class 'torch.nn._functions.thnn.auto.Softshrink'>SoftshrinkBackward                       <class 'torch.nn._functions.thnn.auto.SoftshrinkBackward'>LeakyReLU                                <class 'torch.nn._functions.thnn.auto.LeakyReLU'>LeakyReLUBackward                        <class 'torch.nn._functions.thnn.auto.LeakyReLUBackward'>VolumetricUpSamplingNearest              <class 'torch.nn._functions.thnn.auto.VolumetricUpSamplingNearest'>VolumetricUpSamplingNearestBackward      <class 'torch.nn._functions.thnn.auto.VolumetricUpSamplingNearestBackward'>VolumetricDilatedConvolution             <class 'torch.nn._functions.thnn.auto.VolumetricDilatedConvolution'>VolumetricDilatedConvolutionBackward     <class 'torch.nn._functions.thnn.auto.VolumetricDilatedConvolutionBackward'>Tanh                                     <class 'torch.nn._functions.thnn.auto.Tanh'>TanhBackward                             <class 'torch.nn._functions.thnn.auto.TanhBackward'>TemporalSubSampling                      <class 'torch.nn._functions.thnn.auto.TemporalSubSampling'>TemporalSubSamplingBackward              <class 'torch.nn._functions.thnn.auto.TemporalSubSamplingBackward'>ELU                                      <class 'torch.nn._functions.thnn.auto.ELU'>ELUBackward                              <class 'torch.nn._functions.thnn.auto.ELUBackward'>Hardtanh                                 <class 'torch.nn._functions.thnn.auto.Hardtanh'>HardtanhBackward                         <class 'torch.nn._functions.thnn.auto.HardtanhBackward'>L1Cost                                   <class 'torch.nn._functions.thnn.auto.L1Cost'>L1CostBackward                           <class 'torch.nn._functions.thnn.auto.L1CostBackward'>SpatialSubSampling                       <class 'torch.nn._functions.thnn.auto.SpatialSubSampling'>SpatialSubSamplingBackward               <class 'torch.nn._functions.thnn.auto.SpatialSubSamplingBackward'>Im2Col                                   <class 'torch.nn._functions.thnn.auto.Im2Col'>Im2ColBackward                           <class 'torch.nn._functions.thnn.auto.Im2ColBackward'>KLDivLoss                                <class 'torch.nn._functions.thnn.auto.KLDivLoss'>KLDivLossBackward                        <class 'torch.nn._functions.thnn.auto.KLDivLossBackward'>SmoothL1Loss                             <class 'torch.nn._functions.thnn.auto.SmoothL1Loss'>SmoothL1LossBackward                     <class 'torch.nn._functions.thnn.auto.SmoothL1LossBackward'>ReflectionPad2d                          <class 'torch.nn._functions.thnn.auto.ReflectionPad2d'>ReflectionPad2dBackward                  <class 'torch.nn._functions.thnn.auto.ReflectionPad2dBackward'>CrossMapLRN2d                            <class 'torch.nn._functions.thnn.normalization.CrossMapLRN2d'>EmbeddingBag                             <class 'torch.nn._functions.thnn.sparse.EmbeddingBag'>

一不留神把pytorch支持的所有预定义模块都给展示出来了。本文稍后开始讲解这些预定义模块的实现。

其他有序字典

        self._parameters = OrderedDict() # 模块网络参数
        self._buffers = OrderedDict()       # 驻留内存（不释放，不交换）
        self._backward_hooks = OrderedDict() # 反向钩子函数字典，
        self._forward_hooks = OrderedDict() # 正向钩子函数字典
        self._forward_pre_hooks = OrderedDict() # 正向调用前钩子函数字典
        self._modules = OrderedDict() # 模块列表
        self.training = True # 训练还是验证

模块函数

模块的函数根据名称可以知道其作用，此处仅仅列举，不在详述

作者：readilen
链接：https://www.jianshu.com/p/c5b751123ae8