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本部分多试几次就可以弄得清每一层具体怎么访问了。

step1. 网络定义如下：

require "dpnn"local net = nn.Sequential()net:add(nn.SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3))net:add(nn.SpatialBatchNormalization(64))net:add(nn.ReLU())net:add(nn.SpatialMaxPooling(3, 3, 2, 2, 1, 1))net:add(nn.Inception{     inputSize = 64,     kernelSize = {
   3, 5},     kernelStride = {
   1, 1},     outputSize = {
   128, 32},     reduceSize = {
   96, 16, 32, 64},     pool = nn.SpatialMaxPooling(3, 3, 1, 1, 1, 1),     batchNorm = true   })net:evaluate()

上面的网络，包含conv+BatchNorm+ReLU+Maxpool+Inception层。

step2. 直接通过print(net)便可得到其网络结构：

nn.Sequential {  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]  (1): nn.SpatialConvolution(3 -> 64, 7x7, 2,2, 3,3)  (2): nn.SpatialBatchNormalization  (3): nn.ReLU  (4): nn.SpatialMaxPooling(3x3, 2,2, 1,1)  (5): nn.Inception @ nn.DepthConcat {    input      |`-> (1): nn.Sequential {      |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]      |      (1): nn.SpatialConvolution(64 -> 96, 1x1)      |      (2): nn.SpatialBatchNormalization      |      (3): nn.ReLU      |      (4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)      |      (5): nn.SpatialBatchNormalization      |      (6): nn.ReLU      |    }      |`-> (2): nn.Sequential {      |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]      |      (1): nn.SpatialConvolution(64 -> 16, 1x1)      |      (2): nn.SpatialBatchNormalization      |      (3): nn.ReLU      |      (4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)      |      (5): nn.SpatialBatchNormalization      |      (6): nn.ReLU      |    }      |`-> (3): nn.Sequential {      |      [input -> (1) -> (2) -> (3) -> (4) -> output]      |      (1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)      |      (2): nn.SpatialConvolution(64 -> 32, 1x1)      |      (3): nn.SpatialBatchNormalization      |      (4): nn.ReLU      |    }      |`-> (4): nn.Sequential {             [input -> (1) -> (2) -> (3) -> output]             (1): nn.SpatialConvolution(64 -> 64, 1x1)             (2): nn.SpatialBatchNormalization             (3): nn.ReLU           }       ... -> output  }}

但实际上该网络还包括input，output，gradInput等参数。

step3. 使用下面代码便可输出网络比较详细的参数：

for k,curLayer in pairs(net) do    print(k,curLayer)end

step4. 输出：

_type	torch.DoubleTensor	output	[torch.DoubleTensor with no dimension]gradInput	[torch.DoubleTensor with no dimension]modules	{  1 :     {      dH : 2      dW : 2      nInputPlane : 3      output : DoubleTensor - empty      kH : 7      train : false      gradBias : DoubleTensor - size: 64      padH : 3      bias : DoubleTensor - size: 64      weight : DoubleTensor - size: 64x3x7x7      _type : "torch.DoubleTensor"      gradWeight : DoubleTensor - size: 64x3x7x7      padW : 3      nOutputPlane : 64      kW : 7      gradInput : DoubleTensor - empty    }  2 :     {      gradBias : DoubleTensor - size: 64      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      running_var : DoubleTensor - size: 64      momentum : 0.1      gradWeight : DoubleTensor - size: 64      eps : 1e-05      _type : "torch.DoubleTensor"      affine : true      running_mean : DoubleTensor - size: 64      bias : DoubleTensor - size: 64      weight : DoubleTensor - size: 64      train : false    }  3 :     {      inplace : false      threshold : 0      _type : "torch.DoubleTensor"      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      train : false      val : 0    }  4 :     {      dH : 2      dW : 2      kW : 3      gradInput : DoubleTensor - empty      indices : DoubleTensor - empty      train : false      _type : "torch.DoubleTensor"      padH : 1      ceil_mode : false      output : DoubleTensor - empty      kH : 3      padW : 1    }  5 :     {      outputSize :         {          1 : 128          2 : 32        }      inputSize : 64      gradInput : DoubleTensor - empty      modules :         {          1 :             {              train : false              _type : "torch.DoubleTensor"              output : DoubleTensor - empty              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                  4 : {...}                }              dimension : 2              size : LongStorage - size: 0            }        }      kernelStride :         {          1 : 1          2 : 1        }      _type : "torch.DoubleTensor"      module :         {          train : false          _type : "torch.DoubleTensor"          output : DoubleTensor - empty          gradInput : DoubleTensor - empty          modules :             {              1 :                 {                  _type : "torch.DoubleTensor"                  output : DoubleTensor - empty                  gradInput : DoubleTensor - empty                  modules : {...}                  train : false                }              2 :                 {                  _type : "torch.DoubleTensor"                  output : DoubleTensor - empty                  gradInput : DoubleTensor - empty                  modules : {...}                  train : false                }              3 :                 {                  _type : "torch.DoubleTensor"                  output : DoubleTensor - empty                  gradInput : DoubleTensor - empty                  modules : {...}                  train : false                }              4 :                 {                  _type : "torch.DoubleTensor"                  output : DoubleTensor - empty                  gradInput : DoubleTensor - empty                  modules : {...}                  train : false                }            }          dimension : 2          size : LongStorage - size: 0        }      poolStride : 1      padding : true      reduceStride : {...}      transfer :         {          inplace : false          threshold : 0          _type : "torch.DoubleTensor"          output : DoubleTensor - empty          gradInput : DoubleTensor - empty          val : 0        }      batchNorm : true      train : false      pool :         {          dH : 1          dW : 1          kW : 3          gradInput : DoubleTensor - empty          indices : DoubleTensor - empty          train : false          _type : "torch.DoubleTensor"          padH : 1          ceil_mode : false          output : DoubleTensor - empty          kH : 3          padW : 1        }      poolSize : 3      reduceSize :         {          1 : 96          2 : 16          3 : 32          4 : 64        }      kernelSize :         {          1 : 3          2 : 5        }      output : DoubleTensor - empty    }}train	false

上面的modules中，分别为conv、BatchNorm、ReLU、Maxpool、Inception对应的参数。

step5. 可通过net.modules[1]来索引nn.SpatialConvolution。如print(net.modules[1])得到：

nn.SpatialConvolution(3 -> 64, 7x7, 2,2, 3,3)

step6. 如果想更进一步，输出该层的参数，可以使用如下代码（实际上step4中已经输出了）：

for k,curLayer in pairs(net.modules[1]) do    if type(curLayer) ~= 'userdata' then        print(k,curLayer)    else        local strval = ' '        for i = 1, curLayer:dim() do             strval = strval .. curLayer:size(i) .. " "        end        print(k .. " " .. type(curLayer) .. " " .. string.format("\27[31m size: %s", strval))    endend

step7. 得到的结果为：

dH	2	dW	2	nInputPlane	3	output userdata  size:  	kH	7	train	false	gradBias userdata  size:  64 	padH	3	bias userdata  size:  64 	weight userdata  size:  64 3 7 7 	_type	torch.DoubleTensor	gradWeight userdata  size:  64 3 7 7 	padW	3	nOutputPlane	64	kW	7	gradInput userdata  size:

step8. 对于Inception层，step4中并没有完全显示出来。按照step5中的方式，使用net.modules[5]来得到Inception层。将step6进行更改，可输出：

outputSize	{  1 : 128  2 : 32}inputSize	64	gradInput userdata  size:  	modules	{  1 :     {      train : false      _type : "torch.DoubleTensor"      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      modules :         {          1 :             {              _type : "torch.DoubleTensor"              output : DoubleTensor - empty              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                  4 : {...}                  5 : {...}                  6 : {...}                }              train : false            }          2 :             {              _type : "torch.DoubleTensor"              output : DoubleTensor - empty              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                  4 : {...}                  5 : {...}                  6 : {...}                }              train : false            }          3 :             {              _type : "torch.DoubleTensor"              output : DoubleTensor - empty              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                  4 : {...}                }              train : false            }          4 :             {              _type : "torch.DoubleTensor"              output : DoubleTensor - empty              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              train : false            }        }      dimension : 2      size : LongStorage - size: 0    }}kernelStride	{  1 : 1  2 : 1}_type	torch.DoubleTensor	module	nn.DepthConcat {  input    |`-> (1): nn.Sequential {    |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]    |      (1): nn.SpatialConvolution(64 -> 96, 1x1)    |      (2): nn.SpatialBatchNormalization    |      (3): nn.ReLU    |      (4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)    |      (5): nn.SpatialBatchNormalization    |      (6): nn.ReLU    |    }    |`-> (2): nn.Sequential {    |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]    |      (1): nn.SpatialConvolution(64 -> 16, 1x1)    |      (2): nn.SpatialBatchNormalization    |      (3): nn.ReLU    |      (4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)    |      (5): nn.SpatialBatchNormalization    |      (6): nn.ReLU    |    }    |`-> (3): nn.Sequential {    |      [input -> (1) -> (2) -> (3) -> (4) -> output]    |      (1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)    |      (2): nn.SpatialConvolution(64 -> 32, 1x1)    |      (3): nn.SpatialBatchNormalization    |      (4): nn.ReLU    |    }    |`-> (4): nn.Sequential {           [input -> (1) -> (2) -> (3) -> output]           (1): nn.SpatialConvolution(64 -> 64, 1x1)           (2): nn.SpatialBatchNormalization           (3): nn.ReLU         }     ... -> output}poolStride	1	padding	true	reduceStride	{}transfer	nn.ReLUbatchNorm	true	train	false	pool	nn.SpatialMaxPooling(3x3, 1,1, 1,1)poolSize	3	reduceSize	{  1 : 96  2 : 16  3 : 32  4 : 64}kernelSize	{  1 : 3  2 : 5}output userdata  size:

step9. 在step8中，modules中为对应的inception各层（3*3卷积，5*5卷积，pooling，1*1reduce）。可通过net.modules[5].module来得到这些层。该层也有train，output，gradInput，modules等变量。可通过print(net.modules[5].module)来输出。

step10. 根据step5中的思路，可通过net.modules[5].module.modules[1]来得到3*3卷基层具体情况：

_type	torch.DoubleTensor	output userdata  size:  	gradInput userdata  size:  	modules	{  1 :     {      dH : 1      dW : 1      nInputPlane : 64      output : DoubleTensor - empty      kH : 1      train : false      gradBias : DoubleTensor - size: 96      padH : 0      bias : DoubleTensor - size: 96      weight : DoubleTensor - size: 96x64x1x1      _type : "torch.DoubleTensor"      gradWeight : DoubleTensor - size: 96x64x1x1      padW : 0      nOutputPlane : 96      kW : 1      gradInput : DoubleTensor - empty    }  2 :     {      gradBias : DoubleTensor - size: 96      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      running_var : DoubleTensor - size: 96      momentum : 0.1      gradWeight : DoubleTensor - size: 96      eps : 1e-05      _type : "torch.DoubleTensor"      affine : true      running_mean : DoubleTensor - size: 96      bias : DoubleTensor - size: 96      weight : DoubleTensor - size: 96      train : false    }  3 :     {      inplace : false      threshold : 0      _type : "torch.DoubleTensor"      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      train : false      val : 0    }  4 :     {      dH : 1      dW : 1      nInputPlane : 96      output : DoubleTensor - empty      kH : 3      train : false      gradBias : DoubleTensor - size: 128      padH : 1      bias : DoubleTensor - size: 128      weight : DoubleTensor - size: 128x96x3x3      _type : "torch.DoubleTensor"      gradWeight : DoubleTensor - size: 128x96x3x3      padW : 1      nOutputPlane : 128      kW : 3      gradInput : DoubleTensor - empty    }  5 :     {      gradBias : DoubleTensor - size: 128      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      running_var : DoubleTensor - size: 128      momentum : 0.1      gradWeight : DoubleTensor - size: 128      eps : 1e-05      _type : "torch.DoubleTensor"      affine : true      running_mean : DoubleTensor - size: 128      bias : DoubleTensor - size: 128      weight : DoubleTensor - size: 128      train : false    }  6 :     {      inplace : false      threshold : 0      _type : "torch.DoubleTensor"      output : DoubleTensor - empty      gradInput : DoubleTensor - empty      train : false      val : 0    }}train	false

注意：此处有一个module和一个modules，具体不太明白。

step11. 可通过net.modules[5].module.modules[1].modules进一步查看该层的情况：

1	nn.SpatialConvolution(64 -> 96, 1x1)2	nn.SpatialBatchNormalization3	nn.ReLU4	nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)5	nn.SpatialBatchNormalization6	nn.ReLU

可见，该层包括1*1conv，BatchNorm，ReLU，3*3conv，BatchNorm，Relu这些。

step12. 若要查看step11中的3*3卷基层信息，可使用如下索引：

net.modules[5].module.modules[1].modules[4]

结果如下：

dH	1	dW	1	nInputPlane	96	output userdata  size:  	kH	3	train	false	gradBias userdata  size:  128 	padH	1	bias userdata  size:  128 	weight userdata  size:  128 96 3 3 	_type	torch.DoubleTensor	gradWeight userdata  size:  128 96 3 3 	padW	1	nOutputPlane	128	kW	3	gradInput userdata  size:

step13. 到了step12，已经索引到了step1中网络的最深层。网络中每层均有input，output等。

step14. 对于net.modules[5]的Inception层，net.modules[5].output的结果和net.modules[5].module.output的结果是一样的，如（为方便显示，只显示了一小部分。如果输出net.modules[5].output，可能会有很多全为0的）:

local imgBatch = torch.rand(1,3,128,128)local infer = net:forward(imgBatch)print(net.modules[5].output[1][2][3])print(net.modules[5].module.output[1][2][3])

结果为：

0.01 * 2.7396 2.9070 3.1895 1.5040 1.9784 4.0125 3.2874 3.3137 2.1326 2.3930 2.8170 3.5226 2.3162 2.7308 2.8511 2.5278 3.3325 3.0819 3.2826 3.5363 2.5749 2.8816 2.2393 2.4765 2.4803 3.2553 3.0837 3.1197 2.4632 1.5145 3.7101 2.1888[torch.DoubleTensor of size 32]0.01 * 2.7396 2.9070 3.1895 1.5040 1.9784 4.0125 3.2874 3.3137 2.1326 2.3930 2.8170 3.5226 2.3162 2.7308 2.8511 2.5278 3.3325 3.0819 3.2826 3.5363 2.5749 2.8816 2.2393 2.4765 2.4803 3.2553 3.0837 3.1197 2.4632 1.5145 3.7101 2.1888[torch.DoubleTensor of size 32]