PytorchLearning-testResNet

发表于 | 分类于

PytorchLearning-testResNet

标签： DeepLearning Pytorch

概述

  今天在熟悉pytorch中的nn模块和optim模块，之后利用pytroch搭建一个残差网络-ResNet。

torch相关知识

torch.nn模块

Containers

  Containers(容器)中包含了torch.nn.Module，是所有模型需要包含的基类。例如：

1
2
3
4
5
6
7
8
9
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)# submodule: Conv2d
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
       x = F.relu(self.conv1(x))
       return F.relu(self.conv2(x))

  除此之外，在pytorch的官方文档中这部分还有几个需要牢记的知识点：
   forward( input)：定义了每次执行的 计算步骤。 在所有的子类中都需要重写这个函数。
   modules()：返回一个包含 当前模型 所有模块的迭代器。
   class torch.nn.Sequential( args)：一个时序容器，Modules会以他们传入的顺序被添加到容器中。当然，也可以传入一个OrderedDict。
   其中Sequential()应用举例如下：

1
2
3
4
5
6
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

神经网络层

  图像相关的层主要包括卷积层，池化层，全连接层，具体的参数介绍可以查看官方文档。
   卷积层包括了1-3维卷积层和1-3维解卷积层，其中二维卷积层函数如下：

1
class torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True)

   池化层包括了1-3维的最大池化，平均池化，自适应池化，其中二维最大池化函数如下：

1
class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

   全连接层对输入数据做线性变换，举例如下：

1
class torch.nn.Linear(in_features, out_features, bias=True)

  除CNN，RNN需要的网络的层之外，这部分还包括了一些激活函数，损失函数，可以看为一种特殊的layer。
  除此之外，还有一些其他的网络层，在官网文档中介绍的很详细。

nn.functional

  nn中的functional模块包括了许多神经网络用到的函数，nn中大多数layer在此模块中都有一个对应的函数，不同的是，layer定义的函数会自动提取学习的参数，而nn.function中的函数更想平时所说的函数

nn.init

  n.init模块专门为初始化设计，实现了常用的初始化策略。以从给定均值和标准差的正态分布N(mean,std)中生成值，填充输入的张量或变量的初始化化方法为例，实现如下：

1
torch.nn.init.normal(tensor, mean=0, std=1)

torch.optim模块

  torch.optim模块中包含了DeepLearning中常用的优化方法。若使用此模块进行优化，需要先构建一个optimizer对象；然后为每个参数设置选项，之后进行优化迭代。
  具体实现的算法参考官方文档。

残差网络

  当网络较深的时候，由于存在梯度消失和梯度爆炸等问题，难以训练；所以采用跳远连接的方式，构建可以训练深度网络的ResNets。所谓跳远连接，就是在原来输入的基础上，原始输入数据直接作为后一层的输入进行训练，由于多了这条捷径，实现了跨层连接，来自深层的梯度能直接通过，去到上一层，使得浅层的网络层参数等到有效的训练。这个方法由何凯明等人提出。
  其中ResNets由基本结构参差块组成：
               Markdown
  其中典型的ResNets网络如下：
                 Markdown

代码实现

  利用pytorch构建ResNet34网络，源代码实现参考陈云的实现方法，代码如下：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
from torch import nn
import torch as t
from torch.nn import functional as F

class ResidualBlock(nn.Module):
    def __init__(self,inchannel,outchannel,stride=1,shortcut=None):
        super(ResidualBlock,self).__init__()
        self.left = nn.Sequential(
                 nn.Conv2d(inchannel,outchannel,3,stride,1,bias=False),
                 nn.BatchNorm2d(outchannel),
                 nn.ReLU(inplace=True),
                 nn.Conv2d(outchannel,outchannel,3,1,1,bias=False),
                 nn.BatchNorm2d(outchannel))
        self.right = shortcut

    def forward(self,x):
        out = self.left(x)
        residual = x if self.right is None else self.right(x)
        out += residual
        return F.relu(out)

class ResNet(nn.Module):
    def __init__(self, num_classes = 1000):
        super(ResNet,self).__init__()
        self.pre = nn.Sequential(
                nn.Conv2d(3,64,7,2,3,bias = False),
                nn.BatchNorm2d(64),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(3,2,1))
        self.layer1 = self._make_layer(64,128,3)
        self.layer2 = self._make_layer(128,256,4,stride = 2)
        self.layer3 = self._make_layer(256,512,6,stride = 2)
        self.layer4 = self._make_layer(512,512,3,stride = 2)

        self.fc = nn.Linear(512,num_classes)

    def _make_layer(self,inchannel,outchannel,block_num,stride = 1):
        shortcut = nn.Sequential(
                 nn.Conv2d(inchannel,outchannel,1,stride,bias = False),
                 nn.BatchNorm2d(outchannel))

        layers = []
        layers.append(ResidualBlock(inchannel,outchannel,stride,shortcut))

        for i in range(1,block_num):
            layers.append(ResidualBlock(outchannel,outchannel))
        return nn.Sequential(*layers)

    def forward(self,x):
        x = self.pre(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = F.avg_pool2d(x,7)
        x = x.view(x.size(0),-1)
        return self.fc(x)

model = ResNet()
input = t.autograd.Variable(t.randn(1,3,224,224))
o = model(input)

print model
print o

  在实现ResNets的过程中，先实现了一个基础的ResidualBlock，之后利用了for循环结构循环了ResidualBlock搭建了_make_layer函数，最后在将各个由_make_layer构成的层连接起来，构成了ResNet34.网络结构输出如下：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
$ python ResNet.py 
ResNet (
  (pre): Sequential (
    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (2): ReLU (inplace)
    (3): MaxPool2d (size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
  )
  (layer1): Sequential (
    (0): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      )
      (right): Sequential (
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      )
    )
  )
  (layer2): Sequential (
    (0): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      )
      (right): Sequential (
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (3): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      )
    )
  )
  (layer3): Sequential (
    (0): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
      (right): Sequential (
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (3): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (4): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (5): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
  )
  (layer4): Sequential (
    (0): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
      (right): Sequential (
        (0): Conv2d(512, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): ResidualBlock (
      (left): Sequential (
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU (inplace)
        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
  )
  (fc): Linear (512 -> 1000)
)
Variable containing:
 0.3086 -0.0393 -0.0103  ...   0.5097 -0.1146  0.1722
[torch.FloatTensor of size 1x1000]

说明

  其实pytorch配备的图像工具包torchvision已经实现了DeepLearning中的许多经典的模型，其中包括了ResNet34，实现如下：

1
2
from torchvision import models
model1 = models.resnet34()

  测试输出如下：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
>>> print model1
ResNet (
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
  (relu): ReLU (inplace)
  (maxpool): MaxPool2d (size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
  (layer1): Sequential (
    (0): BasicBlock (
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    )
    (1): BasicBlock (
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    )
    (2): BasicBlock (
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (layer2): Sequential (
    (0): BasicBlock (
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      (downsample): Sequential (
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): BasicBlock (
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    )
    (2): BasicBlock (
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    )
    (3): BasicBlock (
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (layer3): Sequential (
    (0): BasicBlock (
      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (downsample): Sequential (
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): BasicBlock (
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    )
    (2): BasicBlock (
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    )
    (3): BasicBlock (
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    )
    (4): BasicBlock (
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    )
    (5): BasicBlock (
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (layer4): Sequential (
    (0): BasicBlock (
      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      (downsample): Sequential (
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (1): BasicBlock (
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
    )
    (2): BasicBlock (
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU (inplace)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (avgpool): AvgPool2d (
  )
  (fc): Linear (512 -> 1000)
)

  发现实现的ResNet34是参考torchvision的ResNet34并做出简化的版本。