Pytorch-----torch.nn.Module.modules()

饭饭饭饭饭炒蛋2023-08-08 15:35

文章目录

前言

在使用pytorch构建神经网络时，定义的网络模型必须要继承自torch.nn.Module这一父类。在Module类中，有一个函数可以返回网络中所有模块的迭代器。这就是torch.nn.Module.modules()

提示：以下是本篇文章正文内容，下面案例可供参考

一、torch.nn.Module.modules()

源码中的解释如下：

bash 复制代码 
    def modules(self) -> Iterator['Module']:
        r"""Returns an iterator over all modules in the network.

        Yields:
            Module: a module in the network

        Note:
            Duplicate modules are returned only once. In the following
            example, ``l`` will be returned only once.

        Example::

            >>> l = nn.Linear(2, 2)
            >>> net = nn.Sequential(l, l)
            >>> for idx, m in enumerate(net.modules()):
                    print(idx, '->', m)

            0 -> Sequential(
              (0): Linear(in_features=2, out_features=2, bias=True)
              (1): Linear(in_features=2, out_features=2, bias=True)
            )
            1 -> Linear(in_features=2, out_features=2, bias=True)

        """
        for _, module in self.named_modules():
            yield module

不只是返回网络中的某一层，而是所有模块和每一层，例如，在下面的代码中，我首先定义了VGG16的网络结构，然后打印出VGG16的modules()

结果如下：

bash 复制代码 
E:\study\python\anaconda\envs\graduate_project\python.exe E:/study/project/compute_vision/VGG/model.py
0 -> VGG16(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU()
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU()
    (9): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU()
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU()
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU()
    (16): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU()
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU()
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU()
    (23): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU()
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU()
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU()
    (30): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Dropout(p=0.5, inplace=False)
    (1): Linear(in_features=25088, out_features=4096, bias=True)
    (2): Dropout(p=0.5, inplace=False)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
1 -> Sequential(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU()
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (3): ReLU()
  (4): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (6): ReLU()
  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (8): ReLU()
  (9): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (11): ReLU()
  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (13): ReLU()
  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (15): ReLU()
  (16): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
  (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (18): ReLU()
  (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (20): ReLU()
  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (22): ReLU()
  (23): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
  (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (25): ReLU()
  (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (27): ReLU()
  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (29): ReLU()
  (30): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
)
2 -> Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
3 -> ReLU()
4 -> Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
5 -> ReLU()
6 -> MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
7 -> Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
8 -> ReLU()
9 -> Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
10 -> ReLU()
11 -> MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
12 -> Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
13 -> ReLU()
14 -> Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
15 -> ReLU()
16 -> Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
17 -> ReLU()
18 -> MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
19 -> Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
20 -> ReLU()
21 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
22 -> ReLU()
23 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
24 -> ReLU()
25 -> MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
26 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
27 -> ReLU()
28 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
29 -> ReLU()
30 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
31 -> ReLU()
32 -> MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
33 -> Sequential(
  (0): Dropout(p=0.5, inplace=False)
  (1): Linear(in_features=25088, out_features=4096, bias=True)
  (2): Dropout(p=0.5, inplace=False)
  (3): Linear(in_features=4096, out_features=4096, bias=True)
  (4): Linear(in_features=4096, out_features=1000, bias=True)
)
34 -> Dropout(p=0.5, inplace=False)
35 -> Linear(in_features=25088, out_features=4096, bias=True)
36 -> Dropout(p=0.5, inplace=False)
37 -> Linear(in_features=4096, out_features=4096, bias=True)
38 -> Linear(in_features=4096, out_features=1000, bias=True)

Process finished with exit code 0

序号0：整个VGG16模块，包括特征提取模块和分类模块

序号1：特征提取模块

序号2-32：特征提取模块的每一层

序号33：分类模块

序号34-38：分类模块的每一层

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