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6-1构建模型的3种方法

时间：2024-07-14 16:19:35浏览次数：15  
标签：kernel nn 32 模型 stride 构建 64 方法 size

可以使用以下三种方式构建模型：

1.继承nn.Module基类构建自定义模型

2.使用nn.Sequential按层顺序构建模型

3.继承nn.Module基类构建模型并辅助应用模型容器进行封装（nn.Sequentail, nn.ModuleList, nn.ModuleDict）

其中第一种方式最为常见，第二种方式最简单，第三种方式最为灵活也较为复杂。

推荐使用第一种方式构建模型。

import torch
import torchkeras

print('torch.__version__=' + torch.__version__)
print('torchkeras.__version__=' + torchkeras.__version__)

"""
torch.__version__=2.3.1+cu121
torchkeras.__version__=3.9.6
"""

1.继承nn.Module基类构建自定义模型

以下是继承nn.Module基类构建自定义模型的一个范例。模型中的用到的层一般在__init__函数中定义，然后在forward方法中定义模型的正向传播逻辑。

from torch import nn


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.dropout = nn.Dropout2d(p=0.1)
        self.adaptive_pool = nn.AdaptiveMaxPool2d((1, 1))  # 对于输入信号，提供二维自适应最大池化操作
        self.flatten = nn.Flatten()
        self.linear1 = nn.Linear(64, 32)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(32, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.dropout(x)
        x = self.adaptive_pool(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.relu(x)
        y = self.linear2(x)
        return y

net = Net()
print(net)

"""
Net(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
)
"""

from torchkeras import summary

summary(net, input_shape=(3, 32, 32));

"""
--------------------------------------------------------------------------
Layer (type)                            Output Shape              Param #
==========================================================================
Conv2d-1                            [-1, 32, 30, 30]                  896
MaxPool2d-2                         [-1, 32, 15, 15]                    0
Conv2d-3                            [-1, 64, 11, 11]               51,264
MaxPool2d-4                           [-1, 64, 5, 5]                    0
Dropout2d-5                           [-1, 64, 5, 5]                    0
AdaptiveMaxPool2d-6                   [-1, 64, 1, 1]                    0
Flatten-7                                   [-1, 64]                    0
Linear-8                                    [-1, 32]                2,080
ReLU-9                                      [-1, 32]                    0
Linear-10                                    [-1, 1]                   33
==========================================================================
Total params: 54,273
Trainable params: 54,273
Non-trainable params: 0
--------------------------------------------------------------------------
Input size (MB): 0.011719
Forward/backward pass size (MB): 0.359627
Params size (MB): 0.207035
Estimated Total Size (MB): 0.578381
--------------------------------------------------------------------------
"""

2.使用nn.Sequential按层顺序构建模型

使用nn.Sequential按层顺序构建模型无需定义forward方法。仅仅适用于简单的模型。

以下是使用nn.Sequential搭建模型的一些等价方法。

  • 1.利用add_module方法
net = nn.Sequential()
net.add_module("conv1", nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3))
net.add_module("pool1", nn.MaxPool2d(kernel_size=2, stride=2))
net.add_module("conv2", nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5))
net.add_module("pool2", nn.MaxPool2d(kernel_size=2, stride=2))
net.add_module("dropout", nn.Dropout2d(p=0.1))
net.add_module("adaptive_pool", nn.AdaptiveMaxPool2d(1, 1))
net.add_module("flatten", nn.Flatten())
net.add_module("linear1", nn.Linear(64, 32))
net.add_module("relu", nn.ReLU())
net.add_module("linear2", nn.Linear(32, 1))
print(net)

"""
Sequential(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=1)
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
)
"""
  • 2.利用变长参数，这种方式构建时不能给每个层指定名称
net = nn.Sequential(
    nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Dropout2d(p=0.1),
    nn.AdaptiveMaxPool2d((1, 1)),
    nn.Flatten(),
    nn.Linear(64, 32),
    nn.ReLU(),
    nn.Linear(32, 1)
)

print(net)

"""
Sequential(
  (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (4): Dropout2d(p=0.1, inplace=False)
  (5): AdaptiveMaxPool2d(output_size=(1, 1))
  (6): Flatten(start_dim=1, end_dim=-1)
  (7): Linear(in_features=64, out_features=32, bias=True)
  (8): ReLU()
  (9): Linear(in_features=32, out_features=1, bias=True)
)
"""
  • 3.利用OrderedDict
from collections import OrderedDict

net = nn.Sequential(OrderedDict([
    ("conv1", nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)),
    ("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2)),
    ("conv2",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)),
    ("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2)),
    ("dropout",nn.Dropout2d(p = 0.1)),
    ("adaptive_pool",nn.AdaptiveMaxPool2d((1,1))),
    ("flatten",nn.Flatten()),
    ("linear1",nn.Linear(64,32)),
    ("relu",nn.ReLU()),
    ("linear2",nn.Linear(32,1))
]))

print(net)

"""
Sequential(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
)
"""

from torchkeras import summary

summary(net, input_shape=(3, 32, 32));

"""
--------------------------------------------------------------------------
Layer (type)                            Output Shape              Param #
==========================================================================
Conv2d-1                            [-1, 32, 30, 30]                  896
MaxPool2d-2                         [-1, 32, 15, 15]                    0
Conv2d-3                            [-1, 64, 11, 11]               51,264
MaxPool2d-4                           [-1, 64, 5, 5]                    0
Dropout2d-5                           [-1, 64, 5, 5]                    0
AdaptiveMaxPool2d-6                   [-1, 64, 1, 1]                    0
Flatten-7                                   [-1, 64]                    0
Linear-8                                    [-1, 32]                2,080
ReLU-9                                      [-1, 32]                    0
Linear-10                                    [-1, 1]                   33
==========================================================================
Total params: 54,273
Trainable params: 54,273
Non-trainable params: 0
--------------------------------------------------------------------------
Input size (MB): 0.011719
Forward/backward pass size (MB): 0.359627
Params size (MB): 0.207035
Estimated Total Size (MB): 0.578381
--------------------------------------------------------------------------
"""

3.继承nn.Module基类构建模型并辅助应用模型容器进行封装

当模型的结构比较复杂时，我们可以应用模型容器（nn.Sequential, nn.ModuleList, nn.ModuleDict）对模型的部分结构进行封装。

这样做会让模型整体更加有层次干，有时候也能减少代码量。

注意，在下面的范例中，我们每次仅仅使用一种模型容器，但实际上这些模型容器的使用是非常灵活的，可以在一个模型中任意组合任意嵌套使用。

  • nn.Sequential作为模型容器
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Dropout2d(p=0.1),
            nn.AdaptiveMaxPool2d((1, 1))
        )
        self.dense = nn.Sequential(
            nn.Flatten(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 1)
        )

    def forward(self, x):
        x = self.conv(x)
        y = self.dense(x)
        return y
    
net = Net()
print(net)

"""
Net(
  (conv): Sequential(
    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Dropout2d(p=0.1, inplace=False)
    (5): AdaptiveMaxPool2d(output_size=(1, 1))
  )
  (dense): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=64, out_features=32, bias=True)
    (2): ReLU()
    (3): Linear(in_features=32, out_features=1, bias=True)
  )
)
"""
  • nn.ModuleList作为模型容器，注意，下面的ModuleList不能使用Python中的列表代替
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.ModuleList([
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Dropout2d(p=0.1),
            nn.AdaptiveMaxPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 1)
        ])

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x
    
net = Net()
print(net)

"""
Net(
  (layers): ModuleList(
    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Dropout2d(p=0.1, inplace=False)
    (5): AdaptiveMaxPool2d(output_size=(1, 1))
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=64, out_features=32, bias=True)
    (8): ReLU()
    (9): Linear(in_features=32, out_features=1, bias=True)
  )
)
"""

from torchkeras import summary

summary(net, input_shape=(3, 32, 32));

"""
--------------------------------------------------------------------------
Layer (type)                            Output Shape              Param #
==========================================================================
Conv2d-1                            [-1, 32, 30, 30]                  896
MaxPool2d-2                         [-1, 32, 15, 15]                    0
Conv2d-3                            [-1, 64, 11, 11]               51,264
MaxPool2d-4                           [-1, 64, 5, 5]                    0
Dropout2d-5                           [-1, 64, 5, 5]                    0
AdaptiveMaxPool2d-6                   [-1, 64, 1, 1]                    0
Flatten-7                                   [-1, 64]                    0
Linear-8                                    [-1, 32]                2,080
ReLU-9                                      [-1, 32]                    0
Linear-10                                    [-1, 1]                   33
==========================================================================
Total params: 54,273
Trainable params: 54,273
Non-trainable params: 0
--------------------------------------------------------------------------
Input size (MB): 0.011719
Forward/backward pass size (MB): 0.359627
Params size (MB): 0.207035
Estimated Total Size (MB): 0.578381
--------------------------------------------------------------------------
"""
  • nn.ModuleDict作为模型容器，注意，下面的ModuleDict不能用Python中的字典代替
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers_dict = nn.ModuleDict({
            "conv1":nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),
            "pool1": nn.MaxPool2d(kernel_size = 2,stride = 2),
            "conv2":nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
            "pool2": nn.MaxPool2d(kernel_size = 2,stride = 2),
            "dropout": nn.Dropout2d(p = 0.1),
            "adaptive":nn.AdaptiveMaxPool2d((1,1)),
            "flatten": nn.Flatten(),
            "linear1": nn.Linear(64,32),
            "relu":nn.ReLU(),
            "linear2": nn.Linear(32,1)
        })

    def forward(self, x):
        layers = ["conv1", "pool1", "conv2", "pool2","dropout","adaptive", "flatten", "linear1", "relu", "linear2"]
        for layer in layers:
            x = self.layers_dict[layer](x)
        return x
    
net = Net()
print(net)

"""
Net(
  (layers_dict): ModuleDict(
    (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
    (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
    (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (dropout): Dropout2d(p=0.1, inplace=False)
    (adaptive): AdaptiveMaxPool2d(output_size=(1, 1))
    (flatten): Flatten(start_dim=1, end_dim=-1)
    (linear1): Linear(in_features=64, out_features=32, bias=True)
    (relu): ReLU()
    (linear2): Linear(in_features=32, out_features=1, bias=True)
  )
)
"""

标签：kernel,nn,32,模型,stride,构建,64,方法,size
From： https://www.cnblogs.com/lotuslaw/p/18301694

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