【PyTorch】微调修改预训练模型权重参数

介绍

在使用预训练模型微调训练时，我们通常需要根据实际的数据集以及想要达到的效果，修改预训练模型的结构。查阅了其他博客和torch.nn源码后，做个笔记。
为了更方便的了解，将使用torchvision中的模型convnext作介绍。

一、获取模型参数

1、直接print模型

import torch
import torchvision.models as models
import torch.nn as nn

model = models.convnext_tiny(pretrained = False)
print(model)

######################输出的模型结果###################
ConvNeXt(
  (features): Sequential(
    (0): Conv2dNormActivation(
      (0): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))
      (1): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)
    )
    (1): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0058823529411764705, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.011764705882352941, mode=row)
      )
    )
    (2): Sequential(
      (0): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(96, 192, kernel_size=(2, 2), stride=(2, 2))
    )
    (3): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.017647058823529415, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.023529411764705882, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.029411764705882353, mode=row)
      )
    )
    (4): Sequential(
      (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))
    )
    (5): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.03529411764705883, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0411764705882353, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.047058823529411764, mode=row)
      )
      (3): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.052941176470588235, mode=row)
      )
      (4): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.058823529411764705, mode=row)
      )
      (5): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.06470588235294118, mode=row)
      )
      (6): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.07058823529411766, mode=row)
      )
      (7): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.07647058823529412, mode=row)
      )
      (8): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0823529411764706, mode=row)
      )
    )
    (6): Sequential(
      (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))
    )
    (7): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.08823529411764706, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.09411764705882353, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.1, mode=row)
      )
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=1)
  (classifier): Sequential(
    (0): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)
    (1): Flatten(start_dim=1, end_dim=-1)
    (2): Linear(in_features=768, out_features=1000, bias=True)
  )
)

2、model.state_dict()

pytorch 中的 state_dict 是一个简单的python的字典对象,将每一层与它的对应参数建立映射关系.(如model的每一层的weights及偏置等等)。这个方法的作用一方面是方便查看某一个层的权值和偏置数据，另一方面更多的是在模型保存的时候使用。

torch.save(model.state_dict(), 'model_weights.pth')			#保存模型的参数以及权重

#使用预训练的模型时
model = models.convnext_tiny(pretrained = False)			#生成相同的模型结构
model.load_state_dict(torch.load('model_weights.pth'))		#将参数权重加载到模型之中

3、model.parameters()

这个方法也可以获取模型的参数信息，与前面的方法不同的是，model.parameters()方法返回的是一个生成器generator，每一个元素是从开头到结尾的参数，parameters没有对应的key名称，是一个由纯参数组成的generator，而state_dict是一个字典，包含了一个key。

二、修改模型参数

PyTorch中模型参数都是由字典的形式保存，所以当你想要修改模型结构时，直接通过字典的方式调用你想要的结构并且重新定义，就可以修改模型的参数。

model.classifier = nn.Linear(in_features=768, out_features=1000, bias=True)
print(model)

#########################输出的结果如下#####################
ConvNeXt(
  (features): Sequential(
    (0): Conv2dNormActivation(
      (0): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))
      (1): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)
    )
    (1): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0058823529411764705, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.011764705882352941, mode=row)
      )
    )
    (2): Sequential(
      (0): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(96, 192, kernel_size=(2, 2), stride=(2, 2))
    )
    (3): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.017647058823529415, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.023529411764705882, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.029411764705882353, mode=row)
      )
    )
    (4): Sequential(
      (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))
    )
    (5): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.03529411764705883, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0411764705882353, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.047058823529411764, mode=row)
      )
      (3): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.052941176470588235, mode=row)
      )
      (4): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.058823529411764705, mode=row)
      )
      (5): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.06470588235294118, mode=row)
      )
      (6): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.07058823529411766, mode=row)
      )
      (7): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.07647058823529412, mode=row)
      )
      (8): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0823529411764706, mode=row)
      )
    )
    (6): Sequential(
      (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))
    )
    (7): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.08823529411764706, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.09411764705882353, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.1, mode=row)
      )
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=1)
  (classifier): Linear(in_features=768, out_features=1000, bias=True)
)

但是，我们可以看到，当我们直接修改classifier里面的结构，他会将整个classifier都重新定义为你的输入的样子，那么当你只是要修改最后的分类层的话，你就只能重新去定义一整个sequential，并且在重新定义的时候，如果你已经加载了预训练的参数，预训练模型的参数就会丢失，这样就会非常麻烦。

那如何只修改classifier中最后一个线性层呢，我在网络上查找了这个内容，发现比较难找到相关的内容，大家都只能通过访问模型拥有key的部分的结构，对于sequential内部的结构，有人尝试用model.classifier.0去访问，这并不符合python的语法结构。所以我又去查询了nn.sequential的官方文档。

    def _get_item_by_idx(self, iterator, idx) -> T:
        """Get the idx-th item of the iterator"""
        size = len(self)
        idx = operator.index(idx)
        if not -size <= idx < size:
            raise IndexError('index {} is out of range'.format(idx))
        idx %= size
        return next(islice(iterator, idx, None))

可以看到其中有个迭代器，所以我就尝试了用列表的访问方式去访问，结果证明是可行的。

model.classifier[2] = nn.Linear(in_features=768, out_features=4, bias=True)
print(model)

####################输出结果如下########################
ConvNeXt(
  (features): Sequential(
    (0): Conv2dNormActivation(
      (0): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))
      (1): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)
    )
    (1): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0058823529411764705, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)
          (1): Permute()
          (2): LayerNorm((96,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=96, out_features=384, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=384, out_features=96, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.011764705882352941, mode=row)
      )
    )
    (2): Sequential(
      (0): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(96, 192, kernel_size=(2, 2), stride=(2, 2))
    )
    (3): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.017647058823529415, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.023529411764705882, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
          (1): Permute()
          (2): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=192, out_features=768, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=768, out_features=192, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.029411764705882353, mode=row)
      )
    )
    (4): Sequential(
      (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))
    )
    (5): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.03529411764705883, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0411764705882353, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.047058823529411764, mode=row)
      )
      (3): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.052941176470588235, mode=row)
      )
      (4): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.058823529411764705, mode=row)
      )
      (5): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.06470588235294118, mode=row)
      )
      (6): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.07058823529411766, mode=row)
      )
      (7): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.07647058823529412, mode=row)
      )
      (8): CNBlock(
        (block): Sequential(
          (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
          (1): Permute()
          (2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=384, out_features=1536, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=1536, out_features=384, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.0823529411764706, mode=row)
      )
    )
    (6): Sequential(
      (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)
      (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))
    )
    (7): Sequential(
      (0): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.08823529411764706, mode=row)
      )
      (1): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.09411764705882353, mode=row)
      )
      (2): CNBlock(
        (block): Sequential(
          (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
          (1): Permute()
          (2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
          (3): Linear(in_features=768, out_features=3072, bias=True)
          (4): GELU(approximate=none)
          (5): Linear(in_features=3072, out_features=768, bias=True)
          (6): Permute()
        )
        (stochastic_depth): StochasticDepth(p=0.1, mode=row)
      )
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=1)
  (classifier): Sequential(
    (0): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)
    (1): Flatten(start_dim=1, end_dim=-1)
    (2): Linear(in_features=768, out_features=4, bias=True)
  )
)

那么我们能够顺利的访问模型的每个结构的话，修改的话也就十分简单了。

使用add_module()方法添加dropout

model.classifier.add_module("add_dropout",nn.Dropout())
print(model)

参考链接: https://blog.csdn.net/ltochange/article/details/121421776
https://blog.csdn.net/qq_39332551/article/details/124943453