pytorch笔记：Efficientnet微调

我会带着你远行 2021-12-17 11:51 460阅读 0赞

**pytorch中有为efficientnet专门写好的网络模型，写在efficientnet\_pytorch模块中。  
模块包含EfficientNet的op-for-op的pytorch实现，也实现了预训练模型和示例。**

# 安装Efficientnet #

### pytorch Efficientnet ###

**Install via pip:**

pip install efficientnet_pytorch

**Or install from source:**

git clone https://github.com/lukemelas/EfficientNet-PyTorch
    cd EfficientNet-Pytorch
    pip install -e .

#### keras Efficientnet ####

**From PyPI:**

pip install keras_efficientnets

**From Master branch:**

pip install git+https://github.com/titu1994/keras-efficientnets.git
    
    OR
    
    git clone https://github.com/titu1994/keras-efficientnets.git
    cd keras-efficientnets
    pip install .

# 用法 #

###### 加载EfficientNet（只是网络结构，无预训练参数） ######

from efficientnet_pytorch import EfficientNet
    model = EfficientNet.from_name('efficientnet-b0')

###### 加载预训练EfficientNet ######

from efficientnet_pytorch import EfficientNet
    model = EfficientNet.from_pretrained('efficientnet-b0')

# efficientnet-b5为例（加载预训练） #

from efficientnet_pytorch import EfficientNet
    model = EfficientNet.from_pretrained('efficientnet-b5')
    print(model)

**只修改网络的最后几层（原始层结构）：**

(_conv_head): Conv2dStaticSamePadding(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn1): BatchNorm2d(2048, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_fc): Linear(in_features=2048, out_features=1000, bias=True)

**修改\_fc层（最后一层），将输出的分类数由1000改为45：**  
得到`in_features`：

feature = model._fc.in_features
    print(feature)

**结果：**

Loaded pretrained weights for efficientnet-b5
    2048

**修改最后一层：**

from efficientnet_pytorch import EfficientNet
    from torch import nn
    model = EfficientNet.from_pretrained('efficientnet-b5')
    feature = model._fc.in_features
    model._fc = nn.Linear(in_features=feature,out_features=45,bias=True)
    print(model)

**结果：**

**或者：和上述方法一致**

from efficientnet_pytorch import EfficientNet
    model = EfficientNet.from_pretrained('efficientnet-b5')
    model._fc.out_features = 45
    print(model)

**结果：**