pytorch笔记：Efficientnet微调

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：

  (_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=45, bias=True)

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：

  (_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=45, bias=True)