6-2 pytorch中训练模型的3种方法

news2024/11/24 3:04:48

Pytorch通常需要用户编写自定义训练循环，训练循环的代码风格因人而异。（养成自己的习惯）
有3类典型的训练循环代码风格：脚本形式训练循环，函数形式训练循环，类形式训练循环。
下面以minist数据集的多分类模型的训练为例，演示这3种训练模型的风格。
其中类形式训练循环我们同时演示torchkeras.KerasModel和torchkeras.LightModel两种示范。

准备数据

transform = transforms.Compose([transforms.ToTensor()])

ds_train = torchvision.datasets.MNIST(root="./data/mnist/",train=True,download=True,transform=transform)
ds_val = torchvision.datasets.MNIST(root="./data/mnist/",train=False,download=True,transform=transform)

dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=4)
dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=128, shuffle=False, num_workers=4)

print(len(ds_train))
print(len(ds_val))

%matplotlib inline
%config InlineBackend.figure_format = 'svg'

#查看部分样本
from matplotlib import pyplot as plt 

plt.figure(figsize=(8,8)) 
for i in range(9):
    img,label = ds_train[i] 
    img = torch.squeeze(img) # 删除为1的维度
    ax=plt.subplot(3,3,i+1)
    ax.imshow(img.numpy())
    ax.set_title("label = %d"%label)
    ax.set_xticks([])
    ax.set_yticks([]) 
plt.show()

一、脚本风格

脚本风格的训练循环非常常见。

net = nn.Sequential()
net.add_module("conv1",nn.Conv2d(in_channels=1,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(32,10))

print(net)

代码量较多，可以查看最下方链接对应的notebook。

二、函数风格

该风格在脚本形式上做了进一步的函数封装。

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.layers = nn.ModuleList([
            nn.Conv2d(in_channels=1,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(32,10)]
        )
    def forward(self,x):
        for layer in self.layers:
            x = layer(x)
        return x
net = Net()
print(net)

代码量较多，可以查看最下方链接对应的notebook。

三、类风格

此处使用**torchkeras.KerasModel（其源码其实就是脚本风格中的代码）**高层次API接口中的fit方法训练模型。
使用该形式训练模型非常简洁明了。
先构建模型，同一二。

from torchkeras import KerasModel 

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.ModuleList([
            nn.Conv2d(in_channels=1,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(32,10)]
        )
    def forward(self,x):
        for layer in self.layers:
            x = layer(x)
        return x
    
net = Net() 

print(net)

使用kerasModel：

from torchmetrics import Accuracy

model = KerasModel(net,
                   loss_fn=nn.CrossEntropyLoss(),
                   metrics_dict = {"acc":Accuracy(task='multiclass',num_classes=10)},
                   optimizer = torch.optim.Adam(net.parameters(),lr = 0.01)  )

model.fit(
    train_data = dl_train,
    val_data= dl_val,
    epochs=10,
    patience=3,
    monitor="val_acc", 
    mode="max",
    plot=True,
    cpu=True
)