实操教程｜Pytorch-lightning的使用

Pytorch-lightning(以下简称pl)可以非常简洁得构建深度学习代码。但是其实大部分人用不到很多复杂得功能。而pl有时候包装得过于深了，用的时候稍微有一些不灵活。通常来说，在你的模型搭建好之后，大部分的功能都会被封装在一个叫trainer的类里面。一些比较麻烦但是需要的功能通常如下：

1. 
   
   
   保存checkpoints
   


2. 
   
   
   输出log信息
   


3. 
   
   
   resume training 即重载训练，我们希望可以接着上一次的epoch继续训练
   


4. 
   
   
   记录模型训练的过程(通常使用tensorboard)
   


5. 
   
   
   设置seed，即保证训练过程可以复制
   

好在这些功能在pl中都已经实现。

由于doc上的很多解释并不是很清楚，而且网上例子也不是特别多。下面分享一点我自己的使用心得。

首先关于设置全局的种子：

    


     


      


      


      


      


      


     


     
from pytorch_lightning import seed_everything
# Set seedseed = 42seed_everything(seed)


    

只需要import如上的seed_everything函数即可。它应该和如下的函数是等价的：

    


     


      


      


      


      


      


      


      


      


      


      


      


      


      


      


     


     
def seed_all(seed_value):    random.seed(seed_value) # Python    np.random.seed(seed_value) # cpu vars    torch.manual_seed(seed_value) # cpu vars        if torch.cuda.is_available():         print ('CUDA is available')        torch.cuda.manual_seed(seed_value)        torch.cuda.manual_seed_all(seed_value) # gpu vars        torch.backends.cudnn.deterministic = True  #needed        torch.backends.cudnn.benchmark = False
seed=42seed_all(seed)


    

但经过我的测试，好像pl的seed_everything函数应该更全一点。

下面通过一个具体的例子来说明一些使用方法：

先下载、导入必要的包和下载数据集：

    


     


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


     


     
!pip install pytorch-lightning!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip!unzip -q hymenoptera_data.zip!rm hymenoptera_data.zip
import pytorch_lightning as plimport osimport numpy as np import randomimport matplotlib.pyplot as plt
import torchimport torch.nn.functional as Fimport torchvisionimport torchvision.transforms as transforms


    

以下代码种加入!的代码是在terminal中运行的。在google colab中运行linux命令需要在之前加!

如果是使用google colab，由于它创建的是一个虚拟机，不能及时保存，所以如果需要保存，挂载自己google云盘也是有必要的。使用如下的代码：

    


     


      


      


      


      


      


     


     
from google.colab import drivedrive.mount('./content/drive')
import osos.chdir("/content/drive/My Drive/")


    

先如下定义如下的LightningModule和main函数。

class CoolSystem(pl.LightningModule):
    def __init__(self, hparams):        super(CoolSystem, self).__init__()
        self.params = hparams                self.data_dir = self.params.data_dir        self.num_classes = self.params.num_classes 
        ########## define the model ##########         arch = torchvision.models.resnet18(pretrained=True)        num_ftrs = arch.fc.in_features
        modules = list(arch.children())[:-1] # ResNet18 has 10 children        self.backbone = torch.nn.Sequential(*modules) # [bs, 512, 1, 1]        self.final = torch.nn.Sequential(               torch.nn.Linear(num_ftrs, 128),               torch.nn.ReLU(inplace=True),               torch.nn.Linear(128, self.num_classes),               torch.nn.Softmax(dim=1))
    def forward(self, x):        x = self.backbone(x)        x = x.reshape(x.size(0), -1)        x = self.final(x)                return x        def configure_optimizers(self):        # REQUIRED        optimizer = torch.optim.SGD([                {'params': self.backbone.parameters()},                {'params': self.final.parameters(), 'lr': 1e-2}            ], lr=1e-3, momentum=0.9)
        exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)         return [optimizer], [exp_lr_scheduler]
    def training_step(self, batch, batch_idx):        # REQUIRED        x, y = batch        y_hat = self.forward(x)                loss = F.cross_entropy(y_hat, y)                _, preds = torch.max(y_hat, dim=1)        acc = torch.sum(preds == y.data) / (y.shape[0] * 1.0)
        self.log('train_loss', loss)        self.log('train_acc', acc)
        return {'loss': loss, 'train_acc': acc}    
    def validation_step(self, batch, batch_idx):        # OPTIONAL        x, y = batch        y_hat = self.forward(x)        loss = F.cross_entropy(y_hat, y)        _, preds = torch.max(y_hat, 1)        acc = torch.sum(preds == y.data) / (y.shape[0] * 1.0)
        self.log('val_loss', loss)        self.log('val_acc', acc)
        return {'val_loss': loss, 'val_acc': acc}

    def test_step(self, batch, batch_idx):        # OPTIONAL        x, y = batch        y_hat = self.forward(x)        loss = F.cross_entropy(y_hat, y)        _, preds = torch.max(y_hat, 1)        acc = torch.sum(preds == y.data) / (y.shape[0] * 1.0)
        return {'test_loss': loss, 'test_acc': acc}

    def train_dataloader(self):        # REQUIRED
        transform = transforms.Compose([                                transforms.RandomResizedCrop(224),                                transforms.RandomHorizontalFlip(),                                transforms.ToTensor(),                                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])                                ])
        train_set = torchvision.datasets.ImageFolder(os.path.join(self.data_dir, 'train'), transform)        train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True, num_workers=4)
        return train_loader        def val_dataloader(self):      transform = transforms.Compose([                                transforms.Resize(256),                                transforms.CenterCrop(224),                                transforms.ToTensor(),                                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])                                ])                                    val_set = torchvision.datasets.ImageFolder(os.path.join(self.data_dir, 'val'), transform)      val_loader = torch.utils.data.DataLoader(val_set, batch_size=32, shuffle=True, num_workers=4)
      return val_loader
    def test_dataloader(self):      transform = transforms.Compose([                              transforms.Resize(256),                              transforms.CenterCrop(224),                              transforms.ToTensor(),                              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])                              ])                                  val_set = torchvision.datasets.ImageFolder(os.path.join(self.data_dir, 'val'), transform)      val_loader = torch.utils.data.DataLoader(val_set, batch_size=8, shuffle=True, num_workers=4)
      return val_loader




def main(hparams):  model = CoolSystem(hparams)

  trainer = pl.Trainer(      max_epochs=hparams.epochs,      gpus=1,      accelerator='dp'  )  
  trainer.fit(model)

下面是run的部分：

    


     


      


      


      


      


      


      


      


      


      


      


      


      


      


     


     
from argparse import Namespace
args = {    'num_classes': 2,    'epochs': 5,    'data_dir': "/content/hymenoptera_data",}
hyperparams = Namespace(**args)

if __name__ == '__main__':    main(hyperparams)


    

如果希望重载训练的话，可以按如下方式：

    


     


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


     


     
# resume training
RESUME = True
if RESUME:    resume_checkpoint_dir = './lightning_logs/version_0/checkpoints/'    checkpoint_path = os.listdir(resume_checkpoint_dir)[0]    resume_checkpoint_path = resume_checkpoint_dir + checkpoint_path

    args = {    'num_classes': 2,    'data_dir': "/content/hymenoptera_data"}
    hparams = Namespace(**args)
    model = CoolSystem(hparams)
        trainer = pl.Trainer(gpus=1,                 max_epochs=10,                             accelerator='dp',                resume_from_checkpoint = resume_checkpoint_path)
    trainer.fit(model)


    

如果我们想要从checkpoint加载模型，并进行使用可以按如下操作来：

    


     


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


      


     


     
import matplotlib.pyplot as pltimport numpy as np
# functions to show an imagedef imshow(inp):    inp = inp.numpy().transpose((1, 2, 0))    mean = np.array([0.485, 0.456, 0.406])    std = np.array([0.229, 0.224, 0.225])    inp = std * inp + mean    inp = np.clip(inp, 0, 1)    plt.imshow(inp)    plt.show()
classes = ['ants', 'bees']
checkpoint_dir = 'lightning_logs/version_1/checkpoints/'checkpoint_path = checkpoint_dir + os.listdir(checkpoint_dir)[0]
checkpoint = torch.load(checkpoint_path)model_infer = CoolSystem(hparams)model_infer.load_state_dict(checkpoint['state_dict'])
try_dataloader = model_infer.test_dataloader()
inputs, labels = next(iter(try_dataloader))
# print images and ground truthimshow(torchvision.utils.make_grid(inputs))print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(8)))
# inferenceoutputs = model_infer(inputs)
_, preds = torch.max(outputs, dim=1)# print (preds)print (torch.sum(preds == labels.data) / (labels.shape[0] * 1.0))
print('Predicted: ', ' '.join('%5s' % classes[preds[j]] for j in range(8)))


    

预测结果如上。

如果希望检测训练过程（第一部分+重载训练的部分），如下：

    


     


      


      


      


      


     


     
# tensorboard
%load_ext tensorboard%tensorboard --logdir = ./lightning_logs


    

训练过程在tensorboard里面记录，version0是第一次的训练，version1是重载后的结果。

完整的code在这里.

https://colab.research.google.com/gist/calibertytz/a9de31175ce15f384dead94c2a9fad4d/pl_tutorials_1.ipynb

如果觉得有用，就请分享到朋友圈吧！