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：
-
保存checkpoints
-
输出log信息
-
resume training 即重载训练，我们希望可以接着上一次的epoch继续训练
-
记录模型训练的过程(通常使用tensorboard)
-
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 colab中运行linux命令需要在之前加!
如果是使用 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
from .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.
猜您喜欢：
CVPR 2021 | GAN的说话人驱动、3D人脸论文汇总
附下载 |《TensorFlow 2.0 深度学习算法实战》
附下载 | 超100篇！CVPR 2020最全GAN论文梳理汇总！
