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setup: 训练循环开始前设置数据加载器和模型。
configure_optimizers: 设置优化器和学习率调度器。
on_fit_start: 训练过程开始。
on_train_start: 训练开始。
on_train_epoch_start: 每个训练周期开始。
on_train_batch_start: 每个训练批次开始。
on_before_bac…结果
setup: 训练循环开始前设置数据加载器和模型。
configure_optimizers: 设置优化器和学习率调度器。
on_fit_start: 训练过程开始。
on_train_start: 训练开始。
on_train_epoch_start: 每个训练周期开始。
on_train_batch_start: 每个训练批次开始。
on_before_backward: 反向传播之前。
on_after_backward: 反向传播之后。
on_before_zero_grad: 清空梯度之前。
on_after_zero_grad: 清空梯度之后。
on_before_optimizer_step: 优化器步骤之前。
on_train_batch_end: 每个训练批次结束。
on_train_epoch_end: 每个训练周期结束。
on_train_end: 训练结束。
on_fit_end: 训练过程结束。
测试代码
import torch
from torch.utils.data import DataLoader, TensorDataset
from pytorch_lightning import LightningModule, Trainer, Callback
from pytorch_lightning.callbacks import Callback# 定义一个简单的线性回归模型
class LinearRegression(LightningModule):def __init__(self):super().__init__()self.linear torch.nn.Linear(1, 1)def forward(self, x):return self.linear(x)def training_step(self, batch, batch_idx):x, y batchy_hat self(x)loss torch.nn.functional.mse_loss(y_hat, y)return lossdef on_after_backward(self, *args, **kwargs):print(After backward is called!, args, kwargs)return super().on_after_backward(*args, **kwargs)def on_before_zero_grad(self, *args, **kwargs):print(Before zero grad is called!, args, kwargs)return super().on_before_zero_grad(*args, **kwargs)def on_after_zero_grad(self, *args, **kwargs):print(After zero grad is called!, args, kwargs)return super().on_after_zero_grad(*args, **kwargs)def on_before_backward(self, *args, **kwargs):print(Before backward is called!, args, kwargs)return super().on_before_backward(*args, **kwargs)def on_before_optimizer_step(self, *args, **kwargs):print(Before optimizer step is called!, args, kwargs)return super().on_before_optimizer_step(*args, **kwargs)def on_after_optimizer_step(self, *args, **kwargs):print(After optimizer step is called!, args, kwargs)return super().on_after_optimizer_step(*args, **kwargs)def on_fit_start(self, *args, **kwargs):print(Fit is starting!, args, kwargs)return super().on_fit_start(*args, **kwargs)def on_fit_end(self, *args, **kwargs):print(Fit is ending!, args, kwargs)return super().on_fit_end(*args, **kwargs)def setup(self, *args, **kwargs):print(Setup is called!, args, kwargs)return super().setup(*args, **kwargs)def configure_optimizers(self, *args, **kwargs):print(Configure Optimizers is called!, args, kwargs)return super().configure_optimizers(*args, **kwargs)def on_train_start(self, *args, **kwargs):print(Training is starting!, args, kwargs)return super().on_train_start(*args, **kwargs)def on_train_end(self, *args, **kwargs):print(Training is ending!, args, kwargs)return super().on_train_end(*args, **kwargs)def on_train_batch_start(self, *args, **kwargs):print(fTraining batch is starting!, args, kwargs)return super().on_train_batch_start(*args, **kwargs)def on_train_batch_end(self, *args, **kwargs):print(fTraining batch is ending!, args, kwargs)return super().on_train_batch_end(*args, **kwargs)def on_train_epoch_start(self, *args, **kwargs):print(fTraining epoch is starting!, args, kwargs)return super().on_train_epoch_start(*args, **kwargs)def on_train_epoch_end(self, *args, **kwargs):print(fTraining epoch is ending!, args, kwargs)return super().on_train_epoch_end(*args, **kwargs)# 创建数据集
x_train torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtypetorch.float)
y_train torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtypetorch.float)
train_dataset TensorDataset(x_train, y_train)
train_loader DataLoader(train_dataset, batch_size2)# 创建模型和训练器
model LinearRegression()
trainer Trainer(max_epochs2)# 开始训练
trainer.fit(model, train_loader)
