def on_validation_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs: STEP_OUTPUT | None, batch: Any, batch_idx: int, dataloader_idx: int) -> None: pass ``` * `trainer`中含有所有的已经被aggregate过的、各种通过`pl_module.log/log_dict`记录metrics/loss,可通过`trainer....
outputs.append({'val_loss': loss}) # 验证步骤 total_loss = outputs.mean() # 结束 可选方法 如果仍然需要更精细的控制,请为循环定义其他可选方法。 def validation_step(self, batch, batch_idx): preds = ... return preds def validation_epoch_end(self, val_step_outputs): for pred in val_s...
classLitModel(pl.LightningModule):def__init__(...):defforward(...):deftraining_step(...)deftraining_step_end(...)deftraining_epoch_end(...)defvalidation_step(...)defvalidation_step_end(...)defvalidation_epoch_end(...)deftest_step(...)deftest_step_end(...)deftest_epoch_end(.....
hparams.learning_rate) def validation_step(self, batch, batch_idx): x, y = batch preds = self(x) loss = nn.CrossEntropyLoss()(preds,y) return {"loss":loss,"preds":preds.detach(),"y":y.detach()} def validation_step_end(self,outputs): val_acc = self.val_acc(outputs['preds']...
同理,在model_interface中建立class MInterface(pl.LightningModule):类,作为模型的中间接口。__init__()函数中import相应模型类,然后老老实实加入configure_optimizers,training_step,validation_step等函数,用一个接口类控制所有模型。不同部分使用输入参数控制。
LightningModule将PyTorch代码整理成5个部分: Computations (init). Train loop (training_step) Validation loop (validation_step) Test loop (test_step) Optimizers (configure_optimizers) 例子: import pytorch_lightning as pl class LitModel(pl.LightningModule): ...
validation_step(self, batch, batch_idx) test_step(self, batch, batch_idx) 除以上三个主要函数外,还有training_step_end(self,batch_parts) 和 training_epoch_end(self, training_step_outputs)。 -- 即每一个 * 步完成后调用。 -- 即每一个 * 的epoch 完成之后会自动调用。
deftraining_step(self, batch, batch_nb):x, y= batch y_hat = self.forward(x)return {'loss': self.my_loss(y_hat, y)} defvalidation_step(self, batch, batch_nb):x, y= batch y_hat = self.forward(x)return {'val_loss': self.my_loss(y_hat, y)} defvalidation_end(self, output...
def validation_step(self, val_batch, batch_idx): x, y = val_batch logits = self.forward(x) loss = self.cross_entropy_loss(logits, y) self.log('val_loss', loss) def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) ...
h_cls = outputs.last_hidden_state[:,0] logits =self.W(h_cls)returnlogits 另外,training_step()和validation_step()函数分别负责处理训练和验证的逻辑,并记录诸如损失和准确率等关键指标。 3. Training Loop train.py 脚本利用 PyTorch Lightning 的 Trainer 类来控制训练过程。它还包含了模型检查点和提前停...