y)pred=...return{"loss":loss,"pred":pred}deftraining_step_end(self,batch_parts):# 从每个GUP计算到的predictionspredictions=batch_parts["pred"]# 从每个GUP计算到的losseslosses=batch_parts["loss"]gpu_0_prediction=predictions[0]gpu_1_prediction=predictions[1]# 如果metric需要分开计算...
torch lightning为了接近接近原生torch的灵活性,设计了相当多的hook. 不过实际上日常的修改一般只需要自定义其中一部分即可. 'training_epoch_end', 'training_step', 'training_step_end', 'validation_epoch_end', 'validation_step', 'validation_step_end', btw,torch lightning里的prediction_step在一些复杂模型...
prediction = net(x) #看每一步的prediction loss = loss_func(prediction, y) #y是真实值,前者是预测值 optimizer.zero_grad() #这一步是将梯度降为0,防止先前的梯度造成影响 loss.backward() #进行loss值的反向传播 optimizer.step() #用optimizer以学习效率0.5来优化梯度 1. 2. 3. 4. 5. 6. 7. ...
等价Lightning代码:def training_step(self, batch, batch_idx): prediction = ... return prediction def training_epoch_end(self, training_step_outputs): for prediction in predictions: # do something with these 我们需要做的,就是像填空一样,填这些函数。 组件与函数 API页面:/en/latest/common/lightni...
def shared_step(self,batch): x, y = batch prediction = self(x) loss = nn.BCELoss()(prediction,y) preds = torch.where(prediction>0.5,torch.ones_like(prediction),torch.zeros_like(prediction)) acc = pl.metrics.functional.accuracy(preds, y) ...
When you start using LightningModule, the PyTorch code isn't abstracted; it’s organized into six sections: Initialization (__init__ and setup() methods). Train loop (training_step() method). Validation loop (validation_step() method). Test loop (test_step() method). Prediction loop (pr...
LightModel): def shared_step(self,batch): x, y = batch prediction = self(x) loss = nn.BCELoss()(prediction,y) preds = torch.where(prediction>0.5,torch.ones_like(prediction),torch.zeros_like(prediction)) acc = pl.metrics.functional.accuracy(preds, y) # attention: there must be a ...
一,pytorch-lightning的设计哲学 pytorch-lightning 的核心设计哲学是将 深度学习项目中的 研究代码(定义模型) 和 工程代码 (训练模型) 相互分离。 用户只需专注于研究代码(pl.LightningModule)的实现,而工程代码借助训练工具类(pl.Trainer)统一实现。 更详细地说,深度学习项目代码可以分成如下4部分: 研究代码 (Rese...
defforward(self, x):# in lightning, forward defines the prediction/inference actionsembedding =self.encoder(x)returnembedding deftraining_step(self, batch, batch_idx):# training_step defined the train loop.# It is independent of forward...
等价Lightning代码: deftraining_step(self, batch, batch_idx): prediction = ... returnprediction deftraining_epoch_end(self, training_step_outputs): forpredictioninpredictions: # do something with these 我们需要做的,就是像填空一样,填这些函数。