__init__() def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"): print("Predict is ending") def on_train_epoch_end(self, trainer : "pl.Trainer", pl_module: "pl.LightningModule"): epoch_mean_loss = torch.stack([x['train_loss'] for x in pl_module...
# 预测的流程,可以不写 # 实例化trainer之后可以调用trainer.predict()来预测 def predict_step(self, batch, batch_idx): x, y = batch x = x.reshape(x.size(0), -1) scores = self.forward(x) preds = torch.argmax(scores, dim=1) return # 优化器 def configure_optimizers(self): return op...
dm=MyDataModule(args)ifnot is_predict:# 训练 # 定义保存模型的callback,仔细查看后文 checkpoint_callback=ModelCheckpoint(monitor='val_loss')# 定义模型 model=MyModel()# 定义logger logger=TensorBoardLogger('log_dir',name='test_PL')# 定义数据集为训练校验阶段 dm.setup('fit')# 定义trainer train...
self.model.freeze() Predict() 方法接受文本输入,使用分词器对其进行处理,并返回模型的预测: def predict(self, text): inference_sample = {"sentence": text} processed = self.processor.tokenize_data(inference_sample) logits = self.model( torch.tensor([processed["input_ids"]]), torch.tensor([proce...
predict_nn(pred) loss += self.loss(last_hidden, y[step]) # 小示例 loss = loss / max_seq_len return {'loss': loss} 或像CNN图像分类一样 # 在这里定义验证代码 def validation_step(self, batch, batch_idx): x, y = batch # 或者像CNN分类一样 out = self(x) loss = my_loss(out,...
ds_predict, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, pin_memory=True) data_mnist = MNISTDataModule() data_mnist.setup() for features,labels in data_mnist.train_dataloader(): print(features.shape) print(labels.shape) break torch.Size([32, 1, 28, 28]) ...
def predict(model,dl): model.eval() result = torch.cat([model.forward(t[0].to(model.device)) for t in dl]) return(result.data) result = predict(model,dl_valid) 1. 2. 3. 4. 5. 6. result 1. tensor([[9.8850e-01],
pin_memory=True) def test_dataloader(self): return DataLoader(self.ds_test, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, pin_memory=True) def predict_dataloader(self): return DataLoader(self.ds_predict, batch_size=self.batch_size, shuffle=False, num_workers=self...
最后,第三部分提供了一个我总结出来的易用于大型项目、容易迁移、易于复用的模板,有兴趣的可以去GitHub—https://github.com/miracleyoo/pytorch-lightning-template试用。 02 核心 Pytorch-Lighting 的一大特点是把模型和系统分开来看。模型是像Resnet18, RNN之类的纯模型, 而...
预测循环:predict_step() 模型可以从检查点加载权重,或者如果在训练循环之后调用,则模型会自动获取最后一轮或最佳(如果已实现回调)时期的权重。 def test_step( self, batch: list[torch.Tensor, torch.Tensor], batch_idx: int, ): """Function called when using `trainer.test()` with trainer a ...