log_dict({"test_loss": loss, "test_acc": accuracy, "test_f1": f1_score}, on_step=False, on_epoch=True, prog_bar=True) return {'loss': loss, "scores": scores, "y": y} def predict_step(self, batch, batch_idx): x,
# 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...
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...
return decode_prediction(output) print(predict(model, "captcha_images/Z8K5_123.png"))
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...
num_workers, pin_memory=True) def predict_dataloader(self): return DataLoader(self.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(): ...
pred = self.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 ...
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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 ...
pred = self.predict_nn(pred) loss += self.loss(last_hidden, y[step]) #toy example as well loss = loss / max_seq_len return {'loss': loss} 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. ...