AI代码解释 # load checkpointcheckpoint="./lightning_logs/version_0/checkpoints/epoch=0-step=100.ckpt"autoencoder=LitAutoEncoder.load_from_checkpoint(checkpoint,encoder=encoder,decoder=decoder)# choose your trained nn.Moduleencoder=autoencoder.encoder encoder.eval()# embed 4 fake images!fake_image_ba...
model_clone = Model.load_from_checkpoint(trainer.checkpoint_callback.best_model_path) trainer_clone = pl.Trainer(max_epochs=3,gpus=1) result = trainer_clone.test(model_clone,data_module.test_dataloader()) print(result) 1. 2. 3. 4. --- DATALOADER:0 TEST RESULTS {'test_acc': 0.98879998...
最优模型默认保存在 trainer.checkpoint_callback.best_model_path 的目录下,可以直接加载。 print(trainer.checkpoint_callback.best_model_path) print(trainer.checkpoint_callback.best_model_score) 1. 2. model_clone = Model.load_from_checkpoint(trainer.checkpoint_callback.best_model_path) ...
self.model = MyModel.load_from_checkpoint(checkpoint_path="./model.ckpt") defpredict(self, payload): inputs = self.tokenizer.encode_plus(payload["text"], return_tensors="pt") predictions = self.model(**inputs)[0] if(predictions[0] >...
一个Pytorch-Lighting 模型必须含有的部件是: init: 初始化,包括模型和系统的定义。 training_step(self, batch, batch_idx): 即每个batch的处理函数。 参数: batch (Tensor | (Tensor, …) | [Tensor, …]) – The output of your DataLoader. A tensor, tuple or list. ...
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, 而系统定义了一组模型如何相互交互,如GAN(生成器网络与判别器网络)、Seq2Seq(Encoder与Decoder网络)和Bert。同时,有时候问题只涉及一个模型,那么这个系统则可以是一个通用的系统,用于...
classifier = VideoClassifier.load_from_checkpoint(...) # 选项1:使用Trainer和数据模块生成预测 datamodule = VideoClassificationData.from_folders( predict_folder="/path/to/folder", ... ) trainer = Trainer() classifier.serializer = FiftyOneLabels(return_filepath=True) ...
Added strict=False for load_from_checkpoint (#2819) Added saving test predictions on multiple GPUs (#2926) Auto log the computational graph for loggers that support this (#3003) Added warning when changing monitor and using results obj (#3014) Added a hook transfer_batch_to_device to the ...
model=MyLightningModule(hparams)trainer.fit(model)trainer.save_checkpoint("example.ckpt") 3.加载(load_from_checkpoint) model = MyLightingModule.load_from_checkpoint(PATH) 4.加载(Trainer) model = LitModel() trainer = Trainer() # 自动恢复模型 trainer.fit(model, ckpt_path="some/path/to/my_che...
() model = model.load_from_checkpoint("./model-epoch=01-val_loss=0.62.ckpt") model.eval() def predict(path): input = CVModule.prepare_picture(path) pred = model.forward(input) return LABEL_ONE_DIC.get( pred[0].argmax(dim=-1).tolist()[0]),LABEL_TWO_DIC.get( pred[1].argmax...