load_from_checkpoint("/path/to/checkpoint.ckpt") # disable randomness, dropout, etc... model.eval() # 基于训练好的权重得到预测值 y_hat = model(x) LightningModule 允许通过调用 self.save_hyperparameters() 自动保存传递给 init 的所有超参数: class MyLightningModule(LightningModule): def __init...
Cloud Studio代码运行 # 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...
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_checkpoint.ckpt") 参考 pytorch保存模型方法_Obolicaca的博客-...
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) trainer_clone ...
lightning_logs/version_10/checkpoints/epoch=8-step=15470.ckpt tensor(0.0376, device='cuda:0') 1. 2. 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....
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...
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] >...
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 ...
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) ...
from transformersimport(AutoModelForSequenceClassification,AutoConfig,AutoTokenizer)classPythonPredictor:def__init__(self,config):self.device="cpu"self.tokenizer=AutoTokenizer.from_pretrained("albert-base-v2")self.model=MyModel.load_from_checkpoint(checkpoint_path="./model.ckpt")defpredict(self,payload)...