load_from_checkpoint("/path/to/checkpoint.ckpt") print(model.learning_rate) 也可以使用新的超参数覆盖之前保存的超参数: LitModel(in_dim=32, out_dim=10) # uses in_dim=32, out_dim=10 model = LitModel.load_from_checkpoint(PATH) # 用新的超参数覆盖掉之前的超参数 model = LitModel.load_...
load_from_checkpoint(checkpoint_path='my_model_path/heiheihei.ckpt') # 修改测试时需要的参数,例如预测的步数等 model.pred_step = 1000 # 定义trainer, 其中limit_test_batches表示取测试集中的0.05的数据来做测试 trainer = pl.Trainer(gpus=1, precision=16, limit_test_batches=0.05) # 测试,自动调用...
importMyModelfromtraining_code fromtransformersimport( AutoModelForSequenceClassification, AutoConfig, AutoTokenizer ) classPythonPredictor: def__init__(self, config): self.device ="cpu" self.tokenizer = AutoTokenizer.from_pretrained("albert-base-v2...
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...
fromtransformersimport( 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") ...
# 创建一个PyTorch Lightning模型实例lightning_model=MyLightningModel.load_from_checkpoint('path/to/lightning_model.ckpt')# 切换到Evaluation模式lightning_model.eval() 1. 2. 3. 4. 5. 步骤3:创建PyTorch模型 在这一步中,我们需要创建一个与PyTorch Lightning模型结构相同的PyTorch模型。以下代码展示了如何创...
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) ...
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) ...
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...