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
() 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...
model = MyLightingModule.load_from_checkpoint(PATH) print(model.learning_rate) # prints the learning_rate you used in this checkpoint model.eval() y_hat = model(x) 如果需要修改超参数,在写Module的时候进行覆盖: class LitModel(LightningModule): def __init__(self, in_dim, out_dim): super...
importMyModelfromtraining_code fromtransformersimport( AutoModelForSequenceClassification, AutoConfig, AutoTokenizer ) classPythonPredictor: def__init__(self, config): self.device ="cpu" self.tokenizer = AutoTokenizer.from_pretrained("albert-base-v2...
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....
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) ...
model = ProteinModel.load_from_checkpoint(CKPT_PATH) model.to(DEVICE) model.eval() _ = model(torch.randn(1, DatasetConfig.CHANNELS, *DatasetConfig.IMAGE_SIZE[::-1], device=DEVICE)) preprocess = TF.Compose( [ TF.Resize(size=DatasetConfig.IMAGE_SIZE[::-1]), ...
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) ...
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...