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 = 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...
checkpoint=torch.load(checkpoint,map_location=lambdastorage,loc:storage)print(checkpoint["hyper_parameters"])# {"learning_rate": the_value, "another_parameter": the_other_value} 可以直接进行某个超参数的访问:直接用"." model=MyLightningModule.load_from_checkpoint("/path/to/checkpoint.ckpt")print(...
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...
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)...
fromtransformersimport( AutoModelForSequenceClassification, AutoConfig, AutoTokenizer ) classPythonPredictor: def__init__(self, config): self.device ="cpu" self.tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") self.model = MyModel.lo...
pl.LightningModule.load_from_checkpoint( checkpoint_path=checkpoint_path, map_location=None, hparams_file=None, strict=True, **kwargs ) 1. 2. 3. 4. 5. 6. 7. 该方法是从checkpoint 加载模型的主要方法。 4.2 加载模型的权重、偏置和超参数 model = MyLightingModule.load_from_checkpoint(PATH) ...
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 ...
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) ...