使用lightningmodule.load_from_checkpoint 方法来加载模型权重。 python checkpoint_path = "path/to/your/pretrained_model.ckpt" model = MyLightningModel.load_from_checkpoint(checkpoint_path) (可选)对加载的模型进行测试或评估: 你可以通过打印模型结构或进行前向传播测试来验证模型是否成功加载。 python # 打...
🐛 Bug Saving a LightningModule whose constructor takes arguments and attempting to load using load_from_checkpoint errors with TypeError: __init__() missing 1 required positional argument: 'some_param' Please reproduce using the BoringMo...
load_from_checkpoint: TypeError:init() missing 1 required positional argument I have read the issues before, but the things different ismyLightningModuleis inherited from my self-definedLightningModule. How to solve this problem or what is the best practice better suited to my needs?
若是从 checkpoint 初始化模型,可以向trainer传入参数empty_init=True,这样在读取 checkpoint 之前模型的权重不会占用内存空间,且速度更快。 withtrainer.init_module(empty_init=True): model = MyLightningModule.load_from_checkpoint("my/checkpoint/path.ckpt") trainer.fit(model) 要注意,此时必须保证模型的每个...
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-CSDN博客_pytorch保存模型 AIC19:Py...
在使用 MyLightningModule 的load_from_checkpoint 方法加载指定的 checkpoint 时,须用到之前训练该模型的“超参数”,如果忽略了超参数的设置可能会报告类似于这样的错误:TypeError: __init__() missing 1 required positional argument: 'args'。对此有两种解决方案: 使用arg1=arg1, arg2=arg2, ...这样的参数传...
self.model=ColaModel.load_from_checkpoint(model_path)self.model.eval()self.model.freeze() Predict() 方法接受文本输入,使用分词器对其进行处理,并返回模型的预测: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defpredict(self,text):inference_sample={"sentence":text}processed=self.processor.tokenize...
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) --- DATALOADER:0 TEST RESULTS {'test_acc': 0.9887999892234802, 'test_loss': 0.03627564385533333...
self.model = ColaModel.load_from_checkpoint(model_path) self.model.eval() self.model.freeze() Predict() 方法接受文本输入,使用分词器对其进行处理,并返回模型的预测: def predict(self, text): inference_sample = {"sentence": text} processed = self.processor.tokenize_data(inference_sample) ...
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(...