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?
if isinstance(cls, LightningModule): raise ValueError( f"Calling `.load_from_checkpoint` on a model instance is not supported. Please change it to `{type(cls).__name__}.load_from_checkpoint(...)" This method doesn't actually work, since _load_from_checkpoint is called inside the classm...
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...
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...
model = LitMNIST.load_from_checkpoint(PATH, loss_fx=torch.nn.SomeOtherLoss, generator_network=MyGenerator()) 还可以将完整对象(如dict或Namespace)保存到检查点。 # 使用argparse.Namespace class LitMNIST(LightningModule): def __init__(self, conf, *args, **kwargs): ...
checkpoint_callback.best_model_path) print(trainer.checkpoint_callback.best_model_score) lightning_logs/version_10/checkpoints/epoch=8-step=15470.ckpt tensor(0.0376, device='cuda:0') model_clone = Model.load_from_checkpoint(trainer.checkpoint_callback.best_model_path) trainer_clone = pl....
Checkpoints Lightning会自动保存你的模型,一旦你训练好了,你可以通过下面代码来加载检查点 model = LitModel.load_from_checkpoint(path) 1. 上面的检查点包含了初始化模型和设置状态字典所需的所有参数 # load the ckpt ckpt = torch.load('path/to/checkpoint.ckpt') ...
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) ...
# 获取恢复了权重和超参数等的模型 model=MODEL.load_from_checkpoint(checkpoint_path='my_model_path/hei.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_batc...
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...