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
❓ Questions and Help What is your question? load_from_checkpoint: TypeError: init() missing 1 required positional argument I have read the issues before, but the things different is my LightningModule is inherited from my self-defined Li...
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...
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...
enable_model_summary=True, # 显示模型构造 accelerator='auto', devices=1, # 多少个设备 deterministic=True, num_sanity_val_steps=1, # 正式训练之前跑一次validation 测试程序是否出错 benchmark=True, # cudnn加速训练(要确保每个batch同一个大小) ) # mnist_model.load_from_checkpoint('ckpts/exp3/ep...
Bug description I want to load a trained checkpoint to "gpu" in colab, but it seems that load_from_checkpoint loads two copies, and the device of the model is "cpu". The memory of both host and gpu is occupied. If i use: model.to(torch.d...
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) ...
# 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...
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') ...
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): ...