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...
# 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 nn.Moduleencoder=autoencoder.encoder encoder.eval()# embed 4 fake images!fake_image_batch=torch.r...
❓ 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...
pytorch_lightning这里用了一个专门的类Trainer来调用。 训练调用: trainer = Trainer(max_epochs=config.num_epochs, # resume_from_checkpoint = 'ckpts/exp3/epoch=7.ckpt', # 断点续训 callbacks=callbacks, logger=logger, enable_model_summary=True, # 显示模型构造 accelerator='auto', devices=1, # ...
PyTorch-Lightning模型保存与加载 1.自动保存 2.手动保存 3.加载(load_from_checkpoint) 4.加载(Trainer) 参考 argmax不可导问题 最近在一项工作中遇到argmax不可导问题,具体来说是使用了两个不同的网络,需要将前一个网络的输出的概率分布转换成最大类别值,然后将其喂给第二个网络作为输入,然而argmax操作后不能...
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...
[checkpoint_callback]);# 开始训练 trainer.fit(dck,datamodule=dm)else:# 测试阶段 dm.setup('test')# 恢复模型 model=MyModel.load_from_checkpoint(checkpoint_path='trained_model.ckpt')# 定义trainer并测试 trainer=pl.Trainer(gpus=1,precision=16,limit_test_batches=0.05)trainer.test(model=model,...
raise TypeError( TypeError: The classmethod `LightningCloudSegNet.load_from_checkpoint` cannot be called on an instance. Please call it on the class type and make sure the return value is used.` Environment Current environment More info
from torch.utils.data import DataLoader, random_split import pytorch_lightning as pl 1. 2. 3. 4. 5. 6. 7. 8. Step 1: 定义Lightning模型 class LitAutoEncoder(pl.LightningModule): def __init__(self): super().__init__() self.encoder = nn.Sequential( ...
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....