2.手动保存 model=MyLightningModule(hparams)trainer.fit(model)trainer.save_checkpoint("example.ckpt") 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...
# 加载checkpointcheckpoint=torch.load('checkpoint.pth')# 恢复模型和优化器状态model.load_state_dict(checkpoint['model_state'])optimizer.load_state_dict(checkpoint['optimizer_state'])epoch=checkpoint['epoch']loss=checkpoint['loss']print(f'Restored from epoch{epoch}, loss:{loss}') 1. 2. 3. 4...
defload_checkpoint(model,optimizer,checkpoint_path):checkpoint=torch.load(checkpoint_path)model.load_state_dict(checkpoint['model_state_dict'])optimizer.load_state_dict(checkpoint['optimizer_state_dict'])start_epoch=checkpoint['epoch']print(f"Checkpoint loaded; Resuming from epoch{start_epoch}")return...
❓ 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...
checkpoint处理:由于再每次增加或删除node时,会将所有worker kill掉,然后再重新启动所有worker进行训练。因此,在训练代码中要对训练的状态进行保存,以保证重启后能接着上次的状态继续训练。 超参调解:由于node节点数的变化,会导致global batch size的变化,因此我们的learning rate一般也要做相应的调整,保证训练出的模质...
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...
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") ...
optimizer.load_state_dict(checkpoint['optimizer']) start_epoch = checkpoint['epoch'] # 冻结训练 if freeze: freeze_epoch = 5 print("冻结前置特征提取网络权重,训练后面的全连接层") for param in model.feature.parameters(): param.requires_grad = False # 将不更新的参数的requires_grad设置为False,...
使用torch.load()函数加载检查点文件: 从检查点中提取模型参数: 从检查点中提取模型参数: 这里假设检查点文件中的模型参数保存在名为model_state_dict的键下。如果检查点文件中的键名不同,请相应地修改。 可选地,加载其他相关信息,如优化器状态: 可选地,加载其他相关信息,如优化器状态: ...
load_from_checkpoint([PATH TO CHECKPOINT]) model.eval() trainer.test(model, test_dataloaders=dm.test_dataloader()) 我怀疑这个模型没有正确加载,但我不知道该做什么不同。有什么想法吗? 使用PyTorch闪电1.4.4 deep-learning pytorch pytorch-lightning...