初始化回调,监视 'val_loss' checkpoint_callback = ModelCheckpoint(monitor="val_loss") # 4. 向Trainer添加回调 trainer = Trainer(callbacks=[checkpoint_callback]) 2.手动保存 model = MyLightningModule(hparams) trainer.fit(model) trainer.save_checkpoint("example.ckpt") 3.加载(load_from_checkpoint)...
读取Checkpoint 读取保存的checkpoint文件可以使我们方便地恢复模型训练: # 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...
train=False, transform=torchvision.transforms.ToTensor(), download=True) # 创建数据集(测试集) dataloader = DataLoader(dataset, batch_size=64) # 搭建神经网络:
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?
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...
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...
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...
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,...
原因是因为checkpoint设置好的确是保存了相关字段。但是其中设置的train_dataset却已经走过了epoch轮,当你再继续训练时候,train_dataset是从第一个load_data开始。 # -*- coding:utf-8 -*-importosimportnumpyasnpimporttorchimportcv2importtorch.nnasnnfromtorch.utils.dataimportDataLoaderimporttorchvision.transformsas...
Resuming a Keras checkpoint Keras models provide the load_weights() method, which loads the weights from a hdf5 file. To load the model's weights, you just need to add this line after the model definition: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ... # Model Definition model.lo...