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...
AI检测代码解析 size mismatch for block0.affine0.linear1.linear2.weight: copying a param with shape torch.Size([512, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]). size mismatch for block0.affine0.linear1.linear2.bias: copying a param with shape torch.Siz...
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,...
model.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=map_location))#后面正常训练代码optimizer =xxxforepoch:fordatainDataloader: model(data) xxx#训练完成 只需要保存rank 0上的即可#不需要dist.barrior(), all_reduce 操作保证了同步性ifrank ==0: ...
MindSpore:在MindSpore中,优化器的状态是通过Checkpoint模块保存和加载的。可以使用save_checkpoint()方法将优化器的状态保存到磁盘,然后使用load_checkpoint()方法将状态加载到优化器中。 from mindspore.train.serialization import save_checkpoint, load_checkpoint # 保存优化器状态 optimizer = nn.SGD(params=model.trai...
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...
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: 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?
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...
ckp_path = "checkpoint.pt" if os.path.exists(ckp_path): print(f"load checkpoint from {ckp_path}") checkpoint = load_checkpoint(ckp_path) model.load_state_dict(checkpoint["model_state_dict"]) optimizer.load_state_dict(checkpoint["optimize_state_dict"]) first_epoch = checkpoint["epoch"] ...