3. 这个时候意识到,可能本来加载的时候,checkpoint中就没有权值字段, 而这情况恰恰又被overwrite模式直接忽略了,所以通过load之后并没有对初始化的模型进行权值的复写,而后来的evaluate过程使用的是初始化的模型进行测试,当然结果很差 AI检测代码解析 save_checkpoint({ 'epoch': epoch + 1, 'state_dict': model....
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...
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/to/my_che...
则加载checkpoint,并初始化训练状态ifRESUME:path_checkpoint=checkpoint_pathcheckpoint=torch.load(path_che...
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权重都从 checkpoint 加载(或是手动加载),否则模型不完整。 针对使用 FSDP 或 DeepSpeed 训练的大参数模型,就不应使用trainer.init_module()了。对应的,为了加快大参数模型加载速度、减...
model = model.to(device) criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5) # 加载预训练权重 if resume: checkpoint = torch.load(resume, map_location='cpu') model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(ch...
scheduler = DDPM_Scheduler(num_time_steps=num_time_steps)model = UNET().cuda()optimizer = optim.Adam(model.parameters(), lr=lr)ema = ModelEmaV3(model, decay=ema_decay)if checkpoint_path is not None:checkpoint = torch.load(ch...
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...
model = SimpleNet(num_classes=10) model.load_state_dict(checkpoint) model.eval() 注意,如果你的模型使用ImageNet训练的,那么你的num_classes必须为1000而不是10. 代码的所有其它部分维持一致,只有一点不同——如果我们以使用CIFAR10训练的模型进行预测,那么在转换中,要将transforms.CenterCrop(224)改变为transfor...
model.fc=nn.Linear(num_final_in,300)# Now that the architecture is defined sameasabove,let's load the model we would have trained above.checkpoint=torch.load(MODEL_PATH)model.load_state_dict(checkpoint)# Let's freeze the sameasabove.Same codeasabove without the print statements ...