best_acc = checkpoint['best_acc'] model_ft.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer'])
时间: 2023-10-30 08:50:28 浏览: 63
best_acc = checkpoint['best_acc']是从checkpoint中获取了之前保存的最佳准确率(best accuracy)。这个准确率在训练过程中可能会被记录并保存下来,以便在需要的时候进行参考或展示。
model_ft.load_state_dict(checkpoint['state_dict'])是将checkpoint中保存的模型权重加载到当前模型中。通过使用model_ft.load_state_dict()函数,并传入checkpoint中的state_dict字典,可以将之前训练好的模型权重加载到当前模型中,以便进行后续的推理或继续训练。
optimizer.load_state_dict(checkpoint['optimizer'])是将checkpoint中保存的优化器状态加载到当前优化器中。在训练过程中,除了保存模型权重外,还会保存优化器的状态,包括学习率、动量等参数。通过使用optimizer.load_state_dict()函数,并传入checkpoint中的optimizer字典,可以将之前保存的优化器状态加载到当前优化器中,以确保训练过程的连续性。这样,在加载checkpoint后,可以从之前保存的状态继续进行优化器的更新操作。
相关问题
if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) args.start_epoch = checkpoint['epoch'] best_acc = checkpoint['best_acc'] recorder = checkpoint['recorder'] best_acc = best_acc.to() model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True
这是一个 Python 的代码段,它包含了一个条件语句和一些操作。如果 `args.resume` 是真值(即非空或非零),那么它会尝试加载一个文件,读取其中保存的模型参数和优化器状态。如果文件存在,它会输出一条信息说明已经成功加载了检查点;如果文件不存在,它会输出另一条信息说明未能找到检查点。最后一行 `cudnn.benchmark = True` 可能是为了让 CUDA 的深度神经网络库提高性能。
generator.load_state_dict(checkpoint[ generator ]) KeyError: generator
It seems that the key "generator" is not present in the checkpoint dictionary. Make sure that you have saved the generator with the key "generator" while creating the checkpoint.
For example, if you are using PyTorch, you can save the generator like this:
```
torch.save({
'generator': generator.state_dict(),
'optimizer': optimizer.state_dict()
}, checkpoint_path)
```
Then, while loading the checkpoint, make sure to provide the same key to load the generator:
```
checkpoint = torch.load(checkpoint_path)
generator.load_state_dict(checkpoint['generator'])
optimizer.load_state_dict(checkpoint['optimizer'])
```
If the issue persists, try printing the keys in the checkpoint dictionary to see the available keys:
```
print(checkpoint.keys())
```
This will give you an idea if the key "generator" is present in the dictionary or not.