[英]How to use checkpoint model file in pytorch to test the CIFAR-10 dataset?
model = SqueezeNext()
model = model.to(device)
def load_checkpoint(model, optimizer, losslogger, filename='SqNxt_23_1x_Cifar.ckpt'):
# Note: Input model & optimizer should be pre-defined. This routine only updates their states.
start_epoch = 0
if os.path.isfile(filename):
print("=> loading checkpoint '{}'".format(filename))
checkpoint = torch.load(filename)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
losslogger = checkpoint['losslogger']
print("=> loaded checkpoint '{}' (epoch {})"
.format(filename, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(filename))
return model, optimizer, start_epoch, losslogger
model, optimizer, start_epoch, losslogger = load_checkpoint(model, optimizer, losslogger)
TypeError: Traceback (最近调用 last) in () 41 test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=80, num_workers=8, shuffle=False) 42 ---> 43 model = SqueezeNext() 44 model = model.to(device) 45 def load_checkpoint(model, optimizer, losslogger, filename='SqNxt_23_1x_Cifar.ckpt'): TypeError: init () 缺少 3 个必需的位置参数:'width_x'、'blocks' 和 'num_classes'
我想我没有以正确的方式实施这个!!
您的错误不是来自您的检查点功能。 如果我们看一下回溯:
> TypeError: Traceback (most recent call last)
> <ipython-input-51-94c8be648862> in <module>()
> 41 test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=80, num_workers=8, shuffle=False)
> 42
> ---> 43 model = SqueezeNext()
> 44 model = model.to(device)
> 45 def load_checkpoint(model, optimizer, losslogger, filename='SqNxt_23_1x_Cifar.ckpt'): TypeError: __init__() missing 3
> required positional arguments: 'width_x', 'blocks', and 'num_classes'
我们被告知的这条线正在打破第 43 行:
> ---> 43 model = SqueezeNext()
错误是:
> required positional arguments: 'width_x', 'blocks', and 'num_classes'
我假设您正在使用 SqueezeNext 的这个实现,但是无论您使用哪个实现,您都没有传递初始化模型所需的所有参数。 您需要将代码更改为以下内容:
model = SqueezeNext(width_x=1.0, blocks=[6, 6, 8, 1], num_classes=10)
如果您没有使用该实现,则需要找到SqueezeNext
模型的源代码,并查看__init__
函数需要哪些参数。 你可以试试这个:
import inspect
inspect.signature(SqueezeNext.__init__)
这应该给你签名。
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