How to evaluate a trained model in pytorch?

Question

I have trained a model and save model using torch.save. Then after training I have loaded the model using train.load but I am getting this error

 Traceback (most recent call last): File "/home/fsdfs.py", line 219, in <module> test(model, 'cuda', testloader) File "/home/fsdfs.py", line 201, in test model.eval() AttributeError: 'collections.OrderedDict' object has no attribute 'eval'

Here is my code for test part

model = torch.load("train_5.pth") def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to('cuda'), target.to('cuda') output = model(data) #test_loss += f.cross_entropy(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(1, keepdim=True) # get the index of the max log-probability print(pred, target) correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format( correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) test(model, 'cuda', testloader)

I have commented training part of the code in the file, so in a way this and loading the data part is all that is there in the file now.

What am I doing wrong?

How did you save it? It seems like you saved a dictionary not a model. — jodag
– jodag, Commented Mar 19, 2022 at 4:28

Bedir Yilmaz · Accepted Answer · 2022-03-19 05:34:53Z

Like @jodag has said. you probably have saved a state_dict instead of a model, which is recommended by the community as well.

This link explains the difference between two. To keep my answer self contained, I copy the snippet from the documentation. Here is the recommended way:

Save:

torch.save(model.state_dict(), PATH)

Load:

model = TheModelClass(*args, **kwargs) model.load_state_dict(torch.load(PATH)) model.eval()

You could also save the entire model instead of saving the state_dict, if you really need to use the model the way you do.

Save:

torch.save(model, PATH)

Load:

# Model class must be defined somewhere model = torch.load(PATH) model.eval()

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