It seems a bug

[Jackey]

I downloaded the code and run it by the command: python3 main.py --model msdnet -b 64 -j 2 cifar10 --msd-blocks 10 --msd-base 4 --msd-step 2 --msd-stepmode even --growth 6-12-24 --gpu 0 to train on cifar10 data set, but it shows the error

Traceback (most recent call last):
File "main.py", line 445, in
main()
File "main.py", line 139, in main
train(train_loader, model, criterion, optimizer, epoch)
File "main.py", line 212, in train
prec1, prec5, _ = msdnet_accuracy(output, target, input)
File "main.py", line 269, in msdnet_accuracy
top1s.append(tprec1[0])
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to c
onvert a 0-dim tensor to a Python number

[Aviram Bar Haim]

Hi @mjjackey,
This code was developed with Pytorch 0.1.12, and it seems that after version 0.5, 0-dim tensor should be handled with an item() call. I think you can use Pytorch 0.4.0/1.
Pull requests for supporting later versions are welcome of course.

[Jackey]

Thank you very much! @avirambh.
Sorry for the late reply, I have figured it out already.
And I have a question: how to vary the FLOPs or parameters to plot the figure for accuracy? Do I need to open a new issue for this question?

[Aviram Bar Haim]

We used the script referred by the authors of the paper in the original torch repository. We created the graphs manually.

[Jackey]

We used the script referred by the authors of the paper in the original torch repository. We created the graphs manually.

@avirambh Hi, avirambn, I am confused that each classifier has been added in the code, but the FLOPs just be output once, how can I know FLOPs of each classifier for anytime prediction?

[Aviram Bar Haim]

@mjjackey we did it manually. If i'm not mistaken, we added prints and checked different model architectures.

[Jackey]

@avirambh Thank you very much for your timely response! Recently, I am studying this paper, so have a lot of questions. :)
In terms of this question, I have found it the prints adding in the ['Conv2d'], this could be not accurate because 'Conv2d' is not limited in classifiers.