Comments (3)
iwowor
commented on July 1, 2024
I met the same problem
from pytorch-vgg-cifar10.
arnaghosh
commented on July 1, 2024
The model is vgg19!
I just got it working on my end (I did not use DataParallel though). I had to tweak the checkpoint to match the keys as in the VGG model description (in the vgg.py file).
This is what I did:
checkpoint_orig = torch.load(<checkpoint_file_pth.tar>)
checkpoint = {'state_dict':{}}
for k in checkpoint_orig['state_dict'].keys():
checkpoint['state_dict'][k.replace('module.','')] = checkpoint_orig['state_dict'][k]
checkpoint['epoch'] = checkpoint_orig['epoch'] # This line is optional
checkpoint['best_prec1'] = checkpoint_orig['best_prec1'] # This line is optional
del checkpoint_orig # This is to save memory space
model.load_state_dict(checkpoint['state_dict'])
I could replicate 92.43% accuracy on the CIFAR-10 testset.
from pytorch-vgg-cifar10.
LLMWinner
commented on July 1, 2024
Could you please share your test.py? Thanks a lot.
from pytorch-vgg-cifar10.
Related Issues (12)
- License? HOT 1
- How is the data divided among workers
- The version of Pytorch and torchvision
- Should it be decay by 2? HOT 1
- Oversampling in CIFAR-10 HOT 2
- Question regarding VGG cfg HOT 1
- I use your code the result all are 100 or 99.9? HOT 1
- which pytorch version did you use to train this model? i am trying to do torch. load on my CPU but even after i give map_location cpu, it still gives pytorch not compiled for CUDA enabled assertion error HOT 6
- I run your code and always nan loss, can you help me? HOT 1
- Is your code based on a code template? HOT 2
- Why not giving the whole model to DataParallel ？
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from pytorch-vgg-cifar10.