Why does Pytorch complains about dimension mismatch when they match?

I encounter the following problem:

RuntimeError: Error(s) in loading state_dict for SphereFace: While copying the parameter named "fc2.weight", whose dimensions in the model are torch.Size([81391, 512]) and whose dimensions in the checkpoint are torch.Size([81931, 512]).

The error is not telling me what’s wrong.

Is this a bug?

The very weird thing is that if I train with a smaller model with class size 10572 instead of 81931, the same code works (loading trained model). But when it is the larger model with class size 81931, it complains. How can the model being bigger cause an error?

The following is how I save the model:

            'epoch': epoch,
            'arch': args.arch,
            'model_config': {'num_classes': model.num_classes, 
                             'use_prelu': model.use_prelu, 
                             'use_se': model.use_se,
                             'weight_scale': model.weight_scale,
                             'feature_scale': model.feature_scale},
            'state_dict': model.state_dict(),
            'optimizer' : optimizer.state_dict(),
            'loss': best_loss,
            'prec1': top1.avg,
            'prec5': top5.avg
        }, is_best, savedir)

def save_checkpoint(state, is_best, savedir):
    if not os.path.exists(savedir):
    checkpoint_savepath = os.path.join(savedir, 'checkpoint.pth.tar')
    torch.save(state, checkpoint_savepath)
    if is_best:
        best_savename = '_'.join([state['arch'], 'epoch' + str(state['epoch'])]) + '.pth.tar'
        best_savepath = os.path.join(savedir, best_savename)
        shutil.copyfile(checkpoint_savepath, best_savepath)

I think you might have a small type, as you model’s fc2.weight shape is [81391, 512], while in the checkpoint it’s [81931, 512]. Note the reversed 39-93.

Can’t believe I miss that. Thanks!