Hi ,
i am beginner in deep learning and pytorch , in my project i want to extract feature using pre-trained model then used these feature to train SVM classifier, how can i use hinge loss in pytorch? when i use nn.MultiMarginLoss()  i get the error:
Traceback (most recent call last):

  File "<ipython-input-1-740ff909cca9>", line 1, in <module>
    runfile('C:/Users/Windows10/Downloads/Hala3/main-run-vr.py', wdir='C:/Users/Windows10/Downloads/Hala3')

  File "C:\Users\Windows10\Anaconda3\envs\New\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
    execfile(filename, namespace)

  File "C:\Users\Windows10\Anaconda3\envs\New\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/Windows10/Downloads/Hala3/main-run-vr.py", line 548, in <module>
    __main__()

  File "C:/Users/Windows10/Downloads/Hala3/main-run-vr.py", line 546, in __main__
    evalInterval, evalMode, numWorkers, outDir,modelUsed,pretrained,train_test_split,datasetDir,crossValidation,nFolds)

  File "C:/Users/Windows10/Downloads/Hala3/main-run-vr.py", line 421, in main_run
    model,accuracy=modelTrain(modelUsed,pretrained,trainDataset,trainLabels,validationDataset,validationLabels,numEpochs,evalInterval,evalMode,outDir,numWorkers,lr, stepSize, decayRate, trainBatchSize, seqLen,True)

  File "C:/Users/Windows10/Downloads/Hala3/main-run-vr.py", line 303, in modelTrain
    loss = lossFn(labelVariable, outputLabel)

  File "C:\Users\Windows10\Anaconda3\envs\New\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)

  File "C:\Users\Windows10\Anaconda3\envs\New\lib\site-packages\torch\nn\modules\loss.py", line 1151, in forward
    weight=self.weight, reduction=self.reduction)

  File "C:\Users\Windows10\Anaconda3\envs\New\lib\site-packages\torch\nn\functional.py", line 2386, in multi_margin_loss
    return torch._C._nn.multi_margin_loss(input, target, p, margin, weight, reduction_enum)

RuntimeError: _thnn_multi_margin_loss_forward not supported on CPUType for Long

can anyone tell me if my code is ok or not 
lossFn = nn.MultiMarginLoss()
    optimizerFn = torch.optim.RMSprop(trainParams, lr=lr)
    optimizerFn.zero_grad()
    optimScheduler = torch.optim.lr_scheduler.StepLR(optimizerFn, stepSize, decayRate)

    minAccuracy = 50
    train_loss=[]
    val_loss=[]
    train_acc=[]
    val_acc=[]
    bestmodel=None

    for epoch in range(numEpochs):
        optimScheduler.step()
        epochLoss = 0
        numCorrTrain = 0
        iterPerEpoch = 0
        model.train(True)
        print('Epoch = {}'.format(epoch + 1))
        writer.add_scalar('lr', optimizerFn.param_groups[0]['lr'], epoch+1)
        for i, (inputs, targets) in enumerate(trainLoader):
            iterPerEpoch += 1
            optimizerFn.zero_grad()
            if(torch.cuda.is_available()):
                inputVariable1 = Variable(inputs.permute(1, 0, 2, 3, 4).cuda()).cpu()                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          
                labelVariable = Variable(targets.cuda()).cpu()
            
            
            else:
                inputVariable1=Variable(inputs.permute(1,0,2,3,4))
                labelVariable=Variable(targets)
            outputLabel = model(inputVariable1)
            features = model(inputVariable1)
            features = features.detach().cpu().numpy()
            labelVariable=(labelVariable.data).detach().cpu().numpy()
            svclassifier.fit(features, labelVariable)
            outputLabel= svclassifier.predict(features)
            labelVariable = torch.LongTensor(labelVariable)
            outputLabel = torch.LongTensor(outputLabel)
            loss = lossFn(labelVariable, outputLabel)
            loss.backward()
            optimizerFn.step()
            #outputProb = torch.nn.Softmax(dim=1)(outputLabel)
            #_, predicted = torch.max(outputProb.data, 1)
            _, predicted= outputLabel
            if(torch.cuda.is_available()):
                numCorrTrain += (predicted == targets.cuda()).sum()
            else:
                numCorrTrain+=(predicted==targets).sum()
            epochLoss += loss.item()