anyone can guide me how to solve this issue? 
Traceback (most recent call last):
File "<ipython-input-24-740ff909cca9>", 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 543, in <module>
__main__()
File "C:/Users/Windows10/Downloads/Hala3/main-run-vr.py", line 541, 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 416, 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 295, in modelTrain
features = model(inputVariable1)
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\Downloads\Hala3\createModel.py", line 83, in forward
x1 = self.convNet(x1)
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\container.py", line 92, in forward
input = module(input)
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\container.py", line 92, in forward
input = module(input)
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\conv.py", line 338, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
model = ViolenceModel(modelUsed,pretrained)
svclassifier = SVC(kernel=‘linear’)
trainParams = []
for params in model.parameters():
if params.requires_grad:
trainParams += [params]
model.train(True)
if(torch.cuda.is_available()):
model.cuda()
lossFn = nn.CrossEntropyLoss()
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)
svclassifier.fit(features, labelVariable)
outputLabel= svclassifier.predict(inputVariable1)
loss = lossFn(outputLabel, labelVariable)
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()
class ViolenceModel(nn.Module):
def __init__(self,modelused,pretrained):
super(ViolenceModel, self).__init__()
if modelused=='alexnet':
self.mod=models.alexnet(pretrained=pretrained)
self.convNet = nn.Sequential(*(list(self.mod.children()))[:-1])
self.mem_size=256
self.conv_lstm = ConvLSTMCell(256, self.mem_size)
if pretrained:
for param in self.convNet.parameters():
param.requires_grad = False
def forward(self, x):
state = None
seqLen = x.size(0) - 1
for t in range(0, seqLen):
x1 = x[t] - x[t+1]
x1 = self.convNet(x1)
x1=x1.cuda()
state = self.conv_lstm(x1, state)
x = self.maxpool(state[0])
x= x.view(x.size(0), -1)
# x = self.classifier(x)
return x
thanks in advance 