This is my code for applying grad-cam:

```
for idx, (data, target, meta) in enumerate(tqdm(dataloader_test)):
print('idx', idx)
data, target = map(lambda x: x.to(device), (data, target))
output = model(data)
output[:,5].sum().backward()
grads=grad[0].cpu().data.numpy().squeeze()
fmap=activation[0].cpu().data.numpy().squeeze()
tmp=grads.reshape([grads.shape[0],-1])
# Get the mean value of the gradients of every featuremap
weights=np.mean(tmp,axis=1)
cam = np.zeros(grads.shape[1:])
for i,w in enumerate(weights):
cam += w*fmap[i,:]
#relu
cam=(cam>0)*cam
#cam = np.maximum(cam, 0)
#print("cam.shape",cam.shape)
#normalize heatmap
cam=cam/cam.max()*224
# make the heatmap to be a numpy array
# impath = meta['impath']
# impath = cv2.imread(impath)
# print('shape data', data.shape)
# data = torch.squeeze(data, dim=1)
# print('squeeze data', data.shape)
for data in idx:
print('shape data', data.shape)
npic = np.array(torchvision.transforms.ToPILImage()(data).convert('RGB'))
cam = cv2.resize(cam,(npic.shape[1], npic.shape[0]))
heatmap=cv2.applyColorMap(np.uint8(cam),cv2.COLORMAP_JET)
cam_img=npic*0.7+heatmap*0.3
print(cam_img.shape)
cv2.imwrite('./visualize/map'+str(count)+'.jpg', cam_img)
count = count + 1
```

I have added a loop `for data in idx:`

because I want to apply the heatmap per sample instead of per batch but I get this error

`TypeError: 'int' object is not iterable`

Any ideas how to solve it? Thank you very much