text: Title: Wrong heatmap for Swin-Transformer
  Body: My code:
  `if __name__ == '__main__':
      """ python swinT_example.py -image-path <path_to_image>
      Example usage of using cam-methods on a SwinTransformers network.
          
      """
      label_map_path = '../../imagenet1000_clsidx_to_labels.txt'
      with open(label_map_path, 'r') as file:
          label = eval(file.read())
      args = get_args()
      methods = \
          {"gradcam": GradCAM, 
           "scorecam": ScoreCAM, 
           "gradcam++": GradCAMPlusPlus,
           "ablationcam": AblationCAM,
           "xgradcam": XGradCAM,
           "eigencam": EigenCAM,
           "eigengradcam": EigenGradCAM}
  
      if args.method not in list(methods.keys()):
          raise Exception(f"method should be one of {list(methods.keys())}")
  
      model = timm.create_model('swin_tiny_patch4_window7_224',
                                pretrained=True
                                )
      model.eval()
  
      if args.use_cuda:
          model = model.cuda()
  
      target_layer = model.layers[-1].blocks[-2].norm1
  
      if args.method not in methods:
          raise Exception(f"Method {args.method} not implemented")
  
      cam = methods[args.method](model=model, 
                                 target_layer=target_layer,
                                 use_cuda=args.use_cuda,
                                 reshape_transform=reshape_transform)
  
      rgb_img = cv2.imread(args.image_path, 1)[:, :, ::-1]
      rgb_img = cv2.resize(rgb_img, (224, 224))
      rgb_img = np.float32(rgb_img) / 255
      input_tensor = preprocess_image(rgb_img, mean=[0.485, 0.456, 0.406],
                                               std=[0.229, 0.224, 0.225])
  
      # If None, returns the map for the highest scoring category.
      # Otherwise, targets the requested category.
      target_category = None
  
      # AblationCAM and ScoreCAM have batched implementations.
      # You can override the internal batch size for faster computation.
      cam.batch_size = 32
      print(input_tensor.shape)
      grayscale_cam = cam(input_tensor=input_tensor,
                          target_category=target_category,
                          eigen_smooth=args.eigen_smooth,
                          aug_smooth=args.aug_smooth)
      input_tensor = input_tensor.cuda()
      output = model(input_tensor)
      output = nn.Softmax(dim=1)(output)
      sorted,ind = torch.sort(output)
      res_ind = ind[0][-1].item()
      # Here grayscale_cam has only one image in the batch
      grayscale_cam = grayscale_cam[0, :]
      
      cam_image = show_cam_on_image(rgb_img, grayscale_cam)
      print(label[res_ind])
      cv2.imwrite(f'{args.method}_cam_swin.jpg', cam_image)`
  
  My heatmap:
  ![gradcam_cam_swin](https://user-images.githubusercontent.com/52605055/131814402-447e8f81-096c-49b1-addf-f3457344d4e3.jpg)
  ![gradcam_cam_swin](https://user-images.githubusercontent.com/52605055/131814528-2c127b48-688d-41c4-96cb-48e01046823e.jpg)
  Is there something wrong with my code?
  
  