Hello, everyone. I am using the pretrained torchvision MaskRCNN model on a dataset containing several videos, by passing the videos to the model frame by frame. However in the forward pass of the MaskRCNN model I get the following error statement:
RuntimeError                              Traceback (most recent call last)

----> 1 output = model(images,targets)

4 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/generalized_rcnn.py in forward(self, images, targets)
     64             original_image_sizes.append((val[0], val[1]))
     65 
---> 66         images, targets = self.transform(images, targets)
     67         features = self.backbone(images.tensors)
     68         if isinstance(features, torch.Tensor):

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/transform.py in forward(self, images, targets)
     42                 raise ValueError("images is expected to be a list of 3d tensors "
     43                                  "of shape [C, H, W], got {}".format(image.shape))
---> 44             image = self.normalize(image)
     45             image, target_index = self.resize(image, target_index)
     46             images[i] = image

/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/transform.py in normalize(self, image)
     62         mean = torch.as_tensor(self.image_mean, dtype=dtype, device=device)
     63         std = torch.as_tensor(self.image_std, dtype=dtype, device=device)
---> 64         return (image - mean[:, None, None]) / std[:, None, None]
     65 
     66     def torch_choice(self, l):

RuntimeError: ZeroDivisionError


My  images  in this code is a list of size 431, denoting the number of frames in the video, with each item of the list being a torch tensor of shape 3 x 256 x 320, denoting channels x height x width, respectively.  Targets  here contains the labels, masks and bounding boxes coordinates in the form of a dictionary of lists. The length of the  targets  list is also 431, with each item containing information about each individual frame.
The code I am using is similar to the one found in this tutorial 3, except for the fact that that tutorial deals with static images, and I am working with videos, so I made only some minor changes to that code.
It seems that the error arises because in one of the images, the standard deviation of the pixel values comes out to be zero, which is highly unlikely if not impossible.  Is there any other reason for this error to occur?