ValueError when running "Mask_R-CNN_demo.ipynb"

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The Computing the predictions step evaluates to
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-ba06eae97bcc> in <module>
      1 # compute predictions
----> 2 predictions = coco_demo.run_on_opencv_image(image)
      3 imshow(predictions)

~/src/maskrcnn-benchmark_fb/demo/predictor.py in run_on_opencv_image(self, image)
    123                 the BoxList via `prediction.fields()`
    124         """
--> 125         predictions = self.compute_prediction(image)
    126         top_predictions = self.select_top_predictions(predictions)
    127 

~/src/maskrcnn-benchmark_fb/demo/predictor.py in compute_prediction(self, original_image)
    156         # compute predictions
    157         with torch.no_grad():
--> 158             predictions = self.model(image_list)
    159         predictions = [o.to(self.cpu_device) for o in predictions]
    160 

~/.local/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    537             result = self._slow_forward(*input, **kwargs)
    538         else:
--> 539             result = self.forward(*input, **kwargs)
    540         for hook in self._forward_hooks.values():
    541             hook_result = hook(self, input, result)

~/src/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py in forward(self, images, targets)
     53         proposals, proposal_losses = self.rpn(images, features, targets)
     54         if self.roi_heads:
---> 55             x, result, detector_losses = self.roi_heads(features, proposals, targets)
     56         else:
     57             # RPN-only models don't have roi_heads

~/.local/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    537             result = self._slow_forward(*input, **kwargs)
    538         else:
--> 539             result = self.forward(*input, **kwargs)
    540         for hook in self._forward_hooks.values():
    541             hook_result = hook(self, input, result)

~/src/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py in forward(self, features, proposals, targets)
     27         losses = {}
     28         # TODO rename x to roi_box_features, if it doesn't increase memory consumption
---> 29         x, detections, loss_box = self.box(features, proposals, targets)
     30         losses.update(loss_box)
     31         if self.cfg.MODEL.MASK_ON:

~/.local/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    537             result = self._slow_forward(*input, **kwargs)
    538         else:
--> 539             result = self.forward(*input, **kwargs)
    540         for hook in self._forward_hooks.values():
    541             hook_result = hook(self, input, result)

~/src/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py in forward(self, features, proposals, targets)
     53 
     54         if not self.training:
---> 55             result = self.post_processor((class_logits, box_regression), proposals)
     56             return x, result, {}
     57 

~/.local/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    537             result = self._slow_forward(*input, **kwargs)
    538         else:
--> 539             result = self.forward(*input, **kwargs)
    540         for hook in self._forward_hooks.values():
    541             hook_result = hook(self, input, result)

~/src/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py in forward(self, x, boxes)
    122             boxlist = boxlist.clip_to_image(remove_empty=False)
    123             if not self.bbox_aug_enabled:  # If bbox aug is enabled, we will do it later
--> 124                 boxlist = self.filter_results(boxlist, num_classes)
    125             results.append(boxlist)
    126         return results

~/src/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py in filter_results(self, boxlist, num_classes)
    180         else:
    181             keep = self.detections_to_keep(scores)
--> 182         result = result[keep]
    183         return result
    184 

~/src/maskrcnn-benchmark/maskrcnn_benchmark/structures/bounding_box.py in __getitem__(self, item)
    218 
    219     def __getitem__(self, item):
--> 220         bbox = BoxList(self.bbox[item], self.size, self.mode)
    221         for k, v in self.extra_fields.items():
    222             bbox.add_field(k, v[item])

~/src/maskrcnn-benchmark/maskrcnn_benchmark/structures/bounding_box.py in __init__(self, bbox, image_size, mode)
     24         if bbox.ndimension() != 2:
     25             raise ValueError(
---> 26                 "bbox should have 2 dimensions, got {}".format(bbox.ndimension())
     27             )
     28         if bbox.size(-1) != 4:

ValueError: bbox should have 2 dimensions, got 3
facebookresearch / maskrcnn-benchmark

ValueError when running "Mask_R-CNN_demo.ipynb" #1003

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