Traceback (most recent call last):

 "faker/tests/__init__.py", line 213, in test_documentor
print_doc(output=output)

 "faker/cli.py", line 89, in print_doc
formatters = doc.get_formatters(with_args=True, with_defaults=True)

 "faker/documentor.py", line 32, in get_formatters
(provider, self.get_provider_formatters(provider, **kwargs))

 "faker/documentor.py", line 87, in get_provider_formatters
example = self.generator.format(name)

 "faker/generator.py", line 62, in format
return self.get_formatter(formatter)(*args, **kwargs)

 "faker/providers/date_time/__init__.py", line 439, in date_time_this_month
next_month_start = datetime(now.year, now.month + 1 % 12, 1)ValueError: month must be in 1..12
Traceback (most recent call last):


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap

fn(i, *args)


 "/home/ubuntu/self-taught-rcnn/detectron2/engine/launch.py", line 84, in _distributed_worker

main_func(*args)


 "/home/ubuntu/self-taught-rcnn/tools/train_net.py", line 150, in main

return trainer.train()


 "/home/ubuntu/self-taught-rcnn/detectron2/engine/defaults.py", line 380, in train

super().train(self.start_iter, self.max_iter)


 "/home/ubuntu/self-taught-rcnn/detectron2/engine/train_loop.py", line 132, in train

self.run_step()


 "/home/ubuntu/self-taught-rcnn/detectron2/engine/train_loop.py", line 215, in run_step

loss_dict = self.model(data)


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 447, in forward

output = self.module(*inputs[0], **kwargs[0])


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/ubuntu/self-taught-rcnn/detectron2/modeling/meta_arch/rcnn.py", line 130, in forward

_, detector_losses = self.roi_heads(images, features, proposals, gt_instances)


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/ubuntu/self-taught-rcnn/detectron2/modeling/roi_heads/roi_heads.py", line 582, in forward

losses = self._forward_box(features, proposals)


 "/home/ubuntu/self-taught-rcnn/detectron2/modeling/roi_heads/roi_heads.py", line 643, in _forward_box

box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/ubuntu/self-taught-rcnn/detectron2/modeling/poolers.py", line 233, in forward

output[inds] = pooler(x_level, pooler_fmt_boxes_level)


 "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/ubuntu/self-taught-rcnn/detectron2/layers/roi_align.py", line 95, in forward

input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned


 "/home/ubuntu/self-taught-rcnn/detectron2/layers/roi_align.py", line 20, in forward

input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned
RuntimeError: CUDA error: invalid device function (ROIAlign_forward_cuda at /home/ubuntu/self-taught-rcnn/detectron2/layers/csrc/ROIAlign/ROIAlig
