Traceback (most recent call last):


 "./tools/train.py", line 90, in <module>

main()


 "./tools/train.py", line 86, in main

logger=logger)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 57, in train_detector

_dist_train(model, dataset, cfg, validate=validate)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 96, in _dist_train

runner.run(data_loaders, cfg.workflow, cfg.total_epochs)


 "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/runner/runner.py", line 355, in run

epoch_runner(data_loaders[i], **kwargs)


 "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/runner/runner.py", line 261, in train

self.model, data_batch, train_mode=True, **kwargs)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 37, in batch_processor

losses = model(**data)


 "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__

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


 "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 50, in forward

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


 "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__

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


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/detectors/base.py", line 80, in forward

return self.forward_train(img, img_meta, **kwargs)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/detectors/cascade_rcnn.py", line 125, in forward_train

*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss

gt_bboxes_ignore=gt_bboxes_ignore)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/anchor_head.py", line 210, in loss

cfg=cfg)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/core/utils/misc.py", line 24, in multi_apply

return tuple(map(list, zip(*map_results)))


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/anchor_head.py", line 155, in loss_single

cls_score, labels, label_weights, avg_factor=num_total_samples)


 "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/core/loss/losses.py", line 30, in weighted_binary_cross_entropy

reduction='sum')[None] / avg_factor


 "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/functional.py", line 2075, in binary_cross_entropy_with_logits

raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([98304])) must be the same as input size (torch.Size([98304, 1]))

Traceback (most recent call last):


 "/usr/local/lib/python2.7/dist-packages/matplotlib-2.0.0+3486.g8c57dae-py2.7-linux-x86_64.egg/matplotlib/sphinxext/plot_directive.py", line 525, in run_code

six.exec_(code, ns)


 "/usr/local/lib/python2.7/dist-packages/six.py", line 699, in exec_

exec("""exec _code_ in _globs_, _locs_""")


 "<string>", line 1, in <module>


 "<string>", line 55, in <module>


 "/usr/local/lib/python2.7/dist-packages/matplotlib-2.0.0+3486.g8c57dae-py2.7-linux-x86_64.egg/matplotlib/__init__.py", line 916, in __setitem__

raise ValueError("Key %s: %s" % (key, str(ve)))
ValueError: Key contour.negative_linestyle: linestyle must be a string or an even-length sequence of floats.
