float division by zero Error in td.plot and td.summarize when all errors are 0 and maps are 0

[djaym7]

in validate(net, val_data, ctx, eval_metric)
37 if mean_ap[-1]>0.001:
38 td.summarize()
---> 39 td.plot()
40 return map_name,mean_ap

~/SageMaker/PICV/Segmentation Job2/tide_metric.py in plot(self)
20 return self.tide.summarize()
21 def plot(self):
---> 22 self.tide.plot()
23 def update(self, pred_bboxes, pred_labels, pred_scores,
24 gt_bboxes, gt_labels):

~/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/tidecv/quantify.py in plot(self, out_dir)
588 # Do the plotting now
589 for run_name, run in self.runs.items():
--> 590 self.plotter.make_summary_plot(out_dir, errors, run_name, run.mode, hbar_names=True)
591
592

~/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/tidecv/plotting.py in make_summary_plot(self, out_dir, errors, model_name, rec_type, hbar_names)
118 error_types = list(errors['main'][model_name].keys()) + list(errors['special'][model_name].keys())
119 error_sum = sum([e for e in errors['main'][model_name].values()])
--> 120 error_sizes = [e / error_sum for e in errors['main'][model_name].values()] + [0, 0]
121 fig, ax = plt.subplots(1, 1, figsize=(11, 11), dpi=high_dpi)
122 patches, outer_text, inner_text = ax.pie(error_sizes, colors=self.colors_main.values(), labels=error_types,

~/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/tidecv/plotting.py in (.0)
118 error_types = list(errors['main'][model_name].keys()) + list(errors['special'][model_name].keys())
119 error_sum = sum([e for e in errors['main'][model_name].values()])
--> 120 error_sizes = [e / error_sum for e in errors['main'][model_name].values()] + [0, 0]
121 fig, ax = plt.subplots(1, 1, figsize=(11, 11), dpi=high_dpi)
122 patches, outer_text, inner_text = ax.pie(error_sizes, colors=self.colors_main.values(), labels=error_types,

ZeroDivisionError: float division by zero

[PaulZhangIsing]

I found the same error, I suspect is my addtion data got problem. I replace the dummy example like this, but it still occurs
tide.evaluate(datasets.COCO(), datasets.COCO(), mode=TIDE.BOX)

[PaulZhangIsing]

Usually it is because the detection has no overlap with the groundtruth, therefore we have such error

[djaym7]

closed since I am no longer using this