I need some help about Qualitative Results and validation

[seokyeongbaek]

hello.

I proceeded from QCNet train to validation for path prediction.

Could you please provide the ipynb file and code for visual testing such as Qualitative Results?

And, I am trying to validate a model already trained with num_future_steps =60, num_historical_steps=50.
Can you please suggest the code to get minADE, minFDE for num_future_steps 0 to 10 or 0 to 20 in validation?

[ZikangZhou]

The qualitative results are generated with matplotlib. You can, for example, write the prediction results to a pickle file via test.py, and then plot the waypoints stored in the pickle file using matplotlib.

[ZikangZhou]

To obtain the metrics for shorter horizons, you can modify the input sent to the metric calculator. For example during updating min ADE, you can modify the code to be:

self.minADE.update(pred=traj_eval[:, :, :20, :self.output_dim], target=gt_eval[:, :20, :self.output_dim], prob=pi_eval, valid_mask=valid_mask_eval[:, :20])

to obtain minADE from 0 to 20.

[seokyeongbaek]

Thank you for your response. I'll proceed as you suggested with the feedback!

[Mastersrange]

Hi @seokyeongbaek
Could you please share the code file if you have got the quantitative results(visualization).