wqi / wimp Goto Github PK

Hi, first of all, thanks for your awesome work and sharing that to us.

I tried to make inference/visualization code by myself, unfortunately, there were some problems.

Maybe library's mismatching, my insufficient coding skills, or something else.

So, can i get your inference/visualization code or even skeleton base code?

Hi, thanks for sharing your great work~
I am wondering how you deal with the incomplete trajectories problem (agents have less then 2 seconds of history).

1. I notice that for the neighboring agent wrt focal agent, you discard all the agents (code) if their trajectories are not complete
2. how would you deal with those incomplete trajectories for the focal agent? Did you use interpolation or some techniques?

Thanks!

Hey there,

I want to reproduce the results of your ablation study, where you only used Social-Context with EWTA-Loss.

However, I habe problems training the model only with social context.
What are the correct flags I need to set for preprocessing (run_preprocess.py) and for training (main.py)?

Looking forward hearing from you soon!

Best regards

SchDevel

I had this error after following the exact steps in README to train the model.
Did anyone ever have this issue?

Hi, thank you so much for your fantastic work.

Which is the order, and the dimensions, in this function?

def l1_ewta_loss(prediction, target, k=6, eps=1e-7, mr=2.0):
    num_mixtures = prediction.shape[1]

    target = target.unsqueeze(1).expand(-1, num_mixtures, -1, -1)
    l1_loss = nn.functional.l1_loss(prediction, target, reduction='none').sum(dim=[2, 3])

    # Get loss from top-k mixtures for each timestep
    mixture_loss_sorted, mixture_ranks = torch.sort(l1_loss, descending=False)
    mixture_loss_topk = mixture_loss_sorted.narrow(1, 0, k)

    # Aggregate loss across timesteps and batch
    loss = mixture_loss_topk.sum()
    loss = loss / target.size(0)
    loss = loss / target.size(2)
    loss = loss / k
    return loss

I am not able to obtain good results compared to NLL. I have as inputs:

predictions: batch_size x num_modes x pred_len x data_dim (e.g. 1024 x 6 x 30 x 2)
gt: batch_size x pred_len x data_dim (e.g. 1024 x 30 x 2)

Is this correct?

Hello!
First of all, thank you for making your code available for the readers of your great paper.
I am having an issue while running run_preprocess.py. I think while reading the csv something goes wrong since my error is a pandas error. When I try to run the script, it gives me:
KeyError: 'CITY_NAME'
When I go to the script and give "MIA" as the CITY_NAME, just to see what happens, I receive a similar error:
KeyError: 'OBJECT_TYPE'
I checked the paths for the data. It seems fine. What could be the reason?
Thank you!