training problem

[l772308126]

Sorry to bother you again. It's really a nice work! I have found two problem when I was training the main.py.

1. 
   I think this code is wrong，because the unpad_input function has five outputs。
   
   So，it will report an error when I run the training code of main.py. I guess it should be revised to 'q, indices_q, cu_seqlens_q, max_len_q, _ = unpad_input(q, mask_q)' and 'k, indices_kv, cu_seqlens_kv, max_len_kv, _ = unpad_input(k, mask_kv)'. Is that correct?

2. I found that the tokenized sequences obtained by mesh through tokenizer always have vertices 3 and 4. I counted the number of vertices 3 and 4. It even exceeds the sum of the occurrences of the other top 10 vertices. As a result, the final predicted results are all 3 and 4. Have you ever encountered such a problem during training? How to solve it?
   

[l772308126]

I use the g-objaverse dataset and randomly select some glb files containing faces less than 4000.