Missing hand poses and hand poses with start_f & end_f out of range in test_challenge.csv

[Necolizer]

Hi! Thanks for your great work.

There are 21900 samples in test_challenge.csv provided in fine-grained-annotations. When I preprocessed them and convert 30 fps to 60 fps, I found the number of the valid samples is not 21900, but 20882.

I have figured out why those 21900-20882=1018 samples are invalid:

1. In function gather_split_annotations() in this script, 286 samples are ignored since they can not be found in jsons_list_poses. I printed the names of those missing samples. They should have been read from a json file named 'nusar-2021_action_both_9025-c12a_9025_user_id_2021-02-18_111731.json'. But the Google Drive doesn't provide this json file.
2. To convert 30 fps to 60 fps, I wrote the following lines in preprocessing script:

curr_data['start_frame'] = aData.start_frame * 2 + 1        # 30 fps -> 60 fps
curr_data['end_frame'] = aData.end_frame * 2 + 1            # 30 fps -> 60 fps

In main() function of the script, the start_f & end_f are calculated using the following lines:

start_f = max(0, segment['start_frame'] - contex_val) # Adjust start_frame with context
end_f = min(segment['end_frame'] + contex_val + 1, len(hand_labels)) # Adjust end_frame with context

where hand_labels are also 60 fps.
Most samples are handled correctly, but surprisingly, 732 samples get start_f & end_f which are out of range. segment['start_frame'] - contex_val and segment['end_frame'] + contex_val + 1 are both bigger than len(hand_labels), resulting in a strange situation where start_f>end_f=len(hand_labels).

epic_joints_seg = [] # Stores all handposes frame by frame for current segment
start_f = max(0, segment['start_frame'] - contex_val) # Adjust start_frame with context
end_f = min(segment['end_frame'] + contex_val + 1, len(hand_labels)) # Adjust end_frame with context
for img_index in range(start_f, end_f, 1):     # This loop is ignored because start_f>end_f=len(hand_labels)
    ......
    epic_joints_seg.append(landmarks3d)        # So the epic_joints_seg list will be empty

In this case, epic_joints_seg will be empty, so these 732 samples are also invalid.

These 1018 invlid test samples (5% of the test set) has no pose data. They will definitely fail to predict. It may cause lower accuracy reports in your CodaLab Challenge Page, if all 21900 samples in test_challenge.csv are computed. I don't know if this is okay.
Thanks for your help in advance.

Best,
Necca