Hi @Nikronic, I use the same github as here: (it is from this dataloader script (with minimal changes): https://github.com/jeffreyhuang1/two-stream-action-recognition/blob/master/dataloader/motion_dataloader.py 10
So basically as below. It takes clips from a video) and gets two flow images for each frame, it defines 10 channels for each so it amounts to 20 channels per frame with stackopf. I think you might get a better idea with the motion_dataloader script on github.

def getitem(self, idx):
#print (‘mode:’,self.mode,‘calling Dataset:getitem @ idx=%d’%idx)
    if self.mode == 'train':
        self.video, nb_clips = self.keys[idx].split('-')
        self.clips_idx = random.randint(1,int(nb_clips))
    elif self.mode == 'val':
        self.video,self.clips_idx = self.keys[idx].split('-')
    else:
        raise ValueError('There are only train and val mode')

    label = self.values[idx]
    label = int(label)-1 
    data = self.stackopf()

    if self.mode == 'train':
        sample = (data,label)
    elif self.mode == 'val':
        sample = (self.video,data,label)
    else:
        raise ValueError('There are only train and val mode')
    return sample