Helping for Evaluation Video Classification Model (with a IterableDataset)
I am training a Video Classification Model. In the Evaluation Phase, I segment a video into 5 parts, and each segment will be sampled for a clip. Below is the code for myval_datasetandval_loader:val_dataset = pytorchvideo.data.Ucf101(
    data_path=os.path.join(dataset_root_path, "val"),
    clip_sampler=pytorchvideo.data.make_clip_sampler(
        "constant_clips_per_video", clip_duration, 5),  # Note the positional arguments
    decode_audio=False,
    transform=val_transform,
    video_sampler=torch.utils.data.SequentialSampler,
)

valloader = DataLoader(val_dataset, batch_size=5, shuffle=False) # batch_size is 5 because I segment a video into 5, so each loop through valloader is ONLY FOR ONE VIDEO.So how do I configure the Trainer for correctly evaluating? I have looked intobatch_eval_metrics, but is it the right approach in this case?Because I want the accuracy of one video to be the average of 5 clips. Is there a better way to evaluate in my case?Note thatval_datasetis a class ofpytorchvideo.data, and it is anIterableDataset.Thanks for helping.