Consistent argument names for video datasets and VideoClips class

First of all, the new support for Video data is amazing. Really useful. 😃

Just when I was going through the code of the video datasets and utilities, I found this a bit confusing.

Sample Dataset:
https://github.com/pytorch/vision/blob/8635be94d1216f10fb8302da89233bd86445e449/torchvision/datasets/ucf101.py#L27-L36

VideoClips class:
https://github.com/pytorch/vision/blob/8635be94d1216f10fb8302da89233bd86445e449/torchvision/datasets/video_utils.py#L43-L49

From what I understand, the argument frames_per_clip from the dataset is the same as clip_length_in_frames from the VideoClips class.

Similarly, step_between_clips <-> frames_between_clips.

Is there a reason for this difference. Or did I understand it wrong?

Would it be better if both, the video datasets and the VideoClips class had consistent arguments?

In my opinion, frames_per_clip looks better than clip_length_in_frames.
And frames_between_clips can be favored over step_between_clips since the former feels more intuitive.

Any suggestions? @fmassa

Start with the linked argument definitions in torchvision/datasets/ucf101.py and torchvision/datasets/video_utils.py, then compare how the video datasets and VideoClips class name the same concepts. Decide on consistent names for clip length and spacing, and update the affected interfaces so the terminology matches across the video dataset utilities.