ImageFolder work-a-like for regression tasks

🚀 Feature

The requested/proposed feature is a close analog of torchvision.datasets.ImageFolder for regression tasks. The target values could be in a dict or an external JSON file.

Motivation

Thanks to torchvision.models and torchvision.datasets, the workflow for using transfer learning from alexnet, resnet, etc. to custom image classification tasks is very streamlined and polished. Datasets can be handled directly in the file system, which makes it easy to use a number of labeling tools that don't necessarily understand PyTorch, but are able to place images in folders like data/train/class1, data/val/class1, etc.

It's not as easy to do this for regression tasks, despite these being useful variations (e.g. for NSFW scoring rather than binary classification) of popular transfer learning tasks.

Pitch

Ideally, there would be a drop-in replacement for ImageFolder accepting the same arguments (such as transform callables) and an additional target_scores variable accepting a JSON filename or a dict. The method __getitem__ would return a (sample, score) pair.

To facilitate switching from classification to regression tasks, this RegressionImageFolder would ignore the directory structure that's used for target classes in ImageFolder.

Alternatives

It's possible that there's no rationale for having ImageFolder and RegressionImageFolder as separate dataset loaders and the functionality can be folded into what already exists.

cc @pmeier

Start with torchvision.datasets.ImageFolder and its getitem behavior. Compare the proposed target_scores dict or JSON input with the requirement to ignore class-directory structure. Done means an agreed regression dataset API, a documented target format, and coverage for returning sample-score pairs.