Module model structure

[bw4sz]

The goal of this PR is make a Model() class that is independent of the current retinanet structure. This would allow us, and users, to swap in and out the models based on developments in computer vision. As long as they pass a check_input() and check_output() function to match with upstream and downstream workflows. Here is a roadmap

• Create a model.py in deepforest/ and move retinanet.py to a new models/ directory.
• Update tests to reflect the change in name
• Write validation tests that run on model.Model.init() to ensure that the model matches expected shapes and formats.
• Test new workflow by adding a new model, probably RCNN from torchvision
• Write a doc on model creation
• Confirm will download_release work if config and download architecture are different, does it correctly overwrite it?

One of the outstanding issues here is how to handle the parameters specific to each model. My initial idea is to have them as nested parts of the .yml

# Model Architecture
architecture: 'retinanet'
num_classes: 1

# Architecture specific params
retinanet:
    # Non-max supression of overlapping predictions
    nms_thresh: 0.05
    score_thresh: 0.1

which means that when we create_model(), we pass the config. The create_model specific to each model in models/ then looks for the appropriate model params

        model = RetinaNet(backbone=backbone, num_classes=self.config["num_classes"])
        model.nms_thresh = self.config["retinanet"]["nms_thresh"]
        model.score_thresh =  self.config["retinanet"]["score_thresh"]

Pros is that its clean and scalable. Cons its not that readable and explicit, since you can pass the config without knowing whether all the arguments are there.

This relates to a more global problem of being able to specify and pass kwargs through the main module. It would be nice at runtime to be able to either supply a named arg

retinanet.retinanet(nms_thresh=0.1)

or a config file

retinanet.retinanet(config=config)

or both, and have the named args supercede. I will leave this idea until we have achieved the core goals of this PR.

Issue was briefly tagged as #396

[bw4sz]

There are a couple features specific to retinanet that we need to build into a more general purpose model idea. The limiting by score threshold and NMS filtering might need to happen outside of the model predict function. They are useful tools, but shouldn't be coupled to the predictions.

[bw4sz]

@ethanwhite I believe this is ready for your review.

[bw4sz]

sigh, i corrupted this pull request somehow. Will need to be remade and reopened.