Save and load parameters of a pre-processing or augmentation (transforms) pipeline

Description

After training a model, it is useful to be able to save the pre-processing steps used in training. Later, one can load the transforms along with the model at inference or production. At the moment, the composed transforms lack a method that can save the transforms. This feature can help with making monai-based deployments more reliable.

Proposed solution

I would suggest a similar solution to the one devised by albumentations (see https://albumentations.ai/docs/examples/serialization/). For example:

train_transforms = Compose([ScaleIntensity(), EnsureChannelFirst(), Resize((96, 96, 96)), RandRotate90()])
monai.transforms.save(transform, '/tmp/transform.json')
loaded_transform = monai.transforms.load('/tmp/transform.json')

The json would look like this (example from albumentations):

{
  "__version__": "1.3.1",
  "transform": {
    "__class_fullname__": "Compose",
    "p": 1,
    "transforms": [
      {
        "__class_fullname__": "albumentations.transforms.RandomCrop",
        "always_apply": true,
        "p": 1,
        "height": 768,
        "width": 768
      },
      {
        "__class_fullname__": "albumentations.transforms.RGBShift",
        "always_apply": false,
        "p": 0.5,
        "hue_shift_limit": (-20, 20),
        "sat_shift_limit": (-30, 30),
        "val_shift_limit": (-20, 20)
      }
    ],
    "bbox_params": null,
    "keypoint_params": null,
    "additional_targets": {},
    "is_check_shapes": true
  }
}

Alternatives considered

I could pickle the transforms. I think that is a bad solution as it relies on the specifically installed package versions, and is not readable. Therefore, it is not portable.

Additional context

None

The issue names composed transforms and the Compose pipeline but no repository files or tests. Start by locating Compose and the transform serialization entry points, then compare the proposed behavior with the linked Albumentations serialization example. Done means a preprocessing or augmentation pipeline can be saved and loaded in a readable, portable form.