main_umap.py

# Copyright 2023 solo-learn development team.

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# this software and associated documentation files (the "Software"), to deal in
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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import json
import os
from pathlib import Path

from omegaconf import OmegaConf

from solo.args.umap import parse_args_umap
from solo.data.classification_dataloader import prepare_data
from solo.methods import METHODS
from solo.utils.auto_umap import OfflineUMAP


def main():
    args = parse_args_umap()

    # build paths
    ckpt_dir = Path(args.pretrained_checkpoint_dir)
    args_path = ckpt_dir / "args.json"
    ckpt_path = [ckpt_dir / ckpt for ckpt in os.listdir(ckpt_dir) if ckpt.endswith(".ckpt")][0]

    # load arguments
    with open(args_path) as f:
        method_args = json.load(f)
    cfg = OmegaConf.create(method_args)

    # build the model
    model = (
        METHODS[method_args["method"]]
        .load_from_checkpoint(ckpt_path, strict=False, cfg=cfg)
        .backbone
    )
    # prepare data
    train_loader, val_loader = prepare_data(
        args.dataset,
        train_data_path=args.train_data_path,
        val_data_path=args.val_data_path,
        data_format=args.data_format,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        auto_augment=False,
    )

    umap = OfflineUMAP()

    # move model to the gpu
    device = "cuda:0"
    model = model.to(device)

    umap.plot(device, model, train_loader, "im100_train_umap.pdf")
    umap.plot(device, model, val_loader, "im100_val_umap.pdf")


if __name__ == "__main__":
    main()