src/transformers/models/upernet/convert_swin_upernet_to_pytorch.py/get_upernet_config
def get_upernet_config(model_name):
    auxiliary_in_channels = 384
    window_size = 7
    if "tiny" in model_name:
        embed_dim = 96
        depths = (2, 2, 6, 2)
        num_heads = (3, 6, 12, 24)
    elif "small" in model_name:
        embed_dim = 96
        depths = (2, 2, 18, 2)
        num_heads = (3, 6, 12, 24)
    elif "base" in model_name:
        embed_dim = 128
        depths = (2, 2, 18, 2)
        num_heads = (4, 8, 16, 32)
        window_size = 12
        auxiliary_in_channels = 512
    elif "large" in model_name:
        embed_dim = 192
        depths = (2, 2, 18, 2)
        num_heads = (6, 12, 24, 48)
        window_size = 12
        auxiliary_in_channels = 768

    # set label information
    num_labels = 150
    repo_id = "huggingface/label-files"
    filename = "ade20k-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    label2id = {v: k for k, v in id2label.items()}

    backbone_config = SwinConfig(
        embed_dim=embed_dim,
        depths=depths,
        num_heads=num_heads,
        window_size=window_size,
        out_features=["stage1", "stage2", "stage3", "stage4"],
    )
    config = UperNetConfig(
        backbone_config=backbone_config,
        auxiliary_in_channels=auxiliary_in_channels,
        num_labels=num_labels,
        id2label=id2label,
        label2id=label2id,
    )

    return config