{
    "imports": [
        "$import glob",
        "$import os"
    ],
    "cudnn_opt": "$setattr(torch.backends.cudnn, 'benchmark', True)",
    "dataset_dir": "/workspace/data/Task09_Spleen",
    "ckpt_path": "/workspace/data/tutorials/modules/bundles/spleen_segmentation/models/model.pt",
    "download_ckpt": "$monai.apps.utils.download_url('https://huggingface.co/MONAI/example_spleen_segmentation/resolve/main/model.pt', @ckpt_path)",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
    "network_def": {
        "_target_": "UNet",
        "spatial_dims": 3,
        "in_channels": 1,
        "out_channels": 2,
        "channels": [
            16,
            32,
            64,
            128,
            256
        ],
        "strides": [
            2,
            2,
            2,
            2
        ],
        "num_res_units": 2,
        "norm": "batch"
    },
    "network": "$@network_def.to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "LoadImaged",
                "keys": "image"
            },
            {
                "_target_": "EnsureChannelFirstd",
                "keys": "image"
            },
            {
                "_target_": "Orientationd",
                "keys": "image",
                "axcodes": "RAS"
            },
            {
                "_target_": "Spacingd",
                "keys": "image",
                "pixdim": [1.5, 1.5, 2.0],
                "mode": "bilinear"
            },
            {
                "_target_": "ScaleIntensityRanged",
                "keys": "image",
                "a_min": -57,
                "a_max": 164,
                "b_min": 0,
                "b_max": 1,
                "clip": true
            },
            {
                "_target_": "EnsureTyped",
                "keys": "image"
            }
        ]
    },
    "dataset": {
        "_target_": "Dataset",
        "data": "$[{'image': i} for i in @datalist]",
        "transform": "@preprocessing"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 4
    },
    "inferer": {
        "_target_": "SlidingWindowInferer",
        "roi_size": [
            96,
            96,
            96
        ],
        "sw_batch_size": 4,
        "overlap": 0.5
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "softmax": true
            },
            {
                "_target_": "Invertd",
                "keys": "pred",
                "transform": "@preprocessing",
                "orig_keys": "image",
                "meta_key_postfix": "meta_dict",
                "nearest_interp": false,
                "to_tensor": true
            },
            {
                "_target_": "AsDiscreted",
                "keys": "pred",
                "argmax": true
            },
            {
                "_target_": "SaveImaged",
                "keys": "pred",
                "meta_keys": "pred_meta_dict",
                "output_dir": "eval"
            }
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointLoader",
            "_requires_": "@download_ckpt",
            "_disabled_": "$not os.path.exists(@ckpt_path)",
            "load_path": "@ckpt_path",
            "load_dict": {"model": "@network"}
        },
        {
            "_target_": "StatsHandler",
            "iteration_log": false
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "_requires_": "@cudnn_opt",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "val_handlers": "@handlers",
        "amp": false
    }
}