{
    "displayable_configs": {
        "highres": true,
        "sw_overlap": 0.25,
        "sw_batch_size": 1
    },
    "imports": [
        "$import glob",
        "$import os"
    ],
    "bundle_root": ".",
    "output_dir": "$@bundle_root + '/eval'",
    "dataset_dir": "sampledata",
    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "pixdim": "$[1.5, 1.5, 1.5] if @displayable_configs#highres else [3.0, 3.0, 3.0]",
    "modelname": "$'model.pt' if @displayable_configs#highres else 'model_lowres.pt'",
    "network_def": {
        "_target_": "SegResNet",
        "spatial_dims": 3,
        "in_channels": 1,
        "out_channels": 105,
        "init_filters": 32,
        "blocks_down": [
            1,
            2,
            2,
            4
        ],
        "blocks_up": [
            1,
            1,
            1
        ],
        "dropout_prob": 0.2
    },
    "network": "$@network_def.to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "LoadImaged",
                "keys": "image"
            },
            {
                "_target_": "EnsureTyped",
                "keys": "image"
            },
            {
                "_target_": "EnsureChannelFirstd",
                "keys": "image"
            },
            {
                "_target_": "Orientationd",
                "keys": "image",
                "axcodes": "RAS"
            },
            {
                "_target_": "Spacingd",
                "keys": "image",
                "pixdim": "@pixdim",
                "mode": "bilinear"
            },
            {
                "_target_": "NormalizeIntensityd",
                "keys": "image",
                "nonzero": true
            },
            {
                "_target_": "ScaleIntensityd",
                "keys": "image",
                "minv": -1.0,
                "maxv": 1.0
            }
        ]
    },
    "dataset": {
        "_target_": "Dataset",
        "data": "$[{'image': i} for i in @datalist]",
        "transform": "@preprocessing"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 1
    },
    "inferer": {
        "_target_": "SlidingWindowInferer",
        "roi_size": [
            96,
            96,
            96
        ],
        "sw_batch_size": "@displayable_configs#sw_batch_size",
        "overlap": "@displayable_configs#sw_overlap",
        "padding_mode": "replicate",
        "mode": "gaussian",
        "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "softmax": true
            },
            {
                "_target_": "AsDiscreted",
                "keys": "pred",
                "argmax": true
            },
            {
                "_target_": "Invertd",
                "keys": "pred",
                "transform": "@preprocessing",
                "orig_keys": "image",
                "meta_key_postfix": "meta_dict",
                "nearest_interp": true,
                "to_tensor": true
            },
            {
                "_target_": "SaveImaged",
                "keys": "pred",
                "meta_keys": "pred_meta_dict",
                "output_dir": "@output_dir"
            }
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointLoader",
            "load_path": "$@bundle_root + '/models/' + @modelname",
            "load_dict": {
                "model": "@network"
            }
        },
        {
            "_target_": "StatsHandler",
            "iteration_log": false
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "val_handlers": "@handlers",
        "amp": true
    },
    "initialize": [
        "$setattr(torch.backends.cudnn, 'benchmark', True)"
    ],
    "run": [
        "$@evaluator.run()"
    ]
}