{
    "imports": [
        "$import glob"
    ],
    "bundle_root": ".",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "ckpt_path": "$@bundle_root + '/models/model.pt'",
    "output_dir": "./outputs",
    "latent_size": 64,
    "num_samples": 10,
    "network_def": {
        "_target_": "Generator",
        "latent_shape": "@latent_size",
        "start_shape": [
            64,
            8,
            8
        ],
        "channels": [
            32,
            16,
            8,
            1
        ],
        "strides": [
            2,
            2,
            2,
            1
        ]
    },
    "network": "$@network_def.to(@device)",
    "dataset": {
        "_target_": "Dataset",
        "data": "$[torch.rand(@latent_size) for i in range(@num_samples)]"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 0
    },
    "inferer": {
        "_target_": "SimpleInferer"
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "sigmoid": true
            },
            {
                "_target_": "SaveImaged",
                "keys": "pred",
                "output_dir": "@output_dir",
                "output_ext": "png",
                "separate_folder": false,
                "scale": 255,
                "output_dtype": "$np.uint8",
                "meta_key_postfix": null
            }
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointLoader",
            "load_path": "@ckpt_path",
            "load_dict": {
                "model": "@network"
            }
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "prepare_batch": "$lambda batchdata, *_,**__: (batchdata.to(@device),None,(),{})",
        "val_handlers": "@handlers"
    },
    "inferring": [
        "$@evaluator.run()"
    ]
}