configs/evaluate.json

{
    "validate#postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "softmax": true
            },
            {
                "_target_": "Invertd",
                "keys": [
                    "pred",
                    "label"
                ],
                "transform": "@validate#preprocessing",
                "orig_keys": "image",
                "meta_key_postfix": "meta_dict",
                "nearest_interp": [
                    false,
                    true
                ],
                "to_tensor": true
            },
            {
                "_target_": "AsDiscreted",
                "keys": [
                    "pred",
                    "label"
                ],
                "argmax": [
                    true,
                    false
                ],
                "to_onehot": 14
            },
            {
                "_target_": "SaveImaged",
                "keys": "pred",
                "meta_keys": "pred_meta_dict",
                "output_dir": "@output_dir",
                "resample": false,
                "squeeze_end_dims": true
            }
        ]
    },
    "validate#handlers": [
        {
            "_target_": "CheckpointLoader",
            "load_path": "$@ckpt_dir + '/model.pt'",
            "load_dict": {
                "model": "@network"
            }
        },
        {
            "_target_": "StatsHandler",
            "iteration_log": false
        },
        {
            "_target_": "MetricsSaver",
            "save_dir": "@output_dir",
            "metrics": [
                "val_mean_dice",
                "val_acc"
            ],
            "metric_details": [
                "val_mean_dice"
            ],
            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
            "summary_ops": "*"
        }
    ],
    "run": [
        "$@validate#evaluator.run()"
    ]
}