configs/inference.json

{
    "imports": [
        "$import os",
        "$import glob",
        "$import torch",
        "$import scripts"
    ],
    "bundle_root": ".",
    "ckpt_path": "$@bundle_root + '/models/model.pt'",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "dataset_dir": "/workspace/data",
    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/*.nii*')))",
    "output_dir": "./output",
    "network_def": {
        "_target_": "scripts.valve_landmarks.PointRegressor",
        "in_shape": [
            1,
            256,
            256
        ],
        "out_shape": [
            2,
            10
        ],
        "channels": [
            8,
            16,
            32,
            64,
            128
        ],
        "strides": [
            2,
            2,
            2,
            2,
            2
        ]
    },
    "network": "$@network_def.to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "LoadImage",
                "image_only": "true"
            },
            {
                "_target_": "EnsureType",
                "device": "@device"
            },
            {
                "_target_": "Transpose",
                "indices": [
                    2,
                    0,
                    1,
                    3
                ]
            },
            {
                "_target_": "ScaleIntensity"
            }
        ]
    },
    "dataset": {
        "_target_": "Dataset",
        "data": "@datalist",
        "transform": "@preprocessing"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 0
    },
    "inferer": {
        "_target_": "scripts.valve_landmarks.LandmarkInferer",
        "spatial_dim": 2,
        "stack_dim": 1
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "SqueezeDimd",
                "keys": "pred",
                "dim": 0
            },
            {
                "_target_": "scripts.valve_landmarks.NpySaverd",
                "keys": "pred",
                "output_dir": "@output_dir",
                "data_root_dir": "@dataset_dir"
            }
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointLoader",
            "_disabled_": "$not os.path.exists(@ckpt_path)",
            "load_path": "@ckpt_path",
            "load_dict": {
                "net": "@network"
            }
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "val_handlers": "@handlers",
        "decollate": false,
        "prepare_batch": "$lambda batch, dev,nb: (batch.to(dev),())"
    },
    "evaluating": [
        "$@evaluator.run()"
    ]
}