{
    "imports": [
        "$import glob",
        "$import os",
        "$import ignite"
    ],
    "bundle_root": "/workspace/data/tutorials/modules/bundle/spleen_segmentation",
    "ckpt_dir": "$@bundle_root + '/models'",
    "output_dir": "$@bundle_root + '/eval'",
    "dataset_dir": "/workspace/data/Task09_Spleen",
    "images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
    "labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "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": "$@network_def.to(@device)",
    "loss": {
        "_target_": "DiceCELoss",
        "to_onehot_y": true,
        "softmax": true,
        "squared_pred": true,
        "batch": true
    },
    "optimizer": {
        "_target_": "torch.optim.Adam",
        "params": "$@network.parameters()",
        "lr": 0.0001
    },
    "train": {
        "deterministic_transforms": [
            {
                "_target_": "LoadImaged",
                "keys": [
                    "image",
                    "label"
                ]
            },
            {
                "_target_": "EnsureChannelFirstd",
                "keys": [
                    "image",
                    "label"
                ]
            },
            {
                "_target_": "Orientationd",
                "keys": [
                    "image",
                    "label"
                ],
                "axcodes": "RAS"
            },
            {
                "_target_": "Spacingd",
                "keys": [
                    "image",
                    "label"
                ],
                "pixdim": [
                    1.5,
                    1.5,
                    2.0
                ],
                "mode": [
                    "bilinear",
                    "nearest"
                ]
            },
            {
                "_target_": "ScaleIntensityRanged",
                "keys": "image",
                "a_min": -57,
                "a_max": 164,
                "b_min": 0,
                "b_max": 1,
                "clip": true
            },
            {
                "_target_": "EnsureTyped",
                "keys": [
                    "image",
                    "label"
                ]
            }
        ],
        "random_transforms": [
            {
                "_target_": "RandCropByPosNegLabeld",
                "keys": [
                    "image",
                    "label"
                ],
                "label_key": "label",
                "spatial_size": [
                    96,
                    96,
                    96
                ],
                "pos": 1,
                "neg": 1,
                "num_samples": 4,
                "image_key": "image",
                "image_threshold": 0
            }
        ],
        "preprocessing": {
            "_target_": "Compose",
            "transforms": "$@train#deterministic_transforms + @train#random_transforms"
        },
        "dataset": {
            "_target_": "CacheDataset",
            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[:-9], @labels[:-9])]",
            "transform": "@train#preprocessing",
            "cache_rate": 1.0,
            "num_workers": 4
        },
        "dataloader": {
            "_target_": "DataLoader",
            "dataset": "@train#dataset",
            "batch_size": 2,
            "shuffle": true,
            "num_workers": 4
        },
        "inferer": {
            "_target_": "SimpleInferer"
        },
        "postprocessing": {
            "_target_": "Compose",
            "transforms": [
                {
                    "_target_": "Activationsd",
                    "keys": "pred",
                    "softmax": true
                },
                {
                    "_target_": "AsDiscreted",
                    "keys": [
                        "pred",
                        "label"
                    ],
                    "argmax": [
                        true,
                        false
                    ],
                    "to_onehot": 2
                }
            ]
        },
        "handlers": [
            {
                "_target_": "ValidationHandler",
                "validator": "@validate#evaluator",
                "epoch_level": true,
                "interval": 5
            },
            {
                "_target_": "StatsHandler",
                "tag_name": "train_loss",
                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
            },
            {
                "_target_": "TensorBoardStatsHandler",
                "log_dir": "@output_dir",
                "tag_name": "train_loss",
                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
            }
        ],
        "key_metric": {
            "train_accuracy": {
                "_target_": "ignite.metrics.Accuracy",
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "trainer": {
            "_target_": "SupervisedTrainer",
            "max_epochs": 100,
            "device": "@device",
            "train_data_loader": "@train#dataloader",
            "network": "@network",
            "loss_function": "@loss",
            "optimizer": "@optimizer",
            "inferer": "@train#inferer",
            "postprocessing": "@train#postprocessing",
            "key_train_metric": "@train#key_metric",
            "train_handlers": "@train#handlers",
            "amp": true
        }
    },
    "validate": {
        "preprocessing": {
            "_target_": "Compose",
            "transforms": "%train#deterministic_transforms"
        },
        "dataset": {
            "_target_": "CacheDataset",
            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[-9:], @labels[-9:])]",
            "transform": "@validate#preprocessing",
            "cache_rate": 1.0
        },
        "dataloader": {
            "_target_": "DataLoader",
            "dataset": "@validate#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": "%train#postprocessing",
        "handlers": [
            {
                "_target_": "StatsHandler",
                "iteration_log": false
            },
            {
                "_target_": "TensorBoardStatsHandler",
                "log_dir": "@output_dir",
                "iteration_log": false
            },
            {
                "_target_": "CheckpointSaver",
                "save_dir": "@ckpt_dir",
                "save_dict": {
                    "model": "@network"
                },
                "save_key_metric": true,
                "key_metric_filename": "model.pt"
            }
        ],
        "key_metric": {
            "val_mean_dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "additional_metrics": {
            "val_accuracy": {
                "_target_": "ignite.metrics.Accuracy",
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "evaluator": {
            "_target_": "SupervisedEvaluator",
            "device": "@device",
            "val_data_loader": "@validate#dataloader",
            "network": "@network",
            "inferer": "@validate#inferer",
            "postprocessing": "@validate#postprocessing",
            "key_val_metric": "@validate#key_metric",
            "additional_metrics": "@validate#additional_metrics",
            "val_handlers": "@validate#handlers",
            "amp": true
        }
    },
    "training": [
        "$monai.utils.set_determinism(seed=123)",
        "$setattr(torch.backends.cudnn, 'benchmark', True)",
        "$@train#trainer.run()"
    ]
}