{
    "imports": [
        "$import glob",
        "$import os",
        "$import ignite"
    ],
    "bundle_root": ".",
    "ckpt_dir": "$@bundle_root + '/models'",
    "output_dir": "$@bundle_root + '/eval'",
    "dataset_dir": "$@bundle_root + '/data'",
    "images": "$[{'artery':a, 'vein':b, 'excret':c }for a,b,c in  zip(glob.glob(@dataset_dir + '/*/12.nii.gz'), glob.glob(@dataset_dir + '/*/22-.nii.gz'), glob.glob(@dataset_dir + '/*/32-.nii.gz'))]",
    "labels": "$list(glob.glob(@dataset_dir + '/*/merged.nii.gz'))",
    "val_interval": 50,
    "dont_finetune": true,
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "network_def": {
        "_target_": "SegResNet",
        "in_channels": 3,
        "out_channels": 6,
        "init_filters": 32,
        "upsample_mode": "deconv",
        "dropout_prob": 0.2,
        "norm_name": "group",
        "blocks_down": [
            1,
            2,
            2,
            4
        ],
        "blocks_up": [
            1,
            1,
            1
        ]
    },
    "network": "$@network_def.to(@device)",
    "loss": {
        "_target_": "DiceLoss",
        "include_background": false,
        "to_onehot_y": true,
        "sigmoid": true,
        "softmax": false,
        "squared_pred": false,
        "jaccard": false,
        "reduction": "mean",
        "smooth_nr": 0.0,
        "smooth_dr": 1e-05,
        "batch": false
    },
    "optimizer": {
        "_target_": "Novograd",
        "params": "$@network.parameters()",
        "lr": 0.001,
        "betas": [
            0.9,
            0.98
        ],
        "eps": 1e-08,
        "weight_decay": 0,
        "grad_averaging": false,
        "amsgrad": false
    },
    "train": {
        "deterministic_transforms": [
            {
                "_target_": "LoadImaged",
                "keys": [
                    "artery",
                    "vein",
                    "excret",
                    "label"
                ],
                "reader": null,
                "overwriting": false,
                "dtype": "float32",
                "as_closest_canonical": true
            },
            {
                "_target_": "EnsureChannelFirstd",
                "keys": [
                    "artery",
                    "vein",
                    "excret",
                    "label"
                ]
            },
            {
                "_target_": "MapLabelValued",
                "keys": "label",
                "orig_labels": [
                    0,
                    1,
                    2,
                    3,
                    4,
                    5,
                    6
                ],
                "target_labels": [
                    0,
                    1,
                    2,
                    3,
                    4,
                    4,
                    5
                ]
            },
            {
                "_target_": "ToTensord",
                "keys": [
                    "artery",
                    "vein",
                    "excret",
                    "label"
                ]
            },
            {
                "_target_": "Spacingd",
                "keys": [
                    "artery",
                    "vein",
                    "excret",
                    "label"
                ],
                "pixdim": [
                    0.8,
                    0.8,
                    0.8
                ],
                "mode": [
                    "bilinear",
                    "bilinear",
                    "bilinear",
                    "nearest"
                ]
            },
            {
                "_target_": "ScaleIntensityRanged",
                "keys": [
                    "artery",
                    "vein",
                    "excret"
                ],
                "a_min": -1000,
                "a_max": 1000,
                "b_min": 0.0,
                "b_max": 1.0,
                "clip": true
            },
            {
                "_target_": "scripts.my_transforms.ConcatImages",
                "keys_merge": [
                    "artery",
                    "vein",
                    "excret"
                ],
                "keys_out": "image"
            },
            {
                "_target_": "ToTensord",
                "keys": [
                    "image"
                ]
            }
        ],
        "random_transforms": [
            {
                "_target_": "RandZoomd",
                "keys": [
                    "image",
                    "label"
                ],
                "prob": 0.3
            },
            {
                "_target_": "RandAxisFlipd",
                "keys": [
                    "image",
                    "label"
                ],
                "prob": 0.3
            },
            {
                "_target_": "RandRotate90d",
                "keys": [
                    "image",
                    "label"
                ],
                "prob": 0.3
            },
            {
                "_target_": "RandAdjustContrastd",
                "keys": [
                    "image"
                ],
                "prob": 0.5
            },
            {
                "_target_": "RandHistogramShiftd",
                "keys": "image",
                "num_control_points": 10,
                "prob": 0.3
            },
            {
                "_target_": "DivisiblePadd",
                "keys": [
                    "image",
                    "label"
                ],
                "k": 32
            },
            {
                "_target_": "RandCropByLabelClassesd",
                "keys": [
                    "image",
                    "label"
                ],
                "label_key": "label",
                "num_classes": 6,
                "spatial_size": [
                    96,
                    96,
                    96
                ],
                "ratios": [
                    1,
                    2,
                    2,
                    3,
                    3,
                    1
                ],
                "num_samples": 4
            }
        ],
        "preprocessing": {
            "_target_": "Compose",
            "transforms": "$@train#deterministic_transforms + @train#random_transforms"
        },
        "dataset": {
            "_target_": "CacheDataset",
            "data": "$[{'label': l, **i} for i, l in zip(@images, @labels)]",
            "transform": "@train#preprocessing",
            "cache_rate": 1.0,
            "num_workers": 4
        },
        "dataloader": {
            "_target_": "DataLoader",
            "dataset": "@train#dataset",
            "batch_size": 1,
            "shuffle": true,
            "num_workers": 2
        },
        "inferer": {
            "_target_": "SimpleInferer"
        },
        "postprocessing": {
            "_target_": "Compose",
            "transforms": [
                {
                    "_target_": "Activationsd",
                    "keys": "pred",
                    "softmax": false,
                    "sigmoid": true
                },
                {
                    "_target_": "AsDiscreted",
                    "keys": [
                        "pred",
                        "label"
                    ],
                    "argmax": [
                        false,
                        false
                    ],
                    "to_onehot": [
                        null,
                        6
                    ],
                    "threshold": [
                        0.5,
                        null
                    ]
                },
                {
                    "_target_": "SplitChanneld",
                    "keys": [
                        "pred",
                        "label"
                    ],
                    "output_postfixes": [
                        "bck",
                        "ar",
                        "ve",
                        "ur",
                        "tu",
                        "ki"
                    ]
                }
            ]
        },
        "handlers": [
            {
                "_target_": "CheckpointLoader",
                "_disabled_": "@dont_finetune",
                "load_path": "$@ckpt_dir + '/model.pt'",
                "load_dict": {
                    "model": "@network"
                }
            },
            {
                "_target_": "ValidationHandler",
                "validator": "@validate#evaluator",
                "epoch_level": true,
                "interval": "@val_interval"
            },
            {
                "_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/mean_dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "additional_metrics": {
            "train/tu_dice": {
                "_target_": "MeanDice",
                "include_background": true,
                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
            }
        },
        "trainer": {
            "_target_": "SupervisedTrainer",
            "max_epochs": 10000,
            "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",
            "additional_metrics": "@train#additional_metrics",
            "amp": true
        }
    },
    "validate": {
        "preprocessing": {
            "_target_": "Compose",
            "transforms": "$@train#deterministic_transforms"
        },
        "dataset": {
            "_target_": "CacheDataset",
            "data": "$[{'label': l, **i} for i, l in zip(@images, @labels)]",
            "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": 0.25
        },
        "postprocessing": {
            "_target_": "Compose",
            "transforms": [
                {
                    "_target_": "Invertd",
                    "transform": "%validate#preprocessing",
                    "device": "@device",
                    "keys": [
                        "pred",
                        "label"
                    ],
                    "orig_keys": [
                        "artery",
                        "label"
                    ],
                    "meta_keys": [
                        "pred_meta_dict",
                        "label_meta_dict"
                    ],
                    "nearest_interp": [
                        false,
                        true
                    ],
                    "to_tensor": true
                },
                "%train#postprocessing#transforms#0",
                "%train#postprocessing#transforms#1",
                "%train#postprocessing#transforms#2"
            ]
        },
        "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": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "additional_metrics": {
            "ar/dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_ar', 'label_ar'])"
            },
            "ve/dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_ve', 'label_ve'])"
            },
            "ur/dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_ur', 'label_ur'])"
            },
            "ki/dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_ki', 'label_ki'])"
            },
            "tu/dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
            },
            "tu/haunsdorff": {
                "_target_": "HausdorffDistance",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
            },
            "tu/surface": {
                "_target_": "SurfaceDistance",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
            }
        },
        "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": [
        "$monai.utils.set_determinism(seed=42)",
        "$setattr(torch.backends.cudnn, 'benchmark', True)",
        "$@train#trainer.run()"
    ]
}