{
    "imports": [
        "$from functools import partial",
        "$import numpy as np",
        "$import torch",
        "$from ignite.engine import Events"
    ],
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "ckpt_path": "$@bundle_root + '/models/model.pt'",
    "dataset_file": "./allimages3label.npz",
    "network_def": {
        "_target_": "UNet",
        "spatial_dims": 2,
        "in_channels": 1,
        "out_channels": 4,
        "channels": [
            16,
            32,
            64,
            128,
            256
        ],
        "strides": [
            2,
            2,
            2,
            2
        ],
        "num_res_units": 2
    },
    "network": "$@network_def.to(@device)",
    "npz": {
        "_target_": "NPZDictItemDataset",
        "npzfile": "$@dataset_file",
        "keys": {
            "images": "image",
            "segs": "label"
        }
    },
    "partitions": "$monai.data.partition_dataset(np.arange(len(@npz)),(8,2), shuffle=True)",
    "train_sub": "$torch.utils.data.Subset(@npz,@partitions[0])",
    "eval_sub": "$torch.utils.data.Subset(@npz,@partitions[1])",
    "im_shape": "$@train_sub[0]['image'].shape",
    "both_keys": [
        "image",
        "label"
    ],
    "rand_prob": 0.5,
    "train_transforms": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "CastToTyped",
                "keys": "@both_keys",
                "dtype": "$(np.float32, np.int32)"
            },
            {
                "_target_": "ScaleIntensityd",
                "keys": "image"
            },
            {
                "_target_": "AddChanneld",
                "keys": "@both_keys"
            },
            {
                "_target_": "RandAxisFlipd",
                "keys": "@both_keys",
                "prob": "@rand_prob"
            },
            {
                "_target_": "RandRotate90d",
                "keys": "@both_keys",
                "prob": "@rand_prob"
            },
            {
                "_target_": "RandSmoothDeformd",
                "keys": "@both_keys",
                "prob": "@rand_prob",
                "spatial_size": "@im_shape",
                "rand_size": [
                    3,
                    3
                ],
                "pad": 2,
                "def_range": 0.1,
                "field_mode": "$monai.utils.InterpolateMode.BICUBIC",
                "grid_mode": "$(monai.utils.GridSampleMode.BICUBIC, monai.utils.GridSampleMode.NEAREST)",
                "align_corners": true
            },
            {
                "_target_": "RandAffined",
                "keys": "@both_keys",
                "prob": "@rand_prob",
                "rotate_range": 0.5,
                "translate_range": 50,
                "scale_range": 0.25,
                "padding_mode": "$monai.utils.GridSamplePadMode.ZEROS",
                "mode": "$(monai.utils.GridSampleMode.BILINEAR,monai.utils.GridSampleMode.NEAREST)",
                "as_tensor_output": false
            },
            {
                "_target_": "RandSmoothFieldAdjustContrastd",
                "keys": "image",
                "prob": "@rand_prob",
                "spatial_size": "@im_shape",
                "rand_size": [
                    8,
                    8
                ],
                "gamma": [
                    0.25,
                    3
                ],
                "mode": "$monai.utils.InterpolateMode.BICUBIC",
                "align_corners": true
            },
            {
                "_target_": "RandSmoothFieldAdjustIntensityd",
                "keys": "image",
                "prob": "@rand_prob",
                "spatial_size": "@im_shape",
                "rand_size": [
                    5,
                    5
                ],
                "gamma": [
                    0.1,
                    1
                ],
                "mode": "$monai.utils.InterpolateMode.BICUBIC",
                "align_corners": true
            },
            {
                "_target_": "RandGaussianNoised",
                "keys": "image",
                "prob": "@rand_prob",
                "std": 0.05
            },
            {
                "_target_": "ScaleIntensityd",
                "keys": "image"
            },
            {
                "_target_": "CastToTyped",
                "keys": "@both_keys",
                "dtype": "$(np.float32, np.int32)"
            },
            {
                "_target_": "EnsureTyped",
                "keys": "@both_keys"
            }
        ]
    },
    "eval_transforms": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "CastToTyped",
                "keys": "@both_keys",
                "dtype": "$(np.float32, np.int32)"
            },
            {
                "_target_": "ScaleIntensityd",
                "keys": "image"
            },
            {
                "_target_": "AddChanneld",
                "keys": "@both_keys"
            },
            {
                "_target_": "EnsureTyped",
                "keys": "@both_keys"
            }
        ]
    },
    "train_dataset": {
        "_target_": "CacheDataset",
        "data": "@train_sub",
        "transform": "@train_transforms"
    },
    "eval_dataset": {
        "_target_": "CacheDataset",
        "data": "@eval_sub",
        "transform": "@eval_transforms"
    },
    "train_no_aug_dataset": {
        "_target_": "CacheDataset",
        "data": "@train_sub",
        "transform": "@eval_transforms"
    },
    "num_iters": 400,
    "batch_size": 200,
    "num_epochs": 50,
    "num_substeps": 5,
    "sampler": {
        "_target_": "torch.utils.data.WeightedRandomSampler",
        "weights": "$torch.ones(len(@train_dataset))",
        "replacement": true,
        "num_samples": "$@num_iters*@batch_size"
    },
    "train_dataloader": {
        "_target_": "ThreadDataLoader",
        "dataset": "@train_dataset",
        "batch_size": "@batch_size",
        "repeats": "@num_substeps",
        "num_workers": 8,
        "sampler": "@sampler"
    },
    "eval_dataloader": {
        "_target_": "DataLoader",
        "dataset": "@eval_dataset",
        "batch_size": "@batch_size",
        "num_workers": 4
    },
    "lossfn": {
        "_target_": "DiceLoss",
        "include_background": false,
        "to_onehot_y": true,
        "softmax": true
    },
    "optimizer": {
        "_target_": "torch.optim.Adam",
        "params": "$@network.parameters()",
        "lr": 0.001
    },
    "post_transform": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "softmax": true
            },
            {
                "_target_": "AsDiscreted",
                "keys": [
                    "pred",
                    "label"
                ],
                "argmax": [
                    true,
                    false
                ],
                "to_onehot": 4
            }
        ]
    },
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@eval_dataloader",
        "network": "@network",
        "postprocessing": "@post_transform",
        "key_val_metric": {
            "val_mean_dice": {
                "_target_": "MeanDice",
                "include_background": false,
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "val_handlers": [
            {
                "_target_": "StatsHandler",
                "output_transform": "$lambda x: None"
            }
        ]
    },
    "handlers": [
        {
            "_target_": "ValidationHandler",
            "validator": "@evaluator",
            "epoch_level": true,
            "interval": 1
        },
        {
            "_target_": "CheckpointSaver",
            "save_dir": "$@bundle_root + '/models'",
            "save_dict": {
                "model": "@network"
            },
            "save_interval": 1,
            "save_final": true,
            "epoch_level": true
        },
        {
            "_target_": "StatsHandler",
            "tag_name": "train_loss",
            "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
        }
    ],
    "trainer": {
        "_target_": "SupervisedTrainer",
        "max_epochs": "@num_epochs",
        "device": "@device",
        "train_data_loader": "@train_dataloader",
        "network": "@network",
        "loss_function": "@lossfn",
        "optimizer": "@optimizer",
        "postprocessing": "@post_transform",
        "key_train_metric": null,
        "train_handlers": "@handlers"
    },
    "training": [
        "$@trainer.run()"
    ]
}