{
    "imports": [
        "$import os",
        "$import ignite"
    ],
    "lr": 0.001,
    "num_epochs": 4,
    "val_interval": 1,
    "bundle_root": ".",
    "ckpt_dir": "$os.path.join(@bundle_root, 'models')",
    "output_dir": "$os.path.join(@bundle_root, 'log')",
    "training_file": "$os.path.join(@bundle_root, 'training.csv')",
    "validation_file": "$os.path.join(@bundle_root, 'validation.csv')",
    "data_root": "/workspace/data/medical/pathology",
    "region_size": [
        768,
        768
    ],
    "patch_size": [
        224,
        224
    ],
    "grid_shape": [
        3,
        3
    ],
    "number_intensity_ch": 3,
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "network_def": {
        "_target_": "TorchVisionFCModel",
        "model_name": "resnet18",
        "num_classes": 1,
        "use_conv": true,
        "pretrained": true
    },
    "network": "$@network_def.to(@device)",
    "loss": {
        "_target_": "torch.nn.BCEWithLogitsLoss"
    },
    "optimizer": {
        "_target_": "Novograd",
        "params": "$@network.parameters()",
        "lr": "@lr"
    },
    "lr_scheduler": {
        "_target_": "torch.optim.lr_scheduler.CosineAnnealingLR",
        "optimizer": "@optimizer",
        "T_max": "@num_epochs"
    },
    "train": {
        "preprocessing": {
            "_target_": "Compose",
            "transforms": [
                {
                    "_target_": "Lambdad",
                    "keys": [
                        "label"
                    ],
                    "func": "$lambda x: x.reshape((1, *@grid_shape))"
                },
                {
                    "_target_": "GridSplitd",
                    "keys": [
                        "image",
                        "label"
                    ],
                    "grid": "@grid_shape",
                    "size": {
                        "image": "@patch_size",
                        "label": 1
                    }
                },
                {
                    "_target_": "ToTensord",
                    "keys": "image"
                },
                {
                    "_target_": "TorchVisiond",
                    "keys": "image",
                    "name": "ColorJitter",
                    "brightness": 0.25,
                    "contrast": 0.75,
                    "saturation": 0.25,
                    "hue": 0.04
                },
                {
                    "_target_": "ToNumpyd",
                    "keys": "image"
                },
                {
                    "_target_": "RandFlipd",
                    "keys": "image",
                    "prob": 0.5
                },
                {
                    "_target_": "RandRotate90d",
                    "keys": "image",
                    "prob": 0.5,
                    "max_k": 3,
                    "spatial_axes": [
                        -2,
                        -1
                    ]
                },
                {
                    "_target_": "CastToTyped",
                    "keys": "image",
                    "dtype": "float32"
                },
                {
                    "_target_": "RandZoomd",
                    "keys": "image",
                    "prob": 0.5,
                    "min_zoom": 0.9,
                    "max_zoom": 1.1
                },
                {
                    "_target_": "ScaleIntensityRanged",
                    "keys": "image",
                    "a_min": 0.0,
                    "a_max": 255.0,
                    "b_min": -1.0,
                    "b_max": 1.0
                },
                {
                    "_target_": "ToTensord",
                    "keys": [
                        "image",
                        "label"
                    ]
                }
            ]
        },
        "datalist": {
            "_target_": "CSVDataset",
            "src": "@training_file",
            "col_groups": {
                "image": 0,
                "location": [
                    2,
                    1
                ],
                "label": [
                    3,
                    6,
                    9,
                    4,
                    7,
                    10,
                    5,
                    8,
                    11
                ]
            },
            "kwargs_read_csv": {
                "header": null
            },
            "transform": {
                "_target_": "Lambdad",
                "keys": "image",
                "func": "$lambda x: os.path.join(@data_root, 'training/images', x + '.tif')"
            }
        },
        "dataset": {
            "_target_": "monai.data.wsi_datasets.PatchWSIDataset",
            "data": "@train#datalist",
            "patch_level": 0,
            "patch_size": "@region_size",
            "reader": "cucim",
            "transform": "@train#preprocessing"
        },
        "dataloader": {
            "_target_": "DataLoader",
            "dataset": "@train#dataset",
            "batch_size": 128,
            "pin_memory": true,
            "num_workers": 8
        },
        "inferer": {
            "_target_": "SimpleInferer"
        },
        "postprocessing": {
            "_target_": "Compose",
            "transforms": [
                {
                    "_target_": "Activationsd",
                    "keys": "pred",
                    "sigmoid": true
                },
                {
                    "_target_": "AsDiscreted",
                    "keys": "pred",
                    "threshold": 0.5
                }
            ]
        },
        "handlers": [
            {
                "_target_": "ValidationHandler",
                "validator": "@validate#evaluator",
                "epoch_level": true,
                "interval": "@val_interval"
            },
            {
                "_target_": "StatsHandler",
                "tag_name": "train_loss",
                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
            },
            {
                "_target_": "LrScheduleHandler",
                "lr_scheduler": "@lr_scheduler",
                "print_lr": true
            },
            {
                "_target_": "TensorBoardStatsHandler",
                "log_dir": "@output_dir",
                "tag_name": "train_loss",
                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
            }
        ],
        "key_metric": {
            "train_acc": {
                "_target_": "ignite.metrics.Accuracy",
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "trainer": {
            "_target_": "SupervisedTrainer",
            "device": "@device",
            "max_epochs": "@num_epochs",
            "train_data_loader": "@train#dataloader",
            "network": "@network",
            "optimizer": "@optimizer",
            "loss_function": "@loss",
            "inferer": "@train#inferer",
            "amp": true,
            "postprocessing": "@train#postprocessing",
            "key_train_metric": "@train#key_metric",
            "train_handlers": "@train#handlers"
        }
    },
    "validate": {
        "preprocessing": {
            "_target_": "Compose",
            "transforms": [
                {
                    "_target_": "Lambdad",
                    "keys": "label",
                    "func": "$lambda x: x.reshape((1, *@grid_shape))"
                },
                {
                    "_target_": "GridSplitd",
                    "keys": [
                        "image",
                        "label"
                    ],
                    "grid": "@grid_shape",
                    "size": {
                        "image": "@patch_size",
                        "label": 1
                    }
                },
                {
                    "_target_": "CastToTyped",
                    "keys": "image",
                    "dtype": "float32"
                },
                {
                    "_target_": "ScaleIntensityRanged",
                    "keys": "image",
                    "a_min": 0.0,
                    "a_max": 255.0,
                    "b_min": -1.0,
                    "b_max": 1.0
                },
                {
                    "_target_": "ToTensord",
                    "keys": [
                        "image",
                        "label"
                    ]
                }
            ]
        },
        "datalist": {
            "_target_": "CSVDataset",
            "src": "@validation_file",
            "col_groups": {
                "image": 0,
                "location": [
                    2,
                    1
                ],
                "label": [
                    3,
                    6,
                    9,
                    4,
                    7,
                    10,
                    5,
                    8,
                    11
                ]
            },
            "kwargs_read_csv": {
                "header": null
            },
            "transform": {
                "_target_": "Lambdad",
                "keys": "image",
                "func": "$lambda x: os.path.join(@data_root, 'training/images', x + '.tif')"
            }
        },
        "dataset": {
            "_target_": "monai.data.wsi_datasets.PatchWSIDataset",
            "data": "@validate#datalist",
            "patch_level": 0,
            "patch_size": "@region_size",
            "reader": "cucim",
            "transform": "@validate#preprocessing"
        },
        "dataloader": {
            "_target_": "DataLoader",
            "dataset": "@validate#dataset",
            "batch_size": 128,
            "pin_memory": true,
            "shuffle": false,
            "num_workers": 8
        },
        "inferer": {
            "_target_": "SimpleInferer"
        },
        "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": {
            "valid_acc": {
                "_target_": "ignite.metrics.Accuracy",
                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
            }
        },
        "evaluator": {
            "_target_": "SupervisedEvaluator",
            "device": "@device",
            "val_data_loader": "@validate#dataloader",
            "network": "@network",
            "inferer": "@validate#inferer",
            "postprocessing": "@validate#postprocessing",
            "key_val_metric": "@validate#key_metric",
            "val_handlers": "@validate#handlers",
            "amp": true
        }
    },
    "training": [
        "$monai.utils.set_determinism(seed=123)",
        "$setattr(torch.backends.cudnn, 'benchmark', True)",
        "$@train#trainer.run()"
    ]
}