{
    "imports": [
        "$import functools",
        "$import glob",
        "$import scripts"
    ],
    "bundle_root": ".",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "ckpt_path": "$@bundle_root + '/models/model.pt'",
    "dataset_dir": "./MedNIST/Hand",
    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/*.jpeg')))",
    "latent_size": 64,
    "discriminator": {
        "_target_": "Discriminator",
        "in_shape": [
            1,
            64,
            64
        ],
        "channels": [
            8,
            16,
            32,
            64,
            1
        ],
        "strides": [
            2,
            2,
            2,
            2,
            1
        ],
        "num_res_units": 1,
        "kernel_size": 5
    },
    "generator": {
        "_target_": "Generator",
        "latent_shape": "@latent_size",
        "start_shape": [
            64,
            8,
            8
        ],
        "channels": [
            32,
            16,
            8,
            1
        ],
        "strides": [
            2,
            2,
            2,
            1
        ]
    },
    "dnetwork": "$@discriminator.apply(monai.networks.normal_init).to(@device)",
    "gnetwork": "$@generator.apply(monai.networks.normal_init).to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "LoadImaged",
                "keys": "reals"
            },
            {
                "_target_": "EnsureChannelFirstd",
                "keys": "reals"
            },
            {
                "_target_": "ScaleIntensityd",
                "keys": "reals"
            },
            {
                "_target_": "RandRotated",
                "keys": "reals",
                "range_x": "$np.pi/12",
                "prob": 0.5,
                "keep_size": true
            },
            {
                "_target_": "RandFlipd",
                "keys": "reals",
                "spatial_axis": 0,
                "prob": 0.5
            },
            {
                "_target_": "RandZoomd",
                "keys": "reals",
                "min_zoom": 0.9,
                "max_zoom": 1.1,
                "prob": 0.5
            },
            {
                "_target_": "EnsureTyped",
                "keys": "reals"
            }
        ]
    },
    "real_dataset": {
        "_target_": "CacheDataset",
        "data": "$[{'reals': i} for i in @datalist]",
        "transform": "@preprocessing"
    },
    "real_dataloader": {
        "_target_": "DataLoader",
        "dataset": "@real_dataset",
        "batch_size": 600,
        "shuffle": true,
        "num_workers": 12
    },
    "doptimizer": {
        "_target_": "torch.optim.Adam",
        "params": "$@dnetwork.parameters()",
        "lr": 0.0002,
        "betas": [
            0.5,
            0.999
        ]
    },
    "goptimizer": {
        "_target_": "torch.optim.Adam",
        "params": "$@gnetwork.parameters()",
        "lr": 0.0002,
        "betas": [
            0.5,
            0.999
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointSaver",
            "save_dir": "$@bundle_root + '/models'",
            "save_dict": {
                "model": "@gnetwork"
            },
            "save_interval": 0,
            "save_final": true,
            "epoch_level": true
        }
    ],
    "trainer": {
        "_target_": "GanTrainer",
        "device": "@device",
        "max_epochs": 50,
        "train_data_loader": "@real_dataloader",
        "g_network": "@gnetwork",
        "g_optimizer": "@goptimizer",
        "g_loss_function": "$functools.partial(scripts.losses.generator_loss, disc_net=@dnetwork)",
        "d_network": "@dnetwork",
        "d_optimizer": "@doptimizer",
        "d_loss_function": "$functools.partial(scripts.losses.discriminator_loss, disc_net=@dnetwork)",
        "d_train_steps": 5,
        "g_update_latents": true,
        "latent_shape": "@latent_size",
        "key_train_metric": "$None",
        "train_handlers": "@handlers"
    },
    "training": [
        "$@gnetwork.conv.add_module('activation', torch.nn.Sigmoid())",
        "$@trainer.run()"
    ]
}