configs/train_diffusion.json

{
    "ckpt_dir": "$@bundle_root + '/models'",
    "train_batch_size_img": 2,
    "train_batch_size_slice": 50,
    "lr": 5e-05,
    "train_patch_size": [
        256,
        256
    ],
    "latent_shape": [
        "@latent_channels",
        64,
        64
    ],
    "load_autoencoder_path": "$@bundle_root + '/models/model_autoencoder.pt'",
    "load_autoencoder": "$@autoencoder_def.load_state_dict(torch.load(@load_autoencoder_path))",
    "autoencoder": "$@autoencoder_def.to(@device)",
    "network_def": {
        "_target_": "generative.networks.nets.DiffusionModelUNet",
        "spatial_dims": "@spatial_dims",
        "in_channels": "@latent_channels",
        "out_channels": "@latent_channels",
        "num_channels": [
            32,
            64,
            128,
            256
        ],
        "attention_levels": [
            false,
            true,
            true,
            true
        ],
        "num_head_channels": [
            0,
            32,
            32,
            32
        ],
        "num_res_blocks": 2
    },
    "diffusion": "$@network_def.to(@device)",
    "optimizer": {
        "_target_": "torch.optim.Adam",
        "params": "$@diffusion.parameters()",
        "lr": "@lr"
    },
    "lr_scheduler": {
        "_target_": "torch.optim.lr_scheduler.MultiStepLR",
        "optimizer": "@optimizer",
        "milestones": [
            1000
        ],
        "gamma": 0.1
    },
    "scale_factor": "$scripts.utils.compute_scale_factor(@autoencoder,@train#dataloader,@device)",
    "noise_scheduler": {
        "_target_": "generative.networks.schedulers.DDPMScheduler",
        "_requires_": [
            "@load_autoencoder"
        ],
        "beta_schedule": "scaled_linear",
        "num_train_timesteps": 1000,
        "beta_start": 0.0015,
        "beta_end": 0.0195
    },
    "inferer": {
        "_target_": "generative.inferers.LatentDiffusionInferer",
        "scheduler": "@noise_scheduler",
        "scale_factor": "@scale_factor"
    },
    "loss": {
        "_target_": "torch.nn.MSELoss"
    },
    "train": {
        "crop_transforms": [
            {
                "_target_": "DivisiblePadd",
                "keys": "image",
                "k": [
                    32,
                    32,
                    1
                ]
            },
            {
                "_target_": "RandSpatialCropSamplesd",
                "keys": "image",
                "random_size": false,
                "roi_size": "$[@train_patch_size[0], @train_patch_size[1], 1]",
                "num_samples": "@train_batch_size_slice"
            },
            {
                "_target_": "SqueezeDimd",
                "keys": "image",
                "dim": 3
            }
        ],
        "preprocessing": {
            "_target_": "Compose",
            "transforms": "$@preprocessing_transforms + @train#crop_transforms"
        },
        "dataset": {
            "_target_": "monai.apps.DecathlonDataset",
            "root_dir": "@dataset_dir",
            "task": "Task01_BrainTumour",
            "section": "training",
            "cache_rate": 1.0,
            "num_workers": 8,
            "download": false,
            "transform": "@train#preprocessing"
        },
        "dataloader": {
            "_target_": "DataLoader",
            "dataset": "@train#dataset",
            "batch_size": "@train_batch_size_img",
            "shuffle": true,
            "num_workers": 0
        },
        "handlers": [
            {
                "_target_": "LrScheduleHandler",
                "lr_scheduler": "@lr_scheduler",
                "print_lr": true
            },
            {
                "_target_": "CheckpointSaver",
                "save_dir": "@ckpt_dir",
                "save_dict": {
                    "model": "@diffusion"
                },
                "save_interval": 0,
                "save_final": true,
                "epoch_level": true,
                "final_filename": "model.pt"
            },
            {
                "_target_": "StatsHandler",
                "tag_name": "train_diffusion_loss",
                "output_transform": "$lambda x: monai.handlers.from_engine(['loss'], first=True)(x)"
            },
            {
                "_target_": "TensorBoardStatsHandler",
                "log_dir": "@tf_dir",
                "tag_name": "train_diffusion_loss",
                "output_transform": "$lambda x: monai.handlers.from_engine(['loss'], first=True)(x)"
            }
        ],
        "trainer": {
            "_target_": "scripts.ldm_trainer.LDMTrainer",
            "device": "@device",
            "max_epochs": 1000,
            "train_data_loader": "@train#dataloader",
            "network": "@diffusion",
            "autoencoder_model": "@autoencoder",
            "optimizer": "@optimizer",
            "loss_function": "@loss",
            "latent_shape": "@latent_shape",
            "inferer": "@inferer",
            "key_train_metric": "$None",
            "train_handlers": "@train#handlers"
        }
    },
    "initialize": [
        "$monai.utils.set_determinism(seed=0)"
    ],
    "run": [
        "@load_autoencoder",
        "$@autoencoder.eval()",
        "$print('scale factor:',@scale_factor)",
        "$@train#trainer.run()"
    ]
}