score_pretraining.json

{
    "name": "score_mixed_domains",
    "phase": "train", // train (train+val) or test

    "path": {
        "log": "logs",
        "tb_logger": "tb_logger",
        "results": "results/Mixed_domains",
        "checkpoint": "checkpoints/Mixed_domains",
        "resume_state": null
    },
    "datasets": {
        "train": {
            "name": "Mixed_domains", 
            "dataroot": ["../data/potsdam/train/images_256",
                         "../data/potsdam/test/images_256",
                         "../data/DeepGlobe/train/images_256",
                         "../data/DeepGlobe/test/images_256"],
            "resolution": 256, // high resolution
            "batch_size": 1,
            "num_workers": 4,
            "use_shuffle": true,
            "data_len": -1 // -1 represents all data used in train
        },
        "val": {
            "name": "Mixed_domains",
            "resolution": 256,
            "data_len": 10
        }
    },
    "model": {
        "which_model_G": "sr3", // use the ddpm or sr3 network structure
        "finetune_norm": false,
        "unet": {
            "in_channel": 3,
            "out_channel": 3,
            "inner_channel": 128,
            "channel_multiplier": [
                1,
                2,
                4,
                8,
                8
            ],
            "attn_res": [
                16
            ],
            "res_blocks": 2,
            "dropout": 0.2
        },
        "beta_schedule": {
            "train": {
                "schedule": "cosine",
                "n_timestep": 2000,
                "linear_start": 1e-6,
                "linear_end": 1e-2
            },
            "val": {
                "schedule": "cosine",
                "n_timestep": 2000,
                "linear_start": 1e-6,
                "linear_end": 1e-2
            }
        },
        "diffusion": {
            "image_size": 256,
            "channels": 3, //sample channel
            "loss": "l2", //options "l1" or "l2"
            "conditional": false // unconditional generation or unconditional generation(super_resolution)
        }
    },
    "train": {
        "n_iter": 200000,
        "val_freq": 1e4,
        "save_checkpoint_freq": 1e4,
        "print_freq": 100,
        "optimizer": {
            "type": "adamw",
            "lr": 1e-5
        }
    },
    "wandb": {
        "project": "score_mixed_domains"
    }
}