config.json

{
    "name": "Shape3Dgen",
    "n_gpu": 1,
    "arch": {
        "type": "Shape3DModel",
        "args": {
            "num_param": 55,
            "num_functions": 100,
            "num_classes": 13
        }
    },
    "data_loader": {
        "type": "Shape3DLoader",
        "args": {
            "data_dir": "./data/",
            "metadata": "./data/metadata.csv",
            "point_limit": [
                2000,
                9000,
                1000
            ],
            "batch_size": 64,
            "shuffle": true,
            "num_workers": 6,
            "task": "train"
        }
    },
    "valid_data_loader": {
        "type": "Shape3DLoader",
        "args": {
            "data_dir": "./data/",
            "metadata": "./data/metadata.csv",
            "point_limit": [
                2000,
                9000,
                1000
            ],
            "batch_size": 32,
            "shuffle": true,
            "num_workers": 6,
            "task": "val"
        }
    },
    "test_data_loader": {
        "type": "Shape3DLoader",
        "args": {
            "data_dir": "./data/",
            "metadata": "./data/metadata.csv",
            "point_limit": [2000, 9000, 1000],
            "batch_size": 1,
            "shuffle": false,
            "training": false,
            "num_workers": 15,
            "task": "test"
        }
    },
    "optimizer": {
        "type": "Adam",
        "args": {
            "lr": 0.0001,
            "weight_decay": 1e-07,
            "amsgrad": true
        }
    },
    "loss": "total_loss",
    "loss_weights": {
        "on": 2.0,
        "in": 0.1,
        "out": 10.0,
        "normvec": 1.0
    },
    "metrics": [
        "IOU",
        "fscore"
    ],
    "lr_scheduler": {
        "type": "MultiStepLR",
        "args": {
            "milestones":[200, 500, 1000, 3000, 4000, 5500],
            "gamma": 0.3
        }
    },
    "trainer": {
        "epochs": 5000,
        "save_dir": "/mnt/disk1/JYChung/3DShapeGen_saved/",
        "save_period": 10,
        "verbosity": 2,
        "monitor": "min val_Total_loss",
        "early_stop": 1000,
        "tensorboard": true
    }
}