Pointcept/configs/scannet/pretrain-msc-v1m2-0-spunet-csc.py

_base_ = ["../_base_/default_runtime.py"]

# misc custom setting
batch_size = 32  # bs: total bs in all gpus
num_worker = 32
mix_prob = 0
empty_cache = False
enable_amp = False
evaluate = False
find_unused_parameters = False

# model settings
model = dict(
    type="MSC-v1m2",
    backbone=dict(
        type="SpUNet-v1m1",
        in_channels=3,
        num_classes=0,
        channels=(32, 64, 128, 256, 256, 128, 96, 96),
        layers=(2, 3, 4, 6, 2, 2, 2, 2),
    ),
    backbone_in_channels=3,
    backbone_out_channels=96,
    mask_grid_size=0.1,
    mask_rate=0,
    view1_mix_prob=0,
    view2_mix_prob=0,
    matching_max_k=8,
    matching_max_radius=0.03,
    matching_max_pair=8192,
    nce_t=0.4,
    contrast_weight=1,
    reconstruct_weight=1,
    reconstruct_color=False,
    reconstruct_normal=False,
    partitions=4,
    r1=2,
    r2=20,
)

# scheduler settings
epoch = 10
eval_epoch = 10
optimizer = dict(type="SGD", lr=0.1, momentum=0.8, weight_decay=0.0001, nesterov=True)
scheduler = dict(
    type="OneCycleLR",
    max_lr=optimizer["lr"],
    pct_start=0.01,
    anneal_strategy="cos",
    div_factor=10.0,
    final_div_factor=10000.0,
)

# dataset settings
dataset_type = "ScanNetPairDataset"
data_root = "data/scannet_pair"

data = dict(
    num_classes=20,
    ignore_index=-1,
    names=[
        "wall",
        "floor",
        "cabinet",
        "bed",
        "chair",
        "sofa",
        "table",
        "door",
        "window",
        "bookshelf",
        "picture",
        "counter",
        "desk",
        "curtain",
        "refridgerator",
        "shower curtain",
        "toilet",
        "sink",
        "bathtub",
        "otherfurniture",
    ],
    train=dict(
        type=dataset_type,
        data_root=data_root,
        view1_transform=[
            dict(type="CenterShift", apply_z=True),
            dict(type="Copy", keys_dict={"coord": "origin_coord"}),
            # dict(type="RandomScale", scale=[0.9, 1.1]),
            dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=1),
            dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=1),
            dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=1),
            dict(type="RandomFlip", p=0.5),
            dict(type="RandomJitter", sigma=0.005, clip=0.02),
            dict(
                type="RandomColorJitter",
                brightness=0.4,
                contrast=0.4,
                saturation=0.2,
                hue=0.02,
                p=0.8,
            ),
            dict(type="ChromaticJitter", p=0.95, std=0.05),
            dict(
                type="GridSample",
                grid_size=0.025,
                hash_type="fnv",
                mode="train",
                keys=("origin_coord", "coord", "color"),
                return_grid_coord=True,
            ),
            dict(type="NormalizeColor"),
            dict(type="ToTensor"),
            dict(
                type="Collect",
                keys=("origin_coord", "grid_coord", "coord", "color"),
                offset_keys_dict=dict(offset="coord"),
                feat_keys=["color"],
            ),
        ],
        view2_transform=[
            dict(type="CenterShift", apply_z=True),
            dict(type="Copy", keys_dict={"coord": "origin_coord"}),
            # dict(type="RandomScale", scale=[0.9, 1.1]),
            dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=1),
            dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=1),
            dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=1),
            dict(type="RandomFlip", p=0.5),
            dict(type="RandomJitter", sigma=0.005, clip=0.02),
            dict(
                type="RandomColorJitter",
                brightness=0.4,
                contrast=0.4,
                saturation=0.2,
                hue=0.02,
                p=0.8,
            ),
            dict(type="ChromaticJitter", p=0.95, std=0.05),
            dict(
                type="GridSample",
                grid_size=0.025,
                hash_type="fnv",
                mode="train",
                keys=("origin_coord", "coord", "color"),
                return_grid_coord=True,
            ),
            dict(type="NormalizeColor"),
            dict(type="ToTensor"),
            dict(
                type="Collect",
                keys=("origin_coord", "grid_coord", "coord", "color"),
                offset_keys_dict=dict(offset="coord"),
                feat_keys=["color"],
            ),
        ],
        test_mode=False,
    ),
)

hooks = [
    dict(type="CheckpointLoader"),
    dict(type="IterationTimer", warmup_iter=2),
    dict(type="InformationWriter"),
    dict(type="CheckpointSaver", save_freq=None),
]