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

# misc custom setting
batch_size = 12  # bs: total bs in all gpus
mix_prob = 0.8
empty_cache = False
enable_amp = True
evaluate = False

# model settings
model = dict(
    type="DefaultSegmentor",
    backbone=dict(
        type="PT-v2m2",
        in_channels=4,
        num_classes=16,
        patch_embed_depth=1,
        patch_embed_channels=48,
        patch_embed_groups=6,
        patch_embed_neighbours=8,
        enc_depths=(2, 2, 6, 2),
        enc_channels=(96, 192, 384, 512),
        enc_groups=(12, 24, 48, 64),
        enc_neighbours=(16, 16, 16, 16),
        dec_depths=(1, 1, 1, 1),
        dec_channels=(48, 96, 192, 384),
        dec_groups=(6, 12, 24, 48),
        dec_neighbours=(16, 16, 16, 16),
        grid_sizes=(0.15, 0.375, 0.9375, 2.34375),  # x3, x2.5, x2.5, x2.5
        attn_qkv_bias=True,
        pe_multiplier=False,
        pe_bias=True,
        attn_drop_rate=0.0,
        drop_path_rate=0.3,
        enable_checkpoint=False,
        unpool_backend="map",  # map / interp
    ),
    criteria=[
        dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1),
        dict(type="LovaszLoss", mode="multiclass", loss_weight=1.0, ignore_index=-1),
    ],
)

# scheduler settings
epoch = 50
eval_epoch = 50
optimizer = dict(type="AdamW", lr=0.002, weight_decay=0.005)
scheduler = dict(
    type="OneCycleLR",
    max_lr=optimizer["lr"],
    pct_start=0.04,
    anneal_strategy="cos",
    div_factor=10.0,
    final_div_factor=100.0,
)

# dataset settings
dataset_type = "NuScenesDataset"
data_root = "data/nuscenes"
ignore_index = -1
names = [
    "barrier",
    "bicycle",
    "bus",
    "car",
    "construction_vehicle",
    "motorcycle",
    "pedestrian",
    "traffic_cone",
    "trailer",
    "truck",
    "driveable_surface",
    "other_flat",
    "sidewalk",
    "terrain",
    "manmade",
    "vegetation",
]

data = dict(
    num_classes=16,
    ignore_index=ignore_index,
    names=names,
    train=dict(
        type=dataset_type,
        split=["train", "val"],
        data_root=data_root,
        transform=[
            # dict(type="RandomDropout", dropout_ratio=0.2, dropout_application_ratio=0.2),
            # dict(type="RandomRotateTargetAngle", angle=(1/2, 1, 3/2), center=[0, 0, 0], axis='z', p=0.75),
            dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=0.5),
            # dict(type="RandomRotate", angle=[-1/6, 1/6], axis='x', p=0.5),
            # dict(type="RandomRotate", angle=[-1/6, 1/6], axis='y', p=0.5),
            dict(type="RandomScale", scale=[0.9, 1.1]),
            # dict(type="RandomShift", shift=[0.2, 0.2, 0.2]),
            dict(type="RandomFlip", p=0.5),
            dict(type="RandomJitter", sigma=0.005, clip=0.02),
            # dict(type="ElasticDistortion", distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
            # dict(type="GridSample", grid_size=0.05, hash_type="fnv", mode="train",
            #      keys=("coord", "strength", "segment"), return_grid_coord=True),
            # dict(type="SphereCrop", point_max=1000000, mode="random"),
            # dict(type="CenterShift", apply_z=False),
            dict(type="ToTensor"),
            dict(
                type="Collect",
                keys=("coord", "segment"),
                feat_keys=("coord", "strength"),
            ),
        ],
        test_mode=False,
        ignore_index=ignore_index,
    ),
    test=dict(
        type=dataset_type,
        split="test",
        data_root=data_root,
        transform=[],
        test_mode=True,
        test_cfg=dict(
            voxelize=None,
            crop=None,
            post_transform=[
                dict(type="ToTensor"),
                dict(
                    type="Collect",
                    keys=("coord", "index"),
                    feat_keys=("coord", "strength"),
                ),
            ],
            aug_transform=[
                [dict(type="RandomScale", scale=[0.9, 0.9])],
                [dict(type="RandomScale", scale=[0.95, 0.95])],
                [dict(type="RandomScale", scale=[1, 1])],
                [dict(type="RandomScale", scale=[1.05, 1.05])],
                [dict(type="RandomScale", scale=[1.1, 1.1])],
                [
                    dict(type="RandomScale", scale=[0.9, 0.9]),
                    dict(type="RandomFlip", p=1),
                ],
                [
                    dict(type="RandomScale", scale=[0.95, 0.95]),
                    dict(type="RandomFlip", p=1),
                ],
                [dict(type="RandomScale", scale=[1, 1]), dict(type="RandomFlip", p=1)],
                [
                    dict(type="RandomScale", scale=[1.05, 1.05]),
                    dict(type="RandomFlip", p=1),
                ],
                [
                    dict(type="RandomScale", scale=[1.1, 1.1]),
                    dict(type="RandomFlip", p=1),
                ],
            ],
        ),
        ignore_index=ignore_index,
    ),
)