data_preprocessor = dict(
    _scope_='mmseg',
    type='SegDataPreProcessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    bgr_to_rgb=True,
    size=(512, 1024),
    pad_val=0,
    seg_pad_val=255)
architecture = dict(
    _scope_='mmseg',
    type='CascadeEncoderDecoder',
    data_preprocessor=data_preprocessor,
    num_stages=2,
    pretrained=None,
    backbone=dict(
        type='ResNetV1c',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 1, 1),
        strides=(1, 2, 2, 2),
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=4),
    decode_head=[
        dict(
            type='FPNHead',
            in_channels=[256, 256, 256, 256],
            in_index=[0, 1, 2, 3],
            feature_strides=[4, 8, 16, 32],
            channels=128,
            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
        dict(
            type='PointHead',
            in_channels=[256],
            in_index=[0],
            channels=256,
            num_fcs=3,
            coarse_pred_each_layer=True,
            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
    ],
    # model training and testing settings
    train_cfg=dict(
        num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75),
    test_cfg=dict(
        mode='whole',
        subdivision_steps=2,
        subdivision_num_points=8196,
        scale_factor=2))
