# model settings
norm_cfg = dict(type="BN", requires_grad=True)
model = dict(
    type="EncoderDecoder",
    pretrained="open-mmlab://resnet101_v1c",
    backbone=dict(
        type="ResNetV1c",
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 1, 1),
        strides=(1, 2, 2, 2),
        norm_cfg=norm_cfg,
        norm_eval=False,
        style="pytorch",
        contract_dilation=True,
    ),
    decode_head=dict(
        type="UPerHead",
        in_channels=[256, 512, 1024, 2048],
        in_index=[0, 1, 2, 3],
        pool_scales=(1, 2, 3, 6),
        channels=512,
        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
    ),
    auxiliary_head=dict(
        type="FCNHead",
        in_channels=1024,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4),
    ),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode="whole"),
)

checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth"
