# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
model = dict(
    type='EncoderDecoder',
    backbone=dict(
        type='FastSCNN',
        downsample_dw_channels1=32,
        downsample_dw_channels2=48,
        global_in_channels=64,
        global_block_channels=(64, 96, 128),
        global_out_channels=128,
        higher_in_channels=64,
        lower_in_channels=128,
        fusion_out_channels=128,
        scale_factor=4,
        out_indices=(0, 1, 2),
        norm_cfg=norm_cfg,
        align_corners=False),
    decode_head=dict(
        type='DepthwiseSeparableFCNHead',
        in_channels=128,
        channels=128,
        concat_input=False,
        num_classes=19,
        in_index=-1,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.)),
    auxiliary_head=[
        dict(
            type='FCNHead',
            in_channels=128,
            channels=32,
            num_convs=1,
            num_classes=19,
            in_index=-2,
            norm_cfg=norm_cfg,
            concat_input=False,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
            type='FCNHead',
            in_channels=64,
            channels=32,
            num_convs=1,
            num_classes=19,
            in_index=-3,
            norm_cfg=norm_cfg,
            concat_input=False,
            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')