# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://resnet50_v1c',
    backbone=dict(type='ResNetV1c',
                  depth=50,
                  num_stages=4,
                  out_indices=(0, 1, 2, 3),
                  dilations=(1, 1, 2, 4),
                  strides=(1, 2, 1, 1),
                  norm_cfg=norm_cfg,
                  norm_eval=False,
                  style='pytorch',
                  contract_dilation=True),
    decode_head=dict(type='PSPHead',
                     in_channels=2048,
                     in_index=3,
                     channels=512,
                     pool_scales=(1, 2, 3, 6),
                     dropout_ratio=0.1,
                     num_classes=9,
                     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=9,
                        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'))
