# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://msra/hrnetv2_w18',
    backbone=dict(type='HRNet',
                  norm_cfg=norm_cfg,
                  norm_eval=False,
                  extra=dict(stage1=dict(num_modules=1,
                                         num_branches=1,
                                         block='BOTTLENECK',
                                         num_blocks=(4, ),
                                         num_channels=(64, )),
                             stage2=dict(num_modules=1,
                                         num_branches=2,
                                         block='BASIC',
                                         num_blocks=(4, 4),
                                         num_channels=(18, 36)),
                             stage3=dict(num_modules=4,
                                         num_branches=3,
                                         block='BASIC',
                                         num_blocks=(4, 4, 4),
                                         num_channels=(18, 36, 72)),
                             stage4=dict(num_modules=3,
                                         num_branches=4,
                                         block='BASIC',
                                         num_blocks=(4, 4, 4, 4),
                                         num_channels=(18, 36, 72, 144)))),
    decode_head=dict(type='FCNHead',
                     in_channels=[18, 36, 72, 144],
                     in_index=(0, 1, 2, 3),
                     channels=sum([18, 36, 72, 144]),
                     input_transform='resize_concat',
                     kernel_size=1,
                     num_convs=1,
                     concat_input=False,
                     dropout_ratio=-1,
                     num_classes=9,
                     norm_cfg=norm_cfg,
                     align_corners=False,
                     loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
