# model settings norm_cfg = dict(type='SyncBN', requires_grad=True) backbone_norm_cfg = dict(type='LN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) model = dict( type='EncoderDecoder', data_preprocessor=data_preprocessor, pretrained=None, backbone=dict( type='SwinTransformer', pretrain_img_size=224, embed_dims=96, patch_size=4, window_size=7, mlp_ratio=4, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], strides=(4, 2, 2, 2), out_indices=(0, 1, 2, 3), qkv_bias=True, qk_scale=None, patch_norm=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.3, use_abs_pos_embed=False, act_cfg=dict(type='GELU'), norm_cfg=backbone_norm_cfg), decode_head=dict( type='UPerHead', in_channels=[96, 192, 384, 768], 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=384, 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'))