# model settings
embed_dims = [32, 64, 160, 256]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='SimplifiedTransformer',
        img_size=(1024, 1024),
        in_chans=3,
        num_classes=19,
        embed_dims=embed_dims,
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[4, 4, 4, 4],
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.,
        attn_drop_rate=0.,
        drop_path_rate=0.1,
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1]),
    decode_head=dict(
        type='DestHead',
        in_channels=[32, 64, 160, 256],
        in_index=[0, 1, 2, 3],
        channels=256,
        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)),
    # model training and testing settings
    train_cfg=dict(),
    # test_cfg=dict(mode='whole'))
    test_cfg=dict(mode='slide', crop_size=(1024, 1024), stride=(768, 768)))
