_base_ = ['./mswin_par_small_patch4_512x512_160k_ade20k_pretrain_224x224_1K.py']

model = dict(
    pretrained=\
    'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth',
    backbone=dict(
        pretrain_img_size=384,
        embed_dims=128,
        depths=[2, 2, 18, 2],
        num_heads=[4, 8, 16, 32],
        window_size=12),
    decode_head=dict(
        in_channels=[128, 256, 512, 1024],
        mode='par',
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        in_channels=512,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))

data = dict(samples_per_gpu=8)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()

_base_ = ['./mswin_par_small_patch4_512x512_160k_ade20k_pretrain_224x224_1K.py']

model = dict(
    pretrained=\
    'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth',
    backbone=dict(
        pretrain_img_size=384,
        embed_dims=128,
        depths=[2, 2, 18, 2],
        num_heads=[4, 8, 16, 32],
        window_size=12),
    decode_head=dict(
        in_channels=[128, 256, 512, 1024],
        mode='par',
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        in_channels=512,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))

data = dict(samples_per_gpu=8)
# fp16 settings
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
# fp16 placeholder
fp16 = dict()

# fp16 settings # fp16 placeholder