_base_ = [
    '../_base_/models/setr_mla.py',
    '../_base_/datasets/FoodSeg103_768x768.py', '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_80k.py'
]
model = dict(
    backbone=dict(
        img_size=768,
        model_name='vit_base_patch16_224',
        pretrain_weights='pretrained_model/VIT_base_224_ReLeM.pth',
        embed_dim=768,
        depth=12,
        num_heads=12,
        pos_embed_interp=True, 
        drop_rate=0.,
        mla_channels=256,
        mla_index=(5,7,9,11)
        ),
    decode_head=dict(img_size=768,mla_channels=256,mlahead_channels=128,num_classes=104),
    auxiliary_head=[
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=0,
        img_size=768,
        num_classes=104,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=1,
        img_size=768,
        num_classes=104,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=2,
        img_size=768,
        num_classes=104,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
        type='VIT_MLA_AUXIHead',
        in_channels=256,
        channels=512,
        in_index=3,
        img_size=768,
        num_classes=104,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        ])

optimizer = dict(lr=0.002, weight_decay=0.0,
paramwise_cfg = dict(custom_keys={'head': dict(lr_mult=10.)})
)

crop_size = (768, 768)
test_cfg = dict(mode='slide', crop_size=crop_size, stride=(512, 512))
find_unused_parameters = True
data = dict(samples_per_gpu=1)