_base_ = [
    '../_base_/models/setr_mla.py',
    '../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_80k.py'
]
model = dict(
    backbone=dict(img_size=480, pos_embed_interp=True, drop_rate=0.,
                  mla_channels=256, mla_index=(5, 11, 17, 23)),
    decode_head=dict(img_size=480, mla_channels=256,
                     mlahead_channels=128, num_classes=60),
    auxiliary_head=[
        dict(
            type='VIT_MLA_AUXIHead',
            in_channels=256,
            channels=512,
            in_index=0,
            img_size=480,
            num_classes=60,
            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=480,
            num_classes=60,
            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=480,
            num_classes=60,
            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=480,
            num_classes=60,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    ])

optimizer = dict(lr=0.001, weight_decay=0.0,
                 paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)})
                 )

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (520, 520)
crop_size = (480, 480)

test_cfg = dict(mode='slide', crop_size=crop_size, stride=(320, 320))
find_unused_parameters = True
data = dict(samples_per_gpu=2)

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=img_scale,
        img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
        flip=True,
        transforms=[
            dict(type='Resize', keep_ratio=True,
                 crop_size=crop_size, setr_multi_scale=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))