auto_scale_lr = dict(base_batch_size=16, enable=False)
backbone_embed_multi = dict(decay_mult=0.0, lr_mult=0.1)
backbone_norm_multi = dict(decay_mult=0.0, lr_mult=0.1)
crop_size = (
    256,
    512,
)
custom_keys = dict({
    'absolute_pos_embed':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone':
    dict(decay_mult=1.0, lr_mult=0.1),
    'backbone.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.patch_embed.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.0.blocks.0.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.0.blocks.1.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.0.downsample.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.1.blocks.0.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.1.blocks.1.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.1.downsample.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.blocks.0.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.blocks.1.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.blocks.2.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.blocks.3.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.blocks.4.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.blocks.5.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.2.downsample.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.3.blocks.0.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'backbone.stages.3.blocks.1.norm':
    dict(decay_mult=0.0, lr_mult=0.1),
    'level_embed':
    dict(decay_mult=0.0, lr_mult=1.0),
    'query_embed':
    dict(decay_mult=0.0, lr_mult=1.0),
    'query_feat':
    dict(decay_mult=0.0, lr_mult=1.0),
    'relative_position_bias_table':
    dict(decay_mult=0.0, lr_mult=0.1)
})
data_preprocessor = dict(
    bgr_to_rgb=True,
    mean=[
        123.675,
        116.28,
        103.53,
    ],
    pad_val=0,
    seg_pad_val=255,
    size=(
        256,
        512,
    ),
    std=[
        58.395,
        57.12,
        57.375,
    ],
    test_cfg=dict(size_divisor=32),
    type='SegDataPreProcessor')
data_root = '/dataset/cityscapes/'
dataset_type = 'CityscapesDataset'
default_hooks = dict(
    checkpoint=dict(
        by_epoch=False, interval=5000, save_best='mIoU',
        type='CheckpointHook'),
    logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'),
    visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
depths = [
    2,
    2,
    6,
    2,
]
embed_multi = dict(decay_mult=0.0, lr_mult=1.0)
env_cfg = dict(
    cudnn_benchmark=True,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
img_ratios = [
    0.5,
    0.75,
    1.0,
    1.25,
    1.5,
    1.75,
]
launcher = 'pytorch'
load_from = 'work_dirs/mask2former-swint-8xb2-512x1024-90k/mask2former-swint-8xb2-512x1024-90k_ckpt.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
    backbone=dict(
        attn_drop_rate=0.0,
        depths=[
            2,
            2,
            6,
            2,
        ],
        drop_path_rate=0.3,
        drop_rate=0.0,
        embed_dims=96,
        frozen_stages=-1,
        init_cfg=dict(
            checkpoint=
            'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth',
            type='Pretrained'),
        mlp_ratio=4,
        num_heads=[
            3,
            6,
            12,
            24,
        ],
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        patch_norm=True,
        qk_scale=None,
        qkv_bias=True,
        type='SwinTransformer',
        window_size=7,
        with_cp=False),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_val=0,
        seg_pad_val=255,
        size=(
            256,
            512,
        ),
        std=[
            58.395,
            57.12,
            57.375,
        ],
        test_cfg=dict(size_divisor=32),
        type='SegDataPreProcessor'),
    decode_head=dict(
        align_corners=False,
        enforce_decoder_input_project=False,
        feat_channels=256,
        in_channels=[
            96,
            192,
            384,
            768,
        ],
        loss_cls=dict(
            class_weight=[
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                0.1,
            ],
            loss_weight=2.0,
            reduction='mean',
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=False),
        loss_dice=dict(
            activate=True,
            eps=1.0,
            loss_weight=5.0,
            naive_dice=True,
            reduction='mean',
            type='mmdet.DiceLoss',
            use_sigmoid=True),
        loss_mask=dict(
            loss_weight=5.0,
            reduction='mean',
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True),
        num_classes=19,
        num_queries=100,
        num_transformer_feat_level=3,
        out_channels=256,
        pixel_decoder=dict(
            act_cfg=dict(type='ReLU'),
            encoder=dict(
                init_cfg=None,
                layer_cfg=dict(
                    ffn_cfg=dict(
                        act_cfg=dict(inplace=True, type='ReLU'),
                        embed_dims=256,
                        feedforward_channels=1024,
                        ffn_drop=0.0,
                        num_fcs=2),
                    self_attn_cfg=dict(
                        batch_first=True,
                        dropout=0.0,
                        embed_dims=256,
                        im2col_step=64,
                        init_cfg=None,
                        norm_cfg=None,
                        num_heads=8,
                        num_levels=3,
                        num_points=4)),
                num_layers=6),
            init_cfg=None,
            norm_cfg=dict(num_groups=32, type='GN'),
            num_outs=3,
            positional_encoding=dict(normalize=True, num_feats=128),
            type='mmdet.MSDeformAttnPixelDecoder'),
        positional_encoding=dict(normalize=True, num_feats=128),
        strides=[
            4,
            8,
            16,
            32,
        ],
        train_cfg=dict(
            assigner=dict(
                match_costs=[
                    dict(type='mmdet.ClassificationCost', weight=2.0),
                    dict(
                        type='mmdet.CrossEntropyLossCost',
                        use_sigmoid=True,
                        weight=5.0),
                    dict(
                        eps=1.0,
                        pred_act=True,
                        type='mmdet.DiceCost',
                        weight=5.0),
                ],
                type='mmdet.HungarianAssigner'),
            importance_sample_ratio=0.75,
            num_points=12544,
            oversample_ratio=3.0,
            sampler=dict(type='mmdet.MaskPseudoSampler')),
        transformer_decoder=dict(
            init_cfg=None,
            layer_cfg=dict(
                cross_attn_cfg=dict(
                    attn_drop=0.0,
                    batch_first=True,
                    dropout_layer=None,
                    embed_dims=256,
                    num_heads=8,
                    proj_drop=0.0),
                ffn_cfg=dict(
                    act_cfg=dict(inplace=True, type='ReLU'),
                    add_identity=True,
                    dropout_layer=None,
                    embed_dims=256,
                    feedforward_channels=2048,
                    ffn_drop=0.0,
                    num_fcs=2),
                self_attn_cfg=dict(
                    attn_drop=0.0,
                    batch_first=True,
                    dropout_layer=None,
                    embed_dims=256,
                    num_heads=8,
                    proj_drop=0.0)),
            num_layers=9,
            return_intermediate=True),
        type='Mask2FormerHead'),
    test_cfg=dict(mode='whole'),
    train_cfg=dict(),
    type='EncoderDecoder')
num_classes = 19
optim_wrapper = dict(
    clip_grad=dict(max_norm=0.01, norm_type=2),
    optimizer=dict(
        betas=(
            0.9,
            0.999,
        ),
        eps=1e-08,
        lr=0.0001,
        type='AdamW',
        weight_decay=0.05),
    paramwise_cfg=dict(
        custom_keys=dict({
            'absolute_pos_embed':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone':
            dict(decay_mult=1.0, lr_mult=0.1),
            'backbone.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.patch_embed.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.0.blocks.0.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.0.blocks.1.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.0.downsample.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.1.blocks.0.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.1.blocks.1.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.1.downsample.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.blocks.0.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.blocks.1.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.blocks.2.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.blocks.3.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.blocks.4.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.blocks.5.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.2.downsample.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.3.blocks.0.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'backbone.stages.3.blocks.1.norm':
            dict(decay_mult=0.0, lr_mult=0.1),
            'level_embed':
            dict(decay_mult=0.0, lr_mult=1.0),
            'query_embed':
            dict(decay_mult=0.0, lr_mult=1.0),
            'query_feat':
            dict(decay_mult=0.0, lr_mult=1.0),
            'relative_position_bias_table':
            dict(decay_mult=0.0, lr_mult=0.1)
        }),
        norm_decay_mult=0.0),
    type='OptimWrapper')
optimizer = dict(
    betas=(
        0.9,
        0.999,
    ),
    eps=1e-08,
    lr=0.0001,
    type='AdamW',
    weight_decay=0.05)
param_scheduler = [
    dict(
        begin=0,
        by_epoch=False,
        end=90000,
        eta_min=0,
        power=0.9,
        type='PolyLR'),
]
pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth'
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_prefix=dict(
            img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
        data_root='/dataset/cityscapes/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                2048,
                1024,
            ), type='Resize'),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        type='CityscapesDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        2048,
        1024,
    ), type='Resize'),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=90000, type='IterBasedTrainLoop', val_interval=5000)
train_dataloader = dict(
    batch_size=2,
    dataset=dict(
        data_prefix=dict(
            img_path='leftImg8bit/train', seg_map_path='gtFine/train'),
        data_root='/dataset/cityscapes/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations'),
            dict(
                max_size=4096,
                resize_type='ResizeShortestEdge',
                scales=[
                    512,
                    614,
                    716,
                    819,
                    921,
                    1024,
                    1126,
                    1228,
                    1331,
                    1433,
                    1536,
                    1638,
                    1740,
                    1843,
                    1945,
                    2048,
                ],
                type='RandomChoiceResize'),
            dict(
                cat_max_ratio=0.75, crop_size=(
                    256,
                    512,
                ), type='RandomCrop'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PhotoMetricDistortion'),
            dict(type='PackSegInputs'),
        ],
        type='CityscapesDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        max_size=4096,
        resize_type='ResizeShortestEdge',
        scales=[
            512,
            614,
            716,
            819,
            921,
            1024,
            1126,
            1228,
            1331,
            1433,
            1536,
            1638,
            1740,
            1843,
            1945,
            2048,
        ],
        type='RandomChoiceResize'),
    dict(cat_max_ratio=0.75, crop_size=(
        256,
        512,
    ), type='RandomCrop'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(
        transforms=[
            [
                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
            ],
            [
                dict(direction='horizontal', prob=0.0, type='RandomFlip'),
                dict(direction='horizontal', prob=1.0, type='RandomFlip'),
            ],
            [
                dict(type='LoadAnnotations'),
            ],
            [
                dict(type='PackSegInputs'),
            ],
        ],
        type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_prefix=dict(
            img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
        data_root='/dataset/cityscapes/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                2048,
                1024,
            ), type='Resize'),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        type='CityscapesDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='SegLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs/mask2former-swint-8xb2-512x1024-90k'