checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth'
crop_size = (
    1024,
    1024,
)
data_preprocessor = dict(
    bgr_to_rgb=True,
    mean=[
        123.675,
        116.28,
        103.53,
    ],
    pad_val=0,
    seg_pad_val=0,
    size=(
        1024,
        1024,
    ),
    std=[
        58.395,
        57.12,
        57.375,
    ],
    type='SegDataPreProcessor')
data_root = '/root/floor_boundary_segm/'
dataset_type = 'BoundaryDataset'
default_hooks = dict(
    checkpoint=dict(by_epoch=False, interval=2000, 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(draw=True, interval=1, type='SegVisualizationHook'))
default_scope = 'mmseg'
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,
]
load_from = '/opt/sdb/disk-sdb/layout_parser/checkpoints/iter_20000.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
    backbone=dict(
        attn_drop_rate=0.0,
        drop_path_rate=0.1,
        drop_rate=0.0,
        embed_dims=64,
        in_channels=3,
        init_cfg=dict(
            checkpoint=
            'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth',
            type='Pretrained'),
        mlp_ratio=4,
        num_heads=[
            1,
            2,
            5,
            8,
        ],
        num_layers=[
            3,
            8,
            27,
            3,
        ],
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        patch_sizes=[
            7,
            3,
            3,
            3,
        ],
        qkv_bias=True,
        sr_ratios=[
            8,
            4,
            2,
            1,
        ],
        type='MixVisionTransformer'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_val=0,
        seg_pad_val=0,
        size=(
            1024,
            1024,
        ),
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='SegDataPreProcessor'),
    decode_head=dict(
        align_corners=False,
        channels=256,
        dropout_ratio=0.1,
        in_channels=[
            64,
            128,
            320,
            512,
        ],
        in_index=[
            0,
            1,
            2,
            3,
        ],
        loss_decode=dict(
            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
        norm_cfg=dict(requires_grad=True, type='SyncBN'),
        num_classes=5,
        type='SegformerHead'),
    pretrained=None,
    test_cfg=dict(crop_size=(
        1024,
        1024,
    ), mode='slide', stride=(
        768,
        768,
    )),
    train_cfg=dict(),
    type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='SyncBN')
optim_wrapper = dict(
    optimizer=dict(
        betas=(
            0.9,
            0.999,
        ), lr=6e-05, type='AdamW', weight_decay=0.01),
    paramwise_cfg=dict(
        custom_keys=dict(
            head=dict(lr_mult=10.0),
            norm=dict(decay_mult=0.0),
            pos_block=dict(decay_mult=0.0))),
    type='OptimWrapper')
optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
    dict(
        begin=0, by_epoch=False, end=1500, start_factor=1e-06,
        type='LinearLR'),
    dict(
        begin=1500,
        by_epoch=False,
        end=160000,
        eta_min=0.0,
        power=1.0,
        type='PolyLR'),
]
resume = True
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
        data_root='/root/floor_boundary_segm/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1024,
                1024,
            ), type='Resize'),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        type='BoundaryDataset'),
    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(
        interpolation='bicubic',
        keep_ratio=True,
        scale=(
            1024,
            1024,
        ),
        type='Resize'),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=2000)
train_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_prefix=dict(
            img_path='img_dir/train', seg_map_path='ann_dir/train'),
        data_root='/root/floor_boundary_segm/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations'),
            dict(
                keep_ratio=True,
                ratio_range=(
                    0.5,
                    2.0,
                ),
                scale=(
                    1024,
                    1024,
                ),
                type='RandomResize'),
            dict(
                cat_max_ratio=0.75,
                crop_size=(
                    1024,
                    1024,
                ),
                type='RandomCrop'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PhotoMetricDistortion'),
            dict(type='PackSegInputs'),
        ],
        type='BoundaryDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        keep_ratio=True,
        ratio_range=(
            0.5,
            2.0,
        ),
        scale=(
            1024,
            1024,
        ),
        type='RandomResize'),
    dict(cat_max_ratio=0.75, crop_size=(
        1024,
        1024,
    ), 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='img_dir/val', seg_map_path='ann_dir/val'),
        data_root='/root/floor_boundary_segm/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1024,
                1024,
            ), type='Resize'),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        type='BoundaryDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
val_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        1024,
        1024,
    ), type='Resize'),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs'),
]
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='SegLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
        dict(
            init_kwargs=dict(
                group='segformer',
                name='1024x1024',
                project='floorplan parser'),
            type='WandbVisBackend'),
    ])