default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(
        type='CheckpointHook', interval=1, max_keep_ckpts=5, save_best='auto'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer',
    save_dir='./')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = './model.pth'
resume = True
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=12,
    val_interval=12,
    dynamic_intervals=[(10, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(
    type='TestLoop',
    pipeline=[
        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
        dict(type='Resize', scale=(640, 640), keep_ratio=True),
        dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
        dict(
            type='PackDetInputs',
            meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                       'scale_factor'))
    ])
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='CosineAnnealingLR',
        eta_min=1.25e-05,
        begin=6,
        end=12,
        T_max=6,
        by_epoch=True,
        convert_to_iter_based=True)
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.00025, weight_decay=0.05),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
file_client_args = dict(backend='disk')
train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
    dict(
        type='RandomResize',
        scale=(1280, 1280),
        ratio_range=(0.1, 2.0),
        keep_ratio=True),
    dict(
        type='RandomCrop',
        crop_size=(640, 640),
        recompute_bbox=True,
        allow_negative_crop=True),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='CachedMixUp',
        img_scale=(640, 640),
        ratio_range=(1.0, 1.0),
        max_cached_images=20,
        pad_val=(114, 114, 114)),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
tta_model = dict(
    type='DetTTAModel',
    tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(
        type='TestTimeAug',
        transforms=[[{
            'type': 'Resize',
            'scale': (640, 640),
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale': (320, 320),
            'keep_ratio': True
        }, {
            'type': 'Resize',
            'scale': (960, 960),
            'keep_ratio': True
        }],
                    [{
                        'type': 'RandomFlip',
                        'prob': 1.0
                    }, {
                        'type': 'RandomFlip',
                        'prob': 0.0
                    }],
                    [{
                        'type': 'Pad',
                        'size': (960, 960),
                        'pad_val': {
                            'img': (114, 114, 114)
                        }
                    }],
                    [{
                        'type':
                        'PackDetInputs',
                        'meta_keys':
                        ('img_id', 'img_path', 'ori_shape', 'img_shape',
                         'scale_factor', 'flip', 'flip_direction')
                    }]])
]
model = dict(
    type='RTMDet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        batch_augments=None),
    backbone=dict(
        type='CSPNeXt',
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=0.67,
        widen_factor=0.75,
        channel_attention=True,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    neck=dict(
        type='CSPNeXtPAFPN',
        in_channels=[192, 384, 768],
        out_channels=192,
        num_csp_blocks=2,
        expand_ratio=0.5,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='RTMDetInsSepBNHead',
        num_classes=80,
        in_channels=192,
        stacked_convs=2,
        share_conv=True,
        pred_kernel_size=1,
        feat_channels=192,
        act_cfg=dict(type='SiLU', inplace=True),
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        anchor_generator=dict(
            type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_mask=dict(
            type='DiceLoss', loss_weight=2.0, eps=5e-06, reduction='mean')),
    train_cfg=dict(
        assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=400,
        min_bbox_size=0,
        score_thr=0.4,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=50,
        mask_thr_binary=0.5))
train_pipeline_stage2 = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(
        type='RandomResize',
        scale=(640, 640),
        ratio_range=(0.1, 2.0),
        keep_ratio=True),
    dict(
        type='RandomCrop',
        crop_size=(640, 640),
        recompute_bbox=True,
        allow_negative_crop=True),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(type='PackDetInputs')
]
train_dataloader = dict(
    batch_size=2,
    num_workers=1,
    batch_sampler=None,
    pin_memory=True,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='ConcatDataset',
        datasets=[
            dict(
                type='CocoDataset',
                metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
                data_prefix=dict(
                    img=
                    '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
                ),
                ann_file=
                '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json',
                pipeline=[
                    dict(
                        type='LoadImageFromFile',
                        file_client_args=dict(backend='disk')),
                    dict(
                        type='LoadAnnotations',
                        with_bbox=True,
                        with_mask=True,
                        poly2mask=False),
                    dict(
                        type='CachedMosaic',
                        img_scale=(640, 640),
                        pad_val=114.0),
                    dict(
                        type='RandomResize',
                        scale=(1280, 1280),
                        ratio_range=(0.1, 2.0),
                        keep_ratio=True),
                    dict(
                        type='RandomCrop',
                        crop_size=(640, 640),
                        recompute_bbox=True,
                        allow_negative_crop=True),
                    dict(type='YOLOXHSVRandomAug'),
                    dict(type='RandomFlip', prob=0.5),
                    dict(
                        type='Pad',
                        size=(640, 640),
                        pad_val=dict(img=(114, 114, 114))),
                    dict(
                        type='CachedMixUp',
                        img_scale=(640, 640),
                        ratio_range=(1.0, 1.0),
                        max_cached_images=20,
                        pad_val=(114, 114, 114)),
                    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
                    dict(type='PackDetInputs')
                ])
        ]))
val_dataloader = dict(
    batch_size=1,
    num_workers=10,
    dataset=dict(
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ],
        type='CocoDataset',
        metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
        data_prefix=dict(
            img=
            '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
        ),
        ann_file=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_regions2.json',
        test_mode=True),
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False))
test_dataloader = dict(
    batch_size=1,
    num_workers=10,
    dataset=dict(
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ],
        type='CocoDataset',
        metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
        data_prefix=dict(
            img=
            '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
        ),
        ann_file=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_regions2.json',
        test_mode=True),
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False))
max_epochs = 12
stage2_num_epochs = 2
base_lr = 0.00025
interval = 12
val_evaluator = dict(
    proposal_nums=(100, 1, 10),
    metric=['bbox', 'segm'],
    type='CocoMetric',
    ann_file=
    '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json'
)
test_evaluator = dict(
    proposal_nums=(100, 1, 10),
    metric=['bbox', 'segm'],
    type='CocoMetric',
    ann_file=
    '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json'
)
custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        priority=49),
    dict(
        type='PipelineSwitchHook',
        switch_epoch=10,
        switch_pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                poly2mask=False),
            dict(
                type='RandomResize',
                scale=(640, 640),
                ratio_range=(0.1, 2.0),
                keep_ratio=True),
            dict(
                type='RandomCrop',
                crop_size=(640, 640),
                recompute_bbox=True,
                allow_negative_crop=True),
            dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(type='PackDetInputs')
        ])
]
work_dir = '/home/erik/Riksarkivet/Projects/HTR_Pipeline/models/checkpoints/rtmdet_lines_pr_2'
train_batch_size_per_gpu = 2
val_batch_size_per_gpu = 1
train_num_workers = 1
num_classes = 1
metainfo = dict(classes='text_line', palette=[(220, 20, 60)])
icdar_2019 = dict(
    type='CocoDataset',
    metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
    data_prefix=dict(
        img=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/'
    ),
    ann_file=
    '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/gt_files/coco_regions2.json',
    pipeline=[
        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
        dict(
            type='LoadAnnotations',
            with_bbox=True,
            with_mask=True,
            poly2mask=False),
        dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
        dict(
            type='RandomResize',
            scale=(1280, 1280),
            ratio_range=(0.1, 2.0),
            keep_ratio=True),
        dict(
            type='RandomCrop',
            crop_size=(640, 640),
            recompute_bbox=True,
            allow_negative_crop=True),
        dict(type='YOLOXHSVRandomAug'),
        dict(type='RandomFlip', prob=0.5),
        dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
        dict(
            type='CachedMixUp',
            img_scale=(640, 640),
            ratio_range=(1.0, 1.0),
            max_cached_images=20,
            pad_val=(114, 114, 114)),
        dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
        dict(type='PackDetInputs')
    ])
icdar_2019_test = dict(
    type='CocoDataset',
    metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
    data_prefix=dict(
        img=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/'
    ),
    ann_file=
    '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/gt_files/coco_lines.json',
    test_mode=True,
    pipeline=[
        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
        dict(type='Resize', scale=(640, 640), keep_ratio=True),
        dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
        dict(
            type='PackDetInputs',
            meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                       'scale_factor'))
    ])
police_records = dict(
    type='CocoDataset',
    metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
    data_prefix=dict(
        img=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
    ),
    ann_file=
    '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json',
    pipeline=[
        dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
        dict(
            type='LoadAnnotations',
            with_bbox=True,
            with_mask=True,
            poly2mask=False),
        dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
        dict(
            type='RandomResize',
            scale=(1280, 1280),
            ratio_range=(0.1, 2.0),
            keep_ratio=True),
        dict(
            type='RandomCrop',
            crop_size=(640, 640),
            recompute_bbox=True,
            allow_negative_crop=True),
        dict(type='YOLOXHSVRandomAug'),
        dict(type='RandomFlip', prob=0.5),
        dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
        dict(
            type='CachedMixUp',
            img_scale=(640, 640),
            ratio_range=(1.0, 1.0),
            max_cached_images=20,
            pad_val=(114, 114, 114)),
        dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
        dict(type='PackDetInputs')
    ])
train_list = [
    dict(
        type='CocoDataset',
        metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
        data_prefix=dict(
            img=
            '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/'
        ),
        ann_file=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/police_records/gt_files/coco_lines2.json',
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                poly2mask=False),
            dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
            dict(
                type='RandomResize',
                scale=(1280, 1280),
                ratio_range=(0.1, 2.0),
                keep_ratio=True),
            dict(
                type='RandomCrop',
                crop_size=(640, 640),
                recompute_bbox=True,
                allow_negative_crop=True),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='CachedMixUp',
                img_scale=(640, 640),
                ratio_range=(1.0, 1.0),
                max_cached_images=20,
                pad_val=(114, 114, 114)),
            dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
            dict(type='PackDetInputs')
        ])
]
test_list = [
    dict(
        type='CocoDataset',
        metainfo=dict(classes='text_line', palette=[(220, 20, 60)]),
        data_prefix=dict(
            img=
            '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/'
        ),
        ann_file=
        '/media/erik/Elements/Riksarkivet/data/datasets/htr/segmentation/ICDAR-2019/clean/gt_files/coco_lines.json',
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ])
]
pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
launcher = 'pytorch'