model = dict(
    type='CascadeRCNN',
    backbone=dict(
        type='SwinTransformer',
        embed_dims=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        mlp_ratio=4,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.2,
        patch_norm=True,
        out_indices=(0, 1, 2, 3),
        with_cp=False,
        convert_weights=True,
        init_cfg=None),
    neck=dict(
        type='FPN',
        in_channels=[96, 192, 384, 768],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
    roi_head=dict(
        type='CascadeRoIHead_LGF',
        num_stages=3,
        stage_loss_weights=[1, 1, 0.5],
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=[
            dict(
                type='Shared3FCBBoxHead_with_BboxEncoding',
                in_channels=256,
                fc_out_channels=1024,
                bbox_encoding_dim=512,
                roi_feat_size=7,
                num_classes=18,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=True,
                loss_cls=dict(type='FocalLoss'),
                loss_bbox=dict(
                    type='BalancedL1Loss', beta=1.0, loss_weight=1.0)),
            dict(
                type='Shared3FCBBoxHead_with_BboxEncoding',
                in_channels=256,
                fc_out_channels=1024,
                bbox_encoding_dim=512,
                roi_feat_size=7,
                num_classes=18,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(type='FocalLoss'),
                loss_bbox=dict(
                    type='BalancedL1Loss', beta=1.0, loss_weight=1.0)),
            dict(
                type='Shared3FCBBoxHead_with_BboxEncoding',
                in_channels=256,
                fc_out_channels=1024,
                bbox_encoding_dim=512,
                roi_feat_size=7,
                num_classes=18,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(type='FocalLoss'),
                loss_bbox=dict(
                    type='BalancedL1Loss', beta=1.0, loss_weight=1.0))
        ],
        localglobal_fuser=dict(
            type='LocalGlobal_Context_Fuser',
            channels=256,
            roi_size=7,
            reduced_channels=256,
            lg_merge_layer=dict(type='SELayer', channels=256)),
        lgf_shared=False,
        bbox_encoder=dict(
            type='BboxEncoder',
            n_layer=4,
            n_head=4,
            n_embd=512,
            bbox_cord_dim=4,
            bbox_max_num=1024,
            embd_pdrop=0.1,
            attn_pdrop=0.1),
        bbox_encoder_shared=False),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=[
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.6,
                    neg_iou_thr=0.6,
                    min_pos_iou=0.6,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.7,
                    min_pos_iou=0.7,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False)
        ]),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.,
            nms=dict(type='nms', iou_threshold=0.7),
            max_per_img=200)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[216.45, 212.36, 206.76], std=[55.82, 56.04, 55.56], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='AutoAugment',
        policies=[[{
            'type':
            'Resize',
            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                          (736, 1333), (768, 1333), (800, 1333)],
            'multiscale_mode':
            'value',
            'keep_ratio':
            True
        }],
                  [{
                      'type': 'Resize',
                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
                      'multiscale_mode': 'value',
                      'keep_ratio': True
                  }, {
                      'type': 'RandomCrop',
                      'crop_type': 'absolute_range',
                      'crop_size': (384, 600),
                      'allow_negative_crop': True
                  }, {
                      'type':
                      'Resize',
                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),
                                    (576, 1333), (608, 1333), (640, 1333),
                                    (672, 1333), (704, 1333), (736, 1333),
                                    (768, 1333), (800, 1333)],
                      'multiscale_mode':
                      'value',
                      'override':
                      True,
                      'keep_ratio':
                      True
                  }, {
                      'type': 'PhotoMetricDistortion',
                      'brightness_delta': 32,
                      'contrast_range': (0.5, 1.5),
                      'saturation_range': (0.5, 1.5),
                      'hue_delta': 18
                  }, {
                      'type': 'MinIoURandomCrop',
                      'min_ious': (0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
                      'min_crop_size': 0.3
                  }, {
                      'type':
                      'CutOut',
                      'n_holes': (5, 10),
                      'cutout_shape': [(4, 4), (4, 8), (8, 4), (8, 8),
                                       (16, 32), (32, 16), (32, 32), (32, 48),
                                       (48, 32), (48, 48)]
                  }]]),
    dict(type='RandomFlip', flip_ratio=0.1),
    dict(
        type='Normalize',
        mean=[216.45, 212.36, 206.76],
        std=[55.82, 56.04, 55.56],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile', to_float32=True),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.0),
            dict(
                type='Normalize',
                mean=[216.45, 212.36, 206.76],
                std=[55.82, 56.04, 55.56],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=3,
    workers_per_gpu=4,
    train=dict(
        type='CocoDataset',
        ann_file=
        './data/pmc_2022/pmc_coco/element_detection/train.json',
        img_prefix=
        './data/pmc_2022/pmc_coco/element_detection/train/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='AutoAugment',
                policies=[[{
                    'type':
                    'Resize',
                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),
                                  (576, 1333), (608, 1333), (640, 1333),
                                  (672, 1333), (704, 1333), (736, 1333),
                                  (768, 1333), (800, 1333)],
                    'multiscale_mode':
                    'value',
                    'keep_ratio':
                    True
                }],
                          [{
                              'type': 'Resize',
                              'img_scale': [(400, 1333), (500, 1333),
                                            (600, 1333)],
                              'multiscale_mode': 'value',
                              'keep_ratio': True
                          }, {
                              'type': 'RandomCrop',
                              'crop_type': 'absolute_range',
                              'crop_size': (384, 600),
                              'allow_negative_crop': True
                          }, {
                              'type':
                              'Resize',
                              'img_scale': [(480, 1333), (512, 1333),
                                            (544, 1333), (576, 1333),
                                            (608, 1333), (640, 1333),
                                            (672, 1333), (704, 1333),
                                            (736, 1333), (768, 1333),
                                            (800, 1333)],
                              'multiscale_mode':
                              'value',
                              'override':
                              True,
                              'keep_ratio':
                              True
                          }, {
                              'type': 'PhotoMetricDistortion',
                              'brightness_delta': 32,
                              'contrast_range': (0.5, 1.5),
                              'saturation_range': (0.5, 1.5),
                              'hue_delta': 18
                          }, {
                              'type': 'MinIoURandomCrop',
                              'min_ious': (0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
                              'min_crop_size': 0.3
                          }, {
                              'type':
                              'CutOut',
                              'n_holes': (5, 10),
                              'cutout_shape': [(4, 4), (4, 8), (8, 4), (8, 8),
                                               (16, 32), (32, 16), (32, 32),
                                               (32, 48), (48, 32), (48, 48)]
                          }]]),
            dict(type='RandomFlip', flip_ratio=0.1),
            dict(
                type='Normalize',
                mean=[216.45, 212.36, 206.76],
                std=[55.82, 56.04, 55.56],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        classes=[
            'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel',
            'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label',
            'legend_title', 'legend_area', 'mark_label', 'value_label',
            'y_axis_area', 'x_axis_area', 'tick_grouping'
        ]),
    val=dict(
        type='CocoDataset',
        ann_file=
        './data/pmc_2022/pmc_coco/element_detection/val.json',
        img_prefix=
        './data/pmc_2022/pmc_coco/element_detection/val/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=[
            'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel',
            'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label',
            'legend_title', 'legend_area', 'mark_label', 'value_label',
            'y_axis_area', 'x_axis_area', 'tick_grouping'
        ]),
    test=dict(
        type='CocoDataset',
        ann_file=
        './data/pmc_2022/pmc_coco/element_detection/split3_test.json',
        img_prefix=
        './data/pmc_2022/pmc_coco/element_detection/split3_test/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=[
            'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel',
            'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label',
            'legend_title', 'legend_area', 'mark_label', 'value_label',
            'y_axis_area', 'x_axis_area', 'tick_grouping'
        ]))
evaluation = dict(interval=1, metric=['bbox'])
optimizer = dict(
    type='AdamW',
    lr=0.0002,
    betas=(0.9, 0.999),
    weight_decay=0.05,
    paramwise_cfg=dict(
        custom_keys=dict(
            absolute_pos_embed=dict(decay_mult=0.0),
            relative_position_bias_table=dict(decay_mult=0.0),
            norm=dict(decay_mult=0.0))))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=150)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
classes = [
    'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel',
    'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label',
    'legend_title', 'legend_area', 'mark_label', 'value_label', 'y_axis_area',
    'x_axis_area', 'tick_grouping'
]
auto_resume = False
gpu_ids = range(0, 4)