auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
data_root = '/kfs2/projects/pvfleets24/repos/cv-dl-framework'
dataset_type = 'CocoDataset'
default_hooks = dict(
    checkpoint=dict(
        rule='greater', save_best='coco/bbox_mAP_50', type='CheckpointHook'),
    logger=dict(interval=50, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'),
    visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = \
    ('/kfs2/projects/pvfleets24/repos/cv-dl-framework/runs/' +
     '02_06_2025_20_53_05/best_coco_bbox_mAP_50_epoch_15.pth')
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
metainfo = dict(classes=('panel', ))
model = dict(
    backbone=dict(
        base_width=4,
        depth=101,
        frozen_stages=1,
        groups=64,
        init_cfg=None,
        norm_cfg=dict(requires_grad=True, type='BN'),
        norm_eval=True,
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        style='pytorch',
        type='ResNeXt'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_mask=True,
        pad_size_divisor=32,
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='DetDataPreprocessor'),
    neck=dict(
        in_channels=[
            256,
            512,
            1024,
            2048,
        ],
        num_outs=5,
        out_channels=256,
        type='FPN'),
    roi_head=dict(
        bbox_head=dict(
            bbox_coder=dict(
                target_means=[
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                ],
                target_stds=[
                    0.1,
                    0.1,
                    0.2,
                    0.2,
                ],
                type='DeltaXYWHBBoxCoder'),
            fc_out_channels=1024,
            in_channels=256,
            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
            loss_cls=dict(
                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
            num_classes=1,
            reg_class_agnostic=False,
            roi_feat_size=7,
            type='Shared2FCBBoxHead'),
        bbox_roi_extractor=dict(
            featmap_strides=[
                4,
                8,
                16,
                32,
            ],
            out_channels=256,
            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
            type='SingleRoIExtractor'),
        mask_head=dict(
            conv_out_channels=256,
            in_channels=256,
            loss_mask=dict(
                loss_weight=1.0, type='CrossEntropyLoss', use_mask=True),
            num_classes=1,
            num_convs=4,
            type='FCNMaskHead'),
        mask_roi_extractor=dict(
            featmap_strides=[
                4,
                8,
                16,
                32,
            ],
            out_channels=256,
            roi_layer=dict(output_size=14, sampling_ratio=0, type='RoIAlign'),
            type='SingleRoIExtractor'),
        test_cfg=dict(
            mask_thr_binary=0.5,
            max_per_img=100,
            nms=dict(iou_threshold=0.5, type='nms'),
            score_thr=0.05),
        train_cfg=dict(
            assigner=dict(
                ignore_iof_thr=-1,
                match_low_quality=True,
                min_pos_iou=0.5,
                neg_iou_thr=0.5,
                pos_iou_thr=0.5,
                type='MaxIoUAssigner'),
            debug=False,
            mask_size=28,
            pos_weight=-1,
            sampler=dict(
                add_gt_as_proposals=True,
                neg_pos_ub=-1,
                num=512,
                pos_fraction=0.25,
                type='RandomSampler')),
        type='StandardRoIHead'),
    rpn_head=dict(
        anchor_generator=dict(
            ratios=[
                0.5,
                1.0,
                2.0,
            ],
            scales=[
                8,
            ],
            strides=[
                4,
                8,
                16,
                32,
                64,
            ],
            type='AnchorGenerator'),
        bbox_coder=dict(
            target_means=[
                0.0,
                0.0,
                0.0,
                0.0,
            ],
            target_stds=[
                1.0,
                1.0,
                1.0,
                1.0,
            ],
            type='DeltaXYWHBBoxCoder'),
        feat_channels=256,
        in_channels=256,
        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
        loss_cls=dict(
            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
        type='RPNHead'),
    test_cfg=dict(
        rcnn=dict(
            mask_thr_binary=0.5,
            max_per_img=100,
            nms=dict(iou_threshold=0.5, type='nms'),
            score_thr=0.05),
        rpn=dict(
            max_per_img=1000,
            min_bbox_size=0,
            nms=dict(iou_threshold=0.7, type='nms'),
            nms_pre=1000)),
    train_cfg=dict(
        rcnn=dict(
            assigner=dict(
                ignore_iof_thr=-1,
                match_low_quality=True,
                min_pos_iou=0.5,
                neg_iou_thr=0.5,
                pos_iou_thr=0.5,
                type='MaxIoUAssigner'),
            debug=False,
            mask_size=28,
            pos_weight=-1,
            sampler=dict(
                add_gt_as_proposals=True,
                neg_pos_ub=-1,
                num=512,
                pos_fraction=0.25,
                type='RandomSampler')),
        rpn=dict(
            allowed_border=-1,
            assigner=dict(
                ignore_iof_thr=-1,
                match_low_quality=True,
                min_pos_iou=0.3,
                neg_iou_thr=0.3,
                pos_iou_thr=0.7,
                type='MaxIoUAssigner'),
            debug=False,
            pos_weight=-1,
            sampler=dict(
                add_gt_as_proposals=False,
                neg_pos_ub=-1,
                num=256,
                pos_fraction=0.5,
                type='RandomSampler')),
        rpn_proposal=dict(
            max_per_img=1000,
            min_bbox_size=0,
            nms=dict(iou_threshold=0.7, type='nms'),
            nms_pre=2000)),
    type='MaskRCNN')
optim_wrapper = dict(
    optimizer=dict(
        lr=0.0006313442216876994,
        momentum=0.9076082930433617,
        type='SGD',
        weight_decay=0.008844292412907089),
    type='OptimWrapper')
param_scheduler = [
    dict(
        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
    dict(
        begin=0,
        by_epoch=True,
        end=12,
        gamma=0.1,
        milestones=[
            9,
            11,
        ],
        type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=2,
    dataset=dict(
        ann_file=('/kfs2/projects/pvfleets24/repos/cv-dl-framework' +
                  '/test/label_json.json'),
        backend_args=None,
        data_prefix=dict(
            img='/kfs2/projects/pvfleets24/repos/cv-dl-framework/test/images/'
        ),
        data_root='/kfs2/projects/pvfleets24/repos/cv-dl-framework',
        metainfo=dict(classes=('panel', )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(
                poly2mask=False,
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    ann_file=('/kfs2/projects/pvfleets24/repos/cv-dl-framework/' +
              'test/label_json.json'),
    backend_args=None,
    metric=[
        'bbox',
        'segm',
    ],
    type='CocoMetric')
test_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        1333,
        800,
    ), type='Resize'),
    dict(
        poly2mask=False,
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=True),
    dict(
        meta_keys=(
            'img_id',
            'img_path',
            'ori_shape',
            'img_shape',
            'scale_factor',
        ),
        type='PackDetInputs'),
]
train_cfg = dict(max_epochs=80, type='EpochBasedTrainLoop', val_interval=3)
train_dataloader = dict(
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    batch_size=2,
    dataset=dict(
        dataset=dict(
            ann_file=('/kfs2/projects/pvfleets24/repos/cv-dl-framework/' +
                      'train/label_json.json'),
            backend_args=None,
            data_prefix=dict(
                img=('/kfs2/projects/pvfleets24/repos/cv-dl-framework/' +
                     'train/images/')
            ),
            data_root='/kfs2/projects/pvfleets24/repos/cv-dl-framework',
            filter_cfg=dict(filter_empty_gt=True, min_size=32),
            metainfo=dict(classes=('panel', )),
            pipeline=[
                dict(backend_args=None, type='LoadImageFromFile'),
                dict(
                    poly2mask=False,
                    type='LoadAnnotations',
                    with_bbox=True,
                    with_mask=True),
                dict(
                    keep_ratio=True,
                    scale=[
                        (
                            1333,
                            640,
                        ),
                        (
                            1333,
                            800,
                        ),
                    ],
                    type='RandomResize'),
                dict(prob=0.5, type='RandomFlip'),
                dict(type='PackDetInputs'),
            ],
            type='CocoDataset'),
        times=3,
        type='RepeatDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(
        poly2mask=False,
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=True),
    dict(
        keep_ratio=True,
        scale=[
            (
                1333,
                640,
            ),
            (
                1333,
                800,
            ),
        ],
        type='RandomResize'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=2,
    dataset=dict(
        ann_file=('/kfs2/projects/pvfleets24/repos/cv-dl-framework/' +
                  'test/label_json.json'),
        backend_args=None,
        data_prefix=dict(
            img='/kfs2/projects/pvfleets24/repos/cv-dl-framework/test/images/'
        ),
        data_root='/kfs2/projects/pvfleets24/repos/cv-dl-framework',
        metainfo=dict(classes=('panel', )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(
                poly2mask=False,
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    ann_file=('/kfs2/projects/pvfleets24/repos/' +
              'cv-dl-framework/test/label_json.json'),
    backend_args=None,
    metric=[
        'bbox',
        'segm',
    ],
    type='CocoMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='DetLocalVisualizer',
    vis_backends=[
        dict(
            save_dir=('/kfs2/projects/pvfleets24/repos/' +
                      'cv-dl-framework/runs/02_06_2025_20_53_05'),
            type='LocalVisBackend'),
    ])
work_dir = ('/)kfs2/projects/pvfleets24/repos/cv-dl-framework' +
            '/runs/02_06_2025_20_53_05')