default_scope = 'mmyolo'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=10),
    param_scheduler=dict(
        type='YOLOv5ParamSchedulerHook',
        scheduler_type='linear',
        lr_factor=0.01,
        max_epochs=100),
    checkpoint=dict(
        type='CheckpointHook', interval=5, save_best='auto', max_keep_ckpts=2),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='mmdet.DetVisualizationHook'))
env_cfg = dict(
    cudnn_benchmark=True,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='mmdet.DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = 'data/si/TTA_도공/best_pretrained.pth'
resume = False
backend_args = None
_backend_args = None
tta_model = dict(
    type='mmdet.DetTTAModel',
    tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300))
img_scales = [(640, 640), (320, 320), (960, 960)]
_multiscale_resize_transforms = [
    dict(
        type='Compose',
        transforms=[
            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
            dict(
                type='LetterResize',
                scale=(640, 640),
                allow_scale_up=False,
                pad_val=dict(img=114))
        ]),
    dict(
        type='Compose',
        transforms=[
            dict(type='YOLOv5KeepRatioResize', scale=(320, 320)),
            dict(
                type='LetterResize',
                scale=(320, 320),
                allow_scale_up=False,
                pad_val=dict(img=114))
        ]),
    dict(
        type='Compose',
        transforms=[
            dict(type='YOLOv5KeepRatioResize', scale=(960, 960)),
            dict(
                type='LetterResize',
                scale=(960, 960),
                allow_scale_up=False,
                pad_val=dict(img=114))
        ])
]
tta_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='TestTimeAug',
        transforms=[[{
            'type':
            'Compose',
            'transforms': [{
                'type': 'YOLOv5KeepRatioResize',
                'scale': (640, 640)
            }, {
                'type': 'LetterResize',
                'scale': (640, 640),
                'allow_scale_up': False,
                'pad_val': {
                    'img': 114
                }
            }]
        }, {
            'type':
            'Compose',
            'transforms': [{
                'type': 'YOLOv5KeepRatioResize',
                'scale': (320, 320)
            }, {
                'type': 'LetterResize',
                'scale': (320, 320),
                'allow_scale_up': False,
                'pad_val': {
                    'img': 114
                }
            }]
        }, {
            'type':
            'Compose',
            'transforms': [{
                'type': 'YOLOv5KeepRatioResize',
                'scale': (960, 960)
            }, {
                'type': 'LetterResize',
                'scale': (960, 960),
                'allow_scale_up': False,
                'pad_val': {
                    'img': 114
                }
            }]
        }],
                    [{
                        'type': 'mmdet.RandomFlip',
                        'prob': 1.0
                    }, {
                        'type': 'mmdet.RandomFlip',
                        'prob': 0.0
                    }], [{
                        'type': 'mmdet.LoadAnnotations',
                        'with_bbox': True
                    }],
                    [{
                        'type':
                        'mmdet.PackDetInputs',
                        'meta_keys':
                        ('img_id', 'img_path', 'ori_shape', 'img_shape',
                         'scale_factor', 'pad_param', 'flip', 'flip_direction')
                    }]])
]
data_root = '/'
train_ann_file = 'annotations/instances_train2017.json'
train_data_prefix = 'train2017/'
val_ann_file = 'annotations/instances_val2017.json'
val_data_prefix = 'val2017/'
num_classes = 80
train_batch_size_per_gpu = 16
train_num_workers = 8
persistent_workers = True
base_lr = 0.01
max_epochs = 500
close_mosaic_epochs = 10
model_test_cfg = dict(
    multi_label=True,
    nms_pre=30000,
    score_thr=0.001,
    nms=dict(type='nms', iou_threshold=0.7),
    max_per_img=300)
img_scale = (640, 640)
dataset_type = 'YOLOv5CocoDataset'
val_batch_size_per_gpu = 1
val_num_workers = 2
batch_shapes_cfg = None
deepen_factor = 1.0
widen_factor = 1.25
strides = [8, 16, 32]
last_stage_out_channels = 512
num_det_layers = 3
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
affine_scale = 0.9
max_aspect_ratio = 100
tal_topk = 10
tal_alpha = 0.5
tal_beta = 6.0
loss_cls_weight = 0.5
loss_bbox_weight = 7.5
loss_dfl_weight = 0.375
lr_factor = 0.01
weight_decay = 0.0005
save_epoch_intervals = 10
val_interval_stage2 = 1
max_keep_ckpts = 2
model = dict(
    type='YOLODetector',
    data_preprocessor=dict(
        type='YOLOv5DetDataPreprocessor',
        mean=[0.0, 0.0, 0.0],
        std=[255.0, 255.0, 255.0],
        bgr_to_rgb=True),
    backbone=dict(
        type='YOLOv8CSPDarknet',
        arch='P5',
        last_stage_out_channels=512,
        deepen_factor=1.0,
        widen_factor=1.25,
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='SiLU', inplace=True)),
    neck=dict(
        type='YOLOv8PAFPN',
        deepen_factor=1.0,
        widen_factor=1.25,
        in_channels=[256, 512, 512],
        out_channels=[256, 512, 512],
        num_csp_blocks=3,
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='YOLOv8Head',
        head_module=dict(
            type='YOLOv8HeadModule',
            num_classes=4,
            in_channels=[256, 512, 512],
            widen_factor=1.25,
            reg_max=16,
            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
            act_cfg=dict(type='SiLU', inplace=True),
            featmap_strides=[8, 16, 32]),
        prior_generator=dict(
            type='mmdet.MlvlPointGenerator', offset=0.5, strides=[8, 16, 32]),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='none',
            loss_weight=0.5),
        loss_bbox=dict(
            type='IoULoss',
            iou_mode='ciou',
            bbox_format='xyxy',
            reduction='sum',
            loss_weight=7.5,
            return_iou=False),
        loss_dfl=dict(
            type='mmdet.DistributionFocalLoss',
            reduction='mean',
            loss_weight=0.375)),
    train_cfg=dict(
        assigner=dict(
            type='BatchTaskAlignedAssigner',
            num_classes=4,
            use_ciou=True,
            topk=10,
            alpha=0.5,
            beta=6.0,
            eps=1e-09)),
    test_cfg=dict(
        multi_label=True,
        nms_pre=30000,
        score_thr=0.001,
        nms=dict(type='nms', iou_threshold=0.7),
        max_per_img=300))
albu_train_transforms = [
    dict(type='Blur', p=0.01),
    dict(type='MedianBlur', p=0.01),
    dict(type='ToGray', p=0.01),
    dict(type='CLAHE', p=0.01)
]
pre_transform = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True)
]
last_transform = [
    dict(type='RemoveDataElement', keys=['gt_masks']),
    dict(
        type='mmdet.Albu',
        transforms=[
            dict(type='Blur', p=0.01),
            dict(type='MedianBlur', p=0.01),
            dict(type='ToGray', p=0.01),
            dict(type='CLAHE', p=0.01)
        ],
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap=dict(img='image', gt_bboxes='bboxes')),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='LoadAnnotations', with_bbox=True, with_mask=True,
        mask2bbox=True),
    dict(
        type='Mosaic',
        img_scale=(640, 640),
        pad_val=114.0,
        pre_transform=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                mask2bbox=True)
        ]),
    dict(type='YOLOv5CopyPaste', prob=0.3),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        max_aspect_ratio=100.0,
        scaling_ratio_range=(0.09999999999999998, 1.9),
        border=(-320, -320),
        border_val=(114, 114, 114),
        min_area_ratio=0.01,
        use_mask_refine=True),
    dict(
        type='YOLOv5MixUp',
        prob=0.15,
        pre_transform=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                mask2bbox=True),
            dict(
                type='Mosaic',
                img_scale=(640, 640),
                pad_val=114.0,
                pre_transform=[
                    dict(type='LoadImageFromFile', backend_args=None),
                    dict(
                        type='LoadAnnotations',
                        with_bbox=True,
                        with_mask=True,
                        mask2bbox=True)
                ]),
            dict(type='YOLOv5CopyPaste', prob=0.3),
            dict(
                type='YOLOv5RandomAffine',
                max_rotate_degree=0.0,
                max_shear_degree=0.0,
                max_aspect_ratio=100.0,
                scaling_ratio_range=(0.09999999999999998, 1.9),
                border=(-320, -320),
                border_val=(114, 114, 114),
                min_area_ratio=0.01,
                use_mask_refine=True)
        ]),
    dict(type='RemoveDataElement', keys=['gt_masks']),
    dict(
        type='mmdet.Albu',
        transforms=[
            dict(type='Blur', p=0.01),
            dict(type='MedianBlur', p=0.01),
            dict(type='ToGray', p=0.01),
            dict(type='CLAHE', p=0.01)
        ],
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap=dict(img='image', gt_bboxes='bboxes')),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]
train_pipeline_stage2 = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='LoadAnnotations', with_bbox=True, with_mask=True,
        mask2bbox=True),
    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
    dict(
        type='LetterResize',
        scale=(640, 640),
        allow_scale_up=True,
        pad_val=dict(img=114.0)),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(0.09999999999999998, 1.9),
        max_aspect_ratio=100,
        border_val=(114, 114, 114),
        min_area_ratio=0.01,
        use_mask_refine=True),
    dict(type='RemoveDataElement', keys=['gt_masks']),
    dict(
        type='mmdet.Albu',
        transforms=[
            dict(type='Blur', p=0.01),
            dict(type='MedianBlur', p=0.01),
            dict(type='ToGray', p=0.01),
            dict(type='CLAHE', p=0.01)
        ],
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap=dict(img='image', gt_bboxes='bboxes')),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]
train_dataloader = dict(
    batch_size=5,
    num_workers=32,
    persistent_workers=True,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='yolov5_collate'),
    dataset=dict(
        type='YOLOv5CocoDataset',
        data_root='/',
        ann_file='/data/si/TTA_도공/pkl_test_json_new.json',
        data_prefix=dict(img='/'),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                mask2bbox=True),
            dict(
                type='Mosaic',
                img_scale=(640, 640),
                pad_val=114.0,
                pre_transform=[
                    dict(type='LoadImageFromFile', backend_args=None),
                    dict(
                        type='LoadAnnotations',
                        with_bbox=True,
                        with_mask=True,
                        mask2bbox=True)
                ]),
            dict(type='YOLOv5CopyPaste', prob=0.3),
            dict(
                type='YOLOv5RandomAffine',
                max_rotate_degree=0.0,
                max_shear_degree=0.0,
                max_aspect_ratio=100.0,
                scaling_ratio_range=(0.09999999999999998, 1.9),
                border=(-320, -320),
                border_val=(114, 114, 114),
                min_area_ratio=0.01,
                use_mask_refine=True),
            dict(
                type='YOLOv5MixUp',
                prob=0.15,
                pre_transform=[
                    dict(type='LoadImageFromFile', backend_args=None),
                    dict(
                        type='LoadAnnotations',
                        with_bbox=True,
                        with_mask=True,
                        mask2bbox=True),
                    dict(
                        type='Mosaic',
                        img_scale=(640, 640),
                        pad_val=114.0,
                        pre_transform=[
                            dict(type='LoadImageFromFile', backend_args=None),
                            dict(
                                type='LoadAnnotations',
                                with_bbox=True,
                                with_mask=True,
                                mask2bbox=True)
                        ]),
                    dict(type='YOLOv5CopyPaste', prob=0.3),
                    dict(
                        type='YOLOv5RandomAffine',
                        max_rotate_degree=0.0,
                        max_shear_degree=0.0,
                        max_aspect_ratio=100.0,
                        scaling_ratio_range=(0.09999999999999998, 1.9),
                        border=(-320, -320),
                        border_val=(114, 114, 114),
                        min_area_ratio=0.01,
                        use_mask_refine=True)
                ]),
            dict(type='RemoveDataElement', keys=['gt_masks']),
            dict(
                type='mmdet.Albu',
                transforms=[
                    dict(type='Blur', p=0.01),
                    dict(type='MedianBlur', p=0.01),
                    dict(type='ToGray', p=0.01),
                    dict(type='CLAHE', p=0.01)
                ],
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
                keymap=dict(img='image', gt_bboxes='bboxes')),
            dict(type='YOLOv5HSVRandomAug'),
            dict(type='mmdet.RandomFlip', prob=0.5),
            dict(
                type='mmdet.PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'flip', 'flip_direction'))
        ],
        metainfo=dict(
            classes=('ConcreteCrack', 'Efflorescene', 'Exposure',
                     'Spalling'))))
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
    dict(
        type='LetterResize',
        scale=(640, 640),
        allow_scale_up=False,
        pad_val=dict(img=114)),
    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'pad_param'))
]
val_dataloader = dict(
    batch_size=5,
    num_workers=32,
    persistent_workers=True,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='YOLOv5CocoDataset',
        data_root='/',
        test_mode=True,
        data_prefix=dict(img='/'),
        ann_file='/data/si/TTA_도공/pkl_test_json_new.json',
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
            dict(
                type='LetterResize',
                scale=(640, 640),
                allow_scale_up=False,
                pad_val=dict(img=114)),
            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
            dict(
                type='mmdet.PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor', 'pad_param'))
        ],
        batch_shapes_cfg=None,
        metainfo=dict(
            classes=('ConcreteCrack', 'Efflorescene', 'Exposure',
                     'Spalling'))))
test_dataloader = dict(
    batch_size=5,
    num_workers=32,
    persistent_workers=True,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='YOLOv5CocoDataset',
        data_root='/',
        test_mode=True,
        data_prefix=dict(img='/'),
        ann_file='/data/si/TTA_도공/pkl_test_json_new.json',
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
            dict(
                type='LetterResize',
                scale=(640, 640),
                allow_scale_up=False,
                pad_val=dict(img=114)),
            dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
            dict(
                type='mmdet.PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor', 'pad_param'))
        ],
        batch_shapes_cfg=None,
        metainfo=dict(
            classes=('ConcreteCrack', 'Efflorescene', 'Exposure',
                     'Spalling'))))
param_scheduler = None
optim_wrapper = dict(
    type='OptimWrapper',
    clip_grad=dict(max_norm=10.0),
    optimizer=dict(
        type='SGD',
        lr=0.01,
        momentum=0.937,
        weight_decay=0.0005,
        nesterov=True,
        batch_size_per_gpu=16),
    constructor='YOLOv5OptimizerConstructor')
custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        update_buffers=True,
        strict_load=False,
        priority=49),
    dict(
        type='mmdet.PipelineSwitchHook',
        switch_epoch=490,
        switch_pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                mask2bbox=True),
            dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
            dict(
                type='LetterResize',
                scale=(640, 640),
                allow_scale_up=True,
                pad_val=dict(img=114.0)),
            dict(
                type='YOLOv5RandomAffine',
                max_rotate_degree=0.0,
                max_shear_degree=0.0,
                scaling_ratio_range=(0.09999999999999998, 1.9),
                max_aspect_ratio=100,
                border_val=(114, 114, 114),
                min_area_ratio=0.01,
                use_mask_refine=True),
            dict(type='RemoveDataElement', keys=['gt_masks']),
            dict(
                type='mmdet.Albu',
                transforms=[
                    dict(type='Blur', p=0.01),
                    dict(type='MedianBlur', p=0.01),
                    dict(type='ToGray', p=0.01),
                    dict(type='CLAHE', p=0.01)
                ],
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
                keymap=dict(img='image', gt_bboxes='bboxes')),
            dict(type='YOLOv5HSVRandomAug'),
            dict(type='mmdet.RandomFlip', prob=0.5),
            dict(
                type='mmdet.PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'flip', 'flip_direction'))
        ])
]
val_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file='/data/si/TTA_도공/pkl_test_json_new.json',
    metric='bbox')
test_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file='/data/si/TTA_도공/pkl_test_json_new.json',
    metric='bbox')
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=100,
    val_interval=1,
    dynamic_intervals=[(40, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
use_mask2refine = True
min_area_ratio = 0.01
mixup_prob = 0.15
copypaste_prob = 0.3
mosaic_affine_transform = [
    dict(
        type='Mosaic',
        img_scale=(640, 640),
        pad_val=114.0,
        pre_transform=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                mask2bbox=True)
        ]),
    dict(type='YOLOv5CopyPaste', prob=0.3),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        max_aspect_ratio=100.0,
        scaling_ratio_range=(0.09999999999999998, 1.9),
        border=(-320, -320),
        border_val=(114, 114, 114),
        min_area_ratio=0.01,
        use_mask_refine=True)
]
class_list = ('ConcreteCrack', 'Efflorescene', 'Exposure', 'Spalling')
BATCH_SIZE = 5
NUM_WORKER = 32
NUM_CLASS = 4
find_unused_parameters = True
launcher = 'none'
work_dir = './work_dirs/final_model'