dataset_type = 'AirbusShipDataset'
data_root = '/data/share/airbus-ship-detection/'
img_norm_cfg = dict(
    mean=[52.29048625, 73.2539164, 80.97759001],
    std=[53.09640994, 47.58987537, 42.15418378],
    to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RResize', img_scale=(768, 768)),
    dict(
        type='RRandomFlip',
        flip_ratio=[0.25, 0.25, 0.25],
        direction=['horizontal', 'vertical', 'diagonal'],
        version='le90'),
    dict(
        type='PolyRandomRotate',
        rotate_ratio=0.5,
        angles_range=180,
        auto_bound=False,
        version='le90'),
    dict(
        type='Normalize',
        mean=[52.29048625, 73.2539164, 80.97759001],
        std=[53.09640994, 47.58987537, 42.15418378],
        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'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(768, 768),
        flip=False,
        transforms=[
            dict(type='RResize', img_scale=(768, 768)),
            dict(
                type='Normalize',
                mean=[52.29048625, 73.2539164, 80.97759001],
                std=[53.09640994, 47.58987537, 42.15418378],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=20,
    workers_per_gpu=8,
    train=dict(
        type='AirbusShipDataset',
        ann_file='full.csv',
        img_prefix='train_v2/',
        metrics_file='metrics_20240328.csv',
        oversample_rate=5,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RResize', img_scale=(768, 768)),
            dict(
                type='RRandomFlip',
                flip_ratio=[0.25, 0.25, 0.25],
                direction=['horizontal', 'vertical', 'diagonal'],
                version='le90'),
            dict(
                type='PolyRandomRotate',
                rotate_ratio=0.5,
                angles_range=180,
                auto_bound=False,
                version='le90'),
            dict(
                type='Normalize',
                mean=[52.29048625, 73.2539164, 80.97759001],
                std=[53.09640994, 47.58987537, 42.15418378],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        version='le90',
        data_root='/data/share/airbus-ship-detection/'),
    val=dict(
        type='AirbusShipDataset',
        ann_file='valid.csv',
        img_prefix='train_v2/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(768, 768),
                flip=False,
                transforms=[
                    dict(type='RResize', img_scale=(768, 768)),
                    dict(
                        type='Normalize',
                        mean=[52.29048625, 73.2539164, 80.97759001],
                        std=[53.09640994, 47.58987537, 42.15418378],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='le90',
        data_root='/data/share/airbus-ship-detection/'),
    test=dict(
        type='AirbusShipDataset',
        ann_file='valid.csv',
        img_prefix='train_v2/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(768, 768),
                flip=False,
                transforms=[
                    dict(type='RResize', img_scale=(768, 768)),
                    dict(
                        type='Normalize',
                        mean=[52.29048625, 73.2539164, 80.97759001],
                        std=[53.09640994, 47.58987537, 42.15418378],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='le90',
        data_root='/data/share/airbus-ship-detection/'))
evaluation = dict(interval=2, metric='mAP', save_best='mAP')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=2000,
    warmup_ratio=0.05,
    min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=20)
checkpoint_config = dict(interval=10)
log_config = dict(
    interval=200,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'redet_re50_fpn_1x_dota_ms_rr_le90-fc9217b5.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
angle_version = 'le90'
model = dict(
    type='ReDet',
    backbone=dict(
        type='ReResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        style='pytorch',
        pretrained='work_dirs/pretrain/re_resnet50_c8_batch256-25b16846.pth'),
    neck=dict(
        type='ReFPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RotatedRPNHead',
        in_channels=256,
        feat_channels=256,
        version='le90',
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[2, 4],
            ratios=[0.125, 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='RoITransRoIHead',
        version='le90',
        num_stages=2,
        stage_loss_weights=[1, 1],
        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]),
            dict(
                type='RotatedSingleRoIExtractor',
                roi_layer=dict(
                    type='RiRoIAlignRotated',
                    out_size=7,
                    num_samples=2,
                    num_orientations=16,
                    clockwise=True),
                out_channels=256,
                featmap_strides=[4, 8, 16, 32])
        ],
        bbox_head=[
            dict(
                type='RotatedShared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHAHBBoxCoder',
                    angle_range='le90',
                    norm_factor=2,
                    edge_swap=True,
                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.1, 0.1, 0.2, 0.2, 1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='RotatedShared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHAOBBoxCoder',
                    angle_range='le90',
                    norm_factor=None,
                    edge_swap=True,
                    proj_xy=True,
                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.05, 0.05, 0.1, 0.1, 0.5]),
                reg_class_agnostic=False,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ]),
    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,
                gpu_assign_thr=200),
            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,
                    iou_calculator=dict(type='BboxOverlaps2D')),
                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=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1,
                    iou_calculator=dict(type='RBboxOverlaps2D')),
                sampler=dict(
                    type='RRandomSampler',
                    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=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            nms_pre=2000,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(iou_thr=0.1),
            max_per_img=2000)))
img_size = 768
max_keep_ckpts = 1
val_dataloader = dict(samples_per_gpu=20, workers_per_gpu=8)
seed = 1984
gpu_ids = range(0, 1)
device = 'cuda'
work_dir = './logs/redet/2024-03-28-14-45-06'