_base_ = [
    '../../../configs/_base_/datasets/dota.py',
    '../../../configs/_base_/schedules/schedule_1x.py', '../../../configs/_base_/default_runtime.py'
]
num_classes = 15
model = dict(
    type='GFL',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32),
    backbone=dict(
        # _delete_ = True,
        type='MSFA',
        use_sar=True,
        use_wavelet=True,
        input_size = (1024,1024), 
        backbone=dict(
            type='ResNet',
            depth=50,
            num_stages=4,
            out_indices=(0, 1, 2, 3),
            frozen_stages=1,
            norm_cfg=dict(type='BN', requires_grad=True),
            norm_eval=True,
            style='pytorch',
            init_cfg=None
        ),
        init_cfg=dict(type='Pretrained', prefix='backbone', checkpoint='/root/siton-gpfs-archive/yuxuanli/mmpretrain/work_dirs/r50_sar_wavelet/epoch_100.pth'),
    ), 
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='GFLHead',
        num_classes=num_classes,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
        reg_max=16,
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(type='ATSSAssigner', topk=9),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=12)
test_cfg=None
find_unused_parameters = True
# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
