_base_ = [
    "../_base_/datasets/dotav1.py",
    "../_base_/schedules/schedule_6x.py",
    "../_base_/default_runtime.py",
]
angle_version = "le90"

# model settings
model = dict(
    type="RotatedFCOS",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        zero_init_residual=False,
        norm_cfg=dict(type="BN", requires_grad=True),
        norm_eval=True,
        style="pytorch",
        init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_output",  # use P5
        num_outs=5,
        relu_before_extra_convs=True,
    ),
    bbox_head=dict(
        type="RotatedFCOSHead",
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        center_sampling=True,
        center_sample_radius=1.5,
        norm_on_bbox=True,
        centerness_on_reg=True,
        separate_angle=False,
        scale_angle=True,
        bbox_coder=dict(type="DistanceAnglePointCoder", angle_version=angle_version),
        # bbox_coder=dict(
        #     type='DistancePointBBoxCoder', angle_version=angle_version),
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox=dict(type="RotatedIoULoss", loss_weight=1.0),
        # loss_bbox=dict(type='GIoULoss', loss_weight=1.0),
        # loss_l1_bbox =dict(type='SmoothL1Loss', loss_weight=0.1),
        # loss_ellipse_bbox=dict(type='SmoothL1Loss', loss_weight=0.2),
        # loss_bbox_ct_offset = dict(type='SmoothL1Loss', loss_weight=1.0),
        # loss_bbox_uv = dict(type='SmoothL1Loss', loss_weight=1.0),
        # loss_bbox_m = dict(type='SmoothL1Loss', loss_weight=1.0),
        # # loss_bbox_a = dict(type='SmoothL1Loss', loss_weight=1.0),
        # loss_cls_alpha = dict(
        #     type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2),
        loss_centerness=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
        ),
    ),
    # bbox_head=dict(
    #     type='FAM3DHead',
    #     num_classes=1,
    #     in_channels=256,
    #     stacked_convs=4,
    #     feat_channels=256,
    #     strides=[8, 16, 32, 64, 128],
    #     center_sampling=True,
    #     center_sample_radius=1.5,
    #     norm_on_bbox=True,
    #     centerness_on_reg=True,
    #     # use_hbbox_loss = True,
    #     separate_angle=True,
    #     scale_angle=False,
    #     # angle_coder=dict(
    #     #     type='PSCCoder',
    #     #     angle_version=angle_version,
    #     #     dual_freq=True,
    #     #     num_step=3),
    #     bbox_coder=dict(
    #         type='DistanceAnglePointCoder', angle_version=angle_version),
    #     h_bbox_coder=dict(type='DistancePointBBoxCoder'),
    #     loss_cls=dict(
    #         type='FocalLoss',
    #         use_sigmoid=True,
    #         gamma=2.0,
    #         alpha=0.25,
    #         loss_weight=1.0),
    #     loss_bbox=dict(type='RotatedIoULoss', loss_weight=1.0),
    #     # loss_angle=dict(type='L1Loss', loss_weight=0.2),
    #     loss_centerness=dict(
    #         type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    # training and testing settings
    train_cfg=None,
    test_cfg=dict(
        nms_pre=2000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(iou_thr=0.1),
        max_per_img=2000,
    ),
)

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="RResize", img_scale=(1024, 1024)),
    dict(
        type="RRandomFlip",
        flip_ratio=[0.25, 0.25, 0.25],
        direction=["horizontal", "vertical", "diagonal"],
        version=angle_version,
    ),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
data = dict(
    train=dict(pipeline=train_pipeline, version=angle_version),
    val=dict(version=angle_version),
    test=dict(version=angle_version),
)


log_config = dict(
    interval=50,
    hooks=[
        dict(type="TextLoggerHook"),
        dict(type="TensorboardLoggerHook", interval=10, by_epoch=True),
        # dict(type = 'WandbLoggerHook',
        #      init_kwargs = dict(
        #         project = 'new_GED',
        #         name = 'dn_Deform_RDETR_KFLoss'
        #         )
        #      )
    ],
)
