dfdet_r50_dota1.py

_base_ = [
    '../_base_/datasets/dota.py', '../_base_/schedules/schedule_1x.py',
    '../_base_/default_runtime.py'
]

model = dict(
    type='ATSSOBB',
    pretrained=
    'https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth',
    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=False),
        norm_eval=True,
        style='caffe'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        extra_convs_on_inputs=False,  # use P5
        num_outs=5,
        relu_before_extra_convs=True),
    bbox_head=dict(
        type='ScaleAdaptiveHead',
        num_classes=15,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        scale_theta=True,
        reg_loss_wh_thre=5,
        bbox_coder=dict(
            type='OBB2DistCoder',
            target_means=(0., 0., 0., 0., 0.),
            target_stds=(1., 1., 1., 1., 1.)),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='CIoULoss', loss_weight=1.0),
        loss_trig=dict(type="L1Loss", loss_weight=0.2),
        anchor_generator=dict(
            type="Theta0AnchorGenerator",
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            center_offset=0.5,
            strides=[8, 16, 32, 64, 128]),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))

# training and testing settings
train_cfg = dict(
    assigner=dict(type='OBBATSSAssigner', topk=9),
    allowed_border=-1,
    pos_weight=-1,
    debug=False)
test_cfg = dict(
    nms_pre=2000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type='obb_nms', iou_thr=0.1),
    max_per_img=2000)

# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.000025, weight_decay=0.0001)
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2))

# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[55, 88, 121])
total_epochs = 200

checkpoint_config = dict(interval=10)