oriented_reppoints_r50_fpn_1x_dota_le135.py

_base_ = [
    '../_base_/datasets/dotav1.py', '../_base_/schedules/schedule_1x.py',
    '../_base_/default_runtime.py'
]

angle_version = 'le135'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
    type='RotatedRepPoints',
    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_input',
        num_outs=5,
        norm_cfg=norm_cfg),
    bbox_head=dict(
        type='OrientedRepPointsHead',
        num_classes=15,
        in_channels=256,
        feat_channels=256,
        point_feat_channels=256,
        stacked_convs=3,
        num_points=9,
        gradient_mul=0.3,
        point_strides=[8, 16, 32, 64, 128],
        point_base_scale=2,
        norm_cfg=norm_cfg,
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox_init=dict(type='ConvexGIoULoss', loss_weight=0.375),
        loss_bbox_refine=dict(type='ConvexGIoULoss', loss_weight=1.0),
        loss_spatial_init=dict(type='SpatialBorderLoss', loss_weight=0.05),
        loss_spatial_refine=dict(type='SpatialBorderLoss', loss_weight=0.1),
        init_qua_weight=0.2,
        top_ratio=0.4),
    # training and testing settings
    train_cfg=dict(
        init=dict(
            assigner=dict(type='ConvexAssigner', scale=4, pos_num=1),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        refine=dict(
            assigner=dict(
                type='MaxConvexIoUAssigner',
                pos_iou_thr=0.1,
                neg_iou_thr=0.1,
                min_pos_iou=0,
                ignore_iof_thr=-1),
            allowed_border=-1,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        nms_pre=2000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(iou_thr=0.4),
        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))
optimizer = dict(lr=0.008)
checkpoint_config = dict(interval=1)