dcfl_r50_fpn_1x_star_le135.py

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

angle_version = 'le135'
model = dict(
    type='RotatedRetinaNetCrop',
    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),
    bbox_head=dict(
        type='RDCFLHead',
        num_classes=48,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        assign_by_circumhbbox=None,
        dcn_assign = True,
        dilation_rate = 4,
        anchor_generator=dict(
            type='RotatedAnchorGenerator',
            octave_base_scale=4,
            scales_per_octave=1, 
            ratios=[1.0], 
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHAOBBoxCoder',
            angle_range=angle_version,
            norm_factor=1,
            edge_swap=False,
            proj_xy=True,
            target_means=(.0, .0, .0, .0, .0),
            target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        reg_decoded_bbox=True,
        loss_bbox=dict(
            type='RotatedIoULoss',
            loss_weight=1.0)), 
    train_cfg=dict(
        assigner=dict(
            type='C2FAssigner',
            ignore_iof_thr=-1,
            gpu_assign_thr= 1024,
            iou_calculator=dict(type='RBboxMetrics2D'),
            assign_metric='gjsd',
            topk=16,
            topq=12,
            constraint='dgmm',
            gauss_thr=0.6),
        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(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(pipeline=train_pipeline, version=angle_version),
    val=dict(version=angle_version),
    test=dict(version=angle_version))

optimizer = dict(
    _delete_=True,
    type='AdamW',
    lr=0.0001,
    betas=(0.9, 0.999),
    weight_decay=0.05,
    paramwise_cfg=dict(
        custom_keys=dict(
            absolute_pos_embed=dict(decay_mult=0.0),
            relative_position_bias_table=dict(decay_mult=0.0),
            norm=dict(decay_mult=0.0))))

checkpoint_config = dict(interval=1, max_keep_ckpts=1)
evaluation = dict(interval=6, metric='mAP')