cascade_s2anet_1s_r50_fpn_1x_dota.py

# model settings
model = dict(
    type='CascadeS2ANetDetector',
    pretrained='torchvision://resnet50',
    num_stages=1,
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        num_outs=5),
    bbox_head=[
        dict(
            type='CascadeS2ANetHead',
            num_classes=16,
            in_channels=256,
            feat_channels=256,
            stacked_convs=2,
            with_align=True,
            anchor_scales=[4],
            anchor_ratios=[1.0],
            anchor_strides=[8, 16, 32, 64, 128],
            anchor_base_sizes=None,
            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),
            loss_bbox=dict(
                type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    ]
)
# training and testing settings
train_cfg = dict(
    loss_weight=[1.0,],
    stage_cfg=[
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.4,
                min_pos_iou=0,
                ignore_iof_thr=-1,
                iou_calculator=dict(type='BboxOverlaps2D_rotated')),
            bbox_coder=dict(type='DeltaXYWHABBoxCoder',
                            target_means=(0., 0., 0., 0., 0.),
                            target_stds=(1., 1., 1., 1., 1.),
                            clip_border=True),
            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='nms_rotated', iou_thr=0.1),
    max_per_img=2000)
# dataset settings
dataset_type = 'DotaDataset'
data_root = 'data/dota_1024/'
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='RotatedResize', img_scale=(1024, 1024), keep_ratio=True),
    dict(type='RotatedRandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1024, 1024),
        flip=False,
        transforms=[
            dict(type='RotatedResize', img_scale=(1024, 1024), keep_ratio=True),
            dict(type='RotatedRandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'trainval_split/trainval_s2anet.pkl',
        img_prefix=data_root + 'trainval_split/images/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'trainval_split/trainval_s2anet.pkl',
        img_prefix=data_root + 'trainval_split/images/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'test_split/test_s2anet.pkl',
        img_prefix=data_root + 'test_split/images/',
        pipeline=test_pipeline))
evaluation = dict(
    gt_dir='data/dota/test/labelTxt/', # change it to valset for offline validation
    imagesetfile='data/dota/test/test.txt')
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[8, 11])
checkpoint_config = dict(interval=4)
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
    ])
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]