gfl_convnext-t-p4-w7_fpn_1x_aodraw_raw_raw-pretraining.py

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

# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
    imports=['mmpretrain.models'], allow_failed_imports=False)
checkpoint_file = 'convnext-tiny_32xb128-noema_in1k_raw-pretraining-029c2ec096135ef3dfb064e4e4329959.pth'  # noqa

model = dict(
    type='GFL',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32),
    backbone=dict(
        type='mmpretrain.ConvNeXt',
        arch='tiny',
        out_indices=[0, 1, 2, 3],
        drop_path_rate=0.4,
        layer_scale_init_value=1.0,
        gap_before_final_norm=False,
        init_cfg=dict(
            type='Pretrained', checkpoint=checkpoint_file,
            prefix='backbone.')),
    neck=dict(
        type='FPN',
        in_channels=[96, 192, 384, 768],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='GFLHead',
        num_classes=62,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
        reg_max=16,
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(type='ATSSAssigner', topk=9),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))

max_epochs = 12
train_cfg = dict(max_epochs=max_epochs)

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[8, 11],
        gamma=0.1)
]

# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(
    type='AmpOptimWrapper',
    constructor='LearningRateDecayOptimizerConstructor',
    paramwise_cfg={
        'decay_rate': 0.75,
        'decay_type': 'layer_wise',
        'num_layers': 6
    },
    optimizer=dict(
        _delete_=True,
        type='AdamW',
        lr=0.0002,
        betas=(0.9, 0.999),
        weight_decay=0.05), 
    clip_grad=dict(max_norm=3, norm_type=2))

find_unused_parameters = True