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- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa
- model = dict(
- type='KnowledgeDistillationSingleStageDetector',
- 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),
- teacher_config='configs/gfl/gfl_r101_fpn_ms-2x_coco.py',
- teacher_ckpt=teacher_ckpt,
- backbone=dict(
- type='ResNet',
- depth=18,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
- neck=dict(
- type='FPN',
- in_channels=[64, 128, 256, 512],
- out_channels=256,
- start_level=1,
- add_extra_convs='on_output',
- num_outs=5),
- bbox_head=dict(
- type='LDHead',
- num_classes=80,
- 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),
- loss_ld=dict(
- type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10),
- 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))
- optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
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