| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127 |
- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
- model = dict(
- type='LAD',
- 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),
- # student
- backbone=dict(
- type='ResNet',
- depth=101,
- 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://resnet101')),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- add_extra_convs='on_output',
- num_outs=5),
- bbox_head=dict(
- type='LADHead',
- reg_decoded_bbox=True,
- score_voting=True,
- topk=9,
- 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]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
- loss_centerness=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
- # teacher
- teacher_ckpt=teacher_ckpt,
- teacher_backbone=dict(
- type='ResNet',
- depth=50,
- 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'),
- teacher_neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- add_extra_convs='on_output',
- num_outs=5),
- teacher_bbox_head=dict(
- type='LADHead',
- reg_decoded_bbox=True,
- score_voting=True,
- topk=9,
- 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]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
- loss_centerness=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- 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=1000,
- min_bbox_size=0,
- score_thr=0.05,
- score_voting=True,
- nms=dict(type='nms', iou_threshold=0.6),
- max_per_img=100))
- train_dataloader = dict(batch_size=8, num_workers=4)
- optim_wrapper = dict(type='AmpOptimWrapper', optimizer=dict(lr=0.01))
|
