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- _base_ = [
- '../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py',
- '../_base_/datasets/semi_coco_detection.py'
- ]
- detector = _base_.model
- detector.data_preprocessor = dict(
- type='DetDataPreprocessor',
- mean=[103.530, 116.280, 123.675],
- std=[1.0, 1.0, 1.0],
- bgr_to_rgb=False,
- pad_size_divisor=32)
- detector.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=False),
- norm_eval=True,
- style='caffe',
- init_cfg=dict(
- type='Pretrained',
- checkpoint='open-mmlab://detectron2/resnet50_caffe'))
- model = dict(
- _delete_=True,
- type='SoftTeacher',
- detector=detector,
- data_preprocessor=dict(
- type='MultiBranchDataPreprocessor',
- data_preprocessor=detector.data_preprocessor),
- semi_train_cfg=dict(
- freeze_teacher=True,
- sup_weight=1.0,
- unsup_weight=4.0,
- pseudo_label_initial_score_thr=0.5,
- rpn_pseudo_thr=0.9,
- cls_pseudo_thr=0.9,
- reg_pseudo_thr=0.02,
- jitter_times=10,
- jitter_scale=0.06,
- min_pseudo_bbox_wh=(1e-2, 1e-2)),
- semi_test_cfg=dict(predict_on='teacher'))
- # 10% coco train2017 is set as labeled dataset
- labeled_dataset = _base_.labeled_dataset
- unlabeled_dataset = _base_.unlabeled_dataset
- labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@10.json'
- unlabeled_dataset.ann_file = 'semi_anns/' \
- 'instances_train2017.1@10-unlabeled.json'
- unlabeled_dataset.data_prefix = dict(img='train2017/')
- train_dataloader = dict(
- dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))
- # training schedule for 180k
- train_cfg = dict(
- type='IterBasedTrainLoop', max_iters=180000, val_interval=5000)
- val_cfg = dict(type='TeacherStudentValLoop')
- test_cfg = dict(type='TestLoop')
- # learning rate policy
- param_scheduler = [
- dict(
- type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
- dict(
- type='MultiStepLR',
- begin=0,
- end=180000,
- by_epoch=False,
- milestones=[120000, 160000],
- gamma=0.1)
- ]
- # optimizer
- optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
- default_hooks = dict(
- checkpoint=dict(by_epoch=False, interval=10000, max_keep_ckpts=2))
- log_processor = dict(by_epoch=False)
- custom_hooks = [dict(type='MeanTeacherHook')]
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