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_base_ = [ |
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'../_base_/datasets/coco_detection.py', |
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' |
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] |
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teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' |
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|
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model = dict( |
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type='LAD', |
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data_preprocessor=dict( |
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type='DetDataPreprocessor', |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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bgr_to_rgb=True, |
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pad_size_divisor=32), |
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|
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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norm_eval=True, |
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style='pytorch', |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
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neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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start_level=1, |
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add_extra_convs='on_output', |
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num_outs=5), |
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bbox_head=dict( |
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type='LADHead', |
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reg_decoded_bbox=True, |
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score_voting=True, |
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topk=9, |
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num_classes=80, |
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in_channels=256, |
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stacked_convs=4, |
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feat_channels=256, |
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anchor_generator=dict( |
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type='AnchorGenerator', |
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ratios=[1.0], |
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octave_base_scale=8, |
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scales_per_octave=1, |
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strides=[8, 16, 32, 64, 128]), |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[.0, .0, .0, .0], |
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target_stds=[0.1, 0.1, 0.2, 0.2]), |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_bbox=dict(type='GIoULoss', loss_weight=1.3), |
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loss_centerness=dict( |
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), |
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|
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teacher_ckpt=teacher_ckpt, |
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teacher_backbone=dict( |
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type='ResNet', |
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depth=101, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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norm_eval=True, |
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style='pytorch'), |
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teacher_neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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start_level=1, |
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add_extra_convs='on_output', |
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num_outs=5), |
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teacher_bbox_head=dict( |
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type='LADHead', |
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reg_decoded_bbox=True, |
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score_voting=True, |
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topk=9, |
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num_classes=80, |
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in_channels=256, |
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stacked_convs=4, |
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feat_channels=256, |
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anchor_generator=dict( |
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type='AnchorGenerator', |
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ratios=[1.0], |
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octave_base_scale=8, |
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scales_per_octave=1, |
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strides=[8, 16, 32, 64, 128]), |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[.0, .0, .0, .0], |
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target_stds=[0.1, 0.1, 0.2, 0.2]), |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_bbox=dict(type='GIoULoss', loss_weight=1.3), |
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loss_centerness=dict( |
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), |
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|
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train_cfg=dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.1, |
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neg_iou_thr=0.1, |
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min_pos_iou=0, |
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ignore_iof_thr=-1), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False), |
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test_cfg=dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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score_voting=True, |
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nms=dict(type='nms', iou_threshold=0.6), |
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max_per_img=100)) |
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train_dataloader = dict(batch_size=8, num_workers=4) |
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optim_wrapper = dict(type='AmpOptimWrapper', optimizer=dict(lr=0.01)) |
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|
