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- # model settings
- norm_cfg = dict(type='BN', requires_grad=False)
- model = dict(
- type='MaskRCNN',
- data_preprocessor=dict(
- type='DetDataPreprocessor',
- mean=[103.530, 116.280, 123.675],
- std=[1.0, 1.0, 1.0],
- bgr_to_rgb=False,
- pad_mask=True,
- pad_size_divisor=32),
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=3,
- strides=(1, 2, 2),
- dilations=(1, 1, 1),
- out_indices=(2, ),
- frozen_stages=1,
- norm_cfg=norm_cfg,
- norm_eval=True,
- style='caffe',
- init_cfg=dict(
- type='Pretrained',
- checkpoint='open-mmlab://detectron2/resnet50_caffe')),
- rpn_head=dict(
- type='RPNHead',
- in_channels=1024,
- feat_channels=1024,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[2, 4, 8, 16, 32],
- ratios=[0.5, 1.0, 2.0],
- strides=[16]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- roi_head=dict(
- type='StandardRoIHead',
- shared_head=dict(
- type='ResLayer',
- depth=50,
- stage=3,
- stride=2,
- dilation=1,
- style='caffe',
- norm_cfg=norm_cfg,
- norm_eval=True),
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
- out_channels=1024,
- featmap_strides=[16]),
- bbox_head=dict(
- type='BBoxHead',
- with_avg_pool=True,
- roi_feat_size=7,
- in_channels=2048,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- mask_roi_extractor=None,
- mask_head=dict(
- type='FCNMaskHead',
- num_convs=0,
- in_channels=2048,
- conv_out_channels=256,
- num_classes=80,
- loss_mask=dict(
- type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
- # model training and testing settings
- train_cfg=dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=0,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=12000,
- max_per_img=2000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- mask_size=14,
- pos_weight=-1,
- debug=False)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=6000,
- nms=dict(type='nms', iou_threshold=0.7),
- max_per_img=1000,
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100,
- mask_thr_binary=0.5)))
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