mirror of https://github.com/alibaba/EasyCV.git
143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
# model settings
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norm_cfg = dict(type='GN', num_groups=1, requires_grad=True)
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pretrained = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/selfsup/mae/vit-b-1600/warpper_mae_vit-base-p16-1600e.pth'
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model = dict(
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type='MaskRCNN',
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pretrained=pretrained,
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backbone=dict(
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type='ViTDet',
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img_size=1024,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4,
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qkv_bias=True,
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qk_scale=None,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.1,
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use_abs_pos_emb=True,
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aggregation='attn',
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),
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neck=dict(
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type='SFP',
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in_channels=[768, 768, 768, 768],
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out_channels=256,
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norm_cfg=norm_cfg,
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num_outs=5),
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rpn_head=dict(
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type='RPNHead',
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in_channels=256,
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feat_channels=256,
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num_convs=2,
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norm_cfg=norm_cfg,
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anchor_generator=dict(
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type='AnchorGenerator',
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scales=[8],
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ratios=[0.5, 1.0, 2.0],
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strides=[4, 8, 16, 32, 64]),
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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=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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roi_head=dict(
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type='StandardRoIHead',
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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='Shared4Conv1FCBBoxHead',
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conv_out_channels=256,
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norm_cfg=norm_cfg,
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=80,
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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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reg_class_agnostic=False,
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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mask_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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mask_head=dict(
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type='FCNMaskHead',
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norm_cfg=norm_cfg,
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num_convs=4,
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in_channels=256,
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conv_out_channels=256,
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num_classes=80,
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loss_mask=dict(
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type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
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# model training and testing settings
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train_cfg=dict(
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rpn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.7,
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neg_iou_thr=0.3,
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min_pos_iou=0.3,
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match_low_quality=True,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=256,
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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(
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nms_pre=2000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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min_pos_iou=0.5,
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match_low_quality=True,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=512,
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pos_fraction=0.25,
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neg_pos_ub=-1,
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add_gt_as_proposals=True),
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mask_size=28,
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pos_weight=-1,
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debug=False)),
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test_cfg=dict(
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rpn=dict(
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nms_pre=1000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.5),
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max_per_img=100,
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mask_thr_binary=0.5)))
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mmlab_modules = [
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dict(type='mmdet', name='MaskRCNN', module='model'),
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dict(type='mmdet', name='RPNHead', module='head'),
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dict(type='mmdet', name='StandardRoIHead', module='head'),
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]
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