s0.loss_rpn_reg is 0

[tikboaHIT]

According to your config at Faster RCNN, A mistake is encountered when I train Cascade RCNN. s0.loss_rpn_reg in the log information always is 0.....

my config is as follows:

rpn_weight = 0.7

model settings

model = dict( type='CascadeRCNN', num_stages=3, num_rpn_stages=2, pretrained="/home/tikboa/306_server_work/model_zoo/X101_dcn_backbone.pth", backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=4, style='pytorch', dcn=dict( modulated=False, groups=32, deformable_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True),

gcb=dict(ratio=1. / 4., ),

    # stage_with_gcb=(False, True, True, True)
),
neck=dict(
    type='FPN',
    in_channels=[256, 512, 1024, 2048],
    out_channels=256,
    num_outs=5),
rpn_head=[
    dict(
        type='CascadeRPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_scales=[8],
        anchor_ratios=[1.0],
        anchor_strides=[4, 8, 16, 32, 64],
        target_means=[.0, .0, .0, .0],
        target_stds=[0.1, 0.1, 0.5, 0.5],
        with_cls=False,
        dilation=3,
        bridged_feature=True,
        sampling=False,
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            loss_weight=rpn_weight),
        loss_bbox=dict(
            type='IoULoss', linear=True, loss_weight=10.0 * rpn_weight)),
    dict(
        type='CascadeRPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_scales=[8],
        anchor_ratios=[1.0],
        anchor_strides=[4, 8, 16, 32, 64],
        target_means=[.0, .0, .0, .0],
        target_stds=[0.05, 0.05, 0.1, 0.1],
        feat_adapt=True,
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            loss_weight=1.0 * rpn_weight),
        loss_bbox=dict(
            type='IoULoss', linear=True, loss_weight=10.0 * rpn_weight))],
bbox_roi_extractor=dict(
    type='SingleRoIExtractor',
    roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
    out_channels=256,
    featmap_strides=[4, 8, 16, 32]),
bbox_head=[
    dict(
        type='ConvFCBBoxHead',
        num_shared_convs=4,
        num_shared_fcs=1,
        in_channels=256,
        conv_out_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=16,
        target_means=[0., 0., 0., 0.],
        target_stds=[0.1, 0.1, 0.2, 0.2],
        reg_class_agnostic=True,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
    dict(
        type='ConvFCBBoxHead',
        num_shared_convs=4,
        num_shared_fcs=1,
        in_channels=256,
        conv_out_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=16,
        target_means=[0., 0., 0., 0.],
        target_stds=[0.05, 0.05, 0.1, 0.1],
        reg_class_agnostic=True,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
    dict(
        type='ConvFCBBoxHead',
        num_shared_convs=4,
        num_shared_fcs=1,
        in_channels=256,
        conv_out_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=16,
        target_means=[0., 0., 0., 0.],
        target_stds=[0.033, 0.033, 0.067, 0.067],
        reg_class_agnostic=True,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
]

)

model training and testing settings

train_cfg = dict( rpn=[ dict( assigner=dict( type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), allowed_border=-1, pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.7, min_pos_iou=0.3, 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=-1, pos_weight=-1, debug=False)], rpn_proposal=dict( nms_across_levels=False, nms_pre=2000, nms_post=2000, max_num=300, nms_thr=0.8, min_bbox_size=0), rcnn=[ dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.3, neg_iou_thr=0.3, min_pos_iou=0.3, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.4, neg_iou_thr=0.4, min_pos_iou=0.4, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False) ], stage_loss_weights=[1, 0.5, 0.25]) test_cfg = dict( rpn=dict( nms_across_levels=False, nms_pre=1000, nms_post=1000, max_num=300, nms_thr=0.8, min_bbox_size=0), rcnn=dict( score_thr=0.001, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)

soft-nms is also supported for rcnn testing

# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)

)

[thangvubk]

We have plan to provide config file for cascade rcnn.

[thangvubk]

Please check here

[tikboaHIT]

Thank your sharing.