Libra_rcnn detect result is 0

[elfpattern]

I use the libra_rcnn to detect two classes ,I changed the backbone based one senet101, when I test my model, I found that threre exists nothing, Here is my config:

model = dict(
    type='FasterRCNN',
    pretrained='/root/.torch/models/se_resnext101_32x4d-3b2fe3d8.pth',
    backbone=dict(
        type='SENet',
        net_type='se_resnext101_32x4d'),
    neck=[
        dict(
            type='FPN',
            in_channels=[256, 512, 1024, 2048],
            out_channels=256,
            num_outs=5),
        dict(
            type='BFP',
            in_channels=256,
            num_levels=5,
            refine_level=2,
            refine_type='non_local')
    ],
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_scales=[8],
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[4, 8, 16, 32, 64],
        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='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    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='SharedFCBBoxHead',
        num_fcs=2,
        in_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=3,
        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='BalancedL1Loss',
            alpha=0.5,
            gamma=1.5,
            beta=1.0,
            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,
            ignore_iof_thr=-1),
        sampler=dict(
            type='RandomSampler',
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=5,
            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=2000,
        nms_thr=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,
            ignore_iof_thr=-1),
        sampler=dict(
            type='CombinedSampler',
            num=512,
            pos_fraction=0.25,
            add_gt_as_proposals=True,
            pos_sampler=dict(type='InstanceBalancedPosSampler'),
            neg_sampler=dict(
                type='IoUBalancedNegSampler',
                floor=-1,
                floor_thr=0,
                num_bins=3)),
        pos_weight=-1,
        debug=False))
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0),
    rcnn=dict(
        score_thr=0.05, 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)
)
# dataset settings
dataset_type = 'VOCDataset'
data_root = '/root/workspace_new_dibu/'
img_norm_cfg = dict(
    mean=[54.664, 54.664, 54.664], std=[48.490, 48.490, 48.490], to_rgb=True)
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=0,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'VOC2012/ImageSets/Main/train.txt',
        img_prefix=data_root + 'VOC2012/',
        img_scale=(780, 340),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0.5,
        with_mask=False,
        with_crowd=True,
        with_label=True),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'VOC2012/ImageSets/Main/val.txt',
        img_prefix=data_root + 'VOC2012/',
        img_scale=(780, 340),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=False,
        with_crowd=True,
        with_label=True),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'VOC2012/ImageSets/Main/val.txt',
        img_prefix=data_root + 'VOC2012/',
        img_scale=(780, 340),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=False,
        with_label=False,
        test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[8, 11])
checkpoint_config = dict(interval=5)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 100
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = '/root/librcnn_dibu/librcnn_se101_2_classes'
load_from = None
resume_from = None
workflow = [('train', 1)]

who can tell me what happened???

[OceanPang]

@elfpattern As you have modified some code in this framework, I recommend you checking the code first to find the bug (there must a/some bugs during your training or testing). I can't help only with the config.

[elfpattern]

@OceanPang Thanks, I changed nothing except for the backbone , it is senet.py, I don't konw what happened .

[OceanPang]

Considering you are using your own dataset in VOC format, I recommend you training Libra R-CNN use ResNet first. That means, to check whether your modified dataset is right or not. If it's okay, then check the backbone you modified. Just follow the rules to find the bug.

[guohaoyuan]

@OceanPang Thanks, I changed nothing except for the backbone , it is senet.py, I don't konw what happened .

你好，请问你是怎么修改的，我没有看明白，