[Test Error] in loadRes if 'caption' in anns[0]:

[heshuting555]

(mmlab) wangzy@lab517:~/Documents/ShutingHe/Detection/mmdetection$ python tools/test.py configs/cascade_rcnn_r50_fpn_1x.py weights/mask_rcnn_r50_fpn_2x.pth --eval bbox --out ok.pkl
loading annotations into memory... Done (t=0.01s) creating index... index created! unexpected key in source state_dict: bbox_head.fc_cls.weight, bbox_head.fc_cls.bias, bbox_head.fc_reg.weight, bbox_head.fc_reg.bias, bbox_head.shared_fcs.0.weight, bbox_head.shared_fcs.0.bias, bbox_head.shared_fcs.1.weight, bbox_head.shared_fcs.1.bias, mask_head.convs.0.conv.weight, mask_head.convs.0.conv.bias, mask_head.convs.1.conv.weight, mask_head.convs.1.conv.bias, mask_head.convs.2.conv.weight, mask_head.convs.2.conv.bias, mask_head.convs.3.conv.weight, mask_head.convs.3.conv.bias, mask_head.upsample.weight, mask_head.upsample.bias, mask_head.conv_logits.weight, mask_head.conv_logits.bias

missing keys in source state_dict: bbox_head.1.fc_reg.bias, bbox_head.1.shared_fcs.1.bias, bbox_head.0.fc_reg.weight, bbox_head.2.fc_cls.bias, bbox_head.1.shared_fcs.1.weight, bbox_head.1.shared_fcs.0.bias, bbox_head.2.shared_fcs.0.weight, bbox_head.2.shared_fcs.0.bias, bbox_head.0.shared_fcs.0.bias, bbox_head.1.fc_reg.weight, bbox_head.2.fc_reg.bias, bbox_head.0.shared_fcs.0.weight, bbox_head.0.fc_cls.weight, bbox_head.2.fc_cls.weight, bbox_head.1.fc_cls.weight, bbox_head.1.fc_cls.bias, bbox_head.2.shared_fcs.1.weight, bbox_head.0.fc_reg.bias, bbox_head.1.shared_fcs.0.weight, bbox_head.2.fc_reg.weight, bbox_head.0.fc_cls.bias, bbox_head.0.shared_fcs.1.weight, bbox_head.2.shared_fcs.1.bias, bbox_head.0.shared_fcs.1.bias

[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1/1, 1.3 task/s, elapsed: 1s, ETA: 0s writing results to ok.pkl Starting evaluate bbox Loading and preparing results... Traceback (most recent call last): File "tools/test.py", line 189, in main() File "tools/test.py", line 178, in main coco_eval(result_file, eval_types, dataset.coco) File "/home/wangzy/anaconda3/envs/mmlab/lib/python3.6/site-packages/mmdet/core/evaluation/coco_utils.py", line 26, in coco_eval coco_dets = coco.loadRes(result_file) File "/home/wangzy/anaconda3/envs/mmlab/lib/python3.6/site-packages/pycocotools/coco.py", line 318, in loadRes if 'caption' in anns[0]: IndexError: list index out of range

# model settings
model = dict(
    type='CascadeRCNN',
    num_stages=3,
    pretrained='modelzoo://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    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=11,
            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='SharedFCBBoxHead',
            num_fcs=2,
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=11,
            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='SharedFCBBoxHead',
            num_fcs=2,
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=81,
            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))
    ],
    mask_roi_extractor=dict(
        type='SingleRoIExtractor',
        roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
        out_channels=256,
        featmap_strides=[4, 8, 16, 32]),
    mask_head=dict(
        type='FCNMaskHead',
        num_convs=4,
        in_channels=256,
        conv_out_channels=256,
        num_classes=81,
        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,
            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_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='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=28,
            pos_weight=-1,
            debug=False),
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.6,
                neg_iou_thr=0.6,
                min_pos_iou=0.6,
                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=28,
            pos_weight=-1,
            debug=False),
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.7,
                min_pos_iou=0.7,
                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=28,
            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=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,
        mask_thr_binary=0.5),
    keep_all_stages=False)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'train.json',
        img_prefix=data_root + 'train2017/',
        img_scale=(1333, 800),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0.5,
        with_mask=True,
        with_crowd=True,
        with_label=True),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'val.json',
        img_prefix=data_root + 'val2017/',
        img_scale=(1333, 800),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=True,
        with_crowd=True,
        with_label=True),
    test=dict(
        type=dataset_type,
        #ann_file=data_root + 'val.json',
        img_prefix=data_root + 'test2017/',
        img_scale=(1333, 800),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=True,
        with_label=False,
        test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, 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=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/cascade_mask_rcnn_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]

Can you help me fix it. Thank you for your help

[leochangzliao]

@heshuting555 Your model did not converage, check some hyper-paramters.

[heshuting555]

Thank you~

[Stanch-LX]

How do you solve this problem?

[bhaktatejas922]

I have the same question, how did you solve it?

[heshuting555]

You can print the outputs, May be outputs is none

发自我的iPhone

------------------ Original ------------------ From: bhaktatejas922 <notifications@github.com> Date: Wed,Sep 4,2019 1:28 PM To: open-mmlab/mmdetection <mmdetection@noreply.github.com> Cc: heshuting555 <1173718914@qq.com>, State change <state_change@noreply.github.com> Subject: Re: [open-mmlab/mmdetection] [Test Error] in loadRes if 'caption' in anns[0]: (#805)

I have the same question, how did you solve it?

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