I got an error report when training

[Wenting-Xu]

Thanks for your error report and we appreciate it a lot.

Checklist

1. I have searched related issues but cannot get the expected help.
2. The bug has not been fixed in the latest version.

Describe the bug A clear and concise description of what the bug is.

Reproduction

1. What command or script did you run?

   
   python tools/train.py configs/DetectoRS/DetectoRS_mstrain_400_1200_x101_32x4d_40e.py

2. Did you make any modifications on the code or config? Did you understand what you have modified?
conv_cfg = dict(type='ConvAWS')
# model settings
model = dict(
    type='RecursiveFeaturePyramid',
    rfp_steps=2,
    rfp_sharing=False,
    stage_with_rfp=(False, True, True, True),
    num_stages=3,
    pretrained='open-mmlab://resnext101_32x4d',
    interleaved=True,
    #mask_info_flow=True,

    backbone=dict(
        type='ResNeXt',
        depth=101,
        groups=32,
        base_width=4,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        conv_cfg=conv_cfg,
        sac=dict(type='SAC', use_deform=True),
        stage_with_sac=(False, True, True, True),
        norm_cfg=dict(type='BN', requires_grad=True),
        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=22,
            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=22,
            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=22,
            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='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_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),

            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),

            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),

            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=0.001,
        nms=dict(type='soft_nms', iou_thr=0.5),
        max_per_img=100,
        mask_thr_binary=0.5))
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='LoadAnnotations', with_bbox=True),
    dict(
        type='Resize',
        img_scale=[(1333, 400), (1333, 1200)],
        multiscale_mode='range',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    #dict(type='SegRescale', scale_factor=1 / 8),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    imgs_per_gpu=1,
    workers_per_gpu=1,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',

        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric=['bbox'])
# 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=[36, 39])
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 = 40
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/DetectoRS_mstrain_400_1200_x101_32x4d_40e.py'
load_from = None
resume_from = None
workflow = [('train', 1)]

3. What dataset did you use?
i used coco dataset

**Environment**

1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment infomation and paste it here.
ys.platform: linux
Python: 3.7.7 (default, May  7 2020, 21:25:33) [GCC 7.3.0]
CUDA available: True
CUDA_HOME: /usr/local/cuda-10.1
NVCC: Cuda compilation tools, release 10.1, V10.1.243
GPU 0,1,2,3: Tesla V100-DGXS-32GB
GCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
PyTorch: 1.4.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - Intel(R) Math Kernel Library Version 2020.0.1 Product Build 20200208 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CUDA Runtime 10.0
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.3
  - Magma 2.5.1
  - Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, 

TorchVision: 0.5.0
OpenCV: 4.3.0
MMCV: 1.0.4
MMDetection: 1.1.0+612916b
MMDetection Compiler: GCC 7.4
MMDetection CUDA Compiler: 10.1

2. You may add addition that may be helpful for locating the problem, such as
    - How you installed PyTorch [e.g., pip, conda, source]
conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

**Error traceback**
If applicable, paste the error trackback here.

loading annotations into memory... Done (t=0.18s) creating index... index created! /raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py:173: UserWarning: Runner was deprecated, please use EpochBasedRunner instead 'Runner was deprecated, please use EpochBasedRunner instead') /raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/mmcv/runner/base_runner.py:59: UserWarning: batch_processor is deprecated, please implement train_step() and val_step() in the model instead. warnings.warn('batch_processor is deprecated, please implement ' 2020-07-26 15:38:11,938 - mmdet - INFO - Start running, host: xuwt@DGX-2, work_dir: /raid/xuwt/ting/DetectoRS/work_dirs/DetectoRS_mstrain_400_1200_x101_32x4d_40e.py 2020-07-26 15:38:11,938 - mmdet - INFO - workflow: [('train', 1)], max: 40 epochs Traceback (most recent call last): File "tools/train.py", line 142, in main() File "tools/train.py", line 138, in main meta=meta) File "/raid/xuwt/ting/DetectoRS/mmdet/apis/train.py", line 111, in train_detector meta=meta) File "/raid/xuwt/ting/DetectoRS/mmdet/apis/train.py", line 242, in _non_dist_train runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 122, in run epoch_runner(data_loaders[i], kwargs) File "/raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 35, in train self.model, data_batch, train_mode=True, kwargs) File "/raid/xuwt/ting/DetectoRS/mmdet/apis/train.py", line 75, in batch_processor losses = model(data) File "/raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, *kwargs) File "/raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward return self.module(inputs[0], kwargs[0]) File "/raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, kwargs) File "/raid/xuwt/ting/DetectoRS/mmdet/core/fp16/decorators.py", line 49, in new_func return old_func(args, kwargs) File "/raid/xuwt/ting/DetectoRS/mmdet/models/detectors/base.py", line 147, in forward return self.forward_train(img, img_metas, kwargs) File "/raid/xuwt/ting/DetectoRS/mmdet/models/detectors/htc.py", line 216, in forward_train rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) File "/raid/xuwt/ting/DetectoRS/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss gt_bboxes_ignore=gt_bboxes_ignore) File "/raid/xuwt/ting/DetectoRS/mmdet/core/fp16/decorators.py", line 127, in new_func return old_func(*args, *kwargs) File "/raid/xuwt/ting/DetectoRS/mmdet/models/anchor_heads/anchor_head.py", line 189, in loss sampling=self.sampling) File "/raid/xuwt/ting/DetectoRS/mmdet/core/anchor/anchor_target.py", line 63, in anchor_target unmap_outputs=unmap_outputs) File "/raid/xuwt/ting/DetectoRS/mmdet/core/utils/misc.py", line 24, in multi_apply return tuple(map(list, zip(map_results))) File "/raid/xuwt/ting/DetectoRS/mmdet/core/anchor/anchor_target.py", line 116, in anchor_target_single anchors, gt_bboxes, gt_bboxes_ignore, None, cfg) File "/raid/xuwt/ting/DetectoRS/mmdet/core/bbox/assign_sampling.py", line 27, in assign_and_sample bbox_assigner = build_assigner(cfg.assigner) File "/raid/xuwt/ting/DetectoRS/mmdet/core/bbox/assign_sampling.py", line 10, in build_assigner return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs) File "/raid/xuwt/anaconda3/envs/detection_rs/lib/python3.7/site-packages/mmcv/runner/utils.py", line 55, in obj_from_dict return obj_type(args) TypeError: init() got an unexpected keyword argument 'match_low_quality'

**Bug fix**
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!

[Wenting-Xu]

• I solved the problem and now I can train normally.The reason for the problem is that the code related to splitting in the configuration file is not cleaned up. Thanks to the author for sharing the code.