TypeError: __init__() got an unexpected keyword argument 'roi_head'

[testchujtest]

Hi, i tried to train this model on custom data. I built docker image:

ARG PYTORCH="1.4"
ARG CUDA="10.1"
ARG CUDNN="7"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"

RUN apt-get update && apt-get install -y git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 ffmpeg\
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

# Install mmdetection
RUN conda clean --all
RUN git clone --branch v1.2.0 https://github.com/open-mmlab/mmdetection.git /mmdetection
WORKDIR /mmdetection
COPY ./prepareVOC.py /prepareVOC.py
RUN python /prepareVOC.py
ENV FORCE_CUDA="1"
RUN pip install cython --no-cache-dir
RUN pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"

RUN pip install -q mmcv terminaltables
RUN pip install -r "/mmdetection/requirements/optional.txt"
RUN python setup.py install
RUN python setup.py develop
RUN pip install -r "requirements.txt"
RUN pip install pillow==6.2.1
RUN pip install mmcv==0.4.3

RUN mkdir -p /dataset/coco/logs
RUN mkdir -p /cdecnet/model
COPY ./cascade_mask_rcnn_hrnetv2p_w32_20e_v2.py /cascade_mask_rcnn_hrnetv2p_w32_20e_v2.py
COPY ./COCODataset /dataset/coco

CMD python tools/train.py /cascade_mask_rcnn_hrnetv2p_w32_20e_v2.py && /bin/bash

my config file:

# model settings
model = dict(
    type='CascadeRCNN',
    # num_stages=3,
    pretrained='open-mmlab://msra/hrnetv2_w32',
    backbone=dict(
        type='HRNet',
        extra=dict(
            stage1=dict(
                num_modules=1,
                num_branches=1,
                block='BOTTLENECK',
                num_blocks=(4, ),
                num_channels=(64, )),
            stage2=dict(
                num_modules=1,
                num_branches=2,
                block='BASIC',
                num_blocks=(4, 4),
                num_channels=(32, 64)),
            stage3=dict(
                num_modules=4,
                num_branches=3,
                block='BASIC',
                num_blocks=(4, 4, 4),
                num_channels=(32, 64, 128)),
            stage4=dict(
                num_modules=3,
                num_branches=4,
                block='BASIC',
                num_blocks=(4, 4, 4, 4),
                num_channels=(32, 64, 128, 256)))),
    neck=dict(type='HRFPN', in_channels=[32, 64, 128, 256], out_channels=256),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            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)),
    roi_head=dict(
        type='CascadeRoIHead',
        num_stages=3,
        stage_loss_weights=[1, 0.5, 0.25],
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),  # may conflict
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=[
            dict(
                type='Shared2FCBBoxHead',
                # num_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    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='Shared2FCBBoxHead',
                # num_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    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='Shared2FCBBoxHead',
                # num_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    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=80,
            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,
        nms=dict(type='nms', iou_threshold=0.7),
        max_per_img=2000,
        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,
        nms=dict(type='nms', iou_threshold=0.7),
        max_per_img=1000,
        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))
# dataset settings
dataset_type = 'CocoDataset'
data_root = '/dataset/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, with_mask=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            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_train2014.json',
        img_prefix=data_root+'train2014/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2014.json',
        img_prefix=data_root + 'val2014/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2014.json',
        img_prefix=data_root + 'val2014/',
        pipeline=test_pipeline))
# evaluation = dict(interval=1, metric=['bbox'])
# optimizer
optimizer = dict(type='SGD', lr=0.0012, 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=[16, 19])
checkpoint_config = dict(interval=1,create_symlink=False)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 36
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = '/dataset/coco/logs'
load_from = None
resume_from = None#'/new_chunk_cascade_mask_rcnn_hrnetv2p_w32_20e/epoch_30.pth'
workflow = [('train', 1)]

but i'm getting this error:

Traceback (most recent call last):
  File "tools/train.py", line 151, in <module>
    main()
  File "tools/train.py", line 124, in main
    cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
  File "/mmdetection/mmdet/models/builder.py", line 43, in build_detector
    return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg))
  File "/mmdetection/mmdet/models/builder.py", line 15, in build
    return build_from_cfg(cfg, registry, default_args)
  File "/mmdetection/mmdet/utils/registry.py", line 79, in build_from_cfg
    return obj_cls(**args)
TypeError: __init__() got an unexpected keyword argument 'roi_head'

has anyone had such a problem or knows how to solve it?

[hmtrii]

May be your mmdet's version is 2x. Due to the pretrained are trained in mmdet 1x, you should convert it to 2x. Following this link

[shivam07a]

Hi @testchujtest , It seems you are using cascade_mask_rcnn_hrnetv2p_w32_20e_v2.py config file with mmdet's version 1x, while it is meant to be used with mmdet's version 2x.

Using cascade_mask_rcnn_hrnetv2p_w32_20e.py config file with mmdet's version 1x should resolve this error.