KeyError: 'CenterNet is not in the detector registry'

2021-10-25 20:44:37,053 - mmdet - INFO - Distributed training: False 2021-10-25 20:44:41,078 - mmdet - INFO - Config: 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', to_float32=True, color_type='color'), dict(type='LoadAnnotations', with_bbox=True), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict( type='RandomCenterCropPad', crop_size=(512, 512), ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_pad_mode=None), dict(type='Resize', img_scale=(512, 512), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), dict( type='MultiScaleFlipAug', scale_factor=1.0, flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict( type='RandomCenterCropPad', ratios=None, border=None, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_mode=True, test_pad_mode=['logical_or', 31], test_pad_add_pix=1), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict( type='Collect', meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg', 'border'), keys=['img']) ]) ] data = dict( samples_per_gpu=16, workers_per_gpu=1, train=dict( type='RepeatDataset', times=5, dataset=dict( type='CocoDataset', ann_file='data/coco/annotations/instances_train2017.json', img_prefix='data/coco/train2017/', pipeline=[ dict( type='LoadImageFromFile', to_float32=True, color_type='color'), dict(type='LoadAnnotations', with_bbox=True), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict( type='RandomCenterCropPad', crop_size=(512, 512), ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_pad_mode=None), dict(type='Resize', img_scale=(512, 512), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ])), val=dict( type='CocoDataset', ann_file= '/home/user/Downloads/EfficientDet/datasets/coco/annotations/instances_val2017.json', img_prefix='/home/user/Downloads/EfficientDet/datasets/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict( type='MultiScaleFlipAug', scale_factor=1.0, flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict( type='RandomCenterCropPad', ratios=None, border=None, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_mode=True, test_pad_mode=['logical_or', 31], test_pad_add_pix=1), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict( type='Collect', meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg', 'border'), keys=['img']) ]) ]), test=dict( type='CocoDataset', ann_file= '/home/user/Downloads/EfficientDet/datasets/coco/annotations/instances_val2017.json', img_prefix='/home/user/Downloads/EfficientDet/datasets/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict( type='MultiScaleFlipAug', scale_factor=1.0, flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict( type='RandomCenterCropPad', ratios=None, border=None, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_mode=True, test_pad_mode=['logical_or', 31], test_pad_add_pix=1), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict( type='Collect', meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg', 'border'), keys=['img']) ]) ])) evaluation = dict(interval=1, metric='bbox') 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)) lr_config = dict( policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=0.001, step=[18, 24]) runner = dict(type='EpochBasedRunner', max_epochs=28) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [dict(type='NumClassCheckHook')] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] model = dict( type='CenterNet', backbone=dict( type='ResNet', depth=18, norm_eval=False, norm_cfg=dict(type='BN'), init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), neck=dict( type='CTResNetNeck', in_channel=512, num_deconv_filters=(256, 128, 64), num_deconv_kernels=(4, 4, 4), use_dcn=True), bbox_head=dict( type='CenterNetHead', num_classes=1, in_channel=64, feat_channel=64, loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0), loss_wh=dict(type='L1Loss', loss_weight=0.1), loss_offset=dict(type='L1Loss', loss_weight=1.0)), train_cfg=None, test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100)) work_dir = './work_dirs/centernet_resnet18_dcnv2_140e_coco' gpu_ids = range(0, 0)

Backend TkAgg is interactive backend. Turning interactive mode on. Traceback (most recent call last): File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/user/.vscode/extensions/ms-python.python-2021.10.1365161279/pythonFiles/lib/python/debugpy/main.py", line 45, in cli.main() File "/home/user/.vscode/extensions/ms-python.python-2021.10.1365161279/pythonFiles/lib/python/debugpy/../debugpy/server/cli.py", line 444, in main run() File "/home/user/.vscode/extensions/ms-python.python-2021.10.1365161279/pythonFiles/lib/python/debugpy/../debugpy/server/cli.py", line 285, in run_file runpy.run_path(target_as_str, run_name=compat.force_str("main")) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 263, in run_path pkg_name=pkg_name, script_name=fname) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 96, in _run_module_code mod_name, mod_spec, pkg_name, script_name) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/user/Downloads/target_detection/mmdetection/tools/train.py", line 189, in main() File "/home/user/Downloads/target_detection/mmdetection/tools/train.py", line 162, in main test_cfg=cfg.get('test_cfg')) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/models/builder.py", line 77, in build_detector return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg)) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/models/builder.py", line 34, in build return build_from_cfg(cfg, registry, default_args) File "/home/user/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/utils/registry.py", line 172, in build_from_cfg f'{obj_type} is not in the {registry.name} registry') KeyError: 'CenterNet is not in the detector registry'

how to resolve this problem?Thank you!

open-mmlab / mmdetection

KeyError: 'CenterNet is not in the detector registry' #6368