search

open-mmlab / mmdetection

OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io
Apache License 2.0
29.07k stars 9.37k forks source link

YOLOX TypeError: CocoDataset: _init_() got an unexpected keyword argument 'dataset' #6629

Closed UygarUsta closed 2 years ago

UygarUsta commented 2 years ago

I am trying to train a YOLOX model with default configuration settings.I have only changed the number of classes according to my dataset and dataset paths inside the cfg.You can see my configuration below ;

optimizer = dict( type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0005, nesterov=True, paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0)) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='YOLOX', warmup=None, by_epoch=False, warmup_by_epoch=True, warmup_ratio=1, warmup_iters=5, num_last_epochs=15, min_lr_ratio=0.05) runner = dict(type='EpochBasedRunner', max_epochs=5) checkpoint_config = dict(interval=5) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [ dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48), dict( type='SyncRandomSizeHook', ratio_range=(10, 20), img_scale=(640, 640), priority=48), dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48), dict(type='ExpMomentumEMAHook', resume_from=None, priority=49) ] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = './yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth' resume_from = None workflow = [('train', 1)] model = dict( type='YOLOX', backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.375), neck=dict( type='YOLOXPAFPN', in_channels=[96, 192, 384], out_channels=96, num_csp_blocks=1), bbox_head=dict( type='YOLOXHead', num_classes=3, in_channels=96, feat_channels=96), train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)), test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65))) data_root = 'data/coco/' dataset_type = 'CocoDataset' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) img_scale = (640, 640) train_pipeline = [ dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), dict( type='RandomAffine', scaling_ratio_range=(0.5, 1.5), border=(-320, -320)), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Resize', keep_ratio=True), dict(type='Pad', pad_to_square=True, pad_val=114.0), 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']) ] train_dataset = dict( type='MultiImageMixDataset', dataset=dict( type='CocoDataset', ann_file='data/coco/annotations/instances_train2017.json', img_prefix='data/coco/train2017/', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True) ], filter_empty_gt=False), pipeline=[ dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), dict( type='RandomAffine', scaling_ratio_range=(0.5, 1.5), border=(-320, -320)), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Resize', keep_ratio=True), dict(type='Pad', pad_to_square=True, pad_val=114.0), 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']) ], dynamic_scale=(640, 640)) test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(416, 416), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Pad', size=(416, 416), pad_val=114.0), 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']) ]) ] data = dict( samples_per_gpu=8, workers_per_gpu=2, train=dict( type='CocoDataset', dataset=dict( type='CocoDataset', ann_file= '/home/ai/Downloads/detr/Hands_Arm/annotations/instances_train2017.json', img_prefix='/home/ai/Downloads/detr/Hands_Arm/train2017', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True) ], filter_empty_gt=False), pipeline=[ dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), dict( type='RandomAffine', scaling_ratio_range=(0.5, 1.5), border=(-320, -320)), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Resize', keep_ratio=True), dict(type='Pad', pad_to_square=True, pad_val=114.0), 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']) ], dynamic_scale=(640, 640), img_prefix='/home/ai/Downloads/detr/Hands_Arm/train2017', classes=('Arm', 'EmptyHand', 'FullHand'), ann_file= '/home/ai/Downloads/detr/Hands_Arm/annotations/instances_train2017.json' ), val=dict( type='CocoDataset', ann_file= '/home/ai/Downloads/detr/Hands_Arm/annotations/instances_val2017.json', img_prefix='/home/ai/Downloads/detr/Hands_Arm/val2017', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(416, 416), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Pad', size=(416, 416), pad_val=114.0), 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']) ]) ], classes=('Arm', 'EmptyHand', 'FullHand')), test=dict( type='CocoDataset', ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(416, 416), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Pad', size=(416, 416), pad_val=114.0), 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']) ]) ])) interval = 10 evaluation = dict(interval=5, metric='bbox') classes = ('Arm', 'EmptyHand', 'FullHand') seed = 0 gpu_ids = range(0, 1) work_dir = './yolox_checkpoints' total_epochs = 5

Thanks in advance !

hhaAndroid commented 2 years ago

@UygarUsta This is a known issue, the solution can refer to https://github.com/open-mmlab/mmdetection/pull/6526

ZwwWayne commented 2 years ago

Fixed in #6526