Closed 52THANOS closed 1 year ago
data['category_id'] = self.cat_ids[label] IndexError: list index out of range
solved
data['category_id'] = self.cat_ids[label] IndexError: list index out of range
Hello, I have the same problem as you, please tell me how you solved it.
Prerequisite
🐞 Describe the bug
Caught AttributeError in DataLoader worker process 0.
Environment
my config file base = '../base/default_runtime.py'
dataset settings
data_root = 'data/coco/' dataset_type = 'YOLOv5CocoDataset'
parameters that often need to be modified
img_scale = (640, 640) # height, width max_epochs = 300 save_epoch_intervals = 10 train_batch_size_per_gpu = 10 train_num_workers = 8
persistent_workers must be False if num_workers is 0.
persistent_workers = True val_batch_size_per_gpu = 1 val_num_workers = 2 class_name=('ctl',)
only on Val
batch_shapes_cfg = dict( type='BatchShapePolicy', batch_size=val_batch_size_per_gpu, img_size=img_scale[0], size_divisor=32, extra_pad_ratio=0.5)
different from yolov5
anchors = [ [(12, 16), (19, 36), (40, 28)], # P3/8 [(36, 75), (76, 55), (72, 146)], # P4/16 [(142, 110), (192, 243), (459, 401)] # P5/32 ] strides = [8, 16, 32] num_det_layers = 3
num_classes = 80
num_classes = 1 metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
single-scale training is recommended to
be turned on, which can speed up training.
env_cfg = dict(cudnn_benchmark=True)
model = dict( type='YOLODetector', data_preprocessor=dict( type='YOLOv5DetDataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.], bgr_to_rgb=True), backbone=dict( type='YOLOv7Backbone', arch='L', norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True)), neck=dict( type='YOLOv7PAFPN', block_cfg=dict( type='ELANBlock', middle_ratio=0.5, block_ratio=0.25, num_blocks=4, num_convs_in_block=1), upsample_feats_cat_first=False, in_channels=[512, 1024, 1024],
The real output channel will be multiplied by 2
pre_transform = [ dict(type='LoadImageFromFile', file_client_args=base.file_client_args), dict(type='LoadAnnotations', with_bbox=True) ]
mosiac4_pipeline = [ dict( type='Mosaic', img_scale=img_scale, pad_val=114.0, pre_transform=pre_transform), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_translate_ratio=0.2, # note scaling_ratio_range=(0.1, 2.0), # note border=(-img_scale[0] // 2, -img_scale[1] // 2), border_val=(114, 114, 114)), ]
mosiac9_pipeline = [ dict( type='Mosaic9', img_scale=img_scale, pad_val=114.0, pre_transform=pre_transform), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_translate_ratio=0.2, # note scaling_ratio_range=(0.1, 2.0), # note border=(-img_scale[0] // 2, -img_scale[1] // 2), border_val=(114, 114, 114)), ]
randchoice_mosaic_pipeline = dict( type='RandomChoice', transforms=[mosiac4_pipeline, mosiac9_pipeline], prob=[0.8, 0.2])
train_pipeline = [ pre_transform, randchoice_mosaic_pipeline, dict( type='YOLOv5MixUp', alpha=8.0, # note beta=8.0, # note prob=0.15, pre_transform=[pre_transform, randchoice_mosaic_pipeline]), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction')) ]
train_dataloader = dict( batch_size=train_batch_size_per_gpu, num_workers=train_num_workers, persistent_workers=persistent_workers, pin_memory=True, sampler=dict(type='DefaultSampler', shuffle=True), collate_fn=dict(type='yolov5_collate'), # FASTER dataset=dict( type=dataset_type, data_root=data_root, ann_file='annotations/instances_train2017.json', data_prefix=dict(img='train2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32), pipeline=train_pipeline))
test_pipeline = [ dict(type='LoadImageFromFile', file_client_args=base.file_client_args), dict(type='YOLOv5KeepRatioResize', scale=img_scale), dict( type='LetterResize', scale=img_scale, allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, scope='mmdet'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ]
val_dataloader = dict( batch_size=val_batch_size_per_gpu, num_workers=val_num_workers, persistent_workers=persistent_workers, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, data_root=data_root, test_mode=True, data_prefix=dict(img='val2017/'), ann_file='annotations/instances_val2017.json', pipeline=test_pipeline, batch_shapes_cfg=batch_shapes_cfg))
test_dataloader = val_dataloader
param_scheduler = None optim_wrapper = dict( type='OptimWrapper', optimizer=dict( type='SGD', lr=0.01, momentum=0.937, weight_decay=0.0005, nesterov=True, batch_size_per_gpu=train_batch_size_per_gpu), constructor='YOLOv7OptimWrapperConstructor')
default_hooks = dict( param_scheduler=dict( type='YOLOv5ParamSchedulerHook', scheduler_type='cosine', lr_factor=0.1, # note max_epochs=max_epochs), checkpoint=dict( type='CheckpointHook', save_param_scheduler=False, interval=1, save_best='auto', max_keep_ckpts=3))
val_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), # Can be accelerated ann_file=data_root + 'annotations/instances_val2017.json', metric='bbox') test_evaluator = val_evaluator
train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=save_epoch_intervals, dynamic_intervals=[(270, 1)])
custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0001, update_buffers=True, strict_load=False, priority=49) ]
val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop')
randomness = dict(seed=1, deterministic=True)
Additional information
No response