[Bug] When I tried using RMosaic data augmentation and set the workflow to [('train', 1), ('val', 1)], I encountered an error that showed 'KeyError: img'. However, when I changed the workflow back to [('train', 1)], the error disappeared. Why is this happening? #1063
Prerequisite
Task
I have modified the scripts/configs, or I'm working on my own tasks/models/datasets.
Branch
master branch https://github.com/open-mmlab/mmrotate
Environment
fatal: not a git repository (or any of the parent directories): .git sys.platform: win32 Python: 3.8.19 (default, Mar 20 2024, 19:55:45) [MSC v.1916 64 bit (AMD64)] CUDA available: True GPU 0: NVIDIA GeForce RTX 3060 Ti CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8 NVCC: Cuda compilation tools, release 11.8, V11.8.89 MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.40.33811 版 GCC: n/a PyTorch: 1.8.0+cu111 PyTorch compiling details: PyTorch built with:
TorchVision: 0.9.0+cu111 OpenCV: 4.10.0 MMCV: 1.6.0 MMCV Compiler: MSVC 192930137 MMCV CUDA Compiler: 11.1 MMRotate: 0.3.4+
Reproduces the problem - code sample
新配置继承基础模型的设置
from mmdet.core.evaluation import class_names
base = '../../configs/lsknet/lsk_s_fpn_1x_dota_le90.py'
1.数据集设置
dataset_type = 'DOTADataset' data_root = 'data/test/' angle_version = 'le90' classes = ('dam', 'groyne', 'lock', 'sluice', 'weir') 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='RRandomFlip', flip_ratio=[0.25, 0.25, 0.25], direction=['horizontal', 'vertical', 'diagonal'], version=angle_version), dict( type='RMosaic', # 马赛克的数据增强 img_scale=(512, 512), center_ratio_range=(0.8, 1.2), min_bbox_size=6, bbox_clip_border=True, skip_filter=True, version=angle_version), dict( type='PolyRandomRotate', # 对图像和边界框（bbox）进行旋转的数据增强 rotate_ratio=0.5, angles_range=180, auto_bound=False, version=angle_version), dict( type='RandomPhotoMetricDistortion', # 光度变换的数据增强 prob=0.5, brightness_delta=0, # 亮度变化范围 contrast_range=(0.9, 1.1), # 对比度变化范围 saturation_range=(0.9, 1.1), # 饱和度变化范围 hue_delta=18), # 色调变化范围 dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] data = dict( samples_per_gpu=2, workers_per_gpu=4, train=dict( delete=True, type='MultiImageMixDataset', dataset=dict( type=dataset_type, classes=classes, ann_file=data_root + 'train/annfiles/', img_prefix=data_root + 'train/images/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True) ], version=angle_version ), pipeline=train_pipeline, max_refetch=500), val=dict( type=dataset_type, classes=classes, ann_file=data_root + 'val/annfiles/', img_prefix=data_root + 'val/images/'), test=dict( type=dataset_type, classes=classes, ann_file=data_root + 'test/annfiles/', img_prefix=data_root + 'test/images/'))
2. 优化器设置
optimizer = dict( delete=True, type='AdamW', lr=0.0001, #/8*gpu_number, betas=(0.9, 0.999), weight_decay=0.05) lr_config = dict( delete=True, policy='CosineAnnealing', by_epoch=False, min_lr=0, warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3) evaluation = dict(interval=1, metric='mAP', save_best='mAP') runner = dict(type='EpochBasedRunner', max_epochs=12)
3. 结果输出设置
log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), dict(type='TensorboardLoggerHook') ]) workflow = [('train',1),('val',1)]
4. 模型设置
gpu_number = 1 # 修改为自己的GPU数量 model = dict( backbone=dict( init_cfg=dict(type='Pretrained', checkpoint='checkpoints/lsk_s_backbone.pth.tar'), norm_cfg=dict(type='BN', requires_grad=True)), # 单卡训练时将SyncBN改为BN roi_head=dict( bbox_head=dict( num_classes=5)))
Reproduces the problem - command or script
python tools/train.py my_demo/configs/my_lsk_s_orcnn_fpn_1x_dota_le90.py --work-dir my_demo/output/debug/lsk_orcnn
Reproduces the problem - error message
2024-09-04 11:10:24,555 - mmrotate - INFO - workflow: [('train', 1), ('val', 1)], max: 12 epochs 2024-09-04 11:10:24,555 - mmrotate - INFO - Checkpoints will be saved to G:\mmrotate\my_demo\output\debug\lsk_orcnn by HardDiskBackend. D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmdet\models\dense_heads\anchor_head.py:123: UserWarning: DeprecationWarning: anchor_generator is deprecated, please use "prior_generator" instead warnings.warn('DeprecationWarning: anchor_generator is deprecated, ' 2024-09-04 11:11:20,375 - mmrotate - INFO - Epoch [1][50/95] lr: 3.969e-05, eta: 0:20:14, time: 1.114, data_time: 0.271, memory: 7126, loss_rpn_cls: 0.5340, loss_rpn_bbox: 0.1305, loss_cls: 0.3892, acc: 92.8320, loss_bbox: 0.0319, loss: 1.0856, grad_norm: 12.6486 2024-09-04 11:11:51,915 - mmrotate - INFO - Saving checkpoint at 1 epochs [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 7/7, 1.0 task/s, elapsed: 7s, ETA: 0s2024-09-04 11:12:11,302 - mmrotate - INFO - +--------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +--------+-----+------+--------+-------+ | dam | 2 | 129 | 0.000 | 0.000 | | groyne | 0 | 6 | 0.000 | 0.000 | | lock | 0 | 3 | 0.000 | 0.000 | | sluice | 5 | 8 | 0.000 | 0.000 | | weir | 0 | 15 | 0.000 | 0.000 | +--------+-----+------+--------+-------+ | mAP | | | | 0.000 | +--------+-----+------+--------+-------+ 2024-09-04 11:12:11,340 - mmrotate - INFO - Exp name: my_lsk_s_orcnn_fpn_1x_dota_le90.py 2024-09-04 11:12:11,340 - mmrotate - INFO - Epoch(val) [1][7] mAP: 0.0000 Traceback (most recent call last): File "tools/train.py", line 192, in
main()
File "tools/train.py", line 181, in main
train_detector(
File "g:\mmrotate\mmrotate\apis\train.py", line 141, in train_detector
runner.run(data_loaders, cfg.workflow)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmcv\runner\epoch_based_runner.py", line 136, in run
epoch_runner(data_loaders[i], *kwargs)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(args, **kwargs)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmcv\runner\epoch_based_runner.py", line 68, in val
for i, data_batch in enumerate(self.data_loader):
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\utils\data\dataloader.py", line 517, in next
data = self._next_data()
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\utils\data\dataloader.py", line 1199, in _next_data
return self._process_data(data)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\utils\data\dataloader.py", line 1225, in _process_data
data.reraise()
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch_utils.py", line 429, in reraise
raise self.exc_type(msg)
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\utils\data_utils\worker.py", line 202, in _worker_loop
data = fetcher.fetch(index)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmdet\datasets\custom.py", line 218, in getitem
data = self.prepare_train_img(idx)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmdet\datasets\custom.py", line 241, in prepare_train_img
return self.pipeline(results)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmdet\datasets\pipelines\compose.py", line 41, in call
data = t(data)
File "D:\Program\Anaconda3\envs\mmrotate\lib\site-packages\mmdet\datasets\pipelines\transforms.py", line 469, in call
results[key], direction=results['flip_direction'])
KeyError: 'img'
Additional information
This is just a test dataset, so the issue of mAP=0 can be ignored.