JMcarrot
commented
5 months ago I also met this error,how do you solve it?
Open yanshuangying888 opened 5 months ago
JMcarrot
commented
5 months ago I also met this error,how do you solve it?
yanshuangying888
commented
5 months ago I also met this error,how do you solve it? @JMcarrot 你好，朋友，很高兴能够回复你，我现在将我修正后的内容发给你，希望能够帮助到您。 （Hello, my friend, I am very glad to reply to you. Now I will send you the corrected content, and I hope it can help you.）
auto_scale_lr = dict(base_batch_size=256) data_preprocessor = dict( mean=[ 123.675, 116.28, 103.53, ], num_classes=7, std=[ 58.395, 57.12, 57.375, ], to_rgb=True) dataset_type = 'ImageNet' default_hooks = dict( checkpoint=dict(interval=1, type='CheckpointHook'), logger=dict(interval=100, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(enable=False, type='VisualizationHook')) default_scope = 'mmpretrain' env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) launcher = 'none' load_from = 'D:\\epoch_50.pth' log_level = 'INFO' model = dict( backbone=dict( depth=50, num_stages=4, out_indices=(3, ), style='pytorch', type='ResNet'), head=dict( in_channels=2048, loss=dict(loss_weight=1.0, type='CrossEntropyLoss'), num_classes=7, topk=( 1, 5, ), type='LinearClsHead'), neck=dict(type='GlobalAveragePooling'), type='ImageClassifier') optim_wrapper = dict( optimizer=dict(lr=0.1, momentum=0.9, type='SGD', weight_decay=0.0001)) param_scheduler = dict( by_epoch=True, gamma=0.1, milestones=[ 100, 200, 300, 400, 500, ], type='MultiStepLR') randomness = dict(deterministic=False, seed=None) resume = False test_cfg = dict() test_dataloader = dict( batch_size=64, collate_fn=dict(type='default_collate'), dataset=dict( data_prefix='test', data_root='D:\\data', pipeline=[ dict(type='LoadImageFromFile'), dict(edge='short', scale=256, type='ResizeEdge'), dict(crop_size=224, type='CenterCrop'), dict(type='PackInputs'), ], type='CustomDataset'), num_workers=8, persistent_workers=True, pin_memory=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = [ dict(topk=( 1, 5, ), type='Accuracy'), dict( items=[ 'precision', 'recall', 'f1-score', ], type='SingleLabelMetric'), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(edge='short', scale=256, type='ResizeEdge'), dict(crop_size=224, type='CenterCrop'), dict(type='PackInputs'), ] train_cfg = dict(by_epoch=True, max_epochs=500, val_interval=1) train_dataloader = dict( batch_size=64, collate_fn=dict(type='default_collate'), dataset=dict( data_prefix='train', data_root='D:\\data', pipeline=[ dict(type='LoadImageFromFile'), dict(scale=224, type='RandomResizedCrop'), dict(direction='horizontal', prob=0.5, type='RandomFlip'), dict(type='PackInputs'), ], type='CustomDataset'), num_workers=8, persistent_workers=True, pin_memory=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict(type='LoadImageFromFile'), dict(scale=224, type='RandomResizedCrop'), dict(direction='horizontal', prob=0.5, type='RandomFlip'), dict(type='PackInputs'), ] val_cfg = dict() val_dataloader = dict( batch_size=64, collate_fn=dict(type='default_collate'), dataset=dict( data_prefix='val', data_root='D:\\data', pipeline=[ dict(type='LoadImageFromFile'), dict(edge='short', scale=256, type='ResizeEdge'), dict(crop_size=224, type='CenterCrop'), dict(type='PackInputs'), ], type='CustomDataset'), num_workers=8, persistent_workers=True, pin_memory=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = [ dict(topk=( 1, 5, ), type='Accuracy'), dict( items=[ 'precision', 'recall', 'f1-score', ], type='SingleLabelMetric'), ] vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( type='UniversalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ]) work_dir = 'D:\\resnet50_8xb32_in1k_epoch500'你可以重点关注data_prefix=XXX这一部分，将原有的split=XXX进行替换。 （You can focus on the data_prefix=XXX section and replace the original split=XXX.） 希望这能帮助到您。 （Hope this helps you.）
Jayden-ch
commented
5 months ago @yanshuangying888 你好，我也遇到了同样的问题，按照您的方法修改了data_prefix后，依旧没有得到解决
yanshuangying888
commented
5 months ago @yanshuangying888 你好，我也遇到了同样的问题，按照您的方法修改了data_prefix后，依旧没有得到解决
@Jayden-ch 您好，朋友，很高兴能够回复您，我在修正此问题时，参考了以往的mmpretrain版本，我将以往能运行的版本进行了代码保存，并与报错代码进行比对，直到修改了data_prefix后才修正，因为每一位开发者的机型与环境都不一定完全相同，您可以根据以往的版本作为参考，看看能不能修复您的报错，您也可以尝试将我的修正代码改变必要的参数后在您的环境运行，希望能够帮助到您。（When I corrected this problem, I referred to the previous versions of mmpretrain. I saved the code of the previous versions that could run and compared them with the error code. I did not correct it until I modified data_prefix, because the model and environment of each developer may not be exactly the same. You can use the previous version as a reference to see if you can fix your error, you can also try my fix code to change the necessary parameters to run in your environment, I hope it can help you.）
分支
main 分支 (mmpretrain 版本)
描述该错误
config.py内容如下：（The contents of config.py are as follows:）
请问这么解决这个问题呢？感激不尽您的建议。（How do you solve this problem? Your advice is greatly appreciated.）
环境信息
{'sys.platform': 'win32', 'Python': '3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, ' '13:26:23) [MSC v.1916 64 bit (AMD64)]', 'CUDA available': True, 'MUSA available': False, 'numpy_random_seed': 2147483648, 'GPU 0': 'NVIDIA GeForce RTX 4090', 'CUDA_HOME': 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1', 'NVCC': 'Cuda compilation tools, release 12.1, V12.1.105', 'MSVC': '用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.39.33521 版', 'GCC': 'n/a', 'PyTorch': '2.1.2+cu121', 'TorchVision': '0.16.2+cu121', 'OpenCV': '4.9.0', 'MMEngine': '0.10.3', 'MMCV': '2.1.0', 'MMPreTrain': '1.2.0+unknown'}
其他信息
No response