Comments (24)
看起来像是那个目录下没有图片?可以ls检查一下吗? @FYM1209
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看起来像是那个目录下没有图片?可以ls检查一下吗? @FYM1209
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老师,试试把路径两边的尖括号去掉呢
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老师,试试把路径两边的尖括号去掉呢
ok了
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老师,试试把路径两边的尖括号去掉呢
Python==3.9还是3.8
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您好,我检查了一下scikit-learn的版本是这个,您看看您的运行环境是和这个对上的吗
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这个问题已经解决了 但是在开始训练的时候,就直接结束了
是我的she'be设备不行么
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看起来像是您的显存不够?您尝试一下缩小batchsize?
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看起来像是那个目录下没有图片?可以ls检查一下吗? @FYM1209
convert_yolov5_to_efficient( '/python_workspace/efficientteacher/weights/efficient-yolov5s.pt', '/python_workspace/efficientteacher/configs/ssod/custom/yolov5s_custom_ssod.yaml','/python_workspace/efficientteacher/weights/efficient-yolov5s.pt')
你好,(Windows环境)请问在使用convert_pt_to_efficient.py把自己的pt导出为本项目可识别的pt文件时。出现了这种错误,如何解决呢
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@lonelyzyp 您好,像是路径填错了? 需要把代码里的path填成您放权重的绝对路径。
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@lonelyzyp 您好,像是路径填错了? 需要把代码里的path填成您放权重的绝对路径。
你好在吗?我已经修改为绝对路径,convert_yolov5_to_efficient( 'G:\python_workspace\efficientteacher\weights\yolov5s.pt', 'G:\python_workspace\efficientteacher\configs\ssod\custom\yolov5s_custom_ssod.yaml','G:\python_workspace\efficientteacher\weights\efficient-yolov5s.pt')
出现这个问题,请问如何解决呢
KeyError: 'Non-existent config key: SSOD.ignore_thres_low'
之前的操作,按照1.Convert Model,修改了nc为一类 和类别,修改了5s的深度因子和宽度因子
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您好,需要用的yaml文件为sup文件夹哈,或者把您现在用的那个yaml文件里SSOD那一行以下的全注释掉应该就好了
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您好,需要用的yaml文件为sup文件夹哈,或者把您现在用的那个yaml文件里SSOD那一行以下的全注释掉应该就好了
您好啊!我按照您的指导,修改了对应的文件夹,深宽因子和nc、names。不再出现KeyError: 'Non-existent config key: SSOD.ignore_thres_low
现在出现了一下问题,请问时什么原因呢(抱拳期待您的回复)
load weights from u-yolov5...
Model summary: 268 layers, 7022326 parameters, 7022326 gradients
Traceback (most recent call last):
File "G:/python_workspace/efficientteacher/scripts/mula_convertor/convert_pt_to_efficient.py", line 92, in
convert_yolov5_to_efficient( 'G:\python_workspace\efficientteacher\weights\best.pt', 'G:\python_workspace\efficientteacher\configs\sup\custom\yolov5s_custom.yaml','G:\python_workspace\efficientteacher\weights\efficient-yolov5s.pt')
File "G:/python_workspace/efficientteacher/scripts/mula_convertor/convert_pt_to_efficient.py", line 44, in convert_yolov5_to_efficient
model.load_state_dict(new_yolov5s_weight,strict=False)
File "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\nn\modules\module.py", line 1407, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Model:
size mismatch for backbone.stage5_2.cv2.conv.weight: copying a param with shape torch.Size([512, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).
size mismatch for backbone.stage5_2.cv2.bn.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone.stage5_2.cv2.bn.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone.stage5_2.cv2.bn.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone.stage5_2.cv2.bn.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone.sppf.cv2.conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).
size mismatch for backbone.sppf.cv2.bn.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for backbone.sppf.cv2.bn.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for backbone.sppf.cv2.bn.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for backbone.sppf.cv2.bn.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
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您好,能检查一下您的原生YOLOv5版本吗,看起来像是结构和6.0的不一样
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您好,能检查一下您的原生YOLOv5版本吗,看起来像是结构和6.0的不一样
好的,我重新开始检查,时间久了。再请问在power shell下使用时,出现这个问题。文件夹下存在图片
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@lonelyzyp 您好,应该是使用Select-String 替换find命令,具体的使用方法您可以查查
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@lonelyzyp 您好,应该是使用Select-String 替换find命令,具体的使用方法您可以查查
好的,我尝试以下,特别感谢您的答疑,夜已深早点休息,期待和您之后的交流
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@BowieHsu 您好!我换用YOLOV5s-6.0训练后权重,成功转换为本项目可识别模型。下面再训练用遇到两个问题,向您请教!
(1)当我使用自己备好的数据集时,使用(python train.py --cfg configs/ssod/custom/yolov5s_custom_ssod.yaml)
加载有标签数据时,不断出现OSError: [WinError 1455] 页面文件太小,无法完成操作;
Error loading "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.;之后加载完成(Scanning 'data\ear3000_angel\train300labels' images and labels...300 found, 0 missing, 0 empty, 0 corrupted: 100%|);最后报错BrokenPipeError: [Errno 32] Broken pipe,无法进行下一步
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@BowieHsu 您好!我换用YOLOV5s-6.0训练后权重,成功转换为本项目可识别模型。下面再训练用遇到两个问题,向您请教! (1)当我使用自己备好的数据集时,使用(python train.py --cfg configs/ssod/custom/yolov5s_custom_ssod.yaml) 加载有标签数据时,不断出现OSError: [WinError 1455] 页面文件太小,无法完成操作; Error loading "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.;之后加载完成(Scanning 'data\ear3000_angel\train300labels' images and labels...300 found, 0 missing, 0 empty, 0 corrupted: 100%|);最后报错BrokenPipeError: [Errno 32] Broken pipe,无法进行下一步
仅改动yaml如下:改为5s的因子参数 nc:1 batch_size: 4(本地设定很小),另外我没有找到num_workers控制线程,本地8核16线程CPU
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@BowieHsu 您好!我换用YOLOV5s-6.0训练后权重,成功转换为本项目可识别模型。下面再训练用遇到两个问题,向您请教! (1)当我使用自己备好的数据集时,使用(python train.py --cfg configs/ssod/custom/yolov5s_custom_ssod.yaml) 加载有标签数据时,不断出现OSError: [WinError 1455] 页面文件太小,无法完成操作; Error loading "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.;之后加载完成(Scanning 'data\ear3000_angel\train300labels' images and labels...300 found, 0 missing, 0 empty, 0 corrupted: 100%|);最后报错BrokenPipeError: [Errno 32] Broken pipe,无法进行下一步
仅改动yaml如下:改为5s的因子参数 nc:1 batch_size: 4(本地设定很小),另外我没有找到num_workers控制线程,本地8核16线程CPU
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@BowieHsu 当我使用(python val.py --cfg configs/sup/custom/yolov5s.yaml --weights weights/efficient-yolov5s.pt)进行val验证coco数据集,正常运行是否可以时。同样是“页面文件大小,无法完成操作”不停的跳动,最后完成了Val数据集5000张读取。然后就一直卡着不能正常进行了。
eg:
OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\lib\cudnn_cnn_infer64_8.dll" or one of its dependencies.
val: Scanning 'data\coco\val2017' images and labels...5000 found, 0 missing, 48 empty, 0 corrupted: 100%|██| 5000/5000 [01:06<00:00, 75.25it/s]
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@BowieHsu 当我使用(python val.py --cfg configs/sup/custom/yolov5s.yaml --weights weights/efficient-yolov5s.pt)进行val验证coco数据集,正常运行是否可以时。同样是“页面文件大小,无法完成操作”不停的跳动,最后完成了Val数据集5000张读取。然后就一直卡着不能正常进行了。 eg: OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\lib\cudnn_cnn_infer64_8.dll" or one of its dependencies. val: Scanning 'data\coco\val2017' images and labels...5000 found, 0 missing, 48 empty, 0 corrupted: 100%|██| 5000/5000 [01:06<00:00, 75.25it/s]
win10 cuda11.7 i7-6700k rtx3080 python3.7 pytorch1.13存在同样的问题
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@BowieHsu 当我使用(python val.py --cfg configs/sup/custom/yolov5s.yaml --weights weights/efficient-yolov5s.pt)进行val验证coco数据集,正常运行是否可以时。同样是“页面文件大小,无法完成操作”不停的跳动,最后完成了Val数据集5000张读取。然后就一直卡着不能正常进行了。 eg: OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "D:\Anaconda\envs\yolov5_ssda2\lib\site-packages\torch\lib\cudnn_cnn_infer64_8.dll" or one of its dependencies. val: Scanning 'data\coco\val2017' images and labels...5000 found, 0 missing, 48 empty, 0 corrupted: 100%|██| 5000/5000 [01:06<00:00, 75.25it/s]
win10 cuda11.7 i7-6700k rtx3080 python3.7 pytorch1.13存在同样的问题
您好,目前我的问题也是如此,在一台128GB RAM的服务器可以run,但还有其他问题正在处理,我通过问博主和查询,原因有1:机器RAM不足,比如我的24GB RAM 2:伪标签产生量比较多。您可以参考这#18 (comment)
希望对您有所帮助,
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Related Issues (20)
- 请问有人知道怎么复现的吗?我只能自己运行val.py,train.py要怎么操作? HOT 1
- 训练Cityscapes数据集,精度为0怎么回事
- coco_1p的yaml配置文件中的teacher_loss_weight是3.0吗?
- 私人数据集上的数据集在半监督中的划分 HOT 1
- 关于keypoint检测
- yolov7
- 训练血细胞分割得到的map和p和R一直是0 HOT 1
- 加载全监督训练模型进行半监督训练,检测精度低 HOT 1
- not found lables HOT 1
- 模型剪枝
- problem with !bash get_coco.sh
- 有关efficient teacher项目问题
- tp,fp_loc指标同时过高,且模型始终没有收敛
- 错误
- RuntimeError: result type Float can't be cast to the desired output type long int HOT 2
- 半监督训练的时候报错,错误如下
- 在个人数据集上使用efficientteacher项目,疑似出现过拟合严重的情况。 HOT 1
- covert yolov8 to efficient teacher
- Efficient teacher for yolov7
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