Closed ZYB-1997 closed 2 years ago
@ZYB-1997,您好!
感谢您的回答!
还有一个问题,我在运行datagen_maps.py时,无法选择较小的base_size,我把输入的mesh数据调整到10000个三角面片,只能选择base_size==10000,请问这个参数应该参考什么数据来设置?
另外我在训练自己的数据集时报错,错误信息为Check failed: y_items != 0 && x_items % y_items == 0 reshape shape is invalid for input of size 1875
,目前我无法定位错误原因,这个是否跟remesh时的参数设置有关?我尝试调整输入参数无法解决该问题。
.obj
文件和一个经过maps算法后的文件,base_size==10000,depth==1,以及我们标注的数据 (复制到浏览器打开):http://119.29.54.203:8848/test.zip
[i 0902 09:52:36.294000 04 compiler.py:955] Jittor(1.3.5.12) src: d:\software\anaconda3\envs\subdivnet\lib\site-packages\jittor
[i 0902 09:52:36.310000 04 compiler.py:956] cl at C:\Users\Administrator\.cache\jittor\msvc\VC\_\_\_\_\_\bin\cl.exe(19.29.30133)
[i 0902 09:52:36.311000 04 compiler.py:957] cache_path: C:\Users\Administrator\.cache\jittor\jt1.3.5\cl\py3.8.0\Windows-10-10.x93\AMDRyzen95900Xx4a\default
[i 0902 09:52:36.313000 04 install_cuda.py:88] cuda_driver_version: [11, 7, 0]
[i 0902 09:52:36.326000 04 __init__.py:411] Found C:\Users\Administrator\.cache\jittor\jtcuda\cuda11.2_cudnn8_win\bin\nvcc.exe(11.2.67) at C:\Users\Administrator\.cache\jittor\jtcuda\cuda11.2_cudnn8_win\bin\nvcc.exe.
[i 0902 09:52:36.376000 04 compiler.py:1010] cuda key:cu11.2.67
[i 0902 09:52:36.377000 04 __init__.py:227] Total mem: 31.89GB, using 10 procs for compiling.
[i 0902 09:52:37.051000 04 jit_compiler.cc:28] Load cc_path: C:\Users\Administrator\.cache\jittor\msvc\VC\_\_\_\_\_\bin\cl.exe
[i 0902 09:52:37.052000 04 init.cc:62] Found cuda archs: [86,]
[i 0902 09:52:37.085000 04 compile_extern.py:517] mpicc not found, distribution disabled.
[w 0902 09:52:37.133000 04 compile_extern.py:200] CUDA related path found in LD_LIBRARY_PATH or PATH(['', 'C', '\\Users\\Administrator\\.cache\\jittor\\jtcuda\\cuda11.2_cudnn8_win\\lib64', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\mkl\\dnnl_win_2.2.0_cpu_vcomp\\bin', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\mkl\\dnnl_win_2.2.0_cpu_vcomp\\lib', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\jt1.3.5\\cl\\py3.8.0\\Windows-10-10.x93\\AMDRyzen95900Xx4a\\default', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\jt1.3.5\\cl\\py3.8.0\\Windows-10-10.x93\\AMDRyzen95900Xx4a\\default\\cu11.2.67', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\jtcuda\\cuda11.2_cudnn8_win\\bin', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\jtcuda\\cuda11.2_cudnn8_win\\lib\\x64', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\msvc\\win10_kits\\lib\\ucrt\\x64', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\msvc\\win10_kits\\lib\\um\\x64', '', 'C', '\\Users\\Administrator\\.cache\\jittor\\msvc\\VC\\lib', '', 'd', '\\software\\anaconda3\\envs\\subdivnet\\libs', 'C', '\\Users\\Administrator\\.cache\\jittor\\msvc\\VC\\_\\_\\_\\_\\_\\bin', 'D', '\\Software\\Anaconda3\\envs\\subdivnet', 'D', '\\Software\\Anaconda3\\envs\\subdivnet\\Library\\mingw-w64\\bin', 'D', '\\Software\\Anaconda3\\envs\\subdivnet\\Library\\usr\\bin', 'D', '\\Software\\Anaconda3\\envs\\subdivnet\\Library\\bin', 'D', '\\Software\\Anaconda3\\envs\\subdivnet\\Scripts', 'D', '\\Software\\Anaconda3\\envs\\subdivnet\\bin', 'D', '\\Software\\Anaconda3\\condabin', 'C', '\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin', 'C', '\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\libnvvp', '.', '.', '.', 'C', '\\Python310\\Scripts', 'C', '\\Python310', 'C', '\\WINDOWS\\system32', 'C', '\\WINDOWS', 'C', '\\WINDOWS\\System32\\Wbem', 'C', '\\WINDOWS\\System32\\WindowsPowerShell\\v1.0', 'C', '\\WINDOWS\\System32\\OpenSSH', 'C', '\\Program Files\\dotnet', 'C', '\\Program Files\\NVIDIA Corporation\\NVIDIA NvDLISR', 'C', '\\Program Files (x86)\\NVIDIA Corporation\\PhysX\\Common', 'D', '\\Software\\Anaconda3', 'D', '\\Software\\Anaconda3\\Scripts', 'D', '\\Software\\Anaconda3\\Library\\bin', 'D', '\\Software\\Anaconda3\\Library\\mingw-w64\\bin', 'D', '\\Software\\Git\\cmd', 'D', '\\Software\\PowerShell\\7', 'D', '\\Software\\Node', 'C', '\\ProgramData\\chocolatey\\bin', 'C', '\\Program Files (x86)\\dotnet', 'C', '\\Program Files (x86)\\Windows Kits\\8.1\\Windows Performance Toolkit', 'D', '\\Software\\CMake\\bin', 'C', '\\Program Files\\NVIDIA Corporation\\Nsight Compute 2021.1.0', 'C', '\\Users\\Administrator\\AppData\\Local\\Microsoft\\WindowsApps', 'C', '\\Users\\Administrator\\.dotnet\\tools', 'D', '\\Software\\Pycharm\\PyCharm 2022.1.2\\bin', '.', 'D', '\\Software\\Microsoft\\Microsoft VS Code\\bin', 'C', '\\Users\\Administrator\\AppData\\Roaming\\npm']), This path may cause jittor found the wrong libs, please unset LD_LIBRARY_PATH and remove cuda lib path in Path.
Or you can let jittor install cuda for you: python3.x -m jittor_utils.install_cuda
[i 0902 09:52:38.198000 04 cuda_flags.cc:32] CUDA enabled.
name: test
0: 0%| | 0/1 [00:09<?, ?it/s]
Traceback (most recent call last):
File "E:/work/Projects/SubdivNet/train_seg.py", line 167, in <module>
train(net, optim, train_dataset, writer, epoch)
File "E:/work/Projects/SubdivNet/train_seg.py", line 36, in train
outputs = net(mesh_tensor)
File "D:\Software\Anaconda3\envs\subdivnet\lib\site-packages\jittor\__init__.py", line 951, in __call__
return self.execute(*args, **kw)
File "E:\work\Projects\SubdivNet\subdivnet\deeplab.py", line 73, in execute
mid_mesh = self.mid_conv(mid_mesh)
File "D:\Software\Anaconda3\envs\subdivnet\lib\site-packages\jittor\__init__.py", line 951, in __call__
return self.execute(*args, **kw)
File "E:\work\Projects\SubdivNet\subdivnet\deeplab.py", line 29, in execute
mesh = self.mconv1(mesh)
File "D:\Software\Anaconda3\envs\subdivnet\lib\site-packages\jittor\__init__.py", line 951, in __call__
return self.execute(*args, **kw)
File "E:\work\Projects\SubdivNet\subdivnet\mesh_ops.py", line 52, in execute
CKP = mesh_tensor.convolution_kernel_pattern(self.kernel_size, self.dilation)
File "E:\work\Projects\SubdivNet\subdivnet\mesh_tensor.py", line 409, in convolution_kernel_pattern
return self.FAF
File "E:\work\Projects\SubdivNet\subdivnet\mesh_tensor.py", line 108, in FAF
self._cache['FAF'] = self.compute_face_adjacency_faces()
File "E:\work\Projects\SubdivNet\subdivnet\mesh_tensor.py", line 304, in compute_face_adjacency_faces
S = S.reshape(-1, 2)
File "D:\Software\Anaconda3\envs\subdivnet\lib\site-packages\jittor\__init__.py", line 553, in reshape
return origin_reshape(x, shape)
RuntimeError: Wrong inputs arguments, Please refer to examples(help(jt.ops.reshape)).
Types of your inputs are: self = module, args = (Var, tuple, ),
The function declarations are: VarHolder reshape(VarHolder x, NanoVector shape)
Failed reason:[f 0902 09:52:48.483000 04 reshape_op.cc:47] Check failed: y_items != 0 && x_items % y_items == 0 reshape shape is invalid for input of size 1875
Process finished with exit code 1
我已经解决了这个问题,原因是生成的obj数据存在问题。 另外还有个小问题,就是在运行datagen_maps.py时,速度会越来越慢,刚开始时有100+的速度,后面就变成了个位数,请问这个问题是跟硬件有关系吗?如果是的话这个跟什么硬件有关?谢谢!
进度条显示的是MAPS算法的简化过程中顶点的数目,简化程度越高,需要维护的参数化数据越多,合法的简化方式也越少,因此基网格大小越小,速度越慢。一些模型可能因为其自身性质复杂,不可能达到所需要大小的基网格,因此到最后可能永远计算不出来。
您可以增大 base size,减小 remesh 的难度,加快速度。此外,datagen_maps.py 可以设置 timeout 参数,如果网格简化用时太长可以提前退出,使得输入网格可以自适应地达到合适的基网格大小。
谢谢您,问题已解决!
@lzhengning 您好!我也是遇到了在运行datagen_maps.py时,无法选择较小的base_size,只能选择base_size和输入面片数一样才能正常运行,请问这是什么原因造成的,base_size和depth这两个参数怎么设置比较好呢?
@ZYB-1997 您好!您提到问题的原因是生成的obj数据存在问题,请问是什么问题,我想我也可能和您遇到了一样的问题。
@ZYB-1997 您好!您提到问题的原因是生成的obj数据存在问题,请问是什么问题,我想我也可能和您遇到了一样的问题。
@szdxhwz 你好!我现在不确定问题的原因,之前出问题都是因为我采用blender导出的obj数据来作为输入,后面改成用trimesh直接读取就好了。
@ZYB-1997 感谢您的回复,我进行了重采样和模型简化,然后可以运行了,问题已解决。
@ZYB-1997 您好!我现在需要制作我自己的数据集来进行训练,请问您知道怎么在mesh上画标签并导出json嘛,就像您上面给的数据里的json那样?http://119.29.54.203:8848/test.zip
@ZYB-1997 您好!我现在需要制作我自己的数据集来进行训练,请问您知道怎么在mesh上画标签并导出json嘛,就像您上面给的数据里的json那样?http://119.29.54.203:8848/test.zip
我是自己写的软件来进行标注的,用VTK+QT来做的
@ZYB-1997 好的明白了,感谢您的回复!
@lzhengning 您好!
感谢您能够开源这么优秀的项目!
目前我在尝试使用自己的数据集进行训练,但是现在遇到一些问题: 1、数据集预处理部分,那个json文件中的参数我没搞明白,我看过了您在#9 中的回答,那个sub_labels和raw_to_sub的数据是如何获取的,trimesh.proximity.closest_point中的输入是一个mesh数据和一个points,这个points应该选择哪些点的坐标?mesh的顶点吗? 2、在打标签时,应该在原始数据上进行还是在进行remesh后的mesh中进行? 3、我目前需要训练的mesh数据每个都包含了10w个左右的三角面片,请问还需要运行MAPS算法吗?如果需要,是否应该先对mesh进行简化后再运行该算法?
期待您的回答,谢谢!