Hi, Heiden Thank you for sharing such awesome work of DiSECt, where the codebase is solid and cool. But I meet some bugs when try to python examples/basic_cutting.py

The following is my bug info. It seems that cannot find kernels.so ` python examples/basic_cutting.py Rebuilding kernels /home/anabur/anaconda3/envs/robot/lib/python3.6/site-packages/torch/utils/cpp_extension.py:298: UserWarning:

                           !! WARNING !!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Your compiler (clang++-9) is not compatible with the compiler Pytorch was built with for this platform, which is g++ on linux. Please use g++ to to compile your extension. Alternatively, you may compile PyTorch from source using clang++-9, and then you can also use clang++-9 to compile your extension.

See https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md for help with compiling PyTorch from source. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

                          !! WARNING !!

platform=sys.platform)) Detected CUDA files, patching ldflags Emitting ninja build file /media/anabur/E/robot_similation/DiSECt/dflex/kernels/build.ninja... Building extension module kernels... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) 1.10.2.git.kitware.jobserver-1 Loading extension module kernels... Traceback (most recent call last): File "examples/basic_cutting.py", line 20, in from cutting import load_settings, SlicingMotion, CuttingSim File "/media/anabur/E/robot_similation/DiSECt/cutting/init.py", line 10, in from .cutting_sim import * File "/media/anabur/E/robot_similation/DiSECt/cutting/cutting_sim.py", line 25, in from cutting.urdf_loader import load_urdf File "/media/anabur/E/robot_similation/DiSECt/cutting/urdf_loader.py", line 12, in import dflex as df File "/media/anabur/E/robot_similation/DiSECt/dflex/init.py", line 15, in kernel_init() File "/media/anabur/E/robot_similation/DiSECt/dflex/sim.py", line 47, in kernel_init kernels = df.compile() File "/media/anabur/E/robot_similation/DiSECt/dflex/adjoint.py", line 1934, in compile with_pytorch_error_handling=False) File "/home/anabur/anaconda3/envs/robot/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1285, in load_inline keep_intermediates=keep_intermediates) File "/home/anabur/anaconda3/envs/robot/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1362, in _jit_compile return _import_module_from_library(name, build_directory, is_python_module) File "/home/anabur/anaconda3/envs/robot/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1752, in _import_module_from_library module = importlib.util.module_from_spec(spec) ImportError: /media/anabur/E/robot_similation/DiSECt/dflex/kernels/kernels.so: cannot open shared object file: No such file or directory `

I have installed DiSECt on my system:

• Ubuntu 18.04
• NVIDIA GeForce RTX 3090 GPU
• Conda environment with Python 3.7 (find the conda list here)
• nvcc --version gives me CUDA 11.2.
• The dataset/ folder is located in the home directory.

Three of the example scripts seem to be running without errors or warnings.

python examples/basic_cutting.py
python examples/optimize_slicing.py
python examples/parameter_inference.py

The exception is this fourth example:

(disect) seita@takeshi:~/DiSECt (main) $ python examples/render_usd.py 
Using cached kernels
Using log folder at "/home/seita/DiSECt/log".
Converted Young's modulus 43000.0 and Poisson's ratio 0.49 to Lame parameters mu = 14429.530201342282 and lambda = 707046.9798657711
PyANSYS MAPDL Result file object
Title       : Cutting_v5--Static Structural (B5)
Units       : User Defined
Version     : 20.2
Cyclic      : False
Result Sets : 1
Nodes       : 797
Elements    : 3562


Available Results:
ENS : Nodal stresses
ENG : Element energies and volume
EEL : Nodal elastic strains
EUL : Element euler angles
EPT : Nodal temperatures
NSL : Nodal displacements
RF  : Nodal reaction forces

ANSYS Mesh
  Number of Nodes:              797
  Number of Elements:           3562
  Number of Element Types:      2
  Number of Node Components:    1
  Number of Element Components: 0

Loaded mesh with 797 vertices and 3472 tets.
Creating free-floating knife
cut_meshing_cpp took 2.58 ms
224 cut springs have been inserted.
/home/seita/DiSECt/dflex/model.py:2223: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1634272178570/work/torch/csrc/utils/tensor_new.cpp:201.)
  m.shape_transform = torch.tensor(transform_flatten_list(self.shape_transform), dtype=torch.float32, device=adapter)
self.cut_edge_indices: (448, 2)
self.cut_spring_indices: (224, 2)
self.cut_virtual_tri_indices: (790, 3)
self.cut_edge_indices: (448, 2)
self.cut_spring_indices: (224, 2)
self.cut_virtual_tri_indices: (790, 3)
render_demo:   0%|                                                                             | 0/40 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "examples/render_usd.py", line 54, in <module>
    sim.simulate(render=True)
  File "/home/seita/DiSECt/cutting/cutting_sim.py", line 708, in simulate
    self.simulation_step()
  File "/home/seita/DiSECt/cutting/cutting_sim.py", line 650, in simulation_step
    update_mass_matrix=False)
  File "/home/seita/DiSECt/dflex/sim.py", line 2912, in forward
    state_in.joint_qdd.zero_()
RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.
(disect) seita@takeshi:~/DiSECt (main) $ 

This seems to be a PyTorch error (e.g., https://discuss.pytorch.org/t/leaf-variable-was-used-in-an-inplace-operation/308) but are we supposed to have other information stored or loaded?

Hi @eric-heiden

I added in a minor installation change. I was running a Python 3.7 conda env on Ubuntu 18, and was running your basic example after the installation steps (including pip install -r requirements.txt):

(disect) seita@takeshi:~/DiSECt (main) $ python examples/basic_cutting.py 
Rebuilding kernels
Traceback (most recent call last):
  File "examples/basic_cutting.py", line 20, in <module>
    from cutting import load_settings, SlicingMotion, CuttingSim
  File "/home/seita/DiSECt/cutting/__init__.py", line 10, in <module>
    from .cutting_sim import *
  File "/home/seita/DiSECt/cutting/cutting_sim.py", line 25, in <module>
    from cutting.urdf_loader import load_urdf
  File "/home/seita/DiSECt/cutting/urdf_loader.py", line 12, in <module>
    import dflex as df
  File "/home/seita/DiSECt/dflex/__init__.py", line 15, in <module>
    kernel_init()
  File "/home/seita/DiSECt/dflex/sim.py", line 47, in kernel_init
    kernels = df.compile()
  File "/home/seita/DiSECt/dflex/adjoint.py", line 1934, in compile
    with_pytorch_error_handling=False)
  File "/home/seita/miniconda3/envs/disect/lib/python3.7/site-packages/torch/utils/cpp_extension.py", line 1285, in load_inline
    keep_intermediates=keep_intermediates)
  File "/home/seita/miniconda3/envs/disect/lib/python3.7/site-packages/torch/utils/cpp_extension.py", line 1347, in _jit_compile
    is_standalone=is_standalone)
  File "/home/seita/miniconda3/envs/disect/lib/python3.7/site-packages/torch/utils/cpp_extension.py", line 1418, in _write_ninja_file_and_build_library
    verify_ninja_availability()
  File "/home/seita/miniconda3/envs/disect/lib/python3.7/site-packages/torch/utils/cpp_extension.py", line 1474, in verify_ninja_availability
    raise RuntimeError("Ninja is required to load C++ extensions")
RuntimeError: Ninja is required to load C++ extensions
(disect) seita@takeshi:~/DiSECt (main) $

The fix is to do a simple pip install ninja. I've put this in the requirements.txt. (If it would help, I can also write a more detailed overview of how I installed this to make it reproducible in case you don't experience this on your end.)

I've also put a slight clarification into where the instructions are for installing USD libraries in README.md. It might not be as clear on a first glance.