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Differentiable Iso-Surface Extraction Package (DISO)

License: Other

Python 9.84% Cuda 69.89% C++ 20.26%

differentiable dual-marching-cubes marching-cubes iso-surface iso-surface-extraction

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diso's Issues

Cuda not available on any gpus other than 0

I'm using the diso package, and it seems that it can't be put on any gpu device other than 0.
I'm simply using the following line:

device = "cuda:1"
diffmc = DiffMC(dtype=torch.float32).to(device) # or dtype=torch.float64
verts_diso, faces_diso = diffmc(sdf)  # or deform=None

The error seems like:

CUDA error 700: an illegal memory access was encountered (/tmp/pip-install-sw1umf14/diso_21b51e2d772f45e48cb953d19111a5ab/src/cumc.cu:673)
CUDA error 700: an illegal memory access was encountered (/tmp/pip-install-sw1umf14/diso_21b51e2d772f45e48cb953d19111a5ab/src/cumc.cu:679)
CUDA error 700: an illegal memory access was encountered (/tmp/pip-install-sw1umf14/diso_21b51e2d772f45e48cb953d19111a5ab/src/cumc.cu:680)
CUDA error 700: an illegal memory access was encountered (/tmp/pip-install-sw1umf14/diso_21b51e2d772f45e48cb953d19111a5ab/src/cumc.cu:692)
CUDA error 700: an illegal memory access was encountered (/tmp/pip-install-sw1umf14/diso_21b51e2d772f45e48cb953d19111a5ab/src/cumc.cu:721)
Traceback (most recent call last):
  File "test_data.py", line 54, in <module>
    verts_diso, faces_diso = diffmc(sdf)  # or deform=None
  File "/mnt/homes/zhenjun/miniconda3/envs/vtaco/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/homes/zhenjun/miniconda3/envs/vtaco/lib/python3.8/site-packages/diso/__init__.py", line 54, in forward
    verts, tris = self.func.apply(grid, deform, isovalue)
  File "/mnt/homes/zhenjun/miniconda3/envs/vtaco/lib/python3.8/site-packages/diso/__init__.py", line 22, in forward
    verts, tris = mc.forward(grid, isovalue)
RuntimeError: The specified pointer resides on host memory and is not registered with any CUDA device.

There won't be any problem if I set device = "cuda:0", and the same issue occured on DiffDMC.
Could you help me with this? Really appreciate it!

strange box when trying to convert tensoRF to mesh

Hi, I tried to convert tensoRF to mesh using:

verts, faces = diffmc_worker(pytorch_3d_sdf_tensor - level, None)

    verts_numpy = verts.cpu().numpy()
    mesh_points = np.zeros_like(verts_numpy)
    mesh_points[:, 0] = bbox[0,0] + verts_numpy[:, 0] * (bbox[1,0] - bbox[0,0])
    mesh_points[:, 1] = bbox[0,1] + verts_numpy[:, 1] * (bbox[1,1] - bbox[0,1])
    mesh_points[:, 2] = bbox[0,2] + verts_numpy[:, 2] * (bbox[1,2] - bbox[0,2])

    # try writing to the obj file
    mesh = trimesh.Trimesh(vertices=mesh_points, faces=faces.cpu().numpy(), process=False)
    print("saving mesh to %s" % (ply_filename_out))
    mesh.export(ply_filename_out)

However, I get strange result like a lego inside a box:

is there any modification I need inorder to make it right?

Option to disable mesh normalization.

Hi, I noticed in the init.py there is vertices normalization logic

        verts = verts - 1
        verts = verts / (
            torch.tensor([dimX, dimY, dimZ], dtype=verts.dtype, device=verts.device) - 1
        )

I think add an option to disable these will be useful.

batched training with DiffMC

Hi, thanks for sharing this amazing implementation!

I have a question about batched training with diso.
It seems current implementation saves internal states (e.g., used_to_first_mc_tri, used_cell_code) inside the mc struct, which makes it unable to performed batched forward & backward even with a for loop. For example:

loss = 0
for b in range(B):
    v, f = diffmc(sdf[b], deform[b]) # forward multiple times, only the last state is recorded
    loss = loss + loss_func(v)
loss.backward() # wrong gradient, all backwards use the state of the last batch

I wonder is there any workaround to make this work?
For example, maybe move all internal variables outside the struct and into the context of torch Function?

sarahweiii / diso Goto Github PK

diso's People

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diso's Issues

Cuda not available on any gpus other than 0

strange box when trying to convert tensoRF to mesh

Option to disable mesh normalization.

batched training with DiffMC

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