nan in Rasterizer gradients

[shubhtuls]

Description

I encountered a case where two vertices of a triangle had same screen-space XY coordinates, and this led to a 'nan' value in the gradient.

Some interesting things I noted:

• Using CPU instead of GPU (Quadro GP100 in this case) leads to no issues
• Using CPU vs GPU also gives different pix2face in forward pass of the rasterizer. The CPU run ignores the offending triangle's contribution, the GPU run does not. I am not sure why this happens, but I suspect this is what needs to be fixed.

Instructions To Reproduce the Issue:

import torch

import pytorch3d
import pytorch3d.renderer
import numpy as np
import matplotlib.pyplot as plt

device = torch.device("cuda:0")

# using cpu instead of gpu leads no nan values!
# device = torch.device("cpu")

# Note that v0 and v2 of the triangle have same x,y but different Z, so it's not a degenerate case (just a triangle that is parallel to camera)
# These are actual vertex values that occured during a run
# However, my attempts to manually create a simpler vertex location that reproduced this were unsuccessful
vs = torch.Tensor([[0.7922, -0.1992,  6.8850],[0.8408, -0.1622,  6.8568],[0.7922, -0.1992,  6.89]]).to(device)
fs = torch.Tensor([[0,1,2]]).to(device)

vs.requires_grad = True

meshes = pytorch3d.structures.Meshes([vs],[fs])
cameras = pytorch3d.renderer.OpenGLOrthographicCameras(znear=0, zfar=1, device=device)

blend_params = pytorch3d.renderer.BlendParams(sigma=1e-4, gamma=1e-4)
mask_raster_settings = pytorch3d.renderer.RasterizationSettings(
    image_size=256, 
    blur_radius=np.log(1. / 1e-4 - 1.) * blend_params.sigma, 
    faces_per_pixel=20,
    bin_size=0
)
mask_rasterizer = pytorch3d.renderer.MeshRasterizer(
    cameras=cameras, 
    raster_settings=mask_raster_settings
)
mask_shader = pytorch3d.renderer.SoftSilhouetteShader(blend_params=blend_params)
mask_renderer = pytorch3d.renderer.MeshRenderer(mask_rasterizer, mask_shader)

img_mask = mask_renderer(meshes)

img_mask[0,:,:,3].mean().backward()
print(vs.grad)

plt.imshow(img_mask[0,:,:,3].detach().cpu().numpy())
plt.show()

tensor([[nan, nan, nan],
        [nan, nan, nan],
        [nan, nan, nan]], device='cuda:0')

## debugging via checking barycentric coords
pix2face, _, barycentric_coords, _ = mask_rasterizer(meshes)
(pix2face == 0).nonzero()[0]

tensor([  0, 147,  17,   0], device='cuda:0')

print(barycentric_coords[0, 147, 17, 0])

tensor([-3.5279e+26,  0.0000e+00,  3.5279e+26], device='cuda:0',
       grad_fn=<SelectBackward>)

[shubhtuls]

Update: actually, even using a simpler vertex location can reproduce the error e.g.

vs = torch.Tensor([[0., 0.,  1.0],[0.2, 0.2,  2.0],[0., 0.,  3.0]]).to(device)

[nikhilaravi]

@shubhtuls thanks for the detailed explanation of the issue. I will try to reproduce the error as described and get back to you!

[shubhtuls]

I think this is related to some precision issues when computing the face areas. Adding a statement to print the 'face_area' in the cuda rasterizer implementation here shows that it is of the order of 1e-9, which is greater than the kEpsilon=1e-30 used to check for zero area.

I unblocked on my end by additionally defining a 'kEpsilonFace=1e-7' in these lines and using that for the zero area check, but I'm not sure if this is the ideal solution.

[gkioxari]

Small face areas are such a headache! I guess in both the example faces above you have almost 0 face areas. Did nans disappear with 1e-7?

[shubhtuls]

@gkioxari - so far, yes!

[tomguluson92]

@gkioxari - so far, yes!

Thanks for your solution, can I realize your solution aims to avoid gradient vanish problem through a more rigid settings according to each face area?

[gkioxari]

@tomguluson92 In a follow up diff we are setting the kEpsilon value to 1e-8. Yes the issue is the small face areas that are determined based on that value.

[nikhilaravi]

This has been fixed by 487d4d6.