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Fixing two issues in RayMarcher and VolumeRendering #133

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Fixing two issues in RayMarcher and VolumeRendering #133

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6 changes: 3 additions & 3 deletions models/csrc/setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,9 +13,9 @@

setup(
name='vren',
version='2.0',
author='kwea123',
author_email='[email protected]',
version='2.0.1',
author='kwea123, jnhwkim',
author_email='[email protected], [email protected]',
description='cuda volume rendering library',
long_description='cuda volume rendering library',
ext_modules=[
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5 changes: 3 additions & 2 deletions models/csrc/volumerendering.cu
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Expand Up @@ -112,7 +112,7 @@ __global__ void composite_train_bw_kernel(
int samples = 0;
scalar_t R = rgb[ray_idx][0], G = rgb[ray_idx][1], B = rgb[ray_idx][2];
scalar_t O = opacity[ray_idx], D = depth[ray_idx];
scalar_t T = 1.0f, r = 0.0f, g = 0.0f, b = 0.0f, d = 0.0f;
scalar_t T = 1.0f, r = 0.0f, g = 0.0f, b = 0.0f, d = 0.0f, o = 0.0f;

// compute prefix sum of dL_dws * ws
// [a0, a1, a2, a3, ...] -> [a0, a0+a1, a0+a1+a2, a0+a1+a2+a3, ...]
Expand All @@ -130,6 +130,7 @@ __global__ void composite_train_bw_kernel(
r += w*rgbs[s][0]; g += w*rgbs[s][1]; b += w*rgbs[s][2];
d += w*ts[s];
T *= 1.0f-a;
o += w;

// compute gradients by math...
dL_drgbs[s][0] = dL_drgb[ray_idx][0]*w;
Expand All @@ -140,7 +141,7 @@ __global__ void composite_train_bw_kernel(
dL_drgb[ray_idx][0]*(rgbs[s][0]*T-(R-r)) +
dL_drgb[ray_idx][1]*(rgbs[s][1]*T-(G-g)) +
dL_drgb[ray_idx][2]*(rgbs[s][2]*T-(B-b)) + // gradients from rgb
dL_dopacity[ray_idx]*(1-O) + // gradient from opacity
dL_dopacity[ray_idx]*(T-(O-o)) + // gradient from opacity
dL_ddepth[ray_idx]*(ts[s]*T-(D-d)) + // gradient from depth
T*dL_dws[s]-(dL_dws_times_ws_sum-dL_dws_times_ws[s]) // gradient from ws
);
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8 changes: 8 additions & 0 deletions models/custom_functions.py
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Expand Up @@ -104,11 +104,19 @@ def forward(ctx, rays_o, rays_d, hits_t,
def backward(ctx, dL_drays_a, dL_dxyzs, dL_ddirs,
dL_ddeltas, dL_dts, dL_dtotal_samples):
rays_a, ts = ctx.saved_tensors

_, indices = rays_a[:,1].sort() # sort by sample idx for segment_csr
rays_a = rays_a[indices]
_, indices = rays_a[:,0].sort() # sort by ray idx for input grads

segments = torch.cat([rays_a[:, 1], rays_a[-1:, 1]+rays_a[-1:, 2]])
dL_drays_o = segment_csr(dL_dxyzs, segments)
dL_drays_d = \
segment_csr(dL_dxyzs*rearrange(ts, 'n -> n 1')+dL_ddirs, segments)

dL_drays_o = dL_drays_o[indices]
dL_drays_d = dL_drays_d[indices]

return dL_drays_o, dL_drays_d, None, None, None, None, None, None, None


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