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Port RaDe-GS by BaowenZ to new file structure #661

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82 changes: 72 additions & 10 deletions gsplat/cuda/_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -451,16 +451,20 @@ def rasterize_to_pixels(
conics: Tensor, # [C, N, 3] or [nnz, 3]
colors: Tensor, # [C, N, channels] or [nnz, channels]
opacities: Tensor, # [C, N] or [nnz]
ray_ts: Tensor, # [C, N] or [nnz]
ray_planes: Tensor, # [C, N, 2] or [nnz, 2]
normals: Tensor, # [C, N, 3] or [nnz, 3]
image_width: int,
image_height: int,
tile_size: int,
isect_offsets: Tensor, # [C, tile_height, tile_width]
flatten_ids: Tensor, # [n_isects]
backgrounds: Optional[Tensor] = None, # [C, channels]
masks: Optional[Tensor] = None, # [C, tile_height, tile_width]
Ks: Optional[Tensor] = None,
packed: bool = False,
absgrad: bool = False,
) -> Tuple[Tensor, Tensor]:
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""Rasterizes Gaussians to pixels.

Args:
Expand Down Expand Up @@ -493,12 +497,18 @@ def rasterize_to_pixels(
assert conics.shape == (nnz, 3), conics.shape
assert colors.shape[0] == nnz, colors.shape
assert opacities.shape == (nnz,), opacities.shape
assert ray_ts.shape == nnz, means2d.shape
assert ray_planes.shape == (nnz, 2), conics.shape
assert normals.shape == (nnz, 3), colors.shape
else:
N = means2d.size(1)
assert means2d.shape == (C, N, 2), means2d.shape
assert conics.shape == (C, N, 3), conics.shape
assert colors.shape[:2] == (C, N), colors.shape
assert opacities.shape == (C, N), opacities.shape
assert ray_ts.shape == (C, N), means2d.shape
assert ray_planes.shape == (C, N, 2), conics.shape
assert normals.shape == (C, N, 3), colors.shape
if backgrounds is not None:
assert backgrounds.shape == (C, colors.shape[-1]), backgrounds.shape
backgrounds = backgrounds.contiguous()
Expand Down Expand Up @@ -561,24 +571,28 @@ def rasterize_to_pixels(
tile_width * tile_size >= image_width
), f"Assert Failed: {tile_width} * {tile_size} >= {image_width}"

render_colors, render_alphas = _RasterizeToPixels.apply(
render_colors, render_alphas, expected_depths, median_depths, expected_normals = _RasterizeToPixels.apply(
means2d.contiguous(),
conics.contiguous(),
colors.contiguous(),
opacities.contiguous(),
ray_ts.contiguous(),
ray_planes.contiguous(),
normals.contiguous(),
backgrounds,
masks,
image_width,
image_height,
tile_size,
Ks.contiguous(),
isect_offsets.contiguous(),
flatten_ids.contiguous(),
absgrad,
)

if padded_channels > 0:
render_colors = render_colors[..., :-padded_channels]
return render_colors, render_alphas
return render_colors, render_alphas, expected_depths, median_depths, expected_normals


@torch.no_grad()
Expand Down Expand Up @@ -774,7 +788,7 @@ def forward(
)

# "covars" and {"quats", "scales"} are mutually exclusive
radii, means2d, depths, conics, compensations = _make_lazy_cuda_func(
radii, means2d, depths, conics, compensations, ray_ts, ray_planes, normals = _make_lazy_cuda_func(
"projection_ewa_3dgs_fused_fwd"
)(
means,
Expand Down Expand Up @@ -803,10 +817,10 @@ def forward(
ctx.eps2d = eps2d
ctx.camera_model_type = camera_model_type

return radii, means2d, depths, conics, compensations
return radii, means2d, depths, conics, compensations, ray_ts, ray_planes, normals

@staticmethod
def backward(ctx, v_radii, v_means2d, v_depths, v_conics, v_compensations):
def backward(ctx, v_radii, v_means2d, v_depths, v_conics, v_compensations, v_ray_ts, v_ray_planes, v_normals):
(
means,
covars,
Expand Down Expand Up @@ -844,6 +858,9 @@ def backward(ctx, v_radii, v_means2d, v_depths, v_conics, v_compensations):
v_depths.contiguous(),
v_conics.contiguous(),
v_compensations,
v_ray_ts.contiguous(),
v_ray_planes.contiguous(),
v_normals.contiguous(),
ctx.needs_input_grad[4], # viewmats_requires_grad
)
if not ctx.needs_input_grad[0]:
Expand Down Expand Up @@ -886,27 +903,35 @@ def forward(
conics: Tensor, # [C, N, 3]
colors: Tensor, # [C, N, D]
opacities: Tensor, # [C, N]
ray_ts: Tensor, # [C, N]
ray_planes: Tensor, # [C, N, 2]
normals: Tensor, # [C, N, 3]
backgrounds: Tensor, # [C, D], Optional
masks: Tensor, # [C, tile_height, tile_width], Optional
width: int,
height: int,
tile_size: int,
Ks: Tensor, # [C, 3, 3]
isect_offsets: Tensor, # [C, tile_height, tile_width]
flatten_ids: Tensor, # [n_isects]
absgrad: bool,
) -> Tuple[Tensor, Tensor]:
render_colors, render_alphas, last_ids = _make_lazy_cuda_func(
render_colors, render_alphas, expected_depths, median_depths, expected_normals, median_ids, last_ids = _make_lazy_cuda_func(
"rasterize_to_pixels_3dgs_fwd"
)(
means2d,
conics,
colors,
opacities,
ray_ts,
ray_planes,
normals,
backgrounds,
masks,
width,
height,
tile_size,
Ks,
isect_offsets,
flatten_ids,
)
Expand All @@ -916,12 +941,17 @@ def forward(
conics,
colors,
opacities,
ray_ts,
ray_planes,
normals,
backgrounds,
masks,
isect_offsets,
flatten_ids,
render_alphas,
last_ids,
median_ids,
Ks
)
ctx.width = width
ctx.height = height
Expand All @@ -930,25 +960,33 @@ def forward(

# double to float
render_alphas = render_alphas.float()
return render_colors, render_alphas
return render_colors, render_alphas, expected_depths, median_depths, expected_normals

@staticmethod
def backward(
ctx,
v_render_colors: Tensor, # [C, H, W, 3]
v_render_alphas: Tensor, # [C, H, W, 1]
v_render_expected_depths: Tensor, # [C, H, W, 1]
v_render_median_depths: Tensor, # [C, H, W, 1]
v_render_expected_normals: Tensor, # [C, H, W, 1]
):
(
means2d,
conics,
colors,
opacities,
ray_ts,
ray_planes,
normals,
backgrounds,
masks,
isect_offsets,
flatten_ids,
render_alphas,
last_ids,
median_ids,
Ks
) = ctx.saved_tensors
width = ctx.width
height = ctx.height
Expand All @@ -961,29 +999,40 @@ def backward(
v_conics,
v_colors,
v_opacities,
v_ray_ts,
v_ray_planes,
v_normals
) = _make_lazy_cuda_func("rasterize_to_pixels_3dgs_bwd")(
means2d,
conics,
colors,
opacities,
ray_ts,
ray_planes,
normals,
backgrounds,
masks,
width,
height,
tile_size,
Ks,
isect_offsets,
flatten_ids,
render_alphas,
last_ids,
median_ids,
v_render_colors.contiguous(),
v_render_alphas.contiguous(),
v_render_expected_depths.contiguous(),
v_render_median_depths.contiguous(),
v_render_expected_normals.contiguous(),
absgrad,
)

if absgrad:
means2d.absgrad = v_means2d_abs

if ctx.needs_input_grad[4]:
if ctx.needs_input_grad[7]:
v_backgrounds = (v_render_colors * (1.0 - render_alphas).float()).sum(
dim=(1, 2)
)
Expand All @@ -995,6 +1044,9 @@ def backward(
v_conics,
v_colors,
v_opacities,
v_ray_ts,
v_ray_planes,
v_normals,
v_backgrounds,
None,
None,
Expand All @@ -1003,6 +1055,7 @@ def backward(
None,
None,
None,
None
)


Expand Down Expand Up @@ -1042,6 +1095,9 @@ def forward(
depths,
conics,
compensations,
ray_ts,
ray_planes,
normals
) = _make_lazy_cuda_func("projection_ewa_3dgs_packed_fwd")(
means,
covars, # optional
Expand Down Expand Up @@ -1079,7 +1135,7 @@ def forward(
ctx.sparse_grad = sparse_grad
ctx.camera_model_type = camera_model_type

return camera_ids, gaussian_ids, radii, means2d, depths, conics, compensations
return camera_ids, gaussian_ids, radii, means2d, depths, conics, compensations, ray_ts, ray_planes, normals

@staticmethod
def backward(
Expand All @@ -1091,6 +1147,9 @@ def backward(
v_depths,
v_conics,
v_compensations,
v_ray_ts,
v_ray_planes,
v_normals
):
(
camera_ids,
Expand Down Expand Up @@ -1133,6 +1192,9 @@ def backward(
v_depths.contiguous(),
v_conics.contiguous(),
v_compensations,
v_ray_ts.contiguous(),
v_ray_planes.contiguous(),
v_normals.contiguous(),
ctx.needs_input_grad[4], # viewmats_requires_grad
sparse_grad,
)
Expand Down
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