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Running
on
Zero
File size: 2,714 Bytes
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import nvdiffrast.torch as dr
import torch
from ...utils.typing import *
class NVDiffRasterizerContext:
def __init__(self, context_type: str, device: torch.device) -> None:
self.device = device
self.ctx = self.initialize_context(context_type, device)
def initialize_context(
self, context_type: str, device: torch.device
) -> Union[dr.RasterizeGLContext, dr.RasterizeCudaContext]:
if context_type == "gl":
return dr.RasterizeGLContext(device=device)
elif context_type == "cuda":
return dr.RasterizeCudaContext(device=device)
else:
raise ValueError(f"Unknown rasterizer context type: {context_type}")
def vertex_transform(
self, verts: Float[Tensor, "Nv 3"], mvp_mtx: Float[Tensor, "B 4 4"]
) -> Float[Tensor, "B Nv 4"]:
verts_homo = torch.cat(
[verts, torch.ones([verts.shape[0], 1]).to(verts)], dim=-1
)
return torch.matmul(verts_homo, mvp_mtx.permute(0, 2, 1))
def rasterize(
self,
pos: Float[Tensor, "B Nv 4"],
tri: Integer[Tensor, "Nf 3"],
resolution: Union[int, Tuple[int, int]],
):
# rasterize in instance mode (single topology)
return dr.rasterize(self.ctx, pos.float(), tri.int(), resolution, grad_db=True)
def rasterize_one(
self,
pos: Float[Tensor, "Nv 4"],
tri: Integer[Tensor, "Nf 3"],
resolution: Union[int, Tuple[int, int]],
):
# rasterize one single mesh under a single viewpoint
rast, rast_db = self.rasterize(pos[None, ...], tri, resolution)
return rast[0], rast_db[0]
def antialias(
self,
color: Float[Tensor, "B H W C"],
rast: Float[Tensor, "B H W 4"],
pos: Float[Tensor, "B Nv 4"],
tri: Integer[Tensor, "Nf 3"],
) -> Float[Tensor, "B H W C"]:
return dr.antialias(color.float(), rast, pos.float(), tri.int())
def interpolate(
self,
attr: Float[Tensor, "B Nv C"],
rast: Float[Tensor, "B H W 4"],
tri: Integer[Tensor, "Nf 3"],
rast_db=None,
diff_attrs=None,
) -> Float[Tensor, "B H W C"]:
return dr.interpolate(
attr.float(), rast, tri.int(), rast_db=rast_db, diff_attrs=diff_attrs
)
def interpolate_one(
self,
attr: Float[Tensor, "Nv C"],
rast: Float[Tensor, "B H W 4"],
tri: Integer[Tensor, "Nf 3"],
rast_db=None,
diff_attrs=None,
) -> Float[Tensor, "B H W C"]:
return self.interpolate(attr[None, ...], rast, tri, rast_db, diff_attrs)
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