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import copy
import inspect
from typing import Any, Callable, List, Sequence, Tuple, Union
import numpy as np
import torch
from pytorch3d.renderer import (
BlendParams,
DirectionalLights,
FoVPerspectiveCameras,
MeshRasterizer,
MeshRenderer,
RasterizationSettings,
SoftPhongShader,
TexturesVertex,
)
from pytorch3d.renderer.utils import TensorProperties
from pytorch3d.structures import Meshes
from shap_e.models.nn.checkpoint import checkpoint
from .blender.constants import BASIC_AMBIENT_COLOR, BASIC_DIFFUSE_COLOR, UNIFORM_LIGHT_DIRECTION
from .torch_mesh import TorchMesh
from .view_data import ProjectiveCamera
# Using a lower value like 1e-4 seems to result in weird issues
# for our high-poly meshes.
DEFAULT_RENDER_SIGMA = 1e-5
DEFAULT_RENDER_GAMMA = 1e-4
def render_images(
image_size: int,
meshes: Meshes,
cameras: Any,
lights: Any,
sigma: float = DEFAULT_RENDER_SIGMA,
gamma: float = DEFAULT_RENDER_GAMMA,
max_faces_per_bin=100000,
faces_per_pixel=50,
bin_size=None,
use_checkpoint: bool = False,
) -> torch.Tensor:
if use_checkpoint:
# Decompose all of our arguments into a bunch of tensor lists
# so that autograd can keep track of what the op depends on.
verts_list = meshes.verts_list()
faces_list = meshes.faces_list()
assert isinstance(meshes.textures, TexturesVertex)
assert isinstance(lights, BidirectionalLights)
textures = meshes.textures.verts_features_padded()
light_vecs, light_fn = _deconstruct_tensor_props(lights)
camera_vecs, camera_fn = _deconstruct_tensor_props(cameras)
def ckpt_fn(
*args: torch.Tensor,
num_verts=len(verts_list),
num_light_vecs=len(light_vecs),
num_camera_vecs=len(camera_vecs),
light_fn=light_fn,
camera_fn=camera_fn,
faces_list=faces_list
):
args = list(args)
verts_list = args[:num_verts]
del args[:num_verts]
light_vecs = args[:num_light_vecs]
del args[:num_light_vecs]
camera_vecs = args[:num_camera_vecs]
del args[:num_camera_vecs]
textures = args.pop(0)
meshes = Meshes(verts=verts_list, faces=faces_list, textures=TexturesVertex(textures))
lights = light_fn(light_vecs)
cameras = camera_fn(camera_vecs)
return render_images(
image_size=image_size,
meshes=meshes,
cameras=cameras,
lights=lights,
sigma=sigma,
gamma=gamma,
max_faces_per_bin=max_faces_per_bin,
faces_per_pixel=faces_per_pixel,
bin_size=bin_size,
use_checkpoint=False,
)
result = checkpoint(ckpt_fn, (*verts_list, *light_vecs, *camera_vecs, textures), (), True)
else:
raster_settings_soft = RasterizationSettings(
image_size=image_size,
blur_radius=np.log(1.0 / 1e-4 - 1.0) * sigma,
faces_per_pixel=faces_per_pixel,
max_faces_per_bin=max_faces_per_bin,
bin_size=bin_size,
perspective_correct=False,
)
renderer = MeshRenderer(
rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings_soft),
shader=SoftPhongShader(
device=meshes.device,
cameras=cameras,
lights=lights,
blend_params=BlendParams(sigma=sigma, gamma=gamma, background_color=(0, 0, 0)),
),
)
result = renderer(meshes)
return result
def _deconstruct_tensor_props(
props: TensorProperties,
) -> Tuple[List[torch.Tensor], Callable[[List[torch.Tensor]], TensorProperties]]:
vecs = []
names = []
other_props = {}
for k in dir(props):
if k.startswith("__"):
continue
v = getattr(props, k)
if inspect.ismethod(v):
continue
if torch.is_tensor(v):
vecs.append(v)
names.append(k)
else:
other_props[k] = v
def recreate_fn(vecs_arg):
other = type(props)(device=props.device)
for k, v in other_props.items():
setattr(other, k, copy.deepcopy(v))
for name, vec in zip(names, vecs_arg):
setattr(other, name, vec)
return other
return vecs, recreate_fn
def convert_meshes(raw_meshes: Sequence[TorchMesh], default_brightness=0.8) -> Meshes:
meshes = Meshes(
verts=[mesh.verts for mesh in raw_meshes], faces=[mesh.faces for mesh in raw_meshes]
)
rgbs = []
for mesh in raw_meshes:
if mesh.vertex_channels and all(k in mesh.vertex_channels for k in "RGB"):
rgbs.append(torch.stack([mesh.vertex_channels[k] for k in "RGB"], axis=-1))
else:
rgbs.append(
torch.ones(
len(mesh.verts) * default_brightness,
3,
device=mesh.verts.device,
dtype=mesh.verts.dtype,
)
)
meshes.textures = TexturesVertex(verts_features=rgbs)
return meshes
def convert_cameras(
cameras: Sequence[ProjectiveCamera], device: torch.device
) -> FoVPerspectiveCameras:
Rs = []
Ts = []
for camera in cameras:
assert (
camera.width == camera.height and camera.x_fov == camera.y_fov
), "viewports must be square"
assert camera.x_fov == cameras[0].x_fov, "all cameras must have same field-of-view"
R = np.stack([-camera.x, -camera.y, camera.z], axis=0).T
T = -R.T @ camera.origin
Rs.append(R)
Ts.append(T)
return FoVPerspectiveCameras(
R=np.stack(Rs, axis=0),
T=np.stack(Ts, axis=0),
fov=cameras[0].x_fov,
degrees=False,
device=device,
)
def convert_cameras_torch(
origins: torch.Tensor, xs: torch.Tensor, ys: torch.Tensor, zs: torch.Tensor, fov: float
) -> FoVPerspectiveCameras:
Rs = []
Ts = []
for origin, x, y, z in zip(origins, xs, ys, zs):
R = torch.stack([-x, -y, z], axis=0).T
T = -R.T @ origin
Rs.append(R)
Ts.append(T)
return FoVPerspectiveCameras(
R=torch.stack(Rs, dim=0),
T=torch.stack(Ts, dim=0),
fov=fov,
degrees=False,
device=origins.device,
)
def blender_uniform_lights(
batch_size: int,
device: torch.device,
ambient_color: Union[float, Tuple[float]] = BASIC_AMBIENT_COLOR,
diffuse_color: Union[float, Tuple[float]] = BASIC_DIFFUSE_COLOR,
specular_color: Union[float, Tuple[float]] = 0.0,
) -> "BidirectionalLights":
"""
Create a light that attempts to match the light used by the Blender
renderer when run with `--light_mode basic`.
"""
if isinstance(ambient_color, float):
ambient_color = (ambient_color,) * 3
if isinstance(diffuse_color, float):
diffuse_color = (diffuse_color,) * 3
if isinstance(specular_color, float):
specular_color = (specular_color,) * 3
return BidirectionalLights(
ambient_color=(ambient_color,) * batch_size,
diffuse_color=(diffuse_color,) * batch_size,
specular_color=(specular_color,) * batch_size,
direction=(UNIFORM_LIGHT_DIRECTION,) * batch_size,
device=device,
)
class BidirectionalLights(DirectionalLights):
"""
Adapted from here, but effectively shines the light in both positive and negative directions:
https://github.com/facebookresearch/pytorch3d/blob/efea540bbcab56fccde6f4bc729d640a403dac56/pytorch3d/renderer/lighting.py#L159
"""
def diffuse(self, normals, points=None) -> torch.Tensor:
return torch.maximum(
super().diffuse(normals, points=points), super().diffuse(-normals, points=points)
)
def specular(self, normals, points, camera_position, shininess) -> torch.Tensor:
return torch.maximum(
super().specular(normals, points, camera_position, shininess),
super().specular(-normals, points, camera_position, shininess),
)
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