def create_mesh(indices, vertices, normals, uvs):
indices = torch.tensor(indices, dtype = torch.int32, device = pyredner.get_device())
vertices = torch.tensor(vertices, device = pyredner.get_device())
if len(uvs) == 0:
uvs = None
else:
uvs = torch.tensor(uvs, device = pyredner.get_device())
if len(normals) == 0:
normals = None
else:
normals = torch.tensor(normals, device = pyredner.get_device())
return TriangleMesh(vertices, indices, uvs, normals)
def __init__(self,
diffuse_reflectance,
specular_reflectance = None,
roughness = None,
two_sided = False):
if specular_reflectance is None:
specular_reflectance = pyredner.Texture(\
torch.tensor([0.0,0.0,0.0], device = pyredner.get_device()))
if roughness is None:
roughness = pyredner.Texture(\
torch.tensor([1.0], device = pyredner.get_device()))
# Convert to constant texture if necessary
if isinstance(diffuse_reflectance, torch.Tensor):
diffuse_reflectance = pyredner.Texture(diffuse_reflectance)
if isinstance(specular_reflectance, torch.Tensor):
specular_reflectance = pyredner.Texture(specular_reflectance)
if isinstance(roughness, torch.Tensor):
roughness = pyredner.Texture(roughness)
assert(diffuse_reflectance.texels.is_contiguous())
assert(diffuse_reflectance.texels.dtype == torch.float32)
assert(specular_reflectance.texels.is_contiguous())
assert(specular_reflectance.texels.dtype == torch.float32)
assert(roughness.texels.is_contiguous())
assert(roughness.texels.dtype == torch.float32)
if pyredner.get_use_gpu():
assert(diffuse_reflectance.texels.is_cuda)
assert(specular_reflectance.texels.is_cuda)
assert(roughness.texels.is_cuda)
else:
assert(not diffuse_reflectance.texels.is_cuda)
assert(not specular_reflectance.texels.is_cuda)
assert(not roughness.texels.is_cuda)
self.diffuse_reflectance = diffuse_reflectance
self.specular_reflectance = specular_reflectance
self.roughness = roughness
self.two_sided = two_sided
# randomly generate distortion parameters
torch.manual_seed(1234)
target_distort_params = (torch.rand(8) - 0.5) * 0.05
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution,
distortion_params = target_distort_params)
checkerboard_texture = pyredner.imread('scenes/teapot.png')
if pyredner.get_use_gpu():
checkerboard_texture = checkerboard_texture.cuda(device = pyredner.get_device())
mat_checkerboard = pyredner.Material(\
diffuse_reflectance = checkerboard_texture)
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0], device = pyredner.get_device()))
plane = pyredner.Object(vertices = torch.tensor([[-1.0,-1.0, 0.0],
[-1.0, 1.0, 0.0],
[ 1.0,-1.0, 0.0],
[ 1.0, 1.0, 0.0]],
device = pyredner.get_device()),
indices = torch.tensor([[0, 1, 2],
[1, 3, 2]],
dtype = torch.int32,
device = pyredner.get_device()),
def load_obj(filename, obj_group = True):
"""
Load from a Wavefront obj file as PyTorch tensors.
XXX: this is slow, maybe move to C++?
"""
vertices_pool = []
uvs_pool = []
normals_pool = []
indices = []
vertices = []
normals = []
uvs = []
vertices_map = {}
material_map = {}
current_mtllib = {}
current_material_name = None
def create_mesh(indices, vertices, normals, uvs):
indices = torch.tensor(indices, dtype = torch.int32, device = pyredner.get_device())
vertices = torch.tensor(vertices, device = pyredner.get_device())
if len(uvs) == 0:
uvs = None
else:
uvs = torch.tensor(uvs, device = pyredner.get_device())
if len(normals) == 0:
normals = None
else:
normals = torch.tensor(normals, device = pyredner.get_device())
return TriangleMesh(vertices, indices, uvs, normals)
mesh_list = []
light_map = {}
f = open(filename, 'r')
d = os.path.dirname(filename)
cwd = os.getcwd()
if d != '':
os.chdir(d)
for line in f:
line = line.strip()
splitted = re.split('\ +', line)
if splitted[0] == 'mtllib':
current_mtllib = load_mtl(splitted[1])
elif splitted[0] == 'usemtl':
if len(indices) > 0 and obj_group is True:
# Flush
mesh_list.append((current_material_name, create_mesh(indices, vertices, normals, uvs)))
indices = []
vertices = []
normals = []
uvs = []
vertices_map = {}
mtl_name = splitted[1]
current_material_name = mtl_name
if mtl_name not in material_map:
m = current_mtllib[mtl_name]
if m.map_Kd is None:
diffuse_reflectance = torch.tensor(m.Kd,
dtype = torch.float32, device = pyredner.get_device())
else:
diffuse_reflectance = pyredner.imread(m.map_Kd)
if pyredner.get_use_gpu():
diffuse_reflectance = diffuse_reflectance.cuda(device = pyredner.get_device())
if m.map_Ks is None:
specular_reflectance = torch.tensor(m.Ks,
dtype = torch.float32, device = pyredner.get_device())
else:
specular_reflectance = pyredner.imread(m.map_Ks)
if pyredner.get_use_gpu():
specular_reflectance = specular_reflectance.cuda(device = pyredner.get_device())
if m.map_Ns is None:
roughness = torch.tensor([2.0 / (m.Ns + 2.0)],
dtype = torch.float32, device = pyredner.get_device())
else:
roughness = 2.0 / (pyredner.imread(m.map_Ks) + 2.0)
if pyredner.get_use_gpu():
roughness = roughness.cuda(device = pyredner.get_device())
if m.Ke != (0.0, 0.0, 0.0):
light_map[mtl_name] = torch.tensor(m.Ke, dtype = torch.float32)
material_map[mtl_name] = pyredner.Material(\
diffuse_reflectance, specular_reflectance, roughness)
elif splitted[0] == 'v':
vertices_pool.append([float(splitted[1]), float(splitted[2]), float(splitted[3])])
elif splitted[0] == 'vt':
uvs_pool.append([float(splitted[1]), float(splitted[2])])
elif splitted[0] == 'vn':
normals_pool.append([float(splitted[1]), float(splitted[2]), float(splitted[3])])
elif splitted[0] == 'f':
def num_indices(x):
return len(re.split('/', x))
def get_index(x, i):
return int(re.split('/', x)[i])
def parse_face_index(x, i):
f = get_index(x, i)
if f < 0:
if (i == 0):
f += len(vertices)
if (i == 1):
f += len(uvs)
else:
f -= 1
return f
assert(len(splitted) <= 5)
def get_vertex_id(indices):
pi = parse_face_index(indices, 0)
uvi = None
if (num_indices(indices) > 1 and re.split('/', indices)[1] != ''):
uvi = parse_face_index(indices, 1)
ni = None
if (num_indices(indices) > 2 and re.split('/', indices)[2] != ''):
ni = parse_face_index(indices, 2)
key = (pi, uvi, ni)
if key in vertices_map:
return vertices_map[key]
vertex_id = len(vertices)
vertices_map[key] = vertex_id
vertices.append(vertices_pool[pi])
if uvi is not None:
uvs.append(uvs_pool[uvi])
if ni is not None:
normals.append(normals_pool[ni])
return vertex_id
vid0 = get_vertex_id(splitted[1])
vid1 = get_vertex_id(splitted[2])
vid2 = get_vertex_id(splitted[3])
indices.append([vid0, vid1, vid2])
if (len(splitted) == 5):
vid3 = get_vertex_id(splitted[4])
indices.append([vid0, vid2, vid3])
mesh_list.append((current_material_name,
create_mesh(indices, vertices, normals, uvs)))
if d != '':
os.chdir(cwd)
return material_map, mesh_list, light_map
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_green = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.35, 0.75, 0.35],
device = pyredner.get_device()))
mat_red = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.75, 0.35, 0.35],
device = pyredner.get_device()))
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_green,mat_red,mat_black]
tri0_vertices = torch.tensor(\
[[-1.7,1.0,0.0], [1.0,1.0,0.0], [-0.5,-1.0,0.0]],
device = pyredner.get_device())
tri1_vertices = torch.tensor(\
[[-1.0,1.5,1.0], [0.2,1.5,1.0], [0.2,-1.5,1.0]],
device = pyredner.get_device())
tri0_indices = torch.tensor([[0, 1, 2]], dtype = torch.int32, device = pyredner.get_device())
tri1_indices = torch.tensor([[0, 1, 2]], dtype = torch.int32, device = pyredner.get_device())
render = pyredner.RenderFunction.apply
img = render(0, *scene_args)
pyredner.imwrite(img.cpu(), 'results/pose_estimation/target.exr')
pyredner.imwrite(img.cpu(), 'results/pose_estimation/target.png')
target = pyredner.imread('results/pose_estimation/target.exr')
if pyredner.get_use_gpu():
target = target.cuda()
# Now we want to generate the initial guess.
# We want to rotate and translate the teapot. We do this by declaring
# PyTorch tensors of translation and rotation parameters,
# then apply them to all teapot vertices.
# The translation and rotation parameters have very different ranges, so we normalize them
# by multiplying the translation parameters 100 to map to the actual translation amounts.
translation_params = torch.tensor([0.1, -0.1, 0.1],
device = pyredner.get_device(), requires_grad=True)
translation = translation_params * 100.0
euler_angles = torch.tensor([0.1, -0.1, 0.1], requires_grad=True)
# We obtain the teapot vertices we want to apply the transformation on.
shape0_vertices = shapes[0].vertices.clone()
shape1_vertices = shapes[1].vertices.clone()
# We can use pyredner.gen_rotate_matrix to generate 3x3 rotation matrices
rotation_matrix = pyredner.gen_rotate_matrix(euler_angles)
if pyredner.get_use_gpu():
rotation_matrix = rotation_matrix.cuda()
center = torch.mean(torch.cat([shape0_vertices, shape1_vertices]), 0)
# We shift the vertices to the center, apply rotation matrix,
# then shift back to the original space.
shapes[0].vertices = \
(shape0_vertices - center) @ torch.t(rotation_matrix) + \
center + translation
max_bounces = 5) # Set max_bounces = 5 for global illumination
render = pyredner.RenderFunction.apply
img = render(0, *scene_args)
pyredner.imwrite(img.cpu(), 'results/coarse_to_fine_estimation/target.exr')
pyredner.imwrite(img.cpu(), 'results/coarse_to_fine_estimation/target.png')
target = pyredner.imread('results/coarse_to_fine_estimation/target.exr')
if pyredner.get_use_gpu():
target = target.cuda()
# Now let's generate an initial guess by perturbing the reference.
# Let's set all the diffuse color to gray by manipulating material.diffuse_reflectance.
# We also store all the material variables to optimize in a list.
material_vars = []
for mi, m in enumerate(scene.materials):
var = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device(),
requires_grad = True)
material_vars.append(var)
m.diffuse_reflectance = pyredner.Texture(var)
# And let's also slightly perturb the camera up vector and field of view a bit
up = torch.tensor([0.2, 0.8, -0.2], requires_grad = True)
fov = torch.tensor([41.0], requires_grad = True)
cam_vars = [up, fov]
scene.camera = pyredner.Camera(\
position = scene.camera.position,
look_at = scene.camera.look_at,
up = up,
fov = fov,
clip_near = scene.camera.clip_near,
resolution = scene.camera.resolution)
position = torch.tensor([0.0, 2.0, -4.0])
look_at = torch.tensor([0.0, -2.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_shiny = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0], device = pyredner.get_device()),
specular_reflectance = torch.tensor([1.0, 1.0, 1.0], device = pyredner.get_device()),
roughness = torch.tensor([0.0005], device = pyredner.get_device()))
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device()))
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_shiny, mat_grey, mat_black]
floor_vertices = torch.tensor([[-4.0,0.0,-4.0],[-4.0,0.0,4.0],[4.0,0.0,-4.0],[4.0,0.0,4.0]],
device = pyredner.get_device())
floor_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
shape_floor = pyredner.Shape(floor_vertices, floor_indices, None, None, 0)
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 0.0, 1.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position=position,
look_at=look_at,
up=up,
fov=fov,
clip_near=clip_near,
resolution=resolution)
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device()))
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_grey, mat_black]
floor_vertices = torch.tensor([[-20.0, 0.0, -20.0], [-20.0, 0.0, 20.0],
[20.0, 0.0, -20.0], [20.0, 0.0, 20.0]],
device=pyredner.get_device())
floor_indices = torch.tensor([[0, 1, 2], [1, 3, 2]],
device=pyredner.get_device(),
dtype=torch.int32)
shape_floor = pyredner.Shape(floor_vertices, floor_indices, None, None, 0)
blocker_vertices = torch.tensor(\
[[-0.5,10.0,-0.5],[-0.5,10.0,0.5],[0.5,10.0,-0.5],[0.5,10.0,0.5]],
device = pyredner.get_device())
def load_obj(filename: str,
obj_group: bool = True,
flip_tex_coords: bool = True,
use_common_indices: bool = False,
return_objects: bool = False,
device: Optional[torch.device] = None):
"""
Load from a Wavefront obj file as PyTorch tensors.
Args
====
filename: str
Path to the obj file.
obj_group: bool
Split the meshes based on materials.
flip_tex_coords: bool
Flip the v coordinate of uv by applying v' = 1 - v.
use_common_indices: bool
Use the same indices for position, uvs, normals.
Not recommended since texture seams in the objects sharing.
The same positions would cause the optimization to "tear" the object.
return_objects: bool
Output list of Object instead.
If there is no corresponding material for a shape, assign a grey material.
device: Optional[torch.device]
Which device should we store the data in.
If set to None, use the device from pyredner.get_device().
Returns
=======
if return_objects == True, return a list of Object
if return_objects == False, return (material_map, mesh_list, light_map),
material_map -> Map[mtl_name, WavefrontMaterial]
mesh_list -> List[TriangleMesh]
light_map -> Map[mtl_name, torch.Tensor]
"""
if device is None:
device = pyredner.get_device()
vertices_pool = []
uvs_pool = []
normals_pool = []
indices = []
uv_indices = []
normal_indices = []
vertices = []
uvs = []
normals = []
vertices_map = {}
uvs_map = {}
normals_map = {}
material_map = {}
current_mtllib = {}
current_material_name = None
def create_mesh(indices,
uv_indices,
normal_indices,
vertices,
uvs,
normals):
indices = torch.tensor(indices, dtype = torch.int32, device = device)
if len(uv_indices) == 0:
uv_indices = None
else:
uv_indices = torch.tensor(uv_indices, dtype = torch.int32, device = device)
if len(normal_indices) == 0:
normal_indices = None
else:
normal_indices = torch.tensor(normal_indices, dtype = torch.int32, device = device)
vertices = torch.tensor(vertices, device = device)
if len(uvs) == 0:
uvs = None
else:
uvs = torch.tensor(uvs, device = device)
if len(normals) == 0:
normals = None
else:
normals = torch.tensor(normals, device = device)
return TriangleMesh(indices,
uv_indices,
normal_indices,
vertices,
uvs,
normals)
mesh_list = []
light_map = {}
with open(filename, 'r') as f:
d = os.path.dirname(filename)
cwd = os.getcwd()
if d != '':
os.chdir(d)
for line in f:
line = line.strip()
splitted = re.split('\ +', line)
if splitted[0] == 'mtllib':
current_mtllib = load_mtl(splitted[1])
elif splitted[0] == 'usemtl':
if len(indices) > 0 and obj_group is True:
# Flush
mesh_list.append((current_material_name,
create_mesh(indices, uv_indices, normal_indices,
vertices, uvs, normals)))
indices = []
uv_indices = []
normal_indices = []
vertices = []
normals = []
uvs = []
vertices_map = {}
uvs_map = {}
normals_map = {}
mtl_name = splitted[1]
current_material_name = mtl_name
if mtl_name not in material_map:
m = current_mtllib[mtl_name]
if m.map_Kd is None:
diffuse_reflectance = torch.tensor(m.Kd,
dtype = torch.float32, device = device)
else:
diffuse_reflectance = pyredner.imread(m.map_Kd).to(device)
if m.map_Ks is None:
specular_reflectance = torch.tensor(m.Ks,
dtype = torch.float32, device = device)
else:
specular_reflectance = pyredner.imread(m.map_Ks).to(device)
if m.map_Ns is None:
roughness = torch.tensor([2.0 / (m.Ns + 2.0)],
dtype = torch.float32, device = device)
else:
roughness = 2.0 / (pyredner.imread(m.map_Ns) + 2.0)
roughness = roughness.to(device)
if m.Ke != (0.0, 0.0, 0.0):
light_map[mtl_name] = torch.tensor(m.Ke, dtype = torch.float32)
material_map[mtl_name] = pyredner.Material(\
diffuse_reflectance, specular_reflectance, roughness)
elif splitted[0] == 'v':
vertices_pool.append([float(splitted[1]), float(splitted[2]), float(splitted[3])])
elif splitted[0] == 'vt':
u = float(splitted[1])
v = float(splitted[2])
if flip_tex_coords:
v = 1 - v
uvs_pool.append([u, v])
elif splitted[0] == 'vn':
normals_pool.append([float(splitted[1]), float(splitted[2]), float(splitted[3])])
elif splitted[0] == 'f':
def num_indices(x):
return len(re.split('/', x))
def get_index(x, i):
return int(re.split('/', x)[i])
def parse_face_index(x, i):
f = get_index(x, i)
if f > 0:
f -= 1
return f
assert(len(splitted) <= 5)
def get_vertex_id(indices):
pi = parse_face_index(indices, 0)
uvi = None
if (num_indices(indices) > 1 and re.split('/', indices)[1] != ''):
uvi = parse_face_index(indices, 1)
ni = None
if (num_indices(indices) > 2 and re.split('/', indices)[2] != ''):
ni = parse_face_index(indices, 2)
if use_common_indices:
# vertex, uv, normals share the same indexing
key = (pi, uvi, ni)
if key in vertices_map:
vertex_id = vertices_map[key]
return vertex_id, vertex_id, vertex_id
vertex_id = len(vertices)
vertices_map[key] = vertex_id
vertices.append(vertices_pool[pi])
if uvi is not None:
uvs.append(uvs_pool[uvi])
if ni is not None:
normals.append(normals_pool[ni])
return vertex_id, vertex_id, vertex_id
else:
# vertex, uv, normals use separate indexing
vertex_id = None
uv_id = None
normal_id = None
if pi in vertices_map:
vertex_id = vertices_map[pi]
else:
vertex_id = len(vertices)
vertices.append(vertices_pool[pi])
vertices_map[pi] = vertex_id
if uvi is not None:
if uvi in uvs_map:
uv_id = uvs_map[uvi]
else:
uv_id = len(uvs)
uvs.append(uvs_pool[uvi])
uvs_map[uvi] = uv_id
if ni is not None:
if ni in normals_map:
normal_id = normals_map[ni]
else:
normal_id = len(normals)
normals.append(normals_pool[ni])
normals_map[ni] = normal_id
return vertex_id, uv_id, normal_id
vid0, uv_id0, n_id0 = get_vertex_id(splitted[1])
vid1, uv_id1, n_id1 = get_vertex_id(splitted[2])
vid2, uv_id2, n_id2 = get_vertex_id(splitted[3])
indices.append([vid0, vid1, vid2])
if uv_id0 is not None:
assert(uv_id1 is not None and uv_id2 is not None)
uv_indices.append([uv_id0, uv_id1, uv_id2])
if n_id0 is not None:
assert(n_id1 is not None and n_id2 is not None)
normal_indices.append([n_id0, n_id1, n_id2])
if (len(splitted) == 5):
vid3, uv_id3, n_id3 = get_vertex_id(splitted[4])
indices.append([vid0, vid2, vid3])
if uv_id0 is not None:
assert(uv_id3 is not None)
uv_indices.append([uv_id0, uv_id2, uv_id3])
if n_id0 is not None:
assert(n_id3 is not None)
normal_indices.append([n_id0, n_id2, n_id3])
mesh_list.append((current_material_name,
create_mesh(indices, uv_indices, normal_indices, vertices, uvs, normals)))
if d != '':
os.chdir(cwd)
if return_objects:
objects = []
for mtl_name, mesh in mesh_list:
if mtl_name in material_map:
m = material_map[mtl_name]
else:
m = pyredner.Material(diffuse_reflectance = \
torch.tensor((0.5, 0.5, 0.5), device = device))
if mtl_name in light_map:
l = light_map[mtl_name]
else:
l = None
objects.append(pyredner.Object(\
vertices = mesh.vertices,
indices = mesh.indices,
material = m,
light_intensity = l,
uvs = mesh.uvs,
normals = mesh.normals,
uv_indices = mesh.uv_indices,
normal_indices = mesh.normal_indices))
return objects
else:
return material_map, mesh_list, light_map
def backward(ctx,
grad_img):
if not grad_img.is_contiguous():
grad_img = grad_img.contiguous()
scene = ctx.scene
options = ctx.options
d_cam_position = torch.zeros(3)
d_cam_look = torch.zeros(3)
d_cam_up = torch.zeros(3)
d_ndc_to_cam = torch.zeros(3, 3)
d_cam_to_ndc = torch.zeros(3, 3)
d_camera = redner.DCamera(redner.float_ptr(d_cam_position.data_ptr()),
redner.float_ptr(d_cam_look.data_ptr()),
redner.float_ptr(d_cam_up.data_ptr()),
redner.float_ptr(d_ndc_to_cam.data_ptr()),
redner.float_ptr(d_cam_to_ndc.data_ptr()))
d_vertices_list = []
d_uvs_list = []
d_normals_list = []
d_shapes = []
for shape in ctx.shapes:
num_vertices = shape.num_vertices
d_vertices = torch.zeros(num_vertices, 3,
device = pyredner.get_device())
d_uvs = torch.zeros(num_vertices, 2,
device = pyredner.get_device()) if shape.has_uvs() else None
d_normals = torch.zeros(num_vertices, 3,
device = pyredner.get_device()) if shape.has_normals() else None
d_vertices_list.append(d_vertices)
d_uvs_list.append(d_uvs)
d_normals_list.append(d_normals)
d_shapes.append(redner.DShape(\
redner.float_ptr(d_vertices.data_ptr()),
redner.float_ptr(d_uvs.data_ptr() if d_uvs is not None else 0),
redner.float_ptr(d_normals.data_ptr() if d_normals is not None else 0)))
d_diffuse_list = []
d_specular_list = []
d_roughness_list = []
d_materials = []
for material in ctx.materials:
diffuse_size = material.get_diffuse_size()
specular_size = material.get_specular_size()
roughness_size = material.get_roughness_size()
if diffuse_size[0] == 0:
d_diffuse = torch.zeros(3, device = pyredner.get_device())
else:
d_diffuse = torch.zeros(diffuse_size[2],
diffuse_size[1],
diffuse_size[0],
3, device = pyredner.get_device())
if specular_size[0] == 0:
d_specular = torch.zeros(3, device = pyredner.get_device())
else:
d_specular = torch.zeros(specular_size[2],
specular_size[1],
specular_size[0],
3, device = pyredner.get_device())
if roughness_size[0] == 0:
d_roughness = torch.zeros(1, device = pyredner.get_device())
else:
d_roughness = torch.zeros(roughness_size[2],
roughness_size[1],
roughness_size[0],
1, device = pyredner.get_device())
d_diffuse_list.append(d_diffuse)
d_specular_list.append(d_specular)
d_roughness_list.append(d_roughness)
d_diffuse_uv_scale = torch.zeros(2)
d_specular_uv_scale = torch.zeros(2)
d_roughness_uv_scale = torch.zeros(2)
d_diffuse_tex = redner.Texture3(\
redner.float_ptr(d_diffuse.data_ptr()),
diffuse_size[0], diffuse_size[1], diffuse_size[2],
redner.float_ptr(d_diffuse_uv_scale.data_ptr()))
d_specular_tex = redner.Texture3(\
redner.float_ptr(d_specular.data_ptr()),
specular_size[0], specular_size[1], specular_size[2],
redner.float_ptr(d_specular_uv_scale.data_ptr()))
d_roughness_tex = redner.Texture1(\
redner.float_ptr(d_roughness.data_ptr()),
roughness_size[0], roughness_size[1], roughness_size[2],
redner.float_ptr(d_roughness_uv_scale.data_ptr()))
d_materials.append(redner.DMaterial(\
d_diffuse_tex, d_specular_tex, d_roughness_tex))
d_intensity_list = []
d_area_lights = []
for light in ctx.area_lights:
d_intensity = torch.zeros(3, device = pyredner.get_device())
d_intensity_list.append(d_intensity)
d_area_lights.append(\
redner.DAreaLight(redner.float_ptr(d_intensity.data_ptr())))
d_envmap = None
if ctx.envmap is not None:
envmap = ctx.envmap
size = envmap.get_size()
d_envmap_values = \
torch.zeros(size[2],
size[1],
size[0],
3,
device = pyredner.get_device())
d_envmap_uv_scale = torch.zeros(2)
d_envmap_tex = redner.Texture3(\
redner.float_ptr(d_envmap_values.data_ptr()),
size[0], size[1], size[2],
redner.float_ptr(d_envmap_uv_scale.data_ptr()))
d_world_to_env = torch.zeros(4, 4)
d_envmap = redner.DEnvironmentMap(\
d_envmap_tex,
redner.float_ptr(d_world_to_env.data_ptr()))
d_scene = redner.DScene(d_camera,
d_shapes,
d_materials,
d_area_lights,
d_envmap,
pyredner.get_use_gpu(),
pyredner.get_device().index if pyredner.get_device().index is not None else -1)
if not get_use_correlated_random_number():
# Decouple the forward/backward random numbers by adding a big prime number
options.seed += 1000003
options.num_samples = ctx.num_samples[1]
start = time.time()
redner.render(scene, options,
redner.float_ptr(0),
redner.float_ptr(grad_img.data_ptr()),
d_scene,
redner.float_ptr(0))
time_elapsed = time.time() - start
if print_timing:
print('Backward pass, time: %.5f s' % time_elapsed)
# # For debugging
# # pyredner.imwrite(grad_img, 'grad_img.exr')
# # grad_img = torch.ones(256, 256, 3, device = pyredner.get_device())
# debug_img = torch.zeros(256, 256, 3)
# start = time.time()
# redner.render(scene, options,
# redner.float_ptr(0),
# redner.float_ptr(grad_img.data_ptr()),
# d_scene,
# redner.float_ptr(debug_img.data_ptr()))
# time_elapsed = time.time() - start
# if print_timing:
# print('Backward pass, time: %.5f s' % time_elapsed)
# pyredner.imwrite(debug_img, 'debug.exr')
# pyredner.imwrite(-debug_img, 'debug_.exr')
# debug_img = debug_img.numpy()
# print(np.max(debug_img))
# print(np.unravel_index(np.argmax(debug_img), debug_img.shape))
# print(np.min(debug_img))
# print(np.unravel_index(np.argmin(debug_img), debug_img.shape))
# print(np.sum(debug_img) / 3)
# debug_max = 0.5
# debug_min = -0.5
# debug_img = np.clip((debug_img - debug_min) / (debug_max - debug_min), 0, 1)
# debug_img = debug_img[:, :, 0]
# import matplotlib.cm as cm
# debug_img = cm.viridis(debug_img)
# skimage.io.imsave('debug.png', np.power(debug_img, 1/2.2))
# exit()
ret_list = []
ret_list.append(None) # seed
ret_list.append(None) # num_shapes
ret_list.append(None) # num_materials
ret_list.append(None) # num_lights
ret_list.append(d_cam_position)
ret_list.append(d_cam_look)
ret_list.append(d_cam_up)
ret_list.append(d_ndc_to_cam)
ret_list.append(d_cam_to_ndc)
ret_list.append(None) # clip near
ret_list.append(None) # resolution
ret_list.append(None) # fisheye
num_shapes = len(ctx.shapes)
for i in range(num_shapes):
ret_list.append(d_vertices_list[i])
ret_list.append(None) # indices
ret_list.append(d_uvs_list[i])
ret_list.append(d_normals_list[i])
ret_list.append(None) # material id
ret_list.append(None) # light id
num_materials = len(ctx.materials)
for i in range(num_materials):
ret_list.append(d_diffuse_list[i])
ret_list.append(None) # diffuse_uv_scale
ret_list.append(d_specular_list[i])
ret_list.append(None) # specular_uv_scale
ret_list.append(d_roughness_list[i])
ret_list.append(None) # roughness_uv_scale
ret_list.append(None) # two sided
num_area_lights = len(ctx.area_lights)
for i in range(num_area_lights):
ret_list.append(None) # shape id
ret_list.append(d_intensity_list[i].cpu())
ret_list.append(None) # two sided
if ctx.envmap is not None:
ret_list.append(d_envmap_values)
ret_list.append(None) # uv_scale
ret_list.append(None) # env_to_world
ret_list.append(d_world_to_env)
ret_list.append(None) # sample_cdf_ys
ret_list.append(None) # sample_cdf_xs
ret_list.append(None) # pdf_norm
else:
ret_list.append(None)
ret_list.append(None)
ret_list.append(None)
ret_list.append(None)
ret_list.append(None)
ret_list.append(None)
ret_list.append(None)
ret_list.append(None) # num samples
ret_list.append(None) # num bounces
ret_list.append(None) # channels
ret_list.append(None) # sampler type
return tuple(ret_list)
def forward(ctx,
seed,
*args):
"""
Forward rendering pass: given a scene and output an image.
"""
# Unpack arguments
current_index = 0
num_shapes = args[current_index]
current_index += 1
num_materials = args[current_index]
current_index += 1
num_lights = args[current_index]
current_index += 1
cam_position = args[current_index]
current_index += 1
cam_look_at = args[current_index]
current_index += 1
cam_up = args[current_index]
current_index += 1
ndc_to_cam = args[current_index]
current_index += 1
cam_to_ndc = args[current_index]
current_index += 1
clip_near = args[current_index]
current_index += 1
resolution = args[current_index]
current_index += 1
fisheye = args[current_index]
current_index += 1
camera = redner.Camera(resolution[1],
resolution[0],
redner.float_ptr(cam_position.data_ptr()),
redner.float_ptr(cam_look_at.data_ptr()),
redner.float_ptr(cam_up.data_ptr()),
redner.float_ptr(ndc_to_cam.data_ptr()),
redner.float_ptr(cam_to_ndc.data_ptr()),
clip_near,
fisheye)
shapes = []
for i in range(num_shapes):
vertices = args[current_index]
current_index += 1
indices = args[current_index]
current_index += 1
uvs = args[current_index]
current_index += 1
normals = args[current_index]
current_index += 1
material_id = args[current_index]
current_index += 1
light_id = args[current_index]
current_index += 1
assert(vertices.is_contiguous())
assert(indices.is_contiguous())
if uvs is not None:
assert(uvs.is_contiguous())
if normals is not None:
assert(normals.is_contiguous())
shapes.append(redner.Shape(\
redner.float_ptr(vertices.data_ptr()),
redner.int_ptr(indices.data_ptr()),
redner.float_ptr(uvs.data_ptr() if uvs is not None else 0),
redner.float_ptr(normals.data_ptr() if normals is not None else 0),
int(vertices.shape[0]),
int(indices.shape[0]),
material_id,
light_id))
materials = []
for i in range(num_materials):
diffuse_reflectance = args[current_index]
current_index += 1
diffuse_uv_scale = args[current_index]
current_index += 1
specular_reflectance = args[current_index]
current_index += 1
specular_uv_scale = args[current_index]
current_index += 1
roughness = args[current_index]
current_index += 1
roughness_uv_scale = args[current_index]
current_index += 1
two_sided = args[current_index]
current_index += 1
assert(diffuse_reflectance.is_contiguous())
if diffuse_reflectance.dim() == 1:
diffuse_reflectance = redner.Texture3(\
redner.float_ptr(diffuse_reflectance.data_ptr()), 0, 0, 0,
redner.float_ptr(diffuse_uv_scale.data_ptr()))
else:
diffuse_reflectance = redner.Texture3(\
redner.float_ptr(diffuse_reflectance.data_ptr()),
int(diffuse_reflectance.shape[2]), # width
int(diffuse_reflectance.shape[1]), # height
int(diffuse_reflectance.shape[0]), # num levels
redner.float_ptr(diffuse_uv_scale.data_ptr()))
assert(specular_reflectance.is_contiguous())
if specular_reflectance.dim() == 1:
specular_reflectance = redner.Texture3(\
redner.float_ptr(specular_reflectance.data_ptr()), 0, 0, 0,
redner.float_ptr(specular_uv_scale.data_ptr()))
else:
specular_reflectance = redner.Texture3(\
redner.float_ptr(specular_reflectance.data_ptr()),
int(specular_reflectance.shape[2]), # width
int(specular_reflectance.shape[1]), # height
int(specular_reflectance.shape[0]), # num levels
redner.float_ptr(specular_uv_scale.data_ptr()))
assert(roughness.is_contiguous())
if roughness.dim() == 1:
roughness = redner.Texture1(\
redner.float_ptr(roughness.data_ptr()), 0, 0, 0,
redner.float_ptr(roughness_uv_scale.data_ptr()))
else:
assert(roughness.dim() == 4)
roughness = redner.Texture1(\
redner.float_ptr(roughness.data_ptr()),
int(roughness.shape[2]), # width
int(roughness.shape[1]), # height
int(roughness.shape[0]), # num levels
redner.float_ptr(roughness_uv_scale.data_ptr()))
materials.append(redner.Material(\
diffuse_reflectance,
specular_reflectance,
roughness,
two_sided))
area_lights = []
for i in range(num_lights):
shape_id = args[current_index]
current_index += 1
intensity = args[current_index]
current_index += 1
two_sided = args[current_index]
current_index += 1
area_lights.append(redner.AreaLight(\
shape_id,
redner.float_ptr(intensity.data_ptr()),
two_sided))
envmap = None
if args[current_index] is not None:
values = args[current_index]
current_index += 1
envmap_uv_scale = args[current_index]
current_index += 1
env_to_world = args[current_index]
current_index += 1
world_to_env = args[current_index]
current_index += 1
sample_cdf_ys = args[current_index]
current_index += 1
sample_cdf_xs = args[current_index]
current_index += 1
pdf_norm = args[current_index]
current_index += 1
values = redner.Texture3(\
redner.float_ptr(values.data_ptr()),
int(values.shape[2]), # width
int(values.shape[1]), # height
int(values.shape[0]), # num levels
redner.float_ptr(envmap_uv_scale.data_ptr()))
envmap = redner.EnvironmentMap(\
values,
redner.float_ptr(env_to_world.data_ptr()),
redner.float_ptr(world_to_env.data_ptr()),
redner.float_ptr(sample_cdf_ys.data_ptr()),
redner.float_ptr(sample_cdf_xs.data_ptr()),
pdf_norm)
else:
current_index += 7
start = time.time()
scene = redner.Scene(camera,
shapes,
materials,
area_lights,
envmap,
pyredner.get_use_gpu(),
pyredner.get_device().index if pyredner.get_device().index is not None else -1)
time_elapsed = time.time() - start
if print_timing:
print('Scene construction, time: %.5f s' % time_elapsed)
num_samples = args[current_index]
current_index += 1
max_bounces = args[current_index]
current_index += 1
channels = args[current_index]
current_index += 1
sampler_type = args[current_index]
current_index += 1
# check that num_samples is a tuple
if isinstance(num_samples, int):
num_samples = (num_samples, num_samples)
options = redner.RenderOptions(seed, num_samples[0], max_bounces, channels, sampler_type)
num_channels = redner.compute_num_channels(channels)
rendered_image = torch.zeros(resolution[0], resolution[1], num_channels,
device = pyredner.get_device())
start = time.time()
redner.render(scene,
options,
redner.float_ptr(rendered_image.data_ptr()),
redner.float_ptr(0),
None,
redner.float_ptr(0))
time_elapsed = time.time() - start
if print_timing:
print('Forward pass, time: %.5f s' % time_elapsed)
# # For debugging
# debug_img = torch.zeros(256, 256, 3)
# redner.render(scene,
# options,
# redner.float_ptr(rendered_image.data_ptr()),
# redner.float_ptr(0),
# None,
# redner.float_ptr(debug_img.data_ptr()))
# pyredner.imwrite(debug_img, 'debug.exr')
# exit()
ctx.shapes = shapes
ctx.materials = materials
ctx.area_lights = area_lights
ctx.envmap = envmap
ctx.scene = scene
ctx.options = options
ctx.num_samples = num_samples
return rendered_image
def forward(ctx, seed, *args):
"""
Forward rendering pass: given a scene and output an image.
"""
# Unpack arguments
current_index = 0
num_shapes = args[current_index]
current_index += 1
num_materials = args[current_index]
current_index += 1
num_lights = args[current_index]
current_index += 1
cam_to_world = args[current_index]
current_index += 1
world_to_cam = args[current_index]
current_index += 1
fov_factor = args[current_index]
current_index += 1
clip_near = args[current_index]
current_index += 1
resolution = args[current_index]
current_index += 1
fisheye = args[current_index]
current_index += 1
assert (cam_to_world.is_contiguous())
assert (world_to_cam.is_contiguous())
camera = redner.Camera(resolution[1], resolution[0],
redner.float_ptr(cam_to_world.data_ptr()),
redner.float_ptr(world_to_cam.data_ptr()),
fov_factor.item(), clip_near, fisheye)
shapes = []
for i in range(num_shapes):
vertices = args[current_index]
current_index += 1
indices = args[current_index]
current_index += 1
uvs = args[current_index]
current_index += 1
normals = args[current_index]
current_index += 1
material_id = args[current_index]
current_index += 1
light_id = args[current_index]
current_index += 1
assert (vertices.is_contiguous())
assert (indices.is_contiguous())
if uvs is not None:
assert (uvs.is_contiguous())
if normals is not None:
assert (normals.is_contiguous())
shapes.append(redner.Shape(\
redner.float_ptr(vertices.data_ptr()),
redner.int_ptr(indices.data_ptr()),
redner.float_ptr(uvs.data_ptr() if uvs is not None else 0),
redner.float_ptr(normals.data_ptr() if normals is not None else 0),
int(vertices.shape[0]),
int(indices.shape[0]),
material_id,
light_id))
materials = []
for i in range(num_materials):
diffuse_reflectance = args[current_index]
current_index += 1
diffuse_uv_scale = args[current_index]
current_index += 1
specular_reflectance = args[current_index]
current_index += 1
specular_uv_scale = args[current_index]
current_index += 1
roughness = args[current_index]
current_index += 1
roughness_uv_scale = args[current_index]
current_index += 1
two_sided = args[current_index]
current_index += 1
assert (diffuse_reflectance.is_contiguous())
if diffuse_reflectance.dim() == 1:
diffuse_reflectance = redner.Texture3(\
redner.float_ptr(diffuse_reflectance.data_ptr()), 0, 0, 0,
redner.float_ptr(diffuse_uv_scale.data_ptr()))
else:
diffuse_reflectance = redner.Texture3(\
redner.float_ptr(diffuse_reflectance.data_ptr()),
int(diffuse_reflectance.shape[2]), # width
int(diffuse_reflectance.shape[1]), # height
int(diffuse_reflectance.shape[0]), # num levels
redner.float_ptr(diffuse_uv_scale.data_ptr()))
assert (specular_reflectance.is_contiguous())
if specular_reflectance.dim() == 1:
specular_reflectance = redner.Texture3(\
redner.float_ptr(specular_reflectance.data_ptr()), 0, 0, 0,
redner.float_ptr(specular_uv_scale.data_ptr()))
else:
specular_reflectance = redner.Texture3(\
redner.float_ptr(specular_reflectance.data_ptr()),
int(specular_reflectance.shape[2]), # width
int(specular_reflectance.shape[1]), # height
int(specular_reflectance.shape[0]), # num levels
redner.float_ptr(specular_uv_scale.data_ptr()))
assert (roughness.is_contiguous())
if roughness.dim() == 1:
roughness = redner.Texture1(\
redner.float_ptr(roughness.data_ptr()), 0, 0, 0,
redner.float_ptr(roughness_uv_scale.data_ptr()))
else:
roughness = redner.Texture1(\
redner.float_ptr(roughness.data_ptr()),
int(roughness.shape[2]), # width
int(roughness.shape[1]), # height
int(roughness.shape[0]), # num levels
redner.float_ptr(roughness_uv_scale.data_ptr()))
materials.append(redner.Material(\
diffuse_reflectance,
specular_reflectance,
roughness,
two_sided))
lights = []
for i in range(num_lights):
shape_id = args[current_index]
current_index += 1
intensity = args[current_index]
current_index += 1
lights.append(
redner.Light(shape_id, redner.float_ptr(intensity.data_ptr())))
scene = redner.Scene(camera, shapes, materials, lights,
pyredner.get_use_gpu())
num_samples = args[current_index]
current_index += 1
max_bounces = args[current_index]
current_index += 1
options = redner.RenderOptions(seed, num_samples, max_bounces)
rendered_image = torch.zeros(resolution[0],
resolution[1],
3,
device=pyredner.get_device())
redner.render(scene, options,
redner.float_ptr(rendered_image.data_ptr()),
redner.float_ptr(0), None, redner.float_ptr(0))
# # For debugging
# debug_img = torch.zeros(256, 256, 3)
# redner.render(scene,
# options,
# redner.float_ptr(rendered_image.data_ptr()),
# redner.float_ptr(0),
# None,
# redner.float_ptr(debug_img.data_ptr()))
# pyredner.imwrite(debug_img, 'debug.exr')
# exit()
ctx.shapes = shapes
ctx.materials = materials
ctx.lights = lights
ctx.scene = scene
ctx.options = options
return rendered_image
def backward(ctx, grad_img):
if not grad_img.is_contiguous():
grad_img = grad_img.contiguous()
scene = ctx.scene
options = ctx.options
d_fov_factor = torch.zeros(1)
d_cam_to_world = torch.zeros(4, 4)
d_world_to_cam = torch.zeros(4, 4)
d_camera = redner.DCamera(redner.float_ptr(d_cam_to_world.data_ptr()),
redner.float_ptr(d_world_to_cam.data_ptr()),
redner.float_ptr(d_fov_factor.data_ptr()))
d_vertices_list = []
d_uvs_list = []
d_normals_list = []
d_shapes = []
for shape in ctx.shapes:
num_vertices = shape.num_vertices
d_vertices = torch.zeros(num_vertices,
3,
device=pyredner.get_device())
d_uvs = torch.zeros(
num_vertices, 2,
device=pyredner.get_device()) if shape.has_uvs() else None
d_normals = torch.zeros(
num_vertices, 3,
device=pyredner.get_device()) if shape.has_normals() else None
d_vertices_list.append(d_vertices)
d_uvs_list.append(d_uvs)
d_normals_list.append(d_normals)
d_shapes.append(redner.DShape(\
redner.float_ptr(d_vertices.data_ptr()),
redner.float_ptr(d_uvs.data_ptr() if d_uvs is not None else 0),
redner.float_ptr(d_normals.data_ptr() if d_normals is not None else 0)))
d_diffuse_list = []
d_specular_list = []
d_roughness_list = []
d_materials = []
for material in ctx.materials:
diffuse_size = material.get_diffuse_size()
specular_size = material.get_specular_size()
roughness_size = material.get_roughness_size()
if diffuse_size[0] == 0:
d_diffuse = torch.zeros(3, device=pyredner.get_device())
else:
d_diffuse = torch.zeros(diffuse_size[2],
diffuse_size[1],
diffuse_size[0],
3,
device=pyredner.get_device())
if specular_size[0] == 0:
d_specular = torch.zeros(3, device=pyredner.get_device())
else:
d_specular = torch.zeros(specular_size[2],
specular_size[1],
specular_size[0],
3,
device=pyredner.get_device())
if roughness_size[0] == 0:
d_roughness = torch.zeros(1, device=pyredner.get_device())
else:
d_roughness = torch.zeros(roughness_size[2],
roughness_size[1],
roughness_size[0],
device=pyredner.get_device())
d_diffuse_list.append(d_diffuse)
d_specular_list.append(d_specular)
d_roughness_list.append(d_roughness)
d_diffuse_uv_scale = torch.zeros(2)
d_specular_uv_scale = torch.zeros(2)
d_roughness_uv_scale = torch.zeros(2)
d_diffuse_tex = redner.Texture3(\
redner.float_ptr(d_diffuse.data_ptr()),
diffuse_size[0], diffuse_size[1], diffuse_size[2],
redner.float_ptr(d_diffuse_uv_scale.data_ptr()))
d_specular_tex = redner.Texture3(\
redner.float_ptr(d_specular.data_ptr()),
specular_size[0], specular_size[1], specular_size[2],
redner.float_ptr(d_specular_uv_scale.data_ptr()))
d_roughness_tex = redner.Texture1(\
redner.float_ptr(d_roughness.data_ptr()),
roughness_size[0], roughness_size[1], roughness_size[2],
redner.float_ptr(d_roughness_uv_scale.data_ptr()))
d_materials.append(redner.DMaterial(\
d_diffuse_tex, d_specular_tex, d_roughness_tex))
d_intensity_list = []
d_lights = []
for light in ctx.lights:
d_intensity = torch.zeros(3, device=pyredner.get_device())
d_intensity_list.append(d_intensity)
d_lights.append(
redner.DLight(redner.float_ptr(d_intensity.data_ptr())))
d_scene = redner.DScene(d_camera, d_shapes, d_materials, d_lights,
pyredner.get_use_gpu())
if not get_use_correlated_random_number():
# Decouple the forward/backward random numbers by adding a big prime number
options.seed += 1000003
redner.render(scene, options, redner.float_ptr(0),
redner.float_ptr(grad_img.data_ptr()), d_scene,
redner.float_ptr(0))
# # For debugging
# grad_img = torch.ones(256, 256, 3)
# debug_img = torch.zeros(256, 256, 3)
# redner.render(scene, options,
# redner.float_ptr(0),
# redner.float_ptr(grad_img.data_ptr()),
# d_scene,
# redner.float_ptr(debug_img.data_ptr()))
# pyredner.imwrite(debug_img, 'debug.exr')
# exit()
ret_list = []
ret_list.append(None) # seed
ret_list.append(None) # num_shapes
ret_list.append(None) # num_materials
ret_list.append(None) # num_lights
ret_list.append(d_cam_to_world)
ret_list.append(d_world_to_cam)
ret_list.append(d_fov_factor)
ret_list.append(None) # clip near
ret_list.append(None) # resolution
ret_list.append(None) # fisheye
num_shapes = len(ctx.shapes)
for i in range(num_shapes):
ret_list.append(d_vertices_list[i])
ret_list.append(None) # indices
ret_list.append(d_uvs_list[i])
ret_list.append(d_normals_list[i])
ret_list.append(None) # material id
ret_list.append(None) # light id
num_materials = len(ctx.materials)
for i in range(num_materials):
ret_list.append(d_diffuse_list[i])
ret_list.append(None) # diffuse_uv_scale
ret_list.append(d_specular_list[i])
ret_list.append(None) # specular_uv_scale
ret_list.append(d_roughness_list[i])
ret_list.append(None) # roughness_uv_scale
ret_list.append(None) # two sided
num_lights = len(ctx.lights)
for i in range(num_lights):
ret_list.append(None) # shape id
ret_list.append(d_intensity_list[i].cpu())
ret_list.append(None) # num samples
ret_list.append(None) # num bounces
return tuple(ret_list)
import pyredner
import numpy as np
import torch
import scipy
# Optimize for material parameters and camera pose
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/teapot.xml')
# The last material is the teapot material, set it to the target
scene.materials[-1].diffuse_reflectance = \
pyredner.Texture(torch.tensor([0.3, 0.2, 0.2], device = pyredner.get_device()))
scene.materials[-1].specular_reflectance = \
pyredner.Texture(torch.tensor([0.6, 0.6, 0.6], device = pyredner.get_device()))
scene.materials[-1].roughness = \
pyredner.Texture(torch.tensor([0.05], device = pyredner.get_device()))
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 2)
# Alias of the render function
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_teapot_reflectance/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_teapot_reflectance/target.png')
# Set up the scene using Pytorch tensor
position = torch.tensor([0.0, 0.0, -5.0])
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
if pyredner.get_use_gpu():
diffuse = diffuse.cuda(device = pyredner.get_device())
specular = specular.cuda(device = pyredner.get_device())
roughness = roughness.cuda(device = pyredner.get_device())
mat_perlin = pyredner.Material(\
diffuse_reflectance = diffuse,
specular_reflectance = specular,
roughness = roughness)
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0], device = pyredner.get_device()))
materials = [mat_perlin, mat_black]
vertices = torch.tensor([[-1.5,-1.5,0.0], [-1.5,1.5,0.0], [1.5,-1.5,0.0], [1.5,1.5,0.0]],
device = pyredner.get_device())
indices = torch.tensor([[0, 1, 2], [1, 3, 2]], dtype = torch.int32,
device = pyredner.get_device())
uvs = torch.tensor([[0.05, 0.05], [0.05, 0.95], [0.95, 0.05], [0.95, 0.95]],
device = pyredner.get_device())
# Set up the pyredner scene for rendering:
# Setup camera
cam = pyredner.Camera(
position=torch.tensor([0.0, 0.0, -5.0]),
look_at=torch.tensor([0.0, 0.0, 0.0]),
up=torch.tensor([0.0, 1.0, 0.0]),
fov=torch.tensor([45.0]), # in degree
clip_near=1e-2, # needs to > 0
resolution=(256, 256),
fisheye=False)
# Setup materials
mat_grey = pyredner.Material(\
diffuse_reflectance = \
torch.tensor([0.5, 0.5, 0.5], device = pyredner.get_device()))
# The material list of the scene
materials = [mat_grey]
# Setup geometries
shape_triangle = pyredner.Shape(\
vertices = torch.tensor([[-1.7, 1.0, 0.0], [1.0, 1.0, 0.0], [-0.5, -1.0, 0.0]],
device = pyredner.get_device()),
indices = torch.tensor([[0, 1, 2]], dtype = torch.int32,
device = pyredner.get_device()),
uvs = None,
normals = None,
material_id = 0)
# Setup light source shape
shape_light = pyredner.Shape(\
vertices = torch.tensor([[-1.0, -1.0, -7.0],
cam = pyredner.Camera(position = torch.tensor([0.0, 0.0, -5.0]),
look_at = torch.tensor([0.0, 0.0, 0.0]),
up = torch.tensor([0.0, 1.0, 0.0]),
fov = torch.tensor([45.0]), # in degree
clip_near = 1e-2, # needs to > 0
resolution = (256, 256),
fisheye = False)
# Next, we setup the materials for the scene.
# All materials in the scene are stored in a Python list,
# the index of a material in the list is its material id.
# Our simple scene only has a single grey material with reflectance 0.5.
# If you are using GPU, make sure to copy the reflectance to GPU memory.
mat_grey = pyredner.Material(\
diffuse_reflectance = \
torch.tensor([0.5, 0.5, 0.5], device = pyredner.get_device()))
# The material list of the scene
materials = [mat_grey]
# Next, we setup the geometry for the scene.
# 3D objects in redner are called "Shape".
# All shapes in the scene are stored in a Python list,
# the index of a shape in the list is its shape id.
# Right now, a shape is always a triangle mesh, which has a list of
# triangle vertices and a list of triangle indices.
# The vertices are a Nx3 torch float tensor,
# and the indices are a Mx3 torch integer tensor.
# Optionally, for each vertex you can specify its UV coordinate for texture mapping,
# and a normal for Phong interpolation.
# Each shape also needs to be assigned a material using material id,
# which is the index of the material in the material array.
def forward(ctx,
seed,
*args):
"""
Forward rendering pass: given a scene and output an image.
"""
# Unpack arguments
current_index = 0
num_shapes = args[current_index]
current_index += 1
num_materials = args[current_index]
current_index += 1
num_lights = args[current_index]
current_index += 1
cam_position = args[current_index]
current_index += 1
cam_look_at = args[current_index]
current_index += 1
cam_up = args[current_index]
current_index += 1
ndc_to_cam = args[current_index]
current_index += 1
cam_to_ndc = args[current_index]
current_index += 1
clip_near = args[current_index]
current_index += 1
resolution = args[current_index]
current_index += 1
camera_type = args[current_index]
current_index += 1
camera = redner.Camera(resolution[1],
resolution[0],
redner.float_ptr(cam_position.data_ptr()),
redner.float_ptr(cam_look_at.data_ptr()),
redner.float_ptr(cam_up.data_ptr()),
redner.float_ptr(ndc_to_cam.data_ptr()),
redner.float_ptr(cam_to_ndc.data_ptr()),
clip_near,
camera_type)
shapes = []
for i in range(num_shapes):
vertices = args[current_index]
current_index += 1
indices = args[current_index]
current_index += 1
uvs = args[current_index]
current_index += 1
normals = args[current_index]
current_index += 1
material_id = args[current_index]
current_index += 1
light_id = args[current_index]
current_index += 1
assert(vertices.is_contiguous())
assert(indices.is_contiguous())
if uvs is not None:
assert(uvs.is_contiguous())
if normals is not None:
assert(normals.is_contiguous())
shapes.append(redner.Shape(\
redner.float_ptr(vertices.data_ptr()),
redner.int_ptr(indices.data_ptr()),
redner.float_ptr(uvs.data_ptr() if uvs is not None else 0),
redner.float_ptr(normals.data_ptr() if normals is not None else 0),
int(vertices.shape[0]),
int(indices.shape[0]),
material_id,
light_id))
materials = []
for i in range(num_materials):
diffuse_reflectance = args[current_index]
current_index += 1
diffuse_uv_scale = args[current_index]
current_index += 1
specular_reflectance = args[current_index]
current_index += 1
specular_uv_scale = args[current_index]
current_index += 1
roughness = args[current_index]
current_index += 1
roughness_uv_scale = args[current_index]
current_index += 1
normal_map = args[current_index]
current_index += 1
normal_map_uv_scale = args[current_index]
current_index += 1
two_sided = args[current_index]
current_index += 1
assert(diffuse_reflectance.is_contiguous())
if diffuse_reflectance.dim() == 1:
diffuse_reflectance = redner.Texture3(\
redner.float_ptr(diffuse_reflectance.data_ptr()), 0, 0, 0,
redner.float_ptr(diffuse_uv_scale.data_ptr()))
else:
diffuse_reflectance = redner.Texture3(\
redner.float_ptr(diffuse_reflectance.data_ptr()),
int(diffuse_reflectance.shape[2]), # width
int(diffuse_reflectance.shape[1]), # height
int(diffuse_reflectance.shape[0]), # num levels
redner.float_ptr(diffuse_uv_scale.data_ptr()))
assert(specular_reflectance.is_contiguous())
if specular_reflectance.dim() == 1:
specular_reflectance = redner.Texture3(\
redner.float_ptr(specular_reflectance.data_ptr()), 0, 0, 0,
redner.float_ptr(specular_uv_scale.data_ptr()))
else:
specular_reflectance = redner.Texture3(\
redner.float_ptr(specular_reflectance.data_ptr()),
int(specular_reflectance.shape[2]), # width
int(specular_reflectance.shape[1]), # height
int(specular_reflectance.shape[0]), # num levels
redner.float_ptr(specular_uv_scale.data_ptr()))
assert(roughness.is_contiguous())
if roughness.dim() == 1:
roughness = redner.Texture1(\
redner.float_ptr(roughness.data_ptr()), 0, 0, 0,
redner.float_ptr(roughness_uv_scale.data_ptr()))
else:
assert(roughness.dim() == 4)
roughness = redner.Texture1(\
redner.float_ptr(roughness.data_ptr()),
int(roughness.shape[2]), # width
int(roughness.shape[1]), # height
int(roughness.shape[0]), # num levels
redner.float_ptr(roughness_uv_scale.data_ptr()))
if normal_map is not None:
assert(normal_map.dim() == 4)
normal_map = redner.Texture3(\
redner.float_ptr(normal_map.data_ptr()),
int(normal_map.shape[2]), # width
int(normal_map.shape[1]), # height
int(normal_map.shape[0]), # num levels
redner.float_ptr(normal_map_uv_scale.data_ptr()))
else:
normal_map = redner.Texture3(\
redner.float_ptr(0), 0, 0, 0, redner.float_ptr(0))
materials.append(redner.Material(\
diffuse_reflectance,
specular_reflectance,
roughness,
normal_map,
two_sided))
area_lights = []
for i in range(num_lights):
shape_id = args[current_index]
current_index += 1
intensity = args[current_index]
current_index += 1
two_sided = args[current_index]
current_index += 1
area_lights.append(redner.AreaLight(\
shape_id,
redner.float_ptr(intensity.data_ptr()),
two_sided))
envmap = None
if args[current_index] is not None:
values = args[current_index]
current_index += 1
envmap_uv_scale = args[current_index]
current_index += 1
env_to_world = args[current_index]
current_index += 1
world_to_env = args[current_index]
current_index += 1
sample_cdf_ys = args[current_index]
current_index += 1
sample_cdf_xs = args[current_index]
current_index += 1
pdf_norm = args[current_index]
current_index += 1
values = redner.Texture3(\
redner.float_ptr(values.data_ptr()),
int(values.shape[2]), # width
int(values.shape[1]), # height
int(values.shape[0]), # num levels
redner.float_ptr(envmap_uv_scale.data_ptr()))
envmap = redner.EnvironmentMap(\
values,
redner.float_ptr(env_to_world.data_ptr()),
redner.float_ptr(world_to_env.data_ptr()),
redner.float_ptr(sample_cdf_ys.data_ptr()),
redner.float_ptr(sample_cdf_xs.data_ptr()),
pdf_norm)
else:
current_index += 7
# Options
num_samples = args[current_index]
current_index += 1
max_bounces = args[current_index]
current_index += 1
channels = args[current_index]
current_index += 1
sampler_type = args[current_index]
current_index += 1
use_primary_edge_sampling = args[current_index]
current_index += 1
use_secondary_edge_sampling = args[current_index]
current_index += 1
start = time.time()
scene = redner.Scene(camera,
shapes,
materials,
area_lights,
envmap,
pyredner.get_use_gpu(),
pyredner.get_device().index if pyredner.get_device().index is not None else -1,
use_primary_edge_sampling,
use_secondary_edge_sampling)
time_elapsed = time.time() - start
if print_timing:
print('Scene construction, time: %.5f s' % time_elapsed)
# check that num_samples is a tuple
if isinstance(num_samples, int):
num_samples = (num_samples, num_samples)
options = redner.RenderOptions(seed, num_samples[0], max_bounces, channels, sampler_type)
num_channels = redner.compute_num_channels(channels)
rendered_image = torch.zeros(resolution[0], resolution[1], num_channels,
device = pyredner.get_device())
start = time.time()
redner.render(scene,
options,
redner.float_ptr(rendered_image.data_ptr()),
redner.float_ptr(0),
None,
redner.float_ptr(0))
time_elapsed = time.time() - start
if print_timing:
print('Forward pass, time: %.5f s' % time_elapsed)
# # For debugging
# debug_img = torch.zeros(256, 256, 3)
# redner.render(scene,
# options,
# redner.float_ptr(rendered_image.data_ptr()),
# redner.float_ptr(0),
# None,
# redner.float_ptr(debug_img.data_ptr()))
# pyredner.imwrite(debug_img, 'debug.exr')
# exit()
ctx.shapes = shapes
ctx.materials = materials
ctx.area_lights = area_lights
ctx.envmap = envmap
ctx.scene = scene
ctx.options = options
ctx.num_samples = num_samples
return rendered_image
print('scene loaded')
max_bounces = 6
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = max_bounces)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_living_room/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_living_room/target.png')
target = pyredner.imread('results/test_living_room/target.exr')
if pyredner.get_use_gpu():
target = target.cuda(device = pyredner.get_device())
scene.camera.look_at = torch.tensor([-0.556408, 0.951295, -3.98066], requires_grad=True)
scene.camera.position = torch.tensor([0.00419251, 0.973707, -4.80844], requires_grad=True)
scene.camera.up = torch.tensor([-0.00920347, 0.999741, 0.020835], requires_grad=True)
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = max_bounces)
img = render(1, *args)
pyredner.imwrite(img.cpu(), 'results/test_living_room/init.exr')
pyredner.imwrite(img.cpu(), 'results/test_living_room/init.png')
diff = torch.abs(target - img)
pyredner.imwrite(diff.cpu(), 'results/test_living_room/init_diff.png')
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device()))
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_grey, mat_black]
floor_vertices = torch.tensor([[-2.0,0.0,-2.0],[-2.0,0.0,2.0],[2.0,0.0,-2.0],[2.0,0.0,2.0]],
device = pyredner.get_device())
floor_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
shape_floor = pyredner.Shape(floor_vertices, floor_indices, None, None, 0)
blocker_vertices = torch.tensor(\
[[-0.5,3.0,-0.5],[-0.5,3.0,0.5],[0.5,3.0,-0.5],[0.5,3.0,0.5]],
device = pyredner.get_device())
blocker_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device()))
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_grey, mat_black]
floor_vertices = torch.tensor([[-2.0,0.0,-2.0],[-2.0,0.0,2.0],[2.0,0.0,-2.0],[2.0,0.0,2.0]],
device = pyredner.get_device())
floor_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
shape_floor = pyredner.Shape(floor_vertices, floor_indices, None, None, 0)
blocker_vertices = torch.tensor(\
[[-0.5,3.0,-0.5],[-0.5,3.0,0.5],[0.5,3.0,-0.5],[0.5,3.0,0.5]],
device = pyredner.get_device())
blocker_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
import pyredner
import numpy as np
import torch
# Optimize for a textured plane in a specular reflection
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/teapot_specular.xml')
# The last material is the teapot material, set it to a specular material
scene.materials[-1].diffuse_reflectance = \
pyredner.Texture(torch.tensor([0.15, 0.2, 0.15], device = pyredner.get_device()))
scene.materials[-1].specular_reflectance = \
pyredner.Texture(torch.tensor([0.8, 0.8, 0.8], device = pyredner.get_device()))
scene.materials[-1].roughness = \
pyredner.Texture(torch.tensor([0.0001], device = pyredner.get_device()))
args=pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = 2)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_teapot_specular/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_teapot_specular/target.png')
target = pyredner.imread('results/test_teapot_specular/target.exr')
if pyredner.get_use_gpu():
position = torch.tensor([0.0, 0.0, -5.0])
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5], device = pyredner.get_device()))
materials = [mat_grey]
vertices = torch.tensor([[-1.3,1.0,0.0], [1.0,1.0,0.0], [-0.5,-2.0,-7.0]], device = pyredner.get_device())
indices = torch.tensor([[0, 1, 2]], dtype = torch.int32, device = pyredner.get_device())
shape_triangle = pyredner.Shape(vertices, indices, None, None, 0)
light_vertices = torch.tensor([[-1.0,-1.0,-7.0],[1.0,-1.0,-7.0],[-1.0,1.0,-7.0],[1.0,1.0,-7.0]],
device = pyredner.get_device())
light_indices = torch.tensor([[0,1,2],[1,3,2]], dtype = torch.int32, device = pyredner.get_device())
shape_light = pyredner.Shape(light_vertices, light_indices, None, None, 0)
shapes = [shape_triangle, shape_light]
light_intensity = torch.tensor([20.0,20.0,20.0])
light = pyredner.AreaLight(1, light_intensity)
area_lights = [light]
scene = pyredner.Scene(cam, shapes, materials, area_lights)
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device()))
materials = [mat_grey]
vertices = torch.tensor([[-1.7,1.0,0.0], [1.0,1.0,0.0], [-0.5,-1.0,0.0]],
device = pyredner.get_device())
indices = torch.tensor([[0, 1, 2]], dtype = torch.int32,
device = pyredner.get_device())
shape_triangle = pyredner.Shape(vertices, indices, None, None, 0)
light_vertices = torch.tensor([[-1.0,-1.0,-9.0],[1.0,-1.0,-9.0],[-1.0,1.0,-9.0],[1.0,1.0,-9.0]],
device = pyredner.get_device())
light_indices = torch.tensor([[0,1,2],[1,3,2]], dtype = torch.int32,
device = pyredner.get_device())
shape_light = pyredner.Shape(light_vertices, light_indices, None, None, 0)
shapes = [shape_triangle, shape_light]
light_intensity = torch.tensor([30.0,30.0,30.0])
light = pyredner.AreaLight(1, light_intensity)
area_lights = [light]
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
checkerboard_texture = pyredner.imread('checkerboard.exr')
if pyredner.get_use_gpu():
checkerboard_texture = checkerboard_texture.cuda(device = pyredner.get_device())
mat_checkerboard = pyredner.Material(\
diffuse_reflectance = checkerboard_texture)
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_checkerboard, mat_black]
vertices = torch.tensor([[-1.0,-1.0,0.0], [-1.0,1.0,0.0], [1.0,-1.0,0.0], [1.0,1.0,0.0]],
device = pyredner.get_device())
indices = torch.tensor([[0, 1, 2], [1, 3, 2]], dtype = torch.int32,
device = pyredner.get_device())
uvs = torch.tensor([[0.05, 0.05], [0.05, 0.95], [0.95, 0.05], [0.95, 0.95]],
device = pyredner.get_device())
shape_plane = pyredner.Shape(vertices, indices, uvs, None, 0)
light_vertices = torch.tensor([[-1.0,-1.0,-7.0],[1.0,-1.0,-7.0],[-1.0,1.0,-7.0],[1.0,1.0,-7.0]],
import math
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
cam = pyredner.Camera(position = torch.tensor([0.0, 0.0, -5.0]),
look_at = torch.tensor([0.0, 0.0, 0.0]),
up = torch.tensor([0.0, 1.0, 0.0]),
fov = torch.tensor([45.0]), # in degree
clip_near = 1e-2, # needs to > 0
resolution = (256, 256),
fisheye = False)
mat_grey = pyredner.Material(\
diffuse_reflectance = \
torch.tensor([0.4, 0.4, 0.4], device = pyredner.get_device()),
specular_reflectance = \
torch.tensor([0.5, 0.5, 0.5], device = pyredner.get_device()),
roughness = \
torch.tensor([0.05], device = pyredner.get_device()))
materials = [mat_grey]
vertices, indices, uvs, normals = pyredner.generate_sphere(128, 64)
shape_sphere = pyredner.Shape(\
vertices = vertices,
indices = indices,
uvs = uvs,
normals = normals,
material_id = 0)
shapes = [shape_sphere]
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Setup camera
cam = pyredner.Camera(position = torch.tensor([0.0, 0.0, -5.0]),
look_at = torch.tensor([0.0, 0.0, 0.0]),
up = torch.tensor([0.0, 1.0, 0.0]),
fov = torch.tensor([45.0]), # in degree
clip_near = 1e-2, # needs to > 0
resolution = (256, 256),
fisheye = False)
# Setup material
mat_grey = pyredner.Material(\
diffuse_reflectance = \
torch.tensor([0.4, 0.4, 0.4], device = pyredner.get_device()),
specular_reflectance = \
torch.tensor([0.5, 0.5, 0.5], device = pyredner.get_device()),
roughness = \
torch.tensor([0.02], device = pyredner.get_device()))
materials = [mat_grey]
# Setup scene geometry: we use the utility function "generate_sphere" to generate a sphere
# triangle mesh
vertices, indices, uvs, normals = pyredner.generate_sphere(128, 64)
shape_sphere = pyredner.Shape(\
vertices = vertices,
indices = indices,
uvs = uvs,
normals = normals,
material_id = 0)
# Set up the scene using Pytorch tensor
position = torch.tensor([0.0, 0.0, -5.0])
look_at = torch.tensor([0.0, 0.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position=position,
look_at=look_at,
up=up,
fov=fov,
clip_near=clip_near,
resolution=resolution)
if pyredner.get_use_gpu():
diffuse = diffuse.cuda(device=pyredner.get_device())
specular = specular.cuda(device=pyredner.get_device())
roughness = roughness.cuda(device=pyredner.get_device())
mat_perlin = pyredner.Material(\
diffuse_reflectance = diffuse,
specular_reflectance = specular,
roughness = roughness)
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0], device = pyredner.get_device()))
materials = [mat_perlin, mat_black]
vertices = torch.tensor(
[[-1.5, -1.5, 0.0], [-1.5, 1.5, 0.0], [1.5, -1.5, 0.0], [1.5, 1.5, 0.0]],
device=pyredner.get_device())
indices = torch.tensor([[0, 1, 2], [1, 3, 2]],
dtype=torch.int32,
device=pyredner.get_device())
