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nrestir01.py
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nrestir01.py
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from __future__ import (
annotations as __annotations__,
) # Delayed parsing of type annotations
import mitsuba as mi
import drjit as dr
import numpy as np
from drjitstruct import drjitstruct
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
if __name__ == "__main__":
mi.set_variant("cuda_ad_rgb")
from hashgrid import HashGrid
def mis_weight(pdf_a: mi.Float, pdf_b: mi.Float) -> mi.Float:
"""
Compute the Multiple Importance Sampling (MIS) weight given the densities
of two sampling strategies according to the power heuristic.
"""
a2 = dr.sqr(pdf_a)
return dr.detach(dr.select(pdf_a > 0, a2 / dr.fma(pdf_b, pdf_b, a2), 0), True)
def p_hat(f):
return dr.norm(f)
@drjitstruct
class RestirSample:
Li: mi.Vector3f
x0: mi.Point3f
n0: mi.Vector3f
x1: mi.Point3f
pq: mi.Float
@drjitstruct
class RestirReservoir:
z: RestirSample
w: mi.Float
W: mi.Float
M: mi.UInt
def update(
self,
sampler: mi.Sampler,
snew: RestirSample,
wnew: mi.Float,
active: mi.Bool = True,
):
active = mi.Bool(active)
if dr.shape(active)[-1] == 1:
dr.make_opaque(active)
self.w += dr.select(active, wnew, 0)
self.M += dr.select(active, 1, 0)
self.z: RestirSample = dr.select(
active & (sampler.next_1d() < wnew / self.w), snew, self.z
)
def merge(
self, sampler: mi.Sampler, r: "RestirReservoir", p, active: mi.Bool = True
):
active = mi.Bool(active)
M0 = mi.UInt(self.M)
self.update(sampler, r.z, p * r.W * r.M, active)
self.M = dr.select(active, M0 + r.M, M0)
from tinycudann import Encoding as NGPEncoding
class NRField(nn.Module):
def __init__(self, scene: mi.Scene, width=256, n_hidden=8) -> None:
"""Initialize an instance of NRField.
Args:
bb_min (mi.ScalarBoundingBox3f): minimum point of the bounding box
bb_max (mi.ScalarBoundingBox3f): maximum point of the bounding box
"""
super().__init__()
self.bbox = scene.bbox()
enc_config = {
"otype": "HashGrid",
# "type": "Hash",
"base_resolution": 16,
"n_levels": 8,
"n_features_per_level": 4,
"log2_hashmap_size": 22,
}
self.pos_enc = NGPEncoding(3, enc_config)
in_size = 3 * 4 + self.pos_enc.n_output_dims
hidden_layers = []
for _ in range(n_hidden):
hidden_layers.append(nn.Linear(width, width))
hidden_layers.append(nn.ReLU(inplace=True))
self.network = nn.Sequential(
nn.Linear(in_size, width),
nn.ReLU(inplace=True),
*hidden_layers,
nn.Linear(width, 3),
).to("cuda")
def forward(self, si: mi.SurfaceInteraction3f):
"""Forward pass for NRField.
Args:
si (mitsuba.SurfaceInteraction3f): surface interaction
bsdf (mitsuba.BSDF): bidirectional scattering distribution function
Returns:
torch.Tensor
"""
with dr.suspend_grad():
x = ((si.p - self.bbox.min) / (self.bbox.max - self.bbox.min)).torch()
wi = si.to_world(si.wi).torch()
n = si.sh_frame.n.torch()
f_d = si.bsdf().eval_diffuse_reflectance(si).torch()
z_x = self.pos_enc(x)
inp = torch.concat([x, wi, n, f_d, z_x], dim=1)
out = self.network(inp)
out = torch.abs(out)
return out.to(torch.float32)
class GReSTIRIntegrator(mi.SamplingIntegrator):
search_radius = 0.1
angle_threshold = 25 * dr.pi / 180
temporal_M_max = 500
def __init__(self, model: nn.Model):
super().__init__(mi.Properties())
self.max_depth = 8
self.rr_depth = 4
self.n = 0
self.model = model
self.batch_size = 2**14
self.lr = 5e-4
self.losses = []
self.render_mode = "nerad"
def similar(self, s1: RestirSample, s2: RestirSample) -> mi.Bool:
similar = mi.Bool(True)
similar &= dr.dot(s1.n0, s2.n0) > dr.cos(self.angle_threshold)
return similar
def create_reservoirs(self, scene: mi.Scene, n: int):
sampler = mi.load_dict({"type": "independent"}) # type: mi.Sampler
sampler.seed(0, n)
m_area = []
for shape in scene.shapes():
m_area.append(shape.surface_area()[0])
m_area = np.array(m_area)
shape_sampler = mi.DiscreteDistribution(m_area)
self.shape_sampler = shape_sampler
shape_idx = shape_sampler.sample(sampler.next_1d())
shape = dr.gather(mi.ShapePtr, scene.shapes_dr(), shape_idx) # type: mi.Shape
ps = shape.sample_position(0.5, sampler.next_2d()) # type: mi.PositionSample3f
self.reservoirs = dr.zeros(RestirReservoir, n) # type: RestirReservoir
self.reservoirs.z.x0 = ps.p
self.reservoirs.z.n0 = ps.n
self.grid = HashGrid(ps.p, 100, n)
self.n_reservoirs = n
def sample_si(
self, scene: mi.Scene, sampler: mi.Sampler
) -> mi.SurfaceInteraction3f:
shape_idx = self.shape_sampler.sample(sampler.next_1d())
shape = dr.gather(mi.ShapePtr, scene.shapes_dr(), shape_idx) # type: mi.Shape
ps = shape.sample_position(0, sampler.next_2d())
si = mi.SurfaceInteraction3f(ps, dr.zeros(mi.Color0f))
si.shape = shape
bsdf = shape.bsdf()
sample = sampler.next_2d()
active_two_sided = mi.has_flag(bsdf.flags(), mi.BSDFFlags.BackSide)
si.wi = dr.select(
active_two_sided,
mi.warp.square_to_uniform_sphere(sample),
mi.warp.square_to_uniform_hemisphere(sample),
)
# pdf = dr.select(
# active_two_sided,
# mi.warp.square_to_uniform_sphere_pdf(si.wi),
# mi.warp.square_to_uniform_hemisphere_pdf(si.wi),
# )
return si
def sample_ray(
self,
scene: mi.Scene,
sampler: mi.Sampler,
ray: mi.Ray3f,
active: bool = True,
) -> mi.Color3f:
# --------------------- Configure loop state ----------------------
ray = mi.Ray3f(ray)
active = mi.Bool(active)
throughput = mi.Spectrum(1.0)
result = mi.Spectrum(0.0)
eta = mi.Float(1.0)
depth = mi.UInt32(0)
valid_ray = mi.Bool(scene.environment() is not None)
# Variables caching information from the previous bounce
prev_si: mi.SurfaceInteraction3f = dr.zeros(mi.SurfaceInteraction3f)
prev_bsdf_pdf = mi.Float(1.0)
prev_bsdf_delta = mi.Bool(True)
bsdf_ctx = mi.BSDFContext()
loop = mi.Loop(
"Path Tracer",
state=lambda: (
sampler,
ray,
throughput,
result,
eta,
depth,
valid_ray,
prev_si,
prev_bsdf_pdf,
prev_bsdf_delta,
active,
),
)
loop.set_max_iterations(self.max_depth)
while loop(active):
si = scene.ray_intersect(ray) # TODO: not necesarry in first interaction
# ---------------------- Direct emission ----------------------
ds = mi.DirectionSample3f(scene, si, prev_si)
em_pdf = mi.Float(0.0)
em_pdf = scene.pdf_emitter_direction(prev_si, ds, ~prev_bsdf_delta)
mis_bsdf = mis_weight(prev_bsdf_pdf, em_pdf)
result = dr.fma(
throughput,
ds.emitter.eval(si, prev_bsdf_pdf > 0.0) * mis_bsdf,
result,
)
active_next = ((depth + 1) < self.max_depth) & si.is_valid()
bsdf: mi.BSDF = si.bsdf(ray)
# ---------------------- Emitter sampling ----------------------
active_em = active_next & mi.has_flag(bsdf.flags(), mi.BSDFFlags.Smooth)
ds, em_weight = scene.sample_emitter_direction(
si, sampler.next_2d(), True, active_em
)
wo = si.to_local(ds.d)
# ------ Evaluate BSDF * cos(theta) and sample direction -------
sample1 = sampler.next_1d()
sample2 = sampler.next_2d()
bsdf_val, bsdf_pdf, bsdf_sample, bsdf_weight = bsdf.eval_pdf_sample(
bsdf_ctx, si, wo, sample1, sample2
)
# --------------- Emitter sampling contribution ----------------
bsdf_val = si.to_world_mueller(bsdf_val, -wo, si.wi)
mi_em = dr.select(ds.delta, 1.0, mis_weight(ds.pdf, bsdf_pdf))
result[active_em] = dr.fma(throughput, bsdf_val * em_weight * mi_em, result)
# ---------------------- BSDF sampling ----------------------
bsdf_weight = si.to_world_mueller(bsdf_weight, -bsdf_sample.wo, si.wi)
ray = si.spawn_ray(si.to_world(bsdf_sample.wo))
# ------ Update loop variables based on current interaction ------
throughput *= bsdf_weight
eta *= bsdf_sample.eta
valid_ray |= (
active
& si.is_valid()
& ~mi.has_flag(bsdf_sample.sampled_type, mi.BSDFFlags.Null)
)
prev_si = si
prev_bsdf_pdf = bsdf_sample.pdf
prev_bsdf_delta = mi.has_flag(bsdf_sample.sampled_type, mi.BSDFFlags.Delta)
# -------------------- Stopping criterion ---------------------
depth[si.is_valid()] += 1
throughput_max = dr.max(throughput)
rr_prop = dr.minimum(throughput_max * dr.sqr(eta), 0.95)
rr_active = depth >= self.rr_depth
rr_continue = sampler.next_1d() < rr_prop
throughput[rr_active] *= dr.rcp(rr_prop)
active = (
active_next & (~rr_active | rr_continue) & (dr.neq(throughput_max, 0.0))
)
return dr.select(valid_ray, result, 0.0)
def generate_sample(self, scene: mi.Scene, sampler: mi.Sampler) -> RestirSample:
si = self.sample_si(scene, sampler)
bsdf = si.bsdf()
sample = sampler.next_2d()
active_two_sided = mi.has_flag(bsdf.flags(), mi.BSDFFlags.BackSide)
wo = dr.select(
active_two_sided,
mi.warp.square_to_uniform_sphere(sample),
mi.warp.square_to_uniform_hemisphere(sample),
)
pq = dr.select(
active_two_sided,
mi.warp.square_to_uniform_sphere_pdf(wo),
mi.warp.square_to_uniform_hemisphere_pdf(wo),
)
si1 = scene.ray_intersect(
si.spawn_ray(si.to_world(wo))
) # type: mi.SurfaceInteraction3f
Li = self.sample_ray(scene, sampler, si.spawn_ray(si.to_world(wo)))
sample = dr.zeros(RestirSample) # type: RestirSample
sample.Li = Li
sample.pq = pq
sample.x0 = si.p
sample.x1 = si1.p
sample.n0 = si.n
return sample
def temporal_resampling(self, scene: mi.Scene, n):
sampler = mi.load_dict({"type": "independent"}) # type: mi.Sampler
sampler.seed(n, self.batch_size)
new_sample = self.generate_sample(scene, sampler)
cell = self.grid.cell_idx(new_sample.x0)
cell_size = dr.gather(mi.UInt, self.grid.cell_size, cell)
index_in_cell = mi.UInt(dr.floor(sampler.next_1d() * cell_size))
reservoir_idx = self.grid.sample_idx_in_cell(cell, index_in_cell)
R = dr.gather(
RestirReservoir, self.reservoirs, reservoir_idx
) # type: RestirReservoir
Rnew = dr.zeros(RestirReservoir) # type: RestirReservoir
w = dr.select(new_sample.pq > 0, p_hat(new_sample.Li) / new_sample.pq, 0.0)
Rnew.update(sampler, new_sample, w)
similar = self.similar(R.z, new_sample)
Rnew.merge(sampler, R, p_hat(R.z.Li), similar)
Rnew.z.x0 = R.z.x0
Rnew.z.n0 = R.z.n0
phat = p_hat(Rnew.z.Li)
Rnew.W = dr.select(phat * Rnew.M > 0, Rnew.w / (Rnew.M * phat), 0)
Rnew.M = dr.clamp(Rnew.M, 0, self.temporal_M_max)
dr.scatter(self.reservoirs, Rnew, reservoir_idx, similar)
def sample_restir(
self, scene: mi.Scene, sampler: mi.Sampler, si: mi.SurfaceInteraction3f
) -> mi.Color3f:
Rnew = dr.zeros(RestirReservoir) # type: RestirReservoir
# si = scene.ray_intersect(ray) # type: mi.SurfaceInteraction3f
S = dr.zeros(RestirSample) # type: RestirSample
S.x0 = si.p
S.n0 = si.n
for i in range(10):
# offset = mi.warp.square_to_uniform_disk(sampler.next_1d()) * 0.1
offset = (
mi.warp.square_to_uniform_disk(sampler.next_2d()) * self.search_radius
)
# offset = mi.warp.square_to_tent(sampler.next_2d()) * 0.01
p = si.p + si.to_world(mi.Point3f(offset.x, offset.y, 0))
cell = self.grid.cell_idx(p)
cell_size = dr.gather(mi.UInt, self.grid.cell_size, cell)
index_in_cell = mi.UInt(dr.floor(sampler.next_1d() * cell_size))
reservoir_idx = self.grid.sample_idx_in_cell(cell, index_in_cell)
Rn = dr.gather(
RestirReservoir, self.reservoirs, reservoir_idx
) # type: RestirReservoir
similar = self.similar(Rn.z, S)
shadowed = scene.ray_test(si.spawn_ray_to(Rn.z.x1))
Rnew.merge(
sampler,
Rn,
p_hat(Rn.z.Li) * dr.select(~shadowed & similar, 1, 0),
similar,
)
phat = p_hat(Rnew.z.Li)
Rnew.W = dr.select(phat * Rnew.M > 0, Rnew.w / (Rnew.M * phat), 0)
# Final sampling
bsdf = si.bsdf() # type: mi.BSDF
β = bsdf.eval(mi.BSDFContext(), si, si.to_local(dr.normalize(Rnew.z.x1 - si.p)))
emittance = si.emitter(scene).eval(si)
result = Rnew.W * Rnew.z.Li * β + emittance
return result
def sample(
self,
scene: mi.Scene,
sampler: mi.Sampler,
ray: mi.RayDifferential3f,
medium: mi.Medium = None,
active: bool = True,
) -> tuple[mi.Color3f, bool, list[float]]:
if self.render_mode == "nerad":
si = scene.ray_intersect(ray)
lhs = self.sample_mlp(scene, si)
return mi.Color3f(lhs), True, []
elif self.render_mode == "restir":
self.temporal_resampling(scene, self.n)
self.n += 1
si = scene.ray_intersect(ray)
result = self.sample_restir(scene, sampler, si)
return mi.Color3f(result), True, []
def sample_mlp(self, scene: mi.Scene, si: mi.SurfaceInteraction3f, mode="drjit"):
with dr.suspend_grad():
Le = si.emitter(scene).eval(si)
out = self.model(si)
if mode == "drjit":
return mi.Spectrum(out)
elif mode == "torch":
return out
def train(self, scene: mi.Scene, steps: int, debug=False):
optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
tqdm_iterator = tqdm(range(steps))
self.model.train()
for step in tqdm_iterator:
# if step % 100 == 0:
# self.create_reservoirs(scene, self.n_reservoirs)
with dr.suspend_grad():
self.temporal_resampling(scene, step)
optimizer.zero_grad()
sampler = mi.load_dict({"type": "independent"})
sampler.seed(step, self.batch_size)
si = self.sample_si(scene, sampler)
lhs = self.sample_mlp(scene, si, mode="torch")
rhs = self.sample_restir(scene, sampler, si).torch() # type: torch.Tensor
rhs = rhs.reshape(self.batch_size, 3)
loss = torch.nn.MSELoss()(lhs, rhs)
loss.backward()
optimizer.step()
tqdm_iterator.set_description(f"Loos {loss.item():04f}")
self.losses.append(loss.item())
self.model.eval()
if __name__ == "__main__":
torch.manual_seed(0)
scene = mi.cornell_box()
scene["sensor"]["film"]["width"] = 1024
scene["sensor"]["film"]["height"] = 1024
scene["sensor"]["film"]["rfilter"] = mi.load_dict({"type": "box"})
scene = mi.load_dict(scene) # type: mi.Scene
# scene = mi.load_file("./data/scenes/living-room-3/scene.xml")
field = NRField(scene, n_hidden=3, width=256)
integrator = GReSTIRIntegrator(field)
print("Creating Reservoir:")
integrator.create_reservoirs(scene, 1_000_000)
integrator.train(scene, 1000, debug=True)
nerad = mi.render(scene, integrator=integrator, spp=1, seed=0)
mi.util.write_bitmap("out/nerad.png", nerad)
integrator.render_mode = "restir"
restir = mi.render(scene, integrator=integrator, spp=1, seed=0)
mi.util.write_bitmap("out/restir.png", restir)
ref = mi.render(scene, spp=8)
mi.util.write_bitmap("out/ref.png", ref)
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
fig.patch.set_visible(False) # Hide the figure's background
ax[0][0].axis("off") # Remove the axes from the image
ax[0][0].imshow(mi.util.convert_to_bitmap(nerad))
ax[0][1].axis("off")
ax[0][1].imshow(mi.util.convert_to_bitmap(restir))
ax[1][0].axis("off")
ax[1][0].imshow(mi.util.convert_to_bitmap(ref))
ax[1][1].plot(integrator.losses, color="red")
fig.tight_layout() # Remove any extra white spaces around the image
plt.show()