def test03_sample_eval_pdf(variant_scalar_rgb, interaction):
from mitsuba.core.math import InvPi
from mitsuba.core.warp import square_to_uniform_sphere
from mitsuba.render import BSDFContext
from mitsuba.core.xml import load_string
bsdf = load_string("""<bsdf version="2.0.0" type="twosided">
<bsdf type="diffuse">
<rgb name="reflectance" value="0.1, 0.1, 0.1"/>
</bsdf>
<bsdf type="diffuse">
<rgb name="reflectance" value="0.9, 0.9, 0.9"/>
</bsdf>
</bsdf>""")
n = 5
ctx = BSDFContext()
for u in ek.arange(UInt32, n):
for v in ek.arange(UInt32, n):
interaction.wi = square_to_uniform_sphere([u / float(n-1),
v / float(n-1)])
up = ek.dot(interaction.wi, [0, 0, 1]) > 0
for x in ek.arange(UInt32, n):
for y in ek.arange(UInt32, n):
sample = [x / float(n-1), y / float(n-1)]
(bs, s_value) = bsdf.sample(ctx, interaction, 0.5, sample)
if ek.any(s_value > 0):
# Multiply by square_to_cosine_hemisphere_theta
s_value *= bs.wo[2] * InvPi
if not up:
s_value *= -1
e_value = bsdf.eval(ctx, interaction, bs.wo)
p_pdf = bsdf.pdf(ctx, interaction, bs.wo)
assert ek.allclose(s_value, e_value, atol=1e-2)
assert ek.allclose(bs.pdf, p_pdf)
assert not ek.any(ek.isnan(e_value) | ek.isnan(s_value))
def test06_rgb2spec_fetch_eval_mean(variant_scalar_spectral):
from mitsuba.render import srgb_model_fetch, srgb_model_eval, srgb_model_mean
from mitsuba.core import MTS_WAVELENGTH_MIN, MTS_WAVELENGTH_MAX, MTS_WAVELENGTH_SAMPLES
import numpy as np
rgb_values = np.array([
[0, 0, 0],
[0.0129831, 0.0129831, 0.0129831],
[0.0241576, 0.0241576, 0.0241576],
[0.0423114, 0.0423114, 0.0423114],
[0.0561285, 0.0561285, 0.0561285],
[0.0703601, 0.0703601, 0.0703601],
[0.104617, 0.104617, 0.104617],
[0.171441, 0.171441, 0.171441],
[0.242281, 0.242281, 0.242281],
[0.814846, 0.814846, 0.814846],
[0.913098, 0.913098, 0.913098],
[0.930111, 0.930111, 0.930111],
[0.955973, 0.955973, 0.955973],
[0.973445, 0.973445, 0.973445],
[0.982251, 0.982251, 0.982251],
[0.991102, 0.991102, 0.991102],
[1, 1, 1],
])
wavelengths = np.linspace(MTS_WAVELENGTH_MIN, MTS_WAVELENGTH_MAX,
MTS_WAVELENGTH_SAMPLES)
for i in range(rgb_values.shape[0]):
rgb = rgb_values[i, :]
assert np.all((rgb >= 0.0) & (rgb <= 1.0)), "Invalid RGB attempted"
coeff = srgb_model_fetch(rgb)
mean = srgb_model_mean(coeff)
value = srgb_model_eval(coeff, wavelengths)
assert not ek.any(ek.isnan(coeff)), "{} => coeff = {}".format(
rgb, coeff)
assert not ek.any(ek.isnan(mean)), "{} => mean = {}".format(rgb, mean)
assert not ek.any(ek.isnan(value)), "{} => value = {}".format(
rgb, value)
def test47_nan_propagation(m):
for i in range(2):
x = ek.arange(m.Float, 10)
ek.enable_grad(x)
f0 = m.Float(0)
y = ek.select(x < (20 if i == 0 else 0), x, x * (f0 / f0))
ek.backward(y)
g = ek.grad(x)
if i == 0:
assert g == 1
else:
assert ek.all(ek.isnan(g))
for i in range(2):
x = ek.arange(m.Float, 10)
ek.enable_grad(x)
f0 = m.Float(0)
y = ek.select(x < (20 if i == 0 else 0), x, x * (f0 / f0))
ek.forward(x)
g = ek.grad(y)
if i == 0:
assert g == 1
else:
assert ek.all(ek.isnan(g))
diff_vertex_ref = []
diff_vertex_init = []
for j in range(10):
opt = Adam(params_opt, lr = 0.001)
# opt = SGD(params_opt, lr = 0.001, momentum = 0.9)
for i in range(10):
unravel(vertex_positions + Vector3f(0,0,1) * params_opt['displacements'], params[vertex_pos_key])
params.set_dirty(vertex_pos_key)
params.update()
image = render(scene)
if j == 0 and i == 0:
image_init = image
if ek.any(ek.any(ek.isnan(params[vertex_pos_key]))):
print("[WARNING] NaNs in the vertex positions.")
if ek.any(ek.isnan(image)):
print("[WARNING] NaNs in the image.")
# Write a gamma encoded PNG
image_np = image.numpy().reshape(height, width, 3)
output_file = output_path + 'out_%03i_%03i.png' % (j, i)
print("Writing image %s" % (output_file))
Bitmap(image_np).convert(pixel_format=Bitmap.PixelFormat.RGB, component_format=Struct.Type.UInt8, srgb_gamma=True).write(output_file)
# Objective function
if task == 'plain2bumpy' or task == 'bumpy2bumpy':
loss = ek.hsum(ek.hsum(ek.sqr(image - image_ref))) / (height*width*3)
if task == 'bumpy2plain':