How to use the taichi.max function in taichi
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yuanming-hu / taichi / tests / python / test_ad_basics.py View on Github 
grad_test(lambda x: ti.max(x, 1), lambda x: np.maximum(x, 1))
grad_test(lambda x: ti.max(0, x), lambda x: np.maximum(0, x))def dda_particle(eye_pos, d_, t):
grid_res = particle_grid_res
bbox_min = bbox[0]
bbox_max = bbox[1]
hit_pos = ti.Vector([0.0, 0.0, 0.0])
normal = ti.Vector([0.0, 0.0, 0.0])
c = ti.Vector([0.0, 0.0, 0.0])
d = d_
for i in ti.static(range(3)):
if ti.abs(d[i]) < 1e-6:
d[i] = 1e-6
inter, near, far = ray_aabb_intersection(bbox_min, bbox_max, eye_pos, d)
near = ti.max(0, near)
closest_intersection = inf
if inter:
pos = eye_pos + d * (near + eps)
rinv = 1.0 / d
rsign = ti.Vector([0, 0, 0])
for i in ti.static(range(3)):
if d[i] > 0:
rsign[i] = 1
else:
rsign[i] = -1
o = grid_res * pos
ipos = ti.Matrix.floor(o).cast(ti.i32)for p in range(0, n_particles):
base = ti.cast(x[f, p] * inv_dx - 0.5, ti.i32)
fx = x[f, p] * inv_dx - ti.cast(base, ti.i32)
w = [0.5 * ti.sqr(1.5 - fx), 0.75 - ti.sqr(fx - 1), 0.5 * ti.sqr(fx - 0.5)]
new_F = (ti.Matrix.diag(dim=2, val=1) + dt * C[f, p]) @ F[f, p]
J = ti.determinant(new_F)
if particle_type[p] == 0: # fluid
sqrtJ = ti.sqrt(J)
new_F = ti.Matrix([[sqrtJ, 0], [0, sqrtJ]])
F[f + 1, p] = new_F
r, s = ti.polar_decompose(new_F)
act_id = actuator_id[p]
act = actuation[f, ti.max(0, act_id)] * act_strength
if act_id == -1:
act = 0.0
# ti.print(act)
A = ti.Matrix([[0.0, 0.0], [0.0, 1.0]]) * act
cauchy = ti.Matrix([[0.0, 0.0], [0.0, 0.0]])
mass = 0.0
if particle_type[p] == 0:
mass = 4
cauchy = ti.Matrix([[1.0, 0.0], [0.0, 0.1]]) * (J - 1) * E
else:
mass = 1
cauchy = 2 * mu * (new_F - r) @ ti.transposed(new_F) + \
ti.Matrix.diag(2, la * (J - 1) * J)
cauchy += new_F @ A @ ti.transposed(new_F)
stress = -(dt * p_vol * 4 * inv_dx * inv_dx) * cauchydef sdf(o_):
if ti.static(supporter == 0):
o = o_ - ti.Vector([0.5, 0.002, 0.5])
p = o
h = 0.02
ra = 0.29
rb = 0.005
d = (ti.Vector([p[0], p[2]]).norm() - 2.0 * ra + rb, ti.abs(p[1]) - h)
dist = ti.min(ti.max(d[0], d[1]), 0.0) + ti.Vector(
[ti.max(d[0], 0.0), ti.max(d[1], 0)]).norm() - rb
return dist
elif ti.static(supporter == 1):
o = o_ - ti.Vector([0.5, 0.002, 0.5])
dist = (o.abs() - ti.Vector([0.5, 0.02, 0.5])).max()
else:
dist = o_[1] - 0.027
return distdef apply_impulse(t, i, impulse, location, toi_input):
# ti.print(toi)
delta_v = impulse * inverse_mass[i]
delta_omega = cross(location - x[t, i], impulse) * inverse_inertia[i]
toi = ti.min(ti.max(0.0, toi_input), dt)
ti.atomic_add(x_inc[t + 1, i], toi * (-delta_v))
ti.atomic_add(rotation_inc[t + 1, i], toi * (-delta_omega))
ti.atomic_add(v_inc[t + 1, i], delta_v)
ti.atomic_add(omega_inc[t + 1, i], delta_omega)dir[0] = rotated_x
dir[2] = rotated_z
point = camera_origin + (k + 1) * dx * dir
# Convert to coordinates of the density grid box
box_x = point[0] + 0.5
box_y = point[1] + 0.5
box_z = point[2] + 0.5
# Density grid location
index_x = ti.cast(ti.floor(box_x * density_res), ti.i32)
index_y = ti.cast(ti.floor(box_y * density_res), ti.i32)
index_z = ti.cast(ti.floor(box_z * density_res), ti.i32)
index_x = ti.max(0, ti.min(index_x, density_res - 1))
index_y = ti.max(0, ti.min(index_y, density_res - 1))
index_z = ti.max(0, ti.min(index_z, density_res - 1))
flag = 0
if in_box(point[0], point[1], point[2]):
flag = 1
contribution = density[index_z, index_y, index_x] * flag
ti.atomic_add(field[view_id, y, x], contribution)def render_photon_map(t: ti.i32, offset_x: ti.f32, offset_y: ti.f32):
for i in range(n_grid): # Parallelized over GPU threads
for j in range(n_grid):
grad = height_gradient[i, j] * (1 - offset_x) * (1 - offset_y) + \
height_gradient[i + 1, j] * offset_x * (1 - offset_y) + \
height_gradient[i, j + 1] * (1 - offset_x) * offset_y + \
height_gradient[i + 1, j + 1] * offset_x * offset_y
scale = 5.0
sample_x = i - grad[0] * scale + offset_x
sample_y = j - grad[1] * scale + offset_y
sample_x = ti.min(n_grid - 1, ti.max(0, sample_x))
sample_y = ti.min(n_grid - 1, ti.max(0, sample_y))
sample_xi = ti.cast(ti.floor(sample_x), ti.i32)
sample_yi = ti.cast(ti.floor(sample_y), ti.i32)
frac_x = sample_x - sample_xi
frac_y = sample_y - sample_yi
x = sample_xi
y = sample_yi
ti.atomic_add(rendered[x, y], (1 - frac_x) * (1 - frac_y))
ti.atomic_add(rendered[x, y + 1], (1 - frac_x) * frac_y)
ti.atomic_add(rendered[x + 1, y], frac_x * (1 - frac_y))
ti.atomic_add(rendered[x + 1, y + 1], frac_x * frac_y)fx = x[f, p] * inv_dx - ti.cast(base, ti.i32)
w = [0.5 * ti.sqr(1.5 - fx), 0.75 - ti.sqr(fx - 1),
0.5 * ti.sqr(fx - 0.5)]
new_F = (ti.Matrix.diag(dim=2, val=1) + dt * C[f, p]) @ F[f, p]
J = ti.determinant(new_F)
if particle_type[p] == 0: # fluid
sqrtJ = ti.sqrt(J)
new_F = ti.Matrix([[sqrtJ, 0], [0, sqrtJ]])
F[f + 1, p] = new_F
r, s = ti.polar_decompose(new_F)
act_id = actuator_id[p]
act = actuation[f, ti.max(0, act_id)] * act_strength
if act_id == -1:
act = 0.0
# ti.print(act)
A = ti.Matrix([[0.0, 0.0], [0.0, 1.0]]) * act
cauchy = ti.Matrix([[0.0, 0.0], [0.0, 0.0]])
mass = 0.0
if particle_type[p] == 0:
mass = 4
cauchy = ti.Matrix([[1.0, 0.0], [0.0, 0.1]]) * (J - 1) * E
else:
mass = 1
cauchy = 2 * mu * (new_F - r) @ ti.transposed(new_F) + \
ti.Matrix.diag(2, la * (J - 1) * J)
cauchy += new_F @ A @ ti.transposed(new_F)
stress = -(dt * p_vol * 4 * inv_dx * inv_dx) * cauchy
