def test():
a = 0
for i in range(4):
val[i] = i * 10
ti.print(val[i])
if i % 2 == 0:
a += i
ti.print(a)
def p2g(f: ti.i32):
for p in range(n_particles):
base = x[f, p] * inv_dx
ti.print(f)
ti.print(p)
new_F = (F[f, p]) @ F[f, p]
F[f + 1, p] = new_F
def test():
for i in range(n):
ti.print(i)
s = 0
for j in range(10):
s += j
a = ti.Vector([0.4, 0.3])
val[i] = s + ti.cast(a.norm() * 100, ti.i32) + i
def update():
for i in ti.static(range(number_coeffs)):
ti.print(i)
ti.print(coeffs[i][None])
ti.print(coeffs[i].grad[None])
coeffs[i][None] -= learning_rate * coeffs[i].grad[None]
coeffs[i].grad[None] = 0
def func():
while True:
a = 0
ti.print(a)
def func():
for i in range(10):
a = i
ti.print(a)
def func():
if True:
a = 0
else:
a = 1
ti.print(a)
def func():
if 1 > 0:
val = 1
ti.print(val)
def test_numpy(arr: np.ndarray):
for i in range(4):
ti.print(arr[i])
arr[i] = i * i
def kernel2(x: ti.i32, y: ti.f32):
ti.print(x + y)
def kernel(x: ti.i32):
ti.print(x)
def torch_kernel_2(t_grad: ti.ext_arr(), t: ti.ext_arr(),
o_grad: ti.ext_arr()):
for i in range(n):
ti.print(o_grad[i])
t_grad[i] = 2 * t[i] * o_grad[i]
def test_struct():
for i in y:
y[i] = i + 1
ti.print(i)
def test1():
for i in range(4):
val[i] = i * 20
ti.print(val[i])
def torch_kernel_2(t_grad: np.ndarray, t: np.ndarray, o_grad: np.ndarray):
for i in range(n):
ti.print(o_grad[i])
t_grad[i] = 2 * t[i] * o_grad[i]
