How to use the taichi.var function in taichi
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yuanming-hu / taichi / tests / python / test_struct.py View on Github 
def test_linear_nested():
x = ti.var(ti.i32)
y = ti.var(ti.i32)
n = 128
@ti.layout
def place():
ti.root.dense(ti.i, n // 16).dense(ti.i, 16).place(x)
ti.root.dense(ti.i, n // 16).dense(ti.i, 16).place(y)
for i in range(n):
x[i] = i
y[i] = i + 123
for i in range(n):
assert x[i] == i
assert y[i] == i + 123if inside(p, Vector2(0.50, 0.25), 0.25):
if ret == -1:
ret = 0
if inside(p, Vector2(0.50, 0.75), 0.25):
if ret == -1:
ret = 1
if p[0] < 0.5:
if ret == -1:
ret = 1
else:
if ret == -1:
ret = 0
return ret
x = ti.var(ti.f32)
n = 512
ti.cfg.use_llvm = True
@ti.layout
def layout():
ti.root.dense(ti.ij, n).place(x)
@ti.kernel
def paint():
for i in range(n * 4):
for j in range(n * 4):
ret = 1.0 - inside_taichi(Vector2(1.0 * i / n / 4, 1.0 * j / n / 4))
x[i // 4, j // 4] += ret / 16def test_ad_reduce():
x = ti.var(ti.f32)
loss = ti.var(ti.f32)
N = 16
@ti.layout
def place():
ti.root.place(loss, loss.grad).dense(ti.i, N).place(x, x.grad)
@ti.kernel
def func():
for i in x:
loss.atomic_add(ti.sqr(x[i]))
total_loss = 0
for i in range(N):
x[i] = i
total_loss += i * idef test_simple():
x = ti.var(ti.i32)
n = 128
@ti.layout
def place():
ti.root.dense(ti.i, n).place(x)
@ti.kernel
def func():
x[7] = 120
func()
for i in range(n):
if i == 7:
assert x[i] == 120def __init__(self):
self.x = ti.var(ti.f32)
self.total = ti.var(ti.f32)
self.n = 128def test_arg_load():
x = ti.var(ti.i32)
y = ti.var(ti.f32)
@ti.layout
def layout():
ti.root.place(x, y)
@ti.kernel
def set_i32(v: ti.i32):
x[None] = v
@ti.kernel
def set_f32(v: ti.f32):
y[None] = v
@ti.kernel
def set_f64(v: ti.f64):def test_kernel_template_basic():
x = ti.var(ti.i32)
y = ti.var(ti.f32)
n = 16
@ti.layout
def layout():
ti.root.dense(ti.i, n).place(x, y)
@ti.kernel
def inc(a: ti.template(), b: ti.template()):
for i in a:
a[i] += b
inc(x, 1)
inc(y, 2)
for i in range(n):import numpy as np
import random
import taichi as ti
import pickle
# ti.runtime.print_preprocessed = True
# ti.cfg.print_ir = True
input = ti.var(ti.f32)
weight1 = ti.var(ti.f32)
output1 = ti.var(ti.f32)
output1_nonlinear = ti.var(ti.f32)
weight2 = ti.var(ti.f32)
output = ti.var(ti.f32)
output_exp = ti.var(ti.f32)
output_softmax = ti.var(ti.f32)
softmax_sum = ti.var(ti.f32)
gt = ti.var(ti.f32)
loss = ti.var(ti.f32)
learning_rate = ti.var(ti.f32)
n_input = 28**2
n_hidden = 500
n_output = 10scalar = lambda: ti.var(dt=real)
vec = lambda: ti.Vector(2, dt=real)
