Tensors of scalars¶
Declaration¶
-
ti.
var
(dt, shape = None, offset = None)¶ 参数: - dt – (DataType) type of the tensor element
- shape – (optional, scalar or tuple) the shape of tensor
- offset – (optional, scalar or tuple) see Coordinate offsets
For example, this creates a dense tensor with four
int32
as elements:x = ti.var(ti.i32, shape=4)
This creates a 4x3 dense tensor with
float32
elements:x = ti.var(ti.f32, shape=(4, 3))
If shape is
()
(empty tuple), then a 0-D tensor (scalar) is created:x = ti.var(ti.f32, shape=())
Then access it by passing
None
as index:x[None] = 2
If shape is not provided or
None
, the user must manuallyplace
it afterwards:x = ti.var(ti.f32) ti.root.dense(ti.ij, (4, 3)).place(x) # equivalent to: x = ti.var(ti.f32, shape=(4, 3))
注解
Not providing shape
allows you to place the tensor in a layout other than the default dense, see Advanced dense layouts for more details.
警告
All variables should be created and placed before any kernel invocation or any of them accessed from python-scope. For example:
x = ti.var(ti.f32)
x[None] = 1 # ERROR: x not placed!
x = ti.var(ti.f32, shape=())
@ti.kernel
def func():
x[None] = 1
func()
y = ti.var(ti.f32, shape=())
# ERROR: cannot create tensor after kernel invocation!
x = ti.var(ti.f32, shape=())
x[None] = 1
y = ti.var(ti.f32, shape=())
# ERROR: cannot create tensor after any tensor accesses from the Python-scope!
Accessing components¶
You can access an element of the Taichi tensor by an index or indices.
-
a[p, q, ...]
参数: - a – (Tensor) the tensor of scalars
- p – (scalar) index of the first tensor dimension
- q – (scalar) index of the second tensor dimension
返回: (scalar) the element at
[p, q, ...]
This extracts the element value at index
[3, 4]
of tensora
:x = a[3, 4]
This sets the element value at index
2
of 1D tensorb
to5
:b[2] = 5
注解
In Python, x[(exp1, exp2, …, expN)] is equivalent to x[exp1, exp2, …, expN]; the latter is just syntactic sugar for the former.
注解
The returned value can also be
Vector
/Matrix
ifa
is a tensor of vector / matrix, see 向量 for more details.
Meta data¶
-
a.
shape
¶ 参数: a – (Tensor) the tensor 返回: (tuple) the shape of tensor a
x = ti.var(ti.i32, (6, 5)) x.shape # (6, 5) y = ti.var(ti.i32, 6) y.shape # (6,) z = ti.var(ti.i32, ()) z.shape # ()
-
a.
dtype
¶ 参数: a – (Tensor) the tensor 返回: (DataType) the data type of a
x = ti.var(ti.i32, (2, 3)) x.dtype # ti.i32
-
a.
parent
(n = 1)¶ 参数: - a – (Tensor) the tensor
- n – (optional, scalar) the number of parent steps, i.e.
n=1
for parent,n=2
grandparent, etc.
返回: (SNode) the parent of
a
’s containing SNodex = ti.var(ti.i32) y = ti.var(ti.i32) blk1 = ti.root.dense(ti.ij, (6, 5)) blk2 = blk1.dense(ti.ij, (3, 2)) blk1.place(x) blk2.place(y) x.parent() # blk1 y.parent() # blk2 y.parent(2) # blk1
See Structural nodes (SNodes) for more details.