Static Graph Syntax —— Python Built-in Functions

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Python built-in functions supported by the current static graph mode include: int, float, bool, str, tuple, list, dict, getattr, hasattr, len, isinstance, all, any, round, max, min, sum, abs, map, zip 、range、enumerate、super、pow、print、filter、type。 The use of built-in functions in graph mode is similar to the corresponding Python built-in functions.

int

Function: Return the integer value based on the input number or string.

Call: int(x=0, base=10), converted to decimal by default.

Input parameter:

  • x – the object need to be converted to integer, the valid type of x includes int, float, bool, str, Tensor and third-party object (such as numpy.ndarray).

  • base – the base to convert. base is only allowed when x is constant str.

Return value: the converted integer.

For example:

import mindspore as ms

@ms.jit
def func(x):
   a = int(3)
   b = int(3.6)
   c = int('12', 16)
   d = int('0xa', 16)
   e = int('10', 8)
   f = int(x)
   return a, b, c, d, e, f

x = ms.Tensor([-1.0], ms.float32)
a, b, c, d, e, f = func(x)
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)

The result is as follows:

a: 3
b: 3
c: 18
d: 10
e: 8
f: -1

float

Function: Return the floating-point number based on the input number or string.

Calling: float(x=0).

Input parameter: x – the object need to be converted to floating number, the valid type of x includes int, float, bool, str, Tensor and third-party object (such as numpy.ndarray).

Return value: the converted floating-point number.

For example:

import mindspore as ms

@ms.jit
def func(x):
   a = float(1)
   b = float(112)
   c = float(-123.6)
   d = float('123')
   e = float(x.asnumpy())
   return a, b, c, d, e

x = ms.Tensor([-1], ms.int32)
a, b, c, d, e = func(x)
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)

The result is as follows:

a: 1.0
b: 112.0
c: -123.6
d: 123.0
e: -1.0

bool

Function: Return the boolean value based on the input.

Calling: bool(x=false)

Input parameter: x – the object need to be converted to boolean value, the valid type of x includes int, float, bool, str, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: the converted boolean scalar.

For example:

import mindspore as ms

@ms.jit
def func():
   a = bool()
   b = bool(0)
   c = bool("abc")
   d = bool([1, 2, 3, 4])
   e = bool(ms.Tensor([10]).asnumpy())
   return a, b, c, d, e

a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)

The result is as follows:

a: False
b: False
c: True
d: True
e: True

str

Function: Return the string value based on the input.

Calling: str(x='')

Input parameter: x – the object need to be converted to string value, the valid type of x includes int, float, bool, str, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: string converted from x.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = str()
   b = str(0)
   c = str([1, 2, 3, 4])
   d = str(ms.Tensor([10]))
   e = str(np.array([1, 2, 3, 4]))
   return a, b, c, d, e

a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)

The result is as follows:

a:                                             # a is empty string
b: 0
c: [1, 2, 3, 4]
d: Tensor(shape=[1], dtype=Int64, value=[10])
e: [1 2 3 4]

tuple

Function: Return a tuple based on the input object.

Calling: tuple(x=()).

Input parameter: x – the object that need to be converted to tuple, the valid type of x includes list, tuple, dict, Tensor or third-party object (such as numpy.ndarray).

Return value: tuple with elements of x, x is cut based on zero dimension.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = tuple((1, 2, 3))
   b = tuple(np.array([1, 2, 3]))
   c = tuple({'a': 1, 'b': 2, 'c': 3})
   d = tuple(ms.Tensor([1, 2, 3]))
   return a, b, c, d

a, b, c, d = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)

The result is as follows:

a: (1, 2, 3)
b: (1, 2, 3)
c: ('a', 'b', 'c')
d: (Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Int64, value= 2), Tensor(shape=[], dtype=Int64, value= 3))

list

Function: Return a list based on the input object.

Calling: list(x=()).

Input parameter: x – the object that need to be converted to list, the valid type of x includes list, tuple, dict, Tensor or third-party object (such as numpy.ndarray).

Return value: list with elements of x, x is cut based on zero dimension.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = list((1, 2, 3))
   b = list(np.array([1, 2, 3]))
   c = list({'a':1, 'b':2, 'c':3})
   d = list(ms.Tensor([1, 2, 3]))
   return a, b, c, d
a_t, b_t, c_t, d_t = func()
print("a_t: ", a_t)
print("b_t: ", b_t)
print("c_t: ", c_t)
print("d_t: ", d_t)

The result is as follows:

a_t: [1, 2, 3]
b_t: [1, 2, 3]
c_t: ['a', 'b', 'c']
d_t: [Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Int64, value= 2), Tensor(shape=[], dtype=Int64, value= 3)]

dict

Function: Used to create a dictionary.

Valid input: The Key of the dictionary supports only String type. The Value supports only constants, and does not support custom classes.

Looping over dictionaries created by dict() is not supported yet, including dict.keys(), dict.values() and dict.items().

Examples of code usage are as follows:

import mindspore as ms

@ms.jit
def func():
    a = dict()                                          # Create an empty dictionary
    b = dict(a='a', b='b', t='t')                       # Pass in keywords
    c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))   # Mapping function approach to constructing dictionaries
    d = dict([('one', 1), ('two', 2), ('three', 3)])    # Iterable object approach to constructing dictionaries
    return a, b, c, d

a, b, c, d = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)

Output the result:

a: {}
b: {'a': 'a', 'b': 'b', 't': 't'}
c: {'one': 1, 'two': 2, 'three': 3}
d: {'one': 1, 'two': 2, 'three': 3}

getattr

Function: Get the attribute of python object.

Calling: getattr(x, attr, default).

Input parameter:

  • x – The object to get attribute, x can be all types that graph mode supports. Third-party library types are also supported when the JIT syntax support level option is ‘Lax’.

  • attr – The name of the attribute, the type of attr should be str.

  • default – Optional input. If x do not have attr, default will be returned. default can be all types that graph mode supports. Third-party library types are also supported when the JIT syntax support level option is ‘Lax’. If default is not set and x does not have attribute attr, AttributeError will be raised.

Return value: Target attribute or default.

For example:

import numpy as np
import mindspore as ms

@ms.jit_class
class MSClass1:
  def __init__(self):
    self.num0 = 0

ms_obj = MSClass1()

@ms.jit
def func(x):
  a = getattr(ms_obj, 'num0')
  b = getattr(ms_obj, 'num1', 2)
  c = getattr(x.asnumpy(), "shape", np.array([0, 1, 2, 3, 4]))
  return a, b, c

x = ms.Tensor([-1.0], ms.float32)
a, b, c = func(x)
print("a: ", a)
print("b: ", b)
print("c: ", c)

The result is as follows:

a:  0
b:  2
c:  (1,)

The attribute of object in graph mode may be different from that in pynative mode. It is suggested to use default input or call hasattr before using getattr to avoid AttributeError.

‘getattr(x.asnumpy(), “shape”, np.array([0, 1, 2, 3, 4]))’ is a high-level usage, and more introduction can be found in the Extended Syntaxes (LAX level) chapter.

hasattr

Function: Judge whether an object has an attribute.

Calling: hasattr(x, attr).

Input parameter:

  • x – The object to get attribute, x can be all types that graph mode supports. Third-party library types are also supported when the JIT syntax support level option is ‘Lax’.

  • attr – The name of the attribute, the type of attr should be str.

Return value: boolean value indicates whether x has attr.

For example:

import numpy as np
import mindspore as ms
from mindspore import Tensor

@ms.jit_class
class MSClass1:
  def __init__(self):
    self.num0 = 0

ms_obj = MSClass1()

@ms.jit
def func():
   a = hasattr(ms_obj, 'num0')
   b = hasattr(ms_obj, 'num1')
   c = hasattr(Tensor(np.array([1, 2, 3, 4])).asnumpy(), "__len__")
   return a, b, c

a, b, c = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)

The result is as follows:

a: True
b: False
c: True

‘hasattr(Tensor(np.array([1, 2, 3, 4])).asnumpy(), “len”)’ is a high-level usage, and more introduction can be found in the Extended Syntaxes (LAX level) chapter.

len

Function: Return the length of an object (string or other iterable object).

Calling: len(sequence).

Input parameter: sequence – Tuple, List, Dictionary, Tensor or third-party object (such as numpy.ndarray).

Return value: length of the sequence, which is of the int type. If the input parameter is Tensor, the length of dimension 0 is returned.

For example:

import numpy as np
import mindspore as ms

z = ms.Tensor(np.ones((6, 4, 5)))

@ms.jit()
def test(w):
    x = (2, 3, 4)
    y = [2, 3, 4]
    d = {"a": 2, "b": 3}
    n = np.array([1, 2, 3, 4])
    x_len = len(x)
    y_len = len(y)
    d_len = len(d)
    z_len = len(z)
    n_len = len(n)
    w_len = len(w.asnumpy())
    return x_len, y_len, d_len, z_len, n_len, w_len

input_x = ms.Tensor([1, 2, 3, 4])
x_len, y_len, d_len, z_len, n_len, w_len = test(input_x)
print('x_len:{}'.format(x_len))
print('y_len:{}'.format(y_len))
print('d_len:{}'.format(d_len))
print('z_len:{}'.format(z_len))
print('n_len:{}'.format(n_len))
print('w_len:{}'.format(w_len))

The result is as follows:

x_len:3
y_len:3
d_len:2
z_len:6
z_len:4
w_len:4

‘len(w.asnumpy())’ is a high-level usage, and more introduction can be found in the Extended Syntaxes (LAX level) chapter.

isinstance

Function: Determines whether an object is an instance of a class.

Calling: isinstance(obj, type).

Input parameters:

  • obj – Any instance of any supported type.

  • type – bool, int, float, str, list, tuple, dict, Tensor, Parameter, or the types of third-party libraries (e.g. numpy.ndarray) or a tuple containing only those types.

Return value: If obj is an instance of type, return True. Otherwise, return False.

For example:

import mindspore as ms
import numpy as np

z = ms.Tensor(np.ones((6, 4, 5)))

@ms.jit()
def test(w):
    x = (2, 3, 4)
    y = [2, 3, 4]
    x_is_tuple = isinstance(x, tuple)
    y_is_list = isinstance(y, list)
    z_is_tensor = isinstance(z, ms.Tensor)
    w_is_ndarray = isinstance(w.asnumpy(), np.ndarray)
    return x_is_tuple, y_is_list, z_is_tensor, w_is_ndarray

w = ms.Tensor(np.array([-1, 2, 4]))
x_is_tuple, y_is_list, z_is_tensor, w_is_ndarray = test(w)
print('x_is_tuple:{}'.format(x_is_tuple))
print('y_is_list:{}'.format(y_is_list))
print('z_is_tensor:{}'.format(z_is_tensor))
print('w_is_ndarray:{}'.format(w_is_ndarray))

The result is as follows:

x_is_tuple:True
y_is_list:True
z_is_tensor:True
w_is_ndarray:True

‘isinstance(w.asnumpy(), np.ndarray)’ is a high-level usage, and more introduction can be found in the Extended Syntaxes (LAX level) chapter.

all

Function: Judge whether all of the elements in the input is true.

Calling: all(x).

Input parameter: - x – Iterable object, the valid types include list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, return True if all elements are True, otherwise False.

For example:

import numpy as np
import mindspore as ms
from mindspore import Tensor

@ms.jit
def func():
   a = all(['a', 'b', 'c', 'd'])
   b = all(['a', 'b', '', 'd'])
   c = all([0, 1, 2, 3])
   d = all(('a', 'b', 'c', 'd'))
   e = all(('a', 'b', '', 'd'))
   f = all((0, 1, 2, 3))
   g = all([])
   h = all(())
   x = Tensor(np.array([0, 1, 2, 3]))
   i = all(x.asnumpy())
   return a, b, c, d, e, f, g, h, i

a, b, c, d, e, f, g, h, i = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
print("h: ", h)
print("i: ", i)

The result is as follows:

a: True
b: False
c: False
d: True
e: False
f: False
g: True
h: True
i: False

‘all(x.asnumpy())’ is a high-level usage, and more introduction can be found in the Extended Syntaxes (LAX level) chapter.

any

Function: Judge whether any of the elements in the input is true.

Calling: any(x).

Input parameter: - x – Iterable object, the valid types include list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, return False if all elements are False, otherwise True. Elements count as True except for 0, null, and False.

For example:

import numpy as np
import mindspore as ms
from mindspore import Tensor

@ms.jit
def func():
   a = any(['a', 'b', 'c', 'd'])
   b = any(['a', 'b', '', 'd'])
   c = any([0, '', False])
   d = any(('a', 'b', 'c', 'd'))
   e = any(('a', 'b', '', 'd'))
   f = any((0, '', False))
   g = any([])
   h = any(())
   x = Tensor(np.array([0, 1, 2, 3]))
   i = any(x.asnumpy())
   return a, b, c, d, e, f, g, h, i

a, b, c, d, e, f, g, h, i = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
print("h: ", h)
print("i: ", i)

The result is as follows:

a: True
b: True
c: False
d: True
e: True
f: False
g: False
h: False
i: True

round

Function: Return the rounding value of input.

Calling: round(x, digit=0)

Input parameter:

  • x – the object to rounded, the valid types include int, float, bool, Tensor and third-party object that defines magic function __round__().

  • digit – the number of decimal places to round, the default value is 0. digit can be int object or None. If x is Tensor, then round() does not support input digit.

Return value: the value after rounding.

For example:

import mindspore as ms

@ms.jit
def func():
   a = round(10)
   b = round(10.123)
   c = round(10.567)
   d = round(10, 0)
   e = round(10.72, -1)
   f = round(17.12, -1)
   g = round(10.17, 1)
   h = round(10.12, 1)
   return a, b, c, d, e, f, g, h

a, b, c, d, e, f, g, h = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: {:.2f}".format(e))
print("f: {:.2f}".format(f))
print("g: {:.2f}".format(g))
print("h: {:.2f}".format(h))

The result is as follows:

a: 10
b: 10
c: 11
d: 10
e: 10.00
f: 20.00
g: 10.20
h: 10.10

max

Function: Return the maximum of inputs.

Calling: max(*data).

Input parameter: - *data – If *data is single input, max will compare all elements within data and data must be iterable object. If there are multiple inputs, then max() will compare each of them. The valid types of data include int, float, bool, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, the maximum of the inputs.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = max([0, 1, 2, 3])
   b = max((0, 1, 2, 3))
   c = max({1: 10, 2: 20, 3: 3})
   d = max(np.array([1, 2, 3, 4]))
   e = max(('a', 'b', 'c'))
   f = max((1, 2, 3), (1, 4))
   g = max(ms.Tensor([1, 2, 3]))
   return a, b, c, ms.Tensor(d), e, f, g

a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)

The result is as follows:

a: 3
b: 3
c: 3
d: 4
e: c
f: (1, 4)
g: 3

min

Function: Return the minimum of inputs.

Calling: min(*data).

Input parameter: - *data – If *data is single input, then min() will compare all elements within data and data must be iterable object. If there are multiple inputs, then min() will compare each of them. The valid types of data include int, float, bool, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, the minimum of the inputs.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
  a = min([0, 1, 2, 3])
  b = min((0, 1, 2, 3))
  c = min({1: 10, 2: 20, 3: 3})
  d = min(np.array([1, 2, 3, 4]))
  e = min(('a', 'b', 'c'))
  f = min((1, 2, 3), (1, 4))
  g = min(ms.Tensor([1, 2, 3]))
  return a, b, c, ms.Tensor(d), e, f, g

a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)

The result is as follows:

a: 0
b: 0
c: 1
d: 1
e: a
f: (1, 2, 3)
g: 1

sum

Function: Return the sum of input sequence.

Calling: sum(x, n=0).

Input parameter:

  • x – iterable with numbers, the valid types include list, tuple, Tensor and third-party object (such as numpy.ndarray).

  • n – the number that will be added to the sum of x, which is assumed to be 0 if not given.

Return value: the value obtained by summing x and adding it to n.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
  a = sum([0, 1, 2])
  b = sum((0, 1, 2), 10)
  c = sum(np.array([1, 2, 3]))
  d = sum(ms.Tensor([1, 2, 3]), 10)
  e = sum(ms.Tensor([[1, 2], [3, 4]]))
  f = sum([1, ms.Tensor([[1, 2], [3, 4]]), ms.Tensor([[1, 2], [3, 4]])], ms.Tensor([[1, 1], [1, 1]]))
  return a, b, ms.Tensor(c), d, e, f

a, b, c, d, e, f = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)

The result is as follows:

a:  3
b:  13
c:  6
d:  16
e:  [4 6]
f:  [[ 4  6]
     [ 8 10]]

abs

Function: Return the absolute value of the input.

Calling: abs(x).

Input parameter: - x – The valid types of x include int, float, bool, complex, Tensor and third-party object (such as numpy.ndarray).

Return value: the absolute value of the input.

For example:

import mindspore as ms
from mindspore import Tensor

@ms.jit
def func():
   a = abs(-45)
   b = abs(100.12)
   c = abs(Tensor([-1, 2]).asnumpy())
   return a, b, c

a, b, c = func()
print("a: ", a)
print("b: {:.2f}".format(b))
print("c: ", c)

The result is as follows:

a: 45
b: 100.12
c: [1 2]

‘abs(Tensor([-1, 2]).asnumpy())’ is a high-level usage, and more introduction can be found in the Extended Syntaxes (LAX level) chapter.

map

Function: Maps one or more sequences based on the provided functions and generates a new sequence based on the mapping result. The current requirement is that the number of elements in multiple sequences be the same.

Calling: map(func, sequence, ...).

Input parameters:

  • func – Function.

  • sequence – One or more sequences (Tuple or List).

Return value: Return a new sequence.

For example:

import mindspore as ms

def add(x, y):
    return x + y

@ms.jit()
def test():
    elements_a = (1, 2, 3)
    elements_b = (4, 5, 6)
    ret1 = map(add, elements_a, elements_b)
    elements_c = [0, 1, 2]
    elements_d = [6, 7, 8]
    ret2 = map(add, elements_c, elements_d)
    return ret1, ret2

ret1, ret2 = test()
print('ret1:{}'.format(ret1))
print('ret2:{}'.format(ret2))

The result is as follows:

ret1: (5, 7, 9)
ret2: [6, 8, 10]

zip

Function: Packs elements in the corresponding positions in multiple sequences into tuples, and then uses these tuples to form a new sequence. If the number of elements in each sequence is inconsistent, the length of the new sequence is the same as that of the shortest sequence.

Calling: zip(sequence, ...).

Input parameter: sequence – One or more sequences (Tuple or List)`.

Return value: Return a new sequence.

For example:

import mindspore as ms

@ms.jit()
def test():
    elements_a = (1, 2, 3)
    elements_b = (4, 5, 6)
    ret = zip(elements_a, elements_b)
    return ret

ret = test()
print('ret:{}'.format(ret))

The result is as follows:

ret:((1, 4), (2, 5), (3, 6))

range

Function: Creates a Tuple based on the start value, end value, and step.

Calling:

  • range(start, stop, step)

  • range(start, stop)

  • range(stop)

Input parameters:

  • start – start value of the count. The type is int. The default value is 0.

  • stop – end value of the count (exclusive). The type is int.

  • step – Step. The type is int. The default value is 1.

Return value: Return a Tuple.

For example:

import mindspore as ms

@ms.jit()
def test():
    x = range(0, 6, 2)
    y = range(0, 5)
    z = range(3)
    return x, y, z

x, y, z = test()
print('x:{}'.format(x))
print('y:{}'.format(y))
print('z:{}'.format(z))

The result is as follows:

x:(0, 2, 4)
y:(0, 1, 2, 3, 4)
z:(0, 1, 2)

enumerate

Function: Generates an index sequence of a sequence. The index sequence contains data and the corresponding subscript.

Calling:

  • enumerate(sequence, start)

  • enumerate(sequence)

Input parameters:

  • sequence – A sequence (Tuple, List, or Tensor).

  • start – Start position of the subscript. The type is int. The default value is 0.

Return value: A Tuple

For example:

import mindspore as ms
import numpy as np

y = ms.Tensor(np.array([[1, 2], [3, 4], [5, 6]]))

@ms.jit()
def test():
    x = (100, 200, 300, 400)
    m = enumerate(x, 3)
    n = enumerate(y)
    return m, n

m, n = test()
print('m:{}'.format(m))
print('n:{}'.format(n))

The result is as follows:

m:((3, 100), (4, 200), (5, 300), (6, 400))
n:((0, Tensor(shape=[2], dtype=Int64, value= [1, 2])), (1, Tensor(shape=[2], dtype=Int64, value= [3, 4])), (2, Tensor(shape=[2], dtype=Int64, value= [5, 6])))

super

Function: Calls a method of the parent class (super class). Generally, the method of the parent class is called after super.

Calling:

  • super().xxx()

  • super(type, self).xxx()

Input parameters:

  • type – Class.

  • self – Object.

Return value: method of the parent class.

For example:

import mindspore as ms
from mindspore import nn, set_context

set_context(mode=ms.GRAPH_MODE)

class FatherNet(nn.Cell):
    def __init__(self, x):
        super(FatherNet, self).__init__(x)
        self.x = x

    def construct(self, x, y):
        return self.x * x

    def test_father(self, x):
        return self.x + x

class SingleSubNet(FatherNet):
    def __init__(self, x, z):
        super(SingleSubNet, self).__init__(x)
        self.z = z

    def construct(self, x, y):
        ret_father_construct = super().construct(x, y)
        ret_father_test = super(SingleSubNet, self).test_father(x)
        return ret_father_construct, ret_father_test

x = 3
y = 6
z = 9
f_net = FatherNet(x)
net = SingleSubNet(x, z)
out = net(x, y)
print("out:", out)

The result is as follows:

out: (9, 6)

pow

Function: Return the power.

Calling: pow(x, y)

Input parameters:

  • x – Base number, Number, or Tensor.

  • y – Power exponent, Number, or Tensor.

Return value: y power of x, Number, or Tensor

For example:

import mindspore as ms
import numpy as np

x = ms.Tensor(np.array([1, 2, 3]))
y = ms.Tensor(np.array([1, 2, 3]))

@ms.jit()
def test(x, y):
    return pow(x, y)

ret = test(x, y)

print('ret:{}'.format(ret))

The result is as follows:

ret:[ 1  4 27]

print

Function: Prints logs.

Calling: print(arg, ...).

Input parameter: arg – Information to be printed (int, float, bool, String or Tensor, or third-party library data types).

Return value: none

For example:

import mindspore as ms
import numpy as np

x = ms.Tensor(np.array([1, 2, 3]), ms.int32)
y = ms.Tensor(3, ms.int32)

@ms.jit()
def test(x, y):
    print(x)
    print(y)
    return x, y

ret = test(x, y)

The result is as follows:

Tensor(shape=[3], dtype=Int32, value= [1 2 3])
Tensor(shape=[], dtype=Int32, value=3)

Support for printing constants in static graph mode by using native print of Python, which is different from Print operator prints information at a different time. Python native print is triggered during compilation (at compiling time phase printing), while the Print operator requires the graph to be compiled and sent down to the device side to run before printing (at runtime phase printing).

For the sake of understanding, the following examples are given. tensor_sum involves Tensor summing, i.e. the runtime phase to get the result. When calling print, the actual call is the Print operator in the static graph mode. And np_num is the result of adding up two NumPy constants, so when calling print, the native Python print is used. Because of the different timing of the two prints, it ends up showing np_sum before tensor_sum, i.e. the print result of Python native print supported by JIT Fallback will be before the Print operator.

import numpy as np
import mindspore as ms
import mindspore.nn as nn

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()

    def construct(self):
        x = ms.Tensor(np.array([1, 2, 3, 4, 5]))
        y = ms.Tensor(np.array([1, 2, 3, 4, 5]))
        tensor_sum = x + y
        print("tensor_sum: ", tensor_sum)
        x = np.array([1, 2, 3, 4, 5])
        y = np.array([1, 2, 3, 4, 5])
        np_sum = x + y
        print("np_sum: ", np_sum)
        return tensor_sum, ms.Tensor(np_sum)

ms.set_context(mode=ms.GRAPH_MODE)
net = Net()
net()

Output the result:

np_sum:  [ 2  4  6  8 10]
tensor_sum:
Tensor(shape=[5], dtype=Int64, value=[ 2  4  6  8 10])

filter

Function: According to the provided function to judge the elements of a sequence. Each element is passed into the function as a parameter in turn, and the elements whose return result is not 0 or False form a new sequence.

Calling: filter(func, sequence)

Input parameters:

  • func – Function.

  • sequence – A sequence (Tuple or List).

Return value: Return a new sequence.

For example:

import mindspore as ms

def is_odd(x):
    if x % 2:
        return True
    return False

@ms.jit()
def test():
    elements1 = (1, 2, 3, 4, 5)
    ret1 = filter(is_odd, elements1)
    elements2 = [6, 7, 8, 9, 10]
    ret2 = filter(is_odd, elements2)
    return ret1, ret2

ret1, ret2 = test()
print('ret1:{}'.format(ret1))
print('ret2:{}'.format(ret2))

The result is as follows:

ret1:[1, 3, 5]
ret2:[7, 9]

type

Function: Output the type of the input parameter.

Valid inputs: number, list, tuples, dict, numpy.ndarray, constant Tensor.

Examples of code usage are as follows:

import numpy as np
import mindspore as ms

@ms.jit
def func():
    a = type(1)
    b = type(1.0)
    c = type([1, 2, 3])
    d = type((1, 2, 3))
    e = type({'a': 1, 'b': 2})
    f = type(np.array([1, 2, 3]))
    g = type(ms.Tensor([1, 2, 3]))
    return a, b, c, d, e, f, g

a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)

Output the result:

a: <class 'int'>
b: <class 'float'>
c: <class 'list'>
d: <class 'tuple'>
e: <class 'dict'>
f: <class 'numpy.ndarray'>
g: <class 'mindspore.common.tensor.Tensor'>

There is another way to use type as a native Python function, i.e. type(name, bases, dict) returns a class object of type name, which is not supported currently because of the low usage scenario.