Operators
Overview
Operators of MindSpore can be classified based on the operator usage and operator functions. The following example code runs in PyNative mode.
Operator Usage
APIs related to operators include operations, functional, and composite. Operators related to these three APIs can be directly obtained using ops.
The operations API provides a single primitive operator. An operator corresponds to a primitive and is the smallest execution object. An operator can be used only after being instantiated.
The composite API provides some predefined composite operators and complex operators involving graph transformation, such as
GradOperation.The functional API provides objects instantiated by the operations and composite to simplify the operator calling process.
mindspore.ops.operations
The operations API provides all primitive operator APIs, which are the lowest-order operator APIs open to users. For details about the supported operators, see Operator List.
Primitive operators directly encapsulate the implementation of operators at bottom layers such as Ascend, GPU, AICPU, and CPU, providing basic operator capabilities for users.
Primitive operator APIs are the basis for building high-order APIs, automatic differentiation, and network models.
A code example is as follows:
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops.operations as P
input_x = mindspore.Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
input_y = 3.0
pow = P.Pow()
output = pow(input_x, input_y)
print("output =", output)
The following information is displayed:
output = [ 1. 8. 64.]
mindspore.ops.functional
To simplify the calling process of operators without attributes, MindSpore provides the functional version of some operators. For details about the input parameter requirements, see the input and output requirements of the original operator. For details about the supported operators, see Operator List.
For example, the functional version of the P.Pow operator is F.tensor_pow.
A code example is as follows:
import numpy as np
import mindspore
from mindspore import Tensor
from mindspore.ops import functional as F
input_x = mindspore.Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
input_y = 3.0
output = F.tensor_pow(input_x, input_y)
print("output =", output)
The following information is displayed:
output = [ 1. 8. 64.]
mindspore.ops.composite
The composite API provides some operator combinations, including some operators related to clip_by_value and random, and functions (such as GradOperation, HyperMap, and Map) related to graph transformation.
The operator combination can be directly used as a common function. For example, use normal to generate a random distribution:
from mindspore import dtype as mstype
from mindspore.ops import composite as C
from mindspore import Tensor
mean = Tensor(1.0, mstype.float32)
stddev = Tensor(1.0, mstype.float32)
output = C.normal((2, 3), mean, stddev, seed=5)
print("output =", output)
The following information is displayed:
output = [[2.4911082 0.7941146 1.3117087]
[0.30582333 1.772938 1.525996]]
The preceding code runs on the GPU version of MindSpore.
For functions involving graph transformation, users can use MultitypeFuncGraph to define a group of overloaded functions. The implementation varies according to the function type.
A code example is as follows:
import numpy as np
from mindspore.ops.composite import MultitypeFuncGraph
from mindspore import Tensor
import mindspore.ops as ops
add = MultitypeFuncGraph('add')
@add.register("Number", "Number")
def add_scalar(x, y):
return ops.scalar_add(x, y)
@add.register("Tensor", "Tensor")
def add_tensor(x, y):
return ops.add(x, y)
tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
print('tensor', add(tensor1, tensor2))
print('scalar', add(1, 2))
The following information is displayed:
tensor [[2.4 4.2]
[4.4 6.4]]
scalar 3
In addition, the high-order function GradOperation provides the method of computing the gradient function corresponding to the input function. For details, see mindspore.ops.
Combination usage of operations/functional/composite three types of operators
In order to make it easier to use, in addition to the several usages introduced above, we have encapsulated the three operators of operations/functional/composite into mindspore.ops. It is recommended to directly call the interface in mindspore.ops.
The code sample is as follows:
import mindspore.ops.operations as P
pow = P.Pow()
import mindspore.ops as ops
pow = ops.Pow()
The above two methods have the same effect.
Operator Functions
Operators can be classified into seven functional modules: tensor operations, network operations, array operations, image operations, encoding operations, debugging operations, and quantization operations. For details about the supported operators on the Ascend AI processors, GPU, and CPU, see Operator List.
Tensor Operations
The tensor operations include the tensor structure operation and the tensor mathematical operation.
Tensor structure operations include tensor creation, index sharding, dimension transformation, and integration and splitting.
Tensor mathematical operations include scalar operations, vector operations, and matrix operations.
The following describes how to use the tensor mathematical operation and operation broadcast mechanism.
Scalar Operations
Tensor mathematical operators can be classified into scalar operator, vector operator, and matrix operator.
Scalar operators include addition, subtraction, multiplication, division, exponentiation, common functions such as trigonometric function, exponential function, and logarithmic function, and logical comparison operators.
Scalar operators are characterized by performing element-by-element operations on tensors.
Some scalar operators overload commonly used mathematical operators. In addition, the broadcast feature similar to NumPy is supported.
The following code implements the exponentiation, where the base is input_x and the exponent is input_y:
import numpy as np
import mindspore
from mindspore import Tensor
input_x = mindspore.Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
input_y = 3.0
print(input_x**input_y)
The following information is displayed:
[ 1. 8. 64.]
Addition
The following code implements the addition of input_x and input_y:
print(input_x + input_y)
The following information is displayed:
[4. 5. 7.]
Element-wise Multiplication
The following code implements the element-wise multiplication:
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops as ops
input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32)
mul = ops.Mul()
res = mul(input_x, input_y)
print(res)
The following information is displayed:
[4. 10. 18.]
Trigonometric Function
The following code implements Acos:
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops as ops
acos = ops.ACos()
input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32)
output = acos(input_x)
print(output)
The following information is displayed:
[0.7377037 1.5307858 1.2661037 0.97641146]
Vector Operations
Vector operators perform operations on only one particular axis, mapping a vector to a scalar or another vector.
Squeeze
The following code implements the compression of a channel whose dimension of the third channel is 1:
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops as ops
input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32)
squeeze = ops.Squeeze(2)
output = squeeze(input_tensor)
print(output)
The following information is displayed:
[[1. 1.]
[1. 1.]
[1. 1.]]
Matrix Operations
Matrix operations include matrix multiplication, matrix norm, matrix determinant, matrix eigenvalue calculation, and matrix decomposition.
Matrix Multiplication
The following code implements the matrix multiplication of input_x and input_y:
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops as ops
input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32)
input_y = Tensor(np.ones(shape=[3, 4]), mindspore.float32)
matmul = ops.MatMul()
output = matmul(input_x, input_y)
print(output)
The following information is displayed:
[[3. 3. 3. 3.]]
Broadcast Mechanism
Broadcast indicates that when the number of channels of each input variable is inconsistent, change the number of channels to obtain the result.
The following code implements the broadcast mechanism:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
shape = (2, 3)
input_x = Tensor(np.array([1, 2, 3]).astype(np.float32))
broadcast_to = ops.BroadcastTo(shape)
output = broadcast_to(input_x)
print(output)
The following information is displayed:
[[1. 2. 3.]
[1. 2. 3.]]
Network Operations
Network operations include feature extraction, activation function, loss function, and optimization algorithm.
Feature Extraction
Feature extraction is a common operation in machine learning. The core of feature extraction is to extract more representative tensors than the original input.
Convolution Operation
The following code implements the 2D convolution operation which is one of the common convolution operations:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
input = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32)
weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)
conv2d = ops.Conv2D(out_channel=32, kernel_size=3)
res = conv2d(input, weight)
print(res)
The following information is displayed:
[[[[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
...
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]]]
...
[[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
...
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]]
...
[[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
...
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]
[288. 288. 288. ... 288. 288. 288.]]]]
Convolutional Backward Propagation Operator Operation
The following code implements the propagation operation of backward gradient operators. The outputs are stored in dout and weight:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
dout = Tensor(np.ones([10, 32, 30, 30]), mindspore.float32)
weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)
x = Tensor(np.ones([10, 32, 32, 32]))
conv2d_backprop_input = ops.Conv2DBackpropInput(out_channel=32, kernel_size=3)
res = conv2d_backprop_input(dout, weight, ops.shape(x))
print(res)
The following information is displayed:
[[[[ 32. 64. 96. ... 96. 64. 32.]
[ 64. 128. 192. ... 192. 128. 64.]
[ 96. 192. 288. ... 288. 192. 96.]
...
[ 96. 192. 288. ... 288. 192. 96.]
[ 64. 128. 192. ... 192. 128. 64.]
[ 32. 64. 96. ... 96. 64. 32.]]
...
[[ 32. 64. 96. ... 96. 64. 32.]
[ 64. 128. 192. ... 192. 128. 64.]
[ 96. 192. 288. ... 288. 192. 96.]
...
[ 96. 192. 288. ... 288. 192. 96.]
[ 64. 128. 192. ... 192. 128. 64.]
[ 32. 64. 96. ... 96. 64. 32.]]]]
Activation Function
The following code implements the computation of the Softmax activation function:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32)
softmax = ops.Softmax()
res = softmax(input_x)
print(res)
The following information is displayed:
[0.01165623 0.03168492 0.08612853 0.23412164 0.63640857]
Loss Function
L1Loss
The following code implements the L1 loss function:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
loss = ops.SmoothL1Loss()
input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
res = loss(input_data, target_data)
print(res)
The following information is displayed:
[0. 0. 0.5]
Optimization Algorithm
The following code implements the stochastic gradient descent (SGD) algorithm. The output is stored in result.
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
sgd = ops.SGD()
parameters = Tensor(np.array([2, -0.5, 1.7, 4]), mindspore.float32)
gradient = Tensor(np.array([1, -1, 0.5, 2]), mindspore.float32)
learning_rate = Tensor(0.01, mindspore.float32)
accum = Tensor(np.array([0.1, 0.3, -0.2, -0.1]), mindspore.float32)
momentum = Tensor(0.1, mindspore.float32)
stat = Tensor(np.array([1.5, -0.3, 0.2, -0.7]), mindspore.float32)
result = sgd(parameters, gradient, learning_rate, accum, momentum, stat)
print(result)
The following information is displayed:
(Tensor(shape=[4], dtype=Float32, value= [ 1.99000001e+00, -4.90300000e-01, 1.69500005e+00, 3.98009992e+00]),)
Array Operations
Array operations refer to operations on arrays.
DType
Returns a Tensor variable that has the same data type as the input and adapts to MindSpore. It is usually used in a MindSpore project.
The following is a code example:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
typea = ops.DType()(input_tensor)
print(typea)
The following information is displayed:
Float32
Cast
Converts the input data type and outputs variables of the same type as the target data type.
The following is a code example:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_x = Tensor(input_np)
type_dst = mindspore.float16
cast = ops.Cast()
result = cast(input_x, type_dst)
print(result.dtype)
The following information is displayed:
Float16
Shape
Returns the shape of the input data.
The following code implements the operation of returning the input data input_tensor:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32)
shape = ops.Shape()
output = shape(input_tensor)
print(output)
The following information is displayed:
(3, 2, 1)
Image Operations
The image operations include image preprocessing operations, for example, image cropping (for obtaining a large quantity of training samples) and resizing (for constructing an image pyramid).
The following code implements the cropping and resizing operations:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
BATCH_SIZE = 1
NUM_BOXES = 5
IMAGE_HEIGHT = 256
IMAGE_WIDTH = 256
CHANNELS = 3
image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
box_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
crop_size = (24, 24)
crop_and_resize = ops.CropAndResize()
output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index), crop_size)
print(output.asnumpy())
The following information is displayed:
[[[[ 6.51672244e-01 -1.85958534e-01 5.19907832e-01]
[ 1.53466597e-01 4.10562098e-01 6.26138210e-01]
[ 6.62892580e-01 3.81776541e-01 4.69261825e-01]
...
[-5.83377600e-01 -3.53377648e-02 -6.01786733e-01]
[ 1.36125124e+00 5.84172308e-02 -6.41442612e-02]
[-9.11651254e-01 -1.19495761e+00 1.96810793e-02]]
[[ 6.06956100e-03 -3.73778701e-01 1.88935513e-03]
[-1.06859171e+00 2.00272346e+00 1.37180305e+00]
[ 1.69524819e-01 2.90421434e-02 -4.12243098e-01]
...
[[-2.04489112e-01 2.36615837e-01 1.33802962e+00]
[ 1.08329034e+00 -9.00492966e-01 -8.21497202e-01]
[ 7.54147097e-02 -3.72897685e-01 -2.91040149e-02]
...
[ 1.12317121e+00 8.98950577e-01 4.22795087e-01]
[ 5.13781667e-01 5.12095273e-01 -3.68211865e-01]
[-7.04941899e-02 -1.09924078e+00 6.89047515e-01]]]]
The preceding code runs on MindSpore of the Ascend version.
Encoding Operations
The encoding operations include BoundingBox Encoding, BoundingBox Decoding, and IOU computing.
BoundingBoxEncode
The box of the area where the object is located is encoded to obtain more concise information similar to PCA, facilitating subsequent tasks such as feature extraction, object detection, and image restoration.
The following code implements BoundingBox Encoding for anchor_box and groundtruth_box:
from mindspore import Tensor
import mindspore.ops as ops
import mindspore
anchor_box = Tensor([[2,2,2,3],[2,2,2,3]],mindspore.float32)
groundtruth_box = Tensor([[1,2,1,4],[1,2,1,4]],mindspore.float32)
boundingbox_encode = ops.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
res = boundingbox_encode(anchor_box, groundtruth_box)
print(res)
The following information is displayed:
[[-1. 0.25 0. 0.40546513]
[-1. 0.25 0. 0.40546513]]
BoundingBoxDecode
After decoding the area location information, the encoder uses this operator to decode the information.
Code implementation:
from mindspore import Tensor
import mindspore.ops as ops
import mindspore
anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32)
deltas = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32)
boundingbox_decode = ops.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), max_shape=(768, 1280), wh_ratio_clip=0.016)
res = boundingbox_decode(anchor_box, deltas)
print(res)
The following information is displayed:
[[ 4.194528 0. 0. 5.194528 ]
[ 2.1408591 0. 3.8591409 60.59815 ]]
IOU Computing
Computes the proportion of the intersection area and union area of the box where the predicted object is located and the box where the real object is located. It is often used as a loss function to optimize the model.
The following code implements the IOU computing between anchor_boxes and gt_boxes. The output is stored in out:
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
import mindspore
iou = ops.IOU()
anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
out = iou(anchor_boxes, gt_boxes)
print(out)
The following information is displayed:
[[ 0. -0. 0.]
[ 0. -0. 0.]
[ 0. 0. 0.]]
Debugging Operations
The debugging operations refer to some common operators and operations used to debug a network, for example, HookBackward. These operations are very convenient and important for entry-level deep learning, greatly improving learning experience.
HookBackward
Displays the gradient of intermediate variables. It is a common operator. Currently, only the PyNative mode is supported.
The following code implements the function of printing the gradient of the intermediate variable (x,y in this example):
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
from mindspore import dtype as mstype
def hook_fn(grad_out):
print(grad_out)
grad_all = ops.GradOperation(get_all=True)
hook = ops.HookBackward(hook_fn)
def hook_test(x, y):
z = x * y
z = hook(z)
z = z * y
return z
def backward(x, y):
return grad_all(hook_test)(Tensor(x, mstype.float32), Tensor(y, mstype.float32))
print(backward(1, 2))
The following information is displayed:
(Tensor(shape=[], dtype=Float32, value= 2),)
(Tensor(shape=[], dtype=Float32, value= 4), Tensor(shape=[], dtype=Float32, value= 4))