ONNX - Operators

Quiz

Operators in ONNX are the building blocks that define computations in a machine learning model, mapping operations from various frameworks (like TensorFlow, PyTorch, etc.) into a standardized ONNX format.

In this tutorial, well explore what ONNX operators are, the different types, and how they function in ONNX-compatible models.

What are ONNX Operators?

An ONNX operator is a fundamental unit of computation used in an ONNX model. Each operator defines a specific type of operation, such as mathematical computations, data processing, or neural network layers. Operators are identified by a tuple −

<name, domain, version>

Where,

name: The name of the operator.
domain: The namespace to which the operator belongs.
version: The version of the operator (to track updates and changes).

Core Operators in ONNX

Core operators are the standard set of operators that come with ONNX and ONNX-ML. These operators are highly optimized and supported by any ONNX-compatible product. These operators are designed to cover most common machine learning tasks and cannot generally be meaningfully further decomposed into simpler operations.

Key Features of Core Operators −

These are standard operators defined within the ONNX framework.
The ai.onnx domain contains 124 operators, while the ai.onnx.ml domain (focused on machine learning tasks) contains 19 operators.
Core operators support various problem areas such as image classification, recommendation systems, and natural language processing.

The ai.onnx Domain Operators

Following are the list of ai.onnx operators −

S.No	Operator
1	Abs
2	Acos
3	Acosh
4	Add
5	AffineGrid
6	And
7	ArgMax
8	ArgMin
9	Asin
10	Asinh
11	Atan
12	Atanh
13	AveragePool
14	BatchNormalization
15	Bernoulli
16	BitShift
17	BitwiseAnd
18	BitwiseNot
19	BitwiseOr
20	BitwiseXor
21	BlackmanWindow
22	Cast
23	CastLike
24	Ceil
25	Celu
26	CenterCropPad
27	Clip
28	Col2Im
29	Compress
30	Concat
31	ConcatFromSequence
32	Constant
33	ConstantOfShape
34	Conv
35	ConvInteger
36	ConvTranspose
37	Cos
38	Cosh
39	CumSum
40	DFT
41	DeformConv
42	DepthToSpace
43	DequantizeLinear
44	Det
45	Div
46	Dropout
47	DynamicQuantizeLinear
48	Einsum
49	Elu
50	Equal
51	Erf
52	Exp
53	Expand
54	EyeLike
55	Flatten
56	Floor
57	GRU
58	Gather
59	GatherElements
60	GatherND
61	Gelu
62	Gemm
63	GlobalAveragePool
64	GlobalLpPool
65	GlobalMaxPool
66	Greater
67	GreaterOrEqual
68	GridSample
69	GroupNormalization
70	HammingWindow
71	HannWindow
72	HardSigmoid
73	HardSwish
74	Hardmax
75	Identity
76	If
77	ImageDecoder
78	InstanceNormalization
79	IsInf
80	IsNaN
81	LRN
82	LSTM
83	LayerNormalization
84	LeakyRelu
85	Less
86	LessOrEqual
87	Log
88	LogSoftmax
89	Loop
90	LpNormalization
91	LpPool
92	MatMul
93	MatMulInteger
94	Max
95	MaxPool
96	MaxRoiPool
97	MaxUnpool
98	Mean
99	MeanVarianceNormalization
100	MelWeightMatrix
101	Min
102	Mish
103	Mod
104	Mul
105	Multinomial
106	Neg
107	NonMaxSuppression
108	NonZero
109	Not
110	OneHot
111	Optional
112	Or
113	PRelu
114	Pad
115	Pow
116	QLinearAdd
117	QLinearAveragePool
118	QLinearConcat
119	QLinearConv
120	QLinearLeakyRelu
121	QLinearMul
122	QLinearSigmoid
123	QLinearSoftmax
124	QLinearTranspose

The ai.onnx.ml Domain Operators

Below are the list of all available operators in the ai.onnx.ml domain.

S.No	Operator
1	ArrayFeatureExtractor
2	Binarizer
3	CastMap
4	CategoryMapper
5	DictVectorizer
6	FeatureVectorizer
7	Imputer
8	LabelEncoder
9	LinearClassifier
10	LinearRegressor
11	Normalizer
12	OneHotEncoder
13	SVMClassifier
14	SVMRegressor
15	Scaler
16	TreeEnsemble
17	TreeEnsembleClassifier
18	TreeEnsembleRegressor
19	ZipMap

Custom Operators in ONNX

In addition to core operators, ONNX allows developers to define custom operators for more specialized or non-standard tasks.

If a particular operation does not exist in the ONNX operator set, or if a developer creates a new technique or custom activation function, they can define a custom operator.
Custom operators are identified by a custom domain name, distinguishing them from core operators.

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