Summary table

Following table contains the list of the operators that can be imported, if the constraints or limitations are met.

• supported optional fused activation (or non-linearity): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign, abs, acos, acosh, asin, asinh, atan, atanh, ceil, clip, cos, cosh, erf, flexerf, exp, floor, identity, log, logistic, neg, logical_not, prelu, probit, reciprocal, relu_generic, relu_thresholded, round, sign, sin, sinh, softmax_zero, sqrt, swish, tan
  
• supported optional fused integer activation (or non-linearity): prelu, relu, clip, lut, swish, identity, relu6
  
• mixed data operations (i.e hybrid operator) are not natively supported, activations and weights should be quantized
  
• if an operator is not supported in integer, floating point version is used. Converters are automatically added by the code generator.

Common constraints

• input and output tensors must be not dynamic.
  • variable-length batch dimension (i.e. (None,)) is considered as equal to 1
    
  • must not be greater than 6D
    
  • dimension must be in the range [0, 65536[
    
  • batch dimension is partially supported in the axis/axes parameters
    
• data type for the weights/activations tensors must be:
  • float32, int8, uint8
    
  • only int32 for the bias tensor is considered
    
  • for some operators, bool and binary types are also supported
    
• 1D operator is mapped on the respective 2D operator by adding a singleton dimension on the input: (12,3) -> (12, 1, 3)

ABS

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

ADD

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ARG_MAX

Computes the indices of the max elements of the input tensor’s element along the provided axis.

• category: generic layer
  
• input data types: float32
  
• output data types: int32

ARG_MIN

Computes the indices of the min elements of the input tensor’s element along the provided axis.

• category: generic layer
  
• input data types: float32
  
• output data types: int32

ATAN2

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

AVERAGE_POOL_2D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary pool sizes, provided that they are smaller than the input size

BATCH_MATMUL

Fully Connected operation

• category: core layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)

Specific constraints/recommendations:

• Only up to 3D matrix multiplication is supported

BATCH_TO_SPACE_ND

Reshape the batch dimension of a tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

BROADCAST_ARGS

Returns a tensor containing the shape of the input tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: int32

BROADCAST_TO

Constructs a tensor by tiling the input tensor

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• tiling on batch-dimension is not supported

CAST

Cast elements of the input tensor to the specified output tensor data

• category: conversion layer
  
• input data types: bool, int8, uint8, float32
  
• output data types: bool, int8, uint8, float32

Specific constraints/recommendations:

• The attribute saturate different from the default value is not supported

CEIL

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

CONCATENATION

Performs concatenation of a list of inputs

• category: merge operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign

Specific constraints/recommendations:

• concatenating on the batch dimension is not supported

CONV_2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

COS

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

DEPTH_TO_SPACE

Permutes the dimensions of the input according to a given pattern

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• transposing the batch dimension is not supported

DEPTHWISE_CONV_2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

DEQUANTIZE

Computes element-wise data conversion low precision to full precision, based on the scale/zeropoint parameters

• category: conversion layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

DIV

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ELU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

EQUAL

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

EXP

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

EXPAND_DIMS

Reshapes a tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

FILL

Generates a tensor with given value and shape

• category: constant layer
  
• input data types: float32
  
• output data types: float32

FlexErf

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

FLOOR

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

FLOOR_DIV

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

FLOOR_MOD

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

FULLY_CONNECTED

Fully Connected operation

• category: core layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)

Specific constraints/recommendations:

• Only up to 3D matrix multiplication is supported

GATHER

Gathers values along a specified axis

• category: activation function
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• Gather is not supported with indices dimensions > 2 (Batch is not considered), axis > 3and axis = 0, batch_dims attribute is not handled

GATHER_ND

Gathers slices from input tensor into an output tensor with shape specified by indices

• category: activation function
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• Batch_dims attribute is not handled. 4D or more indices (BATCH is not included) are not implementedLast dimension of indices > 4 with 2D inputs tensor case is not handled

GELU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

GREATER

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

GREATER_EQUAL

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

HARD_SWISH

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

L2_NORMALIZATION

Apply Lp-normalization along the provided axis

• category: normalization function
  
• input data types: float32
  
• output data types: float32

LEAKY_RELU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

LESS

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

LESS_EQUAL

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

LOCAL_RESPONSE_NORMALIZATION

Apply Local Response Normalization over local input regions

• category: normalization function
  
• input data types: float32
  
• output data types: float32

LOG

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

LOG_SOFTMAX

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• It is supported only for 1D tensor and only on the channel dimension

LOGICAL_AND

Performs boolean element-wise operation

• category: eltwise operator
  
• input data types: bool
  
• output data types: bool

LOGICAL_NOT

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

LOGICAL_OR

Performs boolean element-wise operation

• category: eltwise operator
  
• input data types: bool
  
• output data types: bool

LOGISTIC

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

MAX_POOL_2D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary pool sizes, provided that they are smaller than the input size

MAXIMUM

Computes the maximum (element-wise) a list of inputs

• category: eltwise operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

MEAN

Computes the Mean of the input tensor’s element along the provided axes

• category: reduction operation
  
• input data types: float32
  
• output data types: float32

MINIMUM

Computes the minimum (element-wise) a list of inputs

• category: eltwise operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

MIRROR_PAD

Pads an input tensor

• category: Reshaping layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• padding ‘edge’ is not supported

MUL

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

NEG

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

NOT_EQUAL

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

PACK

Packs a list of tensors into a tensor along a specified axis

• category: merge operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• related TF operator: tf.stack

PAD

Pads an input tensor

• category: Reshaping layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• padding ‘edge’ is not supported

PADV2

Pads an input tensor

• category: Reshaping layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• padding ‘edge’ is not supported

POW

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

PRELU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

QUANTIZE

Computes element-wise data conversion full precision to low precision, based on the scale/zeropoint parameters

• category: conversion layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

REDUCE_ANY

Computes the logical ‘or’ of elements across dimensions of a tensor

• category: reduction operation
  
• input data types: float32
  
• output data types: float32

REDUCE_MAX

Computes the Max of the input tensor’s element along the provided axes

• category: reduction operation
  
• input data types: float32
  
• output data types: float32

REDUCE_MIN

Computes the Min of the input tensor’s element along the provided axes

• category: reduction operation
  
• input data types: float32
  
• output data types: float32

REDUCE_PROD

Computes the Product of the input tensor’s element along the provided axes

• category: reduction operation
  
• input data types: float32
  
• output data types: float32

RELU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

RELU6

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

RELU_0_TO_1

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

RELU_N1_TO_1

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

RESHAPE

Reshapes a tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

RESIZE_BILINEAR

Resize input tensor using bilinear interpolation

• category: resizing operation
  
• input data types: float32
  
• output data types: float32

RESIZE_NEAREST_NEIGHBOR

Resize input tensor using nearest interpolation mode

• category: resizing operation
  
• input data types: float32
  
• output data types: float32

REVERSE_V2

Reverses specific dimensions of a tensor

• category: Reshape layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• reversing the batch dimension is not supported
  
• support more than 1 axis is not supported

ROUND

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

RSQRT

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

SCATTER_ND

Scatters updates into a tensor of shape equal to shape attribute according indices (TFLite),The output of the ScatterND layer is produced by creating a copy of the input data, and then updating its values to values specified by updates at specific index positions specified by indices (ONNX)

• category: activation function
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• 5D or more indices (BATCH is not included) are not implemented, 5D or more data (BATCH is not included) are not implemented

SELECT

Where layer

• category: generic layer
  
• input data types: float32, int8, uint8, int16, uint16, int32, uint32, bool
  
• output data types: float32, int8, uint8, int16, uint16, int32, uint32, bool

SELECT_V2

Where layer

• category: generic layer
  
• input data types: float32, int8, uint8, int16, uint16, int32, uint32, bool
  
• output data types: float32, int8, uint8, int16, uint16, int32, uint32, bool

SHAPE

Returns a tensor containing the shape of the input tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: int32

SIGN

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

SIN

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

SLICE

Crops the input

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

SOFTMAX

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• It is supported only for 1D tensor and only on the channel dimension

SPACE_TO_BATCH_ND

Divides spatial dimensions

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

SPACE_TO_DEPTH

Rearranges blocks of spatial data into depth

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

SPLIT

Splits a tensor into a list of sub tensors

• category: split operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• Only supported if the number of splits is equal to the size of the splitting dimension

SPLIT_V

Splits a tensor into a list of sub tensors

• category: split operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• Only supported if the number of splits is equal to the size of the splitting dimension

SQRT

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

SQUARE

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

SQUARED_DIFFERENCE

Compute (x-y)(x-y)

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

SQUEEZE

Reshapes a tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

STRIDED_SLICE

Crops the input

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

SUB

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

SUM

Computes the Sum of the input tensor’s element along the provided axes

• category: reduction operation
  
• input data types: float32
  
• output data types: float32

TANH

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

TILE

Constructs a tensor by tiling the input tensor

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• tiling on batch-dimension is not supported

TOPK_V2

Retrieve the top-K largest or smallest elements along a specified axis

• category: topK operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

TRANSPOSE

Permutes the dimensions of the input according to a given pattern

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• transposing the batch dimension is not supported

TRANSPOSE_CONV

Transposed convolutional layer

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size

UNIDIRECTIONAL_SEQUENCE_LSTM

Computes a multi-layer long short-term memory (LSTM) RNN to an input sequence (batch=1, timesteps, features)

• category: recurrent layer
  
• input data types: float32, int8
  
• output data types: float32, int8

Specific constraints/recommendations:

• stateless mode support only
  
• fused activation: sigmoid
  
• fused recurrent activation: sigmoid
  
• return_state not supported
  
• time_major not supported

UNPACK

Unpacks num tensors from values along specified axis

• category: split operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ZEROS_LIKE

Generates a tensor with values 0

• category: zeros like
  
• input data types: float32
  
• output data types: float32