Aidge ONNX API

Import

Method related to the import of an ONNX graph into Aidge.

aidge_onnx.load_onnx(filename)

Load an ONNX file and convert it into a aidge_core.GraphView.

Parameters:

filename (str | bytes | bytearray) – Path to the ONNX file to load, or raw ONNX bytes

Returns:

Aidge aidge_core.GraphView corresponding to the ONNX model described by the onnx file filename

Return type:

aidge_core.GraphView

Register import functions

aidge_onnx.node_import.register_import(key: str, converter_function: ConverterType) → None

Add a new conversion function to the aidge_onnx.node_import.ONNX_NODE_CONVERTER_ dictionary. A conversion function must have the following signature : (onnx.NodeProto, list[aidge_core.Node], int) -> aidge_core.Node

Parameters:

• key (str) – This chain of characters must correspond to the ONNX type (onnx/onnx) of the operator (in lowercase).

• converter_function (Callable[[onnx.NodeProto, list[tuple[aidge_core.Node], int], int], aidge_core.Node]) – Function which take as an input the ONNX node and a list of aidge nodes and output the corresponding Aidge node. This function must not connect the node. If the function fails to convert the operator, it must return None.

aidge_onnx.node_import.supported_operators() → list[str]

Return a list of operators supported by the ONNX import.

Returns:

List of string representing the operators supported by the ONNX import.

Return type:

list[str]

aidge_onnx.node_import.auto_register_import(*args) → Callable[[ConverterType], ConverterType]

Decorator used to register a converter to the aidge_onnx.node_import.ONNX_NODE_CONVERTER_

Example:

@auto_register_import("myOp")
def my_op_onverter(onnx_node, input_nodes, opset):
    ...

Parameters:

args – Set of keys (str) which should correspond to the operator type defined by ONNX (onnx/onnx).

aidge_onnx.node_import.ONNX_NODE_CONVERTER_

This defaultdict maps the ONNX type to a function which can convert an ONNX Node into an Aidge Node. This means that if a key is missing from aidge_onnx.node_import.ONNX_NODE_CONVERTER_, it will return the function aidge_onnx.node_import.generic.import_generic() which import the ONNX node as an Aidge generic operator. It is possible to add keys to this dictionary at runtime using aidge_onnx.node_import.register_converter() or aidge_onnx.node_import.auto_register()

Converters ONNX to Aidge

aidge_onnx.node_import.generic.import_generic(onnx_node: NodeProto, input_nodes: list[tuple[Node, int]], opset: int, inputs_tensor_info: list[ValueInfoProto | None]) → Node | None

Parameters:

• onnx_node (onnx.NodeProto) – ONNX node to convert

• input_nodes (list[tuple[aidge_core.Node, int]]) – List of tuple of Aidge nodes with their output index, which constitute the input of the current node

• opset (int, optional) – Indicate opset version of the ONNX model, default=None

Check ONNX import with Aidge

If you want to verify the layerwise output consistency between Aidge and ONNX Runtime for your ONNX file, you can use the command-line tool:

usage: aidge_onnx_checker [-h] [--log-dir LOG_DIR] [--atol ATOL] [--rtol RTOL]
                          model_path

Compare ONNXRuntime with Aidge

positional arguments:
  model_path         Path to the ONNX model

options:
  -h, --help         show this help message and exit
  --log-dir LOG_DIR  Directory to save result logs (optional)
  --atol ATOL        Set the absolute precision to compare each value of the
                     intermediate outputs
  --rtol RTOL        Set the relative precision to compare each value of the
                     intermediate outputs

This tool compares the output tensors produced by Aidge and ONNX Runtime for each layer of your model and summarizes the results.

The typical output looks like this:

Each edge of the graphs is listed following the model’s topological order. If a layer exists in one framework but not the other, the corresponding column will display a cross (✗) for the missing framework and a check mark (✓) for the available one. This does not necessarily indicate an error in the model loading, it simply means there is no direct equivalent layer between the two frameworks.

The average error shown in the report corresponds to the mean difference between tensor values for all compared layers.

Optionally, the tool can log per-layer error data to a specified folder, making it easier to identify where discrepancies occur within the model.

Clean ONNX file

When exporting a model to ONNX, the exported graph may be messy.

We defined a CLI tool that use Aidge in order to clean the ONNX.

This tool curate multiple functions (recipes) that are applied to the graph using Aidge graph matching and replacement algorithms. These functions are applied depending on the opset targeted and optimizations level desired by the user.

Optimization level are as follow:

• O0: Training safe, with these optimizations the graph is still learnable.

• O1: Accuracy safe, with these optimizations the graph will be optimized for inference performances, potentially breaking learnability.

• O2: Approximation, with these optimizations the inference result may diverge from the original one in order to optimize inference speed.

CLI

The CLI helper is:

usage: onnx_cleaner [-h] [--show-recipes] [-v]
                    [--input-shape INPUT_SHAPE [INPUT_SHAPE ...]]
                    [--ir-version IR_VERSION] [--opset-version OPSET_VERSION]
                    [-O {0,1,2}] [--training-safe] [--allow-nonexact]
                    [--skip SKIP [SKIP ...]] [--compare]
                    [input_model] [output_model]

positional arguments:
  input_model           Path to the input ONNX model.
  output_model          Path to save the simplified ONNX model.

options:
  -h, --help            show this help message and exit

helper / debugging:
  --show-recipes        Show available recipes.
  -v, --verbose         Set the verbosity level of console output. Use -v,
                        -vv, -vvv to increase verbosity.

format options:
  --input-shape INPUT_SHAPE [INPUT_SHAPE ...]
                        Overwrite the input shape. Format:
                        "input_name:dim0,dim1,..." or just "dim0,dim1,..." if
                        only one input. Example: "data:1,3,224,224" or
                        "1,3,224,224".
  --ir-version IR_VERSION
                        Specify ONNX IR version to save the model with.
                        Default: let ONNX decide.
  --opset-version OPSET_VERSION
                        Specify ONNX opset version to save the model with.
                        Default: let ONNX decide.

optimization options:
  -O {0,1,2}, --opt-level {0,1,2}
                        Optimization level: 0 = training-safe (minimal) 1 =
                        accuracy-safe (exact) 2 = approximate perf
                        optimizations
  --training-safe       Resulting graph remains learnable (overrides -O if
                        needed).
  --allow-nonexact      Permit optimizations that may change outputs
                        (accuracy-unsafe) for performance gains (overrides -O
                        if needed).
  --skip SKIP [SKIP ...]
                        Skip specific recipes by class name (e.g. --skip
                        FuseGeLU FuseLayerNorm).
  --compare             Run inference with both original and optimized model,
                        compare results.

Python API

One can directly use the Python API to simplify an ONNX graph using aidge_onnx.onnx_cleaner.clean_onnx() or a Aidge aidge_core.GraphView using aidge_core.recipes.simplify_graph().

aidge_onnx.onnx_cleaner.clean_onnx(onnx_to_clean: ModelProto, input_shape: dict[str, list[int]], name: str, ir_version: int | None = None, opset_version: int | None = None, compare: bool = True, skip: list[str] | None = None, training_safe: bool = True, allow_nonexact: bool = False) → ModelProto

Simplify an ONNX model and return the processed graph.

Converts the ONNX model to an Aidge GraphView, applies simplification recipes, and converts back to ONNX. Optionally compares inference results with the original model to verify equivalence.

Parameters:

• onnx_to_clean (onnx.ModelProto) – ONNX model to simplify

• input_shape (dict[str, list[int]]) – Dict mapping input names to shape lists in nested format. Format: {name: [[dim1, dim2, …]]} If empty dict, shapes will be inferred from the model.

• name (str) – Name for the cleaned model

• ir_version (int | None, optional) – ONNX IR version to use for the output model, default=None

• opset_version (int | None, optional) – ONNX opset version to use for the output model, default=None

• compare (bool, optional) – If True, compare inference results with original model, default=True

• skip (list[str] | None, optional) – List of recipe class names to skip, default=None

• training_safe (bool, optional) – If True, only apply training-safe recipes, default=True

• allow_nonexact (bool, optional) – If True, allow accuracy-unsafe optimizations, default=False

Returns:

Simplified ONNX model

Return type:

onnx.ModelProto

Available recipes

The ONNX cleaner relies on the core Aidge graph manipulation recipes.

If you are looking for the exact Python module reference, see aidge_core.recipes.

Export

Method related to the export of an Aidge aidge_core.GraphView to an ONNX file.

aidge_onnx.export_onnx(graph_view: GraphView, path_to_save: str, inputs_dims: dict[str, list[list[int]]] | None = None, outputs_dims: dict[str, list[list[int]]] | None = None, enable_custom_op: bool = False, opset: int | None = None, ir_version: int | None = None)

Export a aidge_core.GraphView to an ONNX file.

Parameters:

• graph_view (aidge_core.GraphView) – aidge_core.GraphView to convert.

• path_to_save (str) – Path where to save the ONNX file, example test.onnx

• inputs_dims (Mapping[str, list[list[int]]], optional) – input dimensions of the network, if provided, outputs_dims must also be filled, this argument is a map, where the key is the name of the input node and the value is a list of dimensions ordered by the input index, defaults to None

• outputs_dims (Mapping[str, list[list[int]]], optional) – output dimensions of the network, if provided, inputs_dims must also be filled, this argument is a map, where the key is the name of the output node and the value is a list of dimensions ordered by the output index, defaults to None

• enable_custom_op (bool, optional) – If True, export will not fail for aidge_core.GenericOperator and will add the operator schema to a custom aidge domain, defaults to False

• opset (int, optional) – The version of the ONNX opset generated, defaults to None

• ir_version (int, optional) – The version of the ONNX intermediate representation, if None the version will be decided by onnx.helper.make_model, defaults to None

Register export functions

aidge_onnx.node_export.register_export(key, parser_function) → None

Add a new conversion function to the aidge_onnx.node_export.AIDGE_NODE_CONVERTER_ dictionary. A conversion function must have the following signature : (aidge_core.Node, list[str], list[str], int, bool) -> onnx.NodeProto

Parameters:

• key (str) – This chain of characters must correspond to the Aidge type.

• converter_function (Callable[[aidge_core.Node, list[str], list[str], int | None], onnx.NodeProto]) – Function which take as an input an Aidge node, list of inputs name, outputs name and a boolean to handle verbosity level.

aidge_onnx.node_export.supported_operators() → list[str]

Return a list of operators supported by the ONNX export.

Returns:

List of string representing the operators supported by the ONNX import.

Return type:

list[str]

aidge_onnx.node_export.auto_register_export(*args) → Callable

Decorator used to register a converter to the aidge_onnx.node_export.AIDGE_NODE_CONVERTER_

Example:

@auto_register_export("myOp")
def my_op_converter(aidge_node, node_inputs_name, node_outputs_name):
    ...

Parameters:

args – Set of keys (str) which should correspond to the operator type defined by Aidge.

Converters Aidge to ONNX

aidge_onnx.node_export.generic_export.generic_export(aidge_node: Node, node_inputs_name, node_outputs_name, initializer_list: list[TensorProto], opset: int | None = None, enable_custom_op: bool = False) → None

Function to export a aidge_core.GenericOperator to an ONNX node

Parameters:

• aidge_node (aidge_core.Node) – Aidge node containing a aidge_core.GenericOperator

• node_inputs_name (list[str]) – list of names of inputs node

• node_outputs_name (list[str]) – list of names of outputs node

• opset (int, optional) – opset to use for the export, defaults to None

• enable_custom_op (bool, optional) – If True, the export will not fait if the type associated to the aidge_core.GenericOperator is not , defaults to False

Supported operators

59% (113/193)

Supported ONNX operators (✔️ Yes, ❌ No)

Operator	Version	Aidge op.	Import	Export
Abs	13	AbsOp (aidge_core.Abs)	✔️	✔️
Acos	22	—	❌	❌
Acosh	22	—	❌	❌
Add	14	AddOp (aidge_core.Add)	✔️	✔️
AffineGrid	20	—	❌	❌
And	7	AndOp (aidge_core.And)	✔️	✔️
ArgMax	13	ArgMaxOp (aidge_core.ArgMax)	✔️	✔️
ArgMin	13	—	❌	❌
Asin	22	AsinOp (aidge_core.Asin)	✔️	✔️
Asinh	22	—	❌	❌
Atan	22	AtanOp (aidge_core.Atan)	✔️	✔️
Atanh	22	—	❌	❌
AveragePool	22	—	✔️	❌
—		AvgPooling1DOp (aidge_core.AvgPooling1D)	❌	✔️
—		AvgPooling2DOp (aidge_core.AvgPooling2D)	❌	✔️
—		AvgPooling3DOp (aidge_core.AvgPooling3D)	❌	✔️
—		BatchNorm2DOp (aidge_core.BatchNorm2D)	❌	✔️
BatchNormalization	15	—	✔️	❌
Bernoulli	22	—	❌	❌
BitShift	11	BitShiftOp (aidge_core.BitShift)	✔️	✔️
BitwiseAnd	18	—	❌	❌
BitwiseNot	18	—	❌	❌
BitwiseOr	18	—	❌	❌
BitwiseXor	18	—	❌	❌
BlackmanWindow	17	—	❌	❌
Cast	21	CastOp (aidge_core.Cast)	✔️	✔️
CastLike	21	CastLikeOp (aidge_core.CastLike)	❌	❌
Ceil	13	CeilOp (aidge_core.Ceil)	✔️	✔️
Celu	12	—	❌	❌
CenterCropPad	18	—	❌	❌
Clip	13	ClipOp (aidge_core.Clip)	✔️	✔️
Col2Im	18	—	❌	❌
—		ComplexToInnerPairOp (aidge_core.ComplexToInnerPair)	❌	✔️
Compress	11	—	❌	❌
Concat	13	ConcatOp (aidge_core.Concat)	✔️	✔️
ConcatFromSequence	11	—	❌	❌
Constant	21	—	✔️	❌
ConstantOfShape	21	ConstantOfShapeOp (aidge_core.ConstantOfShape)	✔️	✔️
Conv	22	—	✔️	❌
—		Conv1DOp (aidge_core.Conv1D)	❌	✔️
—		Conv2DOp (aidge_core.Conv2D)	❌	✔️
—		Conv3DOp (aidge_core.Conv3D)	❌	✔️
—		ConvDepthWise1DOp (aidge_core.ConvDepthWise1D)	❌	✔️
—		ConvDepthWise2DOp (aidge_core.ConvDepthWise2D)	❌	✔️
ConvInteger	10	—	❌	❌
ConvTranspose	22	—	✔️	❌
—		ConvTranspose1DOp (aidge_core.ConvTranspose1D)	❌	✔️
—		ConvTranspose2DOp (aidge_core.ConvTranspose2D)	❌	✔️
—		ConvTranspose3DOp (aidge_core.ConvTranspose3D)	❌	✔️
Cos	22	CosOp (aidge_core.Cos)	✔️	✔️
Cosh	22	CoshOp (aidge_core.Cosh)	✔️	✔️
CumSum	14	—	❌	❌
DeformConv	22	—	❌	❌
DepthToSpace	13	DepthToSpaceOp (aidge_core.DepthToSpace)	✔️	❌
DequantizeLinear	21	—	✔️	❌
Det	22	—	❌	❌
DFT	20	DFTOp (aidge_core.DFT)	✔️	✔️
Div	14	DivOp (aidge_core.Div)	✔️	✔️
—		DropBlockOp (aidge_core.DropBlock)	❌	✔️
Dropout	22	DropoutOp (aidge_core.Dropout)	✔️	✔️
DynamicQuantizeLinear	11	—	❌	❌
Einsum	12	—	❌	❌
Elu	22	—	❌	❌
Equal	19	EqualOp (aidge_core.Equal)	✔️	✔️
Erf	13	ErfOp (aidge_core.Erf)	✔️	✔️
Exp	13	ExpOp (aidge_core.Exp)	✔️	✔️
Expand	13	ExpandOp (aidge_core.Expand)	✔️	✔️
EyeLike	22	—	❌	❌
—		FCOp (aidge_core.FC)	❌	✔️
Flatten	21	FlattenOp (aidge_core.Flatten)	✔️	✔️
Floor	13	FloorOp (aidge_core.Floor)	✔️	✔️
Gather	13	GatherOp (aidge_core.Gather)	✔️	✔️
GatherElements	13	GatherElementsOp (aidge_core.GatherElements)	✔️	✔️
GatherND	13	GatherNDOp (aidge_core.GatherND)	✔️	✔️
Gelu	20	—	❌	✔️
Gemm	13	—	✔️	✔️
GlobalAveragePool	22	—	✔️	❌
—		GlobalAveragePoolingOp (aidge_core.GlobalAveragePooling)	❌	✔️
GlobalLpPool	22	—	❌	❌
GlobalMaxPool	22	—	❌	❌
Greater	13	GreaterOp (aidge_core.Greater)	✔️	✔️
GreaterOrEqual	16	GreaterOrEqualOp (aidge_core.GreaterOrEqual)	✔️	✔️
GridSample	22	GridSampleOp (aidge_core.GridSample)	✔️	❌
GroupNormalization	21	—	❌	❌
GRU	22	—	✔️	❌
HammingWindow	17	—	❌	❌
HannWindow	17	—	✔️	✔️
Hardmax	13	HardmaxOp (aidge_core.Hardmax)	✔️	✔️
HardSigmoid	22	HardSigmoidOp (aidge_core.HardSigmoid)	✔️	✔️
HardSwish	22	—	✔️	✔️
Identity	21	IdentityOp (aidge_core.Identity)	✔️	✔️
If	21	—	❌	❌
ImageDecoder	20	—	❌	❌
—		InnerPairToComplexOp (aidge_core.InnerPairToComplex)	❌	✔️
—		InstanceNormOp (aidge_core.InstanceNorm)	❌	✔️
InstanceNormalization	22	—	✔️	❌
IsInf	20	—	❌	❌
IsNaN	20	—	❌	❌
LayerNormalization	17	—	✔️	❌
LeakyRelu	16	LeakyReLUOp (aidge_core.LeakyReLU)	✔️	✔️
Less	13	LessOp (aidge_core.Less)	✔️	✔️
LessOrEqual	16	LessOrEqualOp (aidge_core.LessOrEqual)	✔️	✔️
—		LnOp (aidge_core.Ln)	❌	✔️
Log	13	—	✔️	❌
LogSoftmax	13	LogSoftmaxOp (aidge_core.LogSoftmax)	✔️	✔️
Loop	21	—	❌	❌
LpNormalization	22	—	❌	❌
LpPool	22	—	❌	❌
LRN	13	LRNOp (aidge_core.LRN)	✔️	✔️
LSTM	22	—	✔️	❌
MatMul	13	MatMulOp (aidge_core.MatMul)	✔️	✔️
MatMulInteger	10	—	❌	❌
Max	13	MaxOp (aidge_core.Max)	✔️	✔️
MaxPool	22	—	✔️	❌
—		MaxPooling1DOp (aidge_core.MaxPooling1D)	❌	✔️
—		MaxPooling2DOp (aidge_core.MaxPooling2D)	❌	✔️
—		MaxPooling3DOp (aidge_core.MaxPooling3D)	❌	✔️
MaxRoiPool	22	—	❌	❌
MaxUnpool	22	—	❌	❌
Mean	13	—	❌	❌
MeanVarianceNormalization	13	—	❌	❌
MelWeightMatrix	17	—	❌	❌
Min	13	MinOp (aidge_core.Min)	✔️	✔️
Mish	22	—	❌	❌
Mod	13	ModOp (aidge_core.Mod)	✔️	✔️
Mul	14	MulOp (aidge_core.Mul)	✔️	✔️
Multinomial	22	—	❌	❌
Neg	13	NegOp (aidge_core.Neg)	✔️	✔️
NegativeLogLikelihoodLoss	22	—	❌	❌
NonMaxSuppression	11	NonMaxSuppressionOp (aidge_core.NonMaxSuppression)	✔️	✔️
NonZero	13	NonZeroOp (aidge_core.NonZero)	✔️	✔️
Not	1	NotOp (aidge_core.Not)	✔️	✔️
OneHot	11	OneHotOp (aidge_core.OneHot)	✔️	✔️
Optional	15	—	❌	❌
OptionalGetElement	18	—	❌	❌
OptionalHasElement	18	—	❌	❌
Or	7	—	❌	❌
Pad	21	PadOp (aidge_core.Pad)	✔️	✔️
Pow	15	PowOp (aidge_core.Pow)	✔️	✔️
PRelu	16	—	❌	❌
—		ProducerOp (aidge_core.Producer)	❌	✔️
QLinearConv	10	—	✔️	✔️
QLinearMatMul	21	—	✔️	✔️
QuantizeLinear	21	—	✔️	❌
RandomNormal	22	—	❌	❌
RandomNormalLike	22	RandomNormalLikeOp (aidge_core.RandomNormalLike)	✔️	✔️
RandomUniform	22	—	❌	❌
RandomUniformLike	22	—	❌	❌
Range	11	RangeOp (aidge_core.Range)	✔️	✔️
Reciprocal	13	ReciprocalOp (aidge_core.Reciprocal)	✔️	✔️
ReduceL1	18	—	✔️	✔️
ReduceL2	18	—	✔️	✔️
ReduceLogSum	18	—	❌	❌
ReduceLogSumExp	18	—	❌	❌
ReduceMax	20	ReduceMaxOp (aidge_core.ReduceMax)	✔️	✔️
ReduceMean	18	ReduceMeanOp (aidge_core.ReduceMean)	✔️	✔️
ReduceMin	20	ReduceMinOp (aidge_core.ReduceMin)	✔️	✔️
ReduceProd	18	—	❌	❌
ReduceSum	13	ReduceSumOp (aidge_core.ReduceSum)	✔️	✔️
ReduceSumSquare	18	—	❌	❌
RegexFullMatch	20	—	❌	❌
Relu	14	ReLUOp (aidge_core.ReLU)	✔️	✔️
Reshape	21	ReshapeOp (aidge_core.Reshape)	✔️	✔️
Resize	19	ResizeOp (aidge_core.Resize)	✔️	✔️
ReverseSequence	10	—	❌	❌
RNN	22	—	❌	❌
RoiAlign	22	—	❌	❌
Round	22	RoundOp (aidge_core.Round)	✔️	✔️
Scan	21	—	❌	❌
Scatter	11	ScatterOp (aidge_core.Scatter)	❌	✔️
ScatterElements	18	—	✔️	❌
ScatterND	18	—	❌	❌
—		SDLayerNormOp (aidge_core.SDLayerNorm)	❌	✔️
—		SDShiftGELUOp (aidge_core.SDShiftGELU)	❌	✔️
—		SDShiftmaxOp (aidge_core.SDShiftmax)	❌	✔️
—		SDShiftSiLUOp (aidge_core.SDShiftSiLU)	❌	✔️
Selu	22	—	❌	❌
SequenceAt	11	—	❌	❌
SequenceConstruct	11	—	❌	❌
SequenceEmpty	11	—	❌	❌
SequenceErase	11	—	❌	❌
SequenceInsert	11	—	❌	❌
SequenceLength	11	—	❌	❌
SequenceMap	17	—	❌	❌
Shape	21	ShapeOp (aidge_core.Shape)	✔️	✔️
Shrink	9	—	❌	❌
Sigmoid	13	SigmoidOp (aidge_core.Sigmoid)	✔️	✔️
Sign	13	—	❌	❌
Sin	22	SinOp (aidge_core.Sin)	✔️	✔️
Sinh	22	SinhOp (aidge_core.Sinh)	✔️	✔️
Size	21	SizeOp (aidge_core.Size)	✔️	✔️
Slice	13	SliceOp (aidge_core.Slice)	✔️	✔️
Softmax	13	SoftmaxOp (aidge_core.Softmax)	✔️	✔️
SoftmaxCrossEntropyLoss	13	—	❌	❌
Softplus	22	—	✔️	✔️
Softsign	22	—	❌	❌
SpaceToDepth	13	—	❌	❌
Split	18	SplitOp (aidge_core.Split)	✔️	✔️
SplitToSequence	11	—	❌	❌
Sqrt	13	SqrtOp (aidge_core.Sqrt)	✔️	✔️
Squeeze	21	SqueezeOp (aidge_core.Squeeze)	✔️	✔️
STFT	17	STFTOp (aidge_core.STFT)	✔️	✔️
StringConcat	20	—	❌	❌
StringNormalizer	10	—	❌	❌
StringSplit	20	—	❌	❌
Sub	14	SubOp (aidge_core.Sub)	✔️	✔️
Sum	13	SumOp (aidge_core.Sum)	✔️	✔️
Tan	22	TanOp (aidge_core.Tan)	✔️	✔️
Tanh	13	TanhOp (aidge_core.Tanh)	✔️	✔️
TfIdfVectorizer	9	—	❌	❌
ThresholdedRelu	22	—	❌	❌
Tile	13	TileOp (aidge_core.Tile)	✔️	✔️
TopK	11	TopKOp (aidge_core.TopK)	✔️	✔️
Transpose	21	TransposeOp (aidge_core.Transpose)	✔️	✔️
Trilu	14	TriluOp (aidge_core.Trilu)	✔️	✔️
Unique	11	—	❌	❌
Unsqueeze	21	UnsqueezeOp (aidge_core.Unsqueeze)	✔️	✔️
Upsample	10	—	❌	❌
Where	16	WhereOp (aidge_core.Where)	✔️	✔️
Xor	7	—	❌	❌