Contains all the information necessary to run a ONNX command.
Information about the measurement named measurement can be accessed by using the method
measurementInformation() which returns a
FieldInformation
Information about the measurement named measurement can be accessed by using the property
measurementInformation which returns a
FieldInformation
Information about the measurement named measurement can be accessed by using the property
measurement_information which returns a
FieldInformation
Object members
Measurement name
Description
Element type
Indexing
Physical Information
inputCount
The number of inputs of the model.
Unsigned integer
None
COUNT
inputDimensionCount
The number of dimensions expected by the model for the input tensors.
It is generally 4 for 2D images, and 5 for 3D volumes.
Unsigned integer
[inputIndex]
COUNT
inputName
The inputs'names.
String
[inputIndex]
UNKNOWN
inputShape
The input tensors'shapes.
The tensor layout depends on how it has been defined in the model.
It is generally NCHW or NHWC
in 2D, and NDHWC or NCDHW in 3D where N is the number of
images contained in the batch, C is the number of channels, D is the volume depth in 3D,
H is the image height, or number of rows, and W is the image width, or number of columns.
Integer
[inputIndex, dimension]
UNKNOWN
inputType
The inputs'data types.
String
[inputIndex]
UNKNOWN
outputCount
The number of outputs of the model.
Unsigned integer
None
COUNT
outputDimensionCount
The number of dimensions expected for the output tensors.
Unsigned integer
[outputIndex]
COUNT
outputName
The outputs'names.
String
[outputIndex]
UNKNOWN
outputShape
The output tensors'shapes.
Integer
[outputIndex, dimension]
UNKNOWN
outputType
The outputs'data types.
String
[outputIndex]
UNKNOWN
inputOnnxType
The inputs'data ONNX types.
String
[inputIndex]
UNKNOWN
outputOnnxType
The outputs'data ONNX types.
String
[outputIndex]
UNKNOWN
path
The path of the file used to load the Onnx model.
String
None
UNKNOWN
runnerType
The type of runner.
It depends of the capacities of the hosting machine and defines if the model will be applied
on CPU or on GPU.