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
metadata
The metadata of the model.
It contains all information that can be useful at the inference step such as the inference type, the data format expected by the model,
the compatible image size required pre processing, the input and output normalization information ot the tile overlap.
String
None
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.
String
None
UNKNOWN
Object methods
Method
Description
void toDataFrame()
Convert the measurement to an IOLink.DataFrame
One or more "index" columns will be added at the beginning of the dataframe to identify
the different elements. For instance:
index [label]: The index of the label. This index systematically starts from 0
and is the label value minus 1 when all label values are represented.
When there are some missing label values, the corresponding label value cannot be
directly deduced from this index.
index [time]: The sequence index of a time series or of an image stack.
Method
Description
void ToDataFrame()
Convert the measurement to an IOLink.DataFrame
One or more "index" columns will be added at the beginning of the dataframe to identify
the different elements. For instance:
index [label]: The index of the label. This index systematically starts from 0
and is the label value minus 1 when all label values are represented.
When there are some missing label values, the corresponding label value cannot be
directly deduced from this index.
index [time]: The sequence index of a time series or of an image stack.
Method
Description
void to_data_frame()
Convert the measurement to an IOLink.DataFrame
One or more "index" columns will be added at the beginning of the dataframe to identify
the different elements. For instance:
index [label]: The index of the label. This index systematically starts from 0
and is the label value minus 1 when all label values are represented.
When there are some missing label values, the corresponding label value cannot be
directly deduced from this index.
index [time]: The sequence index of a time series or of an image stack.