Object Representation¶
As it is mentioned in the “Algorithms” section of “Getting Started with BEAT” in BEAT documentation, data is available via our backend API to the user algorithms. For example, in Python, the BEAT platform uses NumPy data types to pass data to and from algorithms. For example, when the algorithm reads data for which the format is defined like:
{
"value": "float64"
}
The field value
of an instance named object
of this format is
accessible as object.value
and will have the type numpy.float64
. If the
format would be, instead:
{
"value": [0, 0, "float64"]
}
It would be accessed in the same way (i.e., via object.value
), except that
the type would be numpy.ndarray
and object.value.dtype
would be
numpy.float64
. Naturally, objects which are instances of a format like
this:
{
"x": "int32",
"y": "int32"
}
Could be accessed like object.x
, for the x
value and object.y
, for
the y
value. The type of object.x
and object.y
would be
numpy.int32
.
Conversely, if you write output data in an algorithm, the type of the output objects are checked for compatibility with respect to the value declared on the format. For example, this would be a valid use of the format above, in Python:
import numpy
class Algorithm:
def process(self, inputs, dataloaders, outputs):
# read data
# prepares object to be written
myobj = {"x": numpy.int32(4), "y": numpy.int32(6)}
# write it
outputs["point"].write(myobj) #OK!
If you try to write into an object that is supposed to be of type int32
, a
float64
object, an exception will be raised. For example:
import numpy
class Algorithm:
def process(self, inputs, dataloaders outputs):
# read data
# prepares object to be written
myobj = {"x": numpy.int32(4), "y": numpy.float64(3.14)}
# write it
outputs["point"].write(myobj) #Error: cannot downcast!
The bottomline is: all type casting in the platform must be explicit. It will not automatically downcast or upcast objects for you as to avoid unexpected precision loss leading to errors.