[Python] Improve Python interop usability. #22104
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- Define `version` and `versionInfo` computed properties on `PythonInterface`
for convenience.
- Define labelless failable initializers from `PythonObject`
(representing `numpy.ndarray`) to `ShapedArray` and `Tensor`.
- A labelless initializer was not created for `Array` because `Array` is
always one-dimensional, so the conversion is semantically more significant.
Users can use the labelled `Array(numpyArray:)` initalizer.
rxwei
reviewed
Jan 25, 2019
| // Creates a `ShapedArray` instance from the given `PythonObject` if it is a | ||
| // `numpy.ndarray` instance with a matching scalar datatype. | ||
| public init?(_ pythonObject: PythonObject) { | ||
| self.init(numpyArray: pythonObject) |
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If it's not a numpy array, we should load it as a (potentially multi-dimensional) python array.
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Good point. Added TODO comments in 49f70e4.
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@swift-ci Please test tensorflow |
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@swift-ci please test tensorflow Linux |
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@swift-ci please test tensorflow Linux |
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@swift-ci please test tensorflow Linux |
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@swift-ci please test tensorflow Linux |
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@swift-ci please test tensorflow |
dan-zheng
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`init?(numpy:)` is a bit shorter than the previous `init?(numpyArray:)`. Using a labelless initializer is problematic because it causes type inference problems due to implicit conversions.
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@swift-ci Please test tensorflow |
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@swift-ci Please test tensorflow |
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versionandversionInfocomputed properties onPythonInterfacefor convenience.
PythonObject(representing
numpy.ndarray) toShapedArrayandTensor.ArraybecauseArrayisalways one-dimensional, so the nature of the conversion is semantically bigger.