AnandInguva commented on code in PR #23266:
URL: https://github.com/apache/beam/pull/23266#discussion_r973084809
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sdks/python/apache_beam/ml/inference/base.py:
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@@ -82,6 +82,33 @@ def _to_microseconds(time_ns: int) -> int:
return int(time_ns / _NANOSECOND_TO_MICROSECOND)
+def _convert_to_result(
+ batch: Iterable,
+ predictions: Union[Iterable, Dict[Any, Iterable]],
+ drop_example: bool = False) -> Iterable[PredictionResult]:
+ if isinstance(predictions, dict):
+ # Go from one dictionary of type: {key_type1: Iterable<val_type1>,
+ # key_type2: Iterable<val_type2>, ...} where each Iterable is of
+ # length batch_size, to a list of dictionaries:
+ # [{key_type1: value_type1, key_type2: value_type2}]
+ predictions_per_tensor = [
+ dict(zip(predictions.keys(), v)) for v in zip(*predictions.values())
+ ]
+ if not drop_example:
+ return [
+ PredictionResult(x, y) for x, y in zip(batch, predictions_per_tensor)
+ ]
+ else:
+ return [
+ PredictionResult(None, y) for x,
+ y in zip(batch, predictions_per_tensor)
+ ]
Review Comment:
Done. PTAL
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