ryanthompson591 commented on code in PR #21806:
URL: https://github.com/apache/beam/pull/21806#discussion_r897131988
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sdks/python/apache_beam/ml/inference/pytorch_inference_test.py:
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@@ -237,10 +237,8 @@ def test_run_inference_kwargs_prediction_params(self):
inference_runner = TestPytorchModelHandlerForInferenceOnly(
torch.device('cpu'))
predictions = inference_runner.run_inference(
- batch=KWARGS_TORCH_EXAMPLES,
- model=model,
- prediction_params=prediction_params)
- for actual, expected in zip(predictions, KWARGS_TORCH_PREDICTIONS):
+ batch=KEYED_TORCH_EXAMPLES, model=model, extra_kwargs=extra_kwargs)
Review Comment:
ok I've been thinking about this:
I was playing around with ways to make arguments specific to run_inference
and I think there are only three ways. Either what you have done, anonymous
args, or an if statement
if inference_args:
model_handler.run_inference(model, batch, inference_args)
else:
model_handler.run_inference(model, batch)
I'm not sure what I prefer now that I'm looking at it.
The if statement has the advantage of allowing clients that don't expect
this argument to fail or pass without modifcations.
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