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Pass onnx model output directly to post_inference request handler #476

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Merged
merged 1 commit into from
Sep 19, 2019
Merged

Pass onnx model output directly to post_inference request handler #476

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2 changes: 1 addition & 1 deletion examples/iris-classifier/handlers/pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,5 +15,5 @@ def pre_inference(sample, metadata):


def post_inference(prediction, metadata):
predicted_class_id = int(np.argmax(prediction[0][0]))
predicted_class_id = int(np.argmax(prediction[0].squeeze()))
return labels[predicted_class_id]
14 changes: 4 additions & 10 deletions pkg/workloads/cortex/onnx_serve/api.py
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Expand Up @@ -192,19 +192,13 @@ def predict(app_name, api_name):
) from e

inference_input = convert_to_onnx_input(prepared_sample, input_metadata)
model_outputs = sess.run([], inference_input)
result = []
for model_output in model_outputs:
if type(model_output) is np.ndarray:
result.append(model_output.tolist())
else:
result.append(model_output)

debug_obj("inference", result, debug)
model_output = sess.run([], inference_input)

debug_obj("inference", model_output, debug)
result = model_output
if request_handler is not None and util.has_function(request_handler, "post_inference"):
try:
result = request_handler.post_inference(result, output_metadata)
result = request_handler.post_inference(model_output, output_metadata)
except Exception as e:
raise UserRuntimeException(
api["request_handler"], "post_inference request handler", str(e)
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