mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add
support for TFLite_Detection_PostProcess
URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371276909
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File path: tests/python/frontend/tflite/test_forward.py
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@@ -1113,6 +1113,49 @@ def test_forward_fully_connected():
_test_fully_connected([5, 1, 1, 150], [150, 100], [100])
+#######################################################################
+# Custom Operators
+# -------
+
+def test_detection_postprocess():
+ tf_model_file = tf_testing.get_workload_official(
+ "http://download.tensorflow.org/models/object_detection/";
+ "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz",
Review comment:
This test is a bit misleading because it doesn't actually run ssd mobilenet,
it just test the postprocess op. I couldn't find a way to create the op using
the tflite python API, so what I did instead was take a model that has it and
then run it through the tflite converter but with the converter inputs set to
the inputs of the postprocess op rather than the input to the network.
This has the net effect of producing a single postprocess op, so this should
already be a unit test (and it passes). I can add the end-to-end tests if/when
we resolve the QNN accuracy issue. I'll open an RFC shortly to describe why
rounding is a particularly significant in the case of this operator.
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