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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2021/04/14 23:29:17 UTC
[GitHub] [incubator-mxnet] waytrue17 opened a new pull request #20165: [v1.x] ONNX fix operator batch1
waytrue17 opened a new pull request #20165:
URL: https://github.com/apache/incubator-mxnet/pull/20165
## Description ##
Rewrite `clip` to support more input data type
Rewrite `_rminus_scalar` and `_rdiv_scalar` to fix assertion error
Rewrite `argmax` and `argmin` to fix issue when `axis=None`
Add unittest for 9 operators
## Checklist ##
### Essentials ###
- [ ] PR's title starts with a category (e.g. [BUGFIX], [MODEL], [TUTORIAL], [FEATURE], [DOC], etc)
- [ ] Changes are complete (i.e. I finished coding on this PR)
- [ ] All changes have test coverage
- [ ] Code is well-documented
### Changes ###
- [ ] Feature1, tests, (and when applicable, API doc)
- [ ] Feature2, tests, (and when applicable, API doc)
## Comments ##
- If this change is a backward incompatible change, why must this change be made.
- Interesting edge cases to note here
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[GitHub] [incubator-mxnet] szha merged pull request #20165: [v1.x] ONNX legacy operator fix and test
Posted by GitBox <gi...@apache.org>.
szha merged pull request #20165:
URL: https://github.com/apache/incubator-mxnet/pull/20165
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20165: [v1.x] ONNX fix operator batch1
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20165:
URL: https://github.com/apache/incubator-mxnet/pull/20165#issuecomment-819914305
Hey @waytrue17 , Thanks for submitting the PR
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[GitHub] [incubator-mxnet] Zha0q1 commented on a change in pull request #20165: [v1.x] ONNX fix operator batch1
Posted by GitBox <gi...@apache.org>.
Zha0q1 commented on a change in pull request #20165:
URL: https://github.com/apache/incubator-mxnet/pull/20165#discussion_r614373330
##########
File path: tests/python-pytest/onnx/test_operators.py
##########
@@ -1275,3 +1287,42 @@ def test_onnx_export_contrib_div_sqrt_dim(tmp_path, dtype, shape):
A = mx.nd.random.uniform(-100, 100, shape).astype(dtype)
M = def_model('contrib.div_sqrt_dim')
op_export_test('contrib_div_sqrt_dim', M, [A], tmp_path)
+
+
+# onnxruntime currently does not support int32
+@pytest.mark.parametrize('dtype', ['float16', 'float32', 'int64'])
+@pytest.mark.parametrize('shape', [(1,), (2, 3), (4, 5, 6)])
+def test_onnx_export_clip(tmp_path, dtype, shape):
+ A = mx.nd.random.uniform(-100, 100, shape).astype(dtype)
+ a_min = mx.nd.min(A).astype('float32').asnumpy()[0] + 5
+ a_max = mx.nd.max(A).astype('float32').asnumpy()[0] - 5
+ print(a_min)
+ M = def_model('clip', a_min=a_min, a_max=a_max)
+ op_export_test('clip', M, [A], tmp_path)
+
+
+@pytest.mark.parametrize('dtype', ['float16', 'float32', 'int32', 'int64'])
+@pytest.mark.parametrize('shape', [(3, 4, 5), (6, 7), (8,)])
+@pytest.mark.parametrize('func', [lambda x : x + np.random.rand(1)[0]*100,
+ lambda x : x * np.random.rand(1)[0]*100,
Review comment:
Nice!
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