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Posted to commits@mxnet.apache.org by la...@apache.org on 2020/12/14 20:48:30 UTC
[incubator-mxnet] branch v1.x updated: Improve onnx test suite
(#19662)
This is an automated email from the ASF dual-hosted git repository.
lausen pushed a commit to branch v1.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.x by this push:
new a87d3f4 Improve onnx test suite (#19662)
a87d3f4 is described below
commit a87d3f41faff2f7e60bb4e60f068dc7351d336db
Author: Zhaoqi Zhu <zh...@gmail.com>
AuthorDate: Mon Dec 14 12:47:16 2020 -0800
Improve onnx test suite (#19662)
---
ci/docker/install/ubuntu_onnx.sh | 2 +-
tests/python-pytest/onnx/test_operators.py | 142 ++++++++++++-----------------
2 files changed, 58 insertions(+), 86 deletions(-)
diff --git a/ci/docker/install/ubuntu_onnx.sh b/ci/docker/install/ubuntu_onnx.sh
index 31eb5e8..3fbc167 100755
--- a/ci/docker/install/ubuntu_onnx.sh
+++ b/ci/docker/install/ubuntu_onnx.sh
@@ -31,4 +31,4 @@ apt-get update || true
apt-get install -y libprotobuf-dev protobuf-compiler
echo "Installing pytest, pytest-cov, protobuf, Pillow, ONNX, tabulate and onnxruntime..."
-pip3 install pytest==3.6.3 pytest-cov==2.5.1 protobuf==3.5.2 onnx==1.7.0 Pillow==5.0.0 tabulate==0.7.5 onnxruntime==1.4.0
+pip3 install pytest pytest-cov protobuf==3.5.2 onnx==1.7.0 Pillow==5.0.0 tabulate==0.7.5 onnxruntime==1.4.0
diff --git a/tests/python-pytest/onnx/test_operators.py b/tests/python-pytest/onnx/test_operators.py
index aa66858..22ba237 100644
--- a/tests/python-pytest/onnx/test_operators.py
+++ b/tests/python-pytest/onnx/test_operators.py
@@ -23,17 +23,32 @@ from mxnet.test_utils import assert_almost_equal
import pytest
import tempfile
-def op_export_test(op_name, Model, inputs, tmp_path):
- def export_to_onnx(model, op_name, inputs):
- model_path = '{}/{}'.format(tmp_path, op_name)
+def def_model(op_name, **params):
+ class Model(HybridBlock):
+ def __init__(self, **kwargs):
+ super(Model, self).__init__(**kwargs)
+
+ def hybrid_forward(self, F, *inputs):
+ names = op_name.split('.')
+ func = F
+ for name in names:
+ func = getattr(func, name)
+ out = func(*inputs, **params)
+ return out
+ return Model
+
+def op_export_test(model_name, Model, inputs, tmp_path):
+ def export_to_onnx(model, model_name, inputs):
+ model_path = '{}/{}'.format(tmp_path, model_name)
model.export(model_path, epoch=0)
sym_file = '{}-symbol.json'.format(model_path)
params_file = '{}-0000.params'.format(model_path)
dtype = inputs[0].dtype
- onnx_file = '{}/{}.onnx'.format(tmp_path, op_name)
- mx.contrib.onnx.export_model(sym_file, params_file, [i.shape for i in inputs],
+ onnx_file = '{}/{}.onnx'.format(tmp_path, model_name)
+ mx.contrib.onnx.export_model(sym_file, params_file, [inp.shape for inp in inputs],
dtype, onnx_file)
return onnx_file
+
def onnx_rt(onnx_file, inputs):
sess = rt.InferenceSession(onnx_file)
input_dict = dict((sess.get_inputs()[i].name, inputs[i].asnumpy()) for i in range(len(inputs)))
@@ -45,90 +60,47 @@ def op_export_test(op_name, Model, inputs, tmp_path):
model.initialize(ctx=mx.cpu(0))
model.hybridize()
pred_nat = model(*inputs)
- onnx_file = export_to_onnx(model, op_name, inputs)
+ onnx_file = export_to_onnx(model, model_name, inputs)
pred_onx = onnx_rt(onnx_file, inputs)
assert_almost_equal(pred_nat, pred_onx)
-def test_onnx_export_abs():
- with tempfile.TemporaryDirectory() as tmp_path:
- class Model(HybridBlock):
- def __init__(self, **kwargs):
- super(Model, self).__init__(**kwargs)
- def hybrid_forward(self, F, x):
- out = F.abs(x)
- return out
- x = mx.nd.array([[-2, -1], [0, 99]], dtype='float32')
- op_export_test('abs', Model, [x], tmp_path)
-
-def test_onnx_export_slice():
- with tempfile.TemporaryDirectory() as tmp_path:
- class Model(HybridBlock):
- def __init__(self, **kwargs):
- super(Model, self).__init__(**kwargs)
- def hybrid_forward(self, F, x):
- out = F.slice(x, begin=(0,1), end=(2,4))
- return out
- x = mx.nd.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]], dtype='float32')
- op_export_test('slice', Model, [x], tmp_path)
-
-def test_onnx_export_stack():
- with tempfile.TemporaryDirectory() as tmp_path:
- dtype = 'float32'
- class Model(HybridBlock):
- def __init__(self, **kwargs):
- super(Model, self).__init__(**kwargs)
- def hybrid_forward(self, F, x, y):
- out = F.stack(x, y)
- return out
- x = mx.nd.array([1, 2], dtype=dtype)
- y = mx.nd.array([3, 4], dtype=dtype)
- op_export_test('stack', Model, [x, y], tmp_path)
-
-def test_onnx_export_zeros_like():
- with tempfile.TemporaryDirectory() as tmp_path:
- class Model(HybridBlock):
- def __init__(self, **kwargs):
- super(Model, self).__init__(**kwargs)
- def hybrid_forward(self, F, x):
- out = F.zeros_like(x)
- return out
- x = mx.nd.array([[-2,-1,0],[0,50,99],[4,5,6],[7,8,9]], dtype='float32')
- op_export_test('zeros_like', Model, [x], tmp_path)
+def test_onnx_export_abs(tmp_path):
+ M = def_model('abs')
+ x = mx.nd.array([[-2, -1], [0, 99]], dtype='float32')
+ op_export_test('abs', M, [x], tmp_path)
+
+
+def test_onnx_export_slice(tmp_path):
+ M = def_model('slice', begin=(0,1), end=(2,4))
+ x = mx.nd.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]], dtype='float32')
+ op_export_test('slice', M, [x], tmp_path)
+
+
+def test_onnx_export_stack(tmp_path):
+ M = def_model('stack')
+ x = mx.nd.array([1, 2], dtype='float32')
+ y = mx.nd.array([3, 4], dtype='float32')
+ op_export_test('stack', M, [x, y], tmp_path)
+
+
+def test_onnx_export_zeros_like(tmp_path):
+ M = def_model('zeros_like')
+ x = mx.nd.array([[-2,-1,0],[0,50,99],[4,5,6],[7,8,9]], dtype='float32')
+ op_export_test('zeros_like', M, [x], tmp_path)
+
@pytest.mark.parametrize("dtype", ["float32", "double"])
-def test_onnx_export_arange_like(dtype):
- with tempfile.TemporaryDirectory() as tmp_path:
- class Model(HybridBlock):
- def __init__(self, **kwargs):
- super(Model, self).__init__(**kwargs)
- def hybrid_forward(self, F, x):
- out = F.contrib.arange_like(x)
- return out
- x = mx.nd.array([[-2,-1,0],[0,50,99],[4,5,6],[7,8,9]], dtype=dtype)
- op_export_test('arange_like', Model, [x], tmp_path)
-
-def test_onnx_export_layernorm():
- with tempfile.TemporaryDirectory() as tmp_path:
- dtype = 'float32'
- class Model(HybridBlock):
- def __init__(self, **kwargs):
- super(Model, self).__init__(**kwargs)
- def hybrid_forward(self, F, x, gamma, beta):
- out = F.LayerNorm(x, gamma, beta, axis=1)
- return out
- x = mx.nd.array([[1,3],[2,4]], dtype=dtype)
- gamma = mx.random.uniform(0, 1, x[0].shape).astype(dtype)
- beta = mx.random.uniform(0, 1, x[0].shape).astype(dtype)
- op_export_test('LayerNorm', Model, [x, gamma, beta], tmp_path)
-
-
-if __name__ == '__main__':
- test_onnx_export_abs()
- test_onnx_export_slice()
- test_onnx_export_stack()
- test_onnx_export_zeros_like()
- test_onnx_export_arange_like('float32')
- test_onnx_export_arange_like('double')
- test_onnx_export_layernorm()
+def test_onnx_export_arange_like(tmp_path, dtype):
+ M = def_model('contrib.arange_like')
+ x = mx.nd.array([[-2,-1,0],[0,50,99],[4,5,6],[7,8,9]], dtype=dtype)
+ op_export_test('arange_like', M, [x], tmp_path)
+
+
+def test_onnx_export_layernorm(tmp_path):
+ M = def_model('LayerNorm', axis=1)
+ x = mx.nd.array([[1,3],[2,4]], dtype='float32')
+ gamma = mx.random.uniform(0, 1, x[0].shape, dtype='float32')
+ beta = mx.random.uniform(0, 1, x[0].shape, dtype='float32')
+ op_export_test('LayerNorm', M, [x, gamma, beta], tmp_path)