add tf2onnx wrapper test. (#367)

* add tf2onnx wrapper test.

* install tf 1.15.0

* install tf2onnx

.azure-pipelines/linux-CI-nightly.yml

@@ -36,7 +36,10 @@ jobs:
       conda install -c conda-forge numpy
       conda install -c conda-forge cmake
       python -m pip install $(ONNX_PATH)
+      test '$(python.version)' != '2.7' && python -m pip install tensorflow-cpu==1.15.0
+      python -m pip install tf2onnx
       python -m pip install git+https://github.com/microsoft/onnxconverter-common
+      python -m pip install git+https://github.com/onnx/keras-onnx
       python -m pip install -r requirements.txt
       python -m pip install -r requirements-dev.txt
       python -m pip install $(ORT_PATH)

.azure-pipelines/linux-conda-CI.yml

@@ -50,10 +50,10 @@ jobs:
       conda install -c conda-forge numpy
       conda install -c conda-forge cmake
       pip install $(ONNX_PATH)
-      git clone https://github.com/microsoft/onnxconverter-common
-      cd onnxconverter-common
-      pip install -e .
-      cd ..
+      test '$(python.version)' != '2.7' && python -m pip install tensorflow-cpu==1.15.0
+      python -m pip install tf2onnx
+      python -m pip install git+https://github.com/microsoft/onnxconverter-common
+      python -m pip install git+https://github.com/onnx/keras-onnx
       pip install -r requirements.txt
       pip install -r requirements-dev.txt
       test '$(python.version)' != '2.7' && pip install $(ONNXRT_PATH)

.azure-pipelines/win32-CI-nightly.yml

@@ -37,10 +37,10 @@ jobs:
       python -m pip install --upgrade pip numpy
       echo Test numpy installation... && python -c "import numpy"
       pip install %COREML_PATH% %ONNX_PATH%
-      git clone https://github.com/microsoft/onnxconverter-common
-      cd onnxconverter-common
-      pip install -e .
-      cd ..
+      python -m pip install tensorflow-cpu==1.15.0
+      python -m pip install tf2onnx
+      python -m pip install git+https://github.com/microsoft/onnxconverter-common
+      python -m pip install git+https://github.com/onnx/keras-onnx
       echo Test onnxconverter-common installation... && python -c "import onnxconverter_common"
       pip install -r requirements.txt
       pip install -r requirements-dev.txt

.azure-pipelines/win32-conda-CI.yml

@@ -56,7 +56,10 @@ jobs:
       python -m pip install --upgrade pip numpy
       echo Test numpy installation... && python -c "import numpy"
       python -m pip install %COREML_PATH% %ONNX_PATH%
+      python -m pip install tensorflow-cpu==1.15.0
+      python -m pip install tf2onnx
       python -m pip install git+https://github.com/microsoft/onnxconverter-common
+      python -m pip install git+https://github.com/onnx/keras-onnx
       echo Test onnxconverter-common installation... && python -c "import onnxconverter_common"
       python -m pip install -r requirements.txt
       python -m pip install -r requirements-dev.txt

tests/utils/test_utils.py

@@ -2,7 +2,9 @@
 Tests utilities.
 """
 import os
+import six
 import unittest
+import onnxmltools
 from onnxmltools.utils import load_model, save_model
 from onnxmltools.utils import set_model_version, set_model_domain, set_model_doc_string
 
@@ -53,5 +55,21 @@ def test_set_docstring_blank(self):
         self.assertEqual(onnx_model.doc_string, "")
 
 
+@unittest.skipIf(six.PY2, "Keras and Tensorflow converter not support python 2.x")
+class TestWrapper(unittest.TestCase):
+
+    def test_keras_with_tf2onnx(self):
+        import keras2onnx
+        from keras2onnx.proto import keras
+        from keras2onnx.proto.tfcompat import is_tf2
+        if not is_tf2:  # tf2onnx is not available for tensorflow 2.0 yet.
+            model = keras.Sequential()
+            model.add(keras.layers.Dense(units=4, input_shape=(10,), activation='relu'))
+            model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['binary_accuracy'])
+            graph_def = keras2onnx.export_tf_frozen_graph(model)
+            onnx_model = onnxmltools.convert_tensorflow(graph_def, **keras2onnx.build_io_names_tf2onnx(model))
+            self.assertTrue(len(onnx_model.graph.node) > 0)
+
+
 if __name__ == "__main__":
     unittest.main()