Prepare 1.6.5 release. (#366)

* Prepare 1.6.5 release.

* add pytest in dependencies

* disable coverage in CI

* pytest path issue on Windows

* one more try on version.

* debug the CI build issue.

* using python -m pip

* installation

* restore the docs sub-folder.

Author: Wenbing Li

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

@@ -35,15 +35,12 @@ jobs:
       conda install -c conda-forge protobuf
       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 ..
-      pip install -r requirements.txt
-      pip install -r requirements-dev.txt
-      pip install $(ORT_PATH)
-      pip install pytest
+      python -m pip install $(ONNX_PATH)
+      python -m pip install git+https://github.com/microsoft/onnxconverter-common
+      python -m pip install -r requirements.txt
+      python -m pip install -r requirements-dev.txt
+      python -m pip install $(ORT_PATH)
+      python -m pip install pytest
       git clone --recursive https://github.com/cjlin1/libsvm libsvm
       cd libsvm
       make lib

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

@@ -71,15 +71,6 @@ jobs:
       pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
     displayName: 'pytest - onnxmltools'
 
-  - script: |
-      export PYTHONPATH=$PYTHONPATH:libsvm/python
-      python -c "import onnxconverter_common"
-      python -c "import onnxmltools"
-      coverage run --include=onnxmltools/** tests/main.py
-      coverage report -m
-      coverage html
-    displayName: 'coverage'
-
   - task: PublishTestResults@2
     inputs:
       testResultsFiles: '**/test-results.xml'

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

@@ -58,7 +58,7 @@ jobs:
       call activate py$(python.version)
       set PYTHONPATH=libsvm\python;%PYTHONPATH%
       pip install -e .
-      pytest tests  --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
+      python -m pytest tests  --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
     displayName: 'pytest - onnxmltools'
 
   - task: PublishTestResults@2

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

@@ -55,29 +55,28 @@ jobs:
       call activate py$(python.version)
       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 %COREML_PATH% %ONNX_PATH%
+      python -m pip install git+https://github.com/microsoft/onnxconverter-common
       echo Test onnxconverter-common installation... && python -c "import onnxconverter_common"
-      pip install -r requirements.txt
-      pip install -r requirements-dev.txt
-      pip install %ONNXRT_PATH%
+      python -m pip install -r requirements.txt
+      python -m pip install -r requirements-dev.txt
+      python -m pip install %ONNXRT_PATH%
       echo Test onnxruntime installation... && python -c "import onnxruntime"
       REM install libsvm from github
       git clone --recursive https://github.com/cjlin1/libsvm libsvm
       copy libsvm\windows\*.dll libsvm\python
       set PYTHONPATH=libsvm\python;%PYTHONPATH%
       dir libsvm\python
       echo Test libsvm installation... && python -c "import svmutil"
+      echo "debug environment" && path
+      python -m pip show pytest
     displayName: 'Install dependencies'
 
   - script: |
       call activate py$(python.version)
       set PYTHONPATH=libsvm\python;%PYTHONPATH%
-      pip install -e .
-      pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
+      python -m pip install -e .
+      python -m pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
     displayName: 'pytest - onnxmltools'
 
   - task: PublishTestResults@2

.gitignore

@@ -25,6 +25,7 @@ build/
 # test generated files
 .pytest_cache
 .cache
+tests/temp
 tests/utils/models/coreml_OneHotEncoder_BikeSharing_new.json
 tests/utils/models/coreml_OneHotEncoder_BikeSharing2.onnx
 tests/baseline/outmodels

README.md

@@ -16,6 +16,7 @@ ONNXMLTools enables you to convert models from different machine learning toolki
 * libsvm
 * XGBoost
 * H2O
+<p>Pytorch has its builtin ONNX exporter check <a href="https://pytorch.org/docs/stable/onnx.html">here</a>  for details</p>
 
 ## Install
 You can install latest release of ONNXMLTools from [PyPi](https://pypi.org/project/onnxmltools/):
@@ -96,11 +97,6 @@ onnx_model = onnxmltools.convert_coreml(coreml_model, 'Example Model')
 onnxmltools.utils.save_model(onnx_model, 'example.onnx')
 ```
 
-## Spark ML to ONNX Conversion (experimental)
-Please refer to the following documents:
- * [Conversion Framework](onnxmltools/convert/README.md)
- * [Spark ML to ONNX Model Conversion](onnxmltools/convert/sparkml/README.md)
-
 ## H2O to ONNX Conversion
 Below is a code snippet to convert a H2O MOJO model into an ONNX model. The only pre-requisity is to have a MOJO model saved on the local file-system.
 
@@ -136,17 +132,12 @@ Documentation for the [ONNX Model format](https://github.com/onnx/onnx) and more
 
 ## Test all existing converters
 
-There exists a way
-to automatically check every converter with
+All converter unit test can generate the original model and converted model to automatically be checked with
 [onnxruntime](https://pypi.org/project/onnxruntime/) or
 [onnxruntime-gpu](https://pypi.org/project/onnxruntime-gpu/).
-This process requires the user to clone the *onnxmltools* repository.
-The following command runs all unit tests and generates
-dumps of models, inputs, expected outputs and converted models
-in folder ``TESTDUMP``.
-
+The unit test cases are all the normal python unit test cases, you can run it with pytest command line, for example:
 ```
-python tests/main.py DUMP
+python -m pytest --ignore .\tests\
 ```
 
 It requires *onnxruntime*, *numpy* for most models,

docs/index.rst

@@ -55,7 +55,7 @@ to automatically check every converter with
     from onnxmltools import convert_sklearn
     from onnxmltools.utils import save_model
     from onnxconverter_common.data_types import FloatTensorType
-    initial_type = [('float_input', FloatTensorType([-1, 4]))]
+    initial_type = [('float_input', FloatTensorType([1, 4]))]
     onx = convert_sklearn(clr, initial_types=initial_type)
     save_model(onx, "rf_iris.onnx")
 

docs/tutorial.rst

@@ -54,7 +54,7 @@ to convert other model formats into ONNX. Here we will use
     from onnxmltools.utils import save_model
     from onnxmltools.convert.common.data_types import FloatTensorType
 
-    initial_type = [('float_input', FloatTensorType([-1, 4]))]
+    initial_type = [('float_input', FloatTensorType([1, 4]))]
     onx = convert_sklearn(clr, initial_types=initial_type)
     save_model(onx, "logreg_iris.onnx")
 
@@ -67,9 +67,7 @@ for this machine learning model.
 ::
 
     import onnxruntime as rt
-    import numpy
     sess = rt.InferenceSession("logreg_iris.onnx")
-    label_name = sess.get_outputs()[0].name
     input_name = sess.get_inputs()[0].name
 
     pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]

onnxmltools/__init__.py

@@ -9,7 +9,7 @@
 This framework converts any machine learned model into onnx format
 which is a common language to describe any machine learned model.
 """
-__version__ = "1.6.0"
+__version__ = "1.6.5"
 __author__ = "Microsoft"
 __producer__ = "OnnxMLTools"
 __producer_version__ = __version__

onnxmltools/convert/main.py

@@ -7,9 +7,11 @@
 from ..proto import onnx
 from .common import utils
 import warnings
+import importlib
+
 
 def convert_coreml(model, name=None, initial_types=None, doc_string='', target_opset=None,
-                   targeted_onnx=onnx.__version__ , custom_conversion_functions=None, custom_shape_calculators=None):
+                   targeted_onnx=onnx.__version__, custom_conversion_functions=None, custom_shape_calculators=None):
     if not utils.coreml_installed():
         raise RuntimeError('coremltools is not installed. Please install coremltools to use this feature.')
 
@@ -33,7 +35,7 @@ def convert_keras(model, name=None, initial_types=None, doc_string='',
 
 
 def convert_libsvm(model, name=None, initial_types=None, doc_string='', target_opset=None,
-                     targeted_onnx=onnx.__version__, custom_conversion_functions=None, custom_shape_calculators=None):
+                   targeted_onnx=onnx.__version__, custom_conversion_functions=None, custom_shape_calculators=None):
     if not utils.libsvm_installed():
         raise RuntimeError('libsvm is not installed. Please install libsvm to use this feature.')
 
@@ -62,7 +64,8 @@ def convert_sklearn(model, name=None, initial_types=None, doc_string='', target_
 
     from skl2onnx.convert import convert_sklearn as convert_skl2onnx
     return convert_skl2onnx(model, name, initial_types, doc_string, target_opset,
-                   custom_conversion_functions, custom_shape_calculators)
+                            custom_conversion_functions, custom_shape_calculators)
+
 
 def convert_sparkml(model, name=None, initial_types=None, doc_string='', target_opset=None,
                     targeted_onnx=onnx.__version__, custom_conversion_functions=None,
@@ -74,18 +77,6 @@ def convert_sparkml(model, name=None, initial_types=None, doc_string='', target_
     return convert(model, name, initial_types, doc_string, target_opset, targeted_onnx,
                    custom_conversion_functions, custom_shape_calculators, spark_session)
 
-def convert_tensorflow(frozen_graph_def,
-                       name=None, input_names=None, output_names=None,
-                       doc_string='',
-                       target_opset=None,
-                       channel_first_inputs=None,
-                       debug_mode=False, custom_op_conversions=None):
-    if not utils.keras2onnx_installed():
-        raise RuntimeError('keras2onnx is not installed. Please install it to use this feature.')
-
-    from keras2onnx import convert_tensorflow as convert
-    return convert(frozen_graph_def, name, input_names, output_names, doc_string,
-                   target_opset, channel_first_inputs, debug_mode, custom_op_conversions)
 
 def convert_xgboost(*args, **kwargs):
     if not utils.xgboost_installed():
@@ -94,9 +85,91 @@ def convert_xgboost(*args, **kwargs):
     from .xgboost.convert import convert
     return convert(*args, **kwargs)
 
+
 def convert_h2o(*args, **kwargs):
     if not utils.h2o_installed():
         raise RuntimeError('h2o is not installed. Please install h2o to use this feature.')
 
     from .h2o.convert import convert
     return convert(*args, **kwargs)
+
+
+def _collect_input_nodes(graph, outputs):
+    nodes_to_keep = set()
+    input_nodes = set()
+    node_inputs = [graph.get_tensor_by_name(ts_).op for ts_ in outputs]
+    while node_inputs:
+        nd_ = node_inputs[0]
+        del node_inputs[0]
+        if nd_.type in ['Placeholder', "PlaceholderV2", 'PlaceholderWithDefault']:
+            input_nodes.add(nd_)
+        if nd_ in nodes_to_keep:
+            continue
+
+        nodes_to_keep.add(nd_)
+        node_inputs.extend(in_.op for in_ in nd_.inputs)
+
+    return input_nodes, nodes_to_keep
+
+
+def _convert_tf_wrapper(frozen_graph_def,
+                        name=None, input_names=None, output_names=None,
+                        doc_string='',
+                        target_opset=None,
+                        channel_first_inputs=None,
+                        debug_mode=False, custom_op_conversions=None):
+    """
+    convert a tensorflow graph def into a ONNX model proto, just like how keras does.
+    :param graph_def: the frozen tensorflow graph
+    :param name: the converted onnx model internal name
+    :param input_names: the inputs name list of the model
+    :param output_names: the output name list of the model
+    :param doc_string: doc string
+    :param target_opset: the targeted onnx model opset
+    :param channel_first_inputs: A list of channel first input (not supported yet)
+    :param debug_mode: will enable the log and try to convert as much as possible on conversion
+    :return an ONNX ModelProto
+    """
+    import tensorflow as tf
+    import tf2onnx
+
+    if target_opset is None:
+        target_opset = onnx.defs.onnx_opset_version()
+
+    if not doc_string:
+        doc_string = "converted from {}".format(name)
+
+    tf_graph_def = tf2onnx.tfonnx.tf_optimize(input_names, output_names, frozen_graph_def, True)
+    with tf.Graph().as_default() as tf_graph:
+        tf.import_graph_def(tf_graph_def, name='')
+
+        if not input_names:
+            input_nodes = list(_collect_input_nodes(tf_graph, output_names)[0])
+            input_names = [nd_.outputs[0].name for nd_ in input_nodes]
+        g = tf2onnx.tfonnx.process_tf_graph(tf_graph,
+                                            continue_on_error=debug_mode,
+                                            opset=target_opset,
+                                            custom_op_handlers=custom_op_conversions,
+                                            inputs_as_nchw=channel_first_inputs,
+                                            output_names=output_names,
+                                            input_names=input_names)
+
+        onnx_graph = tf2onnx.optimizer.optimize_graph(g)
+        model_proto = onnx_graph.make_model(doc_string)
+
+    return model_proto
+
+
+def convert_tensorflow(frozen_graph_def,
+                       name=None, input_names=None, output_names=None,
+                       doc_string='',
+                       target_opset=None,
+                       channel_first_inputs=None,
+                       debug_mode=False, custom_op_conversions=None):
+    try:
+        importlib.import_module('tf2onnx')
+    except (ImportError, ModuleNotFoundError) as e:
+        raise RuntimeError('tf2onnx is not installed, please install it before calling this function.')
+
+    return _convert_tf_wrapper(frozen_graph_def, name, input_names, output_names, doc_string,
+                               target_opset, channel_first_inputs, debug_mode, custom_op_conversions)