Fix ImageScaler bias for opset 10 (#322) (#325)

Author: jiafatom

README.md

@@ -7,16 +7,15 @@
 
 # Introduction 
 ONNXMLTools enables you to convert models from different machine learning toolkits into [ONNX](https://onnx.ai). Currently the following toolkits are supported:
+* Keras (a wrapper of [keras2onnx converter](https://github.com/onnx/keras-onnx/))
+* Tensorflow (a wrapper of [tf2onnx converter](https://github.com/onnx/tensorflow-onnx/))
+* scikit-learn (a wrapper of [skl2onnx converter](https://github.com/onnx/sklearn-onnx/))
 * Apple Core ML
-* scikit-learn (subset of models convertible to ONNX)
-* Keras
 * Spark ML (experimental)
 * LightGBM
 * libsvm
 * XGBoost
 
-To convert Tensorflow models to ONNX, see [tensorflow-onnx](https://github.com/onnx/tensorflow-onnx).
-
 ## Install
 You can install latest release of ONNXMLTools from [PyPi](https://pypi.org/project/onnxmltools/):
 ```

onnxmltools/__init__.py

@@ -22,6 +22,7 @@
 from .convert import convert_lightgbm
 from .convert import convert_sklearn
 from .convert import convert_sparkml
+from .convert import convert_tensorflow
 from .convert import convert_xgboost
 
 from .utils import load_model

onnxmltools/convert/__init__.py

@@ -10,4 +10,5 @@
 from .main import convert_lightgbm
 from .main import convert_sklearn
 from .main import convert_sparkml
+from .main import convert_tensorflow
 from .main import convert_xgboost

onnxmltools/convert/main.py

@@ -74,6 +74,19 @@ 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():
         raise RuntimeError('xgboost is not installed. Please install xgboost to use this feature.')