Adds an example to check export issues (#447)

.gitignore

@@ -325,3 +325,9 @@ mlprodict/npy/_cache/*.rst
 *.so
 *.dylib
 *.dll
+_doc/examples/cpp/*
+_doc/examples/numpy/*
+_doc/examples/onnx/*
+_doc/examples/python/*
+_doc/examples/tf2onnx/*
+_doc/examples/xop/*

_doc/examples/plot_export_onnx_tests.py

@@ -0,0 +1,94 @@
+"""
+.. _l-export-onnx-test:
+
+Walk through all methods to export an ONNX model
+================================================
+
+An ONNX model can be exported into many formats
+(see :ref:`l-api-export-onnx`). This example checks the
+availibility through all onnx examples and all formats.
+
+.. contents::
+    :local:
+
+"""
+import os
+import numpy
+from pandas import DataFrame
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from mlprodict.testing.onnx_backend import enumerate_onnx_tests
+from mlprodict.onnx_tools.onnx_export import (
+    export2onnx, export2tf2onnx, export2xop,
+    export2python, export2numpy, export2cpp)
+
+#####################################
+# Load the tests
+# ++++++++++++++
+
+tests = []
+for test in tqdm(enumerate_onnx_tests('node')):
+    tests.append(test)
+
+#####################################
+# Code
+# ++++
+
+conv = dict(onnx=export2onnx,
+            tf2onnx=export2tf2onnx,
+            xop=export2xop,
+            python=export2python,
+            numpy=export2numpy,
+            cpp=export2cpp)
+
+for fmt in conv:
+    if not os.path.exists(fmt):
+        os.mkdir(fmt)
+
+
+data = []
+for test in tqdm(tests):
+    for fmt, fct in conv.items():
+        onx = test.onnx_model
+        ext = ".cpp" if 'cpp' in fmt else ".py"
+        try:
+            code = fct(onx)
+            error = ""
+        except Exception as e:
+            error = str(e)
+            code = None
+        obs = dict(name=test.name, format=fmt, error=error,
+                   ok=1 if error == "" else 0, code=code)
+        data.append(obs)
+        if code is not None:
+            filename = os.path.join(fmt, test.name + ext)
+            with open(filename, "w", encoding="utf-8") as f:
+                f.write(code)
+
+
+#####################################
+# Status and summary
+# ++++++++++++++++++
+
+df = DataFrame(data)
+summary = df.pivot("name", "format", "ok").mean(axis=0).T
+print(summary)
+
+
+#####################################
+# Graph
+# +++++
+
+summary.plot.bar(title="Conversion coverage")
+
+
+#####################################
+# Errors
+# ++++++
+
+for obs in data:
+    if obs['error'] != '':
+        print("%s | %s | %s" % (obs['name'], obs['format'], obs['error']))
+
+
+# plt.show()

_doc/sphinxdoc/source/api/tools.rst

@@ -18,15 +18,21 @@ Accessor
 
 .. autosignature:: mlprodict.onnx_tools.onnx_tools.insert_node
 
-Export
-++++++
+.. _l-api-export-onnx:
+
+Export from onnx to...
+++++++++++++++++++++++
 
 .. autosignature:: mlprodict.onnx_tools.onnx_export.export2numpy
 
 .. autosignature:: mlprodict.onnx_tools.onnx_export.export2onnx
 
+.. autosignature:: mlprodict.onnx_tools.onnx_export.export2python
+
 .. autosignature:: mlprodict.onnx_tools.onnx_export.export2tf2onnx
 
+.. autosignature:: mlprodict.onnx_tools.onnx_export.export2xop
+
 Graphs helper, manipulations
 ++++++++++++++++++++++++++++
 
@@ -115,6 +121,13 @@ Validation of scikit-learn models
 
 .. autosignature:: mlprodict.onnxrt.validate.validate_summary.summary_report
 
+Testing
++++++++
+
+.. autosignature:: mlprodict.testing.onnx_backend.enumerate_onnx_tests
+
+.. autosignature:: mlprodict.testing.onnx_backend.OnnxBackendTest
+
 Visualization
 +++++++++++++
 
@@ -167,6 +180,11 @@ Versions
 
 .. autosignature:: mlprodict.__max_supported_opsets__
 
+skl2onnx
+========
+
+.. autosignature:: mlprodict.onnx_tools.exports.skl2onnx_helper.add_onnx_graph
+
 Type conversion
 ===============
 
@@ -269,8 +287,3 @@ The last example summarizes all the possibilities.
     print()
     for e in errors:
         print(e)
-
-skl2onnx
-========
-
-.. autosignature:: mlprodict.onnx_tools.exports.skl2onnx_helper.add_onnx_graph

mlprodict/onnx_tools/exports/numpy_helper.py

@@ -250,6 +250,11 @@ def _make_numpy_code_onnx(self):
             return "%s = %s %s" % (
                 outs, self.inputs[0], unary_ops_[self.op_type])
 
+        if self.op_type in {'Abs', 'Ceil', 'Cos', 'Cosh',
+                            'Exp', 'Log', 'Sin', 'Sinh',
+                            'Tan', 'Tanh'}:
+            return "%s = numpy.%s(%s)" % (outs, self.op_type.lower(), self.inputs[0])
+
         if self.op_type == 'ArgMax':
             self._make_sure_opsets(12)
             self._make_sure_inputs(1)
@@ -305,9 +310,6 @@ def _make_numpy_code_onnx(self):
             return "%s = numpy.full(%s, %s)" % (
                 outs, shape, value)
 
-        if self.op_type == 'Exp':
-            return "%s = numpy.exp(%s)" % (outs, self.inputs[0])
-
         if self.op_type == 'Max':
             return "%s = numpy.maximum(%s)" % (outs, ", ".join(self.inputs))
 

mlprodict/onnx_tools/onnx_export.py

@@ -87,7 +87,8 @@ def _nodes(graph, rename_name, used, output_names, use_onnx_tensor,
                     use_onnx_tensor=use_onnx_tensor,
                     autopep_options=autopep_options,
                     function_name=fname)
-                subgraphs.append((body, node.op_type + "_" + node.name + "_body"))
+                subgraphs.append(
+                    (body, node.op_type + "_" + node.name + "_body"))
                 attributes.append((at.name, fname + "()"))
                 continue
             if node.op_type == 'If' and at.name in {'then_branch', 'else_branch'}:
@@ -274,7 +275,7 @@ def _python_make_node(onnx_node, version, indent=0):
     name = _python_make_node_name(
         node.domain, version, node.op_type, node=True)
     attributes_str = _python_make_node_make_attribute_str(node)
-    if len(node.input) > 0:
+    if len(node.input) > 0 and len(attributes_str) > 0:
         attributes_str = ", " + attributes_str
     output = ", ".join(node.output)
     text = [sindent, output, " = ", name,

mlprodict/testing/onnx_backend.py

@@ -282,7 +282,8 @@ def enumerate_onnx_tests(series, fct_filter=None):
     Works as an enumerator to start processing them
     without waiting or storing too much of them.
 
-    :param series: which subfolder to load
+    :param series: which subfolder to load, possible values:
+        (`'node'`, ...)
     :param fct_filter: function `lambda testname: boolean`
         to load or skip the test, None for all
     :return: list of @see cl OnnxBackendTest