Force operator Conv to use continuous array

_unittests/ut_onnxrt/test_cpu_ops.py

@@ -55,7 +55,7 @@ def test_cpu_conv_init(self):
         W = numpy.random.rand(24, 96, 1, 1).astype(numpy.float32)
 
         onx = OnnxConv(
-            'X', W, output_names=['Y'],
+            'X', 'W', output_names=['Y'],
             auto_pad='NOTSET', group=1, dilations=[1, 1],
             kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1],
             op_version=get_opset_number_from_onnx())
@@ -80,6 +80,58 @@ def test_cpu_conv_init(self):
                             ii, diff[ii], gotrt['Y'].ravel()[ii], got['Y'].ravel()[ii]))
             self.assertEqualArray(gotrt['Y'], got['Y'], decimal=5)
 
+    def test_cpu_conv_group(self):
+        x = numpy.random.rand(1, 3, 3, 4).astype(numpy.float32)
+        W = numpy.random.rand(9, 1, 3, 3).astype(numpy.float32)
+
+        onx = OnnxConv(
+            'X', 'W', output_names=['Y'],
+            auto_pad='NOTSET', group=3, dilations=[1, 1],
+            kernel_shape=[3, 3], strides=[1, 1],
+            op_version=get_opset_number_from_onnx())
+        model_def = onx.to_onnx({'X': x.astype(numpy.float32),
+                                 'W': W.astype(numpy.float32)},
+                                target_opset=get_opset_number_from_onnx())
+        oinf = OnnxInference(model_def)
+        oinfrt = OnnxInference(model_def, runtime='onnxruntime1')
+        d = oinf.sequence_[-1].ops_.atts_value
+        self.assertIsInstance(d, dict)
+        self.assertEqual(d['kernel_shape'].tolist(), [3, 3])
+
+        xs = [
+            numpy.random.rand(1, 3, 3, 4).astype(numpy.float32),
+            numpy.array([1.0, 4.0, 7.0, 10.0, 13.0, 16.0, 19.0, 22.0, 25.0, 28.0, 31.0,
+                         34.0, 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 20.0, 23.0, 26.0, 29.0,
+                         32.0, 35.0, 3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0, 27.0,
+                         30.0, 33.0, 36.0], dtype=numpy.float32).reshape((1, 3, 3, 4))]
+        Ws = [
+            numpy.random.rand(9, 1, 3, 3).astype(numpy.float32),
+            numpy.array([1.0, 10.0, 19.0, 28.0, 37.0, 46.0, 55.0, 64.0,
+                         73.0, 2.0, 11.0, 20.0, 29.0, 38.0, 47.0, 56.0, 65.0, 74.0,
+                         3.0, 12.0, 21.0, 30.0, 39.0, 48.0, 57.0, 66.0, 75.0, 4.0,
+                         13.0, 22.0, 31.0, 40.0, 49.0, 58.0, 67.0, 76.0, 5.0, 14.0,
+                         23.0, 32.0, 41.0, 50.0, 59.0, 68.0, 77.0, 6.0, 15.0, 24.0,
+                         33.0, 42.0, 51.0, 60.0, 69.0, 78.0, 7.0, 16.0, 25.0, 34.0,
+                         43.0, 52.0, 61.0, 70.0, 79.0, 8.0, 17.0, 26.0, 35.0, 44.0,
+                         53.0, 62.0, 71.0, 80.0, 9.0, 18.0, 27.0, 36.0, 45.0, 54.0,
+                         63.0, 72.0, 81.0], dtype=numpy.float32).reshape((9, 1, 3, 3))]
+
+        for x, W in zip(xs, Ws):
+            x = numpy.asfortranarray(x)
+            W = numpy.asfortranarray(W)
+            got = oinf.run({'X': x, 'W': W})
+            gotrt = oinfrt.run({'X': x, 'W': W})
+            diff = list(numpy.abs((gotrt['Y'] - got['Y']).ravel()))
+            sdiff = list(sorted(diff))
+            if sdiff[-1] > 1e-5:
+                raise AssertionError("runtimes disagree {}".format(sdiff[-5:]))
+            for ii in range(len(diff)):  # pylint: disable=C0200
+                if numpy.isnan(diff[ii]):
+                    raise AssertionError(
+                        "runtimes disagree about nan {}: {} # {} ? {}".format(
+                            ii, diff[ii], gotrt['Y'].ravel()[ii], got['Y'].ravel()[ii]))
+            self.assertEqualArray(gotrt['Y'], got['Y'], decimal=5)
+
 
 if __name__ == "__main__":
     unittest.main()

mlprodict/onnxrt/ops_cpu/_op.py

@@ -274,6 +274,14 @@ def _to_python_numpy(self, inputs, numpy_name):
         return ("import numpy",
                 "return numpy.%s(%s)" % (numpy_name, ", ".join(inputs)))
 
+    @property
+    def atts_value(self):
+        "Returns all parameters in a dictionary."
+        if hasattr(self, 'atts'):
+            return {k: getattr(self, k)
+                    for k in self.atts}  # pylint: disable=E1101
+        return None
+
 
 class OpRunUnary(OpRun):
     """

mlprodict/onnxrt/ops_cpu/op_conv_.cpp

@@ -44,7 +44,9 @@ class Conv {
                   py::array_t<int64_t> pads,
                   py::array_t<int64_t> strides);
 
-        py::array_t<T> compute(py::array_t<T> X, py::array_t<T> W, py::array_t<T> B) const;
+        py::array_t<T> compute(py::array_t<T, py::array::c_style | py::array::forcecast> X,
+                               py::array_t<T, py::array::c_style | py::array::forcecast> W,
+                               py::array_t<T, py::array::c_style | py::array::forcecast> B) const;
 
     private:
 
@@ -117,7 +119,9 @@ void Conv<T>::compute_kernel_shape(const std::vector<int64_t>& weight_shape,
 
 
 template<typename T>
-py::array_t<T> Conv<T>::compute(py::array_t<T> X, py::array_t<T> W, py::array_t<T> B) const {
+py::array_t<T> Conv<T>::compute(py::array_t<T, py::array::c_style | py::array::forcecast> X,
+                                py::array_t<T, py::array::c_style | py::array::forcecast> W,
+                                py::array_t<T, py::array::c_style | py::array::forcecast> B) const {
 
     std::vector<int64_t> x_dims;
     arrayshape2vector(x_dims, X);