def test_onnxt_runtime_cdist(self):
for metric in ['sqeuclidean', 'euclidean']:
with self.subTest(metric=metric):
X = numpy.array([[2, 1], [0, 1]], dtype=float)
Y = numpy.array([[2, 1, 5], [0, 1, 3]], dtype=float).T
Z = cdist(X, Y, metric=metric)
onx = OnnxCDist('X',
'Y',
output_names=['Z'],
metric=metric,
op_version=get_opset_number_from_onnx())
model_def = onx.to_onnx(
{
'X': X.astype(numpy.float32),
'Y': Y.astype(numpy.float32)
},
outputs={'Z': Z.astype(numpy.float32)},
target_opset=get_opset_number_from_onnx())
self.assertIn('s: "%s"' % metric, str(model_def))
oinf = OnnxInference(model_def)
got = oinf.run({'X': X, 'Y': Y})
self.assertEqual(list(sorted(got)), ['Z'])
self.assertEqualArray(Z, got['Z'], decimal=6)
python_tested.append(OnnxCDist)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy, {
'X': X.astype(numpy.float32),
'Y': Y.astype(numpy.float32)
},
tolerance=1e-6)
import onnxruntime as rt
from onnxruntime import InferenceSession
import skl2onnx
from skl2onnx.algebra.custom_ops import OnnxCDist
from skl2onnx.common.data_types import FloatTensorType
X = np.ones((2, 4), dtype=np.float32)
Y = np.ones((3, 4), dtype=np.float32)
Y *= 2
print(cdist(X, Y, metric='euclidean'))
####################################
# ONNX
op = OnnxCDist('X', 'Y', op_version=12, output_names=['Z'], metric='euclidean')
onx = op.to_onnx({'X': X, 'Y': Y}, outputs=[('Z', FloatTensorType())])
print(onx)
########################################
# CDist and onnxruntime
# +++++++++++++++++++++
#
# We compute the output of CDist operator
# with onnxruntime.
sess = InferenceSession(onx.SerializeToString())
res = sess.run(None, {'X': X, 'Y': Y})
print(res)
#####################################
# Benchmark