def test_algebra_to_onnx(self):
X = numpy.random.randn(5, 4)
beta = numpy.array([1, 2, 3, 4]) / 10
beta32 = beta.astype(numpy.float32)
onnxExpM = OnnxExp(OnnxMatMul('X', beta32))
cst = numpy.ones((1, 3), dtype=numpy.float32)
onnxExpM1 = OnnxAdd(onnxExpM, cst)
onnxPred = OnnxDiv(onnxExpM, onnxExpM1)
inputs = {'X': X[:1].astype(numpy.float32)}
model_onnx = onnxPred.to_onnx(inputs)
s1 = str(model_onnx)
model_onnx = onnxPred.to_onnx(inputs)
s2 = str(model_onnx)
assert s1 == s2
def _rewrite_op_no_grad(onx):
"""
Rewrites operators with no gradient.
"""
set_types = set(n.op_type for n in onx.graph.node)
if "Reciprocal" in set_types:
from skl2onnx.algebra.onnx_ops import OnnxDiv # pylint: disable=E0611
from skl2onnx.common.data_types import FloatTensorType
from .onnx_rewriter import onnx_rewrite_operator
opset = None
for op in onx.opset_import:
if op.domain in ('', 'ai.onnx'):
opset = op.version
if opset is None: # pragma: no cover
from .. import get_max_opset
opset = get_max_opset()
node = OnnxDiv(numpy.array([1], dtype=numpy.float32),
'X',
output_names=['Y'],
op_version=opset)
rewrite_onx = node.to_onnx(inputs={'X': FloatTensorType()},
outputs={'Y': FloatTensorType()},
target_opset=opset)
onx = onnx_rewrite_operator(onx, 'Reciprocal', rewrite_onx)
return onx
def test_onnx_rewrite_operator(self):
opset = get_max_opset()
node1 = OnnxReciprocal('X', output_names=['Y'],
op_version=opset)
onx1 = node1.to_onnx(
inputs={'X': FloatTensorType()},
outputs={'Y': FloatTensorType()},
target_opset=opset)
onx1.graph.name = "jjj"
oinf1 = OnnxInference(onx1)
node2 = OnnxDiv(numpy.array([1], dtype=numpy.float32),
'X', output_names=['Y'],
op_version=opset)
onx2 = node2.to_onnx(
inputs={'X': FloatTensorType()},
outputs={'Y': FloatTensorType()},
target_opset=opset)
oinf2 = OnnxInference(onx2)
X = numpy.array([[5, 6]], dtype=numpy.float32)
y1 = oinf1.run({'X': X})['Y']
y2 = oinf2.run({'X': X})['Y']
self.assertEqualArray(y1, y2)
onx3 = onnx_rewrite_operator(onx1, 'Reciprocal', onx2)
self.assertNotIn('Reciprocal', str(onx3))
oinf3 = OnnxInference(onx3)
y3 = oinf3.run({'X': X})['Y']
self.assertEqualArray(y1, y3)
def test_algebra_to_onnx(self):
X = numpy.random.randn(5, 4)
beta = numpy.array([1, 2, 3, 4]) / 10
beta32 = beta.astype(numpy.float32)
onnxExpM = OnnxExp(OnnxMatMul('X', beta32))
cst = numpy.ones((1, 3), dtype=numpy.float32)
onnxExpM1 = OnnxAdd(onnxExpM, cst)
onnxPred = OnnxDiv(onnxExpM, onnxExpM1)
inputs = {'X': X[:1].astype(numpy.float32)}
model_onnx = onnxPred.to_onnx(inputs)
s1 = str(model_onnx)
model_onnx = onnxPred.to_onnx(inputs)
s2 = str(model_onnx)
assert s1 == s2
nin = list(onnxExpM1.enumerate_initial_types())
nno = list(onnxExpM1.enumerate_nodes())
nva = list(onnxExpM1.enumerate_variables())
self.assertEqual(len(nin), 0)
self.assertEqual(len(nno), 3)
self.assertEqual(len(nva), 0)
