def build_model(n_nodes, size, opv=15):
OnnxAdd, OnnxIdentity = loadop('Add', 'Identity')
x = 'X'
for n in range(n_nodes):
y = OnnxAdd(x,
numpy.random.randn(size).astype(numpy.float32),
op_version=opv)
x = y
final = OnnxIdentity(x, op_version=opv, output_names=['Y'])
x = numpy.zeros((10, 10), dtype=numpy.float32)
return final.to_onnx({'X': x}, {'Y': x}, target_opset=opv)
def test_onnx_abs(self):
OnnxAbs = loadop("Abs")
ov = OnnxAbs('X', output_names=['Y'])
onx = ov.to_onnx(numpy.float32, numpy.float32, verbose=0)
sub = OnnxSubOnnx(onx, 'X', output_names=['Y'])
onx = sub.to_onnx(numpy.float32, numpy.float32, verbose=0)
oinf = OnnxInference(onx)
x = numpy.array([-2, 2], dtype=numpy.float32)
got = oinf.run({'X': x})
self.assertEqualArray(numpy.abs(x), got['Y'])
def test_onnx_cast(self):
OnnxCast = loadop("Cast")
ov = OnnxCast('X', to=numpy.int64, output_names=['Y'])
onx = ov.to_onnx(numpy.float32, numpy.float32, verbose=0)
sub = OnnxSubOnnx(onx, 'X', output_names=['Y'])
onx = sub.to_onnx(numpy.float32, numpy.int64, verbose=0)
r = repr(sub)
self.assertStartsWith('OnnxSubOnnx(..., output_name', r)
oinf = OnnxInference(onx)
x = numpy.array([-2.4, 2.4], dtype=numpy.float32)
got = oinf.run({'X': x})
self.assertEqualArray(x.astype(numpy.int64), got['Y'])
def test_onnx_shape_inference(self):
OnnxAdd = loadop('Add')
dtype = numpy.float32
for opset in TestOnnxShapeInferenceXop.opsets:
with self.subTest(opset=opset):
cop = OnnxAdd('X',
numpy.array([[1]], dtype=dtype),
op_version=opset)
cop4 = OnnxAdd(cop,
numpy.array([[2]], dtype=dtype),
output_names=['Y'])
vari = Variable('X', numpy.float32, [None, None])
model_def = cop4.to_onnx([vari], run_shape=False)
rt = OnnxShapeInference(model_def)
out = rt.run()
self.assertIn('X', out)
self.assertIn('Y', out)
y = out['Y']
self.assertEqual(numpy.float32, y.dtype)
self.assertEqual(['_0', '_1'], y.shape)
def test_onnx_lr(self):
X, y = make_regression(n_features=2) # pylint: disable=W0632
lr = LinearRegression()
lr.fit(X, y)
X32 = X.astype(numpy.float32)
OnnxIdentity, OnnxReshape = loadop("Identity", "Reshape")
ov = OnnxIdentity('X')
self.assertRaise(lambda: OnnxSubEstimator(lr, ov), NotImplementedError)
sub = OnnxSubEstimator(lr,
ov,
op_version=max_supported_opset(),
initial_types=X32[:1])
r = repr(sub)
self.assertStartsWith('OnnxSubEstimator(LinearRegression()', r)
last = OnnxReshape(sub,
numpy.array([-1], dtype=numpy.int64),
output_names=['Y'])
onx = last.to_onnx(numpy.float32, numpy.float32, verbose=0)
oinf = OnnxInference(onx)
got = oinf.run({'X': X32})
expected = lr.predict(X32)
self.assertEqualArray(expected, got['Y'], decimal=4)