def test_onnxt_runtime_solve(self):
for transposed in [False, True]:
with self.subTest(transposed=transposed):
A = numpy.array([[2, 1], [0, 1]], dtype=float)
Y = numpy.array([2, 1], dtype=float)
X = solve(A, Y, transposed=transposed)
onx = OnnxSolve('A',
'Y',
output_names=['X'],
transposed=transposed,
op_version=get_opset_number_from_onnx())
model_def = onx.to_onnx(
{
'A': A.astype(numpy.float32),
'Y': Y.astype(numpy.float32)
},
outputs={'X': X.astype(numpy.float32)},
target_opset=get_opset_number_from_onnx())
oinf = OnnxInference(model_def)
got = oinf.run({'A': A, 'Y': Y})
self.assertEqual(list(sorted(got)), ['X'])
self.assertEqualArray(X, got['X'], decimal=6)
python_tested.append(OnnxCDist)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy, {
'A': A.astype(numpy.float32),
'Y': Y.astype(numpy.float32)
})
python_tested.append(OnnxSolve)
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)
def test_onnxt_runtime_complex_abs(self):
for dtype in [numpy.complex64, numpy.complex128]:
with self.subTest(dtype=dtype):
X = numpy.array([[2, 1j], [0, 1j]], dtype=dtype)
Z = numpy.absolute(X)
onx = OnnxComplexAbs('X',
output_names=['Z'],
op_version=TARGET_OPSET)
model_def = onx.to_onnx({'X': X},
outputs={'Z': Z},
target_opset=TARGET_OPSET)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Z'])
self.assertEqualArray(Z, got['Z'], decimal=6)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy, {'X': X}, tolerance=1e-6)
python_tested.append(OnnxComplexAbs)
def test_onnxt_runtime_fft2d(self):
for dim in [2]:
for axis in [None, (-2, -1)]:
with self.subTest(dim=dim, axis=axis):
if dim == 1:
X = numpy.arange(16).astype(numpy.float32)
elif dim == 2:
X = numpy.arange(48).astype(numpy.float32).reshape(
(3, -1))
Y = numpy.fft.fft2(X.astype(numpy.float32), axes=axis)
if axis is not None:
onx = OnnxFFT2D('X',
output_names=['Y'],
axes=axis,
op_version=TARGET_OPSET)
else:
onx = OnnxFFT2D('X',
output_names=['Y'],
op_version=TARGET_OPSET)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
outputs={'Y': Y},
target_opset=TARGET_OPSET)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(Y, got['Y'], decimal=5)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy,
{'X': X.astype(numpy.float32)},
tolerance=1e-5)
for dim in [2]:
for axis in [None, (-2, -1)]:
with self.subTest(dim=dim, axis=axis, length=(8, 8)):
if dim == 1:
X = numpy.arange(16).astype(numpy.float32)
elif dim == 2:
X = numpy.arange(48).astype(numpy.float32).reshape(
(3, -1))
Y = numpy.fft.fft2(X.astype(numpy.float32), (8, 8),
axes=axis)
if axis is not None:
onx = OnnxFFT2D('X',
numpy.array([8, 8], dtype=numpy.int64),
output_names=['Y'],
axes=axis,
op_version=TARGET_OPSET)
else:
onx = OnnxFFT2D('X',
numpy.array([8, 8], dtype=numpy.int64),
output_names=['Y'],
op_version=TARGET_OPSET)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
outputs={'Y': Y},
target_opset=TARGET_OPSET)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(Y, got['Y'], decimal=5)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy,
{'X': X.astype(numpy.float32)},
tolerance=1e-5)
python_tested.append(OnnxRFFT)
def test_onnxt_runtime_rfft(self):
for dim in [1, 2]:
for axis in [-1, 0, 1]:
if axis >= dim:
continue
with self.subTest(dim=dim, axis=axis):
if dim == 1:
X = numpy.arange(16).astype(numpy.float32)
elif dim == 2:
X = numpy.arange(48).astype(numpy.float32).reshape(
(3, -1))
Y = numpy.fft.rfft(X.astype(numpy.float32), axis=axis)
onx = OnnxRFFT('X',
output_names=['Y'],
axis=axis,
op_version=TARGET_OPSET)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
outputs={'Y': Y},
target_opset=TARGET_OPSET)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(Y, got['Y'], decimal=6)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy,
{'X': X.astype(numpy.float32)},
tolerance=1e-6)
for dim in [1, 2]:
for axis in [-1, 0, 1]:
if axis >= dim:
continue
with self.subTest(dim=dim, axis=axis, length=8):
if dim == 1:
X = numpy.arange(16).astype(numpy.float32)
elif dim == 2:
X = numpy.arange(48).astype(numpy.float32).reshape(
(3, -1))
Y = numpy.fft.rfft(X.astype(numpy.float32), 8, axis=axis)
onx = OnnxRFFT('X',
numpy.array([8], dtype=numpy.int64),
output_names=['Y'],
axis=axis,
op_version=TARGET_OPSET)
try:
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
outputs={'Y': Y},
target_opset=TARGET_OPSET)
except NotImplementedError as e:
raise AssertionError(
"Unable to convert due to %r (version=%r)." %
(e, skl2onnx.__version__)) from e
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(Y, got['Y'], decimal=5)
oinfpy = OnnxInference(model_def,
runtime="python",
inplace=True)
validate_python_inference(oinfpy,
{'X': X.astype(numpy.float32)},
tolerance=1e-5)
python_tested.append(OnnxRFFT)