def test_onnx_remove_redundant_subgraphs_full(self):
from skl2onnx.algebra.complex_functions import onnx_squareform_pdist
cop = OnnxAdd(OnnxIdentity('input',
op_version=get_opset_number_from_onnx()),
'input',
op_version=get_opset_number_from_onnx())
cdist = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=get_opset_number_from_onnx())
cdist2 = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=get_opset_number_from_onnx())
cop2 = OnnxAdd(cdist,
cdist2,
output_names=['cdist'],
op_version=get_opset_number_from_onnx())
model_def = cop2.to_onnx({'input': FloatTensorType()},
outputs=[('cdist', FloatTensorType())],
target_opset=get_opset_number_from_onnx())
stats = onnx_statistics(model_def, optim=False)
new_model = onnx_optimisations(model_def)
stats2 = onnx_statistics(new_model, optim=False)
self.assertLess(stats2['size'], stats['size'])
self.assertLess(stats2['nnodes'], stats['nnodes'])
self.assertLess(stats2['op_Identity'], stats['op_Identity'])
def test_onnx_subgraphs2(self):
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd(OnnxIdentity('input', op_version=TARGET_OPSET),
'input',
op_version=TARGET_OPSET)
cdist = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=TARGET_OPSET)
id1 = [id(a) for a in cdist.onx_op.graph_algebra['body']]
cdist2 = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=TARGET_OPSET)
id2 = [id(a) for a in cdist2.onx_op.graph_algebra['body']]
self.assertNotEqual(id1, id2)
cop2 = OnnxAdd(cdist,
cdist2,
output_names=['cdist'],
op_version=TARGET_OPSET)
model_def = cop2.to_onnx({'input': FloatTensorType([None, None])},
outputs=[('cdist',
FloatTensorType([None, None]))],
target_opset=TARGET_OPSET)
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'input': x})
self.assertEqual(len(res), 1)
def test_onnx_remove_redundant_subgraphs(self):
from skl2onnx.algebra.complex_functions import onnx_squareform_pdist
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd(OnnxIdentity('input', op_version=TARGET_OPSET),
'input',
op_version=TARGET_OPSET)
cdist = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=TARGET_OPSET)
cdist2 = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=TARGET_OPSET)
cop2 = OnnxAdd(cdist,
cdist2,
output_names=['cdist'],
op_version=TARGET_OPSET)
model_def = cop2.to_onnx({'input': FloatTensorType()},
outputs=[('cdist', FloatTensorType())],
target_opset=TARGET_OPSET)
c1 = model_def.SerializeToString()
stats = onnx_statistics(model_def, optim=False)
c2 = model_def.SerializeToString()
self.assertEqual(c1, c2)
self.assertIn('subgraphs', stats)
self.assertGreater(stats['subgraphs'], 1)
self.assertGreater(stats['op_Identity'], 2)
new_model = onnx_remove_node_redundant(model_def)
stats2 = onnx_statistics(new_model, optim=False)
self.assertEqual(stats['subgraphs'], 2)
# The test is unstable, probably due to variables names.
# They should be renamed before checking redundancy.
self.assertIn(stats2['subgraphs'], (1, 2))
oinf1 = OnnxInference(model_def)
oinf2 = OnnxInference(new_model)
y1 = oinf1.run({'input': x})['cdist']
y2 = oinf2.run({'input': x})['cdist']
self.assertEqualArray(y1, y2)
new_model = onnx_remove_node_redundant(model_def)
stats3 = onnx_statistics(new_model, optim=False)
self.assertEqual(stats2, stats3)
new_model = onnx_remove_node(model_def)
stats3 = onnx_statistics(new_model, optim=False)
self.assertLess(stats3['size'], stats2['size'])
self.assertLess(stats3['nnodes'], stats2['nnodes'])
self.assertLess(stats3['op_Identity'], stats2['op_Identity'])
def test_onnx_example_pdist_in(self):
opv = _TARGET_OPSET_
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
cop = OnnxAdd('input', 'input', op_version=opv)
cop2 = OnnxIdentity(onnx_squareform_pdist(cop,
dtype=np.float32,
op_version=opv),
output_names=['pdist'],
op_version=opv)
model_def = cop2.to_onnx(inputs=[('input',
FloatTensorType([None, None]))],
outputs=[('pdist', FloatTensorType())])
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'input': x})
exp = squareform(pdist(x * 2, metric="sqeuclidean"))
assert_almost_equal(exp, res[0])
x = np.array([1, 2, 4, 5]).astype(np.float32).reshape((2, 2))
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'input': x})
exp = squareform(pdist(x * 2, metric="sqeuclidean"))
assert_almost_equal(exp, res[0])
x = np.array([1, 2, 4, 5, 5, 6]).astype(np.float32).reshape((2, 3))
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((2, 3))
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'input': x})
exp = squareform(pdist(x * 2, metric="sqeuclidean"))
assert_almost_equal(exp, res[0])
def test_pdist(self):
from skl2onnx.algebra.complex_functions import onnx_squareform_pdist
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('input', 'input', op_version=TARGET_OPSET)
cdist = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=TARGET_OPSET)
cop2 = OnnxIdentity(cdist,
output_names=['cdist'],
op_version=TARGET_OPSET)
model_def = cop2.to_onnx({'input': FloatTensorType()},
outputs=[('cdist', FloatTensorType())],
target_opset=TARGET_OPSET)
sess = OnnxInference(model_def)
res = sess.run({'input': x})
self.assertEqual(list(res.keys()), ['cdist'])
exp = squareform(pdist(x * 2, metric="sqeuclidean"))
self.assertEqualArray(exp, res['cdist'])
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(2, 3))
res = sess.run({'input': x})
self.assertEqual(list(res.keys()), ['cdist'])
def test_onnx_remove_identities2(self):
from skl2onnx.algebra.complex_functions import onnx_squareform_pdist
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxIdentity('input', op_version=get_opset_number_from_onnx())
cdist = onnx_squareform_pdist(cop,
dtype=numpy.float32,
op_version=get_opset_number_from_onnx())
cop2 = OnnxIdentity(cdist,
output_names=['cdist'],
op_version=get_opset_number_from_onnx())
model_def = cop2.to_onnx({'input': FloatTensorType()},
outputs=[('cdist', FloatTensorType())],
target_opset=get_opset_number_from_onnx())
stats = onnx_statistics(model_def, optim=False)
self.assertIn('subgraphs', stats)
self.assertGreater(stats['subgraphs'], 1)
self.assertGreater(stats['op_Identity'], 2)
new_model = onnx_remove_node_identity(model_def)
stats2 = onnx_statistics(new_model, optim=False)
self.assertEqual(stats['subgraphs'], stats2['subgraphs'])
self.assertLesser(stats2['op_Identity'], 2)
oinf1 = OnnxInference(model_def)
oinf2 = OnnxInference(new_model)
y1 = oinf1.run({'input': x})['cdist']
y2 = oinf2.run({'input': x})['cdist']
self.assertEqualArray(y1, y2)
self.assertLesser(stats2['op_Identity'], 1)
def kernel_call_ynone(X,
length_scale=1.2,
periodicity=1.1,
pi=3.141592653589793):
dists = onnx_squareform_pdist(X, metric='euclidean')
arg = dists / periodicity * pi
sin_of_arg = numpy.sin(arg)
K = numpy.exp((sin_of_arg / length_scale)**2 * (-2))
return K
def test_onnxt_pdist_dot(self):
from skl2onnx.algebra.complex_functions import onnx_squareform_pdist # pylint: disable=E0401,E0611
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('input', 'input')
cdist = onnx_squareform_pdist(cop, dtype=numpy.float32)
cop2 = OnnxIdentity(cdist, output_names=['cdist'])
model_def = cop2.to_onnx({'input': x},
outputs=[('cdist', FloatTensorType())])
oinf = OnnxInference(model_def, skip_run=True)
dot = oinf.to_dot(recursive=True)
self.assertIn("B_next_out", dot)
self.assertIn("cluster", dot)
def test_onnx_stat_recursive(self):
from skl2onnx.algebra.complex_functions import onnx_squareform_pdist
cop = OnnxAdd(
OnnxIdentity('input', op_version=__max_supported_opset__),
'input', op_version=__max_supported_opset__)
cdist = onnx_squareform_pdist(
cop, dtype=numpy.float32, op_version=__max_supported_opset__)
cop2 = OnnxIdentity(cdist, output_names=['cdist'],
op_version=__max_supported_opset__)
model_def = cop2.to_onnx(
{'input': FloatTensorType()},
outputs=[('cdist', FloatTensorType())],
target_opset=__max_supported_opset__)
stats = onnx_statistics(model_def)
self.assertIn('subgraphs', stats)
self.assertGreater(stats['subgraphs'], 1)
self.assertGreater(stats['op_Identity'], 2)
