def test_onnx_rename_names_type(self):
rows = []
def flog(*s):
rows.append(" ".join(map(str, s)))
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def = cop4.to_onnx({'X': x})
oinf1 = OnnxInference(model_def)
new_model = onnx_rename_names(model_def,
verbose=1,
fLOG=flog,
strategy='type')
total = "\n".join(rows)
self.assertIn("'Ad_Addcst' -> 'i_05'", total)
oinf2 = OnnxInference(new_model)
y1 = oinf1.run({'X': x})
y2 = oinf2.run({'X': x})
self.assertEqualArray(y1['final'], y2['final'])
def test_onnx_rename_node_scan(self):
def squareform_pdist(X, **kwargs):
opv = TARGET_OPSET
diff = OnnxSub('next_in',
'next',
output_names=['diff'],
op_version=opv)
id_next = OnnxIdentity('next_in',
output_names=['next_out'],
op_version=opv)
norm = OnnxReduceSumSquare(diff,
output_names=['norm'],
axes=[1],
op_version=opv)
flat = OnnxSqueezeApi11(norm,
output_names=['scan_out'],
axes=[1],
op_version=opv)
scan_body = id_next.to_onnx(
OrderedDict([('next_in', FloatTensorType()),
('next', FloatTensorType())]),
outputs=[('next_out', FloatTensorType([None, None])),
('scan_out', FloatTensorType([None]))],
other_outputs=[flat])
node = OnnxScan(X,
X,
output_names=['scan0_{idself}', 'scan1_{idself}'],
num_scan_inputs=1,
body=scan_body.graph,
op_version=opv,
**kwargs)
return node[1]
rows = []
def flog(*s):
rows.append(" ".join(map(str, s)))
opv = TARGET_OPSET
onnx_fct = OnnxIdentity(squareform_pdist('x'),
output_names='Y',
op_version=opv)
model_def = onnx_fct.to_onnx(inputs=[('x', FloatTensorType())])
oinf1 = OnnxInference(model_def)
new_model = onnx_rename_names(model_def,
verbose=1,
fLOG=flog,
strategy='type')
total = "\n".join(rows)
self.assertNotIn('name: "Re_ReduceSumSquare"', str(new_model))
self.assertIn("'Re_ReduceSumSquare' -> 'n_24'", total)
oinf2 = OnnxInference(new_model)
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
y1 = oinf1.run({'x': x})
y2 = oinf2.run({'x': x})
self.assertEqualArray(y1['Y'], y2['Y'])
def test_onnx_rename_names_exc(self):
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def = cop4.to_onnx({'X': x})
self.assertRaise(lambda: onnx_rename_names(model_def, strategy="none"),
ValueError)
def onnx_rename_weights(onx):
"""
Renames ONNX initializers to make sure their name
follows the alphabetical order. The model is
modified inplace. This function calls
:func:`onnx_rename_names
<mlprodict.onnx_tools.onnx_manipulations.onnx_rename_names>`.
:param onx: ONNX model
:return: same model
.. note::
The function does not go into subgraphs.
"""
from mlprodict.onnx_tools.onnx_manipulations import ( # pylint: disable=C0415
onnx_rename_names)
init = [init.name for init in onx.graph.initializer]
ninit = max(1, int(math.log(len(init)) / math.log(10) + 1))
fmt = f"I%0{ninit}d_%s"
new_names = [fmt % (i, name) for i, name in enumerate(init)]
repl = dict(zip(init, new_names))
return onnx_rename_names(onx, recursive=False, replace=repl)