Commit ⋅ sdpython/mlprodict

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mlprodict/onnxrt/onnx_inference_node.py changed.

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mlprodict/onnxrt/onnx_inference.py changed.

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mlprodict/onnxrt/ops_cpu/_op_list.py changed.

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mlprodict/onnxrt/ops_cpu/op_loop.py created.

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HISTORY.rst has changed.

@@ -220,11 +220,11 @@

                ".".format(type(self.ops_)))
        if len(self.outputs) != len(res):
            raise RuntimeError(  # pragma: no cover
                "Mismatch number of outputs got {} for names {} (node='{}')."
                "Mismatch number of outputs got {} != {} for names {} (node='{}')."
                "\n{}".format(
                    len(res), list(self.outputs),
                    len(res), len(self.outputs), list(self.outputs),
                    self.ops_.__class__.__name__,
                    pprint.pformat(self.desc)))
                    pprint.pformat(self.desc, depth=2)))
        for name, value in zip(self.outputs, res):
            values[name] = value
        return values

@@ -610,7 +610,7 @@

        else:
            def dispsimple(arr):
                if hasattr(arr, 'shape'):
                    if len(arr.shape) == 1:
                    if len(arr.shape) <= 1:
                        threshold = 8
                    else:
                        threshold = min(

@@ -52,6 +52,7 @@

from .op_linear_classifier import LinearClassifier
from .op_linear_regressor import LinearRegressor
from .op_log import Log
from .op_loop import Loop
from .op_lp_normalization import LpNormalization
from .op_matmul import MatMul
from .op_max import Max

@@ -0,0 +1,58 @@

1	+	# -- encoding: utf-8 --
2	+	# pylint: disable=E0203,E1101,C0111
3	+	"""
4	+	@file
5	+	@brief Runtime operator.
6	+	"""
7	+	import numpy
8	+	from ._op import OpRun
9	+
10	+
11	+	class Loop(OpRun):
12	+
13	+	atts = {
14	+	'body': None,
15	+	}
16	+
17	+	def __init__(self, onnx_node, desc=None, **options):
18	+	OpRun.__init__(self, onnx_node, desc=desc,
19	+	expected_attributes=Loop.atts,
20	+	**options)
21	+	if not hasattr(self.body, 'run'):
22	+	raise RuntimeError("Parameter 'body' must have a method 'run', "
23	+	"type {}.".format(type(self.body)))
24	+
25	+	self._run_meth = (self.body.run_in_scan
26	+	if hasattr(self.body, 'run_in_scan')
27	+	else self.body.run)
28	+
29	+	def _run(self, M, cond, v_initial, *args): # pylint: disable=W0221
30	+	inputs = {name: None for name in self.body.input_names}
31	+	inputs[self.body.input_names[0]] = cond
32	+	inputs[self.body.input_names[1]] = v_initial
33	+	cond_name = self.body.output_names[0]
34	+	if len(args) > 0:
35	+	begin = len(self.body.input_names) - len(args)
36	+	for name, val in zip(self.body.input_names[begin:], args):
37	+	inputs[name] = val
38	+	it = 0
39	+	while cond and it < M:
40	+	outputs = self._run_meth_then(inputs)
41	+	cond = outputs[cond_name]
42	+	for i, o in zip(self.body.input_names[2:],
43	+	self.body.output_names[2:]):
44	+	inputs[i] = outputs[o]
45	+	it += 1
46	+	if it == 0:
47	+	outputs = {self.body.output_names[1]: cond}
48	+	for i, o in zip(self.body.input_names[2:],
49	+	self.body.output_names[2:]):
50	+	outputs[o] = inputs[i]
51	+	for o in self.body.output_names:
52	+	if o not in outputs:
53	+	outputs[o] = numpy.empty(shape=tuple())
54	+	return tuple([outputs[name] for name in self.body.output_names[1:]])
55	+
56	+	def _infer_shapes(self, M, cond, v_initial, *args): # pylint: disable=W0221
57	+	res = self.body._set_shape_inference_runtime()
58	+	return tuple([res[name] for name in self.body.output_names[1:]])

Files		•	•	•	Coverage
mlprodict	13,595	12,405	0	1,190	91.25%
Project Totals (225 files)	13,595	12,405	0	1,190	91.25%

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610	610		else:
611	611		def dispsimple(arr):
612	612		if hasattr(arr, 'shape'):
613		-	if len(arr.shape) == 1:
	613	+	if len(arr.shape) <= 1:
614	614		threshold = 8
615	615		else:
616	616		threshold = min(

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220	220		".".format(type(self.ops_)))
221	221		if len(self.outputs) != len(res):
222	222		raise RuntimeError( # pragma: no cover
223		-	"Mismatch number of outputs got {} for names {} (node='{}')."
	223	+	"Mismatch number of outputs got {} != {} for names {} (node='{}')."
224	224		"\n{}".format(
225		-	len(res), list(self.outputs),
	225	+	len(res), len(self.outputs), list(self.outputs),
226	226		self.ops_.__class__.__name__,
227		-	pprint.pformat(self.desc)))
	227	+	pprint.pformat(self.desc, depth=2)))
228	228		for name, value in zip(self.outputs, res):
229	229		values[name] = value
230	230		return values

52	52		from .op_linear_classifier import LinearClassifier
53	53		from .op_linear_regressor import LinearRegressor
54	54		from .op_log import Log
	55	+	from .op_loop import Loop
55	56		from .op_lp_normalization import LpNormalization
56	57		from .op_matmul import MatMul
57	58		from .op_max import Max