def __init__(self, X, op_version=None, **kwargs):
"""
:param X: array or OnnxOperatorMixin
:param op_version: opset version
:param kwargs: additional parameter
"""
OnnxOperator.__init__(self, X, op_version=op_version, **kwargs)
def __init__(self,
text,
mark=0,
mincharnum=1,
pad_value='#',
separators=None,
tokenexp='[a-zA-Z0-9_]+',
stopwords=None,
op_version=None,
**kwargs):
"""
:param text: array or OnnxOperatorMixin
:param mark: see :epkg:`Tokenizer`
:param pad_value: see :epkg:`Tokenizer`
:param separators: see :epkg:`Tokenizer`
:param tokenexp: see :epkg:`Tokenizer`
:param stopwords: list of stopwords, addition to :epkg:`Tokenizer`
:param op_version: opset version
:param kwargs: additional parameter
"""
if separators is None:
separators = []
if stopwords is None:
stopwords = []
OnnxOperator.__init__(self,
text,
mark=mark,
mincharnum=mincharnum,
pad_value=pad_value,
separators=separators,
tokenexp=tokenexp,
stopwords=stopwords,
op_version=op_version,
**kwargs)
def __init__(self, a_shape, b_shape, op_version=None, **kwargs):
"""
:param a_shape: The 1st input shape as Tensor.
:param b_shape: The 2nds input shape as Tensor.
:param op_version: opset version
:param kwargs: additional parameter
"""
OnnxOperator.__init__(self,
a_shape,
b_shape,
op_version=op_version,
**kwargs)
def __init__(self, grad, prob, op_version=None, **kwargs):
"""
:param grad: gradient
:param prob: probablities
:param op_version: opset version
:param kwargs: additional parameter
"""
OnnxOperator.__init__(self,
grad,
prob,
op_version=op_version,
**kwargs)
def __init__(self, *args, axis=-1, op_version=None, **kwargs):
"""
:param A: array or OnnxOperatorMixin
:param fft_length: (optional) array or OnnxOperatorMixin (args)
:param axis: axis
:param op_version: opset version
:param kwargs: additional parameter
"""
OnnxOperator.__init__(self,
*args,
axis=axis,
op_version=op_version,
**kwargs)
def __init__(self, X, Y, transA=0, transB=0, op_version=None, **kwargs):
"""
:param X: first matrix
:param Y: second matrix
:param transA: transpose first matrix
:param transB: transpose second matrix
:param op_version: opset version
:param kwargs: additional parameter
"""
OnnxOperator.__init__(self,
X,
Y,
transA=transA,
transB=transB,
op_version=op_version,
**kwargs)
def __init__(self,
X,
non_differentiable_outputs=None,
full_shape_outputs=None,
op_version=None,
**kwargs):
"""
:param X: array or OnnxOperatorMixin
:param non_differentiable_outputs: the indices of the module
outputs that doesn't have a gradient.
:param full_shape_outputs: the indices of the module outputs
that must have full shape.
:param op_version: opset version
:param kwargs: additional parameter
"""
OnnxOperator.__init__(self, X, op_version=op_version, **kwargs)
self.non_differentiable_outputs = non_differentiable_outputs
self.full_shape_outputs = full_shape_outputs
def test_constant(self):
cst = OnnxOperator.ConstantVariable("a")
self.assertEqual(cst.value, "a")
def test_unscoped(self):
var2 = OnnxOperator.UnscopedVariable("a")
var1 = OnnxOperator.UnscopedVariable("a")
self.assertEqual(var1, var2)
self.assertEqual(var1, "a")
self.assertEqual(repr(var1), "UnscopedVariable('a')")