extras={'axis':axis})(inputs, {})
#return _op.take(inputs[0], inputs[1], axis)
+
+class Greater(OnnxOpConverter):
+ """ Operator logical greater.
+ """
+ @classmethod
+ def _impl_v7(cls, inputs, attr, params):
+ return _op.greater(inputs[0], inputs[1])
+
+
+class Less(OnnxOpConverter):
+ """ Operator logical less than.
+ """
+ @classmethod
+ def _impl_v7(cls, inputs, attr, params):
+ return _op.less(inputs[0], inputs[1])
+
+
class LRN(OnnxOpConverter):
""" Operator converter for Local Response Normalization.
"""
'Selu': Selu.get_converter(opset),
'Elu': Elu.get_converter(opset),
'Exp': Renamer('exp'),
+ 'Greater': Greater.get_converter(opset),
+ 'Less': Less.get_converter(opset),
'Log': Renamer('log'),
'Tanh': Renamer('tanh'),
'Pow': Renamer('power'),
verify_binary_ops("Div", x, y, x / y, broadcast=None)
verify_binary_ops("Div", x, z, x / z, broadcast=True)
verify_binary_ops("Sum", x, y, x + y, broadcast=None)
+ verify_binary_ops("Greater", x, y, x > y, broadcast=True)
+ verify_binary_ops("Less", x, y, x < y, broadcast=True)
def test_single_ops():
in_shape = (1, 2, 3, 3)