def _get_kwargs(schema, only_tensor = False):
"""
Get the keywords arguments from the schema.
`schema`
the schema in which to lookup arguments
`only_tensor`: bool
If True list only keyword arguments that can be passed as TensorLists (argument inputs)
If False list all the arguments. False indicates that we list arguments to the
constructor of the operator which does not accept TensorLists (argument inputs) - that
fact will be reflected in specified type
"""
ret = ""
for arg in schema.GetArgumentNames():
if not only_tensor or schema.IsTensorArgument(arg):
arg_name_doc = arg
dtype = schema.GetArgumentType(arg)
type_name = _type_name_convert_to_string(dtype, is_tensor = only_tensor)
if schema.IsArgumentOptional(arg):
type_name += ", optional"
if schema.HasArgumentDefaultValue(arg):
default_value_string = schema.GetArgumentDefaultValueString(arg)
default_value = eval(default_value_string)
type_name += ", default = {}".format(_default_converter(dtype, default_value))
doc = schema.GetArgumentDox(arg)
ret += _numpydoc_formatter(arg, type_name, doc)
ret += '\n'
return ret
def _get_kwargs(schema):
"""
Get the keywords arguments from the schema.
`schema`
the schema in which to lookup arguments
"""
ret = ""
for arg in schema.GetArgumentNames():
allow_tensors = schema.IsTensorArgument(arg)
arg_name_doc = arg
dtype = schema.GetArgumentType(arg)
type_name = _type_name_convert_to_string(dtype,
allow_tensors=allow_tensors)
if schema.IsArgumentOptional(arg):
type_name += ", optional"
if schema.HasArgumentDefaultValue(arg):
default_value_string = schema.GetArgumentDefaultValueString(
arg)
default_value = ast.literal_eval(default_value_string)
type_name += ", default = {}".format(
_default_converter(dtype, default_value))
doc = schema.GetArgumentDox(arg)
ret += _numpydoc_formatter(arg, type_name, doc)
ret += '\n'
return ret
def _get_kwargs(schema):
"""
Get the keywords arguments from the schema.
`schema`
the schema in which to lookup arguments
"""
ret = ""
for arg in schema.GetArgumentNames():
skip_full_doc = False
type_name = ""
dtype = None
doc = ""
deprecation_warning = None
if schema.IsDeprecatedArg(arg):
meta = schema.DeprecatedArgMeta(arg)
msg = meta['msg']
assert msg is not None
deprecation_warning = ".. warning::\n\n " + msg.replace(
"\n", "\n ")
renamed_arg = meta['renamed_to']
# Renamed and removed arguments won't show full documentation (only warning box)
skip_full_doc = renamed_arg or meta['removed']
# Renamed aliases are not fully registered to the schema, that's why we query for the
# info on the renamed_arg name.
if renamed_arg:
dtype = schema.GetArgumentType(renamed_arg)
type_name = _type_name_convert_to_string(
dtype, allow_tensors=schema.IsTensorArgument(renamed_arg))
# Try to get dtype only if not set already (renamed args go through a different path, see above)
if not dtype:
dtype = schema.GetArgumentType(arg)
type_name = _type_name_convert_to_string(
dtype, allow_tensors=schema.IsTensorArgument(arg))
# Add argument documentation if necessary
if not skip_full_doc:
if schema.IsArgumentOptional(arg):
type_name += ", optional"
if schema.HasArgumentDefaultValue(arg):
default_value_string = schema.GetArgumentDefaultValueString(
arg)
default_value = ast.literal_eval(default_value_string)
type_name += ", default = {}".format(
_default_converter(dtype, default_value))
doc += schema.GetArgumentDox(arg)
if deprecation_warning:
doc += "\n\n" + deprecation_warning
elif deprecation_warning:
doc += deprecation_warning
ret += _numpydoc_formatter(arg, type_name, doc)
ret += '\n'
return ret
