def __call__(self, *input_values: NumericData) -> List[NumericData]:
"""Run computation on input values and return result."""
# Input validation
if len(input_values) < len(self.parameters):
raise UserInputError(
"Expected %s params, received not enough %s values.",
len(self.parameters), len(input_values))
param_names = [param.friendly_name for param in self.parameters]
input_shapes = [get_shape(input_value) for input_value in input_values]
if self.network_cache.get(str(input_shapes)) is None:
function = self.function
if self.function.is_dynamic():
function.reshape(
dict(
zip(param_names,
[PartialShape(i) for i in input_shapes])))
self.network_cache[str(input_shapes)] = function
else:
function = self.network_cache[str(input_shapes)]
executable_network = self.runtime.backend.compile_model(
function, self.runtime.backend_name)
for parameter, input in zip(self.parameters, input_values):
parameter_shape = parameter.get_output_partial_shape(0)
input_shape = PartialShape([]) if isinstance(
input, (int, float)) else PartialShape(input.shape)
if not parameter_shape.compatible(input_shape):
raise UserInputError(
"Provided tensor's shape: %s does not match the expected: %s.",
input_shape,
parameter_shape,
)
is_bfloat16 = any(
parameter.get_output_element_type(0) == Type.bf16
for parameter in self.parameters)
if is_bfloat16:
input_values = self.convert_to_tensors(input_values)
request = executable_network.create_infer_request()
result_buffers = request.infer(dict(zip(param_names, input_values)))
# # Note: other methods to get result_buffers from request
# # First call infer with no return value:
# request.infer(dict(zip(param_names, input_values)))
# # Now use any of following options:
# result_buffers = [request.get_tensor(n).data for n in request.outputs]
# result_buffers = [request.get_output_tensor(i).data for i in range(len(request.outputs))]
# result_buffers = [t.data for t in request.output_tensors]
# # Since OV overwrite result data type we have to convert results to the original one.
original_dtypes = [
get_dtype(result.get_output_element_type(0))
for result in self.results
]
converted_buffers = self.convert_buffers(result_buffers,
original_dtypes)
return converted_buffers
def _check_value(op_name, attr_key, value, val_type, cond=None):
# type: (str, str, Any, Type, Optional[Callable[[Any], bool]]) -> bool
"""Check whether provided value satisfies specified criteria.
:param op_name: The operator name which attributes are checked.
:param attr_key: The attribute name.
:param value: The value to check.
:param val_type: Required value type.
:param cond: The optional function running additional checks.
:raises UserInputError:
returns True if attribute satisfies all criterias. Otherwise False.
"""
if not np.issubdtype(type(value), val_type):
raise UserInputError(
'{} operator attribute "{}" value must by of type {}.'.format(
op_name, attr_key, val_type
)
)
if cond is not None and not cond(value):
raise UserInputError(
'{} operator attribute "{}" value does not satisfy provided condition.'.format(
op_name, attr_key
)
)
return True
def assert_list_of_ints(value_list: Iterable[int], message: str) -> None:
"""Verify that the provided value is an iterable of integers."""
try:
for value in value_list:
if not isinstance(value, int):
raise TypeError
except TypeError:
log.warning(message)
raise UserInputError(message, value_list)
def check_valid_attribute(op_name,
attr_dict,
attr_key,
val_type,
cond=None,
required=False):
# type: (str, dict, str, Type, Optional[Callable[[Any], bool]], Optional[bool]) -> bool
"""Check whether specified attribute satisfies given criteria.
@param op_name: The operator name which attributes are checked.
@param attr_dict: Dictionary containing key-value attributes to check.
@param attr_key: Key value for validated attribute.
@param val_type: Value type for validated attribute.
@param cond: Any callable wich accept attribute value and returns True or False.
@param required: Whether provided attribute key is not required. This mean it may be missing
from provided dictionary.
:raises UserInputError:
@return True if attribute satisfies all criterias. Otherwise False.
"""
result = True
if required and attr_key not in attr_dict:
raise UserInputError(
'Provided dictionary is missing {} operator required attribute "{}"'
.format(op_name, attr_key))
if attr_key not in attr_dict:
return result
attr_value = attr_dict[attr_key]
if np.isscalar(attr_value):
result = result and _check_value(op_name, attr_key, attr_value,
val_type, cond)
else:
for v in attr_value:
result = result and _check_value(op_name, attr_key, v, val_type,
cond)
return result
def i420_to_rgb(
arg: NodeInput,
arg_u: Optional[NodeInput] = None,
arg_v: Optional[NodeInput] = None,
name: Optional[str] = None,
) -> Node:
"""Return a node which performs I420toRGB operation.
@param arg: The node providing single or Y plane data.
@param arg_u: The node providing U plane data. Required for separate planes.
@param arg_v: The node providing V plane data. Required for separate planes.
@param name: The optional name for the created output node.
@return The new node performing I420toRGB operation.
"""
if arg_u is None and arg_v is None:
inputs = as_nodes(arg)
elif arg_u is not None and arg_v is not None:
inputs = as_nodes(arg, arg_u, arg_v)
else:
raise UserInputError(
"Operation I420toRGB must have one (single plane) or three (separate planes) inputs provided."
)
return _get_node_factory_opset8().create("I420toRGB", inputs)
def create(
self,
op_type_name: str,
arguments: Optional[List[Union[Node, Output]]] = None,
attributes: Optional[Dict[str, Any]] = None,
) -> Node:
"""Create node object from provided description.
The user does not have to provide all node's attributes, but only required ones.
@param op_type_name: The operator type name.
@param arguments: The operator arguments.
@param attributes: The operator attributes.
@return Node object representing requested operator with attributes set.
"""
if arguments is None and attributes is None:
node = self.factory.create(op_type_name)
node._attr_cache = {}
node._attr_cache_valid = False
return node
if arguments is None and attributes is not None:
raise UserInputError(
'Error: cannot create "{}" op without arguments.'.format(
op_type_name))
if attributes is None:
attributes = {}
assert arguments is not None
arguments = self._arguments_as_outputs(arguments)
node = self.factory.create(op_type_name, arguments, attributes)
# Currently we don't support any attribute getters & setters for TensorIterator node.
if node.get_type_name() == "TensorIterator":
return node
# Set getters and setters for each node's attribute.
# node.get_attribute_name()
# node.set_attribute_name()
# For compound (with more than one level of nesting) attributes of form ie.:
# node.class_member_name.some_metric.attr_name:
# node.get_some_metric_attr_name()
# node.set_some_metric_attr_name()
# Please see test_dyn_attributes.py for more usage examples.
all_attributes = node.get_attributes()
for attr_name in all_attributes.keys():
setattr(
node,
self._normalize_attr_name_getter(attr_name),
partial(NodeFactory._get_node_attr_value, node, attr_name),
)
setattr(
node,
self._normalize_attr_name_setter(attr_name),
partial(NodeFactory._set_node_attr_value, node, attr_name),
)
# Setup helper members for caching attribute values.
# The cache would be lazily populated at first access attempt.
node._attr_cache = {}
node._attr_cache_valid = False
return node
