def __call__(self, *input_values: NumericData) -> List[NumericData]:
"""Run computation on input values and return result."""
input_values = [np.array(input_value) for input_value in input_values]
input_shapes = [get_shape(input_value) for input_value in input_values]
param_names = [param.friendly_name for param in self.parameters]
if self.network_cache.get(str(input_shapes)) is None:
capsule = Function.to_capsule(self.function)
cnn_network = IENetwork(capsule)
if self.function.is_dynamic():
cnn_network.reshape(dict(zip(param_names, input_shapes)))
# Convert unsupported inputs of the network
_convert_inputs(cnn_network)
self.network_cache[str(input_shapes)] = cnn_network
else:
cnn_network = self.network_cache[str(input_shapes)]
executable_network = self.runtime.backend.load_network(
cnn_network, self.runtime.backend_name)
# Input validation
if len(input_values) != len(self.parameters):
raise UserInputError("Expected %s parameters, received %s.",
len(self.parameters), len(input_values))
for parameter, input in zip(self.parameters, input_values):
parameter_shape = parameter.get_output_partial_shape(0)
input_shape = PartialShape(input.shape)
if len(input.shape) > 0 and not parameter_shape.compatible(
input_shape):
raise UserInputError(
"Provided tensor's shape: %s does not match the expected: %s.",
input_shape,
parameter_shape,
)
request = executable_network.requests[0]
request.infer(dict(zip(param_names, input_values)))
# Set order of output blobs compatible with nG Function
result_buffers = [
self.__get_ie_output_blob_buffer(request.output_blobs, result)
for result in self.results
]
# 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 = [
buffer.astype(original_dtype)
for buffer, original_dtype in zip(result_buffers, original_dtypes)
]
return converted_buffers
def __call__(self, *input_values: NumericData) -> List[NumericData]:
"""Run computation on input values and return result."""
input_values = [np.array(input_value) for input_value in input_values]
input_shapes = [get_shape(input_value) for input_value in input_values]
if self.network_cache.get(str(input_shapes)) is None:
capsule = Function.to_capsule(self.function)
cnn_network = IENetwork(capsule)
if self.function.is_dynamic():
param_names = [
param.friendly_name for param in self.parameters
]
cnn_network.reshape(dict(zip(param_names, input_shapes)))
self.network_cache[str(input_shapes)] = cnn_network
else:
cnn_network = self.network_cache[str(input_shapes)]
executable_network = self.runtime.backend.load_network(
cnn_network, self.runtime.backend_name)
# Input validation
if len(input_values) != len(self.parameters):
raise UserInputError("Expected %s parameters, received %s.",
len(self.parameters), len(input_values))
for parameter, input in zip(self.parameters, input_values):
parameter_shape = parameter.get_output_partial_shape(0)
input_shape = PartialShape(input.shape)
if len(input.shape) > 0 and not parameter_shape.compatible(
input_shape):
raise UserInputError(
"Provided tensor's shape: %s does not match the expected: %s.",
input_shape,
parameter_shape,
)
request = executable_network.requests[0]
request.infer(dict(zip(request._inputs_list, input_values)))
return [blob.buffer for blob in request.output_blobs.values()]