140 lines
5.8 KiB
Python
140 lines
5.8 KiB
Python
# ******************************************************************************
|
|
# Copyright 2017-2020 Intel Corporation
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ******************************************************************************
|
|
"""Provide a layer of abstraction for an OpenVINO runtime environment."""
|
|
import logging
|
|
from typing import Dict, List, Union
|
|
|
|
import numpy as np
|
|
from openvino.inference_engine import IECore, IENetwork
|
|
|
|
from ngraph.exceptions import UserInputError
|
|
from ngraph.impl import Function, Node, PartialShape
|
|
from ngraph.utils.types import NumericData, get_shape, get_dtype
|
|
|
|
import tests
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
|
|
def runtime(backend_name: str = "CPU") -> "Runtime":
|
|
"""Create a Runtime object (helper factory)."""
|
|
return Runtime(backend_name)
|
|
|
|
|
|
def get_runtime():
|
|
"""Return runtime object."""
|
|
return runtime(backend_name=tests.BACKEND_NAME)
|
|
|
|
|
|
def convert_i64_to_i32(cnn_network: IENetwork) -> None:
|
|
for cnn_input in cnn_network.input_info:
|
|
if cnn_network.input_info[cnn_input].precision == "I64":
|
|
cnn_network.input_info[cnn_input].precision = "I32"
|
|
|
|
|
|
class Runtime(object):
|
|
"""Represents an nGraph runtime environment."""
|
|
|
|
def __init__(self, backend_name: str) -> None:
|
|
self.backend_name = backend_name
|
|
log.debug("Creating Inference Engine for %s" % backend_name)
|
|
self.backend = IECore()
|
|
assert backend_name in self.backend.available_devices, (
|
|
'The requested device "' + backend_name + '" is not supported!'
|
|
)
|
|
|
|
def set_config(self, config: Dict[str, str]) -> None:
|
|
"""Set the inference engine configuration."""
|
|
self.backend.set_config(config, device_name=self.backend_name)
|
|
|
|
def __repr__(self) -> str:
|
|
return "<Runtime: Backend='{}'>".format(self.backend_name)
|
|
|
|
def computation(self, node_or_function: Union[Node, Function], *inputs: Node) -> "Computation":
|
|
"""Return a callable Computation object."""
|
|
if isinstance(node_or_function, Node):
|
|
ng_function = Function(node_or_function, inputs, node_or_function.name)
|
|
return Computation(self, ng_function)
|
|
elif isinstance(node_or_function, Function):
|
|
return Computation(self, node_or_function)
|
|
else:
|
|
raise TypeError(
|
|
"Runtime.computation must be called with an nGraph Function object "
|
|
"or an nGraph node object an optionally Parameter node objects. "
|
|
"Called with: %s",
|
|
node_or_function,
|
|
)
|
|
|
|
|
|
class Computation(object):
|
|
"""nGraph callable computation object."""
|
|
|
|
def __init__(self, runtime: Runtime, ng_function: Function) -> None:
|
|
self.runtime = runtime
|
|
self.function = ng_function
|
|
self.parameters = ng_function.get_parameters()
|
|
self.results = ng_function.get_results()
|
|
self.network_cache = {}
|
|
|
|
def __repr__(self) -> str:
|
|
params_string = ", ".join([param.name for param in self.parameters])
|
|
return "<Computation: {}({})>".format(self.function.get_name(), params_string)
|
|
|
|
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 inputs of the network from I64 to I32
|
|
convert_i64_to_i32(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)))
|
|
|
|
# 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]
|
|
result_buffers = [blob.buffer for blob in request.output_blobs.values()]
|
|
converted_buffers = [buffer.astype(original_dtype) for buffer, original_dtype in
|
|
zip(result_buffers, original_dtypes)]
|
|
return converted_buffers
|