ac1a1e3e9f
* Change request.infer to use param_names instead of request._input_list because it fix the bug * remove xfail mark * remove xfail_issue_35893 form tests/ __init__.py * Remove xfail_issue_35893. Add OnnxBackendNodeModelTest.test_convtranspose_3d_cpu to xfail_issue_38091 * Remove OnnxBackendNodeModelTest.test_convtranspose_3d_cpu from xfail_issue_38091 in test_backend.py
125 lines
5.1 KiB
Python
125 lines
5.1 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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"""Provide a layer of abstraction for an OpenVINO runtime environment."""
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import logging
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from typing import Dict, List, Union
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import numpy as np
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from openvino.inference_engine import IECore, IENetwork
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from ngraph.exceptions import UserInputError
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from ngraph.impl import Function, Node, PartialShape
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from ngraph.utils.types import NumericData, get_shape
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import tests
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log = logging.getLogger(__name__)
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def runtime(backend_name: str = "CPU") -> "Runtime":
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"""Create a Runtime object (helper factory)."""
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return Runtime(backend_name)
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def get_runtime():
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"""Return runtime object."""
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return runtime(backend_name=tests.BACKEND_NAME)
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class Runtime(object):
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"""Represents an nGraph runtime environment."""
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def __init__(self, backend_name: str) -> None:
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self.backend_name = backend_name
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log.debug("Creating Inference Engine for %s" % backend_name)
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self.backend = IECore()
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assert backend_name in self.backend.available_devices, (
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'The requested device "' + backend_name + '" is not supported!'
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)
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def set_config(self, config: Dict[str, str]) -> None:
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"""Set the inference engine configuration."""
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self.backend.set_config(config, device_name=self.backend_name)
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def __repr__(self) -> str:
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return "<Runtime: Backend='{}'>".format(self.backend_name)
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def computation(self, node_or_function: Union[Node, Function], *inputs: Node) -> "Computation":
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"""Return a callable Computation object."""
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if isinstance(node_or_function, Node):
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ng_function = Function(node_or_function, inputs, node_or_function.name)
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return Computation(self, ng_function)
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elif isinstance(node_or_function, Function):
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return Computation(self, node_or_function)
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else:
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raise TypeError(
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"Runtime.computation must be called with an nGraph Function object "
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"or an nGraph node object an optionally Parameter node objects. "
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"Called with: %s",
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node_or_function,
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)
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class Computation(object):
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"""nGraph callable computation object."""
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def __init__(self, runtime: Runtime, ng_function: Function) -> None:
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self.runtime = runtime
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self.function = ng_function
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self.parameters = ng_function.get_parameters()
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self.results = ng_function.get_results()
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self.network_cache = {}
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def __repr__(self) -> str:
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params_string = ", ".join([param.name for param in self.parameters])
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return "<Computation: {}({})>".format(self.function.get_name(), params_string)
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def __call__(self, *input_values: NumericData) -> List[NumericData]:
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"""Run computation on input values and return result."""
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input_values = [np.array(input_value) for input_value in input_values]
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input_shapes = [get_shape(input_value) for input_value in input_values]
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param_names = [param.friendly_name for param in self.parameters]
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if self.network_cache.get(str(input_shapes)) is None:
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capsule = Function.to_capsule(self.function)
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cnn_network = IENetwork(capsule)
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if self.function.is_dynamic():
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cnn_network.reshape(dict(zip(param_names, input_shapes)))
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self.network_cache[str(input_shapes)] = cnn_network
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else:
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cnn_network = self.network_cache[str(input_shapes)]
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executable_network = self.runtime.backend.load_network(cnn_network, self.runtime.backend_name)
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# Input validation
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if len(input_values) != len(self.parameters):
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raise UserInputError(
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"Expected %s parameters, received %s.", len(self.parameters), len(input_values)
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)
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for parameter, input in zip(self.parameters, input_values):
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parameter_shape = parameter.get_output_partial_shape(0)
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input_shape = PartialShape(input.shape)
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if len(input.shape) > 0 and not parameter_shape.compatible(input_shape):
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raise UserInputError(
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"Provided tensor's shape: %s does not match the expected: %s.",
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input_shape,
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parameter_shape,
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)
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request = executable_network.requests[0]
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request.infer(dict(zip(param_names, input_values)))
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return [blob.buffer for blob in request.output_blobs.values()]
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