Jan Iwaszkiewicz
4247a9d6b0
* Added new Tensor dispatch and improvements, replace injections with inheritance of pybind objects, remove Blob from public python API. * Clean-up tests and API from unused classes * Remove unused import * cpp codestyle * Update AsyncInferQueue with python wrapper * Codestyle cpp * Applying comments * Common tensor setting for requests
139 lines
5.6 KiB
Python
139 lines
5.6 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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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 import Core
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from openvino.exceptions import UserInputError
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from openvino.impl import Function, Node, PartialShape, Type
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from openvino.utils.types import NumericData, get_shape, get_dtype
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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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if tests.BACKEND_NAME is not None:
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return runtime(backend_name=tests.BACKEND_NAME)
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else:
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return runtime()
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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 = Core()
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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 convert_buffers(self, source_buffers, target_dtypes):
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converted_buffers = []
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for i in range(len(source_buffers)):
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target_dtype = target_dtypes[i]
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# custom conversion for bf16
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if self.results[i].get_output_element_type(0) == Type.bf16:
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converted_buffers.append((source_buffers[i].view(np.uint32) >> 16).astype(np.uint16))
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else:
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converted_buffers.append(source_buffers[i].astype(target_dtype))
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return converted_buffers
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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 validation
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if len(input_values) < len(self.parameters):
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raise UserInputError(
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"Expected %s params, received not enough %s values.", len(self.parameters), len(input_values)
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)
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param_names = [param.friendly_name for param in self.parameters]
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input_shapes = [get_shape(input_value) for input_value in input_values]
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if self.network_cache.get(str(input_shapes)) is None:
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function = self.function
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if self.function.is_dynamic():
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function.reshape(dict(zip(param_names, [PartialShape(i) for i in input_shapes])))
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self.network_cache[str(input_shapes)] = function
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else:
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function = self.network_cache[str(input_shapes)]
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executable_network = self.runtime.backend.compile_model(function, self.runtime.backend_name)
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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([]) if isinstance(input, (int, float)) else PartialShape(input.shape)
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if 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.create_infer_request()
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result_buffers = request.infer(dict(zip(param_names, input_values)))
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# # Note: other methods to get result_buffers from request
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# # First call infer with no return value:
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# request.infer(dict(zip(param_names, input_values)))
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# # Now use any of following options:
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# result_buffers = [request.get_tensor(n).data for n in request.outputs]
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# result_buffers = [request.get_output_tensor(i).data for i in range(len(request.outputs))]
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# result_buffers = [t.data for t in request.output_tensors]
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# # Since OV overwrite result data type we have to convert results to the original one.
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original_dtypes = [get_dtype(result.get_output_element_type(0)) for result in self.results]
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converted_buffers = self.convert_buffers(result_buffers, original_dtypes)
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return converted_buffers
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