206 lines
8.0 KiB
Python
206 lines
8.0 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, IENetwork, Blob, DataPtr
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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.opset1.ops import result
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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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def _convert_inputs(cnn_network: IENetwork) -> None:
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"""WA converts unsupported input images formats."""
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precision_map = {
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"FP64": "FP32",
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"I64": "I32",
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"U32": "I32",
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}
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for cnn_input in cnn_network.input_info:
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try:
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_precision = precision_map[cnn_network.input_info[cnn_input].precision]
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cnn_network.input_info[cnn_input].precision = _precision
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except KeyError:
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pass
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def _convert_val(val):
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"""WA converts unsupported input values."""
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if type(val) is np.ndarray:
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if val.dtype == np.float64:
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return np.array(val, dtype=np.float32)
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elif val.dtype == np.int64:
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return np.array(val, dtype=np.int32)
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return np.array(val)
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return np.array(val, dtype=np.float32)
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def apply_ng_type(output: DataPtr, ng_type: Type):
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ng_ie_supported_type_map = {
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Type.boolean.get_type_name(): "BOOL",
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Type.f32.get_type_name(): "FP32",
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Type.i8.get_type_name(): "I8",
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Type.i32.get_type_name(): "I32",
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Type.u8.get_type_name(): "U8",
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}
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if ng_type.get_type_name() in ng_ie_supported_type_map:
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output.precision = ng_ie_supported_type_map[ng_type.get_type_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 = 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 _get_ie_output_blob_name(self, outputs: Dict, ng_result: result) -> str:
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if len(self.results) == 1:
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return next(iter(outputs.keys()))
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else:
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prev_layer = ng_result.input(0).get_source_output()
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out_name = prev_layer.get_node().get_friendly_name()
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if prev_layer.get_node().get_output_size() != 1:
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out_name += "." + str(prev_layer.get_index())
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return out_name
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def _get_ie_output_blob_buffer(self, output_blobs: Dict[str, Blob], ng_result: result) -> np.ndarray:
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out_name = self._get_ie_output_blob_name(output_blobs, ng_result)
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out_blob = output_blobs[out_name]
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if out_blob.tensor_desc.layout == "SCALAR":
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return out_blob.buffer.reshape(())
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else:
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return out_blob.buffer
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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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# ignore not needed input values
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input_values = input_values[:len(self.parameters)]
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input_values = [_convert_val(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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cnn_network = IENetwork(self.function)
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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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# Convert unsupported inputs of the network
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_convert_inputs(cnn_network)
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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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# set output blobs precission based on nG results
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for ng_result in self.results:
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ie_out_name = self._get_ie_output_blob_name(cnn_network.outputs, ng_result)
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apply_ng_type(cnn_network.outputs[ie_out_name], ng_result.get_output_element_type(0))
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executable_network = self.runtime.backend.load_network(cnn_network, 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(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.create_infer_request()
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request.infer(dict(zip(param_names, input_values)))
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# Set order of output blobs compatible with nG Function
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result_buffers = [self._get_ie_output_blob_buffer(request.output_blobs, result)
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for result in self.results]
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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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