119 lines
4.7 KiB
Python
119 lines
4.7 KiB
Python
import sys
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from openvino.runtime import Core
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from openvino.inference_engine import IECore
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model_path = "/openvino_CI_CD/result/install_pkg/tests/test_model_zoo/core/models/ir/add_abc.xml"
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path_to_model = "/openvino_CI_CD/result/install_pkg/tests/test_model_zoo/core/models/ir/add_abc.xml"
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def part0():
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#! [part0]
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core = Core()
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# Read a network in IR, PaddlePaddle, or ONNX format:
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model = core.read_model(model_path)
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# compile a model on AUTO using the default list of device candidates.
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# The following lines are equivalent:
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compiled_model = core.compile_model(model=model)
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compiled_model = core.compile_model(model=model, device_name="AUTO")
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# Optional
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# You can also specify the devices to be used by AUTO.
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# The following lines are equivalent:
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compiled_model = core.compile_model(model=model, device_name="AUTO:GPU,CPU")
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compiled_model = core.compile_model(model=model, device_name="AUTO", config={"MULTI_DEVICE_PRIORITIES": "GPU,CPU"})
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# Optional
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# the AUTO plugin is pre-configured (globally) with the explicit option:
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core.set_property(device_name="AUTO", properties={"MULTI_DEVICE_PRIORITIES":"GPU,CPU"})
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#! [part0]
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def part1():
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#! [part1]
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### IE API ###
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ie = IECore()
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# Read a network in IR, PaddlePaddle, or ONNX format:
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net = ie.read_network(model=path_to_model)
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# Load a network to AUTO using the default list of device candidates.
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# The following lines are equivalent:
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exec_net = ie.load_network(network=net)
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exec_net = ie.load_network(network=net, device_name="AUTO")
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exec_net = ie.load_network(network=net, device_name="AUTO", config={})
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# Optional
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# You can also specify the devices to be used by AUTO in its selection process.
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# The following lines are equivalent:
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exec_net = ie.load_network(network=net, device_name="AUTO:GPU,CPU")
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exec_net = ie.load_network(network=net, device_name="AUTO", config={"MULTI_DEVICE_PRIORITIES": "GPU,CPU"})
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# Optional
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# the AUTO plugin is pre-configured (globally) with the explicit option:
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ie.set_config(config={"MULTI_DEVICE_PRIORITIES":"GPU,CPU"}, device_name="AUTO");
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#! [part1]
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def part3():
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#! [part3]
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core = Core()
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# Read a network in IR, PaddlePaddle, or ONNX format:
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model = core.read_model(model_path)
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# compile a model on AUTO with Performance Hints enabled:
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# To use the “throughput” mode:
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compiled_model = core.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT":"THROUGHPUT"})
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# or the “latency” mode:
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compiled_model = core.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT":"LATENCY"})
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#! [part3]
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def part4():
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#! [part4]
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core = Core()
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model = core.read_model(model_path)
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# Example 1
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compiled_model0 = core.compile_model(model=model, device_name="AUTO", config={"MODEL_PRIORITY":"HIGH"})
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compiled_model1 = core.compile_model(model=model, device_name="AUTO", config={"MODEL_PRIORITY":"MEDIUM"})
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compiled_model2 = core.compile_model(model=model, device_name="AUTO", config={"MODEL_PRIORITY":"LOW"})
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# Assume that all the devices (CPU, GPU, and MYRIAD) can support all the networks.
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# Result: compiled_model0 will use GPU, compiled_model1 will use MYRIAD, compiled_model2 will use CPU.
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# Example 2
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compiled_model3 = core.compile_model(model=model, device_name="AUTO", config={"MODEL_PRIORITY":"HIGH"})
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compiled_model4 = core.compile_model(model=model, device_name="AUTO", config={"MODEL_PRIORITY":"MEDIUM"})
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compiled_model5 = core.compile_model(model=model, device_name="AUTO", config={"MODEL_PRIORITY":"LOW"})
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# Assume that all the devices (CPU, GPU, and MYRIAD) can support all the networks.
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# Result: compiled_model3 will use GPU, compiled_model4 will use GPU, compiled_model5 will use MYRIAD.
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#! [part4]
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def part5():
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#! [part5]
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core = Core()
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model = core.read_model(model_path)
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core.set_property(device_name="CPU", properties={})
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core.set_property(device_name="MYRIAD", properties={})
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compiled_model = core.compile_model(model=model)
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compiled_model = core.compile_model(model=model, device_name="AUTO")
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#! [part5]
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def part6():
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#! [part6]
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core = Core()
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# read a network in IR, PaddlePaddle, or ONNX format
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model = core.read_model(model_path)
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# compile a model on AUTO and set log level to debug
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compiled_model = core.compile_model(model=model, device_name="AUTO", config={"LOG_LEVEL":"LOG_DEBUG"});
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# set log level with set_property and compile model
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core.set_property(device_name="AUTO", properties={"LOG_LEVEL":"LOG_DEBUG"});
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compiled_model = core.compile_model(model=model, device_name="AUTO");
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#! [part6]
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def main():
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part0()
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part1()
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part3()
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part4()
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part5()
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part6()
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if __name__ == '__main__':
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sys.exit(main())
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