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openvino/docs/snippets/ov_auto.py
2022-03-23 16:31:03 +08:00

119 lines
4.7 KiB
Python

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