def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
# --------------------------- Step 1. Initialize OpenVINO Runtime Core --------------------------------------------
core = Core()
# --------------------------- Step 2. Get metrics of available devices --------------------------------------------
log.info('Available devices:')
for device in core.available_devices:
log.info(f'{device} :')
log.info('\tSUPPORTED_METRICS:')
for metric in core.get_metric(device, 'SUPPORTED_METRICS'):
if metric not in ('SUPPORTED_METRICS', 'SUPPORTED_CONFIG_KEYS'):
try:
metric_val = core.get_metric(device, metric)
except TypeError:
metric_val = 'UNSUPPORTED TYPE'
log.info(f'\t\t{metric}: {param_to_string(metric_val)}')
log.info('')
log.info('\tSUPPORTED_CONFIG_KEYS (default values):')
for config_key in core.get_metric(device, 'SUPPORTED_CONFIG_KEYS'):
try:
config_val = core.get_config(device, config_key)
except TypeError:
config_val = 'UNSUPPORTED TYPE'
log.info(f'\t\t{config_key}: {param_to_string(config_val)}')
log.info('')
# -----------------------------------------------------------------------------------------------------------------
return 0
def test_get_metric_tuple_of_three_ints():
ie = Core()
param = ie.get_metric("CPU", "RANGE_FOR_ASYNC_INFER_REQUESTS")
assert isinstance(param, tuple), "Parameter value for 'RANGE_FOR_ASYNC_INFER_REQUESTS' " \
f"metric must be tuple but {type(param)} is returned"
assert all(isinstance(v, int) for v in param), "Not all of the parameter values for " \
"'RANGE_FOR_ASYNC_INFER_REQUESTS' metric are integers!"
def test_get_metric_tuple_of_two_ints():
ie = Core()
param = ie.get_metric("CPU", "RANGE_FOR_STREAMS")
assert isinstance(param, tuple), "Parameter value for 'RANGE_FOR_STREAMS' " \
f"metric must be tuple but {type(param)} is returned"
assert all(isinstance(v, int) for v in param), \
"Not all of the parameter values for 'RANGE_FOR_STREAMS' metric are integers!"
def test_get_metric_list_of_str():
ie = Core()
param = ie.get_metric("CPU", "OPTIMIZATION_CAPABILITIES")
assert isinstance(param, list), "Parameter value for 'OPTIMIZATION_CAPABILITIES' " \
f"metric must be a list but {type(param)} is returned"
assert all(isinstance(v, str) for v in param), \
"Not all of the parameter values for 'OPTIMIZATION_CAPABILITIES' metric are strings!"
def test_get_config(device):
core = Core()
if core.get_metric(device, "FULL_DEVICE_NAME") == "arm_compute::NEON":
pytest.skip("Can't run on ARM plugin due-to CPU dependent test")
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
config = exec_net.get_config("PERF_COUNT")
assert config == "NO"
def test_query_state_write_buffer(device, input_shape, data_type, mode):
core = Core()
if device == "CPU":
if core.get_metric(device, "FULL_DEVICE_NAME") == "arm_compute::NEON":
pytest.skip("Can't run on ARM plugin")
from openvino.runtime import Tensor
from openvino.runtime.utils.types import get_dtype
function = create_function_with_memory(input_shape, data_type)
exec_net = core.compile_model(model=function, device_name=device)
request = exec_net.create_infer_request()
mem_states = request.query_state()
mem_state = mem_states[0]
assert mem_state.name == "var_id_667"
# todo: Uncomment after fix 45611,
# CPU plugin returns outputs and memory state in FP32 in case of FP16 original precision
# assert mem_state.state.tensor_desc.precision == data_type
for i in range(1, 10):
if mode == "set_init_memory_state":
# create initial value
const_init = 5
init_array = np.full(input_shape,
const_init,
dtype=get_dtype(mem_state.state.element_type))
tensor = Tensor(init_array)
mem_state.state = tensor
res = request.infer({0: np.full(input_shape, 1, dtype=data_type)})
expected_res = np.full(input_shape,
1 + const_init,
dtype=data_type)
elif mode == "reset_memory_state":
# reset initial state of ReadValue to zero
mem_state.reset()
res = request.infer({0: np.full(input_shape, 1, dtype=data_type)})
# always ones
expected_res = np.full(input_shape, 1, dtype=data_type)
else:
res = request.infer({0: np.full(input_shape, 1, dtype=data_type)})
expected_res = np.full(input_shape, i, dtype=data_type)
assert np.allclose(res[list(res)[0]], expected_res, atol=1e-6), \
"Expected values: {} \n Actual values: {} \n".format(expected_res, res)
def test_get_metric_str():
ie = Core()
param = ie.get_metric("CPU", "FULL_DEVICE_NAME")
assert isinstance(param, str), "Parameter value for 'FULL_DEVICE_NAME' " \
f"metric must be string but {type(param)} is returned"
# ! [compiled_model_get_rw_property]
perf_mode = compiled_model.get_property("PERFORMANCE_HINT")
# ! [compiled_model_get_rw_property]
# ! [compiled_model_get_ro_property]
nireq = compiled_model.get_property("OPTIMAL_NUMBER_OF_INFER_REQUESTS")
# ! [compiled_model_get_ro_property]
from openvino.inference_engine import IECore
core = IECore()
#! [core_get_metric]
full_device_name = core.get_metric("CPU", "FULL_DEVICE_NAME")
#! [core_get_metric]
#! [core_get_config]
num_streams = core.get_config("CPU", "CPU_THROUGHPUT_STREAMS")
#! [core_get_config]
#! [core_set_config]
core.set_config({"PERF_COUNT": "YES"}, "CPU")
#! [core_set_config]
net = core.read_network("sample.xml")
#! [core_load_network]
exec_network = core.load_network(net, "MULTI", {"DEVICE_PRIORITIES": "CPU, GPU",
"PERFORMANCE_HINT": "THROUGHPUT",
