def test_const_output_get_target_inputs(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
outputs = exec_net.outputs
for node in outputs:
assert isinstance(node.get_target_inputs(), set)
def test_const_output_get_partial_shape(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
node = exec_net.input("data")
expected_partial_shape = PartialShape([1, 3, 32, 32])
assert node.get_partial_shape() == expected_partial_shape
def test_output_type(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
output = exec_net.output(0)
output_type = output.get_element_type().get_type_name()
assert output_type == "f32"
class Runtime(object):
"""Represents an nGraph runtime environment."""
def __init__(self, backend_name: str) -> None:
self.backend_name = backend_name
log.debug("Creating Inference Engine for %s" % backend_name)
self.backend = Core()
assert backend_name in self.backend.available_devices, (
'The requested device "' + backend_name + '" is not supported!')
def set_config(self, config: Dict[str, str]) -> None:
"""Set the inference engine configuration."""
self.backend.set_config(config, device_name=self.backend_name)
def __repr__(self) -> str:
return "<Runtime: Backend='{}'>".format(self.backend_name)
def computation(self, node_or_function: Union[Node, Function],
*inputs: Node) -> "Computation":
"""Return a callable Computation object."""
if isinstance(node_or_function, Node):
ng_function = Function(node_or_function, inputs,
node_or_function.name)
return Computation(self, ng_function)
elif isinstance(node_or_function, Function):
return Computation(self, node_or_function)
else:
raise TypeError(
"Runtime.computation must be called with an nGraph Function object "
"or an nGraph node object an optionally Parameter node objects. "
"Called with: %s",
node_or_function,
)
def test_core_class():
input_shape = [1, 3, 4, 4]
param = ov.parameter(input_shape, np.float32, name="parameter")
relu = ov.relu(param, name="relu")
func = Function([relu], [param], "test")
func.get_ordered_ops()[2].friendly_name = "friendly"
cnn_network = IENetwork(func)
core = Core()
core.set_config({}, device_name="CPU")
executable_network = core.compile_model(cnn_network, "CPU", {})
td = TensorDesc("FP32", input_shape, "NCHW")
# from IPython import embed; embed()
request = executable_network.create_infer_request()
input_data = np.random.rand(*input_shape) - 0.5
expected_output = np.maximum(0.0, input_data)
input_blob = Blob(td, input_data)
request.set_input({"parameter": input_blob})
request.infer()
result = request.get_blob("relu").buffer
assert np.allclose(result, expected_output)
def test_read_network():
ie_core = Core()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
assert isinstance(net, IENetwork)
net = ie_core.read_network(model=test_net_xml)
assert isinstance(net, IENetwork)
def test_read_model_from_ir():
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
assert isinstance(func, Function)
func = core.read_model(model=test_net_xml)
assert isinstance(func, Function)
def test_input_get_index(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
input = exec_net.input(0)
expected_idx = 0
assert input.get_index() == expected_idx
def test_cancel(device):
ie_core = Core()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device)
img = read_image()
td = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
input_blob = Blob(td, img)
request = exec_net.create_infer_request()
def callback(req, code, array):
array.append(42)
data = []
request.set_completion_callback(callback, data)
request.set_input({"data": input_blob})
request.async_infer()
request.cancel()
with pytest.raises(RuntimeError) as e:
request.wait()
assert "[ INFER_CANCELLED ]" in str(e.value)
# check if callback has executed
assert data == [42]
request.async_infer()
status = request.wait()
assert status == StatusCode.OK
assert data == [42, 42]
def test_const_output_docs(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
node = exec_net.input(0)
exptected_string = "openvino.impl.ConstOutput wraps ov::Output<Const ov::Node >"
assert node.__doc__ == exptected_string
def test_inputs(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
inputs = exec_net.inputs
assert isinstance(inputs, list)
assert len(inputs) == 1
def test_output_shape(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
output = exec_net.output(0)
expected_shape = Shape([1, 10])
assert str(output.get_shape()) == str(expected_shape)
def test_tensor_setter(device):
core = Core()
func = core.read_model(test_net_xml, test_net_bin)
exec_net_1 = core.compile_model(model=func, device_name=device)
exec_net_2 = core.compile_model(model=func, device_name=device)
img = read_image()
tensor = Tensor(img)
request1 = exec_net_1.create_infer_request()
request1.set_tensor("data", tensor)
t1 = request1.get_tensor("data")
assert np.allclose(tensor.data, t1.data, atol=1e-2, rtol=1e-2)
res = request1.infer({0: tensor})
res_1 = np.sort(res[0])
t2 = request1.get_tensor("fc_out")
assert np.allclose(t2.data, res[0].data, atol=1e-2, rtol=1e-2)
request = exec_net_2.create_infer_request()
res = request.infer({"data": tensor})
res_2 = np.sort(request.get_tensor("fc_out").data)
assert np.allclose(res_1, res_2, atol=1e-2, rtol=1e-2)
request.set_tensor("data", tensor)
t3 = request.get_tensor("data")
assert np.allclose(t3.data, t1.data, atol=1e-2, rtol=1e-2)
def test_blob_setter(device):
ie_core = Core()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net_1 = ie_core.load_network(network=net, device_name=device)
net.input_info["data"].layout = "NHWC"
exec_net_2 = ie_core.load_network(network=net, device_name=device)
img = read_image()
request1 = exec_net_1.create_infer_request()
tensor_desc = TensorDesc("FP32", [1, 3, img.shape[2], img.shape[3]],
"NCHW")
img_blob1 = Blob(tensor_desc, img)
request1.set_input({"data": img_blob1})
request1.infer()
res_1 = np.sort(request1.get_blob("fc_out").buffer)
img = np.transpose(img, axes=(0, 2, 3, 1)).astype(np.float32)
tensor_desc = TensorDesc("FP32", [1, 3, 32, 32], "NHWC")
img_blob = Blob(tensor_desc, img)
request = exec_net_2.create_infer_request()
request.set_blob("data", img_blob)
request.infer()
res_2 = np.sort(request.get_blob("fc_out").buffer)
assert np.allclose(res_1, res_2, atol=1e-2, rtol=1e-2)
def test_infer_new_request_numpy(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
exec_net = ie.compile_model(func, device)
res = exec_net.infer_new_request({"data": img})
assert np.argmax(res) == 2
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_inputs_docs(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
inputs = exec_net.inputs
input_0 = inputs[0]
expected_string = "openvino.impl.ConstOutput wraps ov::Output<Const ov::Node >"
assert input_0.__doc__ == expected_string
def test_read_net_from_buffer():
ie_core = Core()
with open(test_net_bin, "rb") as f:
bin = f.read()
with open(model_path()[0], "rb") as f:
xml = f.read()
net = ie_core.read_network(model=xml, weights=bin)
assert isinstance(net, IENetwork)
def test_read_net_from_buffer():
core = Core()
with open(test_net_bin, "rb") as f:
bin = f.read()
with open(model_path()[0], "rb") as f:
xml = f.read()
func = core.read_model(model=xml, weights=bin)
assert isinstance(func, Function)
def import_onnx_model(model: onnx.ModelProto) -> Function:
onnx.checker.check_model(model)
model_byte_string = model.SerializeToString()
ie = Core()
func = ie.read_model(bytes(model_byte_string),
Tensor(type=np.uint8, shape=[]))
return func
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_get_version(device):
ie = Core()
version = ie.get_versions(device)
assert isinstance(version, dict), "Returned version must be a dictionary"
assert device in version, "{} plugin version wasn't found in versions"
assert hasattr(version[device], "major"), "Returned version has no field 'major'"
assert hasattr(version[device], "minor"), "Returned version has no field 'minor'"
assert hasattr(version[device], "description"), "Returned version has no field 'description'"
assert hasattr(version[device], "build_number"), "Returned version has no field 'build_number'"
def test_inputs_get_friendly_name(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
inputs = exec_net.inputs
input_0 = inputs[0]
node = input_0.get_node()
name = node.friendly_name
assert name == "data"
def test_get_input(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
input = exec_net.input()
input_node = input.get_node()
name = input_node.friendly_name
assert isinstance(input, ConstOutput)
assert name == "data"
def test_infer_new_request_wrong_port_name(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
tensor = Tensor(img)
exec_net = ie.compile_model(func, device)
with pytest.raises(RuntimeError) as e:
exec_net.infer_new_request({"_data_": tensor})
assert "Port for tensor name _data_ was not found." in str(e.value)
def test_output_set_friendly_name(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)
exec_net = core.compile_model(func, device)
output = exec_net.output(0)
output_node = output.get_node()
output_node.set_friendly_name("output_1")
name = output_node.friendly_name
assert isinstance(output, ConstOutput)
assert name == "output_1"
def test_infer_tensor_wrong_input_data(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = np.ascontiguousarray(img)
tensor = Tensor(img, shared_memory=True)
exec_net = ie.compile_model(func, device)
with pytest.raises(TypeError) as e:
exec_net.infer_new_request({4.5: tensor})
assert "Incompatible key type!" in str(e.value)
def test_infer_new_request_tensor_numpy_copy(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
tensor = Tensor(img)
exec_net = ie.compile_model(func, device)
res_tensor = exec_net.infer_new_request({"data": tensor})
res_img = exec_net.infer_new_request({"data": tensor})
assert np.argmax(res_tensor) == 2
assert np.argmax(res_tensor) == np.argmax(res_img)
