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_network_as_path():
ie_core = Core()
net = ie_core.read_network(model=Path(test_net_xml), weights=Path(test_net_bin))
assert isinstance(net, IENetwork)
net = ie_core.read_network(model=test_net_xml, weights=Path(test_net_bin))
assert isinstance(net, IENetwork)
net = ie_core.read_network(model=Path(test_net_xml))
assert isinstance(net, IENetwork)
def test_read_network_from_blob_valid():
ie_core = Core()
model = open(test_net_xml).read()
blob = blob_from_file(test_net_bin)
net = ie_core.read_network(model=model, blob=blob)
ref_net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
assert net.name == ref_net.name
assert net.batch_size == ref_net.batch_size
ii_net = net.input_info
ii_net2 = ref_net.input_info
o_net = net.outputs
o_net2 = ref_net.outputs
assert ii_net.keys() == ii_net2.keys()
assert o_net.keys() == o_net2.keys()
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_register_plugin():
ie = Core()
ie.register_plugin("MKLDNNPlugin", "BLA")
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie.load_network(net, "BLA")
assert isinstance(exec_net, ExecutableNetwork), \
"Cannot load the network to the registered plugin with name 'BLA'"
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_net_from_buffer_valid():
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)
ref_net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
assert net.name == ref_net.name
assert net.batch_size == ref_net.batch_size
ii_net = net.input_info
ii_net2 = ref_net.input_info
o_net = net.outputs
o_net2 = ref_net.outputs
assert ii_net.keys() == ii_net2.keys()
assert o_net.keys() == o_net2.keys()
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 import_onnx_model(model: onnx.ModelProto) -> Function:
onnx.checker.check_model(model)
model_byte_string = model.SerializeToString()
ie = Core()
ie_network = ie.read_network(bytes(model_byte_string),
Blob(TensorDesc("U8", [], "C")))
ng_function = ie_network.get_function()
return ng_function
def test_query_network(device):
ie = Core()
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
query_res = ie.query_network(network=net, device_name=device)
func_net = net.get_function()
ops_net = func_net.get_ordered_ops()
ops_net_names = [op.friendly_name for op in ops_net]
assert [key for key in query_res.keys() if key not in ops_net_names] == [], \
"Not all network layers present in query_network results"
assert next(iter(set(query_res.values()))) == device, "Wrong device for some layers"
def test_set_zero_batch_size(device):
ie_core = Core()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device)
request = exec_net.create_infer_request()
with pytest.raises(ValueError) as e:
request.set_batch(0)
assert "Batch size should be positive integer number but 0 specified" in str(e.value)
del exec_net
del ie_core
del net
def test_register_plugins():
ie = Core()
if platform == "linux" or platform == "linux2":
ie.register_plugins(plugins_xml)
elif platform == "darwin":
ie.register_plugins(plugins_osx_xml)
elif platform == "win32":
ie.register_plugins(plugins_win_xml)
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie.load_network(net, "CUSTOM")
assert isinstance(exec_net,
ExecutableNetwork), "Cannot load the network to " \
"the registered plugin with name 'CUSTOM' " \
"registred in the XML file"
def test_import_onnx_function():
model_path = os.path.join(os.path.dirname(__file__), "models/add_abc.onnx")
ie = Core()
network = ie.read_network(model=model_path)
ng_function = network.get_function()
dtype = np.float32
value_a = np.array([1.0], dtype=dtype)
value_b = np.array([2.0], dtype=dtype)
value_c = np.array([3.0], dtype=dtype)
runtime = get_runtime()
computation = runtime.computation(ng_function)
result = computation(value_a, value_b, value_c)
assert np.allclose(result, np.array([6], dtype=dtype))
def test_get_perf_counts(device):
ie_core = Core()
net = ie_core.read_network(test_net_xml, test_net_bin)
ie_core.set_config({"PERF_COUNT": "YES"}, device)
exec_net = ie_core.load_network(net, device)
img = read_image()
request = exec_net.create_infer_request()
td = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
input_blob = Blob(td, img)
request.set_input({"data": input_blob})
request.infer()
pc = request.get_perf_counts()
assert pc["29"]["status"] == "EXECUTED"
assert pc["29"]["layer_type"] == "FullyConnected"
del exec_net
del ie_core
del net
def test_import_onnx_with_external_data():
model_path = os.path.join(os.path.dirname(__file__),
"models/external_data.onnx")
ie = Core()
network = ie.read_network(model=model_path)
ng_function = network.get_function()
dtype = np.float32
value_a = np.array([1.0, 3.0, 5.0], dtype=dtype)
value_b = np.array([3.0, 5.0, 1.0], dtype=dtype)
# third input [5.0, 1.0, 3.0] read from external file
runtime = get_runtime()
computation = runtime.computation(ng_function)
result = computation(value_a, value_b)
assert np.allclose(result, np.array([3.0, 3.0, 3.0], dtype=dtype))
def test_set_batch_size(device):
ie_core = Core()
ie_core.set_config({"DYN_BATCH_ENABLED": "YES"}, device)
net = ie_core.read_network(test_net_xml, test_net_bin)
net.batch_size = 10
data = np.ones(shape=net.input_info["data"].input_data.shape)
exec_net = ie_core.load_network(net, device)
data[0] = read_image()[0]
request = exec_net.create_infer_request()
request.set_batch(1)
td = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
input_blob = Blob(td, data)
request.set_input({"data": input_blob})
request.infer()
assert np.allclose(int(round(request.output_blobs["fc_out"].buffer[0][2])), 1), \
"Incorrect data for 1st batch"
del exec_net
del ie_core
del net
def test_load_network(device):
ie = Core()
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie.load_network(net, device)
assert isinstance(exec_net, ExecutableNetwork)
def test_read_network_from_blob():
ie_core = Core()
model = open(test_net_xml).read()
blob = blob_from_file(test_net_bin)
net = ie_core.read_network(model=model, blob=blob)
assert isinstance(net, IENetwork)
def test_read_network_from_onnx_as_path():
ie_core = Core()
net = ie_core.read_network(model=Path(test_net_onnx))
assert isinstance(net, IENetwork)
def test_read_network_from_onnx():
ie_core = Core()
net = ie_core.read_network(model=test_net_onnx)
assert isinstance(net, IENetwork)
