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_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_ie_core_class():
input_shape = [1, 3, 4, 4]
param = ng.parameter(input_shape, np.float32, name="parameter")
relu = ng.relu(param, name="relu")
func = Function([relu], [param], "test")
func.get_ordered_ops()[2].friendly_name = "friendly"
cnn_network = ov.IENetwork(func)
ie_core = ov.Core()
ie_core.set_config({}, device_name="CPU")
executable_network = ie_core.load_network(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_blobs():
input_shape = [1, 3, 4, 4]
input_data_float32 = (np.random.rand(*input_shape) - 0.5).astype(np.float32)
td = TensorDesc("FP32", input_shape, "NCHW")
input_blob_float32 = Blob(td, input_data_float32)
assert np.all(np.equal(input_blob_float32.buffer, input_data_float32))
input_data_int16 = (np.random.rand(*input_shape) + 0.5).astype(np.int16)
td = TensorDesc("I16", input_shape, "NCHW")
input_blob_i16 = Blob(td, input_data_int16)
assert np.all(np.equal(input_blob_i16.buffer, input_data_int16))
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_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_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