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_unregister_plugin(device): ie = Core() ie.unload_plugin(device) func = ie.read_model(model=test_net_xml, weights=test_net_bin) with pytest.raises(RuntimeError) as e: ie.load_network(func, device) assert f"Device with '{device}' name is not registered in the InferenceEngine" in str( e.value)
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 = 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) ie_core = 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_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_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_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
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)