def test_cannot_set_shape_on_preallocated_memory(ref_shape): ones_arr = np.ones(shape=(1, 3, 32, 32), dtype=np.float32) ov_tensor = Tensor(ones_arr) with pytest.raises(RuntimeError) as e: ov_tensor.shape = ref_shape assert "Cannot call setShape for Blobs created on top of preallocated memory" in str( e.value)
def test_init_with_roi_tensor(): array = np.random.normal(size=[1, 3, 48, 48]) ov_tensor1 = Tensor(array) ov_tensor2 = Tensor(ov_tensor1, [0, 0, 24, 24], [1, 3, 48, 48]) assert list(ov_tensor2.shape) == [1, 3, 24, 24] assert ov_tensor2.element_type == ov_tensor2.element_type assert np.shares_memory(ov_tensor1.data, ov_tensor2.data) assert np.array_equal(ov_tensor1.data[0:1, :, 24:, 24:], ov_tensor2.data)
def test_init_with_ngraph(ov_type, numpy_dtype): ov_tensors = [] ov_tensors.append(Tensor(type=ov_type, shape=ov.impl.Shape([1, 3, 32, 32]))) ov_tensors.append(Tensor(type=ov_type, shape=[1, 3, 32, 32])) assert np.all([list(ov_tensor.shape) == [1, 3, 32, 32] for ov_tensor in ov_tensors]) assert np.all(ov_tensor.element_type == ov_type for ov_tensor in ov_tensors) assert np.all(ov_tensor.data.dtype == numpy_dtype for ov_tensor in ov_tensors) assert np.all(ov_tensor.data.shape == (1, 3, 32, 32) for ov_tensor in ov_tensors)
def test_evaluate(): param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1") param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2") add = ops.add(param1, param2) func = Function(add, [param1, param2], "TestFunction") input1 = np.array([2, 1], dtype=np.float32).reshape(2, 1) input2 = np.array([3, 7], dtype=np.float32).reshape(2, 1) out_tensor = Tensor("float32", Shape([2, 1])) assert func.evaluate([out_tensor], [Tensor(input1), Tensor(input2)]) assert np.allclose(out_tensor.data, np.array([5, 8]).reshape(2, 1))
def test_set_tensors(device): core = Core() func = core.read_model(test_net_xml, test_net_bin) exec_net = core.compile_model(func, device) data1 = read_image() tensor1 = Tensor(data1) data2 = np.ones(shape=(1, 10), dtype=np.float32) tensor2 = Tensor(data2) data3 = np.ones(shape=(1, 3, 32, 32), dtype=np.float32) tensor3 = Tensor(data3) data4 = np.zeros(shape=(1, 10), dtype=np.float32) tensor4 = Tensor(data4) request = exec_net.create_infer_request() request.set_tensors({"data": tensor1, "fc_out": tensor2}) t1 = request.get_tensor("data") t2 = request.get_tensor("fc_out") assert np.allclose(tensor1.data, t1.data, atol=1e-2, rtol=1e-2) assert np.allclose(tensor2.data, t2.data, atol=1e-2, rtol=1e-2) request.set_output_tensors({0: tensor2}) output_node = exec_net.outputs[0] t3 = request.get_tensor(output_node) assert np.allclose(tensor2.data, t3.data, atol=1e-2, rtol=1e-2) request.set_input_tensors({0: tensor1}) output_node = exec_net.inputs[0] t4 = request.get_tensor(output_node) assert np.allclose(tensor1.data, t4.data, atol=1e-2, rtol=1e-2) output_node = exec_net.inputs[0] request.set_tensor(output_node, tensor3) t5 = request.get_tensor(output_node) assert np.allclose(tensor3.data, t5.data, atol=1e-2, rtol=1e-2) request.set_input_tensor(tensor3) t6 = request.get_tensor(request.inputs[0]) assert np.allclose(tensor3.data, t6.data, atol=1e-2, rtol=1e-2) request.set_input_tensor(0, tensor1) t7 = request.get_tensor(request.inputs[0]) assert np.allclose(tensor1.data, t7.data, atol=1e-2, rtol=1e-2) request.set_output_tensor(tensor2) t8 = request.get_tensor(request.outputs[0]) assert np.allclose(tensor2.data, t8.data, atol=1e-2, rtol=1e-2) request.set_output_tensor(0, tensor4) t9 = request.get_tensor(request.outputs[0]) assert np.allclose(tensor4.data, t9.data, atol=1e-2, rtol=1e-2)
def test_init_with_numpy_dtype(ov_type, numpy_dtype): shape = (1, 3, 127, 127) ov_shape = ov.impl.Shape(shape) ov_tensors = [] ov_tensors.append(Tensor(type=numpy_dtype, shape=shape)) ov_tensors.append(Tensor(type=np.dtype(numpy_dtype), shape=shape)) ov_tensors.append(Tensor(type=np.dtype(numpy_dtype), shape=np.array(shape))) ov_tensors.append(Tensor(type=numpy_dtype, shape=ov_shape)) ov_tensors.append(Tensor(type=np.dtype(numpy_dtype), shape=ov_shape)) assert np.all(tuple(ov_tensor.shape) == shape for ov_tensor in ov_tensors) assert np.all(ov_tensor.element_type == ov_type for ov_tensor in ov_tensors) assert np.all(isinstance(ov_tensor.data, np.ndarray) for ov_tensor in ov_tensors) assert np.all(ov_tensor.data.dtype == numpy_dtype for ov_tensor in ov_tensors) assert np.all(ov_tensor.data.shape == shape for ov_tensor in ov_tensors)
def test_infer_mixed_keys(device): core = Core() func = core.read_model(test_net_xml, test_net_bin) core.set_config({"PERF_COUNT": "YES"}, device) model = core.compile_model(func, device) img = read_image() tensor = Tensor(img) data2 = np.ones(shape=img.shape, dtype=np.float32) tensor2 = Tensor(data2) request = model.create_infer_request() res = request.infer({0: tensor2, "data": tensor}) assert np.argmax(res) == 2
def test_evaluate_invalid_input_shape(): param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1") param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2") add = ops.add(param1, param2) func = Function(add, [param1, param2], "TestFunction") with pytest.raises(RuntimeError) as e: assert func.evaluate( [Tensor("float32", Shape([2, 1]))], [ Tensor("float32", Shape([3, 1])), Tensor("float32", Shape([3, 1])) ], ) assert "must be compatible with the partial shape: {2,1}" in str(e.value)
def test_infer_mixed_keys(device): core = Core() func = core.read_model(test_net_xml, test_net_bin) core.set_config({"PERF_COUNT": "YES"}, device) exec_net = core.compile_model(func, device) img = read_image() tensor = Tensor(img) data2 = np.ones(shape=(1, 10), dtype=np.float32) tensor2 = Tensor(data2) request = exec_net.create_infer_request() with pytest.raises(TypeError) as e: request.infer({0: tensor, "fc_out": tensor2}) assert "incompatible function arguments!" in str(e.value)
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 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_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_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)
def test_infer_tensor_numpy_shared_memory(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) 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)
def test_infer_tensor_model_from_buffer(device): core = Core() with open(test_net_bin, "rb") as f: bin = f.read() with open(test_net_xml, "rb") as f: xml = f.read() func = core.read_model(model=xml, weights=bin) img = read_image() tensor = Tensor(img) exec_net = core.compile_model(func, device) res = exec_net.infer_new_request({"data": tensor}) assert np.argmax(res) == 2
def test_init_with_numpy(ov_type, numpy_dtype): shape = (1, 3, 127, 127) ov_shape = ng.impl.Shape(shape) ones_arr = np.ones(shape, numpy_dtype) ones_ov_tensor = Tensor(array=ones_arr) ov_tensors = [] ov_tensors.append(Tensor(dtype=numpy_dtype, shape=shape)) ov_tensors.append(Tensor(dtype=np.dtype(numpy_dtype), shape=shape)) ov_tensors.append( Tensor(dtype=np.dtype(numpy_dtype), shape=np.array(shape))) ov_tensors.append(ones_ov_tensor) ov_tensors.append(Tensor(dtype=numpy_dtype, shape=ov_shape)) ov_tensors.append(Tensor(dtype=np.dtype(numpy_dtype), shape=ov_shape)) assert np.all(tuple(ov_tensor.shape) == shape for ov_tensor in ov_tensors) assert np.all(ov_tensor.element_type == ov_type for ov_tensor in ov_tensors) assert np.all( isinstance(ov_tensor.data, np.ndarray) for ov_tensor in ov_tensors) assert np.all(ov_tensor.data.dtype == numpy_dtype for ov_tensor in ov_tensors) assert np.all(ov_tensor.data.shape == shape for ov_tensor in ov_tensors) assert np.shares_memory(ones_arr, ones_ov_tensor.data) assert np.array_equal(ones_ov_tensor.data, ones_arr) assert ones_ov_tensor.size == ones_arr.size assert ones_ov_tensor.byte_size == ones_arr.nbytes
def test_init_with_numpy_copy_memory(ov_type, numpy_dtype): arr = read_image().astype(numpy_dtype) shape = arr.shape ov_tensor = Tensor(array=arr, shared_memory=False) assert tuple(ov_tensor.shape) == shape assert ov_tensor.element_type == ov_type assert isinstance(ov_tensor.data, np.ndarray) assert ov_tensor.data.dtype == numpy_dtype assert ov_tensor.data.shape == shape assert not (np.shares_memory(arr, ov_tensor.data)) assert np.array_equal(ov_tensor.data, arr) assert ov_tensor.size == arr.size assert ov_tensor.byte_size == arr.nbytes
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 import Tensor from openvino.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[0], expected_res, atol=1e-6), \ "Expected values: {} \n Actual values: {} \n".format(expected_res, res)
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" core = Core() model = core.compile_model(func, "CPU", {}) request = model.create_infer_request() input_data = np.random.rand(*input_shape).astype(np.float32) - 0.5 expected_output = np.maximum(0.0, input_data) input_tensor = Tensor(input_data) results = request.infer({"parameter": input_tensor}) assert np.allclose(results, expected_output)
def test_set_shape(ov_type, numpy_dtype): shape = ng.impl.Shape([1, 3, 32, 32]) ref_shape = ng.impl.Shape([1, 3, 48, 48]) ref_shape_np = [1, 3, 28, 28] ov_tensor = Tensor(ov_type, shape) ov_tensor.shape = ref_shape assert list(ov_tensor.shape) == list(ref_shape) ones_arr = np.ones(list(ov_tensor.shape), numpy_dtype) ov_tensor.data[:] = ones_arr assert np.array_equal(ov_tensor.data, ones_arr) ov_tensor.shape = ref_shape_np assert list(ov_tensor.shape) == ref_shape_np zeros = np.zeros(ref_shape_np, numpy_dtype) ov_tensor.data[:] = zeros assert np.array_equal(ov_tensor.data, zeros)
def test_init_with_numpy_fail(): arr = read_image() with pytest.raises(RuntimeError) as e: _ = Tensor(array=arr, shared_memory=True) assert "Tensor with shared memory must be C contiguous" in str(e.value)
def test_cannot_set_shape_incorrect_dims(): ov_tensor = Tensor(np.float32, [1, 3, 48, 48]) with pytest.raises(RuntimeError) as e: ov_tensor.shape = [3, 28, 28] assert "Dims and format are inconsistent" in str(e.value)
def test_write_to_buffer(ov_type, numpy_dtype): ov_tensor = Tensor(ov_type, ng.impl.Shape([1, 3, 32, 32])) ones_arr = np.ones([1, 3, 32, 32], numpy_dtype) ov_tensor.data[:] = ones_arr assert np.array_equal(ov_tensor.data, ones_arr)