def test_query_state(device):
import ngraph as ng
from ngraph.impl import Function
input_data = ng.parameter([5, 7], name="input_data", dtype=np.float32)
rv = ng.read_value(input_data, "var_id_667")
#a = ng.add(rv, input_data)
node = ng.assign(rv, "var_id_667")
res = ng.result(rv, "res")
func = Function([res], sinks=[node], parameters=[input_data], name='test')
caps = Function.to_capsule(func)
net = ie.IENetwork(caps)
ie_core = ie.IECore()
exec_net = ie_core.load_network(network=net,
device_name=device,
num_requests=1)
request = exec_net.requests[0]
mem_states = request.query_state()
mem_state = mem_states[0]
with pytest.raises(ValueError) as e:
ones_arr = np.ones(shape=(1, 800), dtype=np.float32)
mem_state.state.buffer[:] = ones_arr
assert "assignment destination is read-only" in str(e.value)
assert mem_state.name == 'id_1'
assert mem_state.state.tensor_desc.precision == 'FP32'
def test_query_state_write_buffer(device, input_shape, data_type, mode):
ie_core = ie.IECore()
if device == "CPU":
if ie_core.get_metric(device, "FULL_DEVICE_NAME") == "arm_compute::NEON":
pytest.skip("Can't run on ARM plugin")
layout = ["C", "HW", "CHW", "NCHW"]
np_data_type = {"FP32": np.float32, "FP16": np.float16, "I32": np.int32}
from openvino.inference_engine import TensorDesc, Blob
net = ie.IENetwork(create_function_with_memory(input_shape, np_data_type[data_type]))
ie_core = ie.IECore()
exec_net = ie_core.load_network(network=net, device_name=device, num_requests=1)
request = exec_net.requests[0]
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=np_data_type[mem_state.state.tensor_desc.precision])
tensor_desc = TensorDesc(mem_state.state.tensor_desc.precision, input_shape, layout[len(input_shape) - 1])
blob = Blob(tensor_desc, init_array)
mem_state.state = blob
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=np_data_type[data_type])})
expected_res = np.full(input_shape, 1 + const_init, dtype=np_data_type[data_type])
elif mode == "reset_memory_state":
# reset initial state of ReadValue to zero
mem_state.reset()
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=np_data_type[data_type])})
# always ones
expected_res = np.full(input_shape, 1, dtype=np_data_type[data_type])
else:
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=np_data_type[data_type])})
expected_res = np.full(input_shape, i, dtype=np_data_type[data_type])
assert np.allclose(res['MemoryAdd'], expected_res, atol=1e-6), \
"Expected values: {} \n Actual values: {} \n".format(expected_res, res)
