def test_ngraph_preprocess_set_from_np_infer():
shape = [1, 1, 1]
parameter_a = ops.parameter(shape, dtype=np.float32, name="A")
model = parameter_a
function = Model(model, [parameter_a], "TestFunction")
@custom_preprocess_function
def custom_crop(out_node: Output):
start = ops.constant(np.array([1, 1, 1]), dtype=np.int32)
stop = ops.constant(np.array([2, 2, 2]), dtype=np.int32)
step = ops.constant(np.array([1, 1, 1]), dtype=np.int32)
axis = ops.constant(np.array([0, 1, 2]), dtype=np.int32)
return ops.slice(out_node, start, stop, step, axis)
input_data = np.array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
[[9, 10, 11], [12, 13, 14], [15, 16, 17]],
[[18, 19, 20], [21, 22, 23], [24, 25, 26]]]).astype(np.int32)
p = PrePostProcessor(function)
inp = p.input()
inp.tensor().set_from(input_data)
inp.preprocess().convert_element_type().custom(custom_crop)
function = p.build()
assert function.input().shape == ov.Shape([3, 3, 3])
assert function.input().element_type == Type.i32
expected_output = np.array([[[13]]]).astype(np.float32)
runtime = get_runtime()
computation = runtime.computation(function)
output = computation(input_data)
assert np.equal(output, expected_output).all()
def create_advanced_model():
# Advanced example with multi output operation
#
# Parameter->Split---0-->Result
# | `--1-->Relu-->Result
# `----2-->Result
data = ov.opset8.parameter(ov.Shape([1, 3, 64, 64]), ov.Type.f32)
# Create Constant for axis value
axis_const = ov.opset8.constant(ov.Type.i64, ov.Shape({}), [1])
# Create opset8::Split operation that splits input to three slices across 1st dimension
split = ov.opset8.split(data, axis_const, 3)
# Create opset8::Relu operation that takes 1st Split output as input
relu = ov.opset8.relu(split.output(1))
# Results operations will be created automatically based on provided OutputVector
return ov.Model([split.output(0), relu, split.output[2]], [data], "model")
def test_ngraph_preprocess_set_from_tensor():
shape = [1, 224, 224, 3]
inp_shape = [1, 480, 640, 3]
parameter_a = ops.parameter(shape, dtype=np.float32, name="A")
parameter_a.set_layout(ov.Layout("NHWC"))
model = parameter_a
function = Model(model, [parameter_a], "TestFunction")
input_data = ov.Tensor(Type.i32, inp_shape)
p = PrePostProcessor(function)
inp = p.input()
inp.tensor().set_from(input_data)
inp.preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
function = p.build()
assert function.input().shape == ov.Shape(inp_shape)
assert function.input().element_type == Type.i32
assert function.output().shape == ov.Shape(shape)
assert function.output().element_type == Type.f32
input_tensor = ov.Tensor()
infer_request.set_tensor(input_port, input_tensor)
output_tensor = infer_request.get_tensor(output_port)
#! [get_set_tensor_by_port]
infer_request1 = compiled_model.create_infer_request()
infer_request2 = compiled_model.create_infer_request()
#! [cascade_models]
output = infer_request1.get_output_tensor(0)
infer_request2.set_input_tensor(0, output)
#! [cascade_models]
#! [roi_tensor]
# input_tensor points to input of a previous network and
# cropROI contains coordinates of output bounding box **/
input_tensor = ov.Tensor(type=ov.Type.f32, shape=ov.Shape([1, 3, 20, 20]))
begin = [0, 0, 0, 0]
end = [1, 2, 3, 3]
# ...
# roi_tensor uses shared memory of input_tensor and describes cropROI
# according to its coordinates **/
roi_tensor = ov.Tensor(input_tensor, begin, end)
infer_request2.set_tensor("input_name", roi_tensor)
#! [roi_tensor]
#! [remote_tensor]
# NOT SUPPORTED
#! [remote_tensor]