def test_simple_tensor_iterator():
X = ov.parameter(Shape([32, 40, 10]), np.float32, "X")
Y = ov.parameter(Shape([32, 40, 10]), np.float32, "Y")
M = ov.parameter(Shape([32, 2, 10]), np.float32, "M")
X_i = ov.parameter(Shape([32, 2, 10]), np.float32, "X_i")
Y_i = ov.parameter(Shape([32, 2, 10]), np.float32, "Y_i")
M_body = ov.parameter(Shape([32, 2, 10]), np.float32, "M_body")
sum = ov.add(X_i, Y_i)
Zo = ov.multiply(sum, M_body)
body = Model([Zo], [X_i, Y_i, M_body], "body_function")
ti = ov.tensor_iterator()
ti.set_body(body)
ti.set_sliced_input(X_i, X.output(0), 0, 2, 2, 39, 1)
ti.set_sliced_input(Y_i, Y.output(0), 0, 2, 2, -1, 1)
ti.set_invariant_input(M_body, M.output(0))
out0 = ti.get_iter_value(Zo.output(0), -1)
out1 = ti.get_concatenated_slices(Zo.output(0), 0, 2, 2, 39, 1)
result0 = ov.result(out0)
result1 = ov.result(out1)
out0_shape = [32, 2, 10]
out1_shape = [32, 40, 10]
assert list(result0.get_output_shape(0)) == out0_shape
assert list(result1.get_output_shape(0)) == out1_shape
assert list(ti.get_output_shape(0)) == out0_shape
assert list(ti.get_output_shape(1)) == out1_shape
def test_low_latency2():
X = opset8.parameter(Shape([32, 40, 10]), np.float32, "X")
Y = opset8.parameter(Shape([32, 40, 10]), np.float32, "Y")
M = opset8.parameter(Shape([32, 2, 10]), np.float32, "M")
X_i = opset8.parameter(Shape([32, 2, 10]), np.float32, "X_i")
Y_i = opset8.parameter(Shape([32, 2, 10]), np.float32, "Y_i")
M_body = opset8.parameter(Shape([32, 2, 10]), np.float32, "M_body")
sum = opset8.add(X_i, Y_i)
Zo = opset8.multiply(sum, M_body)
body = Model([Zo], [X_i, Y_i, M_body], "body_function")
ti = opset8.tensor_iterator()
ti.set_body(body)
ti.set_sliced_input(X_i, X.output(0), 0, 2, 2, 39, 1)
ti.set_sliced_input(Y_i, Y.output(0), 0, 2, 2, -1, 1)
ti.set_invariant_input(M_body, M.output(0))
out0 = ti.get_iter_value(Zo.output(0), -1)
out1 = ti.get_concatenated_slices(Zo.output(0), 0, 2, 2, 39, 1)
result0 = opset8.result(out0)
result1 = opset8.result(out1)
model = Model([result0, result1], [X, Y, M])
m = Manager()
m.register_pass(LowLatency2())
m.run_passes(model)
# TODO: create TI which will be transformed by LowLatency2
assert count_ops(model, "TensorIterator") == [1]
def simple_if(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
X_t = ov.parameter([2], np.float32, "X")
Y_t = ov.parameter([2], np.float32, "Y")
then_mul = ov.multiply(X_t, Y_t)
then_body_res_1 = ov.result(then_mul)
then_body = Model([then_body_res_1], [X_t, Y_t], "then_body_function")
# else_body
X_e = ov.parameter([2], np.float32, "X")
Y_e = ov.parameter([2], np.float32, "Y")
add_e = ov.add(X_e, Y_e)
else_body_res_1 = ov.result(add_e)
else_body = Model([else_body_res_1], [X_e, Y_e], "else_body_function")
X = ov.constant([3, 4], dtype=np.float32)
Y = ov.constant([2, 1], dtype=np.float32)
if_node = ov.if_op(condition)
if_node.set_then_body(then_body)
if_node.set_else_body(else_body)
if_node.set_input(X.output(0), X_t, X_e)
if_node.set_input(Y.output(0), Y_t, Y_e)
if_res = if_node.set_output(then_body_res_1, else_body_res_1)
relu = ov.relu(if_res)
return relu
def simple_if(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
X_t = ov.parameter([2], np.float32, "X")
Y_t = ov.parameter([2], np.float32, "Y")
then_mul = ov.multiply(X_t, Y_t)
then_body_res_1 = ov.result(then_mul)
then_body = GraphBody([X_t, Y_t], [then_body_res_1])
then_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(2, 1)
]
then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
# else_body
X_e = ov.parameter([2], np.float32, "X")
Y_e = ov.parameter([2], np.float32, "Y")
add_e = ov.add(X_e, Y_e)
else_body_res_1 = ov.result(add_e)
else_body = GraphBody([X_e, Y_e], [else_body_res_1])
else_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(2, 1)
]
else_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
X = ov.constant([3, 4], dtype=np.float32)
Y = ov.constant([2, 1], dtype=np.float32)
if_node = ov.if_op(condition, [X, Y], (then_body, else_body),
(then_body_inputs, else_body_inputs),
(then_body_outputs, else_body_outputs))
relu = ov.relu(if_node)
return relu
def create_function2(shape1=[2, 2], shape2=[2, 2], dtype1=np.float32, dtype2=np.float32):
input1 = ops.parameter(shape1, dtype=dtype1, name="input1")
input1.get_output_tensor(0).set_names({'input1', 'input1a'})
relu1 = ops.relu(input1)
res1 = ops.result(relu1, "res1")
res1.get_output_tensor(0).set_names({'res1', 'res1a'})
input2 = ops.parameter(shape2, dtype=dtype2, name="input2")
input2.get_output_tensor(0).set_names({'input2', 'input2a'})
relu2 = ops.relu(input2)
res2 = ops.result(relu2, "res2")
res2.get_output_tensor(0).set_names({'res2', 'res2a'})
function = Model(results=[res1, res2], parameters=[input1, input2], name="TestFunction")
return function
def create_simple_if_with_two_outputs(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
X_t = ov.parameter([], np.float32, "X")
Y_t = ov.parameter([], np.float32, "Y")
Z_t = ov.parameter([], np.float32, "Z")
add_t = ov.add(X_t, Y_t)
mul_t = ov.multiply(Y_t, Z_t)
then_body_res_1 = ov.result(add_t)
then_body_res_2 = ov.result(mul_t)
then_body = GraphBody([X_t, Y_t, Z_t], [then_body_res_1, then_body_res_2])
then_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(2, 1),
TensorIteratorInvariantInputDesc(3, 2)
]
then_body_outputs = [
TensorIteratorBodyOutputDesc(0, 0),
TensorIteratorBodyOutputDesc(1, 1)
]
# else_body
X_e = ov.parameter([], np.float32, "X")
Z_e = ov.parameter([], np.float32, "Z")
W_e = ov.parameter([], np.float32, "W")
add_e = ov.add(X_e, W_e)
pow_e = ov.power(W_e, Z_e)
else_body_res_1 = ov.result(add_e)
else_body_res_2 = ov.result(pow_e)
else_body = GraphBody([X_e, Z_e, W_e], [else_body_res_1, else_body_res_2])
else_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(3, 1),
TensorIteratorInvariantInputDesc(4, 2)
]
else_body_outputs = [
TensorIteratorBodyOutputDesc(0, 0),
TensorIteratorBodyOutputDesc(1, 1)
]
X = ov.constant(15.0, dtype=np.float32)
Y = ov.constant(-5.0, dtype=np.float32)
Z = ov.constant(4.0, dtype=np.float32)
W = ov.constant(2.0, dtype=np.float32)
if_node = ov.if_op(condition, [X, Y, Z, W], (then_body, else_body),
(then_body_inputs, else_body_inputs),
(then_body_outputs, else_body_outputs))
return if_node
def test_simple_loop():
X = ov.parameter(Shape([32, 1, 10]), np.float32, "X")
Y = ov.parameter(Shape([32, 1, 10]), np.float32, "Y")
M = ov.parameter(Shape([32, 1, 10]), np.float32, "M")
input_shape = Shape([])
current_iteration = ov.parameter(Shape([1]), np.int64)
X_i = ov.parameter(input_shape, np.float32)
Y_i = ov.parameter(input_shape, np.float32)
M_body = ov.parameter(input_shape, np.float32)
bool_val = np.array([1], dtype=np.bool)
bool_val[0] = True
body_condition = ov.constant(bool_val)
trip_count = ov.constant(10, dtype=np.int64)
exec_condition = ov.constant(True, dtype=np.bool)
sum = ov.add(X_i, Y_i)
Zo = ov.multiply(sum, M_body)
body = Model([body_condition, Zo], [current_iteration, X_i, Y_i, M_body],
"body_function")
loop = ov.loop(trip_count, exec_condition)
loop.set_function(body)
loop.set_invariant_input(X_i, X.output(0))
loop.set_invariant_input(Y_i, Y.output(0))
loop.set_merged_input(M_body, M.output(0), Zo.output(0))
loop.set_special_body_ports([-1, 0])
out0 = loop.get_iter_value(body_condition.output(0), -1)
out1 = loop.get_iter_value(Zo.output(0), -1)
out2 = loop.get_concatenated_slices(Zo.output(0), 0, 1, 1, -1, 1)
result0 = ov.result(out0)
result1 = ov.result(out1)
result2 = ov.result(out2)
out0_shape = [1]
out1_shape = [32, 1, 10]
out2_shape = [32, 10, 10]
assert list(result0.get_output_shape(0)) == out0_shape
assert list(result1.get_output_shape(0)) == out1_shape
assert list(result2.get_output_shape(0)) == out2_shape
assert list(loop.get_output_shape(0)) == out0_shape
assert list(loop.get_output_shape(1)) == out1_shape
assert list(loop.get_output_shape(2)) == out2_shape
def test_batched_tensors(device):
core = Core()
# TODO: remove when plugins will support set_input_tensors
core.register_plugin("openvino_template_plugin", "TEMPLATE")
batch = 4
one_shape = [1, 2, 2, 2]
one_shape_size = np.prod(one_shape)
batch_shape = [batch, 2, 2, 2]
data1 = ops.parameter(batch_shape, np.float32)
data1.set_friendly_name("input0")
data1.get_output_tensor(0).set_names({"tensor_input0"})
data1.set_layout(Layout("N..."))
constant = ops.constant([1], np.float32)
op1 = ops.add(data1, constant)
op1.set_friendly_name("Add0")
res1 = ops.result(op1)
res1.set_friendly_name("Result0")
res1.get_output_tensor(0).set_names({"tensor_output0"})
model = Model([res1], [data1])
compiled = core.compile_model(model, "TEMPLATE")
req = compiled.create_infer_request()
# Allocate 8 chunks, set 'user tensors' to 0, 2, 4, 6 chunks
buffer = np.zeros([batch * 2, *batch_shape[1:]], dtype=np.float32)
tensors = []
for i in range(batch):
# non contiguous memory (i*2)
tensors.append(
Tensor(np.expand_dims(buffer[i * 2], 0), shared_memory=True))
req.set_input_tensors(tensors)
with pytest.raises(RuntimeError) as e:
req.get_tensor("tensor_input0")
assert "get_tensor shall not be used together with batched set_tensors/set_input_tensors" in str(
e.value)
actual_tensor = req.get_tensor("tensor_output0")
actual = actual_tensor.data
for test_num in range(0, 5):
for i in range(0, batch):
tensors[i].data[:] = test_num + 10
req.infer() # Adds '1' to each element
# Reference values for each batch:
_tmp = np.array([test_num + 11] * one_shape_size,
dtype=np.float32).reshape([2, 2, 2])
for j in range(0, batch):
assert np.array_equal(actual[j], _tmp)
def create_model_with_memory(input_shape, data_type):
input_data = ops.parameter(input_shape, name="input_data", dtype=data_type)
rv = ops.read_value(input_data, "var_id_667")
add = ops.add(rv, input_data, name="MemoryAdd")
node = ops.assign(add, "var_id_667")
res = ops.result(add, "res")
model = Model(results=[res], sinks=[node], parameters=[input_data], name="name")
return model
def create_function1(shape1=[2, 2]):
input1 = ops.parameter(shape1, dtype=np.float32, name="input1")
input1.get_output_tensor(0).set_names({'input1a', 'input1b'})
relu1 = ops.relu(input1)
res1 = ops.result(relu1, "res1")
res1.get_output_tensor(0).set_names({'res1', 'res1a'})
function = Model(results=[res1], parameters=[input1], name="TestFunction")
return function
def get_model():
param = opset8.parameter(PartialShape([1, 3, 22, 22]), name="parameter")
param.get_output_tensor(0).set_names({"parameter"})
relu = opset8.relu(param)
reshape = opset8.reshape(relu, opset8.shape_of(relu), False)
res = opset8.result(reshape, name="result")
res.get_output_tensor(0).set_names({"result"})
return Model([res], [param], "test")
def test_batched_tensors(device):
batch = 4
one_shape = Shape([1, 2, 2, 2])
batch_shape = Shape([batch, 2, 2, 2])
one_shape_size = np.prod(one_shape)
core = Core()
core.register_plugin("openvino_template_plugin", "TEMPLATE")
data1 = ops.parameter(batch_shape, np.float32)
data1.set_friendly_name("input0")
data1.get_output_tensor(0).set_names({"tensor_input0"})
data1.set_layout(Layout("N..."))
constant = ops.constant([1], np.float32)
op1 = ops.add(data1, constant)
op1.set_friendly_name("Add0")
res1 = ops.result(op1)
res1.set_friendly_name("Result0")
res1.get_output_tensor(0).set_names({"tensor_output0"})
model = Model([res1], [data1])
compiled = core.compile_model(model, "TEMPLATE")
buffer = np.zeros([one_shape_size * batch * 2], dtype=np.float32)
req = compiled.create_infer_request()
tensors = []
for i in range(0, batch):
_start = i * one_shape_size * 2
# Use of special constructor for Tensor.
# It creates a Tensor from pointer, thus it requires only
# one element from original buffer, and shape to "crop".
tensor = Tensor(buffer[_start:(_start + 1)], one_shape)
tensors.append(tensor)
req.set_input_tensors(tensors) # using list overload!
actual_tensor = req.get_tensor("tensor_output0")
actual = actual_tensor.data
for test_num in range(0, 5):
for i in range(0, batch):
tensors[i].data[:] = test_num + 10
req.infer() # Adds '1' to each element
# Reference values for each batch:
_tmp = np.array([test_num + 11] * one_shape_size,
dtype=np.float32).reshape([2, 2, 2])
for j in range(0, batch):
assert np.array_equal(actual[j], _tmp)
def test_replace_output_update_name():
param = opset8.parameter(PartialShape([1, 3, 22, 22]), name="parameter")
relu = opset8.relu(param.output(0))
exp = opset8.exp(relu.output(0))
res = opset8.result(exp.output(0), name="result")
replace_output_update_name(exp.output(0), exp.input_value(0))
assert res.input_value(0).get_node() == exp
def create_diff_if_with_two_outputs(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
X_t = ov.parameter([2], np.float32, "X")
Y_t = ov.parameter([2], np.float32, "Y")
mmul_t = ov.matmul(X_t, Y_t, False, False)
mul_t = ov.multiply(Y_t, X_t)
then_body_res_1 = ov.result(mmul_t)
then_body_res_2 = ov.result(mul_t)
then_body = GraphBody([X_t, Y_t], [then_body_res_1, then_body_res_2])
then_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(2, 1)
]
then_body_outputs = [
TensorIteratorBodyOutputDesc(0, 0),
TensorIteratorBodyOutputDesc(1, 1)
]
# else_body
X_e = ov.parameter([2], np.float32, "X")
Z_e = ov.parameter([], np.float32, "Z")
mul_e = ov.multiply(X_e, Z_e)
else_body_res_1 = ov.result(Z_e)
else_body_res_2 = ov.result(mul_e)
else_body = GraphBody([X_e, Z_e], [else_body_res_1, else_body_res_2])
else_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(3, 1)
]
else_body_outputs = [
TensorIteratorBodyOutputDesc(0, 0),
TensorIteratorBodyOutputDesc(1, 1)
]
X = ov.constant([3, 4], dtype=np.float32)
Y = ov.constant([2, 1], dtype=np.float32)
Z = ov.constant(4.0, dtype=np.float32)
if_node = ov.if_op(condition, [X, Y, Z], (then_body, else_body),
(then_body_inputs, else_body_inputs),
(then_body_outputs, else_body_outputs))
return if_node
def simple_if_without_parameters(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
then_constant = ov.constant(0.7, dtype=np.float)
then_body_res_1 = ov.result(then_constant)
then_body = Model([then_body_res_1], [])
# else_body
else_const = ov.constant(9.0, dtype=np.float)
else_body_res_1 = ov.result(else_const)
else_body = Model([else_body_res_1], [])
if_node = ov.if_op(condition.output(0))
if_node.set_then_body(then_body)
if_node.set_else_body(else_body)
if_res = if_node.set_output(then_body_res_1, else_body_res_1)
relu = ov.relu(if_res)
return relu
def create_simple_if_with_two_outputs(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
X_t = ov.parameter([], np.float32, "X")
Y_t = ov.parameter([], np.float32, "Y")
Z_t = ov.parameter([], np.float32, "Z")
add_t = ov.add(X_t, Y_t)
mul_t = ov.multiply(Y_t, Z_t)
then_body_res_1 = ov.result(add_t)
then_body_res_2 = ov.result(mul_t)
then_body = Model([then_body_res_1, then_body_res_2], [X_t, Y_t, Z_t],
"then_body_function")
# else_body
X_e = ov.parameter([], np.float32, "X")
Z_e = ov.parameter([], np.float32, "Z")
W_e = ov.parameter([], np.float32, "W")
add_e = ov.add(X_e, W_e)
pow_e = ov.power(W_e, Z_e)
else_body_res_1 = ov.result(add_e)
else_body_res_2 = ov.result(pow_e)
else_body = Model([else_body_res_1, else_body_res_2], [X_e, Z_e, W_e],
"else_body_function")
X = ov.constant(15.0, dtype=np.float32)
Y = ov.constant(-5.0, dtype=np.float32)
Z = ov.constant(4.0, dtype=np.float32)
W = ov.constant(2.0, dtype=np.float32)
if_node = ov.if_op(condition)
if_node.set_then_body(then_body)
if_node.set_else_body(else_body)
if_node.set_input(X.output(0), X_t, X_e)
if_node.set_input(Y.output(0), Y_t, None)
if_node.set_input(Z.output(0), Z_t, Z_e)
if_node.set_input(W.output(0), None, W_e)
if_node.set_output(then_body_res_1, else_body_res_1)
if_node.set_output(then_body_res_2, else_body_res_2)
return if_node
def test_replace_source_output():
param = opset8.parameter(PartialShape([1, 3, 22, 22]), name="parameter")
relu = opset8.relu(param.output(0))
res = opset8.result(relu.output(0), name="result")
exp = opset8.exp(param.output(0))
res.input(0).replace_source_output(exp.output(0))
assert len(exp.output(0).get_target_inputs()) == 1
assert len(relu.output(0).get_target_inputs()) == 0
assert next(iter(exp.output(0).get_target_inputs())).get_node() == res
def simple_if_without_parameters(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
then_constant = ov.constant(0.7, dtype=np.float)
then_body_res_1 = ov.result(then_constant)
then_body = GraphBody([], [then_body_res_1])
then_body_inputs = []
then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
# else_body
else_const = ov.constant(9.0, dtype=np.float)
else_body_res_1 = ov.result(else_const)
else_body = GraphBody([], [else_body_res_1])
else_body_inputs = []
else_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
if_node = ov.if_op(condition, [], (then_body, else_body),
(then_body_inputs, else_body_inputs),
(then_body_outputs, else_body_outputs))
relu = ov.relu(if_node)
return relu
def create_diff_if_with_two_outputs(condition_val):
condition = ov.constant(condition_val, dtype=np.bool)
# then_body
X_t = ov.parameter([2], np.float32, "X")
Y_t = ov.parameter([2], np.float32, "Y")
mmul_t = ov.matmul(X_t, Y_t, False, False)
mul_t = ov.multiply(Y_t, X_t)
then_body_res_1 = ov.result(mmul_t)
then_body_res_2 = ov.result(mul_t)
then_body = Model([then_body_res_1, then_body_res_2], [X_t, Y_t],
"then_body_function")
# else_body
X_e = ov.parameter([2], np.float32, "X")
Z_e = ov.parameter([], np.float32, "Z")
mul_e = ov.multiply(X_e, Z_e)
else_body_res_1 = ov.result(Z_e)
else_body_res_2 = ov.result(mul_e)
else_body = Model([else_body_res_1, else_body_res_2], [X_e, Z_e],
"else_body_function")
X = ov.constant([3, 4], dtype=np.float32)
Y = ov.constant([2, 1], dtype=np.float32)
Z = ov.constant(4.0, dtype=np.float32)
if_node = ov.if_op(condition)
if_node.set_then_body(then_body)
if_node.set_else_body(else_body)
if_node.set_input(X.output(0), X_t, X_e)
if_node.set_input(Y.output(0), Y_t, None)
if_node.set_input(Z.output(0), None, Z_e)
if_node.set_output(then_body_res_1, else_body_res_1)
if_node.set_output(then_body_res_2, else_body_res_2)
return if_node
def test_simple_if_basic():
condition = ov.constant(True, dtype=np.bool)
# then_body
X_t = ov.parameter([2], np.float32, "X")
Y_t = ov.parameter([2], np.float32, "Y")
then_mul = ov.multiply(X_t, Y_t)
then_body_res_1 = ov.result(then_mul)
then_body = Model([then_body_res_1], [X_t, Y_t], "then_body_function")
then_body_inputs = [
InvariantInputDescription(1, 0),
InvariantInputDescription(2, 1)
]
else_body_outputs = [BodyOutputDescription(0, 0)]
if_node = ov.if_op(condition.output(0))
if_node.set_function(0, then_body)
assert if_node.get_function(0) == then_body
if_node.set_input_descriptions(0, then_body_inputs)
if_node.set_output_descriptions(1, else_body_outputs)
input_desc = if_node.get_input_descriptions(0)
output_desc = if_node.get_output_descriptions(1)
assert len(input_desc) == len(then_body_inputs)
assert len(output_desc) == len(else_body_outputs)
for i in range(len(input_desc)):
assert input_desc[i].get_type_info(
) == then_body_inputs[i].get_type_info()
assert input_desc[i].input_index == then_body_inputs[i].input_index
assert input_desc[i].body_parameter_index == then_body_inputs[
i].body_parameter_index
for i in range(len(output_desc)):
assert output_desc[i].get_type_info(
) == else_body_outputs[i].get_type_info()
assert output_desc[i].output_index == else_body_outputs[i].output_index
assert output_desc[i].body_value_index == else_body_outputs[
i].body_value_index
def test_sink_function_ctor():
input_data = ops.parameter([2, 2], name="input_data", dtype=np.float32)
rv = ops.read_value(input_data, "var_id_667")
add = ops.add(rv, input_data, name="MemoryAdd")
node = ops.assign(add, "var_id_667")
res = ops.result(add, "res")
function = Model(results=[res],
sinks=[node],
parameters=[input_data],
name="TestFunction")
ordered_ops = function.get_ordered_ops()
op_types = [op.get_type_name() for op in ordered_ops]
assert op_types == ["Parameter", "ReadValue", "Add", "Assign", "Result"]
assert len(function.get_ops()) == 5
assert function.get_output_size() == 1
assert function.get_output_op(0).get_type_name() == "Result"
assert function.get_output_element_type(0) == input_data.get_element_type()
assert list(function.get_output_shape(0)) == [2, 2]
assert (function.get_parameters()[0].get_partial_shape()) == PartialShape(
[2, 2])
assert len(function.get_parameters()) == 1
assert len(function.get_results()) == 1
assert function.get_friendly_name() == "TestFunction"
def get_test_function():
# Parameter->Relu->Result
param = opset8.parameter(PartialShape([1, 3, 22, 22]), name="parameter")
relu = opset8.relu(param.output(0))
res = opset8.result(relu.output(0), name="result")
return Model([res], [param], "test")
