Enable ngraph python tests in OpenVINO-ONNX CI

ngraph/python/tests/__init__.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ******************************************************************************
+import pytest
 
 # test.BACKEND_NAME is a configuration variable determining which
 # nGraph backend tests will use. It's set during pytest configuration time.
@@ -24,3 +25,58 @@
 # configuration time. See `pytest_configure` hook in `conftest.py` for more
 # details.
 ADDITIONAL_MODELS_DIR = None
+
+
+def xfail_test(reason="Mark the test as expected to fail", strict=True):
+    return pytest.mark.xfail(reason=reason, strict=strict)
+
+
+skip_segfault = pytest.mark.skip(reason="Segmentation fault error")
+xfail_issue_35893 = xfail_test(reason="ValueError: could not broadcast input array")
+xfail_issue_35911 = xfail_test(reason="Assertion error: Pad model mismatch error")
+xfail_issue_35912 = xfail_test(reason="RuntimeError: Error of validate layer: B with type: "
+                                      "Pad. Cannot parse parameter pads_end  from IR for layer B. "
+                                      "Value -1,0 cannot be casted to int.")
+xfail_issue_35914 = xfail_test(reason="IndexError: too many indices for array: "
+                                      "array is 0-dimensional, but 1 were indexed")
+xfail_issue_35915 = xfail_test(reason="RuntimeError: Eltwise node with unsupported combination "
+                                      "of input and output types")
+xfail_issue_35916 = xfail_test(reason="RuntimeError: Unsupported input dims count for layer Z")
+xfail_issue_35917 = xfail_test(reason="RuntimeError: Unsupported input dims count for "
+                                      "layer MatMul")
+xfail_issue_35918 = xfail_test(reason="onnx.onnx_cpp2py_export.checker.ValidationError: "
+                                      "Mismatched attribute type in 'test_node : alpha'")
+xfail_issue_35921 = xfail_test(reason="ValueError - shapes mismatch in gemm")
+
+xfail_issue_35923 = xfail_test(reason="RuntimeError: PReLU without weights is not supported")
+xfail_issue_35924 = xfail_test(reason="Assertion error - elu results mismatch")
+xfail_issue_35925 = xfail_test(reason="Assertion error - reduction ops results mismatch")
+xfail_issue_35926 = xfail_test(reason="RuntimeError: [NOT_IMPLEMENTED] Input image format I64 is "
+                                      "not supported yet...")
+xfail_issue_35927 = xfail_test(reason="RuntimeError: B has zero dimension that is not allowable")
+xfail_issue_35929 = xfail_test(reason="CRuntimeError: Incorrect precision f64!")
+xfail_issue_34323 = xfail_test(reason="RuntimeError: data [value] doesn't exist")
+xfail_issue_35930 = xfail_test(reason="onnx.onnx_cpp2py_export.checker.ValidationError: "
+                                      "Required attribute 'to' is missing.")
+xfail_issue_35932 = xfail_test(reason="Assertion error - logsoftmax results mismatch")
+xfail_issue_36437 = xfail_test(reason="RuntimeError: Cannot find blob with name: y")
+xfail_issue_36476 = xfail_test(reason="RuntimeError: [NOT_IMPLEMENTED] Input image format U32 is "
+                               "not supported yet...")
+xfail_issue_36478 = xfail_test(reason="RuntimeError: [NOT_IMPLEMENTED] Input image format U64 is "
+                               "not supported yet...")
+xfail_issue_36479 = xfail_test(reason="Assertion error - basic computation on ndarrays mismatch")
+xfail_issue_36480 = xfail_test(reason="RuntimeError: [NOT_FOUND] Unsupported property dummy_option "
+                               "by CPU plugin")
+xfail_issue_36481 = xfail_test(reason="TypeError: _get_node_factory() takes from 0 to 1 positional "
+                               "arguments but 2 were given")
+xfail_issue_36483 = xfail_test(reason="RuntimeError: Unsupported primitive of type: "
+                               "Ceiling name: Ceiling_22669")
+xfail_issue_34327 = xfail_test(reason="RuntimeError: '<value>' layer has different "
+                                      "IN and OUT channels number")
+xfail_issue_36485 = xfail_test(reason="RuntimeError: Check 'm_group >= 1' failed at "
+                               "/openvino/ngraph/src/ngraph/op/fused/shuffle_channels.cpp:77:")
+xfail_issue_36486 = xfail_test(reason="RuntimeError: HardSigmoid operation should be converted "
+                                      "to HardSigmoid_IE")
+xfail_issue_34314 = xfail_test(reason="RuntimeError: RNNCell operation has a form that is not "
+                               "supported.RNNCell_21204 should be converted to RNNCellIE operation")
+xfail_issue_36487 = xfail_test(reason="Assertion error - mvn operator computation mismatch")

ngraph/python/tests/conftest.py

@@ -73,3 +73,7 @@ def pytest_collection_modifyitems(config, items):
         skip_this_backend = keywords[backend_name]
         if skip_this_backend in item.keywords:
             item.add_marker(skip_markers[backend_name])
+
+        if item.name.startswith(("test_reduce_operation[None-False",
+                                 "test_reduce_operation[axes7-False")):
+            item.add_marker(tests.xfail_issue_35925)

ngraph/python/tests/test_ngraph/test_basic.py

@@ -23,6 +23,13 @@
 from ngraph.impl import Function, PartialShape, Shape
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
+from tests import (xfail_issue_34323,
+                   xfail_issue_35929,
+                   xfail_issue_35926,
+                   xfail_issue_36476,
+                   xfail_issue_36478,
+                   xfail_issue_36479,
+                   xfail_issue_36480)
 
 
 def test_ngraph_function_api():
@@ -53,15 +60,15 @@ def test_ngraph_function_api():
     "dtype",
     [
         np.float32,
-        np.float64,
-        np.int8,
+        pytest.param(np.float64, marks=xfail_issue_35929),
+        pytest.param(np.int8, marks=xfail_issue_36479),
         np.int16,
         np.int32,
-        np.int64,
-        np.uint8,
-        np.uint16,
-        np.uint32,
-        np.uint64,
+        pytest.param(np.int64, marks=xfail_issue_35926),
+        pytest.param(np.uint8, marks=xfail_issue_36479),
+        pytest.param(np.uint16, marks=xfail_issue_36479),
+        pytest.param(np.uint32, marks=xfail_issue_36476),
+        pytest.param(np.uint64, marks=xfail_issue_36478),
     ],
 )
 def test_simple_computation_on_ndarrays(dtype):
@@ -107,6 +114,7 @@ def test_serialization():
         pass
 
 
+@xfail_issue_34323
 def test_broadcast_1():
     input_data = np.array([1, 2, 3])
 
@@ -116,6 +124,7 @@ def test_broadcast_1():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 def test_broadcast_2():
     input_data = np.arange(4)
     new_shape = [3, 4, 2, 4]
@@ -124,6 +133,7 @@ def test_broadcast_2():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 def test_broadcast_3():
     input_data = np.array([1, 2, 3])
     new_shape = [3, 3]
@@ -134,6 +144,7 @@ def test_broadcast_3():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 @pytest.mark.parametrize(
     "destination_type, input_data",
     [(bool, np.zeros((2, 2), dtype=int)), ("boolean", np.zeros((2, 2), dtype=int))],
@@ -148,10 +159,10 @@ def test_convert_to_bool(destination_type, input_data):
 @pytest.mark.parametrize(
     "destination_type, rand_range, in_dtype, expected_type",
     [
-        (np.float32, (-8, 8), np.int32, np.float32),
-        (np.float64, (-16383, 16383), np.int64, np.float64),
-        ("f32", (-8, 8), np.int32, np.float32),
-        ("f64", (-16383, 16383), np.int64, np.float64),
+        pytest.param(np.float32, (-8, 8), np.int32, np.float32, marks=xfail_issue_34323),
+        pytest.param(np.float64, (-16383, 16383), np.int64, np.float64, marks=xfail_issue_35929),
+        pytest.param("f32", (-8, 8), np.int32, np.float32, marks=xfail_issue_34323),
+        pytest.param("f64", (-16383, 16383), np.int64, np.float64, marks=xfail_issue_35929),
     ],
 )
 def test_convert_to_float(destination_type, rand_range, in_dtype, expected_type):
@@ -163,6 +174,7 @@ def test_convert_to_float(destination_type, rand_range, in_dtype, expected_type)
     assert np.array(result).dtype == expected_type
 
 
+@xfail_issue_34323
 @pytest.mark.parametrize(
     "destination_type, expected_type",
     [
@@ -185,6 +197,7 @@ def test_convert_to_int(destination_type, expected_type):
     assert np.array(result).dtype == expected_type
 
 
+@xfail_issue_34323
 @pytest.mark.parametrize(
     "destination_type, expected_type",
     [
@@ -262,13 +275,15 @@ def test_constant_get_data_unsigned_integer(data_type):
     assert np.allclose(input_data, retrieved_data)
 
 
+@xfail_issue_36480
 def test_backend_config():
     dummy_config = {"dummy_option": "dummy_value"}
     # Expect no throw
     runtime = get_runtime()
     runtime.set_config(dummy_config)
 
 
+@xfail_issue_34323
 def test_result():
     node = [[11, 10], [1, 8], [3, 4]]
     result = run_op_node([node], ng.result)

ngraph/python/tests/test_ngraph/test_convolution.py

@@ -20,8 +20,10 @@
 from tests.runtime import get_runtime
 from tests.test_ngraph.test_ops import convolution2d
 from tests.test_ngraph.util import run_op_node
+from tests import xfail_issue_34323
 
 
+@xfail_issue_34323
 def test_convolution_2d():
 
     # input_x should have shape N(batch) x C x H x W
@@ -212,6 +214,7 @@ def test_convolution_backprop_data():
     )
 
 
+@xfail_issue_34323
 def test_convolution_v1():
     input_tensor = np.arange(-128, 128, 1, dtype=np.float32).reshape(1, 1, 16, 16)
     filters = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)

ngraph/python/tests/test_ngraph/test_data_movement.py

@@ -18,6 +18,7 @@
 import ngraph as ng
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
+from tests import xfail_issue_35926, xfail_issue_34323
 
 
 def test_reverse_sequence():
@@ -165,6 +166,7 @@ def test_pad_edge():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_35926
 def test_pad_constant():
     input_data = np.arange(1, 13).reshape([3, 4])
     pads_begin = np.array([0, 1], dtype=np.int32)
@@ -189,6 +191,7 @@ def test_pad_constant():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 def test_select():
     cond = [[False, False], [True, False], [True, True]]
     then_node = [[-1, 0], [1, 2], [3, 4]]

ngraph/python/tests/test_ngraph/test_normalization.py

@@ -19,8 +19,10 @@
 import ngraph as ng
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
+from tests import xfail_issue_34323, xfail_issue_35929
 
 
+@xfail_issue_34323
 def test_lrn():
     input_image_shape = (2, 3, 2, 1)
     input_image = np.arange(int(np.prod(input_image_shape))).reshape(input_image_shape).astype("f")
@@ -56,6 +58,7 @@ def test_lrn():
     )
 
 
+@xfail_issue_34323
 def test_lrn_factory():
     alpha = 0.0002
     beta = 0.5
@@ -101,6 +104,7 @@ def test_lrn_factory():
     assert np.allclose(result, excepted)
 
 
+@xfail_issue_35929
 def test_batch_norm_inference():
     data = [[1.0, 2.0, 3.0], [-1.0, -2.0, -3.0]]
     gamma = [2.0, 3.0, 4.0]

ngraph/python/tests/test_ngraph/test_ops.py

@@ -20,6 +20,7 @@
 from ngraph.impl import AxisSet, Function, Shape, Type
 from ngraph.impl.op import Constant, Parameter
 from tests.runtime import get_runtime
+from tests import xfail_issue_36483, xfail_issue_34323
 
 
 def binary_op(op_str, a, b):
@@ -339,6 +340,7 @@ def test_atan():
     unary_op_exec(op_str, input_list)
 
 
+@xfail_issue_36483
 def test_ceiling():
     input_list = [0.5, 0, 0.4, 0.5]
     op_str = "Ceiling"
@@ -450,6 +452,7 @@ def test_broadcast():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 def test_constant():
     element_type = Type.f32
     parameter_list = []

ngraph/python/tests/test_ngraph/test_ops_binary.py

@@ -21,6 +21,7 @@
 import ngraph as ng
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
+from tests import xfail_issue_34323
 
 
 @pytest.mark.parametrize(
@@ -201,6 +202,7 @@ def test_binary_operators_with_scalar(operator, numpy_function):
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 def test_multiply():
     A = np.arange(48).reshape((8, 1, 6, 1))
     B = np.arange(35).reshape((7, 1, 5))
@@ -211,6 +213,7 @@ def test_multiply():
     assert np.allclose(result, expected)
 
 
+@xfail_issue_34323
 def test_power_v1():
     A = np.arange(48, dtype=np.float32).reshape((8, 1, 6, 1))
     B = np.arange(20, dtype=np.float32).reshape((4, 1, 5))