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[nGraph][ONNX] Rename xfails (#3061)

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This commit is contained in:
Jan Iwaszkiewicz 2020-11-17 10:34:33 +01:00 committed by GitHub
parent 070fc17087
commit cfe143fd33
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GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 38 additions and 38 deletions
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2
ngraph/python/tests/__init__.py
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@ -67,7 +67,7 @@ xfail_issue_34310 = xfail_test(reason="RuntimeError: Error of validate layer: LS
"type: LSTMSequence. Layer is not instance of RNNLayer class")
xfail_issue_34314 = xfail_test(reason="RuntimeError: RNNCell operation has a form that is not "
"supported.RNNCell_<number> should be converted to RNNCellIE operation")
xfail_issue_34323 = xfail_test(reason="RuntimeError: data [value] doesn't exist")
xfail_issue_40957 = xfail_test(reason="RuntimeError: MKLDNNGraph::CreateGraph: No inputs for the topology")
xfail_issue_34327 = xfail_test(reason="RuntimeError: '<value>' layer has different "
"IN and OUT channels number")
xfail_issue_33581 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations:"

4
ngraph/python/tests/test_ngraph/test_normalization.py
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@ -19,10 +19,10 @@ import numpy as np
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
from tests import xfail_issue_40957, xfail_issue_35929
@xfail_issue_34323
@xfail_issue_40957
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")

4
ngraph/python/tests/test_ngraph/test_ops.py
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@ -20,7 +20,7 @@ import ngraph as ng
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_34323
from tests import xfail_issue_40957
def binary_op(op_str, a, b):
@ -481,7 +481,7 @@ def test_broadcast():
assert np.allclose(result, expected)
@xfail_issue_34323
@xfail_issue_40957
def test_constant():
element_type = Type.f32
parameter_list = []

8
ngraph/python/tests/test_ngraph/test_ops_fused.py
View File

@ -18,7 +18,7 @@ import pytest
import ngraph as ng
from tests.runtime import get_runtime
from tests import (xfail_issue_34323,
from tests import (xfail_issue_40957,
skip_segfault,
xfail_issue_34327,
xfail_issue_36485,
@ -27,7 +27,7 @@ from tests import (xfail_issue_34323,
xfail_issue_36487)
@xfail_issue_34323
@xfail_issue_40957
def test_elu_operator_with_scalar_and_array():
runtime = get_runtime()
@ -241,7 +241,7 @@ def test_gelu_operator_with_parameters():
assert np.allclose(result, expected, 0.007, 0.007)
@xfail_issue_34323
@xfail_issue_40957
def test_gelu_operator_with_array():
runtime = get_runtime()
@ -274,7 +274,7 @@ def test_clamp_operator():
assert np.allclose(result, expected)
@xfail_issue_34323
@xfail_issue_40957
def test_clamp_operator_with_array():
runtime = get_runtime()

6
ngraph/python/tests/test_ngraph/test_ops_multioutput.py
View File

@ -17,10 +17,10 @@ import numpy as np
import ngraph as ng
from tests.runtime import get_runtime
from tests import xfail_issue_34323
from tests import xfail_issue_40957
@xfail_issue_34323
@xfail_issue_40957
def test_split():
runtime = get_runtime()
input_tensor = ng.constant(np.array([0, 1, 2, 3, 4, 5], dtype=np.int32))
@ -34,7 +34,7 @@ def test_split():
assert np.allclose(split_results, expected_results)
@xfail_issue_34323
@xfail_issue_40957
def test_variadic_split():
runtime = get_runtime()
input_tensor = ng.constant(np.array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]], dtype=np.int32))

28
ngraph/python/tests/test_ngraph/test_ops_reshape.py
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@ -19,7 +19,7 @@ import pytest
import ngraph as ng
from tests.runtime import get_runtime
from tests.test_ngraph.util import run_op_node, run_op_numeric_data
from tests import xfail_issue_34323, xfail_issue_35929
from tests import xfail_issue_40957, xfail_issue_35929
def test_concat():
@ -37,7 +37,7 @@ def test_concat():
assert np.allclose(result, expected)
@xfail_issue_34323
@xfail_issue_40957
@pytest.mark.parametrize(
"val_type, value", [(bool, False), (bool, np.empty((2, 2), dtype=bool))]
)
@ -50,16 +50,16 @@ def test_constant_from_bool(val_type, value):
@pytest.mark.parametrize(
"val_type, value",
[
pytest.param(np.float32, np.float32(0.1234), marks=xfail_issue_34323),
pytest.param(np.float32, np.float32(0.1234), marks=xfail_issue_40957),
pytest.param(np.float64, np.float64(0.1234), marks=xfail_issue_35929),
pytest.param(np.int8, np.int8(-63), marks=xfail_issue_34323),
pytest.param(np.int16, np.int16(-12345), marks=xfail_issue_34323),
pytest.param(np.int32, np.int32(-123456), marks=xfail_issue_34323),
pytest.param(np.int64, np.int64(-1234567), marks=xfail_issue_34323),
pytest.param(np.uint8, np.uint8(63), marks=xfail_issue_34323),
pytest.param(np.uint16, np.uint16(12345), marks=xfail_issue_34323),
pytest.param(np.uint32, np.uint32(123456), marks=xfail_issue_34323),
pytest.param(np.uint64, np.uint64(1234567), marks=xfail_issue_34323),
pytest.param(np.int8, np.int8(-63), marks=xfail_issue_40957),
pytest.param(np.int16, np.int16(-12345), marks=xfail_issue_40957),
pytest.param(np.int32, np.int32(-123456), marks=xfail_issue_40957),
pytest.param(np.int64, np.int64(-1234567), marks=xfail_issue_40957),
pytest.param(np.uint8, np.uint8(63), marks=xfail_issue_40957),
pytest.param(np.uint16, np.uint16(12345), marks=xfail_issue_40957),
pytest.param(np.uint32, np.uint32(123456), marks=xfail_issue_40957),
pytest.param(np.uint64, np.uint64(1234567), marks=xfail_issue_40957),
],
)
def test_constant_from_scalar(val_type, value):
@ -71,7 +71,7 @@ def test_constant_from_scalar(val_type, value):
@pytest.mark.parametrize(
"val_type",
[
pytest.param(np.float32, marks=xfail_issue_34323),
pytest.param(np.float32, marks=xfail_issue_40957),
pytest.param(np.float64, marks=xfail_issue_35929),
],
)
@ -82,7 +82,7 @@ def test_constant_from_float_array(val_type):
assert np.allclose(result, input_data)
@xfail_issue_34323
@xfail_issue_40957
@pytest.mark.parametrize(
"val_type, range_start, range_end",
[
@ -223,7 +223,7 @@ def test_reshape_v1():
assert np.allclose(result, expected)
@xfail_issue_34323
@xfail_issue_40957
def test_shape_of():
input_tensor = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)

6
ngraph/python/tests/test_ngraph/test_reduction.py
View File

@ -20,7 +20,7 @@ from _pyngraph import PartialShape
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
from tests import xfail_issue_40957
@pytest.mark.parametrize(
@ -161,7 +161,7 @@ def test_roi_align():
assert list(node.get_output_shape(0)) == expected_shape
@xfail_issue_34323
@xfail_issue_40957
@pytest.mark.parametrize(
"input_shape, cumsum_axis, reverse",
[([5, 2], 0, False), ([5, 2], 1, False), ([5, 2, 6], 2, False), ([5, 2], 0, True)],
@ -181,7 +181,7 @@ def test_cum_sum(input_shape, cumsum_axis, reverse):
assert np.allclose(result, expected)
@xfail_issue_34323
@xfail_issue_40957
def test_normalize_l2():
input_shape = [1, 2, 3, 4]
input_data = np.arange(np.prod(input_shape)).reshape(input_shape).astype(np.float32)

4
ngraph/python/tests/test_onnx/test_backend.py
View File

@ -38,7 +38,7 @@ from tests import (BACKEND_NAME,
xfail_issue_33616,
xfail_issue_38086,
xfail_issue_38087,
xfail_issue_34323,
xfail_issue_40957,
xfail_issue_35915,
xfail_issue_34310,
xfail_issue_36476,
@ -202,7 +202,7 @@ tests_expected_to_fail = [
"OnnxBackendNodeModelTest.test_quantizelinear_cpu"),
(xfail_issue_38087,
"OnnxBackendNodeModelTest.test_convtranspose_1d_cpu"),
(xfail_issue_34323,
(xfail_issue_40957,
"OnnxBackendNodeModelTest.test_constant_cpu",
"OnnxBackendNodeModelTest.test_eyelike_populate_off_main_diagonal_cpu",
"OnnxBackendNodeModelTest.test_eyelike_without_dtype_cpu",

4
ngraph/python/tests/test_onnx/test_ops_unary.py
View File

@ -23,7 +23,7 @@ from ngraph.exceptions import NgraphTypeError
from tests.runtime import get_runtime
from tests.test_onnx.utils import get_node_model, import_onnx_model, run_model, run_node
from tests import (xfail_issue_35929,
xfail_issue_34323,
xfail_issue_40957,
xfail_issue_35930)
@ -502,7 +502,7 @@ def test_cast_errors():
@pytest.mark.parametrize("value_type",
[pytest.param(np.float32, marks=xfail_issue_34323),
[pytest.param(np.float32, marks=xfail_issue_40957),
pytest.param(np.float64, marks=xfail_issue_35929)])
def test_constant(value_type):
values = np.random.randn(5, 5).astype(value_type)

10
ngraph/python/tests/test_onnx/test_zoo_models.py
View File

@ -30,7 +30,7 @@ from tests import (
xfail_issue_41814,
xfail_issue_36533,
xfail_issue_39684,
xfail_issue_34323,
xfail_issue_40957,
xfail_issue_39685,
xfail_issue_37957,
xfail_issue_36465,
@ -151,9 +151,9 @@ if len(zoo_models) > 0:
(xfail_issue_39684, "test_onnx_model_zoo_vision_object_detection_segmentation_yolov4_model_yolov4_yolov4_yolov4_cpu"),
(xfail_issue_36533, "test_onnx_model_zoo_text_machine_comprehension_gpt_2_model_gpt2_10_GPT2_model_cpu"),
(xfail_issue_36533, "test_onnx_model_zoo_text_machine_comprehension_gpt_2_model_gpt2_lm_head_10_GPT_2_LM_HEAD_model_cpu"),
(xfail_issue_34323, "test_onnx_model_zoo_text_machine_comprehension_bert_squad_model_bertsquad_10_download_sample_10_bertsquad10_cpu"),
(xfail_issue_34323, "test_onnx_model_zoo_text_machine_comprehension_roberta_model_roberta_base_11_roberta_base_11_roberta_base_11_cpu"),
(xfail_issue_34323, "test_onnx_model_zoo_text_machine_comprehension_bert_squad_model_bertsquad_8_download_sample_8_bertsquad8_cpu"),
(xfail_issue_40957, "test_onnx_model_zoo_text_machine_comprehension_bert_squad_model_bertsquad_10_download_sample_10_bertsquad10_cpu"),
(xfail_issue_40957, "test_onnx_model_zoo_text_machine_comprehension_roberta_model_roberta_base_11_roberta_base_11_roberta_base_11_cpu"),
(xfail_issue_40957, "test_onnx_model_zoo_text_machine_comprehension_bert_squad_model_bertsquad_8_download_sample_8_bertsquad8_cpu"),
(xfail_issue_39685, "test_onnx_model_zoo_text_machine_comprehension_roberta_model_roberta_sequence_classification_9_roberta_sequence_classification_9_roberta_sequence_classification_9_cpu"),
(xfail_issue_39669, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_encoder_12_t5_encoder_cpu"),
(xfail_issue_38084, "test_onnx_model_zoo_vision_object_detection_segmentation_mask_rcnn_model_MaskRCNN_10_mask_rcnn_R_50_FPN_1x_cpu"),
@ -176,7 +176,7 @@ if len(zoo_models) > 0:
(xfail_issue_38084, "test_MSFT_opset10_faster_rcnn_faster_rcnn_R_50_FPN_1x_cpu"),
(xfail_issue_39669, "test_MSFT_opset9_cgan_cgan_cpu"),
(xfail_issue_34323, "test_MSFT_opset10_BERT_Squad_bertsquad10_cpu"),
(xfail_issue_40957, "test_MSFT_opset10_BERT_Squad_bertsquad10_cpu"),
(xfail_issue_41815, "test_MSFT_opset11_tinyyolov3_yolov3_tiny_cpu"),
(xfail_issue_41815, "test_MSFT_opset10_yolov3_yolov3_cpu"),