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[ONNX][Python][Tests] Update ONNX to onnx 1.8 (#3557)

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This commit is contained in:
Tomasz Socha 2020-12-11 11:57:50 +01:00 committed by GitHub
parent 8aabcde925
commit dbf855b320
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GPG Key ID: 4AEE18F83AFDEB23
18 changed files with 384 additions and 92 deletions
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6
ngraph/frontend/onnx_import/include/onnx_import/op/split.hpp
View File

@ -31,6 +31,12 @@ namespace ngraph
} // namespace set_1
namespace set_13
{
OutputVector split(const Node& node);
} // namespace set_13
} // namespace op
} // namespace onnx_import

6
ngraph/frontend/onnx_import/include/onnx_import/op/squeeze.hpp
View File

@ -31,6 +31,12 @@ namespace ngraph
} // namespace set_1
namespace set_13
{
OutputVector squeeze(const Node& node);
} // namespace set_13
} // namespace op
} // namespace onnx_import

7
ngraph/frontend/onnx_import/include/onnx_import/op/unsqueeze.hpp
View File

@ -31,7 +31,12 @@ namespace ngraph
} // namespace set_1
} // namespace op
namespace set_13
{
OutputVector unsqueeze(const Node& node);
} // namespace set_13
} // namespace op
} // namespace onnx_import

25
ngraph/frontend/onnx_import/src/op/split.cpp
View File

@ -49,7 +49,30 @@ namespace ngraph
} // namespace set_1
} // namespace op
namespace set_13
{
OutputVector split(const Node& node)
{
const auto inputs = node.get_ng_inputs();
const auto axis = node.get_attribute_value<int64_t>("axis", 0);
if (inputs.size() < 2)
{
const auto outputs_number = node.get_output_names().size();
return ngraph::builder::opset1::split(inputs.at(0), outputs_number, axis);
}
else
{
const auto axis_node =
default_opset::Constant::create(element::Type_t::i64, Shape{}, {axis});
return {std::make_shared<default_opset::VariadicSplit>(
inputs.at(0), axis_node, inputs.at(1))
->outputs()};
}
}
} // namespace set_13
} // namespace op
} // namespace onnx_import

22
ngraph/frontend/onnx_import/src/op/squeeze.cpp
View File

@ -45,6 +45,28 @@ namespace ngraph
}
} // namespace set_1
namespace set_13
{
OutputVector squeeze(const Node& node)
{
auto inputs = node.get_ng_inputs();
if (inputs.size() < 2)
{
std::vector<int64_t> axes{};
auto axes_node = std::make_shared<default_opset::Constant>(
element::Type_t::u64, Shape{}, axes);
return {std::make_shared<default_opset::Squeeze>(inputs.at(0), axes_node)};
}
else
{
return {
std::make_shared<default_opset::Squeeze>(inputs.at(0), inputs.at(1))};
}
}
} // namespace set_13
} // namespace op
} // namespace onnx_import
} // namespace ngraph

11
ngraph/frontend/onnx_import/src/op/unsqueeze.cpp
View File

@ -41,7 +41,16 @@ namespace ngraph
} // namespace set_1
} // namespace op
namespace set_13
{
OutputVector unsqueeze(const Node& node)
{
auto inputs = node.get_ng_inputs();
return {std::make_shared<default_opset::Unsqueeze>(inputs.at(0), inputs.at(1))};
}
} // namespace set_13
} // namespace op
} // namespace onnx_import

3
ngraph/frontend/onnx_import/src/ops_bridge.cpp
View File

@ -431,8 +431,10 @@ namespace ngraph
REGISTER_OPERATOR("Softsign", 1, softsign);
REGISTER_OPERATOR("SpaceToDepth", 1, space_to_depth);
REGISTER_OPERATOR("Split", 1, split);
REGISTER_OPERATOR("Split", 13, split);
REGISTER_OPERATOR("Sqrt", 1, sqrt);
REGISTER_OPERATOR("Squeeze", 1, squeeze);
REGISTER_OPERATOR("Squeeze", 13, squeeze);
REGISTER_OPERATOR("Sub", 1, sub);
REGISTER_OPERATOR("Sub", 7, sub);
REGISTER_OPERATOR("Sum", 1, sum);
@ -446,6 +448,7 @@ namespace ngraph
REGISTER_OPERATOR("TopK", 11, topk);
REGISTER_OPERATOR("Transpose", 1, transpose);
REGISTER_OPERATOR("Unsqueeze", 1, unsqueeze);
REGISTER_OPERATOR("Unsqueeze", 13, unsqueeze);
REGISTER_OPERATOR("Upsample", 1, upsample);
REGISTER_OPERATOR("Upsample", 9, upsample);
REGISTER_OPERATOR("Where", 1, where);

2
ngraph/python/requirements_test.txt
View File

@ -2,7 +2,7 @@ flake8==3.8.4
flake8-comprehensions==3.3.0
flake8-docstrings==1.5.0
flake8-quotes==3.2.0
onnx==1.7.0
onnx==1.8.0
pydocstyle==5.1.1
pytest==6.1.2
retrying==1.3.3

18
ngraph/python/tests/__init__.py
View File

@ -110,7 +110,6 @@ xfail_issue_38084 = xfail_test(reason="RuntimeError: AssertionFailed: layer->get
"with index 0 contains dynamic shapes: {<value>}. Try to use "
"CNNNetwork::reshape() method in order to specialize shapes "
"before the conversion.")
xfail_issue_38085 = xfail_test(reason="RuntimeError: Interpolate operation should be converted to Interp")
xfail_issue_38086 = xfail_test(reason="RuntimeError: Quantize layer input '<value>' doesn't have blobs")
xfail_issue_38087 = xfail_test(reason="RuntimeError: Cannot cast to tensor desc. Format is unsupported!")
xfail_issue_38091 = xfail_test(reason="AssertionError: Mismatched elements")
@ -170,6 +169,23 @@ xfail_issue_38735 = xfail_test(reason="RuntimeError: nGraph does not support the
"ai.onnx.preview.training.Adagrad")
xfail_issue_38736 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations:"
"NegativeLogLikelihoodLoss")
xfail_issue_43523 = xfail_test(reason="onnx.onnx_cpp2py_export.checker.ValidationError:"
" Unrecognized attribute: axes for operator ReduceSum")
xfail_issue_44839 = xfail_test(reason="Huge computation missmatch")
xfail_issue_44848 = xfail_test(reason="E Unsupported dynamic op: Range")
xfail_issue_44851 = xfail_test(reason="E Unsupported dynamic op: Broadcast")
xfail_issue_44854 = xfail_test(reason="E Unsupported dynamic op: VariadicSplit")
xfail_issue_44858 = xfail_test(reason="E Unsupported dynamic op: Unsqueeze")
xfail_issue_44956 = xfail_test(reason="E Unsupported dynamic op: Loop")
xfail_issue_44957 = xfail_test(reason="E Unsupported dynamic op: NonZero")
xfail_issue_44958 = xfail_test(reason="E Unsupported dynamic op: Interpolate")
xfail_issue_44965 = xfail_test(reason="E RuntimeError: value info has no element")
xfail_issue_44967 = xfail_test(reason="E RuntimeError: unsupported element type: BFLOAT16")
xfail_issue_44968 = xfail_test(reason="E Unsupported dynamic op: Squeeze")
xfail_issue_44970 = xfail_test(reason="Assertion error")
xfail_issue_44976 = xfail_test(reason="E RuntimeError: Quantize layer with name:"
"FakeQuantize_xxx has non const input on 1 port")
# Model ONNX Zoo issues:
xfail_issue_39684 = xfail_test(reason="ngraph.exceptions.UserInputError:"

2
ngraph/python/tests/test_ngraph/test_data_movement.py
View File

@ -14,7 +14,6 @@
# limitations under the License.
# ******************************************************************************
import numpy as np
import pytest
import ngraph as ng
from ngraph.impl import Type
@ -167,7 +166,6 @@ def test_pad_edge():
assert np.allclose(result, expected)
@pytest.mark.xfail(reason="AssertionError")
def test_pad_constant():
input_data = np.arange(1, 13).reshape([3, 4])
pads_begin = np.array([0, 1], dtype=np.int32)

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

@ -19,11 +19,11 @@ import pytest
import ngraph as ng
from tests.runtime import get_runtime
from tests import (xfail_issue_40957,
skip_segfault,
xfail_issue_34327,
xfail_issue_36485,
xfail_issue_36486,
xfail_issue_36487)
xfail_issue_36487,
xfail_issue_44976)
@xfail_issue_40957
@ -58,7 +58,7 @@ def test_elu_operator_with_scalar():
assert np.allclose(result, expected)
@skip_segfault
@xfail_issue_44976
def test_fake_quantize():
runtime = get_runtime()

3
ngraph/python/tests/test_ngraph/test_ops_unary.py
View File

@ -19,6 +19,7 @@ import pytest
import ngraph as ng
from ngraph.impl import Shape, Type
from tests.test_ngraph.util import run_op_node
from tests import xfail_issue_44970
@pytest.mark.parametrize(
@ -110,7 +111,7 @@ def test_sigmoid():
assert np.allclose(result, expected)
@pytest.mark.skip(reason="Wrong results are broadcasted along given axis")
@xfail_issue_44970
def test_softmax():
axis = 0
input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)

5
ngraph/python/tests/test_ngraph/test_sequence_processing.py
View File

@ -18,7 +18,8 @@ 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_36478, skip_issue_38084
from tests import (xfail_issue_36478,
xfail_issue_44848)
def test_onehot():
@ -46,7 +47,7 @@ def test_one_hot():
assert np.allclose(result, excepted)
@skip_issue_38084
@xfail_issue_44848
def test_range():
start = 5
stop = 35

245
ngraph/python/tests/test_onnx/test_backend.py
View File

@ -25,7 +25,6 @@ import onnx.backend.test
from tests.test_onnx.utils.onnx_backend import OpenVinoTestBackend
from tests import (BACKEND_NAME,
skip_issue_38084,
xfail_issue_36535,
xfail_issue_39656,
xfail_issue_39658,
@ -77,7 +76,21 @@ from tests import (BACKEND_NAME,
xfail_issue_38735,
xfail_issue_40319,
xfail_issue_40485,
xfail_issue_41894)
xfail_issue_41894,
xfail_issue_43523,
xfail_issue_43742,
xfail_issue_44839,
xfail_issue_44848,
xfail_issue_44851,
xfail_issue_44854,
xfail_issue_44858,
xfail_issue_44956,
xfail_issue_44957,
xfail_issue_44958,
xfail_issue_44965,
xfail_issue_44967,
xfail_issue_44968,
xfail_issue_44976)
def expect_fail(test_case_path, xfail): # type: (str) -> None
@ -124,21 +137,6 @@ OnnxBackendPyTorchConvertedModelTest = None
globals().update(backend_test.enable_report().test_cases)
tests_expected_to_fail = [
(skip_issue_38084,
"OnnxBackendNodeModelTest.test_expand_dim_changed_cpu",
"OnnxBackendNodeModelTest.test_expand_dim_unchanged_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model1_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model2_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model3_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model4_cpu",
"OnnxBackendNodeModelTest.test_slice_default_axes_cpu",
"OnnxBackendNodeModelTest.test_top_k_cpu",
"OnnxBackendNodeModelTest.test_top_k_negative_axis_cpu",
"OnnxBackendNodeModelTest.test_top_k_smallest_cpu",
"OnnxBackendNodeModelTest.test_nonzero_example_cpu",
"OnnxBackendNodeModelTest.test_range_int32_type_negative_delta_cpu",
"OnnxBackendNodeModelTest.test_range_float_type_positive_delta_cpu",
"OnnxBackendNodeModelTest.test_upsample_nearest_cpu"),
(xfail_issue_34314,
"OnnxBackendNodeModelTest.test_rnn_seq_length_cpu",
"OnnxBackendNodeModelTest.test_simple_rnn_defaults_cpu",
@ -432,22 +430,126 @@ tests_expected_to_fail = [
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_expanded_cpu",
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3_none_no_weight_negative_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1_mean_weight_negative_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3d4d5_none_no_weight_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1_expanded_cpu",
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3d4d5_mean_weight_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NC_expanded_cpu",
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3_sum_weight_high_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_iinput_shape_is_NCd1_weight_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_mean_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_sum_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_mean_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_gather_elements_0_cpu",
"OnnxBackendNodeModelTest.test_gather_elements_negative_indices_cpu",
"OnnxBackendNodeModelTest.test_gather_elements_1_cpu"),
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1_mean_weight_negative_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3d4d5_none_no_weight_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1_expanded_cpu",
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3d4d5_mean_weight_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NC_expanded_cpu",
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2d3_sum_weight_high_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_iinput_shape_is_NCd1_weight_ignore_index_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_mean_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_sum_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_mean_expanded_cpu", # noqa
"OnnxBackendNodeModelTest.test_gather_elements_0_cpu",
"OnnxBackendNodeModelTest.test_gather_elements_negative_indices_cpu",
"OnnxBackendNodeModelTest.test_gather_elements_1_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NC_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NC_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_mean_weight_negative_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_mean_weight_negative_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_weight_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_weight_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1_weight_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_no_weight_reduction_mean_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_reduction_mean_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_reduction_mean_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_reduction_sum_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_reduction_sum_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_reduction_mean_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_reduction_sum_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_reduction_sum_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3_none_no_weight_negative_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3_none_no_weight_negative_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3_sum_weight_high_ii_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3d4d5_mean_weight_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3d4d5_none_no_weight_cpu",
"OnnxBackendNodeModelTest.test_nllloss_NCd1d2d3d4d5_none_no_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1_mean_weight_negative_ii_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1_mean_weight_negative_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1_mean_weight_negative_ii_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_none_no_weight_negative_ii_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_none_no_weight_negative_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_sum_weight_high_ii_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_sum_weight_high_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_sum_weight_high_ii_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3_sum_weight_high_ii_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_mean_weight_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_mean_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_mean_weight_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_none_no_weight_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_none_no_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_3d_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_3d_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_3d_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_3d_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_3d_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_3d_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_3d_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_3d_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_4d_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_4d_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_4d_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_4d_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_no_weight_ii_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_3d_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_3d_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_3d_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_3d_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_4d_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_4d_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_4d_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_4d_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_ii_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_mean_weight_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_none_cpu",
"OnnxBackendNodeModelTest.test_sce_none_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_none_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_none_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_none_weights_cpu",
"OnnxBackendNodeModelTest.test_sce_none_weights_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_none_weights_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_none_weights_log_prob_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_sum_cpu",
"OnnxBackendNodeModelTest.test_sce_sum_expanded_cpu",
"OnnxBackendNodeModelTest.test_sce_sum_log_prob_cpu",
"OnnxBackendNodeModelTest.test_sce_sum_log_prob_expanded_cpu"),
(xfail_issue_38712,
"OnnxBackendNodeModelTest.test_mod_mixed_sign_int16_cpu",
"OnnxBackendNodeModelTest.test_mod_uint8_cpu",
@ -534,7 +636,82 @@ tests_expected_to_fail = [
"OnnxBackendNodeModelTest.test_adagrad_cpu"),
(xfail_issue_41894,
"OnnxBackendNodeModelTest.test_max_uint16_cpu",
"OnnxBackendNodeModelTest.test_mod_int64_fmod_cpu")
"OnnxBackendNodeModelTest.test_mod_int64_fmod_cpu"),
(xfail_issue_43523,
"OnnxBackendNodeModelTest.test_reduce_sum_do_not_keepdims_example_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_do_not_keepdims_random_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_keepdims_example_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_keepdims_random_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_negative_axes_keepdims_example_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_default_axes_keepdims_example_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_default_axes_keepdims_random_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_empty_axes_input_noop_example_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_empty_axes_input_noop_random_cpu",
"OnnxBackendNodeModelTest.test_reduce_sum_negative_axes_keepdims_random_cpu"),
(xfail_issue_43742,
"OnnxBackendNodeModelTest.test_if_cpu",
"OnnxBackendNodeModelTest.test_if_seq_cpu"),
(xfail_issue_44839,
"OnnxBackendNodeModelTest.test_logsoftmax_axis_0_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_axis_0_expanded_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_axis_1_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_axis_1_expanded_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_axis_2_expanded_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_default_axis_expanded_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_large_number_expanded_cpu",
"OnnxBackendNodeModelTest.test_logsoftmax_negative_axis_expanded_cpu",
"OnnxBackendNodeModelTest.test_softmax_axis_0_cpu",
"OnnxBackendNodeModelTest.test_softmax_axis_0_expanded_cpu",
"OnnxBackendNodeModelTest.test_softmax_axis_1_cpu",
"OnnxBackendNodeModelTest.test_softmax_axis_1_expanded_cpu",
"OnnxBackendNodeModelTest.test_softmax_axis_2_expanded_cpu",
"OnnxBackendNodeModelTest.test_softmax_default_axis_cpu",
"OnnxBackendNodeModelTest.test_softmax_default_axis_expanded_cpu",
"OnnxBackendNodeModelTest.test_softmax_large_number_expanded_cpu",
"OnnxBackendNodeModelTest.test_softmax_negative_axis_expanded_cpu",
"OnnxBackendNodeModelTest.test_hardmax_axis_0_cpu",
"OnnxBackendNodeModelTest.test_hardmax_axis_1_cpu",
"OnnxBackendNodeModelTest.test_hardmax_default_axis_cpu",),
(xfail_issue_44848,
"OnnxBackendNodeModelTest.test_range_float_type_positive_delta_cpu",
"OnnxBackendNodeModelTest.test_range_int32_type_negative_delta_cpu",),
(xfail_issue_44851,
"OnnxBackendNodeModelTest.test_expand_dim_changed_cpu",
"OnnxBackendNodeModelTest.test_expand_dim_unchanged_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model1_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model2_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model3_cpu",
"OnnxBackendSimpleModelTest.test_expand_shape_model4_cpu",),
(xfail_issue_44854,
"OnnxBackendNodeModelTest.test_split_variable_parts_1d_cpu",
"OnnxBackendNodeModelTest.test_split_variable_parts_2d_cpu",
"OnnxBackendNodeModelTest.test_split_variable_parts_default_axis_cpu",),
(xfail_issue_44858,
"OnnxBackendNodeModelTest.test_unsqueeze_axis_0_cpu",
"OnnxBackendNodeModelTest.test_unsqueeze_axis_1_cpu",
"OnnxBackendNodeModelTest.test_unsqueeze_axis_2_cpu",
"OnnxBackendNodeModelTest.test_unsqueeze_negative_axes_cpu",
"OnnxBackendNodeModelTest.test_unsqueeze_three_axes_cpu",
"OnnxBackendNodeModelTest.test_unsqueeze_two_axes_cpu",
"OnnxBackendNodeModelTest.test_unsqueeze_unsorted_axes_cpu",),
(xfail_issue_44956,
"OnnxBackendNodeModelTest.test_loop11_cpu"),
(xfail_issue_44957,
"OnnxBackendNodeModelTest.test_nonzero_example_cpu"),
(xfail_issue_44958,
"OnnxBackendNodeModelTest.test_upsample_nearest_cpu"),
(xfail_issue_44965,
"OnnxBackendNodeModelTest.test_loop13_seq_cpu",
"OnnxBackendNodeModelTest.test_sequence_insert_at_back_cpu",
"OnnxBackendNodeModelTest.test_sequence_insert_at_front_cpu",),
(xfail_issue_44967,
"OnnxBackendNodeModelTest.test_cast_BFLOAT16_to_FLOAT_cpu",
"OnnxBackendNodeModelTest.test_cast_FLOAT_to_BFLOAT16_cpu",),
(xfail_issue_44968,
"OnnxBackendNodeModelTest.test_squeeze_cpu",
"OnnxBackendNodeModelTest.test_squeeze_negative_axes_cpu",),
(xfail_issue_44976,
"OnnxBackendNodeModelTest.test_quantizelinear_axis_cpu",)
]
for test_group in tests_expected_to_fail:

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

@ -19,7 +19,7 @@ import pytest
from onnx.helper import make_graph, make_model, make_tensor_value_info
from tests.test_onnx.utils import run_model
from tests import skip_segfault
from tests import xfail_issue_44970
def import_and_compute(op_type, input_data_left, input_data_right, opset=7, **node_attributes):
@ -38,7 +38,7 @@ def import_and_compute(op_type, input_data_left, input_data_right, opset=7, **no
return run_model(model, inputs)[0]
@skip_segfault
@xfail_issue_44970
def test_add_opset4():
assert np.array_equal(import_and_compute("Add", 1, 2, opset=4), np.array(3, dtype=np.float32))
@ -111,7 +111,7 @@ def test_add_opset7(left_shape, right_shape):
assert np.array_equal(import_and_compute("Add", left_input, right_input), left_input + right_input)
@skip_segfault
@xfail_issue_44970
def test_sub():
assert np.array_equal(import_and_compute("Sub", 20, 1), np.array(19, dtype=np.float32))
@ -125,7 +125,7 @@ def test_sub():
)
@skip_segfault
@xfail_issue_44970
def test_mul():
assert np.array_equal(import_and_compute("Mul", 2, 3), np.array(6, dtype=np.float32))
@ -139,7 +139,7 @@ def test_mul():
)
@skip_segfault
@xfail_issue_44970
def test_div():
assert np.array_equal(import_and_compute("Div", 6, 3), np.array(2, dtype=np.float32))

26
ngraph/python/tests/test_onnx/test_ops_reduction.py
View File

@ -18,11 +18,11 @@ import onnx
import pytest
from tests.test_onnx.utils import run_node
from tests import xfail_issue_35925
from tests import (xfail_issue_35925,
xfail_issue_43523)
reduce_data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
reduce_axis_parameters = [
None,
(0,),
(1,),
(2,),
@ -36,7 +36,7 @@ reduce_operation_parameters = [
("ReduceMax", np.max),
("ReduceMin", np.min),
("ReduceMean", np.mean),
("ReduceSum", np.sum),
pytest.param("ReduceSum", np.sum, marks=xfail_issue_43523),
("ReduceProd", np.prod)
]
@ -47,15 +47,23 @@ def import_and_compute(op_type, input_data, **node_attrs):
return run_node(node, data_inputs).pop()
@pytest.mark.parametrize("operation, ref_operation", [
("ReduceMax", np.max),
("ReduceMin", np.min),
("ReduceMean", np.mean),
("ReduceSum", np.sum),
("ReduceProd", np.prod)
])
def test_reduce_operation_keepdims_none_axes(operation, ref_operation):
assert np.array_equal(import_and_compute(operation, reduce_data, keepdims=True),
ref_operation(reduce_data, keepdims=True))
@pytest.mark.parametrize("operation, ref_operation", reduce_operation_parameters)
@pytest.mark.parametrize("axes", reduce_axis_parameters)
def test_reduce_operation_keepdims(operation, ref_operation, axes):
if axes:
assert np.array_equal(import_and_compute(operation, reduce_data, axes=axes, keepdims=True),
ref_operation(reduce_data, keepdims=True, axis=axes))
else:
assert np.array_equal(import_and_compute(operation, reduce_data, keepdims=True),
ref_operation(reduce_data, keepdims=True))
assert np.array_equal(import_and_compute(operation, reduce_data, axes=axes, keepdims=True),
ref_operation(reduce_data, keepdims=True, axis=axes))
@pytest.mark.parametrize("axes", [

68
ngraph/python/tests/test_onnx/test_ops_reshape.py
View File

@ -26,7 +26,10 @@ from tests.test_onnx.utils import (
run_model,
run_node,
)
from tests import xfail_issue_35927
from tests import (xfail_issue_35927,
xfail_issue_44854,
xfail_issue_44858,
xfail_issue_44968)
def test_reshape():
@ -228,36 +231,43 @@ def test_concat():
assert np.array_equal(ng_results, [expected_output])
@xfail_issue_44968
def test_squeeze():
data = np.arange(6, dtype=np.int32).reshape([1, 2, 3, 1])
expected_output = data.reshape([2, 3])
node = onnx.helper.make_node("Squeeze", inputs=["x"], outputs=["y"], axes=[0, 3])
ng_results = run_node(node, [data])
axes = np.array([0, 3]).astype(np.int64)
node = onnx.helper.make_node("Squeeze", inputs=["x", "axes"], outputs=["y"])
ng_results = run_node(node, [data, axes])
assert np.array_equal(ng_results, [expected_output])
data = np.random.randn(1, 3, 4, 5).astype(np.float32)
expected_output = np.squeeze(data, axis=0)
node = onnx.helper.make_node("Squeeze", inputs=["x"], outputs=["y"], axes=[0])
ng_results = run_node(node, [data])
axes = np.array([0]).astype(np.int64)
node = onnx.helper.make_node("Squeeze", inputs=["x", "axes"], outputs=["y"])
ng_results = run_node(node, [data, axes])
assert np.array_equal(ng_results, [expected_output])
@xfail_issue_44858
def test_unsqueeze():
data = np.random.randn(3, 4, 5).astype(np.float32)
expected_output = np.expand_dims(data, axis=0)
node = onnx.helper.make_node("Unsqueeze", inputs=["x"], outputs=["y"], axes=[0])
ng_results = run_node(node, [data])
axes = np.array([0]).astype(np.int64)
node = onnx.helper.make_node("Unsqueeze", inputs=["x", "axes"], outputs=["y"])
ng_results = run_node(node, [data, axes])
assert np.array_equal(ng_results, [expected_output])
expected_output = np.reshape(data, [1, 3, 4, 5, 1])
node = onnx.helper.make_node("Unsqueeze", inputs=["x"], outputs=["y"], axes=[0, 4])
ng_results = run_node(node, [data])
axes = np.array([0, 4]).astype(np.int64)
node = onnx.helper.make_node("Unsqueeze", inputs=["x", "axes"], outputs=["y"])
ng_results = run_node(node, [data, axes])
assert np.array_equal(ng_results, [expected_output])
expected_output = np.reshape(data, [1, 3, 1, 4, 5])
node = onnx.helper.make_node("Unsqueeze", inputs=["x"], outputs=["y"], axes=[0, 2])
ng_results = run_node(node, [data])
axes = np.array([0, 2]).astype(np.int64)
node = onnx.helper.make_node("Unsqueeze", inputs=["x", "axes"], outputs=["y"])
ng_results = run_node(node, [data, axes])
assert np.array_equal(ng_results, [expected_output])
@ -300,16 +310,6 @@ def test_unsqueeze():
np.array([[3], [7]], dtype=np.int32),
],
),
# Split into 2 unequal parts along axis=1
(
onnx.helper.make_node(
"Split", inputs=["x"], outputs=["a", "b"], axis=1, split=(3, 1)
),
[
np.array([[0, 1, 2], [4, 5, 6]], dtype=np.int32),
np.array([[3], [7]], dtype=np.int32),
],
),
],
)
def test_split_2d(node, expected_output):
@ -318,6 +318,22 @@ def test_split_2d(node, expected_output):
assert all_arrays_equal(ng_results, expected_output)
@xfail_issue_44854
def test_split_2d_splits_input():
data = np.arange(8, dtype=np.int32).reshape(2, 4)
splits = np.array([3, 1]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["x", "splits"], outputs=["a", "b"], axis=1
)
expected_outputs = [
np.array([[0, 1, 2], [4, 5, 6]], dtype=np.int32),
np.array([[3], [7]], dtype=np.int32),
]
ng_results = run_node(node, [data, splits])
assert all_arrays_equal(ng_results, expected_outputs)
@xfail_issue_44854
def test_split_1d():
# 1D
data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
@ -330,15 +346,16 @@ def test_split_1d():
ng_results = run_node(node, [data])
assert all_arrays_equal(ng_results, expected_outputs)
splits = np.array([2, 3, 1]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input"], outputs=["y", "z", "w"], axis=0, split=[2, 3, 1]
"Split", inputs=["input", "splits"], outputs=["y", "z", "w"], axis=0
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0]).astype(np.float32),
np.array([6.0]).astype(np.float32),
]
ng_results = run_node(node, [data])
ng_results = run_node(node, [data, splits])
assert all_arrays_equal(ng_results, expected_outputs)
# Default values
@ -353,14 +370,15 @@ def test_split_1d():
ng_results = run_node(node, [data])
assert all_arrays_equal(ng_results, expected_outputs)
splits = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input"], outputs=["y", "z"], split=[2, 4]
"Split", inputs=["input", "splits"], outputs=["y", "z"], split=[2, 4]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
ng_results = run_node(node, [data])
ng_results = run_node(node, [data, splits])
assert all_arrays_equal(ng_results, expected_outputs)

11
ngraph/python/tests/test_onnx/test_ops_unary.py
View File

@ -302,13 +302,13 @@ def test_logsoftmax():
ng_results = run_node(node, [data])
assert np.allclose(ng_results, [expected])
# default axis is 1
node = onnx.helper.make_node("LogSoftmax", inputs=["x"], outputs=["y"])
node = onnx.helper.make_node("LogSoftmax", inputs=["x"], outputs=["y"], axis=2)
expected = logsoftmax_2d(data.reshape(12, 5)).reshape(3, 4, 5)
ng_results = run_node(node, [data])
assert np.allclose(ng_results, [expected])
node = onnx.helper.make_node("LogSoftmax", inputs=["x"], outputs=["y"], axis=2)
expected = logsoftmax_2d(data.reshape(12, 5)).reshape(3, 4, 5)
# default axis is -1
node = onnx.helper.make_node("LogSoftmax", inputs=["x"], outputs=["y"])
ng_results = run_node(node, [data])
assert np.allclose(ng_results, [expected])
@ -388,8 +388,7 @@ def test_cast_to_bool(val_type, input_data):
"val_type, range_start, range_end, in_dtype",
[
(np.dtype(np.float32), -8, 8, np.dtype(np.int32)),
pytest.param(np.dtype(np.float64), -16383, 16383, np.dtype(np.int64),
marks=pytest.mark.xfail(reason="RuntimeError: Unsupported type")),
(np.dtype(np.float64), -16383, 16383, np.dtype(np.int64)),
],
)
def test_cast_to_float(val_type, range_start, range_end, in_dtype):