IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Revise ONNX Mod operator (Add support for attribute fmod == 0) (#5173)

Browse Source
This commit is contained in:
Bartosz Sledz 2021-04-13 10:24:51 +02:00 committed by GitHub
parent d2adcb354e
commit 94e9b632e8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 548 additions and 33 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
ngraph/core/reference/include/ngraph/runtime/reference/floor_mod.hpp
View File

@ -26,7 +26,10 @@ namespace ngraph
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x - y * std::floor(x / y);
// Cast to double is needed for integer input,
// otherwise std::floor will act like std::trunc
const double divisor = static_cast<double>(y);
return x - y * std::floor(x / divisor);
});
}
}

23
ngraph/frontend/onnx_import/src/op/mod.cpp
View File

@ -25,10 +25,25 @@ namespace ngraph
Output<ngraph::Node> divisor{node.get_ng_inputs().at(1)};
std::int64_t fmod = node.get_attribute_value<std::int64_t>("fmod", 0);
CHECK_VALID_NODE(
node, fmod == 1, "Only 'fmod=1' mode is supported for mod operator.");
return {std::make_shared<default_opset::Mod>(dividend, divisor)};
OutputVector output;
if (fmod == 1)
{
output = {std::make_shared<default_opset::Mod>(dividend, divisor)};
}
else if (fmod == 0)
{
NGRAPH_CHECK(dividend.get_element_type().is_integral() &&
divisor.get_element_type().is_integral(),
"If the input type is floating point, then `fmod` attribute "
"must be set to 1.");
output = {std::make_shared<default_opset::FloorMod>(dividend, divisor)};
}
else
{
throw ngraph_error(
"Unsupported value of 'fmod' attribute (should be: 0 or 1)");
}
return output;
}
} // namespace set_1

3
ngraph/python/tests/__init__.py
View File

@ -81,9 +81,6 @@ xfail_issue_38706 = xfail_test(reason="RuntimeError: output_3.0 has zero dimensi
xfail_issue_38708 = xfail_test(reason="RuntimeError: While validating ONNX node '<Node(Slice): y>': "
"Axes input must be constant")
xfail_issue_38710 = xfail_test(reason="RuntimeError: roi has zero dimension which is not allowed")
xfail_issue_38712 = xfail_test(reason="RuntimeError: Check '(fmod == 1) "
"While validating ONNX node '<Node(Mod): z>': "
"Only 'fmod=1' mode is supported for mod operator.")
xfail_issue_38713 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations:"
"ai.onnx.preview.training.Momentum")
xfail_issue_43742 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations:"

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

@ -26,7 +26,6 @@ from tests import (BACKEND_NAME,
xfail_issue_38701,
xfail_issue_38706,
xfail_issue_38708,
xfail_issue_38712,
xfail_issue_38713,
xfail_issue_38722,
xfail_issue_38723,
@ -273,16 +272,6 @@ tests_expected_to_fail = [
"OnnxBackendNodeModelTest.test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn_cpu"),
(xfail_issue_33581,
"OnnxBackendNodeModelTest.test_gather_elements_negative_indices_cpu"),
(xfail_issue_38712,
"OnnxBackendNodeModelTest.test_mod_mixed_sign_int16_cpu",
"OnnxBackendNodeModelTest.test_mod_uint8_cpu",
"OnnxBackendNodeModelTest.test_mod_uint64_cpu",
"OnnxBackendNodeModelTest.test_mod_uint32_cpu",
"OnnxBackendNodeModelTest.test_mod_uint16_cpu",
"OnnxBackendNodeModelTest.test_mod_mixed_sign_int8_cpu",
"OnnxBackendNodeModelTest.test_mod_mixed_sign_int64_cpu",
"OnnxBackendNodeModelTest.test_mod_broadcast_cpu",
"OnnxBackendNodeModelTest.test_mod_mixed_sign_int32_cpu"),
(xfail_issue_38713,
"OnnxBackendNodeModelTest.test_momentum_cpu",
"OnnxBackendNodeModelTest.test_nesterov_momentum_cpu",

58
ngraph/test/models/onnx/mod_incorrect_fmod.prototxt Normal file
View File

@ -0,0 +1,58 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
attribute {
name: "fmod"
i: 2
type: INT
}
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

13
ngraph/test/models/onnx/mod_sign.prototxt
View File

@ -6,18 +6,13 @@ graph {
input: "B"
output: "Y"
op_type: "Mod"
attribute {
name: "fmod"
i: 1
type: INT
}
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 7
elem_type: 6
shape {
dim {
dim_value: 6
@ -26,11 +21,11 @@ graph {
}
}
}
input {
input {
name: "B"
type {
tensor_type {
elem_type: 7
elem_type: 6
shape {
dim {
dim_value: 6
@ -43,7 +38,7 @@ graph {
name: "Y"
type {
tensor_type {
elem_type: 7
elem_type: 6
shape {
dim {
dim_value: 6

53
ngraph/test/models/onnx/mod_sign_broadcast.prototxt Normal file
View File

@ -0,0 +1,53 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 1
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

53
ngraph/test/models/onnx/mod_sign_f32.prototxt Normal file
View File

@ -0,0 +1,53 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 6
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

58
ngraph/test/models/onnx/mod_sign_fmod.prototxt Normal file
View File

@ -0,0 +1,58 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
attribute {
name: "fmod"
i: 1
type: INT
}
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

58
ngraph/test/models/onnx/mod_sign_fmod_broadcast.prototxt Normal file
View File

@ -0,0 +1,58 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
attribute {
name: "fmod"
i: 1
type: INT
}
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 1
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 6
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

58
ngraph/test/models/onnx/mod_sign_fmod_f32.prototxt Normal file
View File

@ -0,0 +1,58 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
attribute {
name: "fmod"
i: 1
type: INT
}
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 6
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

53
ngraph/test/models/onnx/mod_sign_i64.prototxt Normal file
View File

@ -0,0 +1,53 @@
ir_version: 5
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Mod"
}
name: "test_mod"
input {
name: "A"
type {
tensor_type {
elem_type: 7
shape {
dim {
dim_value: 6
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 7
shape {
dim {
dim_value: 6
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 7
shape {
dim {
dim_value: 6
}
}
}
}
}
}
opset_import {
version: 10
}

111
ngraph/test/onnx/onnx_import.in.cpp
View File

@ -3012,19 +3012,122 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_matmul_float_type)
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod)
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<int64_t>({-8, 3, 4, 9, -17, 1});
test_case.add_input<int64_t>({22, -13, 8, -3, 7, 2});
test_case.add_expected_output<int64_t>(Shape{6}, {-8, 3, 4, 0, -3, 1});
test_case.add_input<int32_t>({-4, 7, 5, 4, -7, 8});
test_case.add_input<int32_t>({2, -3, 8, -2, 3, 5});
test_case.add_expected_output<int32_t>(Shape{6}, {0, -2, 5, 0, 2, 3});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign_i64)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign_i64.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<int64_t>({-4, 7, 5, 4, -7, 8});
test_case.add_input<int64_t>({2, -3, 8, -2, 3, 5});
test_case.add_expected_output<int64_t>(Shape{6}, {0, -2, 5, 0, 2, 3});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign_broadcast)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign_broadcast.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<int32_t>({-8, 3, 4, 9, -17, 1});
test_case.add_input<int32_t>({3});
test_case.add_expected_output<int32_t>(Shape{6}, {1, 0, 1, 0, 1, 1});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign_f32)
{
try
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign_f32.prototxt"));
FAIL() << "Expected exception was not thrown";
}
catch (const ngraph::ngraph_error& e)
{
EXPECT_HAS_SUBSTRING(e.what(),
std::string("If the input type is floating point, then `fmod` attribute must be set to 1."));
}
catch (...)
{
FAIL() << "Expected ngraph_error exception was not thrown";
}
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign_fmod)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign_fmod.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<int32_t>({-8, 3, 4, 9, -17, 1});
test_case.add_input<int32_t>({22, -13, 8, -3, 7, 2});
test_case.add_expected_output<int32_t>(Shape{6}, {-8, 3, 4, 0, -3, 1});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign_fmod_broadcast)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign_fmod_broadcast.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<int32_t>({-8, 3, 4, 9, -17, 1});
test_case.add_input<int32_t>({3});
test_case.add_expected_output<int32_t>(Shape{6}, {-2, 0, 1, 0, -2, 1});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_sign_fmod_f32)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_sign_fmod_f32.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<float>({-4.3, 7.2, 5.0, 4.3, -7.2, 8.0});
test_case.add_input<float>({2.1, -3.4, 8.0, -2.1, 3.4, 5.0});
test_case.add_expected_output<float>(Shape{6}, {-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_mod_incorrect_fmod)
{
try
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/mod_incorrect_fmod.prototxt"));
FAIL() << "Expected exception was not thrown";
}
catch (const ngraph::ngraph_error& e)
{
EXPECT_HAS_SUBSTRING(e.what(),
std::string("Unsupported value of 'fmod' attribute (should be: 0 or 1)"));
}
catch (...)
{
FAIL() << "Expected ngraph_error exception was not thrown";
}
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_scatterND_param_i64_indices)
{
const auto function = onnx_import::import_onnx_model(

22
ngraph/test/op_eval/floor_mod.cpp
View File

@ -37,3 +37,25 @@ TEST(op_eval, floor_mod)
for (size_t i = 0; i < expected_result.size(); i++)
EXPECT_NEAR(result_data[i], expected_result[i], 0.000001);
}
TEST(op_eval, floor_mod_i32)
{
auto a = make_shared<op::Parameter>(element::i32, Shape{6});
auto b = make_shared<op::Parameter>(element::i32, Shape{6});
auto floor_mod = make_shared<op::v1::FloorMod>(a, b);
auto fun = make_shared<Function>(OutputVector{floor_mod}, ParameterVector{a, b});
std::vector<int32_t> a_value{-4, 7, 5, 4, -7, 8};
std::vector<int32_t> b_value{2, -3, 8, -2, 3, 5};
std::vector<int32_t> expected_result{0, -2, 5, 0, 2, 3};
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::i32>(Shape{6}, a_value),
make_host_tensor<element::Type_t::i32>(Shape{6}, b_value)}));
EXPECT_EQ(result->get_element_type(), element::i32);
EXPECT_EQ(result->get_shape(), Shape{6});
auto result_data = read_vector<int32_t>(result);
for (size_t i = 0; i < expected_result.size(); i++)
EXPECT_NEAR(result_data[i], expected_result[i], 0.000001);
}

2
ngraph/test/runtime/ie/unit_test.manifest
View File

@ -88,7 +88,7 @@ onnx_model_conv_transpose_w_groups
# [NOT_IMPLEMENTED] Input image format I64 is not supported yet...
onnx_model_global_lp_pool_p0
onnx_model_mod
onnx_model_mod_sign_i64
onnx_model_constant_of_shape_float_zeros
onnx_model_constant_of_shape_int_ones
ab_plus_c_inference