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Add support (limited, based on Interpolate-1) to Resize-11 ONNX op (#1364)

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* Implementation of Resize-11

* Added support to sizes input

* Add tests to sizes input

* Added missing comment

* fixed tests

* fixed tests

* Fixed test. part 2.

* review remaks. part 1.

* review remarks. part 2.

Co-authored-by: Tomasz Socha <tomasz.socha@intel.com>

* Added more tests

Co-authored-by: Tomasz Socha <tomasz.socha@intel.com>
This commit is contained in:
Mateusz Bencer 2020-07-27 15:42:00 +02:00 committed by GitHub
parent 0846f2050e
commit 5ff59eb711
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GPG Key ID: 4AEE18F83AFDEB23
14 changed files with 1122 additions and 46 deletions
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194
ngraph/src/ngraph/frontend/onnx_import/op/resize.cpp
View File

@ -18,6 +18,7 @@
#include "default_opset.hpp"
#include "exceptions.hpp"
#include "ngraph/op/util/op_types.hpp"
#include "utils/common.hpp"
namespace ngraph
{
@ -25,17 +26,49 @@ namespace ngraph
{
namespace op
{
namespace set_1
namespace
{
NodeVector resize(const onnx_import::Node& node)
std::shared_ptr<ngraph::Node> calculate_output_shape_based_on_scales(
const std::shared_ptr<ngraph::Node>& data,
const std::shared_ptr<ngraph::Node>& scales)
{
const auto inputs = node.get_ng_inputs();
const auto data = inputs.at(0);
const auto scales = inputs.at(1);
const auto& data_shape = data->get_output_partial_shape(0);
const auto& scales_shape = scales->get_output_partial_shape(0);
const auto data_shape = data->get_output_partial_shape(0);
const auto scales_shape = scales->get_output_partial_shape(0);
if (ngraph::op::is_constant(scales) && data_shape.is_static())
{
const auto scales_const =
as_type_ptr<default_opset::Constant>(scales->shared_from_this());
const auto scales_vector = scales_const->cast_vector<float>();
const auto data_static_shape = data_shape.to_shape();
std::vector<int64_t> output_shape;
for (size_t i = 0; i < data_static_shape.size(); ++i)
{
output_shape.push_back(
std::floor(data_static_shape.at(i) * scales_vector.at(i)));
}
auto output_shape_const = default_opset::Constant::create(
element::u64, Shape({output_shape.size()}), output_shape);
return output_shape_const;
}
const auto shape_of_data = std::make_shared<default_opset::Convert>(
std::make_shared<default_opset::ShapeOf>(data), scales->get_element_type());
const auto multiply =
std::make_shared<default_opset::Multiply>(shape_of_data, scales);
const auto output_shape =
std::make_shared<default_opset::Convert>(multiply, ngraph::element::i64);
return output_shape;
}
NodeVector build_resize(const Node& node,
const std::shared_ptr<ngraph::Node>& output_shape,
const AxisSet& axes)
{
const auto mode = node.get_attribute_value<std::string>("mode", "nearest");
std::unordered_set<std::string> supported_modes = {"nearest", "linear"};
@ -58,54 +91,123 @@ namespace ngraph
supported_modes_str);
}
CHECK_VALID_NODE(
node,
(scales_shape.is_static() || data_shape.rank().is_static()),
" Data rank or shape of Scales input is required to be static.");
size_t axes_size = scales_shape.is_static() ? scales_shape.to_shape().at(0)
: data_shape.rank().get_length();
AxisSet axes;
for (int ax = 0; ax < axes_size; ++ax)
{
axes.insert(ax);
}
auto attrs = ngraph::op::v0::InterpolateAttrs();
attrs.axes = axes;
attrs.mode = mode;
attrs.align_corners = false;
if (ngraph::op::is_constant(scales) && data_shape.is_static())
{
const auto scales_const =
as_type_ptr<default_opset::Constant>(scales->shared_from_this());
const auto inputs = node.get_ng_inputs();
const auto& data = inputs.at(0);
auto scales_vector = scales_const->cast_vector<float>();
auto data_static_shape = data_shape.to_shape();
std::vector<int64_t> output_shape;
for (size_t i = 0; i < data_static_shape.size(); ++i)
{
output_shape.push_back(
std::floor(data_static_shape.at(i) * scales_vector.at(i)));
}
auto output_shape_const = default_opset::Constant::create(
element::u64, Shape({output_shape.size()}), output_shape);
return {std::make_shared<default_opset::Interpolate>(
data, output_shape_const, attrs)};
}
auto shape_of_data = std::make_shared<default_opset::Convert>(
std::make_shared<default_opset::ShapeOf>(data), ngraph::element::f32);
auto multiply =
std::make_shared<default_opset::Multiply>(shape_of_data, scales);
auto output_shape = std::make_shared<default_opset::Convert>(
std::make_shared<default_opset::Floor>(multiply), ngraph::element::i64);
return {
std::make_shared<default_opset::Interpolate>(data, output_shape, attrs)};
}
}
namespace set_11
{
NodeVector resize(const onnx_import::Node& node)
{
// cubic_coeff_a, extrapolation_value attributes are ignored
// (they do not have influence on supported modes)
const auto coordinate_transformation_mode =
node.get_attribute_value<std::string>("coordinate_transformation_mode",
"half_pixel");
CHECK_VALID_NODE(
node,
coordinate_transformation_mode == "asymmetric",
coordinate_transformation_mode,
" - this type of coordinate transformation mode is not supported."
" Only asymmetric mode is supported by current version.");
const auto exclude_outside =
node.get_attribute_value<int64_t>("exclude_outside ", 0);
CHECK_VALID_NODE(node,
exclude_outside == 0,
"Expected exclude_outside=",
exclude_outside,
" mode is not supported. ",
"Current version supports only exclude_outside equals `0`.");
const auto mode = node.get_attribute_value<std::string>("mode", "nearest");
if (mode == "nearest")
{
const auto nearest_mode = node.get_attribute_value<std::string>(
"nearest_mode", "round_prefer_floor");
CHECK_VALID_NODE(
node,
nearest_mode == "floor",
"Expected nearest_mode=",
nearest_mode,
" mode is not supported. ",
"Current version support only nearest_mode equals `floor`");
}
// roi input (inputs.at(2)) is ignored because it is used only
// in "tf_crop_and_resize" which is not handled now
const auto inputs = node.get_ng_inputs();
const auto& data = inputs.at(0);
const auto& data_shape = data->get_output_partial_shape(0);
if (inputs.size() == 4) // sizes input is provided
{
const auto& sizes = inputs.at(3);
const auto& sizes_shape = sizes->get_output_partial_shape(0);
CHECK_VALID_NODE(
node,
(sizes_shape.is_static() || data_shape.rank().is_static()),
" Data rank or shape of sizes input is required to be static.");
size_t axes_size = sizes_shape.is_static() ? sizes_shape[0].get_length()
: data_shape.rank().get_length();
return build_resize(
node, sizes, AxisSet(common::get_monotonic_range(axes_size)));
}
const auto& scales = inputs.at(2);
const auto& scales_shape = scales->get_output_partial_shape(0);
CHECK_VALID_NODE(
node,
(scales_shape.is_static() || data_shape.rank().is_static()),
" Data rank or shape of scales input is required to be static.");
size_t axes_size = scales_shape.is_static() ? scales_shape[0].get_length()
: data_shape.rank().get_length();
const auto output_shape = calculate_output_shape_based_on_scales(data, scales);
return build_resize(
node, output_shape, AxisSet(common::get_monotonic_range(axes_size)));
}
} // namespace set_11
namespace set_1
{
NodeVector resize(const onnx_import::Node& node)
{
const auto inputs = node.get_ng_inputs();
const auto& data = inputs.at(0);
const auto& scales = inputs.at(1);
const auto& data_shape = data->get_output_partial_shape(0);
const auto& scales_shape = scales->get_output_partial_shape(0);
CHECK_VALID_NODE(
node,
(scales_shape.is_static() || data_shape.rank().is_static()),
" Data rank or shape of scales input is required to be static.");
size_t axes_size = scales_shape.is_static() ? scales_shape[0].get_length()
: data_shape.rank().get_length();
const auto output_shape = calculate_output_shape_based_on_scales(data, scales);
return build_resize(
node, output_shape, AxisSet(common::get_monotonic_range(axes_size)));
}
} // namespace set_1

5
ngraph/src/ngraph/frontend/onnx_import/op/resize.hpp
View File

@ -31,6 +31,11 @@ namespace ngraph
} // namespace set_1
namespace set_11
{
NodeVector resize(const Node& node);
}
} // namespace op
} // namespace onnx_import

1
ngraph/src/ngraph/frontend/onnx_import/ops_bridge.cpp
View File

@ -356,6 +356,7 @@ namespace ngraph
REGISTER_OPERATOR("Relu", 1, relu);
REGISTER_OPERATOR("Reshape", 1, reshape);
REGISTER_OPERATOR("Resize", 1, resize);
REGISTER_OPERATOR("Resize", 11, resize);
REGISTER_OPERATOR("ReverseSequence", 1, reverse_sequence);
REGISTER_OPERATOR("RNN", 1, rnn);
REGISTER_OPERATOR("RoiAlign", 1, roi_align);

1
ngraph/test/CMakeLists.txt
View File

@ -305,6 +305,7 @@ set(MULTI_TEST_SRC
backend/gather.in.cpp
backend/gelu.in.cpp
backend/group_convolution.in.cpp
backend/interpolate.in.cpp
backend/log.in.cpp
backend/logical_or.in.cpp
backend/logical_xor.in.cpp

62
ngraph/test/backend/interpolate.in.cpp Normal file
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@ -0,0 +1,62 @@
//*****************************************************************************
// Copyright 2017-2020 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/runtime/tensor.hpp"
#include "runtime/backend.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/engine/test_engines.hpp"
#include "util/known_element_types.hpp"
#include "util/ndarray.hpp"
#include "util/test_case.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
NGRAPH_TEST(${BACKEND_NAME}, interpolate_down_scales_const_linear)
{
Shape input_shape{1, 1, 2, 4};
Shape output_shape{1, 1, 1, 2};
op::v0::InterpolateAttrs attrs;
attrs.axes = AxisSet{0, 1, 2, 3};
attrs.mode = "linear";
attrs.align_corners = false;
const auto input = make_shared<op::Parameter>(element::f32, input_shape);
const auto output_shape_input = op::Constant::create(element::i64, {4}, {1, 1, 1, 2});
std::vector<float> intput_data{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
auto interpolate = make_shared<op::Interpolate>(input, output_shape_input, attrs);
auto f = make_shared<Function>(interpolate, ParameterVector{input});
auto backend = runtime::Backend::create("IE_CPU");
auto input_tensor = backend->create_tensor(element::f32, input_shape);
auto result_tensor = backend->create_tensor(element::f32, output_shape);
auto handle = backend->compile(f);
copy_data(input_tensor, intput_data);
handle->call_with_validate({result_tensor}, {input_tensor});
vector<float> expected_output{1.0f, 2.66666651f};
EXPECT_TRUE(test::all_close_f(expected_output, read_vector<float>(result_tensor)));
}

124
ngraph/test/models/onnx/resize11_scales_down_linear.prototxt Normal file
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@ -0,0 +1,124 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "scales"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 0.6
float_data: 0.6
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "roi"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 8
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: "roi"
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "exclude_outside"
i: 0
type: INT
}
attribute {
name: "extrapolation_value"
f: 0
type: FLOAT
}
attribute {
name: "mode"
s: "linear"
type: STRING
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 4
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

129
ngraph/test/models/onnx/resize11_scales_nearest_asymmetric_floor.prototxt Normal file
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@ -0,0 +1,129 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "scales"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 2.0
float_data: 0.5
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "roi"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 8
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: "roi"
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "exclude_outside"
i: 0
type: INT
}
attribute {
name: "extrapolation_value"
f: 0
type: FLOAT
}
attribute {
name: "mode"
s: "nearest"
type: STRING
}
attribute {
name: "nearest_mode"
s: "floor"
type: STRING
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
dim {
dim_value: 1
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

117
ngraph/test/models/onnx/resize11_scales_nearest_asymmetric_floor_dynamic_scales.prototxt Normal file
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@ -0,0 +1,117 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
input: "X"
input: "roi"
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "exclude_outside"
i: 0
type: INT
}
attribute {
name: "extrapolation_value"
f: 0
type: FLOAT
}
attribute {
name: "mode"
s: "nearest"
type: STRING
}
attribute {
name: "nearest_mode"
s: "floor"
type: STRING
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
input {
name: "roi"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 8
}
}
}
}
}
input {
name: "scales"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 4
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
dim {
dim_value: 1
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

129
ngraph/test/models/onnx/resize11_scales_up_linear_asymmetric.prototxt Normal file
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@ -0,0 +1,129 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "scales"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 2.0
float_data: 4.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "roi"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 8
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: "roi"
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "exclude_outside"
i: 0
type: INT
}
attribute {
name: "extrapolation_value"
f: 0
type: FLOAT
}
attribute {
name: "mode"
s: "linear"
type: STRING
}
attribute {
name: "nearest_mode"
s: "floor"
type: STRING
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

139
ngraph/test/models/onnx/resize11_sizes_linear.prototxt Normal file
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@ -0,0 +1,139 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "scales"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 1
float_data: 1.0
data_type: 1
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 2
int64_data: 1
int64_data: 4
int64_data: 8
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "roi"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 8
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: "roi"
input: "scales"
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "exclude_outside"
i: 0
type: INT
}
attribute {
name: "extrapolation_value"
f: 0
type: FLOAT
}
attribute {
name: "mode"
s: "linear"
type: STRING
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

144
ngraph/test/models/onnx/resize11_sizes_nearest_asymmetric_floor.prototxt Normal file
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@ -0,0 +1,144 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "scales"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 1
float_data: 1.0
data_type: 1
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 2
int64_data: 1
int64_data: 4
int64_data: 1
name: "const_tensor"
}
type: TENSOR
}
}
node {
output: "roi"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 8
data_type: 1
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
float_data: 1.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: "roi"
input: "scales"
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "exclude_outside"
i: 0
type: INT
}
attribute {
name: "extrapolation_value"
f: 0
type: FLOAT
}
attribute {
name: "mode"
s: "nearest"
type: STRING
}
attribute {
name: "nearest_mode"
s: "floor"
type: STRING
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
dim {
dim_value: 1
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

107
ngraph/test/onnx/onnx_import.in.cpp
View File

@ -1148,6 +1148,113 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_resize10_up_scales_const_nearest)
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_down_linear)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_scales_down_linear.prototxt"));
const Shape expected_output_shape{1, 1, 1, 2};
auto test_case = test::TestCase<TestEngine>(function);
const size_t input_size = 8;
std::vector<float> input_data(input_size);
std::iota(std::begin(input_data), std::end(input_data), 1.0f);
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape, {1.0f, 2.66666651f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_up_linear_asymmetric)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_scales_up_linear_asymmetric.prototxt"));
const Shape expected_output_shape{2, 1, 4, 8};
auto test_case = test::TestCase<TestEngine>(function);
const size_t input_size = 8;
std::vector<float> input_data{1.0f, 3.0f, 4.0f, 8.0f, 6.0f, 2.0f, 7.0f, 11.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{1.0f, 1.5f, 2.0f, 2.5f, 3.0f, 3.0f, 3.0f, 3.0f, 2.5f, 3.25f, 4.0f,
4.75f, 5.5f, 5.5f, 5.5f, 5.5f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f,
8.0f, 8.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 8.0f, 8.0f,
6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 2.0f, 2.0f, 2.0f, 6.5f, 6.5f, 6.5f,
6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 11.0f,
11.0f, 11.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 11.0f, 11.0f, 11.0f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_nearest_asymmetric_floor)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_scales_nearest_asymmetric_floor.prototxt"));
const Shape expected_output_shape{2, 1, 4, 1};
auto test_case = test::TestCase<TestEngine>(function);
const std::vector<float> input_data{1.0f, 3.0f, 4.0f, 8.0f, 6.0f, 2.0f, 7.0f, 11.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape,
{1.0f, 1.0f, 4.0f, 4.0f, 6.0f, 6.0f, 7.0f, 7.0f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_nearest_asymmetric_floor_dynamic_sizes)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_scales_nearest_asymmetric_floor_dynamic_scales.prototxt"));
const Shape expected_output_shape{2, 1, 4, 1};
auto test_case = test::TestCase<TestEngine>(function);
const std::vector<float> input_data{1.0f, 3.0f, 4.0f, 8.0f, 6.0f, 2.0f, 7.0f, 11.0f};
test_case.add_input<float>(input_data);
test_case.add_input<float>(
std::vector<float>{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}); // roi
test_case.add_input<float>(std::vector<float>{1.0f, 1.0f, 2.0f, 0.5f}); // scales
test_case.add_expected_output<float>(expected_output_shape,
{1.0f, 1.0f, 4.0f, 4.0f, 6.0f, 6.0f, 7.0f, 7.0f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_sizes_nearest_asymmetric_floor)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_sizes_nearest_asymmetric_floor.prototxt"));
const Shape expected_output_shape{2, 1, 4, 1};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f, 3.0f, 4.0f, 8.0f, 6.0f, 2.0f, 7.0f, 11.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape,
{1.0f, 1.0f, 4.0f, 4.0f, 6.0f, 6.0f, 7.0f, 7.0f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_sizes_linear)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_sizes_linear.prototxt"));
const Shape expected_output_shape{2, 1, 4, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{2.0f, 4.0f, 1.0f, 3.0f, 7.0f, 8.0f, 9.0f, 6.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{2.0f, 2.5f, 3.0f, 3.5f, 4.0f, 4.0f, 4.0f, 4.0f, 1.5f, 2.0f, 2.5f, 3.0f, 3.5f,
3.5f, 3.5f, 3.5f, 1.0f, 1.5f, 2.0f, 2.5f, 3.0f, 3.0f, 3.0f, 3.0f, 1.0f, 1.5f,
2.0f, 2.5f, 3.0f, 3.0f, 3.0f, 3.0f, 7.0f, 7.25f, 7.5f, 7.75f, 8.0f, 8.0f, 8.0f,
8.0f, 8.0f, 7.75f, 7.5f, 7.25f, 7.0f, 7.0f, 7.0f, 7.0f, 9.0f, 8.25f, 7.5f, 6.75f,
6.0f, 6.0f, 6.0f, 6.0f, 9.0f, 8.25f, 7.5f, 6.75f, 6.0f, 6.0f, 6.0f, 6.0f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_shape)
{
auto function =

9
ngraph/test/runtime/ie/unit_test.manifest
View File

@ -1087,8 +1087,17 @@ IE_CPU.atanh
IE_CPU.asinh
IE_CPU.acosh
# Interpolate-1 in linear mode
# 2.666666507720947266 is not close to 3 at index 1
IE_CPU.interpolate_down_scales_const_linear
# Result mismatch 2.666666507720947266 is not close to 3 at index 1
IE_CPU.onnx_resize10_down_scales_const_linear
IE_CPU.onnx_resize11_scales_down_linear
# C++ exception with description "Interpolate operation should be converted to Interp
# not constant output_shape passed to Interpolate-1
IE_CPU.onnx_resize11_scales_nearest_asymmetric_floor_dynamic_sizes
# GRUCell operation has a form that is not supported
IE_CPU.onnx_model_gru_defaults_fwd

7
ngraph/test/runtime/interpreter/unit_test.manifest
View File

@ -20,6 +20,13 @@ INTERPRETER.onnx_upsample8_linear_infer
INTERPRETER.onnx_upsample9_scales_const_nearest_infer
INTERPRETER.onnx_upsample9_scales_const_linear_infer
INTERPRETER.onnx_upsample9_scales_input_nearest_infer
INTERPRETER.onnx_resize11_scales_down_linear
INTERPRETER.onnx_resize11_scales_up_linear_asymmetric
INTERPRETER.onnx_resize11_scales_nearest_asymmetric_floor
INTERPRETER.onnx_resize11_sizes_nearest_asymmetric_floor
INTERPRETER.onnx_resize11_sizes_linear
INTERPRETER.onnx_resize11_scales_nearest_asymmetric_floor_dynamic_sizes
INTERPRETER.interpolate_down_scales_const_linear
# ONNX Loop
onnx_controlflow_loop_2d_add_execution