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Extend test coverage for ONNX Resize Operator (#3086)

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This commit is contained in:
Bartosz Sledz
2020-11-16 15:20:01 +01:00
committed by GitHub
parent 749d70bb63
commit ffebfe7f41
18 changed files with 1698 additions and 54 deletions
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0
ngraph/test/models/onnx/resize11_scales_down_linear.prototxt → ngraph/test/models/onnx/resize11_down_scales_linear_asymmetric.prototxt
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104
ngraph/test/models/onnx/resize11_down_sizes_cubic_half_pixel.prototxt Normal file
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@@ -0,0 +1,104 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 3
int64_data: 3
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "half_pixel"
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: "cubic"
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: 4
}
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: 3
}
dim {
dim_value: 3
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

109
ngraph/test/models/onnx/resize11_down_sizes_linear_pytorch_half_pixel.prototxt Normal file
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@@ -0,0 +1,109 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 3
int64_data: 1
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "pytorch_half_pixel"
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: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
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: 9
}
dim {
dim_value: 10
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

104
ngraph/test/models/onnx/resize11_down_sizes_tf_half_pixel.prototxt Normal file
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@@ -0,0 +1,104 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 3
int64_data: 2
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "tf_half_pixel_for_nn"
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
}
}
name: "test-model"
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
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: 3
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

103
ngraph/test/models/onnx/resize11_up_scales_cubic_align_corners.prototxt Normal file
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@@ -0,0 +1,103 @@
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: 2.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "align_corners"
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: "cubic"
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: 4
}
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: 8
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

0
ngraph/test/models/onnx/resize11_scales_up_linear_asymmetric.prototxt → ngraph/test/models/onnx/resize11_up_scales_linear_asymmetric.prototxt
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103
ngraph/test/models/onnx/resize11_up_scales_tf_half_pixel.prototxt Normal file
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@@ -0,0 +1,103 @@
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: 2.0
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "tf_half_pixel_for_nn"
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: "cubic"
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: 4
}
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: 8
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

79
ngraph/test/models/onnx/resize11_up_sizes_all_attributes_default.prototxt Normal file
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@@ -0,0 +1,79 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 7
int64_data: 8
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
}
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: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 7
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

104
ngraph/test/models/onnx/resize11_up_sizes_cubic_half_pixel.prototxt Normal file
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@@ -0,0 +1,104 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 9
int64_data: 10
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "half_pixel"
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: "cubic"
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: 4
}
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: 9
}
dim {
dim_value: 10
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

100
ngraph/test/models/onnx/resize11_up_sizes_cubic_half_pixel_dynamic_sizes.prototxt Normal file
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@@ -0,0 +1,100 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "half_pixel"
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: "cubic"
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: 4
}
dim {
dim_value: 4
}
}
}
}
}
input {
name: "sizes"
type {
tensor_type {
elem_type: 7
shape {
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: 9
}
dim {
dim_value: 10
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

0
ngraph/test/models/onnx/resize11_sizes_linear.prototxt → ngraph/test/models/onnx/resize11_up_sizes_linear_asymmetric.prototxt
View File

109
ngraph/test/models/onnx/resize11_up_sizes_nearest_ceil_half_pixel.prototxt Normal file
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@@ -0,0 +1,109 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 8
int64_data: 8
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "half_pixel"
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: "ceil"
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: 4
}
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: 8
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

109
ngraph/test/models/onnx/resize11_up_sizes_nearest_floor_align_corners.prototxt Normal file
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@@ -0,0 +1,109 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 8
int64_data: 8
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "align_corners"
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: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 4
}
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: 8
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

109
ngraph/test/models/onnx/resize11_up_sizes_nearest_prefer_ceil_asymmetric.prototxt Normal file
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@@ -0,0 +1,109 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 8
int64_data: 8
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
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: "round_prefer_ceil"
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: 4
}
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: 8
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

104
ngraph/test/models/onnx/resize11_up_sizes_nearest_round_prefer_floor_half_pixel.prototxt Normal file
View File

@@ -0,0 +1,104 @@
ir_version: 7
producer_name: "onnx-importer-test"
graph {
node {
output: "sizes"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 4
data_type: 7
int64_data: 1
int64_data: 1
int64_data: 7
int64_data: 8
name: "const_tensor"
}
type: TENSOR
}
}
node {
input: "X"
input: ""
input: ""
input: "sizes"
output: "Y"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "half_pixel"
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
}
}
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: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 7
}
dim {
dim_value: 8
}
}
}
}
}
}
opset_import {
domain: ""
version: 11
}

513
ngraph/test/onnx/onnx_import.in.cpp
View File

@@ -1128,6 +1128,30 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_resize10_import_only)
EXPECT_EQ(count_ops_of_type<onnx_import::default_opset::Constant>(resize_fn), 1);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_empty_constant_as_input)
{
// this model contains a Constant node with an empty underlying tensor
// this node is connected to the "roi" input of the Resize op but this input should be
// ignored since the Resize coordinate_transformation_mode is set to asymmetric
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_empty_constant_as_input.prototxt"));
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>(
Shape{1, 2, 4, 8},
{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(${BACKEND_NAME}, onnx_resize10_down_scales_const_nearest)
{
const auto function = onnx_import::import_onnx_model(
@@ -1181,10 +1205,10 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_resize10_up_scales_const_nearest)
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_down_linear)
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_down_scales_linear_asymmetric)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_scales_down_linear.prototxt"));
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_down_scales_linear_asymmetric.prototxt"));
const Shape expected_output_shape{1, 1, 1, 2};
auto test_case = test::TestCase<TestEngine>(function);
@@ -1215,45 +1239,10 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_nearest_asymmetric_floor_dynam
test_case.run_with_tolerance_as_fp();
}
NGRAPH_TEST(${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_with_tolerance_as_fp();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_sizes_linear)
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_scales_linear_asymmetric)
{
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_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_up_linear_asymmetric)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_scales_up_linear_asymmetric.prototxt"));
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_up_scales_linear_asymmetric.prototxt"));
const Shape expected_output_shape{2, 1, 4, 8};
auto test_case = test::TestCase<TestEngine>(function);
@@ -1288,28 +1277,444 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_scales_nearest_asymmetric_floor)
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_empty_constant_as_input)
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_scales_cubic_align_corners)
{
// this model contains a Constant node with an empty underlying tensor
// this node is connected to the "roi" input of the Resize op but this input should be
// ignored since the Resize coordinate_transformation_mode is set to asymmetric
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_empty_constant_as_input.prototxt"));
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_scales_cubic_align_corners.prototxt"));
const Shape expected_output_shape{1, 1, 8, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{
1.0f, 1.34110787f, 1.80029155f, 2.32944606f, 2.67055394f, 3.19970845f,
3.65889213f, 4.0f, 2.36443149f, 2.70553936f, 3.16472303f, 3.69387755f,
4.03498542f, 4.56413994f, 5.02332362f, 5.36443149f, 4.20116618f, 4.54227405f,
5.00145773f, 5.53061224f, 5.87172012f, 6.40087464f, 6.86005831f, 7.20116618f,
6.31778426f, 6.65889213f, 7.1180758f, 7.64723032f, 7.98833819f, 8.51749271f,
8.97667638f, 9.31778426f, 7.68221574f, 8.02332362f, 8.48250729f, 9.01166181f,
9.35276968f, 9.8819242f, 10.34110787f, 10.68221574f, 9.79883382f, 10.13994169f,
10.59912536f, 11.12827988f, 11.46938776f, 11.99854227f, 12.45772595f, 12.79883382f,
11.63556851f, 11.97667638f, 12.43586006f, 12.96501458f, 13.30612245f, 13.83527697f,
14.29446064f, 14.6355685f, 13.0f, 13.34110787f, 13.80029155f, 14.32944606f,
14.67055394f, 15.19970845f, 15.65889213f, 16.0f,
});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_scales_tf_half_pixel)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_up_scales_tf_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 8, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{1.95703f, 2.43359f, 3.0625f, 3.46875f, 4.09766f, 4.57422f, 4.87109f, 4.80078f,
3.86328f, 4.33984f, 4.96875f, 5.375f, 6.00391f, 6.48047f, 6.77734f, 6.70703f,
6.37891f, 6.85547f, 7.48438f, 7.89063f, 8.51953f, 8.99609f, 9.29297f, 9.22266f,
8.00391f, 8.48047f, 9.10938f, 9.51563f, 10.1445f, 10.6211f, 10.918f, 10.8477f,
10.5195f, 10.9961f, 11.625f, 12.0313f, 12.6602f, 13.1367f, 13.4336f, 13.3633f,
12.4258f, 12.9023f, 13.5313f, 13.9375f, 14.5664f, 15.043f, 15.3398f, 15.2695f,
13.6133f, 14.0898f, 14.7188f, 15.125f, 15.7539f, 16.2305f, 16.5273f, 16.457f,
13.332f, 13.8086f, 14.4375f, 14.8438f, 15.4727f, 15.9492f, 16.2461f, 16.1758f});
test_case.run_with_tolerance_as_fp(2.0e-2f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_all_attributes_default)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_sizes_all_attributes_default.prototxt"));
const Shape expected_output_shape{1, 1, 7, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f, 2.0f, 3.0f, 4.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 2.0f, 1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f,
2.0f, 2.0f, 1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 2.0f, 1.0f, 1.0f, 1.0f, 1.0f,
2.0f, 2.0f, 2.0f, 2.0f, 3.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 4.0f, 3.0f, 3.0f,
3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 4.0f});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_sizes_nearest_asymmetric_floor)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_sizes_nearest_asymmetric_floor.prototxt"));
const Shape expected_output_shape{2, 1, 4, 1};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f, 3.0f, 4.0f, 8.0f, 6.0f, 2.0f, 7.0f, 11.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape,
{1.0f, 1.0f, 4.0f, 4.0f, 6.0f, 6.0f, 7.0f, 7.0f});
test_case.run_with_tolerance_as_fp();
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_linear_asymmetric)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_up_sizes_linear_asymmetric.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>(
Shape{1, 2, 4, 8},
{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,
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});
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_with_tolerance_as_fp(2.0e-5f);
}
test_case.run();
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_down_sizes_cubic_half_pixel)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_down_sizes_cubic_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 3, 3};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape,
{1.6307871,
3.0046299,
4.3784733,
7.1261587,
8.5,
9.873844,
12.621532,
13.995373,
15.369216});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_down_sizes_linear_pytorch_half_pixel)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_down_sizes_linear_pytorch_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 3, 1};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape, {1.666666f, 7.0f, 12.333333f});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_cubic_half_pixel)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_up_sizes_cubic_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 9, 10};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{0.45507922f, 0.64057922f, 0.97157922f, 1.42257922f, 1.90732922, 2.22332922f,
2.70807922f, 3.15907922f, 3.49007922f, 3.67557922, 1.39437963f, 1.57987963f,
1.91087963f, 2.36187963f, 2.84662963, 3.16262963f, 3.64737963f, 4.09837963f,
4.42937963f, 4.61487963, 2.95130693f, 3.13680693f, 3.46780693f, 3.91880693f,
4.40355693, 4.71955693f, 5.20430693f, 5.65530693f, 5.98630693f, 6.17180693,
5.20525069f, 5.39075069f, 5.72175069f, 6.17275069f, 6.65750069, 6.97350069f,
7.45825069f, 7.90925069f, 8.24025069f, 8.42575069, 6.88975f, 7.07525f,
7.40625f, 7.85725f, 8.342, 8.658f, 9.14275f, 9.59375f,
9.92475f, 10.11025f, 8.57424931f, 8.75974931f, 9.09074931f, 9.54174931f,
10.02649931, 10.34249931f, 10.82724931f, 11.27824931f, 11.60924931f, 11.79474931,
10.82819307f, 11.01369307f, 11.34469307f, 11.79569307f, 12.28044307, 12.59644307f,
13.08119307f, 13.53219307f, 13.86319307f, 14.04869307, 12.38512037f, 12.57062037f,
12.90162037f, 13.35262037f, 13.83737037, 14.15337037f, 14.63812037f, 15.08912037f,
15.42012037f, 15.60562037, 13.32442078f, 13.50992078f, 13.84092078f, 14.29192078f,
14.77667078, 15.09267078f, 15.57742078f, 16.02842078f, 16.35942078f, 16.54492078});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_cubic_half_pixel_dynamic_sizes)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_sizes_cubic_half_pixel_dynamic_sizes.prototxt"));
const Shape expected_output_shape{1, 1, 9, 10};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_input<float>(std::vector<float>{1, 1, 9, 10}); // sizes
test_case.add_expected_output<float>(
expected_output_shape,
{0.45507922f, 0.64057922f, 0.97157922f, 1.42257922f, 1.90732922, 2.22332922f,
2.70807922f, 3.15907922f, 3.49007922f, 3.67557922, 1.39437963f, 1.57987963f,
1.91087963f, 2.36187963f, 2.84662963, 3.16262963f, 3.64737963f, 4.09837963f,
4.42937963f, 4.61487963, 2.95130693f, 3.13680693f, 3.46780693f, 3.91880693f,
4.40355693, 4.71955693f, 5.20430693f, 5.65530693f, 5.98630693f, 6.17180693,
5.20525069f, 5.39075069f, 5.72175069f, 6.17275069f, 6.65750069, 6.97350069f,
7.45825069f, 7.90925069f, 8.24025069f, 8.42575069, 6.88975f, 7.07525f,
7.40625f, 7.85725f, 8.342, 8.658f, 9.14275f, 9.59375f,
9.92475f, 10.11025f, 8.57424931f, 8.75974931f, 9.09074931f, 9.54174931f,
10.02649931, 10.34249931f, 10.82724931f, 11.27824931f, 11.60924931f, 11.79474931,
10.82819307f, 11.01369307f, 11.34469307f, 11.79569307f, 12.28044307, 12.59644307f,
13.08119307f, 13.53219307f, 13.86319307f, 14.04869307, 12.38512037f, 12.57062037f,
12.90162037f, 13.35262037f, 13.83737037, 14.15337037f, 14.63812037f, 15.08912037f,
15.42012037f, 15.60562037, 13.32442078f, 13.50992078f, 13.84092078f, 14.29192078f,
14.77667078, 15.09267078f, 15.57742078f, 16.02842078f, 16.35942078f, 16.54492078});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_nearest_round_prefer_floor_half_pixel)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_sizes_nearest_round_prefer_floor_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 7, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f, 2.0f, 3.0f, 4.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 2.0f, 1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f,
2.0f, 2.0f, 1.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 2.0f, 1.0f, 1.0f, 1.0f, 1.0f,
2.0f, 2.0f, 2.0f, 2.0f, 3.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 4.0f, 3.0f, 3.0f,
3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 4.0f});
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_nearest_prefer_ceil_asymmetric)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_sizes_nearest_prefer_ceil_asymmetric.prototxt"));
const Shape expected_output_shape{1, 1, 8, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{
1.0f, 2.0f, 2.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 5.0f, 6.0f, 6.0f,
7.0f, 7.0f, 8.0f, 8.0f, 8.0f, 5.0f, 6.0f, 6.0f, 7.0f, 7.0f, 8.0f,
8.0f, 8.0f, 9.0f, 10.0f, 10.0f, 11.0f, 11.0f, 12.0f, 12.0f, 12.0f, 9.0f,
10.0f, 10.0f, 11.0f, 11.0f, 12.0f, 12.0f, 12.0f, 13.0f, 14.0f, 14.0f, 15.0f,
15.0f, 16.0f, 16.0f, 16.0f, 13.0f, 14.0f, 14.0f, 15.0f, 15.0f, 16.0f, 16.0f,
16.0f, 13.0f, 14.0f, 14.0f, 15.0f, 15.0f, 16.0f, 16.0f, 16.0f,
});
test_case.run_with_tolerance_as_fp(2.0e-2f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_nearest_ceil_half_pixel)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_sizes_nearest_ceil_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 8, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{1.0f, 2.0f, 2.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, 5.0f, 6.0f, 6.0f, 7.0f, 7.0f,
8.0f, 8.0f, 8.0f, 5.0f, 6.0f, 6.0f, 7.0f, 7.0f, 8.0f, 8.0f, 8.0f, 9.0f, 10.0f,
10.0f, 11.0f, 11.0f, 12.0f, 12.0f, 12.0f, 9.0f, 10.0f, 10.0f, 11.0f, 11.0f, 12.0f, 12.0f,
12.0f, 13.0f, 14.0f, 14.0f, 15.0f, 15.0f, 16.0f, 16.0f, 16.0f, 13.0f, 14.0f, 14.0f, 15.0f,
15.0f, 16.0f, 16.0f, 16.0f, 13.0f, 14.0f, 14.0f, 15.0f, 15.0f, 16.0f, 16.0f, 16.0f});
test_case.run_with_tolerance_as_fp(2.0e-2f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_up_sizes_nearest_floor_align_corners)
{
const auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/resize11_up_sizes_nearest_floor_align_corners.prototxt"));
const Shape expected_output_shape{1, 1, 8, 8};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(
expected_output_shape,
{1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f, 4.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f,
3.0f, 3.0f, 4.0f, 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f, 4.0f, 5.0f, 5.0f,
5.0f, 6.0f, 6.0f, 7.0f, 7.0f, 8.0f, 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 7.0f, 7.0f,
8.0f, 9.0f, 9.0f, 9.0f, 10.0f, 10.0f, 11.0f, 11.0f, 12.0f, 9.0f, 9.0f, 9.0f, 10.0f,
10.0f, 11.0f, 11.0f, 12.0f, 13.0f, 13.0f, 13.0f, 14.0f, 14.0f, 15.0f, 15.0f, 16.0f});
test_case.run_with_tolerance_as_fp(2.0e-2f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_resize11_down_sizes_tf_half_pixel)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/resize11_down_sizes_tf_half_pixel.prototxt"));
const Shape expected_output_shape{1, 1, 3, 2};
auto test_case = test::TestCase<TestEngine>(function);
std::vector<float> input_data{1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
6.0f,
7.0f,
8.0f,
9.0f,
10.0f,
11.0f,
12.0f,
13.0f,
14.0f,
15.0f,
16.0f};
test_case.add_input<float>(input_data);
test_case.add_expected_output<float>(expected_output_shape,
{6.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f});
test_case.run_with_tolerance_as_fp(2.0e-2f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_shape)

1
ngraph/test/runtime/ie/unit_test.manifest
View File

@@ -1116,6 +1116,7 @@ IE_CPU.builder_opset1_collapse_dyn_shape
# nGraph Interpolate operation with name: Y cannot be converted to Interpolate layer with name:
# Y because output with index 0 contains dynamic shapes: {?,?,?,?}
IE_CPU.onnx_resize11_scales_nearest_asymmetric_floor_dynamic_sizes
IE_CPU.onnx_resize11_up_sizes_cubic_half_pixel_dynamic_sizes
# Input data precision not supported. Expected float.
ctc_greedy_decoder_f16

1
ngraph/test/runtime/interpreter/unit_test.manifest
View File

@@ -10,6 +10,7 @@ reduce_sum_keep_large_1d_to_scalar
# Temporarily disabled:
INTERPRETER.onnx_resize11_scales_nearest_asymmetric_floor_dynamic_sizes
INTERPRETER.onnx_resize11_up_sizes_cubic_half_pixel_dynamic_sizes
INTERPRETER.interpolate_down_scales_const_linear
INTERPRETER.onnx_resize10_up_scales_const_nearest
INTERPRETER.onnx_resize10_up_scales_const_linear