saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-05-14 20:48:00 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

[Feature Request] Support Resize opset 19 (#15633)

Browse source
This commit is contained in:
liqun Fu 2023-05-01 10:49:17 -07:00 committed by GitHub
parent d58fa9805b
commit 62fc6ed5a8
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
5 changed files with 114 additions and 22 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
docs/OperatorKernels.md
View file

@ -295,7 +295,8 @@ Do not modify directly.*
|||13|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[5, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[1, 4]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Resize|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T1**<br> *in* roi:**T2**<br> *in* scales:**tensor(float)**<br> *in* sizes:**tensor(int64)**<br> *out* Y:**T1**|18+|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|Resize|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T1**<br> *in* roi:**T2**<br> *in* scales:**tensor(float)**<br> *in* sizes:**tensor(int64)**<br> *out* Y:**T1**|19+|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||18|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||[13, 17]|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||[11, 12]|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||10|**T** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|

34
onnxruntime/core/providers/cpu/cpu_execution_provider.cc
View file

@ -840,10 +840,10 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 17, double, LayerNormalization);
// Opset 18
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, float, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int32_t, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int8_t, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, uint8_t, Resize);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, float, Resize);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, int32_t, Resize);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, int8_t, Resize);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, uint8_t, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, float, ReduceL1);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int32_t, ReduceL1);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int64_t, ReduceL1);
@ -922,6 +922,11 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Op
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, OptionalGetElement);
#endif
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, float, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, int32_t, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, int8_t, Resize);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, uint8_t, Resize);
// !!PLEASE READ BELOW!! Following that, add new entries above this comment
/* *** IMPORTANT! ***
@ -2175,15 +2180,14 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
LayerNormalization)>,
// Opset 18
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18,
float, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18,
int32_t, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18,
int8_t, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18,
uint8_t, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, float,
Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, int32_t,
Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, int8_t,
Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 18, uint8_t,
Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, float,
ReduceL1)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int32_t,
@ -2296,6 +2300,10 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, OptionalHasElement)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, OptionalGetElement)>,
#endif
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, float, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, int32_t, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, int8_t, Resize)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, uint8_t, Resize)>,
};
for (auto& function_table_entry : function_table) {

41
onnxruntime/core/providers/cpu/tensor/resize.cc
View file

@ -93,32 +93,63 @@ ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>()),
Resize<uint8_t>);
ONNX_CPU_OPERATOR_TYPED_KERNEL(
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
Resize,
18,
18,
float,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<float>()),
Resize<float>);
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
Resize,
18,
18,
int32_t,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
Resize<int32_t>);
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
Resize,
18,
18,
int8_t,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<int8_t>()),
Resize<int8_t>);
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
Resize,
18,
18,
uint8_t,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>()),
Resize<uint8_t>);
ONNX_CPU_OPERATOR_TYPED_KERNEL(
Resize,
19,
float,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<float>()),
Resize<float>);
ONNX_CPU_OPERATOR_TYPED_KERNEL(
Resize,
18,
19,
int32_t,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
Resize<int32_t>);
ONNX_CPU_OPERATOR_TYPED_KERNEL(
Resize,
18,
19,
int8_t,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<int8_t>()),
Resize<int8_t>);
ONNX_CPU_OPERATOR_TYPED_KERNEL(
Resize,
18,
19,
uint8_t,
KernelDefBuilder().TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>()),
Resize<uint8_t>);
} // namespace onnxruntime

15
onnxruntime/core/providers/cpu/tensor/upsamplebase.h
View file

@ -41,7 +41,7 @@ enum ResizeCoordinateTransformationMode {
TF_HALF_PIXEL_FOR_NN = 3,
ALIGN_CORNERS = 4,
TF_CROP_AND_RESIZE = 5,
CoordinateTransformationModeCount = 6,
HALF_PIXEL_SYMMETRIC = 6,
};
enum ResizeNearestMode {
@ -50,7 +50,6 @@ enum ResizeNearestMode {
ROUND_PREFER_CEIL = 2,
FLOOR = 3,
CEIL = 4,
NearestModeCount = 5,
};
enum class AspectRatioPolicy {
@ -237,6 +236,9 @@ class UpsampleBase {
if (coordinate_transform_mode_name == "half_pixel") {
return HALF_PIXEL;
}
if (coordinate_transform_mode_name == "half_pixel_symmetric") {
return HALF_PIXEL_SYMMETRIC;
}
ORT_THROW("coordinate_transform_mode:[" + coordinate_transform_mode_name + "] is not supportted!");
}
@ -267,6 +269,15 @@ class UpsampleBase {
: 0.5 * (roi_start + roi_end) * (length_original - 1);
return static_cast<float>(orig);
};
case HALF_PIXEL_SYMMETRIC:
return [](float x_resized, float x_scale, float length_resized, float length_original, float, float) {
float output_width = x_scale * length_original;
float adjustment = length_resized / output_width;
float center = length_original / 2;
float offset = center * (1 - adjustment);
auto orig = offset + (x_resized + 0.5) / x_scale - 0.5;
return static_cast<float>(orig);
};
default: // "half_pixel"
return [](float x_resized, float x_scale, float, float, float, float) {
return ((x_resized + 0.5f) / x_scale) - 0.5f;

43
onnxruntime/test/providers/cpu/tensor/resize_op_test.cc
View file

@ -1772,6 +1772,48 @@ TEST(ResizeOpTest, ResizeOpTypeCheck_Ver18) {
ResizeOpTypeCheck_Ver_11_13_18<uint8_t>(18);
}
TEST(ResizeOpTest, ResizeOpHalfPixelSymmetricDownSample_ver19) {
OpTester test("Resize", 19);
test.AddAttribute("mode", "linear");
test.AddAttribute("coordinate_transformation_mode", "half_pixel_symmetric");
constexpr int64_t N = 1, C = 1, H = 1, W = 4;
std::vector<float> X = {1.0f, 2.0f, 3.0f, 4.0f};
test.AddInput<float>("X", {N, C, H, W}, X);
std::vector<float> roi{};
test.AddInput<float>("roi", {0}, roi);
std::vector<float> scales{1.0f, 1.0f, 1.0f, 0.6f};
test.AddInput<float>("scales", {4}, scales);
std::vector<float> Y = {1.666667f, 3.333333f};
test.AddOutput<float>("Y", {N, C, static_cast<int64_t>(H * scales[2]), static_cast<int64_t>(W * scales[3])}, Y);
test.Run();
}
TEST(ResizeOpTest, ResizeOpHalfPixelSymmetricUpSample_ver19) {
OpTester test("Resize", 19);
test.AddAttribute("mode", "linear");
test.AddAttribute("coordinate_transformation_mode", "half_pixel_symmetric");
constexpr int64_t N = 1, C = 1, H = 2, W = 2;
std::vector<float> X = {1.0f, 2.0f, 3.0f, 4.0f};
test.AddInput<float>("X", {N, C, H, W}, X);
std::vector<float> roi{};
test.AddInput<float>("roi", {0}, roi);
std::vector<float> scales{1.0f, 1.0f, 2.3f, 2.94f};
test.AddInput<float>("scales", {4}, scales);
std::vector<float> Y = {1.0f, 1.159864f, 1.5f, 1.840136f, 2.0f,
1.565217f, 1.725081f, 2.065217f, 2.405353f, 2.565217f,
2.434782f, 2.594647f, 2.934783f, 3.274919f, 3.434783f,
3.0f, 3.159864f, 3.5f, 3.840136f, 4.0f};
test.AddOutput<float>("Y", {N, C, static_cast<int64_t>(H * scales[2]), static_cast<int64_t>(W * scales[3])}, Y);
test.Run();
}
/*
* Most of TestCase against Anti-aliasing will have the attribute of "exclude_outside" as 1.
* It's as Pillow 's Resize is corresponding to ONNX op with exclude_outside equaling 1.
@ -2186,6 +2228,5 @@ TEST(ResizeOpTest, Antialias_Use_Extrapolation) {
},
{4, 4, 4}, X, {3, 3, 3}, Y);
}
} // namespace test
} // namespace onnxruntime