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Upsample opset 9 support for CPU provider (#135)

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* Upsample opset 9 support for CPU provider

* remove template from UpsampleBase

* resolve build error on Linux

* Fix build warning
This commit is contained in:
Hector Li 2018-12-10 11:15:29 -08:00 committed by GitHub
parent d71376010b
commit 69a5ff3300
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GPG key ID: 4AEE18F83AFDEB23
5 changed files with 102 additions and 32 deletions
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8
onnxruntime/core/providers/cpu/cpu_execution_provider.cc
View file

@ -182,8 +182,8 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Squ
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Tile);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Transpose);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Unsqueeze);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, float, Upsample);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, int32_t, Upsample);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 9, float, Upsample);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 9, int32_t, Upsample);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, float, Expand);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, Scan);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Scale);
@ -367,8 +367,8 @@ void RegisterOnnxOperatorKernels(std::function<void(KernelCreateInfo&&)> fn) {
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Tile)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Transpose)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Unsqueeze)>());
fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, float, Upsample)>());
fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, int32_t, Upsample)>());
fn(BuildKernel<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 9, float, Upsample)>());
fn(BuildKernel<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 9, int32_t, Upsample)>());
fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, float, Expand)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, Scan)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Scale)>());

34
onnxruntime/core/providers/cpu/tensor/upsample.cc
View file

@ -8,16 +8,16 @@ using namespace ::onnxruntime::common;
using namespace std;
namespace onnxruntime {
ONNX_CPU_OPERATOR_TYPED_KERNEL(
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
Upsample,
7,
7, 9,
float,
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
Upsample<float>);
ONNX_CPU_OPERATOR_TYPED_KERNEL(
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
Upsample,
7,
7, 9,
int32_t,
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<int32_t>()),
Upsample<int32_t>);
@ -191,24 +191,24 @@ void upsampleBilinear(
}
template <typename T>
Status Upsample<T>::Compute(OpKernelContext* context) const {
Status Upsample<T>::BaseCompute(OpKernelContext* context, const std::vector<float>& scales) const {
const Tensor* X = context->Input<Tensor>(0);
ONNXRUNTIME_ENFORCE(X != nullptr);
const std::vector<int64_t>& dims = X->Shape().GetDims();
if (dims.size() != scales_.size()) {
if (dims.size() != scales.size()) {
return Status(ONNXRUNTIME, INVALID_ARGUMENT, "Upsample: input tensor's dimension does not match the scales.");
}
std::vector<int64_t> Y_dims;
for (std::size_t i = 0; i < dims.size(); i++) {
Y_dims.push_back(static_cast<int64_t>(scales_[i] * dims[i]));
Y_dims.push_back(static_cast<int64_t>(scales[i] * dims[i]));
}
Tensor* Y = context->Output(0, Y_dims);
switch (mode_) {
case UpsampleMode::NN:
return upsampleNearest<T>(X->template Data<T>(), Y->template MutableData<T>(), X->Shape(), Y->Shape(), scales_);
return upsampleNearest<T>(X->template Data<T>(), Y->template MutableData<T>(), X->Shape(), Y->Shape(), scales);
case UpsampleMode::LINEAR: {
//What's the correct behavior of linear mode is not clear right now,
//Only support bilinear with 4D tensor to keep consistent with previous behavior
@ -219,13 +219,27 @@ Status Upsample<T>::Compute(OpKernelContext* context) const {
const int64_t input_height = dims[2], input_width = dims[3];
upsampleBilinear(batch_size, num_channels, input_height, input_width,
scales_[2], scales_[3], X->template Data<T>(), Y->template MutableData<T>());
scales[2], scales[3], X->template Data<T>(), Y->template MutableData<T>());
return Status::OK();
//return upsampleLiner<T>(X->template Data<T>(), Y->template MutableData<T>(), X->Shape(), Y->Shape(), scales_);
}
default:
return Status(ONNXRUNTIME, FAIL, "Upsample: unexpected mode");
}
}
template <typename T>
Status Upsample<T>::Compute(OpKernelContext* context) const {
if (OpKernel::Node().InputDefs().size() == 1 || scales_cached_) {
return BaseCompute(context, scales_);
}
const Tensor* scales = context->Input<Tensor>(1);
ONNXRUNTIME_ENFORCE(scales != nullptr);
int64_t scales_size = scales->Shape().Size();
std::vector<float> scales_arrary(scales_size);
ParseScalesData(scales, scales_arrary);
return BaseCompute(context, scales_arrary);
}
} // namespace onnxruntime

59
onnxruntime/core/providers/cpu/tensor/upsample.h
View file

@ -10,33 +10,25 @@ namespace onnxruntime {
constexpr const char* UpsampleModeNN = "nearest";
constexpr const char* UpsampleModeLinear = "linear";
enum UpsampleMode {
NN = 0, // nearest neighbour
LINEAR = 1, // linear interpolation
};
enum UpsampleMode {
NN = 0, // nearest neighbour
LINEAR = 1, // linear interpolation
};
class UpsampleBase {
protected:
UpsampleBase(OpKernelInfo info) {
std::string mode;
ONNXRUNTIME_ENFORCE(info.GetAttr<std::string>("mode", &mode).IsOK());
mode_ = StringToUpsampleMode(mode);
ONNXRUNTIME_ENFORCE(info.GetAttrs<float>("scales", scales_).IsOK());
for (auto& scale : scales_) {
ONNXRUNTIME_ENFORCE(scale >= 1, "Scale value should be greater than or equal to 1.");
}
if (UpsampleMode::LINEAR == mode_) {
ONNXRUNTIME_ENFORCE(((scales_[0] == 1) && (scales_[1] == 1)),
"Upsample: linear mode upsample only support bilinear, the first 2 scales should be 1.");
if (info.GetInputCount() == 1) {
ONNXRUNTIME_ENFORCE(info.GetAttrs<float>("scales", scales_).IsOK());
ScalesValidation(scales_, mode_);
}
}
UpsampleMode mode_;
std::vector<float> scales_;
UpsampleMode StringToUpsampleMode(const std::string& mode) {
@ -49,15 +41,50 @@ class UpsampleBase {
UpsampleModeNN + "(default) or " + UpsampleModeLinear + ".");
}
}
void ScalesValidation(const std::vector<float>& scales, const UpsampleMode mode) const {
for (auto& scale : scales) {
ONNXRUNTIME_ENFORCE(scale >= 1, "Scale value should be greater than or equal to 1.");
}
if (UpsampleMode::LINEAR == mode) {
ONNXRUNTIME_ENFORCE(scales.size() == 4, "Upsample: linear mode upsample only support bilinear with 4 dimension.");
ONNXRUNTIME_ENFORCE(((scales[0] == 1) && (scales[1] == 1)),
"Upsample: linear mode upsample only support bilinear, the first 2 scales should be 1.");
}
}
};
template <typename T>
class Upsample : public UpsampleBase, public OpKernel {
public:
Upsample(OpKernelInfo info) : UpsampleBase(info), OpKernel(info) {
Upsample(OpKernelInfo info) : UpsampleBase(info), OpKernel(info), scales_cached_(false) {
if (info.GetInputCount() > 1) {
const Tensor* scale;
bool get_scale = info.TryGetConstantInput(1, &scale);
if (get_scale) {
ParseScalesData(scale, scales_);
scales_cached_ = true;
}
}
}
Status Compute(OpKernelContext* context) const override;
Status BaseCompute(OpKernelContext* context, const std::vector<float>& scales) const;
private:
void ParseScalesData(const Tensor* scale, std::vector<float>& scales) const {
const float* scale_data = scale->template Data<float>();
int64_t scales_size = scale->Shape().Size();
ONNXRUNTIME_ENFORCE(scales_size > 0, "scales size should be greater than 0.");
memcpy(scales.data(), scale_data, scales_size * sizeof(float));
ScalesValidation(scales, mode_);
}
private:
bool scales_cached_;
};
} // namespace onnxruntime

2
onnxruntime/core/providers/cuda/tensor/upsample.h
View file

@ -10,8 +10,6 @@
namespace onnxruntime {
namespace cuda {
struct TVMState;
template <typename T>
class Upsample : public UpsampleBase, public CudaKernel {
public:

31
onnxruntime/test/providers/cpu/tensor/upsample_op_test.cc
View file

@ -319,5 +319,36 @@ TEST(UpsampleOpTest, UpsampleOpNearestTest_1D) {
test.AddOutput<float>("Y", {10}, Y);
test.Run();
}
TEST(UpsampleOpTest, UpsampleOpNearest2XTest_opset9) {
OpTester test("Upsample", 9);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 2.0f};
test.AddAttribute("mode", "nearest");
const int64_t N = 1, C = 2, H = 2, W = 2;
std::vector<int32_t> X = {1, 3,
3, 5,
3, 5,
7, 9};
test.AddInput<int32_t>("X", {N, C, H, W}, X);
test.AddInput<float>("scales", {4}, scales);
std::vector<int32_t> Y = {
1, 1, 3, 3,
1, 1, 3, 3,
3, 3, 5, 5,
3, 3, 5, 5,
3, 3, 5, 5,
3, 3, 5, 5,
7, 7, 9, 9,
7, 7, 9, 9};
test.AddOutput<int32_t>("Y", {N, C, (int64_t)(H * scales[2]), (int64_t)(W * scales[3])}, Y);
test.Run();
}
} // namespace test
} // namespace onnxruntime