mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-06-06 00:03:22 +00:00
Added more input data types for pad (#2179)
* added more input data types for pad * replacing the comments * replacing the comments * added first set of tests * added tests * added more tests * keep NGRAPH test * avoid type cast * avoid type conversion for value float to T * fixed tabs * Update tests exclusion list * Nits * comments fix * Format files * Nit updates * rebased
This commit is contained in:
Negin Raoof
Hariharan Seshadri
parent
b23cc04f34
commit
ff2c4a4d7e
5 changed files with 293 additions and 151 deletions
|
|
@ -407,7 +407,10 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Ga
|
|||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t, BitShift);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint32_t, BitShift);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint64_t, BitShift);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Pad);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, Pad);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, Pad);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, Pad);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, Pad);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, GatherND);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Range);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Unique);
|
||||
|
|
@ -1059,7 +1062,10 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t, BitShift)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint32_t, BitShift)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint64_t, BitShift)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Pad)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, Pad)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, Pad)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, Pad)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, Pad)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, GatherND)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Range)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Unique)>,
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ namespace contrib {
|
|||
// once Keras Mask RCNN is shipped with all ONNX domain ops
|
||||
|
||||
// Currently this kernel is required to support Keras Mask-RCNN
|
||||
// only float type is supported
|
||||
ONNX_OPERATOR_KERNEL_EX(Pad,
|
||||
kMSDomain,
|
||||
1,
|
||||
|
|
@ -31,6 +32,7 @@ ONNX_OPERATOR_KERNEL_EX(Pad,
|
|||
|
||||
#endif
|
||||
|
||||
// only float type is supported for opset-10
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Pad,
|
||||
2, 10,
|
||||
|
|
@ -40,11 +42,30 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
// The interface for the 'Pad' op was changed in opset-11
|
||||
// 'pads' and 'value' (attributes previously) became inputs in this version
|
||||
// The core logic remains the same
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Pad,
|
||||
11,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pad<float>);
|
||||
float,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()), Pad<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Pad,
|
||||
11,
|
||||
double,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()), Pad<double>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Pad,
|
||||
11,
|
||||
int32_t,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<int32_t>()), Pad<int32_t>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Pad,
|
||||
11,
|
||||
int64_t,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<int64_t>()), Pad<int64_t>);
|
||||
|
||||
// This is the general padding method to n-dimensionally do edge or reflection padding (based on the inputDelta values)
|
||||
template <typename T>
|
||||
|
|
@ -115,12 +136,12 @@ static void ReshapePads(const std::vector<int64_t>& src_pad, size_t src_dim_coun
|
|||
reshaped_pad[inner_axis + new_dim_count] = src_pad[inner_axis + src_dim_count] * inner_no_pad_size;
|
||||
}
|
||||
|
||||
template <>
|
||||
Status PadCpuImpl<float>(OpKernelContext* ctx,
|
||||
const std::vector<int64_t>& pads,
|
||||
const std::vector<int64_t>& slices,
|
||||
const Mode& mode,
|
||||
float value) {
|
||||
template <typename T1, typename T2>
|
||||
Status PadCpuImpl(OpKernelContext* ctx,
|
||||
const std::vector<int64_t>& pads,
|
||||
const std::vector<int64_t>& slices,
|
||||
const Mode& mode,
|
||||
T2 value) {
|
||||
const auto& input_tensor = *ctx->Input<Tensor>(0);
|
||||
std::vector<int64_t> output_dims(input_tensor.Shape().GetDims());
|
||||
size_t data_rank = output_dims.size();
|
||||
|
|
@ -160,11 +181,11 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
TensorShape output_shape(output_dims);
|
||||
|
||||
TensorShape input_shape(reshaped_input_dims);
|
||||
SliceIterator<float> input(input_tensor, input_shape, input_starts, input_extents, {});
|
||||
SliceIterator<T1> input(input_tensor, input_shape, input_starts, input_extents, {});
|
||||
|
||||
// output_shape need to keep original.
|
||||
auto& output_tensor = *ctx->Output(0, output_shape);
|
||||
auto* output = output_tensor.template MutableData<float>();
|
||||
auto* output = output_tensor.template MutableData<T1>();
|
||||
|
||||
TensorPitches output_pitches(reshaped_output_dims);
|
||||
size_t alignSkip = 0; // Amount to skip to align to where the next input tensor data needs to be written
|
||||
|
|
@ -183,24 +204,24 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
while (input_counters) {
|
||||
output += alignSkip;
|
||||
{
|
||||
float* axisStart = output;
|
||||
T1* axisStart = output;
|
||||
output = input.CopyInnermostAxisSolitaryInnerStep(output);
|
||||
|
||||
int64_t prePad = reshaped_pad[inner_axis];
|
||||
int64_t postPad = reshaped_pad[inner_axis + new_dims_count];
|
||||
PadAxisConstant(axisStart - prePad, value, prePad);
|
||||
PadAxisConstant(output, value, postPad);
|
||||
PadAxisConstant(axisStart - prePad, static_cast<T1>(value), prePad);
|
||||
PadAxisConstant(output, static_cast<T1>(value), postPad);
|
||||
output += postPad;
|
||||
alignSkip = prePad;
|
||||
}
|
||||
// Calculate the size of the next block of padding (skipping over the innermost axis since that's already done)
|
||||
while (input_counters.Increment()) {
|
||||
ptrdiff_t inner_pitch = output_pitches[input_counters.Axis()];
|
||||
float* axisStart = output - inner_pitch * input_extents[input_counters.Axis()];
|
||||
T1* axisStart = output - inner_pitch * input_extents[input_counters.Axis()];
|
||||
int64_t prePad = reshaped_pad[input_counters.Axis()];
|
||||
int64_t postPad = reshaped_pad[input_counters.Axis() + new_dims_count];
|
||||
PadAxisConstant(axisStart - prePad * inner_pitch, value, prePad * inner_pitch);
|
||||
PadAxisConstant(output, value, postPad * inner_pitch);
|
||||
PadAxisConstant(axisStart - prePad * inner_pitch, static_cast<T1>(value), prePad * inner_pitch);
|
||||
PadAxisConstant(output, static_cast<T1>(value), postPad * inner_pitch);
|
||||
output += inner_pitch * postPad;
|
||||
alignSkip += inner_pitch * prePad;
|
||||
}
|
||||
|
|
@ -214,7 +235,7 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
while (input_counters) {
|
||||
output += alignSkip;
|
||||
{
|
||||
float* axisStart = output;
|
||||
T1* axisStart = output;
|
||||
output = input.CopyInnermostAxisSolitaryInnerStep(output);
|
||||
|
||||
int64_t prePad = reshaped_pad[inner_axis];
|
||||
|
|
@ -227,7 +248,7 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
// Calculate the size of the next block of padding (skipping over the innermost axis since that's already done)
|
||||
while (input_counters.Increment()) {
|
||||
ptrdiff_t inner_pitch = output_pitches[input_counters.Axis()];
|
||||
float* axisStart = output - inner_pitch * input_extents[input_counters.Axis()];
|
||||
T1* axisStart = output - inner_pitch * input_extents[input_counters.Axis()];
|
||||
int64_t prePad = reshaped_pad[input_counters.Axis()];
|
||||
int64_t postPad = reshaped_pad[input_counters.Axis() + new_dims_count];
|
||||
PadAxis(axisStart - prePad * inner_pitch, axisStart, 1, -inner_pitch, inner_pitch, prePad);
|
||||
|
|
@ -245,7 +266,7 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
while (input_counters) {
|
||||
output += alignSkip;
|
||||
{
|
||||
float* axisStart = output;
|
||||
T1* axisStart = output;
|
||||
output = input.CopyInnermostAxisSolitaryInnerStep(output);
|
||||
|
||||
int64_t prePad = reshaped_pad[inner_axis];
|
||||
|
|
@ -258,7 +279,7 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
// Calculate the size of the next block of padding (skipping over the innermost axis since that's already done)
|
||||
while (input_counters.Increment()) {
|
||||
ptrdiff_t inner_pitch = output_pitches[input_counters.Axis()];
|
||||
float* axisStart = output - inner_pitch * input_extents[input_counters.Axis()];
|
||||
T1* axisStart = output - inner_pitch * input_extents[input_counters.Axis()];
|
||||
int64_t prePad = reshaped_pad[input_counters.Axis()];
|
||||
int64_t postPad = reshaped_pad[input_counters.Axis() + new_dims_count];
|
||||
PadAxis(axisStart - prePad * inner_pitch, axisStart + prePad * inner_pitch, 1, -inner_pitch * 2, inner_pitch, prePad);
|
||||
|
|
@ -273,8 +294,8 @@ Status PadCpuImpl<float>(OpKernelContext* ctx,
|
|||
return Status::OK();
|
||||
}
|
||||
|
||||
template <>
|
||||
Status Pad<float>::Compute(OpKernelContext* ctx) const {
|
||||
template <typename T>
|
||||
Status Pad<T>::Compute(OpKernelContext* ctx) const {
|
||||
// kOnnxDomain Pad opset >= 11 (Or) kMsDomain opset == 1
|
||||
if (is_dynamic_) {
|
||||
const Tensor& input_tensor = *ctx->Input<Tensor>(0);
|
||||
|
|
@ -307,19 +328,21 @@ Status Pad<float>::Compute(OpKernelContext* ctx) const {
|
|||
}
|
||||
}
|
||||
|
||||
float value = 0;
|
||||
T value = 0;
|
||||
const Tensor* value_tensor = ctx->Input<Tensor>(2);
|
||||
if (nullptr != value_tensor) {
|
||||
ORT_ENFORCE(value_tensor->DataType() == DataTypeImpl::GetType<float>() &&
|
||||
ORT_ENFORCE(value_tensor->DataType() == DataTypeImpl::GetType<T>() &&
|
||||
value_tensor->Shape().Size() == 1,
|
||||
"Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor");
|
||||
value = value_tensor->template Data<float>()[0];
|
||||
value = value_tensor->template Data<T>()[0];
|
||||
}
|
||||
|
||||
return PadCpuImpl<float>(ctx, pads, slices, mode_, value);
|
||||
return PadCpuImpl<T, T>(ctx, pads, slices, mode_, value);
|
||||
} else {
|
||||
// kOnnxDomain Pad opset < 11
|
||||
return PadCpuImpl<float>(ctx, pads_, slices_, mode_, value_);
|
||||
// In the earlier opset versions of Pad, the type for 'value' attribute was always float,
|
||||
// irrespective of the data type of the actual input to be padded
|
||||
return PadCpuImpl<T, float>(ctx, pads_, slices_, mode_, value_);
|
||||
}
|
||||
}
|
||||
}; // namespace onnxruntime
|
||||
|
|
|
|||
|
|
@ -435,8 +435,6 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
|
|||
{"resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", "Bad onnx test output. Needs test fix."},
|
||||
{"bitshift_right_uint16", "BitShift(11) uint16 support not enabled currently"},
|
||||
{"bitshift_left_uint16", "BitShift(11) uint16 support not enabled currently"},
|
||||
{"reflect_pad", "Pad(11) int32 support not enabled currently"},
|
||||
{"edge_pad", "Pad(11) int32 support not enabled currently"},
|
||||
{"maxunpool_export_with_output_shape", "Invalid output in ONNX test. See https://github.com/onnx/onnx/issues/2398" },
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,30 @@
|
|||
namespace onnxruntime {
|
||||
namespace test {
|
||||
|
||||
static void RunTest(
|
||||
// There is support for int32, int64, float, and double types for opset-11 Pad alone in ORT
|
||||
template <typename T>
|
||||
static void RunOpset11TypedTest(
|
||||
const std::vector<int64_t>& input_dims,
|
||||
const std::vector<T>& input,
|
||||
const std::vector<int64_t>& pads,
|
||||
T value,
|
||||
const std::vector<int64_t>& output_dims,
|
||||
const std::vector<T>& output,
|
||||
std::string mode = "constant") {
|
||||
// ONNX domain opset-11
|
||||
OpTester test("Pad", 11);
|
||||
if (mode != "constant")
|
||||
test.AddAttribute("mode", mode);
|
||||
test.AddInput<T>("data", input_dims, input);
|
||||
test.AddInput<int64_t>("pads", {static_cast<int64_t>(pads.size())}, pads);
|
||||
test.AddInput<T>("value", {1}, {value});
|
||||
test.AddOutput<T>("output", output_dims, output);
|
||||
// NGraph and TensorRT do not yet support opset-11 and builds break on this test, hence exclude the EP
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kNGraphExecutionProvider, kTensorrtExecutionProvider});
|
||||
}
|
||||
|
||||
// There is only support for float type for opset-10 and MSDomain kernel in ORT
|
||||
static void RunAllOpsetAllDomainPadTests(
|
||||
const std::vector<int64_t>& input_dims,
|
||||
const std::vector<float>& input,
|
||||
const std::vector<int64_t>& pads,
|
||||
|
|
@ -25,17 +48,16 @@ static void RunTest(
|
|||
test1.Run();
|
||||
|
||||
// ONNX domain opset-11
|
||||
OpTester test2("Pad", 11);
|
||||
if (mode != "constant") test2.AddAttribute("mode", mode);
|
||||
test2.AddInput<float>("data", input_dims, input);
|
||||
test2.AddInput<int64_t>("pads", {static_cast<int64_t>(pads.size())}, pads);
|
||||
test2.AddInput<float>("value", {1}, {value});
|
||||
test2.AddOutput<float>("output", output_dims, output);
|
||||
// NGraph and TensorRT do not yet support opset-11 and builds break on this test, hence exclude the EP
|
||||
test2.Run(OpTester::ExpectResult::kExpectSuccess, "", {kNGraphExecutionProvider, kTensorrtExecutionProvider});
|
||||
RunOpset11TypedTest<float>(input_dims,
|
||||
input,
|
||||
pads,
|
||||
value,
|
||||
output_dims,
|
||||
output,
|
||||
mode);
|
||||
|
||||
#ifndef DISABLE_CONTRIB_OPS
|
||||
|
||||
#ifndef DISABLE_CONTRIB_OPS
|
||||
|
||||
// MSFT domain opset-1 (contrib op)
|
||||
OpTester test3("Pad", 1, kMSDomain);
|
||||
if (mode != "constant") test3.AddAttribute("mode", mode);
|
||||
|
|
@ -46,144 +68,237 @@ static void RunTest(
|
|||
//TensorRT does not support pads as an input
|
||||
test3.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
|
||||
|
||||
#endif
|
||||
#endif
|
||||
}
|
||||
|
||||
// Some of the tests can't run on TensorrtExecutionProvider because only constant mode and value 0 of "Pad" node is supported.
|
||||
// Those tests will fallback to other EP.
|
||||
|
||||
TEST(TensorOpTest, Pad_Spec_Example) {
|
||||
RunTest({3, 2},
|
||||
{1.0f, 1.2f, 2.3f, 3.4f, 4.5f, 5.7f},
|
||||
{0, 2, 0, 0},
|
||||
0.f,
|
||||
{3, 4},
|
||||
{0.0f, 0.0f, 1.0f, 1.2f, 0.0f, 0.0f, 2.3f, 3.4f, 0.0f, 0.0f, 4.5f, 5.7f});
|
||||
RunAllOpsetAllDomainPadTests({3, 2},
|
||||
{1.0f, 1.2f, 2.3f, 3.4f, 4.5f, 5.7f},
|
||||
{0, 2, 0, 0},
|
||||
0.f,
|
||||
{3, 4},
|
||||
{0.0f, 0.0f, 1.0f, 1.2f, 0.0f, 0.0f, 2.3f, 3.4f, 0.0f, 0.0f, 4.5f, 5.7f});
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_1D_int) {
|
||||
std::vector<int32_t> X = {1, 2, 3, 4, 5, 6};
|
||||
int32_t value = 1234;
|
||||
std::vector<int32_t> Y = {1234, 1234, 1, 2, 1234, 1234, 3, 4, 1234, 1234, 5, 6};
|
||||
RunOpset11TypedTest({3, 2},
|
||||
X,
|
||||
{0, 2, 0, 0},
|
||||
value,
|
||||
{3, 4},
|
||||
Y);
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_1D_long) {
|
||||
std::vector<int64_t> X = {1, 2, 3, 4, 5, 6};
|
||||
int64_t value = 1234;
|
||||
std::vector<int64_t> Y = {1234, 1234, 1, 2, 1234, 1234, 3, 4, 1234, 1234, 5, 6};
|
||||
RunOpset11TypedTest({3, 2},
|
||||
X,
|
||||
{0, 2, 0, 0},
|
||||
value,
|
||||
{3, 4},
|
||||
Y);
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_1D_double) {
|
||||
std::vector<double> X = {1., 2., 3., 4., 5., 6.};
|
||||
double value = 0.;
|
||||
std::vector<double> Y = {0., 0., 1., 2., 0., 0., 3., 4., 0., 0., 5., 6.};
|
||||
RunOpset11TypedTest({3, 2},
|
||||
X,
|
||||
{0, 2, 0, 0},
|
||||
value,
|
||||
{3, 4},
|
||||
Y);
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_1D) {
|
||||
RunTest({2},
|
||||
{1.0f, 2.0f},
|
||||
{1, 2},
|
||||
1234.f,
|
||||
{5},
|
||||
{1234.0f, 1.0f, 2.0f, 1234.0f, 1234.0f});
|
||||
RunAllOpsetAllDomainPadTests({2},
|
||||
{1.0f, 2.0f},
|
||||
{1, 2},
|
||||
1234.f,
|
||||
{5},
|
||||
{1234.0f, 1.0f, 2.0f, 1234.0f, 1234.0f});
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_1D_Zero) {
|
||||
RunTest({2},
|
||||
{1.0f, 2.0f},
|
||||
{0, 0},
|
||||
1234.f,
|
||||
{2},
|
||||
{1.0f, 2.0f});
|
||||
RunAllOpsetAllDomainPadTests({2},
|
||||
{1.0f, 2.0f},
|
||||
{0, 0},
|
||||
1234.f,
|
||||
{2},
|
||||
{1.0f, 2.0f});
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_2D) {
|
||||
RunTest({2, 2},
|
||||
{11.0f, 21.0f,
|
||||
12.0f, 22.0f},
|
||||
{1, 2, 1, 2},
|
||||
1234.f,
|
||||
{4, 6},
|
||||
{1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 11.0f, 21.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 12.0f, 22.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f});
|
||||
RunAllOpsetAllDomainPadTests({2, 2},
|
||||
{11.0f, 21.0f,
|
||||
12.0f, 22.0f},
|
||||
{1, 2, 1, 2},
|
||||
1234.f,
|
||||
{4, 6},
|
||||
{1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 11.0f, 21.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 12.0f, 22.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f, 1234.0f});
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_2D_negative) {
|
||||
RunTest({2, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f},
|
||||
{1, 2, 1, -1},
|
||||
1234.f,
|
||||
{4, 4},
|
||||
{1234.0f, 1234.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 11.0f, 21.0f,
|
||||
1234.0f, 1234.0f, 12.0f, 22.0f,
|
||||
1234.0f, 1234.0f, 1234.0f, 1234.0f});
|
||||
RunAllOpsetAllDomainPadTests({2, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f},
|
||||
{1, 2, 1, -1},
|
||||
1234.f,
|
||||
{4, 4},
|
||||
{1234.0f, 1234.0f, 1234.0f, 1234.0f,
|
||||
1234.0f, 1234.0f, 11.0f, 21.0f,
|
||||
1234.0f, 1234.0f, 12.0f, 22.0f,
|
||||
1234.0f, 1234.0f, 1234.0f, 1234.0f});
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_3D_complex) {
|
||||
RunTest({2, 2, 2},
|
||||
{111.0f, 112.0f,
|
||||
121.0f, 122.0f,
|
||||
RunAllOpsetAllDomainPadTests({2, 2, 2},
|
||||
{111.0f, 112.0f,
|
||||
121.0f, 122.0f,
|
||||
|
||||
211.0f, 212.0f,
|
||||
221.0f, 222.0f},
|
||||
{1, 0, 0, -1, 0, 0},
|
||||
0.f,
|
||||
{2, 2, 2},
|
||||
{0.0f, 0.0f,
|
||||
0.0f, 0.0f,
|
||||
211.0f, 212.0f,
|
||||
221.0f, 222.0f},
|
||||
{1, 0, 0, -1, 0, 0},
|
||||
0.f,
|
||||
{2, 2, 2},
|
||||
{0.0f, 0.0f,
|
||||
0.0f, 0.0f,
|
||||
|
||||
111.0f, 112.0f,
|
||||
121.0f, 122.0f});
|
||||
111.0f, 112.0f,
|
||||
121.0f, 122.0f});
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Edge_2D) {
|
||||
RunTest({2, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f},
|
||||
{2, 2, 2, 2},
|
||||
0.f,
|
||||
{6, 7},
|
||||
{11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f},
|
||||
"edge");
|
||||
RunAllOpsetAllDomainPadTests({2, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f},
|
||||
{2, 2, 2, 2},
|
||||
0.f,
|
||||
{6, 7},
|
||||
{11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f},
|
||||
"edge");
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Edge_3D) {
|
||||
RunTest({1, 2, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f},
|
||||
{1, 2, 2, 1, 2, 2},
|
||||
0.f,
|
||||
{3, 6, 7},
|
||||
{11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
RunAllOpsetAllDomainPadTests({1, 2, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f},
|
||||
{1, 2, 2, 1, 2, 2},
|
||||
0.f,
|
||||
{3, 6, 7},
|
||||
{11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f},
|
||||
"edge");
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
11.0f, 11.0f, 11.0f, 21.0f, 31.0f, 31.0f, 31.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f,
|
||||
12.0f, 12.0f, 12.0f, 22.0f, 32.0f, 32.0f, 32.0f},
|
||||
"edge");
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Reflect_2D) {
|
||||
RunTest({3, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f,
|
||||
13.0f, 23.0f, 33.0f},
|
||||
{2, 2, 2, 2},
|
||||
0.f,
|
||||
{7, 7},
|
||||
{33.0f, 23.0f, 13.0f, 23.0f, 33.0f, 23.0f, 13.0f,
|
||||
32.0f, 22.0f, 12.0f, 22.0f, 32.0f, 22.0f, 12.0f,
|
||||
31.0f, 21.0f, 11.0f, 21.0f, 31.0f, 21.0f, 11.0f,
|
||||
32.0f, 22.0f, 12.0f, 22.0f, 32.0f, 22.0f, 12.0f,
|
||||
33.0f, 23.0f, 13.0f, 23.0f, 33.0f, 23.0f, 13.0f,
|
||||
32.0f, 22.0f, 12.0f, 22.0f, 32.0f, 22.0f, 12.0f,
|
||||
31.0f, 21.0f, 11.0f, 21.0f, 31.0f, 21.0f, 11.0f},
|
||||
"reflect");
|
||||
RunAllOpsetAllDomainPadTests({3, 3},
|
||||
{11.0f, 21.0f, 31.0f,
|
||||
12.0f, 22.0f, 32.0f,
|
||||
13.0f, 23.0f, 33.0f},
|
||||
{2, 2, 2, 2},
|
||||
0.f,
|
||||
{7, 7},
|
||||
{33.0f, 23.0f, 13.0f, 23.0f, 33.0f, 23.0f, 13.0f,
|
||||
32.0f, 22.0f, 12.0f, 22.0f, 32.0f, 22.0f, 12.0f,
|
||||
31.0f, 21.0f, 11.0f, 21.0f, 31.0f, 21.0f, 11.0f,
|
||||
32.0f, 22.0f, 12.0f, 22.0f, 32.0f, 22.0f, 12.0f,
|
||||
33.0f, 23.0f, 13.0f, 23.0f, 33.0f, 23.0f, 13.0f,
|
||||
32.0f, 22.0f, 12.0f, 22.0f, 32.0f, 22.0f, 12.0f,
|
||||
31.0f, 21.0f, 11.0f, 21.0f, 31.0f, 21.0f, 11.0f},
|
||||
"reflect");
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_2D_int) {
|
||||
std::vector<int32_t> X = {11, 21, 31,
|
||||
12, 22, 32};
|
||||
int32_t value = 0;
|
||||
std::vector<int32_t> Y = {11, 11, 11, 21, 31, 31, 31,
|
||||
11, 11, 11, 21, 31, 31, 31,
|
||||
11, 11, 11, 21, 31, 31, 31,
|
||||
12, 12, 12, 22, 32, 32, 32,
|
||||
12, 12, 12, 22, 32, 32, 32,
|
||||
12, 12, 12, 22, 32, 32, 32};
|
||||
RunOpset11TypedTest({2, 3},
|
||||
X,
|
||||
{2, 2, 2, 2},
|
||||
value,
|
||||
{6, 7},
|
||||
Y,
|
||||
"edge");
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_2D_long) {
|
||||
std::vector<int64_t> X = {11, 21, 31,
|
||||
12, 22, 32};
|
||||
int64_t value = 0;
|
||||
std::vector<int64_t> Y = {11, 11, 11, 21, 31, 31, 31,
|
||||
11, 11, 11, 21, 31, 31, 31,
|
||||
11, 11, 11, 21, 31, 31, 31,
|
||||
12, 12, 12, 22, 32, 32, 32,
|
||||
12, 12, 12, 22, 32, 32, 32,
|
||||
12, 12, 12, 22, 32, 32, 32};
|
||||
RunOpset11TypedTest({2, 3},
|
||||
X,
|
||||
{2, 2, 2, 2},
|
||||
value,
|
||||
{6, 7},
|
||||
Y,
|
||||
"edge");
|
||||
}
|
||||
|
||||
TEST(TensorOpTest, Pad_Constant_2D_double) {
|
||||
std::vector<double> X = {11., 21., 31.,
|
||||
12., 22., 32.};
|
||||
double value = 0.;
|
||||
std::vector<double> Y = {11., 11., 11., 21., 31., 31., 31.,
|
||||
11., 11., 11., 21., 31., 31., 31.,
|
||||
11., 11., 11., 21., 31., 31., 31.,
|
||||
12., 12., 12., 22., 32., 32., 32.,
|
||||
12., 12., 12., 22., 32., 32., 32.,
|
||||
12., 12., 12., 22., 32., 32., 32.};
|
||||
RunOpset11TypedTest({2, 3},
|
||||
X,
|
||||
{2, 2, 2, 2},
|
||||
value,
|
||||
{6, 7},
|
||||
Y,
|
||||
"edge");
|
||||
}
|
||||
|
||||
} // namespace test
|
||||
|
|
|
|||
|
|
@ -93,9 +93,7 @@ def other_tests_failing_permanently_filters():
|
|||
|
||||
def test_with_types_disabled_due_to_binary_size_concerns_filters():
|
||||
filters = ['^test_bitshift_right_uint16_cpu',
|
||||
'^test_bitshift_left_uint16_cpu',
|
||||
'^test_edge_pad_cpu',
|
||||
'^test_reflect_pad_cpu']
|
||||
'^test_bitshift_left_uint16_cpu']
|
||||
|
||||
return filters
|
||||
|
||||
|
|
@ -137,7 +135,9 @@ def create_backend_test(testname=None):
|
|||
'^test_reduce_[a-z1-9_]*_negative_axes_.*',
|
||||
'test_squeeze_negative_axes_cpu',
|
||||
'test_unsqueeze_negative_axes_cpu',
|
||||
'test_constant_pad_cpu']
|
||||
'test_constant_pad_cpu',
|
||||
'test_edge_pad_cpu',
|
||||
'test_reflect_pad_cpu']
|
||||
|
||||
if c2.supports_device('MKL-DNN'):
|
||||
current_failing_tests += ['^test_range_float_type_positive_delta_expanded_cpu',
|
||||
|
|
|
|||
Loading…
Reference in a new issue