saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-06-04 23:59:56 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Relax shape inferencing error handling if model uses an old opset (#2199)

Browse source
This commit is contained in:
Scott McKay 2019-10-21 10:51:22 -07:00 committed by Changming Sun
parent 02dc3a9dcb
commit 3cda9f717b
14 changed files with 212 additions and 88 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
include/onnxruntime/core/graph/graph.h
View file

@ -1009,6 +1009,9 @@ class Graph {
// Model IR version.
Version ir_version_{ONNX_NAMESPACE::Version::IR_VERSION};
// Is model using latest ONNX opset
bool using_latest_onnx_opset_{false};
int name_generator_ = 0;
ResolveContext resolve_context_;

16
include/onnxruntime/core/graph/node_arg.h
View file

@ -59,13 +59,21 @@ class NodeArg {
as the shape information is stored as part of TypeProto. */
void SetShape(const ONNX_NAMESPACE::TensorShapeProto& shape);
/** Clears shape info.
@remarks If there is a mismatch during shape inferencing that can't be resolved the shape info may be removed. */
void ClearShape();
/** Validate and merge type [and shape] info from input_type.
@returns Success unless there is existing type or shape info that can't be cleanly updated. */
common::Status UpdateTypeAndShape(const ONNX_NAMESPACE::TypeProto& input_type);
@param strict If true, the shape update will fail if there are incompatible values.
If false, will be lenient and merge only shape info that can be validly processed.
@returns Success unless there is existing type or shape info that can't be successfully updated. */
common::Status UpdateTypeAndShape(const ONNX_NAMESPACE::TypeProto& input_type, bool strict = true);
/** Validate and merge type [and shape] info from node_arg.
@returns Success unless there is existing type or shape info that can't be cleanly updated. */
common::Status UpdateTypeAndShape(const NodeArg& node_arg);
@param strict If true, the shape update will fail if there are incompatible values.
If false, will be lenient and merge only shape info that can be validly processed.
@returns Success unless there is existing type or shape info that can't be successfully updated. */
common::Status UpdateTypeAndShape(const NodeArg& node_arg, bool strict = true);
/** Gets this NodeArg as a ValueInfoProto. */
const NodeArgInfo& ToProto() const noexcept { return node_arg_info_; }

69
onnxruntime/core/graph/graph.cc
View file

@ -40,12 +40,42 @@ namespace onnxruntime {
GraphProtoSyncNeeded(sync_needed); \
} while (0)
static bool UsingLatestOnnxOpset(const DomainToVersionMap& opset_versions) {
bool is_latest_opset = false;
auto onnx_opset = opset_versions.find(kOnnxDomain);
if (onnx_opset != opset_versions.cend()) {
static int latest_onnx_version =
ONNX_NAMESPACE::OpSchemaRegistry::DomainToVersionRange().Map().at(ONNX_NAMESPACE::ONNX_DOMAIN).second;
if (onnx_opset->second == latest_onnx_version) {
is_latest_opset = true;
}
}
return is_latest_opset;
}
static Status MergeShapeInfo(const std::string& output_name,
const TypeProto_Tensor& source, TypeProto_Tensor& target) {
const TypeProto_Tensor& source, TypeProto_Tensor& target,
bool strict) {
try {
ONNX_NAMESPACE::mergeInShapeInfo(source, target);
} catch (const ONNX_NAMESPACE::InferenceError& ex) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Output:", output_name, " ", ex.what());
// if this model was not created with the latest onnx version, allow the shape inferencing failure (strict == false).
// we do this to have strict testing of the latest inferencing to detect bugs, but lenient shape inferencing for
// older models in case later changes to the ONNX shape inferencing or ORT break them.
if (!strict) {
// mergeInShapeInfo does nothing unless source.shape() is not null, and there would be no conflict if
// target.shape() was empty. 'assert' just in case that ever changes.
assert(utils::HasShape(source) && utils::HasShape(target));
LOGS_DEFAULT(WARNING) << "Error merging shape info for output. '" << output_name
<< "' source:" << source.shape() << " target:" << target.shape()
<< ". Falling back to lenient merge.";
ONNX_NAMESPACE::UnionShapeInfo(source.shape(), target);
} else {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Output:", output_name, " ", ex.what());
}
}
return Status::OK();
@ -158,7 +188,25 @@ void NodeArg::SetShape(const TensorShapeProto& shape) {
}
}
common::Status NodeArg::UpdateTypeAndShape(const ONNX_NAMESPACE::TypeProto& input_type) {
void NodeArg::ClearShape() {
const auto type_case = node_arg_info_.type().value_case();
switch (type_case) {
case TypeProto::kTensorType:
node_arg_info_.mutable_type()->mutable_tensor_type()->clear_shape();
break;
case TypeProto::kSparseTensorType:
node_arg_info_.mutable_type()->mutable_sparse_tensor_type()->clear_shape();
break;
case TypeProto::kSequenceType:
case TypeProto::kMapType:
case TypeProto::kOpaqueType:
case TypeProto::VALUE_NOT_SET:
default:
return;
}
}
common::Status NodeArg::UpdateTypeAndShape(const ONNX_NAMESPACE::TypeProto& input_type, bool strict) {
if (!utils::HasType(node_arg_info_)) {
*node_arg_info_.mutable_type() = input_type;
type_ = DataTypeUtils::ToType(node_arg_info_.type());
@ -187,7 +235,7 @@ common::Status NodeArg::UpdateTypeAndShape(const ONNX_NAMESPACE::TypeProto& inpu
if (utils::HasShape(input_tensor_type)) {
auto& current_tensor_type = *current_type.mutable_tensor_type();
if (utils::HasShape(current_tensor_type)) {
ORT_RETURN_IF_ERROR(MergeShapeInfo(Name(), input_tensor_type, current_tensor_type));
ORT_RETURN_IF_ERROR(MergeShapeInfo(Name(), input_tensor_type, current_tensor_type, strict));
} else {
current_tensor_type = input_tensor_type;
}
@ -225,11 +273,11 @@ common::Status NodeArg::UpdateTypeAndShape(const ONNX_NAMESPACE::TypeProto& inpu
return Status::OK();
}
common::Status NodeArg::UpdateTypeAndShape(const NodeArg& node_arg) {
common::Status NodeArg::UpdateTypeAndShape(const NodeArg& node_arg, bool strict) {
auto status = Status::OK();
if (utils::HasType(node_arg.node_arg_info_))
status = UpdateTypeAndShape(node_arg.node_arg_info_.type());
status = UpdateTypeAndShape(node_arg.node_arg_info_.type(), strict);
return status;
}
@ -657,6 +705,7 @@ Graph::Graph(GraphProto* graph_proto, const std::unordered_map<std::string, int>
domain_to_version_(domain_to_version),
model_functions_(model_functions),
ir_version_(ir_version),
using_latest_onnx_opset_(UsingLatestOnnxOpset(domain_to_version)),
parent_graph_(parent_graph),
parent_node_(parent_node) {
ORT_ENFORCE(graph_proto != nullptr, "graph_proto cannot be null");
@ -1612,12 +1661,16 @@ Status Graph::InferAndVerifyTypeMatch(Node& node, const OpSchema& op) {
// that have no values.
TypeProto_Tensor merge_target;
(*merge_target.mutable_shape()) = *output_def->Shape();
auto status = MergeShapeInfo(output_def->Name(), tensor_type, merge_target);
auto status = MergeShapeInfo(output_def->Name(), tensor_type, merge_target, using_latest_onnx_opset_);
if (!status.IsOK()) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Node:", node_name, " ", status.ErrorMessage());
}
output_def->SetShape(merge_target.shape());
// we may have cleared the shape if there was a mismatch so handle that
if (utils::HasShape(merge_target))
output_def->SetShape(merge_target.shape());
else
output_def->ClearShape();
}
}
}

8
onnxruntime/test/contrib_ops/element_wise_ops_test.cc
View file

@ -13,7 +13,7 @@ namespace onnxruntime {
namespace test {
TEST(MathOpTest, AffineDefaultAttributes) {
OpTester test("Affine");
OpTester test("Affine", 7);
std::vector<int64_t> dims{2, 2};
test.AddInput<float>("A", dims, {0.0f, 1.0f, 2.0f, 3.0f});
test.AddOutput<float>("B", dims, {0.0f, 1.0f, 2.0f, 3.0f});
@ -21,7 +21,7 @@ TEST(MathOpTest, AffineDefaultAttributes) {
}
TEST(MathOpTest, Affine) {
OpTester test("Affine");
OpTester test("Affine", 7);
std::vector<int64_t> dims{2, 2};
test.AddAttribute("alpha", 2.0f);
test.AddAttribute("beta", 1.0f);
@ -31,7 +31,7 @@ TEST(MathOpTest, Affine) {
}
TEST(MathOpTest, Scale) {
OpTester test("Scale");
OpTester test("Scale", 7);
std::vector<int64_t> dims{2, 2};
test.AddAttribute("scale", 2.0f);
test.AddInput<float>("A", dims, {0.0f, 1.0f, 2.0f, 3.0f});
@ -40,7 +40,7 @@ TEST(MathOpTest, Scale) {
}
TEST(MathOpTest, Scale_Default) {
OpTester test("Scale");
OpTester test("Scale", 7);
std::vector<int64_t> dims{2, 2};
test.AddInput<float>("A", dims, {0.0f, 1.0f, 2.0f, 3.0f});
test.AddOutput<float>("B", dims, {0.0f, 1.0f, 2.0f, 3.0f});

7
onnxruntime/test/contrib_ops/tensor_op_test.cc
View file

@ -5,7 +5,6 @@
#include "test/common/tensor_op_test_utils.h"
#include "contrib_ops/cpu/crop.h"
using namespace ONNX_NAMESPACE;
using namespace onnxruntime::test;
namespace onnxruntime {
@ -29,7 +28,7 @@ TEST(CropContribOpTest, CropBorderOnly) {
5.0f, 6.0f};
OpTester test("Crop");
OpTester test("Crop", 7);
test.AddAttribute("border", border);
test.AddInput<float>("input", {N, C, H, W}, X);
test.AddOutput<float>("output", {N, C, (H - border[2] - border[0]), (W - border[3] - border[1])}, output);
@ -56,7 +55,7 @@ TEST(CropContribOpTest, CropBorderAndScale) {
4.0f, 5.0f,
5.0f, 6.0f};
OpTester test("Crop");
OpTester test("Crop", 7);
test.AddAttribute("border", border);
test.AddAttribute("scale", scale);
test.AddInput<float>("input", {N, C, H, W}, X);
@ -83,7 +82,7 @@ TEST(ImageScalerContribOpTest, ImageScalerTest) {
8.0f, 12.0f,
16.0f, 20.0f};
OpTester test("ImageScaler");
OpTester test("ImageScaler", 7);
test.AddAttribute("scale", scale);
test.AddAttribute("bias", bias);
test.AddInput<float>("input", {N, C, H, W}, X);

50
onnxruntime/test/framework/inference_session_test.cc
View file

@ -1471,6 +1471,56 @@ TEST(InferenceSessionTests, TestL1AndL2Transformers) {
}
}
// fallback to lenient merging of shape info if model opset is not the latest
TEST(InferenceSessionTests, TestLenientShapeInferencing) {
// latest opset should fail
std::vector<int64_t> input_shape{2, 2};
std::vector<float> input_data{0.f, 1.f, 2.f, 3.f};
std::vector<int64_t> invalid_output_shape{1, 2}; // valid shape is {2} as output data is input_shape
std::vector<int64_t> output_data{2, 2};
OpTester latest_opset("Shape");
latest_opset.AddInput("data", input_shape, input_data);
latest_opset.AddOutput<int64_t>("output", invalid_output_shape, output_data);
latest_opset.Run(OpTester::ExpectResult::kExpectFailure,
"Mismatch between number of source and target dimensions. Source=1 Target=2");
// older opset should allow the mismatch with a warning.
// we also need for the output to be valid so OpTester doesn't throw so add an Unsqueeze after the Shape.
// This should result in a warning log message but successful run.
class OpTesterWithReshape : public OpTester {
public:
OpTesterWithReshape() : OpTester("Shape", 7) {
}
protected:
void AddNodes(onnxruntime::Graph& graph,
std::vector<onnxruntime::NodeArg*>& graph_input_defs,
std::vector<onnxruntime::NodeArg*>& graph_output_defs,
std::vector<std::function<void(onnxruntime::Node& node)>>& add_attribute_funcs) override {
// we need to create an intermediate output with a different name
auto tmp_output_defs = graph_output_defs;
auto type_info = *tmp_output_defs[0]->TypeAsProto(); // copy
auto& shape_output = graph.GetOrCreateNodeArg("shape_output", &type_info);
tmp_output_defs[0] = &shape_output;
// call base implementation to add the Shape node with invalid output shape
OpTester::AddNodes(graph, graph_input_defs, tmp_output_defs, add_attribute_funcs);
// add Unsqueeze node to fix the output shape
auto& unsqueeze = graph.AddNode("unsqueeze", "Unsqueeze", "Fix output shape", tmp_output_defs, graph_output_defs);
unsqueeze.AddAttribute("axes", std::vector<int64_t>{0});
}
};
OpTesterWithReshape old_opset;
old_opset.AddInput("data", input_shape, input_data);
old_opset.AddOutput<int64_t>("output", invalid_output_shape, output_data);
old_opset.Run();
}
#ifdef USE_CUDA
TEST(InferenceSessionTests, TestParallelExecutionWithCudaProvider) {

10
onnxruntime/test/providers/cpu/controlflow/scan_test.cc
View file

@ -335,7 +335,7 @@ static void RunTest_v9(const std::string test_name, int64_t sequence_len, int64_
CreateSubgraph(graph, options, options.add_bad_shape ? failure_message : "");
auto& proto = graph.ToGraphProto();
ScanOpTester test{9};
ScanOpTester test{11}; // use latest version - no significant change over 9
test.AddAttribute("body", proto);
test.AddAttribute<int64_t>("num_scan_inputs", 2);
@ -553,9 +553,10 @@ static void OuterScopeAccess_NoShapeInMainGraph_NoTypeAndShapeInSubgraph(bool is
TEST_8_AND_9(OuterScopeAccess_NoShapeInMainGraph_NoTypeAndShapeInSubgraph);
static void BadShape(bool is_v8) {
// shape inferencing is only strict for the latest version so only test BadShape with that
TEST(Scan9, BadShape) {
RunOptions options{};
options.is_v8 = is_v8;
options.is_v8 = false;
options.include_dim_values_in_main_graph = false;
options.include_types_in_subgraph = true;
options.include_dim_values_in_subgraph = true;
@ -566,9 +567,6 @@ static void BadShape(bool is_v8) {
"Node:concat Output:concat_out_1 [ShapeInferenceError] Mismatch between number of source and target dimensions. "
"Source=2 Target=1");
}
TEST_8_AND_9(BadShape);
TEST(Scan8, ShortSequenceTwoInBatchOneLoopStateVar) {
const int64_t batch_size = 2;
const int64_t sequence_len = 2;

8
onnxruntime/test/providers/cpu/math/logsoftmax_test.cc
View file

@ -101,7 +101,7 @@ TEST(LogSoftmaxOperator, ThreeDimsAxis0) {
-4.042971f, -4.2982683f, -3.5933442f, -4.538994f, -5.307373f,
-4.2677402f, -4.44635f, -3.5821702f, -3.8414123f, -4.267664f};
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 0, false);// axis=0 is not supported by TensorRT
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 0, false); // axis=0 is not supported by TensorRT
}
TEST(LogSoftmaxOperator, ThreeDimsAxis1) {
@ -127,7 +127,7 @@ TEST(LogSoftmaxOperator, ThreeDimsAxis1) {
-2.9822054f, -3.2375026f, -2.5325785f, -3.4782279f, -4.246608f,
-3.2069747f, -3.3855844f, -2.5214045f, -2.7806466f, -3.206898f};
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 1, false);// This test failed on TensorRT
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 1, false); // This test failed on TensorRT
}
TEST(LogSoftmaxOperator, ThreeDimsAxis2) {
@ -194,7 +194,9 @@ TEST(LogSoftmaxOperator, InvalidAxis) {
/* invalid axis */ -7,
false,
OpTester::ExpectResult::kExpectFailure,
"-7 is not in valid range [-2,1]");//TensorRT parser: Assertion failed: axis >= 0 && axis < nbDims
// ONNX has a bug in the error message generation so this is somewhat cryptic until it's fixed. Message should be:
// "[ShapeInferenceError] 'axis' must be in [-2 , 1]. Its actual value is: -7"
", 1]. Its actual value is: -7"); //TensorRT parser: Assertion failed: axis >= 0 && axis < nbDims
}
} // namespace test

6
onnxruntime/test/providers/cpu/math/softmax_test.cc
View file

@ -96,7 +96,7 @@ TEST(SoftmaxOperator, ThreeDimsAxis0) {
0.017545262f, 0.0135920765f, 0.027506188f, 0.010684152f, 0.0049549243f,
0.01401341f, 0.011721271f, 0.027815264f, 0.021463264f, 0.014014485f};
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 0, false);// Axis=0 is not supported by TensorRT
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 0, false); // Axis=0 is not supported by TensorRT
}
TEST(SoftmaxOperator, ThreeDimsAxis1) {
@ -188,7 +188,9 @@ TEST(SoftmaxOperator, InvalidAxis) {
dimensions,
/* invalid axis */ -10, false,
OpTester::ExpectResult::kExpectFailure,
"-10 is not in valid range [-2,1]");
// bug in ONNX error message currently. Message should be
// "[ShapeInferenceError] 'axis' must be in [-2 , 1]. Its actual value is: -10"
", 1]. Its actual value is: -10");
}
} // namespace test

67
onnxruntime/test/providers/cpu/nn/tfidfvectorizer_test.cc
View file

@ -10,9 +10,6 @@ namespace onnxruntime {
namespace test {
namespace tfidfvectorizer_test {
constexpr const char* domain = kOnnxDomain;
const int opset_ver = 9;
void InitTestAttr(OpTester& test, const std::string& mode,
int64_t min_gram_length, int64_t max_gram_length, int64_t max_skip_count,
const std::vector<int64_t>& ngram_counts,
@ -49,7 +46,7 @@ using namespace tfidfvectorizer_test;
// into consideration or not.With all = false, we only consider N - grams.
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -71,7 +68,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0) {
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim1Fail) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -89,11 +86,13 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim1Fail) {
std::vector<float> output = {};
test.AddOutput<float>("Y", out_dims, output);
test.Run(OpTester::ExpectResult::kExpectFailure);
test.Run(OpTester::ExpectResult::kExpectFailure,
"Can't merge shape info. "
"Both source and target dimension have values but they differ. Source=7 Target=0 Dimension=0");
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim1Success) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -115,7 +114,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim1Success) {
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim2) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -133,11 +132,12 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim2) {
std::vector<float> output = {0, 0, 0, 0, 0, 0, 0};
test.AddOutput<float>("Y", out_dims, output);
test.Run(OpTester::ExpectResult::kExpectFailure);
test.Run(OpTester::ExpectResult::kExpectFailure,
"Mismatch between number of source and target dimensions. Source=2 Target=1");
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip01_Empty_Dim2) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -155,11 +155,12 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip01_Empty_Dim2) {
std::vector<float> output = {0, 0, 0, 0, 0, 0, 0};
test.AddOutput<float>("Y", out_dims, output);
test.Run(OpTester::ExpectResult::kExpectFailure);
test.Run(OpTester::ExpectResult::kExpectFailure,
"Mismatch between number of source and target dimensions. Source=2 Target=1");
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim2N) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -174,7 +175,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim2N) {
test.AddInput<int32_t>("T", dims, input);
std::vector<int64_t> out_dims{2, 7};
std::vector<float> output = {0, 0, 0, 0, 0, 0, 0,
std::vector<float> output = {0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0};
test.AddOutput<float>("Y", out_dims, output);
@ -182,7 +183,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip0_Empty_Dim2N) {
}
TEST(TfIdfVectorizerTest, Int32_TF_BatchOnlyBigrams_Skip0) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -207,7 +208,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_BatchOnlyBigrams_Skip0) {
}
TEST(TfIdfVectorizerTest, String_TF_OnlyBigrams_Skip0) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, string
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -230,7 +231,7 @@ TEST(TfIdfVectorizerTest, String_TF_OnlyBigrams_Skip0) {
}
TEST(TfIdfVectorizerTest, String_TF_BatchOnlyBigrams_Skip0) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, string
InitTestAttr(test, "TF", 2, 2, 0,
{0, 4},
@ -257,7 +258,7 @@ TEST(TfIdfVectorizerTest, String_TF_BatchOnlyBigrams_Skip0) {
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_LevelEmpty) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=0, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 0,
{0, 0}, // no unigrams, bi-grams start immediately
@ -283,7 +284,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_LevelEmpty) {
}
TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 5,
{0, 4},
@ -307,7 +308,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_TF_BatchOnlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, , Min=Max=2, weights empty, int32
InitTestAttr(test, "TF", 2, 2, 5,
{0, 4},
@ -333,7 +334,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_BatchOnlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_TF_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, , Min=Max=2, weights empty, string
InitTestAttr(test, "TF", 2, 2, 5,
{0, 4},
@ -358,7 +359,7 @@ TEST(TfIdfVectorizerTest, String_TF_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_TF_BatchOnlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, , Min=Max=2, weights empty, string
InitTestAttr(test, "TF", 2, 2, 5,
{0, 4},
@ -382,7 +383,7 @@ TEST(TfIdfVectorizerTest, String_TF_BatchOnlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_TF_UniAndBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, , Min=1, Max=2, weights empty, int32
InitTestAttr(test, "TF", 1, 2, 5,
{0, 4},
@ -405,7 +406,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_UniAndBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_TF_BatchUniAndBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=1, Max=2, weights empty, int32
InitTestAttr(test, "TF", 1, 2, 5,
{0, 4},
@ -430,7 +431,7 @@ TEST(TfIdfVectorizerTest, Int32_TF_BatchUniAndBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_TF_UniAndBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=1, Max=2, weights empty, string
InitTestAttr(test, "TF", 1, 2, 5,
{0, 4},
@ -453,7 +454,7 @@ TEST(TfIdfVectorizerTest, String_TF_UniAndBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_TF_BatchUniAndBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=1, Max=2, weights empty, string
InitTestAttr(test, "TF", 1, 2, 5,
{0, 4},
@ -478,7 +479,7 @@ TEST(TfIdfVectorizerTest, String_TF_BatchUniAndBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_IDF_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights empty, int32
// We change to IDF but do not supply weights so
// we should get all 1.0f where count is not zero
@ -502,7 +503,7 @@ TEST(TfIdfVectorizerTest, Int32_IDF_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_IDF_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights empty, string
InitTestAttr(test, "IDF", 2, 2, 5,
{0, 4},
@ -525,7 +526,7 @@ TEST(TfIdfVectorizerTest, String_IDF_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_TFIDF_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights empty, int32
// We change to TFIDF but do not supply weights so
// we should all get the original values as weights are 1.0f by
@ -550,7 +551,7 @@ TEST(TfIdfVectorizerTest, Int32_TFIDF_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_TFIDF_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights empty, string
InitTestAttr(test, "TFIDF", 2, 2, 5,
{0, 4},
@ -573,7 +574,7 @@ TEST(TfIdfVectorizerTest, String_TFIDF_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_IDFWeights_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights specified, int32
// We change to IDF with supplied weights. All
// with non-zero counts must be replaced with the supplied weights
@ -597,7 +598,7 @@ TEST(TfIdfVectorizerTest, Int32_IDFWeights_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_IDFWeights_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights specified, string
InitTestAttr(test, "IDF", 2, 2, 5,
{0, 4},
@ -620,7 +621,7 @@ TEST(TfIdfVectorizerTest, String_IDFWeights_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, Int32_TFIDFWeights_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights specified, int32
// We change to TFIDF with supplied weights.
// We should have all counts scaled by weights
@ -644,7 +645,7 @@ TEST(TfIdfVectorizerTest, Int32_TFIDFWeights_onlyBigrams_Skip5) {
}
TEST(TfIdfVectorizerTest, String_TFIDFWeights_onlyBigrams_Skip5) {
OpTester test("TfIdfVectorizer", opset_ver, domain);
OpTester test("TfIdfVectorizer");
// s=5, Min=Max=2, weights specified, string
InitTestAttr(test, "TFIDF", 2, 2, 5,
{0, 4},

8
onnxruntime/test/providers/cpu/tensor/gather_op_test.cc
View file

@ -81,7 +81,7 @@ TEST(GatherOpTest, Gather_invalid_index_cpu) {
#ifdef USE_CUDA
TEST(GatherOpTest, Gather_invalid_index_gpu) {
OpTester test("Gather");
OpTester test("Gather", 7); // no GPU Gather for opset 11 yet (change was to add negative axis support)
// Invalid index 3. data[3] does not exist.
test.AddAttribute<int64_t>("axis", 0LL);
test.AddInput<float>("data", {3, 4},
@ -186,7 +186,7 @@ TEST(GatherOpTest, Gather_axis1_indices2d_int32) {
{1, 0, 2, 1,
11, 10, 12, 11,
21, 20, 22, 21});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Input batch size is inconsistent
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Input batch size is inconsistent
}
TEST(GatherOpTest, Gather_axis1_indices2d_uint32) {
@ -254,7 +254,7 @@ TEST(GatherOpTest, Gather_axis1_indices2d_int8) {
{1, 0, 2, 1,
11, 10, 12, 11,
21, 20, 22, 21});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Assertion `regionRanges != nullptr' failed
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Assertion `regionRanges != nullptr' failed
}
TEST(GatherOpTest, Gather_axis1_indices2d_string) {
@ -320,7 +320,7 @@ TEST(GatherOpTest, Gather_axis1_neg_indices2d_int8) {
{1, 0, 2, 1,
11, 10, 12, 11,
21, 20, 22, 21});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Assertion `regionRanges != nullptr' failed
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Assertion `regionRanges != nullptr' failed
}
} // namespace test

9
onnxruntime/test/providers/cpu/tensor/unsqueeze_op_test.cc
View file

@ -51,7 +51,9 @@ TEST(TensorOpTest, Unsqueeze_Duplicate) {
test.AddAttribute("axes", std::vector<int64_t>{2, 1, 0, 2});
test.AddInput<float>("input", {2, 3, 4}, std::vector<float>(2 * 3 * 4, 1.0f));
test.AddOutput<float>("output", {1, 1, 1, 2, 3, 4}, std::vector<float>(2 * 3 * 4, 1.0f));
test.Run(OpTester::ExpectResult::kExpectFailure, "'axes' has a duplicate axis", {kTensorrtExecutionProvider}); //TensorRT failed
test.Run(OpTester::ExpectResult::kExpectFailure,
"[ShapeInferenceError] 'axes' attribute must not contain any duplicates",
{kTensorrtExecutionProvider}); //TensorRT failed
}
TEST(TensorOpTest, Unsqueeze_OutOfRange) {
@ -60,7 +62,8 @@ TEST(TensorOpTest, Unsqueeze_OutOfRange) {
test.AddAttribute("axes", std::vector<int64_t>{4});
test.AddInput<float>("input", {2, 3, 4}, std::vector<float>(2 * 3 * 4, 1.0f));
test.AddOutput<float>("output", {2, 1, 3, 4}, std::vector<float>(2 * 3 * 4, 1.0f));
test.Run(OpTester::ExpectResult::kExpectFailure, "Mismatch between number of source and target dimensions.");
test.Run(OpTester::ExpectResult::kExpectFailure,
"[ShapeInferenceError] values in 'axes' are beyond the bounds of the computed output shape");
}
TEST(TensorOpTest, UnsqueezeNegAxis_3) {
@ -70,7 +73,7 @@ TEST(TensorOpTest, UnsqueezeNegAxis_3) {
test.AddInput<float>("input", {2, 3, 4}, std::vector<float>(2 * 3 * 4, 1.0f));
test.AddOutput<float>("output", {1, 1, 1, 2, 3, 4}, std::vector<float>(2 * 3 * 4, 1.0f));
// nGraph and TensorRT does not support negative axis.
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kNGraphExecutionProvider, kTensorrtExecutionProvider});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kNGraphExecutionProvider, kTensorrtExecutionProvider});
}
} // namespace test

29
onnxruntime/test/providers/cpu/tensor/upsample_op_test.cc
View file

@ -12,7 +12,7 @@ namespace test {
// and limited data types. Those tests will fallback to other EPs
TEST(UpsampleOpTest, UpsampleOpNearestTest) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 3.0f};
test.AddAttribute("mode", "nearest");
@ -43,7 +43,7 @@ TEST(UpsampleOpTest, UpsampleOpNearestTest) {
}
TEST(UpsampleOpTest, UpsampleOpNearestTest_int32) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 3.0f};
test.AddAttribute("mode", "nearest");
@ -74,7 +74,7 @@ TEST(UpsampleOpTest, UpsampleOpNearestTest_int32) {
}
TEST(UpsampleOpTest, UpsampleOpNearestTest_uint8) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 3.0f};
test.AddAttribute("mode", "nearest");
@ -105,7 +105,7 @@ TEST(UpsampleOpTest, UpsampleOpNearestTest_uint8) {
}
TEST(UpsampleOpTest, UpsampleOpNearest2XTest) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 2.0f};
test.AddAttribute("mode", "nearest");
@ -136,7 +136,7 @@ TEST(UpsampleOpTest, UpsampleOpNearest2XTest) {
}
TEST(UpsampleOpTest, UpsampleOpNearest222XTest) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{2.0f, 1.0f, 2.0f, 2.0f};
test.AddAttribute("mode", "nearest");
@ -177,7 +177,7 @@ TEST(UpsampleOpTest, UpsampleOpNearest222XTest) {
}
TEST(UpsampleOpTest, UpsampleOpNearest15XTest) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 1.5f};
test.AddAttribute("mode", "nearest");
@ -208,7 +208,7 @@ TEST(UpsampleOpTest, UpsampleOpNearest15XTest) {
}
TEST(UpsampleOpTest, UpsampleOpNearestTest_NoScale) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 1.0f, 1.0f};
test.AddAttribute("mode", "nearest");
@ -234,7 +234,7 @@ TEST(UpsampleOpTest, UpsampleOpNearestTest_NoScale) {
}
TEST(UpsampleOpTest, UpsampleOpNearest2XTest_int32) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 2.0f};
test.AddAttribute("mode", "nearest");
@ -265,7 +265,7 @@ TEST(UpsampleOpTest, UpsampleOpNearest2XTest_int32) {
}
TEST(UpsampleOpTest, UpsampleOp4DBilinearTest) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 4.0f};
test.AddAttribute("mode", "linear");
@ -296,7 +296,7 @@ TEST(UpsampleOpTest, UpsampleOp4DBilinearTest) {
}
TEST(UpsampleOpTest, UpsampleOp2DBilinearTest) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{2.0f, 4.0f};
test.AddAttribute("mode", "linear");
@ -312,15 +312,14 @@ TEST(UpsampleOpTest, UpsampleOp2DBilinearTest) {
1.0f, 1.5f, 2.0f, 2.5f, 3.0f, 3.0f, 3.0f, 3.0f,
2.0f, 2.5f, 3.0f, 3.5f, 4.0f, 4.0f, 4.0f, 4.0f,
3.0f, 3.5f, 4.0f, 4.5f, 5.0f, 5.0f, 5.0f, 5.0f,
3.0f, 3.5f, 4.0f, 4.5f, 5.0f, 5.0f, 5.0f, 5.0f
};
3.0f, 3.5f, 4.0f, 4.5f, 5.0f, 5.0f, 5.0f, 5.0f};
test.AddOutput<float>("Y", {(int64_t)(H * scales[0]), (int64_t)(W * scales[1])}, Y);
test.Run();
}
TEST(UpsampleOpTest, UpsampleOp4DBilinearTest_ScalesNoOp) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 1.0f, 1.0f};
test.AddAttribute("mode", "linear");
@ -346,7 +345,7 @@ TEST(UpsampleOpTest, UpsampleOp4DBilinearTest_ScalesNoOp) {
}
TEST(UpsampleOpTest, UpsampleOp4DBilinearTest_int32) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{1.0f, 1.0f, 2.0f, 4.0f};
test.AddAttribute("mode", "linear");
@ -377,7 +376,7 @@ TEST(UpsampleOpTest, UpsampleOp4DBilinearTest_int32) {
}
TEST(UpsampleOpTest, UpsampleOpNearestTest_1D) {
OpTester test("Upsample");
OpTester test("Upsample", 7);
std::vector<float> scales{2.0f};
test.AddAttribute("mode", "nearest");

10
onnxruntime/test/providers/provider_test_utils.h
View file

@ -242,8 +242,14 @@ const SequenceTensorTypeProto<ElemType> SequenceTensorType<ElemType>::s_sequence
// explanatory
class OpTester {
public:
explicit OpTester(const char* op, int opset_version = 7, const char* domain = onnxruntime::kOnnxDomain)
: op_(op), domain_(domain), opset_version_(opset_version) {}
explicit OpTester(const char* op, int opset_version = -1, const char* domain = onnxruntime::kOnnxDomain)
: op_(op), domain_(domain), opset_version_(opset_version) {
if (opset_version_ < 0) {
static int latest_onnx_version =
ONNX_NAMESPACE::OpSchemaRegistry::DomainToVersionRange().Map().at(ONNX_NAMESPACE::ONNX_DOMAIN).second;
opset_version_ = latest_onnx_version;
}
}
~OpTester();