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Bump onnx to latest (#1756)

Browse source 
* Bump onnx to latest

Update onnx.in.proto with changes for SparseTensor.

* add temp skip tests

* remove passed tests from skip list

* skip more tests for new ops in opset 11

* skip crashing tests

* update handling of new attribute types sparse tensor and sparse tensors

* advance onnx commit and remove skip cpu_flaky_tests

* temporarily skip yolo3 model test due to resize opset10 shape inference regression

* update proto for onnxruntime server

* advance onnx commit further
This commit is contained in:
Bowen Bao 2019-09-12 11:46:49 -07:00 committed by GitHub
parent f8c3442880
commit 8712a523a4
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GPG key ID: 4AEE18F83AFDEB23
14 changed files with 265 additions and 49 deletions
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2
cgmanifest.json
View file

@ -49,7 +49,7 @@
"component": {
"type": "git",
"git": {
"commitHash": "7d90796473295ca3cdf976ed772215c5980ad3e0",
"commitHash": "568b65aaa2bd94b04d8fdac24b398dc25c507b50",
"repositoryUrl": "https://github.com/onnx/onnx.git"
}
}

2
cmake/external/onnx vendored

@ -1 +1 @@
Subproject commit 7d90796473295ca3cdf976ed772215c5980ad3e0
Subproject commit 568b65aaa2bd94b04d8fdac24b398dc25c507b50

1
include/onnxruntime/core/graph/graph.h
View file

@ -252,6 +252,7 @@ class Node {
ADD_ATTR_INTERFACES(std::string)
ADD_ATTR_INTERFACES(ONNX_NAMESPACE::TensorProto)
ADD_ATTR_INTERFACES(ONNX_NAMESPACE::GraphProto)
ADD_ATTR_INTERFACES(ONNX_NAMESPACE::SparseTensorProto)
/** Remove the specified attribute from this Node */
bool ClearAttribute(const std::string& attr_name);

2
onnxruntime/core/framework/op_node_proto_helper.cc
View file

@ -153,8 +153,10 @@ uint32_t OpNodeProtoHelper<Impl_t>::GetPrimitiveAttrElementCount(AttributeProto_
case AttributeProto_AttributeType_UNDEFINED:
case AttributeProto_AttributeType_TENSOR:
case AttributeProto_AttributeType_GRAPH:
case AttributeProto_AttributeType_SPARSE_TENSOR:
case AttributeProto_AttributeType_TENSORS:
case AttributeProto_AttributeType_GRAPHS:
case AttributeProto_AttributeType_SPARSE_TENSORS:
default:
return 0;
}

2
onnxruntime/core/graph/graph.cc
View file

@ -479,11 +479,13 @@ ADD_BASIC_ATTR_IMPL(float, AttributeProto_AttributeType::AttributeProto_Attribut
ADD_BASIC_ATTR_IMPL(int64_t, AttributeProto_AttributeType::AttributeProto_AttributeType_INT, i)
ADD_BASIC_ATTR_IMPL(std::string, AttributeProto_AttributeType::AttributeProto_AttributeType_STRING, s)
ADD_ATTR_IMPL(TensorProto, AttributeProto_AttributeType::AttributeProto_AttributeType_TENSOR, t)
ADD_ATTR_IMPL(SparseTensorProto, AttributeProto_AttributeType::AttributeProto_AttributeType_SPARSE_TENSOR, sparse_tensor)
ADD_LIST_ATTR_IMPL(float, AttributeProto_AttributeType::AttributeProto_AttributeType_FLOATS, floats)
ADD_LIST_ATTR_IMPL(int64_t, AttributeProto_AttributeType::AttributeProto_AttributeType_INTS, ints)
ADD_LIST_ATTR_IMPL(std::string, AttributeProto_AttributeType::AttributeProto_AttributeType_STRINGS, strings)
ADD_LIST_ATTR_IMPL(TensorProto, AttributeProto_AttributeType::AttributeProto_AttributeType_TENSORS, tensors)
ADD_LIST_ATTR_IMPL(GraphProto, AttributeProto_AttributeType::AttributeProto_AttributeType_GRAPHS, graphs)
ADD_LIST_ATTR_IMPL(SparseTensorProto, AttributeProto_AttributeType::AttributeProto_AttributeType_SPARSE_TENSORS, sparse_tensors)
bool Node::ClearAttribute(const std::string& attr_name) {
graph_->SetGraphResolveNeeded();

4
onnxruntime/core/graph/op.cc
View file

@ -51,6 +51,8 @@ Status TypeUtils::GetType(const AttributeProto& attr, AttrType& type) {
type = AttrType::AttributeProto_AttributeType_TENSOR;
} else if (attr.has_g()) {
type = AttrType::AttributeProto_AttributeType_GRAPH;
} else if (attr.has_sparse_tensor()) {
type = AttrType::AttributeProto_AttributeType_SPARSE_TENSOR;
} else if (attr.floats_size()) {
type = AttrType::AttributeProto_AttributeType_FLOATS;
} else if (attr.ints_size()) {
@ -61,6 +63,8 @@ Status TypeUtils::GetType(const AttributeProto& attr, AttrType& type) {
type = AttrType::AttributeProto_AttributeType_TENSORS;
} else if (attr.graphs_size()) {
type = AttrType::AttributeProto_AttributeType_GRAPHS;
} else if (attr.sparse_tensors_size()) {
type = AttrType::AttributeProto_AttributeType_SPARSE_TENSORS;
} else {
return Status(ONNXRUNTIME, FAIL, "Invalid AttributeProto.");
}

4
onnxruntime/core/graph/op.h
View file

@ -33,7 +33,9 @@ AttributeProto_AttributeType_FLOATS = 6,
AttributeProto_AttributeType_INTS = 7,
AttributeProto_AttributeType_STRINGS = 8,
AttributeProto_AttributeType_TENSORS = 9,
AttributeProto_AttributeType_GRAPHS = 10
AttributeProto_AttributeType_GRAPHS = 10,
AttributeProto_AttributeType_SPARSE_TENSOR = 22,
AttributeProto_AttributeType_SPARSE_TENSORS = 23,
*/
static constexpr const char* kAttrTypeStrings[] =
{

57
onnxruntime/core/protobuf/onnx-ml.proto
View file

@ -86,7 +86,13 @@ enum Version {
// IR VERSION 5 published on March 18, 2019
// - Add message TensorAnnotation.
// - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
IR_VERSION = 0x0000000000000005;
IR_VERSION_2019_3_18 = 0x0000000000000005;
// IR VERSION 6 published on <TBD>
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
}
// Attributes
@ -106,12 +112,14 @@ message AttributeProto {
STRING = 3;
TENSOR = 4;
GRAPH = 5;
SPARSE_TENSOR = 11;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
SPARSE_TENSORS = 12;
}
// The name field MUST be present for this version of the IR.
@ -140,6 +148,7 @@ message AttributeProto {
optional bytes s = 4; // UTF-8 string
optional TensorProto t = 5; // tensor value
optional GraphProto g = 6; // graph
optional SparseTensorProto sparse_tensor = 22; // sparse tensor value
// Do not use field below, it's deprecated.
// optional ValueProto v = 12; // value - subsumes everything but graph
@ -148,6 +157,7 @@ message AttributeProto {
repeated bytes strings = 9; // list of UTF-8 strings
repeated TensorProto tensors = 10; // list of tensors
repeated GraphProto graphs = 11; // list of graph
repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
}
// Defines information on value, including the name, the type, and
@ -277,6 +287,9 @@ message GraphProto {
// MAY also appear in the input list.
repeated TensorProto initializer = 5;
// Initializers (see above) stored in sparse format.
repeated SparseTensorProto sparse_initializer = 15;
// A human-readable documentation for this graph. Markdown is allowed.
optional string doc_string = 10;
@ -446,6 +459,28 @@ message TensorProto {
repeated uint64 uint64_data = 11 [packed = true];
}
// A serialized sparse-tensor value
message SparseTensorProto {
// The sequence of non-default values are encoded as a tensor of shape [NNZ].
// The default-value is zero for numeric tensors, and empty-string for string tensors.
optional TensorProto values = 1;
// The indices of the non-default values, which may be stored in one of two formats.
// (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
// corresponding to the j-th index of the i-th value (in the values tensor).
// (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
// must be the linearized-index of the i-th value (in the values tensor).
// The linearized-index can be converted into an index tuple (k_1,...,k_rank)
// using the shape provided below.
// The indices must appear in ascending order without duplication.
// In the first format, the ordering is lexicographic-ordering:
// e.g., index-value [1,4] must appear before [2,1]
optional TensorProto indices = 2;
// The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
repeated int64 dims = 3;
}
// Defines a tensor shape. A dimension can be either an integer value
// or a symbolic variable. A symbolic variable represents an unknown
// dimension.
@ -478,6 +513,14 @@ message TypeProto {
optional TensorShapeProto shape = 2;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// repeated T
message Sequence {
@ -506,19 +549,13 @@ message TypeProto {
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
oneof value {
// The type of a tensor.
Tensor tensor_type = 1;
SparseTensor sparse_tensor_type = 8;
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
// as input and output to graphs and nodes. These types are needed to naturally
@ -533,8 +570,6 @@ message TypeProto {
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
}
// An optional denotation can be used to denote the whole

57
onnxruntime/core/protobuf/onnx-ml.proto3
View file

@ -86,7 +86,13 @@ enum Version {
// IR VERSION 5 published on March 18, 2019
// - Add message TensorAnnotation.
// - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
IR_VERSION = 0x0000000000000005;
IR_VERSION_2019_3_18 = 0x0000000000000005;
// IR VERSION 6 published on <TBD>
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
}
// Attributes
@ -106,12 +112,14 @@ message AttributeProto {
STRING = 3;
TENSOR = 4;
GRAPH = 5;
SPARSE_TENSOR = 11;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
SPARSE_TENSORS = 12;
}
// The name field MUST be present for this version of the IR.
@ -140,6 +148,7 @@ message AttributeProto {
bytes s = 4; // UTF-8 string
TensorProto t = 5; // tensor value
GraphProto g = 6; // graph
SparseTensorProto sparse_tensor = 22; // sparse tensor value
// Do not use field below, it's deprecated.
// optional ValueProto v = 12; // value - subsumes everything but graph
@ -148,6 +157,7 @@ message AttributeProto {
repeated bytes strings = 9; // list of UTF-8 strings
repeated TensorProto tensors = 10; // list of tensors
repeated GraphProto graphs = 11; // list of graph
repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
}
// Defines information on value, including the name, the type, and
@ -277,6 +287,9 @@ message GraphProto {
// MAY also appear in the input list.
repeated TensorProto initializer = 5;
// Initializers (see above) stored in sparse format.
repeated SparseTensorProto sparse_initializer = 15;
// A human-readable documentation for this graph. Markdown is allowed.
string doc_string = 10;
@ -446,6 +459,28 @@ message TensorProto {
repeated uint64 uint64_data = 11 [packed = true];
}
// A serialized sparse-tensor value
message SparseTensorProto {
// The sequence of non-default values are encoded as a tensor of shape [NNZ].
// The default-value is zero for numeric tensors, and empty-string for string tensors.
TensorProto values = 1;
// The indices of the non-default values, which may be stored in one of two formats.
// (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
// corresponding to the j-th index of the i-th value (in the values tensor).
// (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
// must be the linearized-index of the i-th value (in the values tensor).
// The linearized-index can be converted into an index tuple (k_1,...,k_rank)
// using the shape provided below.
// The indices must appear in ascending order without duplication.
// In the first format, the ordering is lexicographic-ordering:
// e.g., index-value [1,4] must appear before [2,1]
TensorProto indices = 2;
// The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
repeated int64 dims = 3;
}
// Defines a tensor shape. A dimension can be either an integer value
// or a symbolic variable. A symbolic variable represents an unknown
// dimension.
@ -478,6 +513,14 @@ message TypeProto {
TensorShapeProto shape = 2;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
int32 elem_type = 1;
TensorShapeProto shape = 2;
}
// repeated T
message Sequence {
@ -506,19 +549,13 @@ message TypeProto {
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
int32 elem_type = 1;
TensorShapeProto shape = 2;
}
oneof value {
// The type of a tensor.
Tensor tensor_type = 1;
SparseTensor sparse_tensor_type = 8;
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
// as input and output to graphs and nodes. These types are needed to naturally
@ -533,8 +570,6 @@ message TypeProto {
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
}
// An optional denotation can be used to denote the whole

57
onnxruntime/core/protobuf/onnx.in.proto
View file

@ -83,7 +83,13 @@ enum Version {
// IR VERSION 5 published on March 18, 2019
// - Add message TensorAnnotation.
// - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
IR_VERSION = 0x0000000000000005;
IR_VERSION_2019_3_18 = 0x0000000000000005;
// IR VERSION 6 published on <TBD>
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
}
// Attributes
@ -103,12 +109,14 @@ message AttributeProto {
STRING = 3;
TENSOR = 4;
GRAPH = 5;
SPARSE_TENSOR = 11;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
SPARSE_TENSORS = 12;
}
// The name field MUST be present for this version of the IR.
@ -137,6 +145,7 @@ message AttributeProto {
optional bytes s = 4; // UTF-8 string
optional TensorProto t = 5; // tensor value
optional GraphProto g = 6; // graph
optional SparseTensorProto sparse_tensor = 22; // sparse tensor value
// Do not use field below, it's deprecated.
// optional ValueProto v = 12; // value - subsumes everything but graph
@ -145,6 +154,7 @@ message AttributeProto {
repeated bytes strings = 9; // list of UTF-8 strings
repeated TensorProto tensors = 10; // list of tensors
repeated GraphProto graphs = 11; // list of graph
repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
}
// Defines information on value, including the name, the type, and
@ -274,6 +284,9 @@ message GraphProto {
// MAY also appear in the input list.
repeated TensorProto initializer = 5;
// Initializers (see above) stored in sparse format.
repeated SparseTensorProto sparse_initializer = 15;
// A human-readable documentation for this graph. Markdown is allowed.
optional string doc_string = 10;
@ -443,6 +456,28 @@ message TensorProto {
repeated uint64 uint64_data = 11 [packed = true];
}
// A serialized sparse-tensor value
message SparseTensorProto {
// The sequence of non-default values are encoded as a tensor of shape [NNZ].
// The default-value is zero for numeric tensors, and empty-string for string tensors.
optional TensorProto values = 1;
// The indices of the non-default values, which may be stored in one of two formats.
// (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
// corresponding to the j-th index of the i-th value (in the values tensor).
// (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
// must be the linearized-index of the i-th value (in the values tensor).
// The linearized-index can be converted into an index tuple (k_1,...,k_rank)
// using the shape provided below.
// The indices must appear in ascending order without duplication.
// In the first format, the ordering is lexicographic-ordering:
// e.g., index-value [1,4] must appear before [2,1]
optional TensorProto indices = 2;
// The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
repeated int64 dims = 3;
}
// Defines a tensor shape. A dimension can be either an integer value
// or a symbolic variable. A symbolic variable represents an unknown
// dimension.
@ -475,6 +510,14 @@ message TypeProto {
optional TensorShapeProto shape = 2;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// #if ONNX-ML
// repeated T
@ -504,20 +547,14 @@ message TypeProto {
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// #endif
oneof value {
// The type of a tensor.
Tensor tensor_type = 1;
SparseTensor sparse_tensor_type = 8;
// #if ONNX-ML
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
@ -533,8 +570,6 @@ message TypeProto {
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
// #endif
}

57
onnxruntime/server/protobuf/onnx-ml.proto
View file

@ -86,7 +86,13 @@ enum Version {
// IR VERSION 5 published on March 18, 2019
// - Add message TensorAnnotation.
// - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
IR_VERSION = 0x0000000000000005;
IR_VERSION_2019_3_18 = 0x0000000000000005;
// IR VERSION 6 published on <TBD>
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
}
// Attributes
@ -106,12 +112,14 @@ message AttributeProto {
STRING = 3;
TENSOR = 4;
GRAPH = 5;
SPARSE_TENSOR = 11;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
SPARSE_TENSORS = 12;
}
// The name field MUST be present for this version of the IR.
@ -140,6 +148,7 @@ message AttributeProto {
optional bytes s = 4; // UTF-8 string
optional TensorProto t = 5; // tensor value
optional GraphProto g = 6; // graph
optional SparseTensorProto sparse_tensor = 22; // sparse tensor value
// Do not use field below, it's deprecated.
// optional ValueProto v = 12; // value - subsumes everything but graph
@ -148,6 +157,7 @@ message AttributeProto {
repeated bytes strings = 9; // list of UTF-8 strings
repeated TensorProto tensors = 10; // list of tensors
repeated GraphProto graphs = 11; // list of graph
repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
}
// Defines information on value, including the name, the type, and
@ -277,6 +287,9 @@ message GraphProto {
// MAY also appear in the input list.
repeated TensorProto initializer = 5;
// Initializers (see above) stored in sparse format.
repeated SparseTensorProto sparse_initializer = 15;
// A human-readable documentation for this graph. Markdown is allowed.
optional string doc_string = 10;
@ -446,6 +459,28 @@ message TensorProto {
repeated uint64 uint64_data = 11 [packed = true];
}
// A serialized sparse-tensor value
message SparseTensorProto {
// The sequence of non-default values are encoded as a tensor of shape [NNZ].
// The default-value is zero for numeric tensors, and empty-string for string tensors.
optional TensorProto values = 1;
// The indices of the non-default values, which may be stored in one of two formats.
// (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
// corresponding to the j-th index of the i-th value (in the values tensor).
// (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
// must be the linearized-index of the i-th value (in the values tensor).
// The linearized-index can be converted into an index tuple (k_1,...,k_rank)
// using the shape provided below.
// The indices must appear in ascending order without duplication.
// In the first format, the ordering is lexicographic-ordering:
// e.g., index-value [1,4] must appear before [2,1]
optional TensorProto indices = 2;
// The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
repeated int64 dims = 3;
}
// Defines a tensor shape. A dimension can be either an integer value
// or a symbolic variable. A symbolic variable represents an unknown
// dimension.
@ -478,6 +513,14 @@ message TypeProto {
optional TensorShapeProto shape = 2;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// repeated T
message Sequence {
@ -506,19 +549,13 @@ message TypeProto {
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST have a valid TensorProto.DataType value
// This field MUST be present for this version of the IR.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
oneof value {
// The type of a tensor.
Tensor tensor_type = 1;
SparseTensor sparse_tensor_type = 8;
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
// as input and output to graphs and nodes. These types are needed to naturally
@ -533,8 +570,6 @@ message TypeProto {
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
}
// An optional denotation can be used to denote the whole

35
onnxruntime/test/onnx/main.cc
View file

@ -411,7 +411,40 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
{"cumsum_1d_reverse_exclusive", "not implemented yet"},
{"cumsum_1d_reverse", "not implemented yet"},
{"cumsum_1d_exclusive", "not implemented yet"},
{"cumsum_1d", "not implemented yet"},
{"cumsum_1d", "not implemented yet"},
{"range_float_type_positive_delta", "not implemented yet"},
{"range_float_type_positive_delta_expanded", "not implemented yet"},
{"range_int32_type_negative_delta", "not implemented yet"},
{"range_int32_type_negative_delta_expanded", "not implemented yet"},
{"det_2d", "not implemented yet"},
{"det_nd", "not implemented yet"},
{"gathernd_example_float32", "not implemented yet"},
{"gathernd_example_int32", "not implemented yet"},
{"resize_downsample_scales_cubic_A_n0p5_exclude_outside", "not implemented yet"},
{"resize_downsample_scales_cubic_align_corners", "not implemented yet"},
{"resize_downsample_scales_cubic", "not implemented yet"},
{"resize_downsample_scales_linear_align_corners", "not implemented yet"},
{"resize_downsample_scales_linear", "not implemented yet"},
{"resize_downsample_scales_nearest", "not implemented yet"},
{"resize_downsample_sizes_cubic", "not implemented yet"},
{"resize_downsample_sizes_linear_pytorch_half_pixel", "not implemented yet"},
{"resize_downsample_sizes_nearest", "not implemented yet"},
{"resize_downsample_sizes_nearest_tf_half_pixel_for_nn", "not implemented yet"},
{"resize_tf_crop_and_resize", "not implemented yet"},
{"resize_upsample_scales_cubic_A_n0p5_exclude_outside", "not implemented yet"},
{"resize_upsample_scales_cubic_align_corners", "not implemented yet"},
{"resize_upsample_scales_cubic_asymmetric", "not implemented yet"},
{"resize_upsample_scales_cubic", "not implemented yet"},
{"resize_upsample_scales_linear_align_corners", "not implemented yet"},
{"resize_upsample_scales_linear", "not implemented yet"},
{"resize_upsample_scales_nearest", "not implemented yet"},
{"resize_upsample_sizes_cubic", "not implemented yet"},
{"resize_upsample_sizes_nearest_ceil_half_pixel", "not implemented yet"},
{"resize_upsample_sizes_nearest", "not implemented yet"},
{"resize_upsample_sizes_nearest_floor_align_corners", "not implemented yet"},
{"resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", "not implemented yet"},
{"scatternd", "not implemented yet"},
{"yolov3", "regression in resize opset 10 shape inference"},
};
#ifdef USE_NGRAPH

32
onnxruntime/test/python/onnx_backend_test_series.py
View file

@ -120,6 +120,38 @@ def create_backend_test(testname=None):
'^test_unique_*',
'^test_mod_float_mixed_sign_example_cpu.*', #onnxruntime::Mod::Compute fmod_ was false. fmod attribute must be true for float, float16 and double types
'^test_shrink_cpu.*', #Invalid rank for input: x Got: 1 Expected: 2 Please fix either the inputs or the model.
'^test_range_float_type_positive_delta_cpu.*',
'^test_range_float_type_positive_delta_expanded_cpu.*',
'^test_range_int32_type_negative_delta_cpu.*',
'^test_range_int32_type_negative_delta_expanded_cpu.*',
'^test_det_2d_cpu.*',
'^test_det_nd_cpu.*',
'^test_gathernd_example_float32_cpu.*',
'^test_gathernd_example_int32_cpu.*',
'^test_resize_downsample_scales_cubic_A_n0p5_exclude_outside_cpu.*',
'^test_resize_downsample_scales_cubic_align_corners_cpu.*',
'^test_resize_downsample_scales_cubic_cpu.*',
'^test_resize_downsample_scales_linear_align_corners_cpu.*',
'^test_resize_downsample_scales_linear_cpu.*',
'^test_resize_downsample_scales_nearest_cpu.*',
'^test_resize_downsample_sizes_cubic_cpu.*',
'^test_resize_downsample_sizes_linear_pytorch_half_pixel_cpu.*',
'^test_resize_downsample_sizes_nearest_cpu.*',
'^test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn_cpu.*',
'^test_resize_tf_crop_and_resize_cpu.*',
'^test_resize_upsample_scales_cubic_A_n0p5_exclude_outside_cpu.*',
'^test_resize_upsample_scales_cubic_align_corners_cpu.*',
'^test_resize_upsample_scales_cubic_asymmetric_cpu.*',
'^test_resize_upsample_scales_cubic_cpu.*',
'^test_resize_upsample_scales_linear_align_corners_cpu.*',
'^test_resize_upsample_scales_linear_cpu.*',
'^test_resize_upsample_scales_nearest_cpu.*',
'^test_resize_upsample_sizes_cubic_cpu.*',
'^test_resize_upsample_sizes_nearest_ceil_half_pixel_cpu.*',
'^test_resize_upsample_sizes_nearest_cpu.*',
'^test_resize_upsample_sizes_nearest_floor_align_corners_cpu.*',
'^test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric_cpu.*',
'^test_scatternd_cpu.*',
)
# Example of how to disable tests for a specific provider.

2
tools/ci_build/github/linux/docker/scripts/install_onnx.sh
View file

@ -13,7 +13,7 @@ version2tag=(5af210ca8a1c73aa6bae8754c9346ec54d0a756e-onnx123
bae6333e149a59a3faa9c4d9c44974373dcf5256-onnx130
9e55ace55aad1ada27516038dfbdc66a8a0763db-onnx141
7d7bc83d29a328233d3e8affa4c4ea8b3e3599ef-onnx150
7d90796473295ca3cdf976ed772215c5980ad3e0-onnxtip)
568b65aaa2bd94b04d8fdac24b398dc25c507b50-onnxtip)
for v2t in ${version2tag[*]}; do
onnx_version="$(cut -d'-' -f1<<<${v2t})"
onnx_tag="$(cut -d'-' -f2<<<${v2t})"