dotnet
/
machinelearning
зеркало из
1
0
Форкнуть 0
Код Задачи Пакеты Проекты Релизы Вики Активность

Ensure ONNX export is compatible with Windows RS5 (#550) 

* remove domain from onnx operators for non-ML types.

* Make ONNX compatible with Windows RS5 and add more tests.

* PR feedback.

* PR feedback.

* fix build.
This commit is contained in:
Zeeshan Siddiqui 2018-07-23 09:33:55 -07:00 коммит произвёл GitHub
Родитель 8ce2a232e9
Коммит 8c11759ea8
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: 4AEE18F83AFDEB23
13 изменённых файлов: 1518 добавлений и 735 удалений
Показать статистику Скачать Patch файл Скачать Diff файл Показать все файлы Свернуть все файлы

4
src/Microsoft.ML.Data/Prediction/Calibrator.cs
Просмотреть файл

@ -1437,13 +1437,13 @@ namespace Microsoft.ML.Runtime.Internal.Calibration
string opType = "Affine";
string linearOutput = ctx.AddIntermediateVariable(null, "linearOutput", true);
var node = ctx.CreateNode(opType, new[] { scoreProbablityColumnNames[0] },
new[] { linearOutput }, ctx.GetNodeName(opType), "ai.onnx");
new[] { linearOutput }, ctx.GetNodeName(opType), "");
node.AddAttribute("alpha", ParamA * -1);
node.AddAttribute("beta", -0.0000001);
opType = "Sigmoid";
node = ctx.CreateNode(opType, new[] { linearOutput },
new[] { scoreProbablityColumnNames[1] }, ctx.GetNodeName(opType), "ai.onnx");
new[] { scoreProbablityColumnNames[1] }, ctx.GetNodeName(opType), "");
return true;
}

1
src/Microsoft.ML.Data/Transforms/ConcatTransform.cs
Просмотреть файл

@ -723,7 +723,6 @@ namespace Microsoft.ML.Runtime.Data
var node = ctx.CreateNode(opType, inputList.Select(t => t.Key),
new[] { ctx.AddIntermediateVariable(outColType, outName) }, ctx.GetNodeName(opType));
node.AddAttribute("inputList", inputList.Select(x => x.Key));
node.AddAttribute("inputdimensions", inputList.Select(x => x.Value));
}
}

5
src/Microsoft.ML.Data/Transforms/TermTransform.cs
Просмотреть файл

@ -719,7 +719,10 @@ namespace Microsoft.ML.Runtime.Data
var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
node.AddAttribute("classes_strings", terms.DenseValues());
node.AddAttribute("default_int64", -1);
node.AddAttribute("default_string", DvText.Empty);
//default_string needs to be an empty string but there is a BUG in Lotus that
//throws a validation error when default_string is empty. As a work around, set
//default_string to a space.
node.AddAttribute("default_string", " ");
return true;
}

4
src/Microsoft.ML.Onnx/OnnxUtils.cs
Просмотреть файл

@ -252,10 +252,10 @@ namespace Microsoft.ML.Runtime.Model.Onnx
model.Domain = domain;
model.ProducerName = producerName;
model.ProducerVersion = producerVersion;
model.IrVersion = (long)UniversalModelFormat.Onnx.Version.IrVersion;
model.IrVersion = (long)Version.IrVersion;
model.ModelVersion = modelVersion;
model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "ai.onnx.ml", Version = 1 });
model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "ai.onnx", Version = 6 });
model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "", Version = 7 });
model.Graph = new GraphProto();
var graph = model.Graph;
graph.Node.Add(nodes);

4
src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs
Просмотреть файл

@ -238,10 +238,10 @@ namespace Microsoft.ML.Runtime.Learners
string opType = "LinearRegressor";
var node = ctx.CreateNode(opType, new[] { featureColumn }, outputs, ctx.GetNodeName(opType));
// Selection of logit or probit output transform. enum {'NONE', 'LOGIT', 'PROBIT}
node.AddAttribute("post_transform", 0);
node.AddAttribute("post_transform", "NONE");
node.AddAttribute("targets", 1);
node.AddAttribute("coefficients", Weight.DenseValues());
node.AddAttribute("intercepts", Bias);
node.AddAttribute("intercepts", new float[] { Bias });
return true;
}

6
src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/MulticlassLogisticRegression.cs
Просмотреть файл

@ -845,12 +845,12 @@ namespace Microsoft.ML.Runtime.Learners
string opType = "LinearClassifier";
var node = ctx.CreateNode(opType, new[] { featureColumn }, outputs, ctx.GetNodeName(opType));
// Selection of logit or probit output transform. enum {'NONE', 'LOGIT', 'PROBIT}
node.AddAttribute("post_transform", 0);
// Selection of logit or probit output transform. enum {'NONE', 'SOFTMAX', 'LOGISTIC', 'SOFTMAX_ZERO', 'PROBIT}
node.AddAttribute("post_transform", "NONE");
node.AddAttribute("multi_class", true);
node.AddAttribute("coefficients", _weights.SelectMany(w => w.DenseValues()));
node.AddAttribute("intercepts", _biases);
node.AddAttribute("classlabels_strings", _labelNames);
node.AddAttribute("classlabels_ints", Enumerable.Range(0, _numClasses).Select(x => (long)x));
return true;
}

4
src/Microsoft.ML.Transforms/NAReplaceTransform.cs
Просмотреть файл

@ -632,13 +632,13 @@ namespace Microsoft.ML.Runtime.Data
node.AddAttribute("replaced_value_float", Single.NaN);
if (!Infos[iinfo].TypeSrc.IsVector)
node.AddAttribute("imputed_value_float", Enumerable.Repeat((float)_repValues[iinfo], 1));
node.AddAttribute("imputed_value_floats", Enumerable.Repeat((float)_repValues[iinfo], 1));
else
{
if (_repIsDefault[iinfo] != null)
node.AddAttribute("imputed_value_floats", (float[])_repValues[iinfo]);
else
node.AddAttribute("imputed_value_float", Enumerable.Repeat((float)_repValues[iinfo], 1));
node.AddAttribute("imputed_value_floats", Enumerable.Repeat((float)_repValues[iinfo], 1));
}
return true;

542
test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationFastTreeSaveModelToOnnxTest.json Normal file
Просмотреть файл

@ -0,0 +1,542 @@
{
"irVersion": "3",
"producerName": "ML.NET",
"producerVersion": "##VERSION##",
"domain": "Onnx",
"graph": {
"node": [
{
"input": [
"F1"
],
"output": [
"F10"
],
"name": "Imputer",
"opType": "Imputer",
"attribute": [
{
"name": "replaced_value_float",
"f": "NaN",
"type": "FLOAT"
},
{
"name": "imputed_value_floats",
"floats": [
0
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"F10"
],
"output": [
"F11"
],
"name": "Scaler",
"opType": "Scaler",
"attribute": [
{
"name": "offset",
"floats": [
0
],
"type": "FLOATS"
},
{
"name": "scale",
"floats": [
0.1
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"F2"
],
"output": [
"F20"
],
"name": "LabelEncoder",
"opType": "LabelEncoder",
"attribute": [
{
"name": "classes_strings",
"strings": [
"NA==",
"MQ==",
"OA==",
"MTA=",
"Mg==",
"Mw==",
"Nw==",
"NQ==",
"Ng==",
"OQ=="
],
"type": "STRINGS"
},
{
"name": "default_int64",
"i": "-1",
"type": "INT"
},
{
"name": "default_string",
"s": "IA==",
"type": "STRING"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"F20"
],
"output": [
"F21"
],
"name": "OneHotEncoder",
"opType": "OneHotEncoder",
"attribute": [
{
"name": "cats_int64s",
"ints": [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10"
],
"type": "INTS"
},
{
"name": "zeros",
"i": "1",
"type": "INT"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"F11",
"F21"
],
"output": [
"Features"
],
"name": "FeatureVectorizer",
"opType": "FeatureVectorizer",
"attribute": [
{
"name": "inputdimensions",
"ints": [
"1",
"10"
],
"type": "INTS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Features"
],
"output": [
"Score"
],
"name": "TreeEnsembleRegressor",
"opType": "TreeEnsembleRegressor",
"attribute": [
{
"name": "post_transform",
"s": "Tk9ORQ==",
"type": "STRING"
},
{
"name": "n_targets",
"i": "1",
"type": "INT"
},
{
"name": "base_values",
"floats": [
0
],
"type": "FLOATS"
},
{
"name": "aggregate_function",
"s": "U1VN",
"type": "STRING"
},
{
"name": "nodes_treeids",
"ints": [
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_nodeids",
"ints": [
"0",
"1",
"2"
],
"type": "INTS"
},
{
"name": "nodes_featureids",
"ints": [
"2",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_modes",
"strings": [
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg=="
],
"type": "STRINGS"
},
{
"name": "nodes_values",
"floats": [
0.5,
0,
0
],
"type": "FLOATS"
},
{
"name": "nodes_truenodeids",
"ints": [
"1",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_falsenodeids",
"ints": [
"2",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_missing_value_tracks_true",
"ints": [
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_treeids",
"ints": [
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_nodeids",
"ints": [
"1",
"2"
],
"type": "INTS"
},
{
"name": "target_ids",
"ints": [
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_weights",
"floats": [
0.504761934,
-0.979112267
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Score"
],
"output": [
"linearOutput"
],
"name": "Affine",
"opType": "Affine",
"attribute": [
{
"name": "alpha",
"f": 0.4,
"type": "FLOAT"
},
{
"name": "beta",
"f": -1E-07,
"type": "FLOAT"
}
]
},
{
"input": [
"linearOutput"
],
"output": [
"Probability"
],
"name": "Sigmoid",
"opType": "Sigmoid"
},
{
"input": [
"Probability"
],
"output": [
"PredictedLabel"
],
"name": "Binarizer",
"opType": "Binarizer",
"attribute": [
{
"name": "threshold",
"f": 0.5,
"type": "FLOAT"
}
],
"domain": "ai.onnx.ml"
}
],
"name": "BinaryClassificationFastTreeSaveModelToOnnxTest",
"input": [
{
"name": "F1",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "F2",
"type": {
"tensorType": {
"elemType": "STRING",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
}
],
"output": [
{
"name": "PredictedLabel",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Score",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Probability",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
}
],
"valueInfo": [
{
"name": "F10",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "F11",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "F20",
"type": {
"tensorType": {
"elemType": "INT64",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "F21",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "10"
}
]
}
}
}
},
{
"name": "Features",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "11"
}
]
}
}
}
}
]
},
"opsetImport": [
{
"domain": "ai.onnx.ml",
"version": "1"
},
{
"version": "7"
}
]
}

254
test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationLRSaveModelToOnnxTest.json Normal file
Просмотреть файл

@ -0,0 +1,254 @@
{
"irVersion": "3",
"producerName": "ML.NET",
"producerVersion": "##VERSION##",
"domain": "Onnx",
"graph": {
"node": [
{
"input": [
"Features"
],
"output": [
"Features0"
],
"name": "Scaler",
"opType": "Scaler",
"attribute": [
{
"name": "offset",
"floats": [
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"type": "FLOATS"
},
{
"name": "scale",
"floats": [
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Features0"
],
"output": [
"Score"
],
"name": "LinearRegressor",
"opType": "LinearRegressor",
"attribute": [
{
"name": "post_transform",
"s": "Tk9ORQ==",
"type": "STRING"
},
{
"name": "targets",
"i": "1",
"type": "INT"
},
{
"name": "coefficients",
"floats": [
2.6596148,
1.67937,
1.94177353,
1.42409551,
0.852847636,
2.93048549,
1.74959826,
1.58030283,
0.5948697
],
"type": "FLOATS"
},
{
"name": "intercepts",
"floats": [
-6.183617
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Score"
],
"output": [
"linearOutput"
],
"name": "Affine",
"opType": "Affine",
"attribute": [
{
"name": "alpha",
"f": 1,
"type": "FLOAT"
},
{
"name": "beta",
"f": -1E-07,
"type": "FLOAT"
}
]
},
{
"input": [
"linearOutput"
],
"output": [
"Probability"
],
"name": "Sigmoid",
"opType": "Sigmoid"
},
{
"input": [
"Probability"
],
"output": [
"PredictedLabel"
],
"name": "Binarizer",
"opType": "Binarizer",
"attribute": [
{
"name": "threshold",
"f": 0.5,
"type": "FLOAT"
}
],
"domain": "ai.onnx.ml"
}
],
"name": "BinaryClassificationLRSaveModelToOnnxTest",
"input": [
{
"name": "Features",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "9"
}
]
}
}
}
}
],
"output": [
{
"name": "PredictedLabel",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Score",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Probability",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
}
],
"valueInfo": [
{
"name": "Features0",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "9"
}
]
}
}
}
}
]
},
"opsetImport": [
{
"domain": "ai.onnx.ml",
"version": "1"
},
{
"version": "7"
}
]
}

285
test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationLightGBMSaveModelToOnnxTest.json Normal file
Просмотреть файл

@ -0,0 +1,285 @@
{
"irVersion": "3",
"producerName": "ML.NET",
"producerVersion": "##VERSION##",
"domain": "Onnx",
"graph": {
"node": [
{
"input": [
"Features"
],
"output": [
"Score"
],
"name": "TreeEnsembleRegressor",
"opType": "TreeEnsembleRegressor",
"attribute": [
{
"name": "post_transform",
"s": "Tk9ORQ==",
"type": "STRING"
},
{
"name": "n_targets",
"i": "1",
"type": "INT"
},
{
"name": "base_values",
"floats": [
0
],
"type": "FLOATS"
},
{
"name": "aggregate_function",
"s": "U1VN",
"type": "STRING"
},
{
"name": "nodes_treeids",
"ints": [
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_nodeids",
"ints": [
"0",
"1",
"2"
],
"type": "INTS"
},
{
"name": "nodes_featureids",
"ints": [
"1",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_modes",
"strings": [
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg=="
],
"type": "STRINGS"
},
{
"name": "nodes_values",
"floats": [
2.5,
0,
0
],
"type": "FLOATS"
},
{
"name": "nodes_truenodeids",
"ints": [
"1",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_falsenodeids",
"ints": [
"2",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_missing_value_tracks_true",
"ints": [
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_treeids",
"ints": [
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_nodeids",
"ints": [
"1",
"2"
],
"type": "INTS"
},
{
"name": "target_ids",
"ints": [
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_weights",
"floats": [
-1.799208,
-0.34535858
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Score"
],
"output": [
"linearOutput"
],
"name": "Affine",
"opType": "Affine",
"attribute": [
{
"name": "alpha",
"f": 0.5,
"type": "FLOAT"
},
{
"name": "beta",
"f": -1E-07,
"type": "FLOAT"
}
]
},
{
"input": [
"linearOutput"
],
"output": [
"Probability"
],
"name": "Sigmoid",
"opType": "Sigmoid"
},
{
"input": [
"Probability"
],
"output": [
"PredictedLabel"
],
"name": "Binarizer",
"opType": "Binarizer",
"attribute": [
{
"name": "threshold",
"f": 0.5,
"type": "FLOAT"
}
],
"domain": "ai.onnx.ml"
}
],
"name": "BinaryClassificationLightGBMSaveModelToOnnxTest",
"input": [
{
"name": "Features",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "9"
}
]
}
}
}
}
],
"output": [
{
"name": "PredictedLabel",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Score",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Probability",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
}
]
},
"opsetImport": [
{
"domain": "ai.onnx.ml",
"version": "1"
},
{
"version": "7"
}
]
}

716
test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/SaveModelToOnnxTest.json
Просмотреть файл

@ -1,716 +0,0 @@
{
"irVersion": "3",
"producerName": "ML.NET",
"producerVersion": "##VERSION##",
"domain": "Onnx",
"graph": {
"node": [
{
"input": [
"Features"
],
"output": [
"Score"
],
"name": "TreeEnsembleRegressor",
"opType": "TreeEnsembleRegressor",
"attribute": [
{
"name": "post_transform",
"s": "Tk9ORQ==",
"type": "STRING"
},
{
"name": "n_targets",
"i": "1",
"type": "INT"
},
{
"name": "base_values",
"floats": [
0
],
"type": "FLOATS"
},
{
"name": "aggregate_function",
"s": "U1VN",
"type": "STRING"
},
{
"name": "nodes_treeids",
"ints": [
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"1",
"1",
"1",
"1",
"1",
"1",
"1",
"1",
"1",
"2",
"2",
"2",
"2",
"2",
"2",
"2",
"2",
"2",
"3",
"3",
"3",
"3",
"3",
"3",
"3",
"3",
"3",
"4",
"4",
"4",
"4",
"4",
"4",
"4",
"4",
"4"
],
"type": "INTS"
},
{
"name": "nodes_nodeids",
"ints": [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8"
],
"type": "INTS"
},
{
"name": "nodes_featureids",
"ints": [
"1",
"2",
"5",
"0",
"0",
"0",
"0",
"0",
"0",
"1",
"5",
"2",
"0",
"0",
"0",
"0",
"0",
"0",
"1",
"5",
"2",
"0",
"0",
"0",
"0",
"0",
"0",
"2",
"6",
"5",
"1",
"0",
"0",
"0",
"0",
"0",
"1",
"5",
"0",
"1",
"0",
"0",
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_modes",
"strings": [
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"QlJBTkNIX0xFUQ==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg==",
"TEVBRg=="
],
"type": "STRINGS"
},
{
"name": "nodes_values",
"floats": [
2.5,
2.5,
5.5,
5.5,
0,
0,
0,
0,
0,
3.5,
3.5,
1.5,
3.5,
0,
0,
0,
0,
0,
3.5,
2.5,
2.5,
5.5,
0,
0,
0,
0,
0,
3.5,
3.5,
2.5,
4.5,
0,
0,
0,
0,
0,
2.5,
1.5,
6.5,
5.5,
0,
0,
0,
0,
0
],
"type": "FLOATS"
},
{
"name": "nodes_truenodeids",
"ints": [
"2",
"3",
"4",
"5",
"0",
"0",
"0",
"0",
"0",
"1",
"4",
"3",
"6",
"0",
"0",
"0",
"0",
"0",
"1",
"4",
"3",
"6",
"0",
"0",
"0",
"0",
"0",
"1",
"4",
"6",
"5",
"0",
"0",
"0",
"0",
"0",
"2",
"3",
"4",
"5",
"0",
"0",
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_falsenodeids",
"ints": [
"1",
"6",
"7",
"8",
"0",
"0",
"0",
"0",
"0",
"5",
"2",
"7",
"8",
"0",
"0",
"0",
"0",
"0",
"5",
"2",
"7",
"8",
"0",
"0",
"0",
"0",
"0",
"3",
"2",
"7",
"8",
"0",
"0",
"0",
"0",
"0",
"1",
"6",
"7",
"8",
"0",
"0",
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "nodes_missing_value_tracks_true",
"ints": [
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_treeids",
"ints": [
"0",
"0",
"0",
"0",
"0",
"1",
"1",
"1",
"1",
"1",
"2",
"2",
"2",
"2",
"2",
"3",
"3",
"3",
"3",
"3",
"4",
"4",
"4",
"4",
"4"
],
"type": "INTS"
},
{
"name": "target_nodeids",
"ints": [
"4",
"5",
"6",
"7",
"8",
"4",
"5",
"6",
"7",
"8",
"4",
"5",
"6",
"7",
"8",
"4",
"5",
"6",
"7",
"8",
"4",
"5",
"6",
"7",
"8"
],
"type": "INTS"
},
{
"name": "target_ids",
"ints": [
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0",
"0"
],
"type": "INTS"
},
{
"name": "target_weights",
"floats": [
-0.9755501,
-0.8947368,
0.8347107,
0.75,
1,
-0.8157646,
0.75331986,
-0.8725711,
0.6149202,
1.05311215,
-0.739901066,
0.65209645,
-0.618561566,
0.6576947,
0.7696665,
-0.663163662,
0.229835972,
-0.49779135,
0.670133352,
0.660453737,
-0.620322645,
-0.634804964,
0.513690054,
0.650555968,
0.6567067
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Score"
],
"output": [
"linearOutput"
],
"name": "Affine",
"opType": "Affine",
"attribute": [
{
"name": "alpha",
"f": 0.4,
"type": "FLOAT"
},
{
"name": "beta",
"f": -1E-07,
"type": "FLOAT"
}
],
"domain": "ai.onnx"
},
{
"input": [
"linearOutput"
],
"output": [
"Probability"
],
"name": "Sigmoid",
"opType": "Sigmoid",
"domain": "ai.onnx"
},
{
"input": [
"Probability"
],
"output": [
"PredictedLabel"
],
"name": "Binarizer",
"opType": "Binarizer",
"attribute": [
{
"name": "threshold",
"f": 0.5,
"type": "FLOAT"
}
],
"domain": "ai.onnx.ml"
}
],
"name": "SaveModelToOnnxTest",
"input": [
{
"name": "Features",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "9"
}
]
}
}
}
}
],
"output": [
{
"name": "PredictedLabel",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Score",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Probability",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
}
]
},
"opsetImport": [
{
"domain": "ai.onnx.ml",
"version": "1"
},
{
"domain": "ai.onnx",
"version": "6"
}
]
}

205
test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLRSaveModelToOnnxTest.json Normal file
Просмотреть файл

@ -0,0 +1,205 @@
{
"irVersion": "3",
"producerName": "ML.NET",
"producerVersion": "##VERSION##",
"domain": "Onnx",
"graph": {
"node": [
{
"input": [
"Features"
],
"output": [
"Features0"
],
"name": "Scaler",
"opType": "Scaler",
"attribute": [
{
"name": "offset",
"floats": [
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"type": "FLOATS"
},
{
"name": "scale",
"floats": [
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1
],
"type": "FLOATS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"Features0"
],
"output": [
"PredictedLabel",
"Score"
],
"name": "LinearClassifier",
"opType": "LinearClassifier",
"attribute": [
{
"name": "post_transform",
"s": "Tk9ORQ==",
"type": "STRING"
},
{
"name": "multi_class",
"i": "1",
"type": "INT"
},
{
"name": "coefficients",
"floats": [
-1.58059466,
-0.82541883,
-1.05039084,
-0.792811334,
-0.385914773,
-1.59029973,
-1.01633251,
-0.8349969,
-0.3322066,
1.58059633,
0.8254174,
1.05039155,
0.7928113,
0.385914057,
1.59029937,
1.01633251,
0.8349978,
0.332206637
],
"type": "FLOATS"
},
{
"name": "intercepts",
"floats": [
3.36230779,
-3.36230469
],
"type": "FLOATS"
},
{
"name": "classlabels_ints",
"ints": [
"0",
"1"
],
"type": "INTS"
}
],
"domain": "ai.onnx.ml"
}
],
"name": "MultiClassificationLRSaveModelToOnnxTest",
"input": [
{
"name": "Features",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "9"
}
]
}
}
}
}
],
"output": [
{
"name": "PredictedLabel",
"type": {
"tensorType": {
"elemType": "INT64",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "1"
}
]
}
}
}
},
{
"name": "Score",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "2"
}
]
}
}
}
}
],
"valueInfo": [
{
"name": "Features0",
"type": {
"tensorType": {
"elemType": "FLOAT",
"shape": {
"dim": [
{
"dimValue": "1"
},
{
"dimValue": "9"
}
]
}
}
}
}
]
},
"opsetImport": [
{
"domain": "ai.onnx.ml",
"version": "1"
},
{
"version": "7"
}
]
}

223
test/Microsoft.ML.Tests/OnnxTests.cs
Просмотреть файл

@ -8,6 +8,7 @@ using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.RunTests;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System.IO;
using System.Text.RegularExpressions;
using Xunit;
@ -25,6 +26,14 @@ namespace Microsoft.ML.Tests
{
public float Label;
public float F1;
public DvText F2;
}
public class BreastCancerDataAllColumns
{
public float Label;
[VectorType(9)]
public float[] Features;
}
@ -35,8 +44,86 @@ namespace Microsoft.ML.Tests
public DvBool Cancerous;
}
public class BreastCancerMCPrediction
{
[ColumnName("Score")]
public float[] Scores;
}
[Fact]
public void BinaryClassificationSaveModelToOnnxTest()
public void BinaryClassificationFastTreeSaveModelToOnnxTest()
{
string dataPath = GetDataPath(@"breast-cancer.txt");
var pipeline = new LearningPipeline();
pipeline.Add(new Data.TextLoader(dataPath)
{
Arguments = new TextLoaderArguments
{
Separator = new[] { '\t' },
HasHeader = true,
Column = new[]
{
new TextLoaderColumn()
{
Name = "Label",
Source = new [] { new TextLoaderRange(0) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "F1",
Source = new [] { new TextLoaderRange(1, 1) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "F2",
Source = new [] { new TextLoaderRange(2, 2) },
Type = Data.DataKind.TX
}
}
}
});
pipeline.Add(new MissingValueSubstitutor("F1"));
pipeline.Add(new MinMaxNormalizer("F1"));
pipeline.Add(new CategoricalOneHotVectorizer("F2"));
pipeline.Add(new ColumnConcatenator("Features", "F1", "F2"));
pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 2, NumTrees = 1, MinDocumentsInLeafs = 2 });
var model = pipeline.Train<BreastCancerData, BreastCancerPrediction>();
var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer");
var onnxPath = GetOutputPath(subDir, "BinaryClassificationFastTreeSaveModelToOnnxTest.onnx");
DeleteOutputPath(onnxPath);
var onnxAsJsonPath = GetOutputPath(subDir, "BinaryClassificationFastTreeSaveModelToOnnxTest.json");
DeleteOutputPath(onnxAsJsonPath);
OnnxConverter converter = new OnnxConverter()
{
InputsToDrop = new[] { "Label" },
OutputsToDrop = new[] { "Label", "F1", "F2", "Features" },
Onnx = onnxPath,
Json = onnxAsJsonPath,
Domain = "Onnx"
};
converter.Convert(model);
// Strip the version.
var fileText = File.ReadAllText(onnxAsJsonPath);
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(onnxAsJsonPath, fileText);
CheckEquality(subDir, "BinaryClassificationFastTreeSaveModelToOnnxTest.json");
Done();
}
[Fact]
public void BinaryClassificationLightGBMSaveModelToOnnxTest()
{
string dataPath = GetDataPath(@"breast-cancer.txt");
var pipeline = new LearningPipeline();
@ -66,14 +153,14 @@ namespace Microsoft.ML.Tests
}
});
pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
pipeline.Add(new LightGbmBinaryClassifier() { NumLeaves = 2, NumBoostRound = 1, MinDataPerLeaf = 2 });
var model = pipeline.Train<BreastCancerData, BreastCancerPrediction>();
var model = pipeline.Train<BreastCancerDataAllColumns, BreastCancerPrediction>();
var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer");
var onnxPath = GetOutputPath(subDir, "SaveModelToOnnxTest.pb");
var onnxPath = GetOutputPath(subDir, "BinaryClassificationLightGBMSaveModelToOnnxTest.onnx");
DeleteOutputPath(onnxPath);
var onnxAsJsonPath = GetOutputPath(subDir, "SaveModelToOnnxTest.json");
var onnxAsJsonPath = GetOutputPath(subDir, "BinaryClassificationLightGBMSaveModelToOnnxTest.json");
DeleteOutputPath(onnxAsJsonPath);
OnnxConverter converter = new OnnxConverter()
@ -92,8 +179,132 @@ namespace Microsoft.ML.Tests
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(onnxAsJsonPath, fileText);
CheckEquality(subDir, "SaveModelToOnnxTest.json");
CheckEquality(subDir, "BinaryClassificationLightGBMSaveModelToOnnxTest.json");
Done();
}
[Fact]
public void BinaryClassificationLRSaveModelToOnnxTest()
{
string dataPath = GetDataPath(@"breast-cancer.txt");
var pipeline = new LearningPipeline();
pipeline.Add(new Data.TextLoader(dataPath)
{
Arguments = new TextLoaderArguments
{
Separator = new[] { '\t' },
HasHeader = true,
Column = new[]
{
new TextLoaderColumn()
{
Name = "Label",
Source = new [] { new TextLoaderRange(0) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Features",
Source = new [] { new TextLoaderRange(1, 9) },
Type = Data.DataKind.Num
}
}
}
});
pipeline.Add(new LogisticRegressionBinaryClassifier() { UseThreads = false });
var model = pipeline.Train<BreastCancerDataAllColumns, BreastCancerPrediction>();
var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer");
var onnxPath = GetOutputPath(subDir, "BinaryClassificationLRSaveModelToOnnxTest.onnx");
DeleteOutputPath(onnxPath);
var onnxAsJsonPath = GetOutputPath(subDir, "BinaryClassificationLRSaveModelToOnnxTest.json");
DeleteOutputPath(onnxAsJsonPath);
OnnxConverter converter = new OnnxConverter()
{
InputsToDrop = new[] { "Label" },
OutputsToDrop = new[] { "Label", "Features" },
Onnx = onnxPath,
Json = onnxAsJsonPath,
Domain = "Onnx"
};
converter.Convert(model);
// Strip the version.
var fileText = File.ReadAllText(onnxAsJsonPath);
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(onnxAsJsonPath, fileText);
CheckEquality(subDir, "BinaryClassificationLRSaveModelToOnnxTest.json");
Done();
}
[Fact]
public void MultiClassificationLRSaveModelToOnnxTest()
{
string dataPath = GetDataPath(@"breast-cancer.txt");
var pipeline = new LearningPipeline();
pipeline.Add(new Data.TextLoader(dataPath)
{
Arguments = new TextLoaderArguments
{
Separator = new[] { '\t' },
HasHeader = true,
Column = new[]
{
new TextLoaderColumn()
{
Name = "Label",
Source = new [] { new TextLoaderRange(0) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Features",
Source = new [] { new TextLoaderRange(1, 9) },
Type = Data.DataKind.Num
}
}
}
});
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new LogisticRegressionClassifier() { UseThreads = false });
var model = pipeline.Train<BreastCancerDataAllColumns, BreastCancerMCPrediction>();
var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "MultiClassClassification", "BreastCancer");
var onnxPath = GetOutputPath(subDir, "MultiClassificationLRSaveModelToOnnxTest.onnx");
DeleteOutputPath(onnxPath);
var onnxAsJsonPath = GetOutputPath(subDir, "MultiClassificationLRSaveModelToOnnxTest.json");
DeleteOutputPath(onnxAsJsonPath);
OnnxConverter converter = new OnnxConverter()
{
InputsToDrop = new[] { "Label" },
OutputsToDrop = new[] { "Label", "Features" },
Onnx = onnxPath,
Json = onnxAsJsonPath,
Domain = "Onnx"
};
converter.Convert(model);
// Strip the version.
var fileText = File.ReadAllText(onnxAsJsonPath);
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(onnxAsJsonPath, fileText);
CheckEquality(subDir, "MultiClassificationLRSaveModelToOnnxTest.json");
Done();
}
}
}