41return new OnnxScoringEstimator(env, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse); 69return new OnnxScoringEstimator(env, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse); 105return new OnnxScoringEstimator(env, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary); 141return new OnnxScoringEstimator(env, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary); 173return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName }, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse); 205return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName }, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse); 221return new OnnxScoringEstimator(env, options.OutputColumns, options.InputColumns, options.ModelFile, 259return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName }, 297return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName }, 323return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse); 348return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse); 378return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary); 408return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary); 440return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary, recursionLimit); 472return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary, recursionLimit);

46var prepEstimator = new OnnxScoringEstimator(env, new[] { "PreprocessedInput" }, new[] { "OriginalInput" }, Path.Combine(modelDir, "AlexNetPrepOnnx", "AlexNetPreprocess.onnx")); 47var mainEstimator = new OnnxScoringEstimator(env, new[] { "Dropout234_Output_0" }, new[] { "Input140" }, Path.Combine(modelDir, "AlexNetOnnx", "AlexNet.onnx"));

46var prepEstimator = new OnnxScoringEstimator(env, new[] { "PreprocessedInput" }, new[] { "OriginalInput" }, Path.Combine(modelDir, "ResNetPrepOnnx", "ResNetPreprocess.onnx")); 47var mainEstimator = new OnnxScoringEstimator(env, new[] { "Pooling2286_Output_0" }, new[] { "Input1600" }, Path.Combine(modelDir, "ResNet101Onnx", "ResNet101.onnx"));

46var prepEstimator = new OnnxScoringEstimator(env, new[] { "PreprocessedInput" }, new[] { "OriginalInput" }, Path.Combine(modelDir, "ResNetPrepOnnx", "ResNetPreprocess.onnx")); 47var mainEstimator = new OnnxScoringEstimator(env, new[] { "Pooling395_Output_0" }, new[] { "Input247" }, Path.Combine(modelDir, "ResNet18Onnx", "ResNet18.onnx"));

46var prepEstimator = new OnnxScoringEstimator(env, new[] { "PreprocessedInput" }, new[] { "OriginalInput" }, Path.Combine(modelDir, "ResNetPrepOnnx", "ResNetPreprocess.onnx")); 47var mainEstimator = new OnnxScoringEstimator(env, new[] { "Pooling1096_Output_0" }, new[] { "Input750" }, Path.Combine(modelDir, "ResNet50Onnx", "ResNet50.onnx"));

60var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 106var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 158var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu);

25/// a chain of two <see cref="OnnxScoringEstimator"/>. 92/// <see cref="OnnxScoringEstimator"/>s (one for preprocessing and one with a pretrained image DNN) with specific models

17/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column. 19/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 38public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, string modelFile, int? gpuDeviceId = null, bool fallbackToCpu = false) 45/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column. 47/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 66public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, Stream modelBytes, int? gpuDeviceId = null, bool fallbackToCpu = false) 73/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column. 75/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 98public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 109/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column. 111/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 134public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 145/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column. 146/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 165public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 177/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column. 178/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 197public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 209/// Create a <see cref="OnnxScoringEstimator"/> using the specified <see cref="OnnxOptions"/>. 210/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 217/// <param name="options">Options for the <see cref="OnnxScoringEstimator"/>.</param> 218public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, OnnxOptions options) 226/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column. 227/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 250public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 264/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column. 265/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 288public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 302/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns. 303/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 315public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 327/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns. 328/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 340public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 352/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns. 353/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 369public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 382/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns. 383/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 399public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 412/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns. 413/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 430public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 444/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns. 445/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies, 462public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, 482/// <see cref="OnnxScoringEstimator"/> (one for preprocessing and one with a pretrained image DNN) with specific models

10/// The options for an <see cref="OnnxScoringEstimator"/>.

38/// <see cref="ITransformer"/> resulting from fitting an <see cref="OnnxScoringEstimator"/>. 39/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,

156var pipe = useOptionsCtor ? 196var est = ML.Transforms.ApplyOnnxModel(outputNames, inputNames, modelFile, gpuDeviceId, fallbackToCpu); 369var pipeline = ML.Transforms.ApplyOnnxModel("softmaxout_1", "data_0", modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 408var pipeline = ML.Transforms.ApplyOnnxModel("softmaxout_1", "data_0", modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 436var pipeline = ML.Transforms.ApplyOnnxModel(new[] { "outa", "outb" }, new[] { "ina", "inb" }, modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 476var pipeline = ML.Transforms.ApplyOnnxModel(new[] { "outb", "outa" }, new[] { "ina", "inb" }, modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 536var pipeline = ML.Transforms.ApplyOnnxModel(modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 562var pipeline = ML.Transforms.ApplyOnnxModel(modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 694var pipeline = ML.Transforms.ApplyOnnxModel(new[] { "output" }, new[] { "input" }, modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 747var pipeline = ML.Transforms.ApplyOnnxModel(new[] { "output" }, new[] { "input" }, modelFile, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 902var pipeline = new OnnxScoringEstimator[3]; 972var pipeline = ML.Transforms.ApplyOnnxModel(new[] { "outa", "outb" }, new[] { "ina", "inb" }, 1072var pipeline = ML.Transforms.ApplyOnnxModel(nameof(PredictionWithCustomShape.argmax),

84var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);

28var pipeline = mlContext.Transforms.ApplyOnnxModel(modelPath);

62/// call <see cref="OnnxScoringEstimator"/> to evaluate that file. The outputs of <see cref="OnnxScoringEstimator"/> are checked against the original 826var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 988var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 1515var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 1632var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 1743var pipeline = mlContext.Transforms.ApplyOnnxModel(onnxModelPath); 1806var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 1819var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu); 2254var onnxEstimator = ML.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu);

46var pipeline = ctx.Transforms.ApplyOnnxModel( 85var pipeline = ctx.Transforms.ApplyOnnxModel(