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// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
using Microsoft.ML.Transforms.Onnx;
namespace Microsoft.ML
{
public static class OnnxCatalog
{
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column.
/// Input/output columns are determined based on the input/output columns of the provided ONNX model.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <remarks>
/// The name/type of input columns must exactly match name/type of the ONNX model inputs.
/// The name/type of the produced output columns will match name/type of the ONNX model outputs.
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, string modelFile, int? gpuDeviceId = null, bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column.
/// Input/output columns are determined based on the input/output columns of the provided ONNX model.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <remarks>
/// The name/type of input columns must exactly match name/type of the ONNX model inputs.
/// The name/type of the produced output columns will match name/type of the ONNX model outputs.
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, Stream modelBytes, int? gpuDeviceId = null, bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column.
/// Input/output columns are determined based on the input/output columns of the provided ONNX model.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <remarks>
/// The name/type of input columns must exactly match name/type of the ONNX model inputs.
/// The name/type of the produced output columns will match name/type of the ONNX model outputs.
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelFile"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string modelFile,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column.
/// Input/output columns are determined based on the input/output columns of the provided ONNX model.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <remarks>
/// The name/type of input columns must exactly match name/type of the ONNX model inputs.
/// The name/type of the produced output columns will match name/type of the ONNX model outputs.
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelBytes"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
Stream modelBytes,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">The output column resulting from the transformation.</param>
/// <param name="inputColumnName">The input column.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string outputColumnName,
string inputColumnName,
string modelFile,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName }, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">The output column resulting from the transformation.</param>
/// <param name="inputColumnName">The input column.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string outputColumnName,
string inputColumnName,
Stream modelBytes,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName }, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/> using the specified <see cref="OnnxOptions"/>.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <remarks>
/// If the options.GpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="options">Options for the <see cref="OnnxScoringEstimator"/>.</param>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog, OnnxOptions options)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, options.GpuDeviceId, options.FallbackToCpu);
return new OnnxScoringEstimator(env, options.OutputColumns, options.InputColumns, options.ModelFile,
gpuDeviceIdToUse, fallbackToCpuToUse, options.ShapeDictionary, options.RecursionLimit, options.InterOpNumThreads, options.IntraOpNumThreads);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">The output column resulting from the transformation.</param>
/// <param name="inputColumnName">The input column.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelFile"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string outputColumnName,
string inputColumnName,
string modelFile,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName },
modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">The output column resulting from the transformation.</param>
/// <param name="inputColumnName">The input column.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelBytes"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string outputColumnName,
string inputColumnName,
Stream modelBytes,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, new[] { outputColumnName }, new[] { inputColumnName },
modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
/// <param name="inputColumnNames">The input columns.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
string modelFile,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
/// <param name="inputColumnNames">The input columns.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
Stream modelBytes,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
/// <param name="inputColumnNames">The input columns.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelFile"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
string modelFile,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
/// <param name="inputColumnNames">The input columns.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelBytes"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
Stream modelBytes,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
/// <param name="inputColumnNames">The input columns.</param>
/// <param name="modelFile">The path of the file containing the ONNX model.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelFile"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <param name="recursionLimit">Optional, specifies the Protobuf CodedInputStream recursion limit. Default value is 100.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
string modelFile,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false,
int recursionLimit = 100)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelFile, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary, recursionLimit);
}
/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// Please refer to <see cref="OnnxScoringEstimator"/> to learn more about the necessary dependencies,
/// and how to run it on a GPU.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
/// <param name="inputColumnNames">The input columns.</param>
/// <param name="modelBytes">The <see cref="System.IO.Stream"/> containing the model bytes.</param>
/// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelBytes"/>.
/// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
/// is particularly useful for working with variable dimension inputs and outputs.
/// </param>
/// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
/// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
/// <param name="recursionLimit">Optional, specifies the Protobuf CodedInputStream recursion limit. Default value is 100.</param>
/// <remarks>
/// If the gpuDeviceId value is <see langword="null" /> the <see cref="P:MLContext.GpuDeviceId"/> value will be used if it is not <see langword="null" />.
/// </remarks>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
Stream modelBytes,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false,
int recursionLimit = 100)
{
var (env, gpuDeviceIdToUse, fallbackToCpuToUse) = GetGpuDeviceId(catalog, gpuDeviceId, fallbackToCpu);
return new OnnxScoringEstimator(env, outputColumnNames, inputColumnNames, modelBytes, gpuDeviceIdToUse, fallbackToCpuToUse, shapeDictionary: shapeDictionary, recursionLimit);
}
/// <summary>
/// Create <see cref="DnnImageFeaturizerEstimator"/>, which applies one of the pre-trained DNN models in
/// <see cref="DnnImageModelSelector"/> to featurize an image.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">The name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="modelFactory">An extension method on the <see cref="DnnImageModelSelector"/> that creates a chain of two
/// <see cref="OnnxScoringEstimator"/> (one for preprocessing and one with a pretrained image DNN) with specific models
/// included in a package together with that extension method.</param>
/// <param name="inputColumnName">Name of column to transform.
/// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static DnnImageFeaturizerEstimator DnnFeaturizeImage(this TransformsCatalog catalog,
string outputColumnName,
Func<DnnImageFeaturizerInput, EstimatorChain<ColumnCopyingTransformer>> modelFactory,
string inputColumnName = null)
=> new DnnImageFeaturizerEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, modelFactory, inputColumnName);
private static (Runtime.IHostEnvironment, int?, bool) GetGpuDeviceId(TransformsCatalog catalog, int? gpuDeviceId, bool fallbackToCpu)
{
Runtime.IHostEnvironment env = CatalogUtils.GetEnvironment(catalog);
if (gpuDeviceId == null && env is Runtime.IHostEnvironmentInternal localEnvironment && localEnvironment.GpuDeviceId != null)
{
gpuDeviceId = localEnvironment.GpuDeviceId;
fallbackToCpu = localEnvironment.FallbackToCpu;
}
return (env, gpuDeviceId, fallbackToCpu);
}
}
}
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