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Add a DNN Image Featurizer Transform Estimator (#1447)
Added a DNN Image Featurizer using pretrained models.
1 parent 08947ef commit f92c90d

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‎Microsoft.ML.sln

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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Recommender", "src\Microsoft.ML.Recommender\Microsoft.ML.Recommender.csproj", "{C8E1772B-DFD9-4A4D-830D-6AAB1C668BB3}"
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.DnnImageFeaturizer.ResNet18", "src\Microsoft.ML.DnnImageFeaturizer.ResNet18\Microsoft.ML.DnnImageFeaturizer.ResNet18.csproj", "{9222FC9D-599A-49A5-B685-08CC9A5C81D7}"
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.DnnImageFeaturizer.AlexNet", "src\Microsoft.ML.DnnImageFeaturizer.AlexNet\Microsoft.ML.DnnImageFeaturizer.AlexNet.csproj", "{6C29AA9B-054B-4762-BEA5-D305B932AA80}"
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.DnnImageFeaturizer.ResNet50", "src\Microsoft.ML.DnnImageFeaturizer.ResNet50\Microsoft.ML.DnnImageFeaturizer.ResNet50.csproj", "{4805129D-78C8-46D4-9519-0AD9B0574D6D}"
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.DnnImageFeaturizer.ResNet101", "src\Microsoft.ML.DnnImageFeaturizer.ResNet101\Microsoft.ML.DnnImageFeaturizer.ResNet101.csproj", "{DB7CEB5E-8BE6-48A7-87BE-B91D9AE96F71}"
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‎pkg/Microsoft.ML.DnnImageFeaturizer.AlexNet/Microsoft.ML.DnnImageFeaturizer.AlexNet.nupkgproj

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<Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<PackageDescription>ML.NET component for pretrained AlexNet image featurization</PackageDescription>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.nupkgproj" />
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<Content Include="$(ObjDir)\DnnImageModels\AlexNetOnnx\AlexNet.onnx" Pack="true" PackagePath="tools\DnnImageModels\AlexNetOnnx" />
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‎pkg/Microsoft.ML.DnnImageFeaturizer.AlexNet/Microsoft.ML.DnnImageFeaturizer.AlexNet.symbols.nupkgproj

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<Project DefaultTargets="Pack">
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<Import Project="Microsoft.ML.DnnImageFeaturizer.AlexNet.nupkgproj" />
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‎pkg/Microsoft.ML.DnnImageFeaturizer.ResNet101/Microsoft.ML.DnnImageFeaturizer.ResNet101.nupkgproj

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<Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<PackageDescription>ML.NET component for pretrained ResNet101 image featurization</PackageDescription>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.nupkgproj" />
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‎pkg/Microsoft.ML.DnnImageFeaturizer.ResNet101/Microsoft.ML.DnnImageFeaturizer.ResNet101.symbols.nupkgproj

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<Import Project="Microsoft.ML.DnnImageFeaturizer.ResNet101.nupkgproj" />
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‎pkg/Microsoft.ML.DnnImageFeaturizer.ResNet18/Microsoft.ML.DnnImageFeaturizer.ResNet18.nupkgproj

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<Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<PackageDescription>ML.NET component for pretrained ResNet18 image featurization</PackageDescription>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.nupkgproj" />
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<ItemGroup>
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<Content Include="$(ObjDir)\DnnImageModels\ResNet18Onnx\ResNet18.onnx" Pack="true" PackagePath="tools\DnnImageModels\ResNet18Onnx" />
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<Content Include="$(ObjDir)\DnnImageModels\ResNetPrepOnnx\ResNetPreprocess.onnx" Pack="true" PackagePath="tools\DnnImageModels\ResNetPrepOnnx" />
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<Content Include="..\common\DnnImageFeaturizer.props" Pack="true" PackagePath="build\netstandard2.0\$(MSBuildProjectName).props" />
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‎pkg/Microsoft.ML.DnnImageFeaturizer.ResNet18/Microsoft.ML.DnnImageFeaturizer.ResNet18.symbols.nupkgproj

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<Project DefaultTargets="Pack">
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<Import Project="Microsoft.ML.DnnImageFeaturizer.ResNet18.nupkgproj" />
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‎pkg/Microsoft.ML.DnnImageFeaturizer.ResNet50/Microsoft.ML.DnnImageFeaturizer.ResNet50.nupkgproj

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<Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<PackageDescription>ML.NET component for pretrained ResNet50 image featurization</PackageDescription>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.nupkgproj" />
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</ItemGroup>
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<ItemGroup>
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<Content Include="$(ObjDir)\DnnImageModels\ResNet50Onnx\ResNet50.onnx" Pack="true" PackagePath="tools\DnnImageModels\ResNet50Onnx" />
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<Content Include="$(ObjDir)\DnnImageModels\ResNetPrepOnnx\ResNetPreprocess.onnx" Pack="true" PackagePath="tools\DnnImageModels\ResNetPrepOnnx" />
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<Content Include="..\common\DnnImageFeaturizer.props" Pack="true" PackagePath="build\netstandard2.0\$(MSBuildProjectName).props" />
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</ItemGroup>
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‎pkg/Microsoft.ML.DnnImageFeaturizer.ResNet50/Microsoft.ML.DnnImageFeaturizer.ResNet50.symbols.nupkgproj

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<Project DefaultTargets="Pack">
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<Import Project="Microsoft.ML.DnnImageFeaturizer.ResNet50.nupkgproj" />
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‎pkg/common/DnnImageFeaturizer.props

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<?xml version="1.0" encoding="utf-8"?>
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<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
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<ItemGroup>
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<Content Include="$(MSBuildThisFileDirectory)..\..\tools\DnnImageModels\**\*.*">
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<Link>DnnImageModels\%(RecursiveDir)%(Filename)%(Extension)</Link>
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</Content>
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</ItemGroup>
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</Project>

‎src/Microsoft.ML.DnnImageFeaturizer.AlexNet/AlexNetExtension.cs

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// Licensed to the .NET Foundation under one or more agreements.
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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using Microsoft.ML.Runtime;
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using Microsoft.ML.Runtime.Data;
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using System.IO;
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using System.Reflection;
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namespace Microsoft.ML.Transforms
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{
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/// <summary>
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/// This is an extension method to be used with the <see cref="DnnImageFeaturizerEstimator"/> in order to use a pretrained AlexNet model.
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/// The NuGet containing this extension is also guaranteed to include the binary model file.
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/// </summary>
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public static class AlexNetExtension
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{
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/// <summary>
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/// Returns an estimator chain with the two corresponding models (a preprocessing one and a main one) required for the AlexNet pipeline.
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/// Also includes the renaming ColumnsCopyingTransforms required to be able to use arbitrary input and output column names.
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/// This assumes both of the models are in the same location as the file containing this method, which they will be if used through the NuGet.
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/// This should be the default way to use AlexNet if importing the model from a NuGet.
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/// </summary>
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public static EstimatorChain<ColumnsCopyingTransformer> AlexNet(this DnnImageModelSelector dnnModelContext, IHostEnvironment env, string inputColumn, string outputColumn)
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{
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return AlexNet(dnnModelContext, env, inputColumn, outputColumn, Path.Combine(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location), "DnnImageModels"));
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}
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/// <summary>
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/// This allows a custom model location to be specified. This is useful is a custom model is specified,
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/// or if the model is desired to be placed or shipped separately in a different folder from the main application. Note that because Onnx models
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/// must be in a directory all by themsleves for the OnnxTransform to work, this method appends a AlexNetOnnx/AlexNetPrepOnnx subdirectory
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/// to the passed in directory to prevent having to make that directory manually each time.
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/// </summary>
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public static EstimatorChain<ColumnsCopyingTransformer> AlexNet(this DnnImageModelSelector dnnModelContext, IHostEnvironment env, string inputColumn, string outputColumn, string modelDir)
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{
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var modelChain = new EstimatorChain<ColumnsCopyingTransformer>();
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var inputRename = new ColumnsCopyingEstimator(env, new[] { (inputColumn, "OriginalInput") });
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var midRename = new ColumnsCopyingEstimator(env, new[] { ("PreprocessedInput", "Input140") });
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var endRename = new ColumnsCopyingEstimator(env, new[] { ("Dropout234_Output_0", outputColumn) });
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// There are two estimators created below. The first one is for image preprocessing and the second one is the actual DNN model.
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var prepEstimator = new OnnxScoringEstimator(env, Path.Combine(modelDir, "AlexNetPrepOnnx", "AlexNetPreprocess.onnx"), new[] { "OriginalInput" }, new[] { "PreprocessedInput" });
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var mainEstimator = new OnnxScoringEstimator(env, Path.Combine(modelDir, "AlexNetOnnx", "AlexNet.onnx"), new[] { "Input140" }, new[] { "Dropout234_Output_0" });
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modelChain = modelChain.Append(inputRename);
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var modelChain2 = modelChain.Append(prepEstimator);
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modelChain = modelChain2.Append(midRename);
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modelChain2 = modelChain.Append(mainEstimator);
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modelChain = modelChain2.Append(endRename);
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return modelChain;
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}
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}
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}

‎src/Microsoft.ML.DnnImageFeaturizer.AlexNet/Microsoft.ML.DnnImageFeaturizer.AlexNet.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<IncludeInPackage>Microsoft.ML.DnnImageFeaturizer.AlexNet</IncludeInPackage>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.csproj" />
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</ItemGroup>
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</Project>

‎src/Microsoft.ML.DnnImageFeaturizer.ResNet101/Microsoft.ML.DnnImageFeaturizer.ResNet101.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<IncludeInPackage>Microsoft.ML.DnnImageFeaturizer.ResNet101</IncludeInPackage>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.csproj" />
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</ItemGroup>
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</Project>

‎src/Microsoft.ML.DnnImageFeaturizer.ResNet101/ResNet101Extension.cs

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// Licensed to the .NET Foundation under one or more agreements.
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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using Microsoft.ML.Runtime;
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using Microsoft.ML.Runtime.Data;
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using System.IO;
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using System.Reflection;
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namespace Microsoft.ML.Transforms
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{
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/// <summary>
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/// This is an extension method to be used with the <see cref="DnnImageFeaturizerEstimator"/> in order to use a pretrained ResNet101 model.
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/// The NuGet containing this extension is also guaranteed to include the binary model file.
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/// </summary>
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public static class ResNet101Extension
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{
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/// <summary>
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/// Returns an estimator chain with the two corresponding models (a preprocessing one and a main one) required for the ResNet pipeline.
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/// Also includes the renaming ColumnsCopyingTransforms required to be able to use arbitrary input and output column names.
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/// This assumes both of the models are in the same location as the file containing this method, which they will be if used through the NuGet.
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/// This should be the default way to use ResNet101 if importing the model from a NuGet.
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/// </summary>
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public static EstimatorChain<ColumnsCopyingTransformer> ResNet101(this DnnImageModelSelector dnnModelContext, IHostEnvironment env, string inputColumn, string outputColumn)
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{
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return ResNet101(dnnModelContext, env, inputColumn, outputColumn, Path.Combine(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location), "DnnImageModels"));
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}
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/// <summary>
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/// This allows a custom model location to be specified. This is useful is a custom model is specified,
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/// or if the model is desired to be placed or shipped separately in a different folder from the main application. Note that because Onnx models
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/// must be in a directory all by themsleves for the OnnxTransform to work, this method appends a ResNet101Onnx/ResNetPrepOnnx subdirectory
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/// to the passed in directory to prevent having to make that directory manually each time.
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/// </summary>
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public static EstimatorChain<ColumnsCopyingTransformer> ResNet101(this DnnImageModelSelector dnnModelContext, IHostEnvironment env, string inputColumn, string outputColumn, string modelDir)
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{
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var modelChain = new EstimatorChain<ColumnsCopyingTransformer>();
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var inputRename = new ColumnsCopyingEstimator(env, new[] { (inputColumn, "OriginalInput") });
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var midRename = new ColumnsCopyingEstimator(env, new[] { ("PreprocessedInput", "Input1600") });
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var endRename = new ColumnsCopyingEstimator(env, new[] { ("Pooling2286_Output_0", outputColumn) });
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// There are two estimators created below. The first one is for image preprocessing and the second one is the actual DNN model.
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var prepEstimator = new OnnxScoringEstimator(env, Path.Combine(modelDir, "ResNetPrepOnnx", "ResNetPreprocess.onnx"), new[] { "OriginalInput" }, new[] { "PreprocessedInput" });
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var mainEstimator = new OnnxScoringEstimator(env, Path.Combine(modelDir, "ResNet101Onnx", "ResNet101.onnx"), new[] { "Input1600" }, new[] { "Pooling2286_Output_0" });
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modelChain = modelChain.Append(inputRename);
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var modelChain2 = modelChain.Append(prepEstimator);
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modelChain = modelChain2.Append(midRename);
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modelChain2 = modelChain.Append(mainEstimator);
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modelChain = modelChain2.Append(endRename);
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return modelChain;
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}
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}
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}

‎src/Microsoft.ML.DnnImageFeaturizer.ResNet18/Microsoft.ML.DnnImageFeaturizer.ResNet18.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<IncludeInPackage>Microsoft.ML.DnnImageFeaturizer.ResNet18</IncludeInPackage>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.csproj" />
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</ItemGroup>
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</Project>

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