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Allow user to overwrite unknown shapes loaded from ONNX model #3963

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Merged
merged 3 commits into from
Aug 6, 2019
Merged

Allow user to overwrite unknown shapes loaded from ONNX model #3963

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ced8c42
Allow user to specify ONNX shape
wschin Jul 2, 2019
ab6424c
Address comments
wschin Jul 10, 2019
5b923e1
Merge branch 'master' into add-shape
wschin Aug 5, 2019
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82 changes: 80 additions & 2 deletions src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
// See the LICENSE file in the project root for more information.

using System;
using System.Collections.Generic;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
using Microsoft.ML.Transforms.Onnx;
Expand Down Expand Up @@ -36,6 +37,34 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
bool fallbackToCpu = false)
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), modelFile, gpuDeviceId, fallbackToCpu);

/// <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.
/// </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.
/// </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 shape should be used to over those loaded from <paramref name="modelFile"/>.</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">
/// <![CDATA[
/// [!code-csharp[ApplyOnnxModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs)]
/// ]]>
/// </format>
/// </example>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string modelFile,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), modelFile, gpuDeviceId, fallbackToCpu,
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@artidoro artidoro Jul 9, 2019 •
edited by wschin
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I think this should be a breaking change. I am afraid API compat is not active for this assembly. I will open an issue about it and fix it in the meantime.
Unfortunately the signature of the function changes if you add another parameter. The only way to do this change without being a breaking change is to add a new overload with the new parameter. #Resolved

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There is already a PR on reactivating API compat, could you approve it and rebase afterwards?

In reply to: 301718819 [](ancestors = 301718819)

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Sure. Thanks.

In reply to: 301768225 [](ancestors = 301768225,301718819)

shapeDictionary: shapeDictionary);

/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
/// </summary>
Expand All @@ -58,7 +87,53 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
string modelFile,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName }, modelFile, gpuDeviceId, fallbackToCpu);
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName },
modelFile, gpuDeviceId, fallbackToCpu);

/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
/// </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 shape should be used to over those loaded from <paramref name="modelFile"/>.</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">
/// <![CDATA[
/// [!code-csharp[ApplyOnnxModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs)]
/// ]]>
/// </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)
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName },
modelFile, gpuDeviceId, fallbackToCpu, shapeDictionary: shapeDictionary);

/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
/// </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>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
string modelFile,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames,
modelFile, gpuDeviceId, fallbackToCpu);

/// <summary>
/// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
Expand All @@ -67,15 +142,18 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
/// <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 shape should be used to over those loaded from <paramref name="modelFile"/>.</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>
public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
string modelFile,
IDictionary<string, int[]> shapeDictionary,
int? gpuDeviceId = null,
bool fallbackToCpu = false)
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames, modelFile, gpuDeviceId, fallbackToCpu);
=> new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames,
modelFile, gpuDeviceId, fallbackToCpu, shapeDictionary: shapeDictionary);

/// <summary>
/// Create <see cref="DnnImageFeaturizerEstimator"/>, which applies one of the pre-trained DNN models in
Expand Down
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