ML.net -在ML.net中使用ONNX时出错

Question

在我的ML.net核心应用程序中使用.NET，我正在尝试使用一个KERAS模型，并将其导出到ONNX文件中。这是我的代码：

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Onnx;

public void getmodel(mydata[] data1)
{
    string modelPath = "C:\\MyStuff\\ONNXtest.onnx";
    MLContext mlContext = new MLContext();

    IDataView data = mlContext.Data.LoadFromEnumerable<mydata>(data1);

    OnnxScoringEstimator pipeline = mlContext.Transforms.ApplyOnnxModel(new[] { "output" }, new[] { "input" }, modelPath);

    IEnumerable<mydata> testdata = mlContext.Data.CreateEnumerable<mydata>(data, reuseRowObject: true);
    foreach (mydata row in testdata)
    {
        System.Diagnostics.Debug.WriteLine(row.myval[0]);
        System.Diagnostics.Debug.WriteLine(row.myval[1]);
        System.Diagnostics.Debug.WriteLine(row.myval[2]);
        System.Diagnostics.Debug.WriteLine(row.myval[3]);
        System.Diagnostics.Debug.WriteLine(row.myval[4]);
    }

    OnnxTransformer test = pipeline.Fit(data);

    IDataView transformedValues = test.Transform(data);

    IEnumerable<float[]> results = transformedValues.GetColumn<float[]>("output");

    double result = Convert.ToDouble(results.ElementAtOrDefault(0).GetValue(0));

}

mydata类如下所示：

public class mydata
{
     [VectorType(1,5,1)]
     [ColumnName("input")]   
     public float[] myval { get; set; }
}

我希望在模型中输入5个值，并查看"System.Diagnostics.Debug.WriteLine“输出，看起来一切都正常，IDataView数据包含5个用于输入模型的值。但是，"System.ArgumentOutOfRangeException“行在Microsoft.ML.OnnxTransformer.dll错误中会导致pipeline.Fit(data)。

下面还有在python中训练和导出LSTM的代码：

regressor = Sequential()
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1),name ='input'))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))

regressor.add(Dense(units = 1,name ='output'))
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)

from winmltools import convert_keras
model_onnx = convert_keras(regressor, 7, name='sequential_7')
from winmltools.utils import save_model
save_model(model_onnx, 'C:\\MyStuff\\ONNXtest.onnx')

这是导出的网络的摘要：

Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input (LSTM)                 (None, 5, 50)             10400     
_________________________________________________________________
dropout_25 (Dropout)         (None, 5, 50)             0         
_________________________________________________________________
lstm_21 (LSTM)               (None, 5, 50)             20200     
_________________________________________________________________
dropout_26 (Dropout)         (None, 5, 50)             0         
_________________________________________________________________
lstm_22 (LSTM)               (None, 5, 50)             20200     
_________________________________________________________________
dropout_27 (Dropout)         (None, 5, 50)             0         
_________________________________________________________________
lstm_23 (LSTM)               (None, 50)                20200     
_________________________________________________________________
dropout_28 (Dropout)         (None, 50)                0         
_________________________________________________________________
output (Dense)               (None, 1)                 51        
=================================================================
Total params: 71,051
Trainable params: 71,051
Non-trainable params: 0

如果我只使用Python中的模型，它就能很好地工作，一种非常类似的设置(实际上，我认为它是同一段代码)在ML.net中很好地工作。但是现在我甚至不确定我的错误是在python上还是在c#端。有人能帮我找出如何处理这个错误吗？

Answer 1

据我所知，这是C#方面的一个错误，因为ML.NET不允许多维输入数组。VectorTypeAttribute(dims)的文档在这一点上还不清楚，但似乎是一个很快就会解决的问题。请参阅：https://github.com/dotnet/machinelearning/issues/5273