Dealing with Semantic Segmentation

**I loaded a "deeplabv3_mnv2_dm05_pascal_trainaug" model from TensorFlow Model Zoo, which is a .pb file. I've met these problems:**
**1. As far as I can see, the input image and the output image of deeplabv3 should both be an unknown-size image. Does ML.NET support unknow-size input and output? I tried but failed. So I have to add a "ResizeImages" before "ExtractPixels". Below is my pipeline code:**
`var pipeline =mlContext.Transforms.LoadImages(outputColumnName:"imageinput", ImagesFolderPath,nameof(ImageData.ImagePath))
.Append(mlContext.Transforms.ResizeImages(outputColumnName: "imageinput", inputColumnName: "imageinput",imageHeight:ImageProperty.ImageHeight,imageWidth:ImageProperty.ImageWidth))
.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "ImageTensor", inputColumnName: "imageinput",outputAsFloatArray:false))
.Append(mlContext.Transforms.LoadImages(outputColumnName: "annotinput", ImagesFolderPath, nameof(ImageData.AnnotPath)))
.Append(mlContext.Transforms.ResizeImages(outputColumnName: "annotinput", inputColumnName: "annotinput", imageHeight:ImageProperty.ImageHeight,imageWidth:ImageProperty.ImageWidth))
.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "SemanticPredictions", inputColumnName: "annotinput", outputAsFloatArray: false))
.Append(tensorFlowModel.ScoreTensorFlowModel( outputColumnNames:
new[] { "SemanticPredictions" },inputColumnNames: new[] { "ImageTensor" }, addBatchDimensionInput: true));`
2. **I got this error message while predicting:**
The first dimension of paddings must be the rank of inputs[3,2] [1,513,513,3] [[{{node Pad}}]]”

**And here is my prediction class:**
` class SemanticPredictions
{
[ColumnName(nameof(SemanticPredictions))]
[VectorType(513,513)]
public Int64[] Output { get; set; }
}`

I really need your help.
Best Regards!

Inizia riproducendo la pipeline segnalata con il modello deeplabv3_mnv2_dm05_pascal_trainaug .pb, inclusi i passaggi ResizeImages ed ExtractPixels, e analizza l’errore di Pad durante la predizione. Confronta le forme di input e output con la dichiarazione VectorType(513,513) della classe SemanticPredictions. Il lavoro è completato quando la gestione supportata dei tensori di dimensione sconosciuta e la causa dell’errore di forma segnalato sono documentate o corrette.

Scritto dal modello di indicizzazione a partire dal testo della issue.