Hello,
i switched from tensorflow to fastai for my C# project and now i am facing a small problem.
I used the Resnet34 Model for training in Python with imagenet_normalisation of my databunch.
data = ImageDataBunch.from_folder(path, train=".", bs = bs, size=256, num_workers=6, valid_pct=0.2, device=torch.device('cuda'))
data.normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet34, metrics=error_rate)
learn.model.cuda()
which i transform after learning to the onnx format
learn.model.eval()
x = torch.randn(1,3,256,256, requires_grad=False).cuda()
torch_out = torch.onnx._export(learn.model, x, stage-5-5-256.onnx", export_params=True)
But when i use it with my C# code i get negative probabilities for my two classes (-0.9087, 1.1822) which i think come from the wrong image preperation.
The code for loading the onnx-Model:
string inputName = "input_1";
string outputName = "dense_1";
var onnxPipeline = mLContext.Transforms.ResizeImages(resizing:
ImageResizingEstimator.ResizingKind.Fill, outputColumnName: inputName, imageWidth: ImageSettings.imageWidth, imageHeight: ImageSettings.imageHeight, inputColumnName: nameof(ImageInputData.Image))
.Append(mLContext.Transforms.ExtractPixels(outputColumnName: inputName, interleavePixelColors: true, scaleImage: 1 / 255f))
.Append(mLContext.Transforms.ApplyOnnxModel(outputColumnName: outputName, inputColumnName: inputName, modelFile: onnxModelPath));
var emptyData = mLContext.Data.LoadFromEnumerable(new List<ImageInputData>());
var onnxModel = onnxPipeline.Fit(emptyData);
return onnxModel;
For the tensorflow-model this code works fine, and my suspicion is, that there is a problem with the “.ExctractPixels” part. I think there might be some transformations to the Image i have to add there, but i dont know which.
Does anyone have some experience with the .net ml framework, or some clues of what i am missing?
Any help would be appreciated!
I hope the code is readable and i am sorry for the grammatical errors this topic might have.
