public async Task <IDictionary <string, float> > EvaluateAsync(SoftwareBitmap bitmap) { var videoFrame = VideoFrame.CreateWithSoftwareBitmap(bitmap); var imageFeatureValue = ImageFeatureValue.CreateFromVideoFrame(videoFrame); var input = new SmartInkModelInput() { data = imageFeatureValue }; var output = new SmartInkModelOutput(); _binding.Bind("data", input.data); _binding.Bind("classLabel", output.ClassLabel); _binding.Bind("loss", output.Loss); LearningModelEvaluationResult result = await _session.EvaluateAsync(_binding, "0"); output.ClassLabel = result.Outputs["classLabel"] as TensorString;//).GetAsVectorView()[0]; output.Loss = result.Outputs["loss"] as IList <IDictionary <string, float> >; var dict = new Dictionary <string, float>(); foreach (var key in output.Loss[0].Keys) { dict.Add(key, output.Loss[0][key]); } return(dict); }
private async Task LoadAndEvaluateModelAsync(VideoFrame _inputFrame, string _modelFileName) { LearningModelBinding _binding = null; VideoFrame _outputFrame = null; LearningModelSession _session; try { //Load and create the model if (_model == null) { var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///{_modelFileName}")); _model = await LearningModel.LoadFromStorageFileAsync(modelFile); } // Create the evaluation session with the model _session = new LearningModelSession(_model); // Get input and output features of the model var inputFeatures = _model.InputFeatures.ToList(); var outputFeatures = _model.OutputFeatures.ToList(); // Create binding and then bind input/ output features _binding = new LearningModelBinding(_session); _inputImageDescriptor = inputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Tensor) as TensorFeatureDescriptor; _outputTensorDescriptor = outputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Tensor) as TensorFeatureDescriptor; TensorFloat outputTensor = TensorFloat.Create(_outputTensorDescriptor.Shape); ImageFeatureValue imageTensor = ImageFeatureValue.CreateFromVideoFrame(_inputFrame); // Bind inputs +outputs _binding.Bind(_inputImageDescriptor.Name, imageTensor); _binding.Bind(_outputTensorDescriptor.Name, outputTensor); // Evaluate and get the results var results = await _session.EvaluateAsync(_binding, "test"); Debug.WriteLine("ResultsEvaluated: " + results.ToString()); var outputTensorList = outputTensor.GetAsVectorView(); var resultsList = new List <float>(outputTensorList.Count); for (int i = 0; i < outputTensorList.Count; i++) { resultsList.Add(outputTensorList[i]); } } catch (Exception ex) { Debug.WriteLine($"error: {ex.Message}"); _model = null; } }
public async Task<ShuffleNetOutput> EvaluateAsync(ShuffleNetInput input) { binding.Bind("gpu_0/data_0", input.gpu_00data_0); var result = await session.EvaluateAsync(binding, "0"); var output = new ShuffleNetOutput(); output.gpu_00softmax_1 = result.Outputs["gpu_0/softmax_1"] as TensorFloat; return output; }
public async Task <MultiObjectDetectionModelv8Output> EvaluateAsync(MultiObjectDetectionModelv8Input input) { var output = new MultiObjectDetectionModelv8Output(); if (input != null) { binding.Bind("image", input.Image); var result = await session.EvaluateAsync(binding, "0"); output.Grid = result.Outputs["grid"] as TensorFloat; } else { throw new NullReferenceException(); } return(output); }
public async Task <TensorFloat> EvaluateAsync(TensorFloat input) { binding.Bind(inName, input); binding.Bind(outName, TensorFloat.Create(new long[] { 1, 1, InHeight, OutHeight })); var result = await session.EvaluateAsync(binding, inName); return(result.Outputs[outName] as TensorFloat); }
public async Task <AlexNetOutput> EvaluateAsync(AlexNetInput input) { binding.Bind("data_0", input.data_0); var result = await session.EvaluateAsync(binding, "0"); var output = new AlexNetOutput(); output.prob_1 = result.Outputs["prob_1"] as TensorFloat; return(output); }
public async Task <ImageClassifierOutput> EvaluateAsync(ImageClassifierInput input) { binding.Bind("modelInput", input.modelInput); var result = await session.EvaluateAsync(binding, "0"); var output = new ImageClassifierOutput(); output.modelOutput = result.Outputs["modelOutput"] as TensorFloat; return(output); }
public async Task <HandwriteOutput> EvaluateAsync(HandwriteInput input) { binding.Bind("Input3", input.Input3); var result = await session.EvaluateAsync(binding, "0"); var output = new HandwriteOutput(); output.Plus214_Output_0 = result.Outputs["Plus214_Output_0"] as TensorFloat; return(output); }
public async Task <mnistOutput> EvaluateAsync(mnistInput input) { binding.Bind("fc1x", input.fc1x); var result = await session.EvaluateAsync(binding, "0"); var output = new mnistOutput(); output.activation3y = result.Outputs["activation3y"] as TensorFloat; return(output); }
public async Task <mnistOutput> EvaluateAsync(mnistInput input) { binding.Bind("port", input.port); var result = await session.EvaluateAsync(binding, "0"); var output = new mnistOutput(); output.dense3port = result.Outputs["dense3port"] as TensorFloat; return(output); }
public async Task <FER_Emotion_RecognitionOutput> EvaluateAsync(FER_Emotion_RecognitionInput input) { binding.Bind("Input3", input.Input3); var result = await session.EvaluateAsync(binding, "0"); var output = new FER_Emotion_RecognitionOutput(); output.Plus692_Output_0 = result.Outputs["Plus692_Output_0"] as TensorFloat; return(output); }
public async Task <emotion_ferplusOutput> EvaluateAsync(emotion_ferplusInput input) { binding.Bind("Input338", input.Input338); var result = await session.EvaluateAsync(binding, "0"); var output = new emotion_ferplusOutput(); output.Plus692_Output_0 = result.Outputs["Plus692_Output_0"] as TensorFloat; return(output); }
public async Task <IList <PredictionModel> > PredictImageAsync(VideoFrame image) { var imageFeature = ImageFeatureValue.CreateFromVideoFrame(image); var bindings = new LearningModelBinding(this.Session); bindings.Bind("data", imageFeature); var result = await Session.EvaluateAsync(bindings, ""); return(Postprocess(result.Outputs["model_outputs0"] as TensorFloat)); }
public async Task <rain_princessOutput> EvaluateAsync(rain_princessInput input) { binding.Bind("input1", input.input1); var result = await session.EvaluateAsync(binding, "0"); var output = new rain_princessOutput(); output.output1 = result.Outputs["output1"] as TensorFloat; return(output); }
public async Task <Output> EvaluateAsync(Input input) { binding.Bind("data", input.data); var result = await session.EvaluateAsync(binding, "0"); var output = new Output(); output.mobilenetv20_output_flatten0_reshape0 = result.Outputs["mobilenetv20_output_flatten0_reshape0"] as TensorFloat; return(output); }
public async Task <Output> EvaluateAsync(Input input) { binding.Bind("image", input.image); var result = await session.EvaluateAsync(binding, "0"); var output = new Output(); output.grid = result.Outputs["grid"] as TensorFloat; return(output); }
public async Task <modelOutput> EvaluateAsync(modelInput input) { binding.Bind("data_0", input.data_0); var result = await session.EvaluateAsync(binding, "0"); var output = new modelOutput(); output.softmaxout_1 = result.Outputs["softmaxout_1"] as TensorFloat; return(output); }
public async Task <model1Output> EvaluateAsync(model1Input input) { binding.Bind("dense_1_input", input.dense_1_input); var result = await session.EvaluateAsync(binding, "0"); var output = new model1Output(); output.activation_10Softmax00 = result.Outputs["activation_1/Softmax:0"] as TensorFloat; return(output); }
public async Task <mnistOutput> EvaluateAsync(mnistInput input) { binding.Bind("input_placeholer:0", input.input_placeholer00); var result = await session.EvaluateAsync(binding, "0"); var output = new mnistOutput(); output.stage_30mid_conv70BiasAdd00 = result.Outputs["stage_3/mid_conv7/BiasAdd:0"] as TensorFloat; return(output); }
public async Task <resnet100Output> EvaluateAsync(resnet100Input input) { binding.Bind("data", input.data); var result = await session.EvaluateAsync(binding, "0"); var output = new resnet100Output(); output.fc1 = result.Outputs["fc1"] as TensorFloat; return(output); }
public async Task <pointilismOutput> EvaluateAsync(pointilismInput input) { binding.Bind("input1", input.input1); var result = await session.EvaluateAsync(binding, "0"); var output = new pointilismOutput(); output.output1 = result.Outputs["output1"] as TensorFloat; return(output); }
public async Task <modelOutput> EvaluateAsync(modelInput input) { binding.Bind("data", input.data); var result = await session.EvaluateAsync(binding, "0"); var output = new modelOutput(); output.model_outputs0 = result.Outputs["model_outputs0"] as TensorFloat; return(output); }
public async Task <BedroomOutput> EvaluateAsync(BedroomInput input) { binding.Bind("img_placeholder:0", input.img_placeholder00); var result = await session.EvaluateAsync(binding, "0"); var output = new BedroomOutput(); output.add_3700 = result.Outputs["add_3700"] as TensorFloat; return(output); }
public async Task <sthv2_tpnOutput> EvaluateAsync(sthv2_tpnInput input) { binding.Bind("input", input.input); var result = await session.EvaluateAsync(binding, "0"); var output = new sthv2_tpnOutput(); output.output = result.Outputs["output"] as TensorFloat; return(output); }
public async Task <modelOutput> EvaluateAsync(modelInput input) { binding.Bind("Input3", input.Input3); var result = await session.EvaluateAsync(binding, "0"); var output = new modelOutput(); output.Plus692_Output_0 = result.Outputs["Plus692_Output_0"] as TensorFloat; return(output); }
public async Task <Inceptionv3_convertedOutput> EvaluateAsync(Inceptionv3_convertedInput input) { binding.Bind("input_1_0", input.input_1_0); var result = await session.EvaluateAsync(binding, "0"); var output = new Inceptionv3_convertedOutput(); output.dense_2_Softmax_01 = result.Outputs["dense_2_Softmax_01"] as TensorFloat; return(output); }
public async Task <hangulOutput> EvaluateAsync(hangulInput input) { binding.Bind("input:0", input.input00); binding.Bind("keep_prob:0", input.keep_prob); var result = await session.EvaluateAsync(binding, "0"); var output = new hangulOutput(); output.output00 = result.Outputs["output:0"] as TensorFloat; return(output); }
public async Task <Output> EvaluateAsync(Input input) { binding.Bind("data", input.data); var result = await session.EvaluateAsync(binding, "0"); var output = new Output(); output.classLabel = result.Outputs["classLabel"] as TensorString; output.loss = result.Outputs["loss"] as IList <Dictionary <string, float> >; return(output); }
public async Task <SentimentPolarityOutput> EvaluateAsync(SentimentPolarityInput input) { binding.Bind("input", input.input); var result = await session.EvaluateAsync(binding, "0"); var output = new SentimentPolarityOutput(); output.classLabel = result.Outputs["classLabel"] as TensorString; output.classProbability = result.Outputs["classProbability"] as IList <Dictionary <string, float> >; return(output); }
public async Task <MyCustomVisionModelOutput> EvaluateAsync(MyCustomVisionModelInput input) { binding.Bind("data", input.Data); var result = await session.EvaluateAsync(binding, "0"); var output = new MyCustomVisionModelOutput(); output.ClassLabel = result.Outputs["classLabel"] as TensorString; output.Loss = result.Outputs["loss"] as IList <IDictionary <string, float> >; return(output); }