public static void Evaluate(this Function func, Dictionary <string, Value> arguments, Dictionary <string, Value> outputs, DeviceDescriptor computeDevice)
{
// Evaluate the rootFunction.
var argMap = new UnorderedMapVariableValuePtr();
foreach (var p in arguments)
{
var variable = func.Arguments.Where(v => string.Equals(v.Name, p.Key)).Single();
if (variable == null)
{
throw new KeyNotFoundException("No input variable '" + p.Key + "' found.");
}
argMap.Add(variable, p.Value);
}
var outMap = new UnorderedMapVariableValuePtr();
foreach (var p in outputs)
{
var variable = func.Outputs.Where(v => string.Equals(v.Name, p.Key)).Single();
if (variable == null)
{
throw new KeyNotFoundException("No output variable '" + p.Key + "' found.");
}
outMap.Add(variable, p.Value);
}
func.Evaluate(argMap, outMap, computeDevice);
foreach (var p in outMap)
{
outputs[p.Key.Name] = p.Value;
}
}
/// <summary>
/// Evaluate the model.
/// </summary>
/// <param name="featureData">The data to evaluate the model on</param>
/// <param name="model">The model to use (defaults to trained model)</param>
/// <returns>The output of the model</returns>
public IList <IList <float> > Evaluate(float[][] featureData, CNTK.Function model = null)
{
// get the current model
if (model == null)
{
model = this.Model;
}
// get the current batch
var featureBatch = (SequenceLength == 1) ?
features.GetBatch(featureData, 0, featureData.Length) :
features.GetSequenceBatch(SequenceLength, featureData, 0, featureData.Length);
// evaluate the model
var inputs = new Dictionary <CNTK.Variable, CNTK.Value>()
{
{ features, featureBatch }
};
var outputs = new Dictionary <CNTK.Variable, CNTK.Value>()
{
{ model.Output, null }
};
model.Evaluate(inputs, outputs, NetUtil.CurrentDevice);
// return result
var result = outputs[model.Output];
var outputData = result.GetDenseData <float>(model.Output);
return(outputData);
}
/// <summary>
/// Reshapes the dataset to new specified shape.
/// </summary>
/// <param name="shape">The new shape on the dataset.</param>
/// <exception cref="System.ArgumentException"></exception>
public void Reshape(params int[] shape)
{
CNTK.Variable features = CNTK.Variable.InputVariable(new int[] { Shape[1], Shape[0] }, DataType.Float);
int total = Shape.Aggregate((d1, d2) => d1 * d2);
if (shape.Aggregate((d1, d2) => d1 * d2) != total)
{
throw new ArgumentException(string.Format("Cannot reshape array of size {0} into shape {1}", total, string.Concat(shape)));
}
//shape.ToList().Insert(0, Data.Count);
CNTK.Variable outfeatures = CNTK.Variable.InputVariable(shape, DataType.Float);
//Variable outfeatures = new Variable(shape, VariableKind.Output, DataType.Float, null, false, new AxisVector(), false, "", "");
CNTK.Function reshapeFunc = CNTKLib.Reshape(features, shape);
List <float> vectorData = new List <float>();
foreach (var item in Data)
{
vectorData.AddRange(item);
}
Value v = Value.CreateBatch <float>(Shape, vectorData, GlobalParameters.Device);
Dictionary <CNTK.Variable, Value> inputs = new Dictionary <CNTK.Variable, Value>()
{
{ features, v }
};
Dictionary <CNTK.Variable, Value> outputs = new Dictionary <CNTK.Variable, Value>()
{
{ outfeatures, null }
};
reshapeFunc.Evaluate(inputs, outputs, GlobalParameters.Device);
var res = outputs[outfeatures].GetDenseData <float>(outfeatures);
Data = new List <List <float> >();
foreach (var item in res)
{
Data.Add(item.ToList());
}
}
public static void Evaluate(this Function func, Dictionary <Variable, Value> arguments, Dictionary <Variable, Value> outputs, DeviceDescriptor computeDevice)
{
// Evaluate the rootFunction.
var argMap = new UnorderedMapVariableValuePtr();
foreach (var p in arguments)
{
argMap.Add(p.Key, p.Value);
}
var outMap = new UnorderedMapVariableValuePtr();
foreach (var p in outputs)
{
outMap.Add(p.Key, p.Value);
}
func.Evaluate(argMap, outMap, computeDevice);
foreach (var p in outMap)
{
outputs[p.Key] = p.Value;
}
}
/// <summary>
/// Predicts the specified BMP.
/// </summary>
/// <param name="bmp">The image in bitmap format.</param>
/// <param name="topK">The top k accurate result to return.</param>
/// <returns></returns>
public List <PredResult> Predict(Bitmap bmp, int topK = 3)
{
try
{
Variable inputVar = modelFunc.Arguments[0];
NDShape inputShape = inputVar.Shape;
int imageWidth = inputShape[0];
int imageHeight = inputShape[1];
var resized = bmp.Resize(imageWidth, imageHeight, true);
List <float> resizedCHW = resized.ParallelExtractCHW();
// Create input data map
var inputDataMap = new Dictionary <Variable, Value>();
var inputVal = Value.CreateBatch(inputShape, resizedCHW, GlobalParameters.Device);
inputDataMap.Add(inputVar, inputVal);
inputVar = modelFunc.Arguments[1];
//inputDataMap.Add(inputVar, null);
Variable outputVar = modelFunc.Outputs.Where(x => (x.Shape.TotalSize == 1000)).ToList()[0];
// Create output data map. Using null as Value to indicate using system allocated memory.
// Alternatively, create a Value object and add it to the data map.
var outputDataMap = new Dictionary <Variable, Value>();
outputDataMap.Add(outputVar, null);
// Start evaluation on the device
modelFunc.Evaluate(inputDataMap, outputDataMap, GlobalParameters.Device);
// Get evaluate result as dense output
var outputVal = outputDataMap[outputVar];
var outputData = outputVal.GetDenseData <float>(outputVar);
Dictionary <int, float> outputPred = new Dictionary <int, float>();
for (int i = 0; i < outputData[0].Count; i++)
{
outputPred.Add(i, outputData[0][i]);
}
var topList = outputPred.OrderByDescending(x => (x.Value)).Take(topK).ToList();
List <PredResult> result = new List <PredResult>();
float sumpredresult = outputPred.Sum(x => (x.Value));
float avgpredresult = outputPred.Average(x => (x.Value));
float min = outputPred.Min(x => (x.Value));
float max = outputPred.Max(x => (x.Value));
foreach (var item in topList)
{
result.Add(new PredResult()
{
Score = item.Value,
Name = actualValues[item.Key]
});
}
Logging.WriteTrace("Prediction Completed");
return(result);
}
catch (Exception ex)
{
Logging.WriteTrace(ex);
throw ex;
}
}
/// <summary>
/// Predicts the specified image.
/// </summary>
/// <param name="bmp">The image in bitmap format.</param>
/// <param name="confidence">The confidence level for the prediction result.</param>
/// <returns></returns>
public List <PredResult> Predict(Bitmap bmp, double confidence = 0.5)
{
try
{
proposedBoxes = new List <Rectangle>();
var resized = bmp.Resize(1000, 1000, true);
List <float> roiList = GenerateROIS(resized, model);
List <float> resizedCHW = resized.ParallelExtractCHW();
//CalculateROI(resizedCHW);
// Create input data map
var inputDataMap = new Dictionary <Variable, Value>();
var inputVal1 = Value.CreateBatch(modelFunc.Arguments.First().Shape, resizedCHW, GlobalParameters.Device);
inputDataMap.Add(modelFunc.Arguments.First(), inputVal1);
var inputVal2 = Value.CreateBatch(modelFunc.Arguments[1].Shape, roiList, GlobalParameters.Device);
inputDataMap.Add(modelFunc.Arguments[1], inputVal2);
Variable outputVar = GetOutputVar(model);
// Create output data map. Using null as Value to indicate using system allocated memory.
// Alternatively, create a Value object and add it to the data map.
var outputDataMap = new Dictionary <Variable, Value>();
outputDataMap.Add(outputVar, null);
// Start evaluation on the device
modelFunc.Evaluate(inputDataMap, outputDataMap, GlobalParameters.Device);
// Get evaluate result as dense output
var outputVal = outputDataMap[outputVar];
var outputData = outputVal.GetDenseData <float>(outputVar);
List <PredResult> result = new List <PredResult>();
var labels = GetLabels(model);
int numLabels = labels.Length;
int numRois = outputData[0].Count / numLabels;
int numBackgroundRois = 0;
for (int i = 0; i < numRois; i++)
{
var outputForRoi = outputData[0].Skip(i * numLabels).Take(numLabels).ToList();
// Retrieve the predicted label as the argmax over all predictions for the current ROI
var max = outputForRoi.IndexOf(outputForRoi.Max());
if (max > 0)
{
result.Add(new PredResult()
{
Name = labels[max],
BBox = proposedBoxes[i],
Score = outputForRoi.Max()
});
//Console.WriteLine("Outcome for ROI {0}: {1} \t({2})", i, max, labels[max]);
}
else
{
numBackgroundRois++;
}
}
var groupBoxes = result.GroupBy(x => (x.Name)).ToList();
result = new List <PredResult>();
foreach (var item in groupBoxes)
{
int counter = 0;
Rectangle unionRect = new Rectangle();
foreach (var rect in item.ToList())
{
if (counter == 0)
{
unionRect = rect.BBox;
continue;
}
unionRect = Rectangle.Union(unionRect, rect.BBox);
}
//var orderedList = item.ToList().OrderByDescending(x => (x.BBox.Width * x.BBox.Height)).ToList();
foreach (var rect in item.ToList())
{
unionRect = Rectangle.Intersect(unionRect, rect.BBox);
}
var goodPred = item.ToList().OrderByDescending(x => (x.Score)).ToList()[0];
goodPred.BBox = unionRect;
result.Add(goodPred);
}
//foreach (var item in result)
//{
// img.Draw(item.BBox, new Bgr(0, 255, 0));
//}
//img.Save("objdet_pred.jpg");
Logging.WriteTrace("Prediction Completed");
return(result);
}
catch (Exception ex)
{
Logging.WriteTrace(ex);
throw ex;
}
}
