/// <summary>
/// Test cntk model stored at 'modelPath' against array of image paths
/// </summary>
/// <param name="modelPath"></param>
/// <param name="vector"></param>
/// <param name="device"></param>
/// <returns></returns>
public static List <int> TestModel(string modelPath, string[] imagePaths, DeviceDescriptor device)
{
try
{
//
FileInfo fi = new FileInfo(modelPath);
if (!fi.Exists)
{
throw new Exception($"The '{fi.FullName}' does not exist. Make sure the model is places at this location.");
}
//load the model from disk
var model = Function.Load(fi.FullName, device);
//get input feature
var features = model.Arguments.ToList();
var labels = model.Outputs.ToList();
var stremsConfig = MLFactory.CreateStreamConfiguration(features, labels);
var mapFile = "testMapFile";
File.WriteAllLines(mapFile, imagePaths.Select(x => $"{x}\t0"));
var testMB = new MinibatchSourceEx(MinibatchType.Image, stremsConfig.ToArray(), features, labels, mapFile, null, 30, false, 0);
//
var vars = features.Union(labels).ToList();
var retVal = new List <int>();
var mbSize = imagePaths.Count();
if (mbSize > 30)
{
mbSize = 30;
}
while (true)
{
bool isSweepEnd = false;
var inputMap = testMB.GetNextMinibatch((uint)mbSize, ref isSweepEnd, vars, device);
//prepare data for trainer
//var inputMap = new Dictionary<Variable, Value>();
//inputMap.Add(features.First(), nextMB.Where(x => x.Key.m_name.Equals(features.First().Name)).Select(x => x.Value.data).FirstOrDefault());
var outputMap = new Dictionary <Variable, Value>();
outputMap.Add(labels.First(), null);
//evaluate model
model.Evaluate(inputMap, outputMap, device);
var result = outputMap[labels.First()].GetDenseData <float>(labels.First());
//extract result
foreach (var r in result)
{
var l = MLValue.GetResult(r);
retVal.Add((int)l);
}
if (/*nextMB.Any(x => x.Value.sweepEnd)*/ isSweepEnd)
{
break;
}
}
return(retVal);
}
catch (Exception)
{
throw;
}
}
