/// <summary>
/// Creates an instance of the <see cref="ModelEvaluator"/> class.
/// </summary>
/// <param name="modelFilePath">The model file path</param>
/// <param name="numThreads">The number of concurrent threads for the model</param>
/// <param name="id">A unique id for the model</param>
/// <remarks>The id is used only for debugging purposes</remarks>
private ModelEvaluator(string modelFilePath, int numThreads, int id)
{
m_modelInstance = id;
m_model = new IEvaluateModelManagedF();
// Configure the model to run with a specific number of threads
m_model.Init(string.Format("numCPUThreads={0}", numThreads));
// Load model
m_model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Generate random input values in the appropriate structure and size
var inDims = m_model.GetNodeDimensions(NodeGroup.Input);
m_inKey = inDims.First().Key;
m_inputs = new Dictionary <string, List <float> >()
{
{ m_inKey, null }
};
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = m_model.GetNodeDimensions(NodeGroup.Output);
m_outKey = outDims.First().Key;
}
public void EvalManagedEvaluateNoInputTest()
{
string modelDefinition = @"precision = ""float""
traceLevel = 1
run=NDLNetworkBuilder
NDLNetworkBuilder=[
v1 = Constant(1)
v2 = Constant(2)
o1 = Plus(v1, v2, tag=""output"")
FeatureNodes = (v1)
]";
using (var model = new IEvaluateModelManagedF())
{
model.CreateNetwork(modelDefinition);
var inDims = model.GetNodeDimensions(NodeGroup.Input);
Assert.AreEqual(inDims.Count(), 0);
var outDims = model.GetNodeDimensions(NodeGroup.Output);
Assert.AreEqual(outDims.Count(), 1);
Assert.AreEqual(outDims.First().Key, "o1");
Assert.AreEqual(outDims.First().Value, 1);
var outputVal = model.Evaluate(outDims.First().Key);
var expected = new List <float>()
{
3
};
CollectionAssert.AreEqual(expected, outputVal);
}
}
public NetEvaluation(string modelpath, List <string> OutputNodesLists)
{
Model = new IEvaluateModelManagedF();
Model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelpath), deviceId: -1, outputNodeNames: OutputNodesLists);
inDims = Model.GetNodeDimensions(NodeGroup.Input);
outDims = Model.GetNodeDimensions(NodeGroup.Output);
inputs = new Dictionary <string, List <float> >();
}
/// <summary>
/// Evaluates a trained model and obtains multiple layers output (including hidden layer)
/// </summary>
/// <remarks>
/// This example requires the 01_OneHidden trained model
/// </remarks>
private static void EvaluateModelMultipleLayers()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\MNIST\Data\");
Dictionary <string, List <float> > outputs;
using (var model = new IEvaluateModelManagedF())
{
// Desired output layers
const string hiddenLayerName = "h1.z";
const string outputLayerName = "ol.z";
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
if (!File.Exists(modelFilePath))
{
Console.WriteLine("Error: The model {0} does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to create the model.", modelFilePath);
throw new FileNotFoundException(string.Format("File {0} not found.", modelFilePath));
}
var desiredOutputLayers = new List <string>()
{
hiddenLayerName, outputLayerName
};
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1, outputNodeNames: desiredOutputLayers);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll get both the hidden layer and the output layer
var outDims = model.GetNodeDimensions(NodeGroup.Output);
// We can preallocate the output structure and pass it in (multiple output layers)
outputs = new Dictionary <string, List <float> >()
{
{ hiddenLayerName, GetFloatArray(outDims[hiddenLayerName], 1) },
{ outputLayerName, GetFloatArray(outDims[outputLayerName], 1) }
};
model.Evaluate(inputs, outputs);
}
OutputResults(outputs);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// This method shows how to evaluate a trained image classification model
/// </summary>
public static void EvaluateImageClassificationModel()
{
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\Miscellaneous\ImageNet\ResNet
// along with a sample image file named "zebra.jpg".
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\Miscellaneous\ImageNet\ResNet");
Environment.CurrentDirectory = initialDirectory;
List <float> outputs;
using (var model = new IEvaluateModelManagedF())
{
string modelFilePath = Path.Combine(workingDirectory, "ResNet_18.model");
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 224 * 224 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
string imageFileName = Path.Combine(workingDirectory, "zebra.jpg");
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
var resized = bmp.Resize(224, 224, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary <string, List <float> >()
{
{ inDims.First().Key, resizedCHW }
};
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// Retrieve the outcome index (so we can compare it with the expected index)
var max = outputs.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
Console.WriteLine("Outcome: {0}", max);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// This method shows how to evaluate a trained image classification model, with
/// explicitly created feature vectors.
/// </summary>
public static List <float> EvaluateImageInputUsingFeatureVector()
{
List <float> outputs = null;
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\Classification\ResNet
// along with a sample image file named "zebra.jpg".
Environment.CurrentDirectory = initialDirectory;
using (var model = new IEvaluateModelManagedF())
{
model.CreateNetwork(string.Format("modelPath=\"{0}\"", resnetModelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != resNetImageSize * resNetImageSize * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
var resized = bmp.Resize(resNetImageSize, resNetImageSize, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary <string, List <float> >()
{
{ inDims.First().Key, resizedCHW }
};
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// Retrieve the outcome index (so we can compare it with the expected index)
var max = outputs.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
Console.WriteLine("EvaluateImageInputUsingFeatureVector: Outcome = {0}", max);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
return(outputs);
}
/// <summary>
/// Evaluates a trained model and obtains multiple layers output (including hidden layer)
/// </summary>
/// <remarks>
/// This example requires the 01_OneHidden trained model
/// </remarks>
private static void EvaluateModelMultipleLayers()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\GettingStarted");
Dictionary <string, List <float> > outputs;
using (var model = new IEvaluateModelManagedF())
{
// Desired output layers
const string hiddenLayerName = "out.h1";
const string outputLayerName = "out.z";
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @".\Output\Models\01_OneHidden");
ThrowIfFileNotExist(modelFilePath,
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/GettingStarted to create the model.", modelFilePath));
var desiredOutputLayers = new List <string>()
{
hiddenLayerName, outputLayerName
};
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1, outputNodeNames: desiredOutputLayers);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll get both the hidden layer and the output layer
var outDims = model.GetNodeDimensions(NodeGroup.Output);
// We can preallocate the output structure and pass it in (multiple output layers)
outputs = new Dictionary <string, List <float> >()
{
{ hiddenLayerName, GetFloatArray(outDims[hiddenLayerName], 1) },
{ outputLayerName, GetFloatArray(outDims[outputLayerName], 1) }
};
model.Evaluate(inputs, outputs);
}
OutputResults(outputs);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
}
public void EvalManagedEvaluateSingleOutputTest()
{
string modelDefinition = @"precision = ""float""
traceLevel = 1
run=NDLNetworkBuilder
NDLNetworkBuilder=[
i1 = Input(1)
o1 = Times(Constant(3), i1, tag=""output"")
FeatureNodes = (i1)
]";
using (var model = new IEvaluateModelManagedF())
{
model.CreateNetwork(modelDefinition);
var inDims = model.GetNodeDimensions(NodeGroup.Input);
Assert.AreEqual(inDims.Count(), 1);
Assert.AreEqual(inDims.First().Key, "i1");
Assert.AreEqual(inDims.First().Value, 1);
var inputs = new Dictionary <string, List <float> >()
{
{ inDims.First().Key, new List <float>()
{
2
} }
};
var outDims = model.GetNodeDimensions(NodeGroup.Output);
Assert.AreEqual(outDims.Count(), 1);
Assert.AreEqual(outDims.First().Key, "o1");
Assert.AreEqual(outDims.First().Value, 1);
var outputs = new Dictionary <string, List <float> >()
{
{ outDims.First().Key, new List <float>()
{
0
} }
};
model.Evaluate(inputs, outputs);
var expected = new List <float>()
{
6
};
CollectionAssert.AreEqual(expected, outputs.First().Value);
}
}
public async Task<string[]> EvaluateCustomDNN(string imageUrl)
{
string domainBaseDirectory = AppDomain.CurrentDomain.BaseDirectory;
string workingDirectory = Environment.CurrentDirectory;
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
int numThreads = 1;
List<float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// initialize the evaluation engine
// TODO: initializing the evaluation engine should be one only once at the beginning
var initParams = string.Format("numCPUThreads={0}", numThreads);
model.Init(initParams);
// load the model
string modelFilePath = Path.Combine(domainBaseDirectory, @"CNTK\Models\ResNet_18.model");
var modelOption = string.Format("modelPath=\"{0}\"", modelFilePath);
model.CreateNetwork(modelOption, deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 224 * 224 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
System.Net.Http.HttpClient httpClient = new HttpClient();
Stream imageStream = await httpClient.GetStreamAsync(imageUrl);
Bitmap bmp = new Bitmap(Bitmap.FromStream(imageStream));
var resized = bmp.Resize(224, 224, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary<string, List<float>>() { { inDims.First().Key, resizedCHW } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
List<string> o = new List<string>();
foreach (float f in outputs)
{
o.Add(f.ToString());
}
return o.ToArray<string>();
}
catch (Exception ex)
{
return new string[] { string.Format("domainBase directory {0}, workingDirectory {1}, exception details: {2}.", domainBaseDirectory, workingDirectory, ex.ToString()) };
}
}
/// <summary>
/// Evaluates a trained model and obtains a single layer output
/// </summary>
/// <remarks>
/// This example requires the 01_OneHidden trained model
/// </remarks>
private static void EvaluateModelSingleLayer()
{
try
{
string outputLayerName;
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\MNIST\Data\");
List <float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
if (!File.Exists(modelFilePath))
{
Console.WriteLine("Error: The model {0} does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to create the model.", modelFilePath);
throw new FileNotFoundException(string.Format("File {0} not found.", modelFilePath));
}
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputLayerName = outDims.First().Key;
// We can call the evaluate method and get back the results (single layer)...
outputs = model.Evaluate(inputs, outputLayerName);
}
OutputResults(outputLayerName, outputs);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// Evaluates a trained model and obtains a single layer output
/// </summary>
/// <remarks>
/// This example requires the 01_OneHidden trained model
/// </remarks>
private static void EvaluateModelSingleLayer()
{
try
{
string outputLayerName;
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\GettingStarted");
List <float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @".\Output\Models\01_OneHidden");
ThrowIfFileNotExist(modelFilePath,
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/GettingStarted to create the model.", modelFilePath));
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputLayerName = outDims.First().Key;
// We can call the evaluate method and get back the results (single layer)...
outputs = model.Evaluate(inputs, outputLayerName);
}
OutputResults(outputLayerName, outputs);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
}
/// <summary>
/// Evaluates a network (without a model, but requiring input) and obtains a single layer output
/// </summary>
private static void EvaluateNetworkSingleLayer()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Other\Simple2d\Config");
Environment.CurrentDirectory = initialDirectory;
List <float> outputs;
string outputLayerName;
using (var model = new IEvaluateModelManagedF())
{
// Create the network
// This network (AddOperatorConstant_ndl_deprecated.cntk) is a simple network consisting of a single binary operator (Plus)
// operating over a single input and a constant
string networkFilePath = Path.Combine(workingDirectory, @"AddOperatorConstant_ndl_deprecated.cntk");
if (!File.Exists(networkFilePath))
{
Console.WriteLine("Error: The network configuration file {0} does not exist.", networkFilePath);
throw new FileNotFoundException(string.Format("File {0} not found.", networkFilePath));
}
string networkDescription = File.ReadAllText(networkFilePath);
model.CreateNetwork(networkDescription, deviceId: -1);
// Prepare input value in the appropriate structure and size
var inputs = new Dictionary <string, List <float> >()
{
{ "features", new List <float>()
{
1.0f
} }
};
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputLayerName = outDims.First().Key;
outputs = model.Evaluate(inputs, outputLayerName);
}
OutputResults(outputLayerName, outputs);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// Evaluates a trained model and obtains multiple layers output
/// </summary>
private static void EvaluateModelMultipleLayers()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\MNIST\Data\");
Dictionary<string, List<float>> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId:-1);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.nodeInput);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = model.GetNodeDimensions(NodeGroup.nodeOutput);
string outputLayerName = outDims.First().Key;
// We can preallocate the output structure and pass it in (multiple output layers)
outputs = GetDictionary(outputLayerName, outDims[outputLayerName], 1);
model.Evaluate(inputs, outputs);
}
OutputResults(outputs);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// Evaluates a trained model and obtains multiple layers output
/// </summary>
private static void EvaluateModelMultipleLayers()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\MNIST\Data\");
Dictionary <string, List <float> > outputs;
using (var model = new IEvaluateModelManagedF())
{
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.nodeInput);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = model.GetNodeDimensions(NodeGroup.nodeOutput);
string outputLayerName = outDims.First().Key;
// We can preallocate the output structure and pass it in (multiple output layers)
outputs = GetDictionary(outputLayerName, outDims[outputLayerName], 1);
model.Evaluate(inputs, outputs);
}
OutputResults(outputs);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// Creates an instance of the <see cref="ModelEvaluator"/> class.
/// </summary>
/// <param name="modelFilePath">The model file path</param>
/// <param name="numThreads">The number of concurrent threads for the model</param>
/// <param name="id">A unique id for the model</param>
/// <remarks>The id is used only for debugging purposes</remarks>
private ModelEvaluator(string modelFilePath, int numThreads, int id)
{
m_modelInstance = id;
m_model = new IEvaluateModelManagedF();
// Configure the model to run with a specific number of threads
m_model.Init(string.Format("numCPUThreads={0}", numThreads));
// Load model
m_model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Generate random input values in the appropriate structure and size
var inDims = m_model.GetNodeDimensions(NodeGroup.Input);
m_inKey = inDims.First().Key;
m_inputs = new Dictionary<string, List<float>>() { { m_inKey, null } };
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = m_model.GetNodeDimensions(NodeGroup.Output);
m_outKey = outDims.First().Key;
}
/// <summary>
/// This method shows how to evaluate a trained image classification model
/// </summary>
public static void EvaluateImageClassificationModel()
{
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\Miscellaneous\ImageNet\ResNet
// along with a sample image file named "zebra.jpg".
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\Miscellaneous\ImageNet\ResNet");
Environment.CurrentDirectory = initialDirectory;
List<float> outputs;
using (var model = new IEvaluateModelManagedF())
{
string modelFilePath = Path.Combine(workingDirectory, "ResNet_18.model");
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 224 * 224 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
string imageFileName = Path.Combine(workingDirectory, "zebra.jpg");
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
var resized = bmp.Resize(224, 224, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary<string, List<float>>() { {inDims.First().Key, resizedCHW } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// Retrieve the outcome index (so we can compare it with the expected index)
var max = outputs.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
Console.WriteLine("Outcome: {0}", max);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
public void EvalManagedEvaluateHiddenLayerTest()
{
string modelDefinition = @"deviceId = -1
precision = ""float""
traceLevel = 1
run=NDLNetworkBuilder
NDLNetworkBuilder=[
i1 = Input(1)
pool5 = Times(Constant(2), i1)
OutputNodes.z = Plus(pool5, Constant(7), tag=""output"")
FeatureNodes = (i1)
]";
using (var model = new IEvaluateModelManagedF())
{
var desiredOutputLayers = new List <string>()
{
"pool5", "OutputNodes.z"
};
model.CreateNetwork(modelDefinition, deviceId: -1, outputNodeNames: desiredOutputLayers);
var inDims = model.GetNodeDimensions(NodeGroup.Input);
Assert.AreEqual(inDims.Count(), 1);
Assert.AreEqual(inDims["i1"], 1);
var inputs = new Dictionary <string, List <float> >()
{
{ "i1", new List <float>()
{
5
} }
};
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = model.GetNodeDimensions(NodeGroup.Output);
Assert.AreEqual(outDims.Count(), 2);
Assert.AreEqual(outDims["pool5"], 1);
Assert.AreEqual(outDims["OutputNodes.z"], 1);
var outputs = new Dictionary <string, List <float> >()
{
// The order of node name below is different than that returned by outDims,
// in order to test whether the output values are correctly mapped to the name.
{ "pool5", new List <float>()
{
0
} },
{ "OutputNodes.z", new List <float>()
{
0
} }
};
// We can call the evaluate method and get back the results (single layer)...
model.Evaluate(inputs, outputs);
var expected1 = new List <float>()
{
10
};
var expected2 = new List <float>()
{
17
};
CollectionAssert.AreEqual(expected1, outputs["pool5"]);
CollectionAssert.AreEqual(expected2, outputs["OutputNodes.z"]);
}
}
/// <summary>
/// Evaluates a network (without a model) and obtains a single layer output
/// </summary>
private static void EvaluateNetworkSingleLayer()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Other\Simple2d\Config");
Environment.CurrentDirectory = initialDirectory;
List<float> outputs;
string outputLayerName;
using (var model = new IEvaluateModelManagedF())
{
// Create the network
// This network (AddOperatorConstant.cntk) is a simple network consisting of a single binary operator (Plus)
// operating over a single input and a constant
string networkDescription = File.ReadAllText(Path.Combine(workingDirectory, @"AddOperatorConstant.cntk"));
model.CreateNetwork(networkDescription, deviceId:-1);
// Generate random input value in the appropriate structure and size
var inputs = new Dictionary<string, List<float>>() { { "features", new List<float>() { 1.0f } } };
// We can call the evaluate method and get back the results (single layer)...
var outDims = model.GetNodeDimensions(NodeGroup.nodeOutput);
outputLayerName = outDims.First().Key;
outputs = model.Evaluate(inputs, outputLayerName);
}
OutputResults(outputLayerName, outputs);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// This method shows how to evaluate a trained image classification model, with
/// explicitly created feature vectors.
/// </summary>
public static List<float> EvaluateImageInputUsingFeatureVector()
{
List<float> outputs = null;
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\Classification\ResNet
// along with a sample image file named "zebra.jpg".
Environment.CurrentDirectory = initialDirectory;
using (var model = new IEvaluateModelManagedF())
{
model.CreateNetwork(string.Format("modelPath=\"{0}\"", resnetModelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != resNetImageSize * resNetImageSize * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
var resized = bmp.Resize(resNetImageSize, resNetImageSize, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary<string, List<float>>() { {inDims.First().Key, resizedCHW } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// Retrieve the outcome index (so we can compare it with the expected index)
var max = outputs.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
Console.WriteLine("EvaluateImageInputUsingFeatureVector: Outcome = {0}", max);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
return outputs;
}
/// <summary>
/// This method shows how to evaluate a trained FastR-CNN object detection model
/// </summary>
public static void EvaluateObjectDetectionModel()
{
initialDirectory = @"C:\local\cntk";
try
{
// This example requires the Fast-RCNN_grocery100 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/Models/FRCN_Grocery/Fast-RCNN_grocery100.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\PretrainedModels\
// It further requires the grocery image data set.
// Please run 'python install_fastrcnn.py' from <cntkroot>\Examples\Image\Detection\FastRCNN to get the data.
//string imageDirectory = Path.Combine(initialDirectory, @"\Examples\Image\DataSets\grocery\testImages");
//string modelDirectory = Path.Combine(initialDirectory, @"\Examples\Image\PretrainedModels");
string imageDirectory = (initialDirectory + @"\Examples\Image\DataSets\grocery\testImages");
string modelDirectory = (initialDirectory + @"\Examples\Image\PretrainedModels");
Environment.CurrentDirectory = initialDirectory;
List <float> outputs;
using (var model = new IEvaluateModelManagedF())
{
string modelFilePath = Path.Combine(modelDirectory, "Fast-RCNN_grocery100.model");
ThrowIfFileNotExist(modelFilePath,
string.Format("Error: The model '{0}' does not exist. Please download the model from https://www.cntk.ai/Models/FRCN_Grocery/Fast-RCNN_grocery100.model " +
"and save it under ..\\..\\Examples\\Image\\PretrainedModels.", modelFilePath));
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 1000 * 1000 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 1000 * 1000 * 3), string.Empty);
}
// Transform the image
string imageFileName = Path.Combine(imageDirectory, "WIN_20160803_11_28_42_Pro.jpg");
ThrowIfFileNotExist(imageFileName, string.Format("Error: The test image file '{0}' does not exist.", imageFileName));
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
// TODO: preserve aspect ratio while scaling and pad the remaining pixels with (114, 114, 114)
var resized = bmp.Resize(1000, 1000, true);
var resizedCHW = resized.ParallelExtractCHW();
// TODO: generate ROI proposals using an external library, e.g. selective search,
// TODO: project them to the 1000 x 1000 image size and compute (x, y, w, h) relative to the image dimensions.
// TODO: Alternative workaround: run script 'A1_GenerateInputROIs.py' from <cntkroot>\Examples\Image\Detection\FastRCNN and read rois from file.
// parse rois: groups of 4 floats corresponding to (x, y, w, h) for an ROI
string roiCoordinates = "0.219 0.0 0.165 0.29 0.329 0.025 0.07 0.115 0.364 0.0 0.21 0.13 0.484 0.0 0.075 0.06 0.354 0.045 0.055 0.09 0.359 0.075 0.095 0.07 0.434 0.155 0.04 0.085 0.459 0.165 0.145 0.08 0.404 0.12 0.055 0.06 0.714 0.235 0.06 0.12 0.659 0.31 0.065 0.075 0.299 0.16 0.1 0.07 0.449 0.18 0.19 0.15 0.284 0.21 0.135 0.115 0.254 0.205 0.07 0.055 0.234 0.225 0.075 0.095 0.239 0.23 0.07 0.085 0.529 0.235 0.075 0.13 0.229 0.24 0.09 0.085 0.604 0.285 0.12 0.105 0.514 0.335 0.1 0.045 0.519 0.335 0.08 0.045 0.654 0.205 0.08 0.055 0.614 0.215 0.115 0.065 0.609 0.205 0.115 0.075 0.604 0.225 0.115 0.055 0.524 0.23 0.06 0.095 0.219 0.315 0.065 0.075 0.629 0.31 0.095 0.08 0.639 0.325 0.085 0.06 0.219 0.41 0.25 0.11 0.354 0.46 0.185 0.11 0.439 0.515 0.09 0.075 0.359 0.455 0.175 0.125 0.449 0.525 0.08 0.07 0.574 0.46 0.06 0.105 0.579 0.46 0.105 0.1 0.529 0.47 0.15 0.145 0.584 0.475 0.085 0.09 0.354 0.52 0.08 0.06 0.219 0.52 0.115 0.1 0.229 0.53 0.1 0.08 0.229 0.575 0.105 0.045 0.339 0.56 0.085 0.045 0.354 0.535 0.075 0.06 0.299 0.59 0.145 0.05 0.304 0.58 0.12 0.045 0.594 0.555 0.075 0.05 0.534 0.58 0.14 0.06 0.504 0.66 0.07 0.06 0.494 0.73 0.075 0.09 0.504 0.695 0.07 0.095 0.219 0.665 0.075 0.145 0.494 0.755 0.085 0.075 0.704 0.665 0.07 0.21 0.434 0.72 0.055 0.1 0.569 0.695 0.205 0.185 0.219 0.73 0.29 0.13 0.574 0.665 0.08 0.055 0.634 0.665 0.095 0.045 0.499 0.725 0.08 0.135 0.314 0.71 0.155 0.065 0.264 0.72 0.19 0.105 0.264 0.725 0.185 0.095 0.249 0.725 0.12 0.11 0.379 0.77 0.08 0.055 0.509 0.785 0.055 0.06 0.644 0.875 0.13 0.085 0.664 0.875 0.11 0.075 0.329 0.025 0.08 0.115 0.639 0.235 0.135 0.15 0.354 0.46 0.185 0.12 0.354 0.46 0.185 0.135 0.229 0.225 0.08 0.095 0.219 0.72 0.29 0.14 0.569 0.67 0.205 0.21 0.219 0.315 0.1 0.075 0.219 0.23 0.09 0.085 0.219 0.41 0.295 0.11 0.219 0.665 0.27 0.145 0.219 0.225 0.09 0.14 0.294 0.665 0.2 0.05 0.579 0.46 0.105 0.145 0.549 0.46 0.14 0.145 0.219 0.41 0.295 0.125 0.219 0.59 0.11 0.05 0.639 0.235 0.135 0.155 0.629 0.235 0.145 0.155 0.314 0.71 0.155 0.115 0.334 0.56 0.09 0.045 0.264 0.72 0.225 0.1 0.264 0.72 0.225 0.105 0.219 0.71 0.29 0.15 0.249 0.725 0.125 0.11 0.219 0.665 0.27 0.17 0.494 0.73 0.075 0.115 0.494 0.73 0.085 0.115 0.219 0.0 0.14 0.14 0.219 0.07 0.14 0.14 0.219 0.14 0.14 0.14";
var rois = roiCoordinates.Split(' ').Select(x => float.Parse(x)).ToList();
// inputs are the image itself and the ROI coordinates
var inputs = new Dictionary <string, List <float> >()
{
{ inDims.First().Key, resizedCHW }, { inDims.Last().Key, rois }
};
// We can call the evaluate method and get back the results (predictions per ROI and per class (no softmax applied yet!)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// the object classes used in the grocery example
var labels = new[] { "__background__",
"avocado", "orange", "butter", "champagne", "eggBox", "gerkin", "joghurt", "ketchup",
"orangeJuice", "onion", "pepper", "tomato", "water", "milk", "tabasco", "mustard" };
int numLabels = labels.Length;
int numRois = outputs.Count / numLabels;
Console.WriteLine("Only showing predictions for non-background ROIs...");
int numBackgroundRois = 0;
for (int i = 0; i < numRois; i++)
{
var outputForRoi = outputs.Skip(i * numLabels).Take(numLabels).ToList();
// Retrieve the predicted label as the argmax over all predictions for the current ROI
var max = outputForRoi.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
if (max > 0)
{
Console.WriteLine("Outcome for ROI {0}: {1} \t({2})", i, max, labels[max]);
}
else
{
numBackgroundRois++;
}
}
Console.WriteLine("Number of background ROIs: {0}", numBackgroundRois);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
}
/// <summary>
/// Evaluates a trained model and obtains multiple layers output (including hidden layer)
/// </summary>
/// <remarks>
/// This example requires the 01_OneHidden trained model
/// </remarks>
private static void EvaluateModelMultipleLayers()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\GettingStarted");
Dictionary<string, List<float>> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Desired output layers
const string hiddenLayerName = "out.h1";
const string outputLayerName = "out.z";
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @".\Output\Models\01_OneHidden");
ThrowIfFileNotExist(modelFilePath,
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/GettingStarted to create the model.", modelFilePath));
var desiredOutputLayers = new List<string>() { hiddenLayerName, outputLayerName };
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1, outputNodeNames: desiredOutputLayers);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll get both the hidden layer and the output layer
var outDims = model.GetNodeDimensions(NodeGroup.Output);
// We can preallocate the output structure and pass it in (multiple output layers)
outputs = new Dictionary<string, List<float>>()
{
{ hiddenLayerName, GetFloatArray(outDims[hiddenLayerName], 1) },
{ outputLayerName, GetFloatArray(outDims[outputLayerName], 1) }
};
model.Evaluate(inputs, outputs);
}
OutputResults(outputs);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
}
/// <summary>
/// Evaluates a trained model and obtains a single layer output
/// </summary>
/// <remarks>
/// This example requires the 01_OneHidden trained model
/// </remarks>
private static void EvaluateModelSingleLayer()
{
try
{
string outputLayerName;
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
Environment.CurrentDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\GettingStarted");
List<float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @".\Output\Models\01_OneHidden");
ThrowIfFileNotExist(modelFilePath,
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/GettingStarted to create the model.", modelFilePath));
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Generate random input values in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
var inputs = GetDictionary(inDims.First().Key, inDims.First().Value, 255);
// We request the output layer names(s) and dimension, we'll use the first one.
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputLayerName = outDims.First().Key;
// We can call the evaluate method and get back the results (single layer)...
outputs = model.Evaluate(inputs, outputLayerName);
}
OutputResults(outputLayerName, outputs);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
}
public async Task <string[]> EvaluateCustomDNN(string imageUrl)
{
string domainBaseDirectory = AppDomain.CurrentDomain.BaseDirectory;
string workingDirectory = Environment.CurrentDirectory;
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
int numThreads = 1;
List <float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// initialize the evaluation engine
// TODO: initializing the evaluation engine should be one only once at the beginning
var initParams = string.Format("numCPUThreads={0}", numThreads);
model.Init(initParams);
// load the model
string modelFilePath = Path.Combine(domainBaseDirectory, @"CNTK\Models\ResNet_18.model");
var modelOption = string.Format("modelPath=\"{0}\"", modelFilePath);
model.CreateNetwork(modelOption, deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 224 * 224 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
System.Net.Http.HttpClient httpClient = new HttpClient();
Stream imageStream = await httpClient.GetStreamAsync(imageUrl);
Bitmap bmp = new Bitmap(Bitmap.FromStream(imageStream));
var resized = bmp.Resize(224, 224, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary <string, List <float> >()
{
{ inDims.First().Key, resizedCHW }
};
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
List <string> o = new List <string>();
foreach (float f in outputs)
{
o.Add(f.ToString());
}
return(o.ToArray <string>());
}
catch (Exception ex)
{
return(new string[] { string.Format("domainBase directory {0}, workingDirectory {1}, exception details: {2}.", domainBaseDirectory, workingDirectory, ex.ToString()) });
}
}
/// <summary>
/// Evaluates a network (without a model, but requiring input) and obtains a single layer output
/// </summary>
private static void EvaluateNetworkSingleLayer()
{
try
{
// The examples assume the executable is running from the data folder
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Other\Simple2d\Config");
Environment.CurrentDirectory = initialDirectory;
List<float> outputs;
string outputLayerName;
using (var model = new IEvaluateModelManagedF())
{
// Create the network
// This network (AddOperatorConstant_ndl_deprecated.cntk) is a simple network consisting of a single binary operator (Plus)
// operating over a single input and a constant
string networkFilePath = Path.Combine(workingDirectory, @"AddOperatorConstant_ndl_deprecated.cntk");
ThrowIfFileNotExist(networkFilePath, string.Format("Error: The network configuration file '{0}' does not exist.", networkFilePath));
string networkDescription = File.ReadAllText(networkFilePath);
model.CreateNetwork(networkDescription, deviceId: -1);
// Prepare input value in the appropriate structure and size
var inputs = new Dictionary<string, List<float>>() { { "features", new List<float>() { 1.0f } } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputLayerName = outDims.First().Key;
outputs = model.Evaluate(inputs, outputLayerName);
}
OutputResults(outputLayerName, outputs);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
}
