/// <summary>
/// Evaluates a network (without a model and without input) and obtains a single layer output
/// </summary>
private static void EvaluateNetworkSingleLayerNoInput()
{
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;
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
model.Init("deviceId=-1");
// Create the network
string networkDescription = GetFileContents(Path.Combine(workingDirectory, @"AddOperatorConstantNoInput.cntk"));
model.CreateNetwork(networkDescription);
// We can call the evaluate method and get back the results (single layer)...
outputs = model.Evaluate("ol", 1);
}
OutputResults("ol", 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;
}
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()) };
}
}
private static List <float> EvaluateGoModel(string dataDirectory, List <float> feature)
{
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
//Path.Combine(initialDirectory, @"..\..\Examples\Image\MNIST\Data\");
//Dictionary<string, List<float>> outputs;
//using (var model = new IEvaluateModelManagedF())
//{
if (model == null)
{
Environment.CurrentDirectory = dataDirectory; // @"D:\Aricie\Go\cntk\Data";
model = new IEvaluateModelManagedF();
// Initialize model evaluator
string config = GetFileContents(Path.Combine(Environment.CurrentDirectory, @"..\Config\02_Convolution.cntk"));
model.Init(config);
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\02_Convolution");
//model.LoadModel(modelFilePath);
model.CreateNetwork(string.Format("deviceId=0\nmodelPath=\"{0}\"", modelFilePath));
// Generate random input values in the appropriate structure and size
//var inputs = GetDictionary("features", 28 * 28, 255);
// We can preallocate the output structure and pass it in (multiple output layers)
//outputs = GetDictionary("ol.z", 10, 1);
}
var inputs = new Dictionary <string, List <float> >();
inputs.Add("features", feature);
return(model.Evaluate(inputs, "ol.act", 361));
//}
//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} CallStack: {1} Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException?.Message ?? "No Inner Exception");
}
return(null);
}
/// <summary>
/// Program entry point
/// </summary>
/// <param name="args">Program arguments (ignored)</param>
private static void Main(string[] args)
{
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(Environment.CurrentDirectory, @"..\..\Examples\Image\MNIST\Data\");
Dictionary<string, List<float>> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
string config = GetConfig();
model.Init(config);
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
model.LoadModel(modelFilePath);
// Generate random input values in the appropriate structure and size
var inputs = GetDictionary("features", 28*28, 255);
// We can call the evaluate method and get back the results (single layer)...
// List<float> outputList = model.Evaluate(inputs, "ol.z", 10);
// ... or we can preallocate the structure and pass it in (multiple output layers)
outputs = GetDictionary("ol.z", 10, 1);
model.Evaluate(inputs, outputs);
}
Console.WriteLine("--- Output results ---");
foreach (var item in outputs)
{
Console.WriteLine("Output layer: {0}", item.Key);
foreach (var entry in item.Value)
{
Console.WriteLine(entry);
}
}
}
catch (Exception ex)
{
Console.WriteLine("Error: {0} \n {1}", ex, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
Console.WriteLine("Press <Enter> to terminate.");
Console.ReadLine();
}
/// <summary>
/// Program entry point
/// </summary>
/// <param name="args">Program arguments (ignored)</param>
private static void Main(string[] args)
{
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(Environment.CurrentDirectory, @"..\..\Examples\Image\MNIST\Data\");
Dictionary <string, List <float> > outputs;
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
string config = GetConfig();
model.Init(config);
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
model.LoadModel(modelFilePath);
// Generate random input values in the appropriate structure and size
var inputs = GetDictionary("features", 28 * 28, 255);
// We can call the evaluate method and get back the results (single layer)...
// List<float> outputList = model.Evaluate(inputs, "ol.z", 10);
// ... or we can preallocate the structure and pass it in (multiple output layers)
outputs = GetDictionary("ol.z", 10, 1);
model.Evaluate(inputs, outputs);
}
Console.WriteLine("--- Output results ---");
foreach (var item in outputs)
{
Console.WriteLine("Output layer: {0}", item.Key);
foreach (var entry in item.Value)
{
Console.WriteLine(entry);
}
}
}
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");
}
Console.WriteLine("Press <Enter> to terminate.");
Console.ReadLine();
}
private void Evaluate(string imagePath)
{
this.LoadImage(imagePath);
var configFilePath = this.ConfigFilePath;
var modelFilePath = this.ModelFilePath;
var labelMappingFilePath = this.LabelMappingFilePath;
// Important
Environment.CurrentDirectory = Path.GetDirectoryName(this.ConfigFilePath);
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
var lines = File.ReadAllLines(configFilePath);
var config = string.Join("\n", lines);
model.Init(config);
// Load model
model.CreateNetwork(string.Format("deviceId=-1\nmodelPath=\"{0}\"", modelFilePath));
// Generate random input values in the appropriate structure and size
var feature = this.GetFeature().ToList();
var inputs = new Dictionary <string, List <float> >();
inputs.Add("features", feature);
var labels = System.IO.File.ReadAllLines(labelMappingFilePath);
var labelCount = labels.Length;
var outputNodeName = string.Format("{0}.z", this.OutputNodeName);
var outputs = model.Evaluate(inputs, outputNodeName, labelCount);
// convert to probability
var results = Softmax(outputs);
var output = labels.Select((label, index) => new CntkResult
{
Label = label,
Probability = results[index],
ProbabilityText = results[index].ToString("F32")
}).ToList();
this.Output = new ListCollectionView(output);
}
}
public static List <float> Evaluate(string modelFilePath, string configContent, Dictionary <string, List <float> > inputs, string outputKey, int outputSize)
{
//Dictionary<string, List<float>> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
model.Init(configContent);
// Load model
// model.LoadModel(modelFilePath);
model.CreateNetwork($"deviceId=0\nmodelPath=\"{modelFilePath}\"");
// We can call the evaluate method and get back the results (single layer)...
// List<float> outputList = model.Evaluate(inputs, "ol.z", 10);
return(model.Evaluate(inputs, outputKey, outputSize));
// ... or we can preallocate the structure and pass it in (multiple output layers)
//var toReturn = new List<float>(outputSize);
//for (int i = 0; i < outputSize; i++)
//{
// toReturn.Add(1);
//}
//outputs = new Dictionary<string, List<float>>();
//outputs.Add("ol.z", toReturn);
//model.Evaluate(inputs, outputs);
//Console.WriteLine("--- Output results ---");
//foreach (var item in outputs)
//{
// Console.WriteLine("Output layer: {0}", item.Key);
// foreach (var entry in item.Value)
// {
// Console.WriteLine(entry);
// }
//}
}
}
/// <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>
/// 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())
{
// Initialize model evaluator
string config = GetFileContents(Path.Combine(Environment.CurrentDirectory, @"..\Config\01_OneHidden.cntk"));
model.Init(config);
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
model.CreateNetwork(string.Format("deviceId=-1\nmodelPath=\"{0}\"", modelFilePath));
// Generate random input values in the appropriate structure and size
var inputs = GetDictionary("features", 28 * 28, 255);
// We can preallocate the output structure and pass it in (multiple output layers)
outputs = GetDictionary("ol.z", 10, 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 a single layer output
/// </summary>
private static void EvaluateModelSingleLayer()
{
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\");
List<float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
string config = GetFileContents(Path.Combine(Environment.CurrentDirectory, @"..\Config\01_OneHidden.cntk"));
model.Init(config);
// Load model
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
model.CreateNetwork(string.Format("deviceId=-1\nmodelPath=\"{0}\"", modelFilePath));
// Generate random input values in the appropriate structure and size
var inputs = GetDictionary("features", 28*28, 255);
// We can call the evaluate method and get back the results (single layer)...
outputs = model.Evaluate(inputs, "ol.z", 10);
}
OutputResults("ol.z", 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");
}
}
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 and without input) and obtains a single layer output
/// </summary>
private static void EvaluateNetworkSingleLayerNoInput()
{
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;
using (var model = new IEvaluateModelManagedF())
{
// Initialize model evaluator
model.Init("deviceId=-1");
// Create the network
string networkDescription = GetFileContents(Path.Combine(workingDirectory, @"AddOperatorConstantNoInput.cntk"));
model.CreateNetwork(networkDescription);
// We can call the evaluate method and get back the results (single layer)...
outputs = model.Evaluate("ol", 1);
}
OutputResults("ol", 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");
}
}
