public bool Run()
{
PrepareData();
var graph = new Graph();
//import GraphDef from pb file
graph.Import(Path.Join(Config.Args.ModelFilePath));
// モデルに応じて決まる
var input_name = graph.First().name;
var output_name = graph.Last().name;
var input_operation = graph.OperationByName(input_name);
var output_operation = graph.OperationByName(output_name);
var labels = File.ReadAllLines(Path.Join(Config.Args.LabelFilePath));
var result_labels = new List <string>();
var sw = new Stopwatch();
var predicts = new PredictResult()
{
Items = new List <PredictResultItem>(),
};
using (var sess = tf.Session(graph))
{
//foreach (var nd in file_ndarrays)
for (int i = 0; i < file_ndarrays.Count; i++)
{
var nd = file_ndarrays[i];
var files = this.imagePathList[i];
sw.Restart();
var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd));
results = np.squeeze(results);
var singleNd = results.ToArray <float>();
var idxs = np.argsort <double>(results)["::-1"]; // high confident order
var idx = idxs[0]; // prediction result
Console.WriteLine($"{files[0]} in {sw.ElapsedMilliseconds}ms", Color.Tan);
for (int j = 0; j < idxs.Shape[0]; j++)
{
Console.WriteLine($" {idxs[j]}) {labels[idxs[j]]} {singleNd[idxs[j]]}", Color.Tan);
}
result_labels.Add(labels[idx]);
var resultArr = results.ToArray <float>();
predicts.Items.Add(new PredictResultItem()
{
PathList = new List <string>(files),
Predicts = new List <float>(resultArr),
TopIndex = idx,
});;
}
}
// output file
predicts.WriteFile(Config.Args.OutputPath, labels);
return(predicts.Items.Count > 0);
}
public bool Run()
{
var graph = new Graph();
//import GraphDef from pb file
graph.Import(Path.Join(Config.Args.ModelFilePath));
// モデルに応じて決まる
var input_name = graph.First().name;
var output_name = graph.Last().name;
var input_operation = graph.OperationByName(input_name);
var output_operation = graph.OperationByName(output_name);
var labels = File.ReadAllLines(Path.Join(Config.Args.LabelFilePath));
var result_labels = new List <string>();
var sw = new Stopwatch();
sw.Start();
var predicts = new PredictResult()
{
Items = new List <PredictResultItem>(),
};
using (var sess = tf.Session(graph))
{
Console.WriteLine($"Initialized in {sw.ElapsedMilliseconds}ms", Color.Tan);
sw.Restart();
// mini batch
foreach (BatchContainer bc in PrepareData(Config.Args.BatchSize))
{
// source
var files = new List <string[]>(bc.Files);
var nd = bc.Nd;
// prediction process
var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd));
// per file pair
double elapsed = (double)sw.ElapsedMilliseconds / files.Count;
// output
for (int i = 0; i < files.Count; i++)
{
var singleResults = results[i];
var singleNd = singleResults.ToArray <float>();
var singleFiles = files[i];
var idxs = np.argsort <double>(singleResults)["::-1"]; // high confident order
var idx = idxs[0]; // prediction result
Console.WriteLine($"{singleFiles[0]} in {elapsed}ms", Color.Tan);
for (int j = 0; j < idxs.Shape[0]; j++)
{
Console.WriteLine($" {idxs[j]}) {labels[idxs[j]]} {singleNd[idxs[j]]}", Color.Tan);
}
result_labels.Add(labels[idx]);
predicts.Items.Add(new PredictResultItem()
{
PathList = new List <string>(singleFiles),
Predicts = new List <float>(singleNd),
TopIndex = idx,
});
}
sw.Restart();
}
}
// output file
predicts.WriteFile(Config.Args.OutputPath, labels);
return(predicts.Items.Count > 0);
}
