public void TestMean()
{
List <string> rgDs = new List <string>()
{
"MNIST", "CIFAR-10", "MNIST"
};
IXImageDatabase db = new MyCaffeImageDatabase();
foreach (string strDs in rgDs)
{
SettingsCaffe settings = new SettingsCaffe();
Stopwatch sw = new Stopwatch();
sw.Start();
db.InitializeWithDsName(settings, strDs);
string str = sw.ElapsedMilliseconds.ToString();
Trace.WriteLine(strDs + " Initialization Time: " + str + " ms.");
DatasetDescriptor ds = db.GetDatasetByName(strDs);
SimpleDatum d1 = db.QueryImageMean(ds.TrainingSource.ID);
SimpleDatum d2 = db.QueryImageMeanFromDataset(ds.ID);
SimpleDatum d3 = db.GetImageMean(ds.TrainingSource.ID);
byte[] rgB1 = d1.ByteData;
byte[] rgB2 = d2.ByteData;
byte[] rgB3 = d3.ByteData;
Assert.AreEqual(rgB1.Length, rgB2.Length);
Assert.AreEqual(rgB2.Length, rgB3.Length);
for (int i = 0; i < rgB1.Length; i++)
{
Assert.AreEqual(rgB1[i], rgB2[i]);
Assert.AreEqual(rgB2[i], rgB3[i]);
}
}
db.CleanUp();
IDisposable idisp = db as IDisposable;
if (idisp != null)
{
idisp.Dispose();
}
}
public void TestLoadLimitNextSequential(IMAGEDB_LOAD_METHOD loadMethod, int nLoadLimit)
{
List <string> rgDs = new List <string>()
{
"MNIST", "CIFAR-10", "MNIST"
};
IXImageDatabase db = new MyCaffeImageDatabase();
foreach (string strDs in rgDs)
{
DatasetFactory df = new DatasetFactory();
int nDs = df.GetDatasetID(strDs);
if (nDs == 0)
{
throw new Exception("The dataset '" + strDs + "' does not exist - you need to load it.");
}
SettingsCaffe settings = new SettingsCaffe();
settings.ImageDbLoadMethod = loadMethod;
settings.ImageDbLoadLimit = nLoadLimit;
Stopwatch sw = new Stopwatch();
sw.Start();
db.InitializeWithDsName(settings, strDs);
db.SetSelectionMethod(IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.NONE);
string str = sw.ElapsedMilliseconds.ToString();
Trace.WriteLine(strDs + " Initialization Time: " + str + " ms.");
DatasetDescriptor ds = db.GetDatasetByName(strDs);
Dictionary <int, List <SimpleDatum> > rg = new Dictionary <int, List <SimpleDatum> >();
Dictionary <int, List <SimpleDatum> > rgFirst = new Dictionary <int, List <SimpleDatum> >();
int nTotal = ds.TrainingSource.ImageCount;
int nCount = 0;
double dfTotalMs = 0;
while (nCount < nTotal)
{
for (int i = 0; i < nLoadLimit; i++)
{
sw.Reset();
sw.Start();
SimpleDatum d1 = db.QueryImage(ds.TrainingSource.ID, i);
dfTotalMs += sw.ElapsedMilliseconds;
sw.Stop();
if (!rg.Keys.Contains(d1.Index))
{
rg.Add(d1.Index, new List <SimpleDatum>()
{
d1
});
}
else
{
rg[d1.Index].Add(d1);
}
if (nCount == 0)
{
if (!rgFirst.Keys.Contains(d1.Index))
{
rgFirst.Add(d1.Index, new List <SimpleDatum>()
{
d1
});
}
else
{
rgFirst[d1.Index].Add(d1);
}
}
}
db.LoadNextSet(null);
nCount += nLoadLimit;
}
str = (dfTotalMs / (double)nCount).ToString();
Trace.WriteLine("Average Query Time: " + str + " ms.");
// Verify that all items have been queried
Assert.AreEqual(nTotal, rg.Count);
Dictionary <int, List <SimpleDatum> > rgWrapAround = new Dictionary <int, List <SimpleDatum> >();
for (int i = 0; i < nLoadLimit; i++)
{
SimpleDatum d1 = db.QueryImage(ds.TrainingSource.ID, i);
if (!rgWrapAround.Keys.Contains(d1.Index))
{
rgWrapAround.Add(d1.Index, new List <SimpleDatum>()
{
d1
});
}
else
{
rgWrapAround[d1.Index].Add(d1);
}
}
// Verify that the reads wrap around to the start.
Assert.AreEqual(rgWrapAround.Count, rgFirst.Count);
List <KeyValuePair <int, List <SimpleDatum> > > rg1 = new List <KeyValuePair <int, List <SimpleDatum> > >();
List <KeyValuePair <int, List <SimpleDatum> > > rg2 = new List <KeyValuePair <int, List <SimpleDatum> > >();
foreach (KeyValuePair <int, List <SimpleDatum> > kv in rgWrapAround)
{
rg1.Add(kv);
}
foreach (KeyValuePair <int, List <SimpleDatum> > kv in rgFirst)
{
rg2.Add(kv);
}
for (int i = 0; i < rg1.Count; i++)
{
Assert.AreEqual(rg1[i].Key, rg2[i].Key);
Assert.AreEqual(rg1[i].Value.Count, rg2[i].Value.Count);
for (int j = 0; j < rg1[i].Value.Count; j++)
{
Assert.AreEqual(rg1[i].Value[j].Label, rg2[i].Value[j].Label);
}
}
}
db.CleanUp();
IDisposable idisp = db as IDisposable;
if (idisp != null)
{
idisp.Dispose();
}
}
public void TestQueryPair(IMAGEDB_LOAD_METHOD loadMethod, int nLoadLimit)
{
List <string> rgDs = new List <string>()
{
"MNIST", "CIFAR-10", "MNIST"
};
IXImageDatabase db = new MyCaffeImageDatabase();
foreach (string strDs in rgDs)
{
SettingsCaffe settings = new SettingsCaffe();
settings.ImageDbLoadMethod = loadMethod;
settings.ImageDbLoadLimit = nLoadLimit;
Stopwatch sw = new Stopwatch();
sw.Start();
db.InitializeWithDsName(settings, strDs);
db.SetSelectionMethod(IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.NONE);
string str = sw.ElapsedMilliseconds.ToString();
Trace.WriteLine(strDs + " Initialization Time: " + str + " ms.");
DatasetDescriptor ds = db.GetDatasetByName(strDs);
Dictionary <int, List <SimpleDatum> > rg = new Dictionary <int, List <SimpleDatum> >();
int nCount = 100;
double dfTotalMs = 0;
for (int i = 0; i < nCount; i++)
{
sw.Reset();
sw.Start();
SimpleDatum d1 = db.QueryImage(ds.TrainingSource.ID, i);
SimpleDatum d2 = db.QueryImage(ds.TrainingSource.ID, i, IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.PAIR);
dfTotalMs += sw.ElapsedMilliseconds;
sw.Stop();
if (!rg.Keys.Contains(d1.Index))
{
rg.Add(d1.Index, new List <SimpleDatum>()
{
d1
});
}
else
{
rg[d1.Index].Add(d1);
}
if (nLoadLimit > 0)
{
Assert.AreEqual(true, d1.Index == d2.Index - 1 || d1.Index == nLoadLimit - 1 && d2.Index == 0);
}
else
{
Assert.AreEqual(d1.Index, d2.Index - 1);
}
}
str = (dfTotalMs / (double)nCount).ToString();
Trace.WriteLine("Average Query Time: " + str + " ms.");
// Verify that all labels are hit.
if (nLoadLimit > 0)
{
Assert.AreEqual(rg.Count, nLoadLimit);
}
}
db.CleanUp();
IDisposable idisp = db as IDisposable;
if (idisp != null)
{
idisp.Dispose();
}
}
public void TestQuerySequential4(IMAGEDB_LOAD_METHOD loadMethod, int nLoadLimit)
{
List <string> rgDs = new List <string>()
{
"MNIST", "CIFAR-10", "MNIST"
};
IXImageDatabase db = new MyCaffeImageDatabase();
foreach (string strDs in rgDs)
{
SettingsCaffe settings = new SettingsCaffe();
settings.ImageDbLoadMethod = loadMethod;
settings.ImageDbLoadLimit = nLoadLimit;
Stopwatch sw = new Stopwatch();
sw.Start();
db.InitializeWithDsName(settings, strDs);
db.SetSelectionMethod(IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.NONE);
string str = sw.ElapsedMilliseconds.ToString();
Trace.WriteLine(strDs + " Initialization Time: " + str + " ms.");
DatasetDescriptor ds = db.GetDatasetByName(strDs);
Dictionary <int, List <SimpleDatum> > rg = new Dictionary <int, List <SimpleDatum> >();
int nCount = 100;
double dfTotalMs = 0;
List <int> rgIdx = new List <int>();
for (int i = 0; i < nCount; i++)
{
sw.Reset();
sw.Start();
SimpleDatum d = db.QueryImage(ds.TrainingSource.ID, i);
dfTotalMs += sw.ElapsedMilliseconds;
sw.Stop();
if (!rg.Keys.Contains(d.Index))
{
rg.Add(d.Index, new List <SimpleDatum>()
{
d
});
}
else
{
rg[d.Index].Add(d);
}
rgIdx.Add(d.Index);
}
str = (dfTotalMs / (double)nCount).ToString();
Trace.WriteLine("Average Query Time: " + str + " ms.");
// Verify sequential selection.
int nIdx = 0;
rgIdx.Sort();
foreach (KeyValuePair <int, List <SimpleDatum> > kv in rg)
{
int nIdx1 = rgIdx[nIdx];
Assert.AreEqual(kv.Value.Count, (nLoadLimit == 0) ? 1 : nLoadLimit);
Assert.AreEqual(rg[nIdx1][0].Index, (nLoadLimit == 0) ? nIdx1 : nIdx1 % nLoadLimit);
nIdx++;
}
}
db.CleanUp();
IDisposable idisp = db as IDisposable;
if (idisp != null)
{
idisp.Dispose();
}
}
public void TestQuerySequential(IMAGEDB_LOAD_METHOD loadMethod, int nLoadLimit)
{
List <string> rgDs = new List <string>()
{
"MNIST", "CIFAR-10", "MNIST"
};
IXImageDatabase db = new MyCaffeImageDatabase();
foreach (string strDs in rgDs)
{
DatasetFactory df = new DatasetFactory();
int nDs = df.GetDatasetID(strDs);
if (nDs == 0)
{
throw new Exception("The dataset '" + strDs + "' does not exist - you need to load it.");
}
SettingsCaffe settings = new SettingsCaffe();
settings.ImageDbLoadMethod = loadMethod;
settings.ImageDbLoadLimit = nLoadLimit;
Stopwatch sw = new Stopwatch();
sw.Start();
db.InitializeWithDsName(settings, strDs);
string str = sw.ElapsedMilliseconds.ToString();
Trace.WriteLine(strDs + " Initialization Time: " + str + " ms.");
DatasetDescriptor ds = db.GetDatasetByName(strDs);
Dictionary <int, List <SimpleDatum> > rg = new Dictionary <int, List <SimpleDatum> >();
int nCount = 100;
double dfTotalMs = 0;
for (int i = 0; i < nCount; i++)
{
sw.Reset();
sw.Start();
SimpleDatum d = db.QueryImage(ds.TrainingSource.ID, 0, IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.NONE);
dfTotalMs += sw.ElapsedMilliseconds;
sw.Stop();
if (!rg.Keys.Contains(d.Index))
{
rg.Add(d.Index, new List <SimpleDatum>()
{
d
});
}
else
{
rg[d.Index].Add(d);
}
}
str = (dfTotalMs / (double)nCount).ToString();
Trace.WriteLine("Average Query Time: " + str + " ms.");
// Verify sequential selection, so all indexes should be the same.
foreach (KeyValuePair <int, List <SimpleDatum> > kv in rg)
{
Assert.AreEqual(kv.Value.Count, nCount);
}
}
db.CleanUp();
IDisposable idisp = db as IDisposable;
if (idisp != null)
{
idisp.Dispose();
}
}
public void TestQueryRandom2(IMAGEDB_LOAD_METHOD loadMethod, int nLoadLimit)
{
List <string> rgDs = new List <string>()
{
"MNIST", "CIFAR-10", "MNIST"
};
foreach (string strDs in rgDs)
{
IXImageDatabase db = new MyCaffeImageDatabase();
SettingsCaffe settings = new SettingsCaffe();
settings.ImageDbLoadMethod = loadMethod;
settings.ImageDbLoadLimit = nLoadLimit;
Stopwatch sw = new Stopwatch();
sw.Start();
db.InitializeWithDsName(settings, strDs);
db.SetSelectionMethod(IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.NONE);
string str = sw.ElapsedMilliseconds.ToString();
Trace.WriteLine(strDs + " Initialization Time: " + str + " ms.");
DatasetDescriptor ds = db.GetDatasetByName(strDs);
Dictionary <int, List <SimpleDatum> > rg = new Dictionary <int, List <SimpleDatum> >();
Dictionary <int, int> rgCounts = new Dictionary <int, int>();
int nCount = 10000;
double dfTotalMs = 0;
int nCount1 = 0;
double dfTotalMs1 = 0;
Stopwatch swTimer = new Stopwatch();
swTimer.Start();
// Randomly query each image and count up the number if times a given label is hit.
for (int i = 0; i < nCount; i++)
{
sw.Reset();
sw.Start();
SimpleDatum d = db.QueryImage(ds.TrainingSource.ID, 0, IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.RANDOM);
sw.Stop();
dfTotalMs += sw.ElapsedMilliseconds;
dfTotalMs1 += sw.ElapsedMilliseconds;
nCount1++;
if (!rg.Keys.Contains(d.Index))
{
rg.Add(d.Index, new List <SimpleDatum>()
{
d
});
}
else
{
rg[d.Index].Add(d);
}
if (!rgCounts.Keys.Contains(d.Label))
{
rgCounts.Add(d.Label, 1);
}
else
{
rgCounts[d.Label]++;
}
if (swTimer.Elapsed.TotalMilliseconds > 2000)
{
double dfPct = (double)i / (double)nCount;
Trace.WriteLine("(" + dfPct.ToString("P") + ") ave time = " + (dfTotalMs1 / nCount1).ToString("N3") + " ms.");
dfTotalMs1 = 0;
nCount1 = 0;
swTimer.Restart();
}
}
// Total the label counts and calculate the average and stddev.
List <KeyValuePair <int, int> > rgCountsNoLabelBalancing = rgCounts.OrderBy(p => p.Key).ToList();
Trace.WriteLine("NO LABEL BALANCING COUNTS");
CalculationArray ca = new CalculationArray();
foreach (KeyValuePair <int, int> kv in rgCountsNoLabelBalancing)
{
ca.Add(kv.Value);
Trace.WriteLine(kv.Key + " -> " + kv.Value.ToString("N0"));
}
double dfAve = ca.Average;
double dfStdDev1 = ca.CalculateStandardDeviation(dfAve);
Trace.WriteLine("Average = " + dfAve.ToString());
Trace.WriteLine("StdDev = " + dfStdDev1.ToString());
// Load the labels by first selecting the label randomly and then the image randomly from the label set.
rg = new Dictionary <int, List <SimpleDatum> >();
rgCounts = new Dictionary <int, int>();
for (int i = 0; i < nCount; i++)
{
sw.Reset();
sw.Start();
SimpleDatum d = db.QueryImage(ds.TrainingSource.ID, 0, IMGDB_LABEL_SELECTION_METHOD.RANDOM, IMGDB_IMAGE_SELECTION_METHOD.RANDOM);
dfTotalMs += sw.ElapsedMilliseconds;
sw.Stop();
if (!rg.Keys.Contains(d.Index))
{
rg.Add(d.Index, new List <SimpleDatum>()
{
d
});
}
else
{
rg[d.Index].Add(d);
}
if (!rgCounts.Keys.Contains(d.Label))
{
rgCounts.Add(d.Label, 1);
}
else
{
rgCounts[d.Label]++;
}
}
// Total the balanced label counts and calculate the average and stddev.
List <KeyValuePair <int, int> > rgCountsLabelBalancing = rgCounts.OrderBy(p => p.Key).ToList();
Trace.WriteLine("LABEL BALANCING COUNTS");
ca = new CalculationArray();
foreach (KeyValuePair <int, int> kv in rgCountsLabelBalancing)
{
ca.Add(kv.Value);
Trace.WriteLine(kv.Key + " -> " + kv.Value.ToString("N0"));
}
dfAve = ca.Average;
double dfStdDev2 = ca.CalculateStandardDeviation(dfAve);
Trace.WriteLine("Average = " + dfAve.ToString());
Trace.WriteLine("StdDev = " + dfStdDev2.ToString());
Assert.AreEqual(true, dfStdDev2 < dfStdDev1 * 1.5);
str = (dfTotalMs / (double)(nCount * 2)).ToString();
Trace.WriteLine("Average Query Time: " + str + " ms.");
db.CleanUp();
IDisposable idisp = db as IDisposable;
if (idisp != null)
{
idisp.Dispose();
}
}
}