/// <summary>
/// Evaluate the detection output.
/// </summary>
/// <param name="colBottom">bottom input Blob vector (Length 2)
/// -# @f$ (1 \times 1 \times N \times 7) @f$ N detection results.
/// -# @f$ (1 \times 1 \times M \times 7) @f$ M ground truth.
/// </param>
/// <param name="colTop">top otuput Blob vector (Length 1)
/// -# @f$ (1 \times 1 \times N \times 4) @f$ N is the number of detections, and each row is: [image_id, label, confidence, true_pos, false_pos].
/// </param>
protected override void forward(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
float[] rgfDetData = convertF(colBottom[0].mutable_cpu_data);
float[] rgfGtData = convertF(colBottom[1].mutable_cpu_data);
// Retrieve all detection results.
Dictionary <int, LabelBBox> rgAllDetections = m_bboxUtil.GetDetectionResults(rgfDetData, colBottom[0].height, m_nBackgroundLabelId);
// Retrieve all ground truth (including difficult ones).
Dictionary <int, LabelBBox> rgAllGtBboxes = m_bboxUtil.GetGroundTruthEx(rgfGtData, colBottom[1].height, m_nBackgroundLabelId, true);
colTop[0].SetData(0);
float[] rgfTopData = convertF(colTop[0].mutable_cpu_data);
int nNumDet = 0;
// Insert number of ground truth for each label.
Dictionary <int, int> rgNumPos = new Dictionary <int, int>();
List <KeyValuePair <int, LabelBBox> > rgAllGtBboxList = rgAllGtBboxes.ToList();
foreach (KeyValuePair <int, LabelBBox> kv in rgAllGtBboxList)
{
List <KeyValuePair <int, List <NormalizedBBox> > > kvLabels = kv.Value.ToList();
foreach (KeyValuePair <int, List <NormalizedBBox> > kvLabel in kvLabels)
{
int nCount = 0;
if (m_bEvaluateDifficultGt)
{
nCount = kvLabel.Value.Count;
}
else
{
// Get number of non difficult ground truth.
for (int i = 0; i < kvLabel.Value.Count; i++)
{
if (kvLabel.Value[i].difficult)
{
nCount++;
}
}
}
if (!rgNumPos.ContainsKey(kvLabel.Key))
{
rgNumPos.Add(kvLabel.Key, nCount);
}
else
{
rgNumPos[kvLabel.Key] += nCount;
}
}
}
for (int c = 0; c < m_nNumClasses; c++)
{
if (c == m_nBackgroundLabelId)
{
continue;
}
rgfTopData[nNumDet * 5 + 0] = -1;
rgfTopData[nNumDet * 5 + 1] = c;
if (!rgNumPos.ContainsKey(c))
{
rgfTopData[nNumDet * 5 + 2] = 0;
}
else
{
rgfTopData[nNumDet * 5 + 2] = rgNumPos[c];
}
rgfTopData[nNumDet * 5 + 3] = -1;
rgfTopData[nNumDet * 5 + 4] = -1;
nNumDet++;
}
// Insert detection evaluate status.
foreach (KeyValuePair <int, LabelBBox> kv in rgAllDetections)
{
int nImageId = kv.Key;
LabelBBox detections = kv.Value;
// No ground truth for current image. All detections become false_pos.
if (!rgAllGtBboxes.ContainsKey(nImageId))
{
List <KeyValuePair <int, List <NormalizedBBox> > > kvLabels = detections.ToList();
foreach (KeyValuePair <int, List <NormalizedBBox> > kvLabel in kvLabels)
{
int nLabel = kvLabel.Key;
if (nLabel == -1)
{
continue;
}
List <NormalizedBBox> bboxes = kvLabel.Value;
for (int i = 0; i < bboxes.Count; i++)
{
rgfTopData[nNumDet * 5 + 0] = nImageId;
rgfTopData[nNumDet * 5 + 1] = nLabel;
rgfTopData[nNumDet * 5 + 2] = bboxes[i].score;
rgfTopData[nNumDet * 5 + 3] = 0;
rgfTopData[nNumDet * 5 + 4] = 1;
nNumDet++;
}
}
}
// Gound truth's exist for current image.
else
{
LabelBBox label_bboxes = rgAllGtBboxes[nImageId];
List <KeyValuePair <int, List <NormalizedBBox> > > kvLabels = detections.ToList();
foreach (KeyValuePair <int, List <NormalizedBBox> > kvLabel in kvLabels)
{
int nLabel = kvLabel.Key;
if (nLabel == -1)
{
continue;
}
List <NormalizedBBox> bboxes = kvLabel.Value;
// No ground truth for current label. All detectiosn become false_pos
if (!label_bboxes.Contains(nLabel))
{
for (int i = 0; i < bboxes.Count; i++)
{
rgfTopData[nNumDet * 5 + 0] = nImageId;
rgfTopData[nNumDet * 5 + 1] = nLabel;
rgfTopData[nNumDet * 5 + 2] = bboxes[i].score;
rgfTopData[nNumDet * 5 + 3] = 0;
rgfTopData[nNumDet * 5 + 4] = 1;
nNumDet++;
}
}
// Ground truth for current label found.
else
{
List <NormalizedBBox> gt_bboxes = label_bboxes[nLabel];
// Scale ground truth if needed.
if (!m_bUseNormalizedBbox)
{
m_log.CHECK_LE(m_nCount, m_rgSizes.Count, "The count must be <= the sizes count.");
for (int i = 0; i < gt_bboxes.Count; i++)
{
gt_bboxes[i] = m_bboxUtil.Output(gt_bboxes[i], m_rgSizes[m_nCount], m_resizeParam);
}
}
List <bool> rgbVisited = Utility.Create <bool>(gt_bboxes.Count, false);
// Sort detections in decending order based on scores.
if (bboxes.Count > 1)
{
bboxes.Sort(new Comparison <NormalizedBBox>(sortBboxDescending));
}
for (int i = 0; i < bboxes.Count; i++)
{
rgfTopData[nNumDet * 5 + 0] = nImageId;
rgfTopData[nNumDet * 5 + 1] = nLabel;
rgfTopData[nNumDet * 5 + 2] = bboxes[i].score;
if (!m_bUseNormalizedBbox)
{
bboxes[i] = m_bboxUtil.Output(bboxes[i], m_rgSizes[m_nCount], m_resizeParam);
}
// Compare with each ground truth bbox.
float fOverlapMax = -1;
int nJmax = -1;
for (int j = 0; j < gt_bboxes.Count; j++)
{
float fOverlap = m_bboxUtil.JaccardOverlap(bboxes[i], gt_bboxes[j], m_bUseNormalizedBbox);
if (fOverlap > fOverlapMax)
{
fOverlapMax = fOverlap;
nJmax = j;
}
}
if (fOverlapMax >= m_fOverlapThreshold)
{
if (m_bEvaluateDifficultGt || (!m_bEvaluateDifficultGt && !gt_bboxes[nJmax].difficult))
{
// True positive.
if (!rgbVisited[nJmax])
{
rgfTopData[nNumDet * 5 + 3] = 1;
rgfTopData[nNumDet * 5 + 4] = 0;
rgbVisited[nJmax] = true;
}
// False positive (multiple detectioN).
else
{
rgfTopData[nNumDet * 5 + 3] = 0;
rgfTopData[nNumDet * 5 + 4] = 1;
}
}
}
else
{
// False positive.
rgfTopData[nNumDet * 5 + 3] = 0;
rgfTopData[nNumDet * 5 + 4] = 1;
}
nNumDet++;
}
}
}
}
if (m_rgSizes.Count > 0)
{
m_nCount++;
// Reset count after a full iteration through the DB.
if (m_nCount == m_rgSizes.Count)
{
m_nCount = 0;
}
}
}
colTop[0].mutable_cpu_data = convert(rgfTopData);
}
/// <summary>
/// Do non-maximum suppression (nms) on prediction results.
/// </summary>
/// <param name="colBottom">bottom input Blob vector (at least 2)
/// -# @f$ (N \times C1 \times 1 \times 1) @f$ the location predictions with C1 predictions.
/// -# @f$ (N \times C2 \times 1 \times 1) @f$ the confidence predictions with C2 predictions.
/// -# @f$ (N \times 2 \times C3 \times 1) @f$ the prior bounding boxes with C3 values.
/// </param>
/// <param name="colTop">top otuput Blob vector (Length 1)
/// -# @f$ (1 \times 1 \times N \times 7) @f$ N is the number of detections after, and each row is: [image_id, label, confidence, xmin, ymin, xmax, ymax].
/// </param>
protected override void forward(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
float[] rgfLocData = convertF(colBottom[0].mutable_cpu_data);
float[] rgfConfData = convertF(colBottom[1].mutable_cpu_data);
float[] rgfPriorData = convertF(colBottom[2].mutable_cpu_data);
int nNum = colBottom[0].num;
// Retrieve all location predictions.
List <LabelBBox> rgAllLocPreds = m_bboxUtil.GetLocPredictions(rgfLocData, nNum, m_nNumPriors, m_nNumLocClasses, m_bShareLocations);
// Retrieve all confidence scores.
List <Dictionary <int, List <float> > > rgAllConfScores = m_bboxUtil.GetConfidenceScores(rgfConfData, nNum, m_nNumPriors, m_nNumClasses);
// Retrieve all prior bboxes, which is the same within a batch since we assume all
// images in a batch are of the same dimension.
List <List <float> > rgrgPriorVariances;
List <NormalizedBBox> rgPriorBboxes = m_bboxUtil.GetPrior(rgfPriorData, m_nNumPriors, out rgrgPriorVariances);
// Decode all loc predictions to bboxes.
bool bClipBbox = false;
List <LabelBBox> rgAllDecodeBboxes = m_bboxUtil.DecodeAll(rgAllLocPreds, rgPriorBboxes, rgrgPriorVariances, nNum, m_bShareLocations, m_nNumLocClasses, m_nBackgroundLabelId, m_codeType, m_bVarianceEncodedInTarget, bClipBbox);
int nNumKept = 0;
List <Dictionary <int, List <int> > > rgAllIndices = new List <Dictionary <int, List <int> > >();
for (int i = 0; i < nNum; i++)
{
LabelBBox decode_bboxes = rgAllDecodeBboxes[i];
Dictionary <int, List <float> > rgConfScores = rgAllConfScores[i];
Dictionary <int, List <int> > rgIndices = new Dictionary <int, List <int> >();
int nNumDet = 0;
for (int c = 0; c < m_nNumClasses; c++)
{
// Ignore background class.
if (c == m_nBackgroundLabelId)
{
continue;
}
// Something bad happened if there are no predictions for the current label.
if (!rgConfScores.ContainsKey(c))
{
m_log.FAIL("Could not find confidence predictions for label '" + c.ToString() + "'!");
}
List <float> rgfScores = rgConfScores[c];
int nLabel = (m_bShareLocations) ? -1 : c;
// Something bad happened if there are no locations for the current label.
if (!decode_bboxes.Contains(nLabel))
{
m_log.FAIL("Could not find location predictions for the label '" + nLabel.ToString() + "'!");
}
List <NormalizedBBox> rgBboxes = decode_bboxes[nLabel];
List <int> rgIndexes;
m_bboxUtil.ApplyNMSFast(rgBboxes, rgfScores, m_fConfidenceThreshold, m_fNmsThreshold, m_fEta, m_nTopK, out rgIndexes);
rgIndices[c] = rgIndexes;
nNumDet += rgIndices[c].Count;
}
if (m_nKeepTopK > -1 && nNumDet > m_nKeepTopK)
{
List <Tuple <float, Tuple <int, int> > > rgScoreIndexPairs = new List <Tuple <float, Tuple <int, int> > >();
foreach (KeyValuePair <int, List <int> > kv in rgIndices)
{
int nLabel = kv.Key;
List <int> rgLabelIndices = kv.Value;
// Something bad happend for the current label.
if (!rgConfScores.ContainsKey(nLabel))
{
m_log.FAIL("Could not find location predictions for label " + nLabel.ToString() + "!");
}
List <float> rgScores = rgConfScores[nLabel];
for (int j = 0; j < rgLabelIndices.Count; j++)
{
int nIdx = rgLabelIndices[j];
m_log.CHECK_LT(nIdx, rgScores.Count, "The current index must be less than the number of scores!");
rgScoreIndexPairs.Add(new Tuple <float, Tuple <int, int> >(rgScores[nIdx], new Tuple <int, int>(nLabel, nIdx)));
}
}
// Keep top k results per image.
rgScoreIndexPairs = rgScoreIndexPairs.OrderByDescending(p => p.Item1).ToList();
if (rgScoreIndexPairs.Count > m_nKeepTopK)
{
rgScoreIndexPairs = rgScoreIndexPairs.Take(m_nKeepTopK).ToList();
}
// Store the new indices.
Dictionary <int, List <int> > rgNewIndices = new Dictionary <int, List <int> >();
for (int j = 0; j < rgScoreIndexPairs.Count; j++)
{
int nLabel = rgScoreIndexPairs[j].Item2.Item1;
int nIdx = rgScoreIndexPairs[j].Item2.Item2;
if (!rgNewIndices.ContainsKey(nLabel))
{
rgNewIndices.Add(nLabel, new List <int>());
}
rgNewIndices[nLabel].Add(nIdx);
}
rgAllIndices.Add(rgNewIndices);
nNumKept += m_nKeepTopK;
}
else
{
rgAllIndices.Add(rgIndices);
nNumKept += nNumDet;
}
}
List <int> rgTopShape = Utility.Create <int>(2, 1);
rgTopShape.Add(nNumKept);
rgTopShape.Add(7);
float[] rgfTopData = null;
if (nNumKept == 0)
{
m_log.WriteLine("WARNING: Could not find any detections.");
rgTopShape[2] = nNum;
colTop[0].Reshape(rgTopShape);
colTop[0].SetData(-1);
rgfTopData = convertF(colTop[0].mutable_cpu_data);
int nOffset = 0;
// Generate fake results per image.
for (int i = 0; i < nNum; i++)
{
rgfTopData[nOffset + 0] = i;
nOffset += 7;
}
}
else
{
colTop[0].Reshape(rgTopShape);
rgfTopData = convertF(colTop[0].mutable_cpu_data);
}
int nCount = 0;
string strDir = m_strOutputDir;
for (int i = 0; i < nNum; i++)
{
Dictionary <int, List <float> > rgConfScores = rgAllConfScores[i];
LabelBBox decode_bboxes = rgAllDecodeBboxes[i];
foreach (KeyValuePair <int, List <int> > kv in rgAllIndices[i])
{
int nLabel = kv.Key;
// Something bad happened if there are no predictions for the current label.
if (!rgConfScores.ContainsKey(nLabel))
{
m_log.FAIL("Could not find confidence predictions for label '" + nLabel.ToString() + "'!");
}
List <float> rgfScores = rgConfScores[nLabel];
int nLocLabel = (m_bShareLocations) ? -1 : nLabel;
// Something bad happened if therea re no predictions for the current label.
if (!decode_bboxes.Contains(nLocLabel))
{
m_log.FAIL("COuld not find location predictions for label '" + nLabel.ToString() + "'!");
}
List <NormalizedBBox> rgBboxes = decode_bboxes[nLocLabel];
List <int> rgIndices = kv.Value;
if (m_bNeedSave)
{
m_log.CHECK(m_rgLabelToName.ContainsKey(nLabel), "The label to name mapping does not contain the label '" + nLabel.ToString() + "'!");
m_log.CHECK_LT(m_nNameCount, m_rgstrNames.Count, "The name count must be less than the number of names.");
}
for (int j = 0; j < rgIndices.Count; j++)
{
int nIdx = rgIndices[j];
rgfTopData[nCount * 7 + 0] = i;
rgfTopData[nCount * 7 + 1] = nLabel;
rgfTopData[nCount * 7 + 2] = rgfScores[nIdx];
NormalizedBBox bbox = rgBboxes[nIdx];
rgfTopData[nCount * 7 + 3] = bbox.xmin;
rgfTopData[nCount * 7 + 4] = bbox.ymin;
rgfTopData[nCount * 7 + 5] = bbox.xmax;
rgfTopData[nCount * 7 + 6] = bbox.ymax;
if (m_bNeedSave)
{
NormalizedBBox out_bbox = m_bboxUtil.Output(bbox, m_rgSizes[m_nNameCount], m_resizeParam);
float fScore = rgfTopData[nCount * 7 + 2];
float fXmin = out_bbox.xmin;
float fYmin = out_bbox.ymin;
float fXmax = out_bbox.xmax;
float fYmax = out_bbox.ymax;
PropertyTree pt_xmin = new PropertyTree();
pt_xmin.Put("", Math.Round(fXmin * 100) / 100);
PropertyTree pt_ymin = new PropertyTree();
pt_ymin.Put("", Math.Round(fYmin * 100) / 100);
PropertyTree pt_wd = new PropertyTree();
pt_wd.Put("", Math.Round((fXmax - fXmin) * 100) / 100);
PropertyTree pt_ht = new PropertyTree();
pt_ht.Put("", Math.Round((fYmax - fYmin) * 100) / 100);
PropertyTree cur_bbox = new PropertyTree();
cur_bbox.AddChild("", pt_xmin);
cur_bbox.AddChild("", pt_ymin);
cur_bbox.AddChild("", pt_wd);
cur_bbox.AddChild("", pt_ht);
PropertyTree cur_det = new PropertyTree();
cur_det.Put("image_id", m_rgstrNames[m_nNameCount]);
if (m_outputFormat == SaveOutputParameter.OUTPUT_FORMAT.ILSVRC)
{
cur_det.Put("category_id", nLabel);
}
else
{
cur_det.Put("category_id", m_rgLabelToName[nLabel]);
}
cur_det.AddChild("bbox", cur_bbox);
cur_det.Put("score", fScore);
m_detections.AddChild("", cur_det);
}
nCount++;
}
}
if (m_bNeedSave)
{
m_nNameCount++;
if (m_nNameCount % m_nNumTestImage == 0)
{
if (m_outputFormat == SaveOutputParameter.OUTPUT_FORMAT.VOC)
{
Dictionary <string, StreamWriter> rgOutFiles = new Dictionary <string, StreamWriter>();
for (int c = 0; c < m_nNumClasses; c++)
{
if (c == m_nBackgroundLabelId)
{
continue;
}
string strLabelName = m_rgLabelToName[c];
string strFile = getFileName(strLabelName, "txt");
rgOutFiles.Add(strLabelName, new StreamWriter(strFile));
}
foreach (PropertyTree pt in m_detections.Children)
{
string strLabel = pt.Get("category_id").Value;
if (!rgOutFiles.ContainsKey(strLabel))
{
m_log.WriteLine("WARNING! Cannot find '" + strLabel + "' label in the output files!");
continue;
}
string strImageName = pt.Get("image_id").Value;
float fScore = (float)pt.Get("score").Numeric;
List <int> bbox = new List <int>();
foreach (Property elm in pt.GetChildren("bbox"))
{
bbox.Add((int)elm.Numeric);
}
string strLine = strImageName;
strLine += " " + fScore.ToString();
strLine += " " + bbox[0].ToString() + " " + bbox[1].ToString();
strLine += " " + (bbox[0] + bbox[2]).ToString();
strLine += " " + (bbox[1] + bbox[3]).ToString();
rgOutFiles[strLabel].WriteLine(strLine);
}
for (int c = 0; c < m_nNumClasses; c++)
{
if (c == m_nBackgroundLabelId)
{
continue;
}
string strLabel = m_rgLabelToName[c];
rgOutFiles[strLabel].Flush();
rgOutFiles[strLabel].Close();
rgOutFiles[strLabel].Dispose();
}
}
else if (m_outputFormat == SaveOutputParameter.OUTPUT_FORMAT.COCO)
{
string strFile = getFileName("", "json");
using (StreamWriter sw = new StreamWriter(strFile))
{
PropertyTree output = new PropertyTree();
output.AddChild("detections", m_detections);
string strOut = output.ToJson();
sw.Write(strOut);
}
}
else if (m_outputFormat == SaveOutputParameter.OUTPUT_FORMAT.ILSVRC)
{
string strFile = getFileName("", "txt");
using (StreamWriter sw = new StreamWriter(strFile))
{
foreach (PropertyTree pt in m_detections.Children)
{
int nLabel = (int)pt.Get("category_id").Numeric;
string strImageName = pt.Get("image_id").Value;
float fScore = (float)pt.Get("score").Numeric;
List <int> bbox = new List <int>();
foreach (Property elm in pt.GetChildren("bbox"))
{
bbox.Add((int)elm.Numeric);
}
string strLine = strImageName;
strLine += " " + fScore.ToString();
strLine += " " + bbox[0].ToString() + " " + bbox[1].ToString();
strLine += " " + (bbox[0] + bbox[2]).ToString();
strLine += " " + (bbox[1] + bbox[3]).ToString();
sw.WriteLine(strLine);
}
}
}
m_nNameCount = 0;
m_detections.Clear();
}
}
if (m_bVisualize)
{
#warning DetectionOutputLayer - does not visualize detections yet.
// TBD.
}
}
colTop[0].mutable_cpu_data = convert(rgfTopData);
colTop[0].type = BLOB_TYPE.MULTIBBOX;
}
