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Code Packages Releases Wiki Activity

Support YOLOv3 model from Darknet

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This commit is contained in:
Dmitry Kurtaev
2018-04-13 18:53:12 +03:00
parent 2129db6e91
commit 97fec07d96
8 changed files with 412 additions and 309 deletions
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modules/dnn/src/layers/region_layer.cpp
+45 -42
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@@ -59,7 +59,7 @@ class RegionLayerImpl CV_FINAL : public RegionLayer
public:
int coords, classes, anchors, classfix;
float thresh, nmsThreshold;
bool useSoftmaxTree, useSoftmax;
bool useSoftmax, useLogistic;
RegionLayerImpl(const LayerParams& params)
{
@@ -71,15 +71,17 @@ public:
classes = params.get<int>("classes", 0);
anchors = params.get<int>("anchors", 5);
classfix = params.get<int>("classfix", 0);
useSoftmaxTree = params.get<bool>("softmax_tree", false);
useSoftmax = params.get<bool>("softmax", false);
useLogistic = params.get<bool>("logistic", false);
nmsThreshold = params.get<float>("nms_threshold", 0.4);
CV_Assert(nmsThreshold >= 0.);
CV_Assert(coords == 4);
CV_Assert(classes >= 1);
CV_Assert(anchors >= 1);
CV_Assert(useSoftmaxTree || useSoftmax);
CV_Assert(useLogistic || useSoftmax);
if (params.get<bool>("softmax_tree", false))
CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -89,7 +91,7 @@ public:
{
CV_Assert(inputs.size() > 0);
CV_Assert(inputs[0][3] == (1 + coords + classes)*anchors);
outputs = std::vector<MatShape>(inputs.size(), shape(inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
outputs = std::vector<MatShape>(1, shape(inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
return false;
}
@@ -124,14 +126,13 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
// TODO: implement a logistic activation to classification scores.
if (useLogistic)
return false;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
if (useSoftmaxTree) { // Yolo 9000
CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
return false;
}
CV_Assert(inputs.size() >= 1);
int const cell_size = classes + coords + 1;
UMat blob_umat = blobs[0].getUMat(ACCESS_READ);
@@ -203,6 +204,7 @@ public:
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(inputs.size() >= 1);
CV_Assert(outputs.size() == 1);
int const cell_size = classes + coords + 1;
const float* biasData = blobs[0].ptr<float>();
@@ -214,6 +216,9 @@ public:
int rows = inpBlob.size[1];
int cols = inpBlob.size[2];
CV_Assert(inputs.size() < 2 || inputs[1]->dims == 4);
int hNorm = inputs.size() > 1 ? inputs[1]->size[2] : rows;
int wNorm = inputs.size() > 1 ? inputs[1]->size[3] : cols;
const float *srcData = inpBlob.ptr<float>();
float *dstData = outBlob.ptr<float>();
@@ -225,49 +230,47 @@ public:
dstData[index + 4] = logistic_activate(x); // logistic activation
}
if (useSoftmaxTree) { // Yolo 9000
CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
}
else if (useSoftmax) { // Yolo v2
if (useSoftmax) { // Yolo v2
// softmax activation for Probability, for each grid cell (X x Y x Anchor-index)
for (int i = 0; i < rows*cols*anchors; ++i) {
int index = cell_size*i;
softmax_activate(srcData + index + 5, classes, 1, dstData + index + 5);
}
for (int x = 0; x < cols; ++x)
for(int y = 0; y < rows; ++y)
for (int a = 0; a < anchors; ++a) {
int index = (y*cols + x)*anchors + a; // index for each grid-cell & anchor
int p_index = index * cell_size + 4;
float scale = dstData[p_index];
if (classfix == -1 && scale < .5) scale = 0; // if(t0 < 0.5) t0 = 0;
int box_index = index * cell_size;
dstData[box_index + 0] = (x + logistic_activate(srcData[box_index + 0])) / cols;
dstData[box_index + 1] = (y + logistic_activate(srcData[box_index + 1])) / rows;
dstData[box_index + 2] = exp(srcData[box_index + 2]) * biasData[2 * a] / cols;
dstData[box_index + 3] = exp(srcData[box_index + 3]) * biasData[2 * a + 1] / rows;
int class_index = index * cell_size + 5;
if (useSoftmaxTree) {
CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
}
else {
for (int j = 0; j < classes; ++j) {
float prob = scale*dstData[class_index + j]; // prob = IoU(box, object) = t0 * class-probability
dstData[class_index + j] = (prob > thresh) ? prob : 0; // if (IoU < threshold) IoU = 0;
}
}
}
}
else if (useLogistic) { // Yolo v3
for (int i = 0; i < rows*cols*anchors; ++i)
{
int index = cell_size*i;
const float* input = srcData + index + 5;
float* output = dstData + index + 5;
for (int i = 0; i < classes; ++i)
output[i] = logistic_activate(input[i]);
}
}
for (int x = 0; x < cols; ++x)
for(int y = 0; y < rows; ++y)
for (int a = 0; a < anchors; ++a) {
int index = (y*cols + x)*anchors + a; // index for each grid-cell & anchor
int p_index = index * cell_size + 4;
float scale = dstData[p_index];
if (classfix == -1 && scale < .5) scale = 0; // if(t0 < 0.5) t0 = 0;
int box_index = index * cell_size;
dstData[box_index + 0] = (x + logistic_activate(srcData[box_index + 0])) / cols;
dstData[box_index + 1] = (y + logistic_activate(srcData[box_index + 1])) / rows;
dstData[box_index + 2] = exp(srcData[box_index + 2]) * biasData[2 * a] / hNorm;
dstData[box_index + 3] = exp(srcData[box_index + 3]) * biasData[2 * a + 1] / wNorm;
int class_index = index * cell_size + 5;
for (int j = 0; j < classes; ++j) {
float prob = scale*dstData[class_index + j]; // prob = IoU(box, object) = t0 * class-probability
dstData[class_index + j] = (prob > thresh) ? prob : 0; // if (IoU < threshold) IoU = 0;
}
}
if (nmsThreshold > 0) {
do_nms_sort(dstData, rows*cols*anchors, thresh, nmsThreshold);
}
}
}