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PaddleDetection
提交 192d2938 编写于 作者: D dengkaipeng
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use stable Sigmoid Cross Entropy implement. test=develop

上级 245b1f05
隐藏空白更改
内联 并排
Showing with 208 addition and 174 deletion
paddle/fluid/operators/yolov3_loss_op.cc
浏览文件 @ 192d2938
......@@ -99,6 +99,10 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>("anchors",
"The anchor width and height, "
"it will be parsed pair by pair.");
AddAttr<int>("input_size",
"The input size of YOLOv3 net, "
"generally this is set as 320, 416 or 608.")
.SetDefault(406);
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.");
AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
......
paddle/fluid/operators/yolov3_loss_op.h
浏览文件 @ 192d2938
......@@ -33,87 +33,91 @@ static inline bool isZero(T x) {
}
template <typename T>
static inline T sigmoid(T x) {
return 1.0 / (exp(-1.0 * x) + 1.0);
}
static inline T CalcMSEWithWeight(const Tensor& x, const Tensor& y,
const Tensor& weight, const T mf) {
int numel = static_cast<int>(x.numel());
const T* x_data = x.data<T>();
const T* y_data = y.data<T>();
const T* weight_data = weight.data<T>();
template <typename T>
static inline T CalcMaskPointNum(const Tensor& mask) {
auto mask_t = EigenVector<int>::Flatten(mask);
T count = 0.0;
for (int i = 0; i < mask_t.dimensions()[0]; i++) {
if (mask_t(i)) {
count += 1.0;
}
T error_sum = 0.0;
for (int i = 0; i < numel; i++) {
T xi = x_data[i];
T yi = y_data[i];
T weighti = weight_data[i];
error_sum += pow(yi - xi, 2) * weighti;
}
return count;
return error_sum / mf;
}
template <typename T>
static inline T CalcMSEWithMask(const Tensor& x, const Tensor& y,
const Tensor& mask) {
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
T error_sum = 0.0;
T points = 0.0;
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
error_sum += pow(x_t(i) - y_t(i), 2);
points += 1;
}
static void CalcMSEGradWithWeight(Tensor* grad, const Tensor& x,
const Tensor& y, const Tensor& weight,
const T mf) {
int numel = static_cast<int>(grad->numel());
T* grad_data = grad->data<T>();
const T* x_data = x.data<T>();
const T* y_data = y.data<T>();
const T* weight_data = weight.data<T>();
for (int i = 0; i < numel; i++) {
grad_data[i] = 2.0 * weight_data[i] * (x_data[i] - y_data[i]) / mf;
}
return (error_sum / points);
}
template <typename T>
static void CalcMSEGradWithMask(Tensor* grad, const Tensor& x, const Tensor& y,
const Tensor& mask, T mf) {
auto grad_t = EigenVector<T>::Flatten(*grad).setConstant(0.0);
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
grad_t(i) = 2.0 * (x_t(i) - y_t(i)) / mf;
}
struct SigmoidCrossEntropyForward {
T operator()(const T& x, const T& label) const {
T term1 = (x > 0) ? x : 0;
T term2 = x * label;
T term3 = std::log(static_cast<T>(1.0) + std::exp(-(std::abs(x))));
return term1 - term2 + term3;
}
}
};
template <typename T>
static inline T CalcBCEWithMask(const Tensor& x, const Tensor& y,
const Tensor& mask) {
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
struct SigmoidCrossEntropyBackward {
T operator()(const T& x, const T& label) const {
T sigmoid_x =
static_cast<T>(1.0) / (static_cast<T>(1.0) + std::exp(-1.0 * x));
return sigmoid_x - label;
}
};
T error_sum = 0.0;
T points = 0.0;
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
error_sum +=
-1.0 * (y_t(i) * log(x_t(i)) + (1.0 - y_t(i)) * log(1.0 - x_t(i)));
points += 1;
}
template <typename T>
static inline T CalcSCEWithWeight(const Tensor& x, const Tensor& labels,
const Tensor& weight, const T mf) {
int numel = x.numel();
const T* x_data = x.data<T>();
const T* labels_data = labels.data<T>();
const T* weight_data = weight.data<T>();
T loss = 0.0;
for (int i = 0; i < numel; i++) {
T xi = x_data[i];
T labeli = labels_data[i];
T weighti = weight_data[i];
loss += ((xi > 0.0 ? xi : 0.0) - xi * labeli +
std::log(1.0 + std::exp(-1.0 * std::abs(xi)))) *
weighti;
}
return (error_sum / points);
return loss / mf;
}
template <typename T>
static inline void CalcBCEGradWithMask(Tensor* grad, const Tensor& x,
const Tensor& y, const Tensor& mask,
T mf) {
auto grad_t = EigenVector<T>::Flatten(*grad).setConstant(0.0);
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
grad_t(i) = ((1.0 - y_t(i)) / (1.0 - x_t(i)) - y_t(i) / x_t(i)) / mf;
}
static inline void CalcSCEGradWithWeight(Tensor* grad, const Tensor& x,
const Tensor& labels,
const Tensor& weight, const T mf) {
int numel = grad->numel();
T* grad_data = grad->data<T>();
const T* x_data = x.data<T>();
const T* labels_data = labels.data<T>();
const T* weight_data = weight.data<T>();
for (int i = 0; i < numel; i++) {
grad_data[i] = (1.0 / (1.0 + std::exp(-1.0 * x_data[i])) - labels_data[i]) *
weight_data[i] / mf;
}
}
......@@ -139,21 +143,20 @@ static void CalcPredResult(const Tensor& input, Tensor* pred_conf,
for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
for (int j = 0; j < h; j++) {
for (int k = 0; k < w; k++) {
pred_x_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx, j, k));
pred_x_t(i, an_idx, j, k) = input_t(i, box_attr_num * an_idx, j, k);
pred_y_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx + 1, j, k));
input_t(i, box_attr_num * an_idx + 1, j, k);
pred_w_t(i, an_idx, j, k) =
input_t(i, box_attr_num * an_idx + 2, j, k);
pred_h_t(i, an_idx, j, k) =
input_t(i, box_attr_num * an_idx + 3, j, k);
pred_conf_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx + 4, j, k));
input_t(i, box_attr_num * an_idx + 4, j, k);
for (int c = 0; c < class_num; c++) {
pred_class_t(i, an_idx, j, k, c) =
sigmoid(input_t(i, box_attr_num * an_idx + 5 + c, j, k));
input_t(i, box_attr_num * an_idx + 5 + c, j, k);
}
}
}
......@@ -188,21 +191,22 @@ static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
template <typename T>
static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
const float ignore_thresh, std::vector<int> anchors,
const int grid_size, Tensor* obj_mask,
Tensor* noobj_mask, Tensor* tx, Tensor* ty,
Tensor* tw, Tensor* th, Tensor* tconf,
Tensor* tclass) {
const int input_size, const int grid_size,
Tensor* obj_mask, Tensor* noobj_mask, Tensor* tx,
Tensor* ty, Tensor* tw, Tensor* th, Tensor* tweight,
Tensor* tconf, Tensor* tclass) {
const int n = gt_box.dims()[0];
const int b = gt_box.dims()[1];
const int anchor_num = anchors.size() / 2;
auto gt_box_t = EigenTensor<T, 3>::From(gt_box);
auto gt_label_t = EigenTensor<int, 2>::From(gt_label);
auto obj_mask_t = EigenTensor<int, 4>::From(*obj_mask).setConstant(0);
auto noobj_mask_t = EigenTensor<int, 4>::From(*noobj_mask).setConstant(1);
auto obj_mask_t = EigenTensor<T, 4>::From(*obj_mask).setConstant(0);
auto noobj_mask_t = EigenTensor<T, 4>::From(*noobj_mask).setConstant(1);
auto tx_t = EigenTensor<T, 4>::From(*tx).setConstant(0.0);
auto ty_t = EigenTensor<T, 4>::From(*ty).setConstant(0.0);
auto tw_t = EigenTensor<T, 4>::From(*tw).setConstant(0.0);
auto th_t = EigenTensor<T, 4>::From(*th).setConstant(0.0);
auto tweight_t = EigenTensor<T, 4>::From(*tweight).setConstant(0.0);
auto tconf_t = EigenTensor<T, 4>::From(*tconf).setConstant(0.0);
auto tclass_t = EigenTensor<T, 5>::From(*tclass).setConstant(0.0);
......@@ -216,8 +220,8 @@ static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
int cur_label = gt_label_t(i, j);
T gx = gt_box_t(i, j, 0) * grid_size;
T gy = gt_box_t(i, j, 1) * grid_size;
T gw = gt_box_t(i, j, 2) * grid_size;
T gh = gt_box_t(i, j, 3) * grid_size;
T gw = gt_box_t(i, j, 2) * input_size;
T gh = gt_box_t(i, j, 3) * input_size;
int gi = static_cast<int>(gx);
int gj = static_cast<int>(gy);
......@@ -234,15 +238,17 @@ static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
best_an_index = an_idx;
}
if (iou > ignore_thresh) {
noobj_mask_t(i, an_idx, gj, gi) = 0;
noobj_mask_t(i, an_idx, gj, gi) = static_cast<T>(0.0);
}
}
obj_mask_t(i, best_an_index, gj, gi) = 1;
noobj_mask_t(i, best_an_index, gj, gi) = 0;
obj_mask_t(i, best_an_index, gj, gi) = static_cast<T>(1.0);
noobj_mask_t(i, best_an_index, gj, gi) = static_cast<T>(0.0);
tx_t(i, best_an_index, gj, gi) = gx - gi;
ty_t(i, best_an_index, gj, gi) = gy - gj;
tw_t(i, best_an_index, gj, gi) = log(gw / anchors[2 * best_an_index]);
th_t(i, best_an_index, gj, gi) = log(gh / anchors[2 * best_an_index + 1]);
tweight_t(i, best_an_index, gj, gi) =
2.0 - gt_box_t(i, j, 2) * gt_box_t(i, j, 3);
tclass_t(i, best_an_index, gj, gi, cur_label) = 1;
tconf_t(i, best_an_index, gj, gi) = 1;
}
......@@ -295,27 +301,22 @@ static void AddAllGradToInputGrad(
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
grad_t(i, j * attr_num, k, l) =
grad_x_t(i, j, k, l) * pred_x_t(i, j, k, l) *
(1.0 - pred_x_t(i, j, k, l)) * loss * loss_weight_xy;
grad_x_t(i, j, k, l) * loss * loss_weight_xy;
grad_t(i, j * attr_num + 1, k, l) =
grad_y_t(i, j, k, l) * pred_y_t(i, j, k, l) *
(1.0 - pred_y_t(i, j, k, l)) * loss * loss_weight_xy;
grad_y_t(i, j, k, l) * loss * loss_weight_xy;
grad_t(i, j * attr_num + 2, k, l) =
grad_w_t(i, j, k, l) * loss * loss_weight_wh;
grad_t(i, j * attr_num + 3, k, l) =
grad_h_t(i, j, k, l) * loss * loss_weight_wh;
grad_t(i, j * attr_num + 4, k, l) =
grad_conf_target_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss * loss_weight_conf_target;
grad_conf_target_t(i, j, k, l) * loss * loss_weight_conf_target;
grad_t(i, j * attr_num + 4, k, l) +=
grad_conf_notarget_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss *
grad_conf_notarget_t(i, j, k, l) * loss *
loss_weight_conf_notarget;
for (int c = 0; c < class_num; c++) {
grad_t(i, j * attr_num + 5 + c, k, l) =
grad_class_t(i, j, k, l, c) * pred_class_t(i, j, k, l, c) *
(1.0 - pred_class_t(i, j, k, l, c)) * loss * loss_weight_class;
grad_class_t(i, j, k, l, c) * loss * loss_weight_class;
}
}
}
......@@ -333,6 +334,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
auto* loss = ctx.Output<Tensor>("Loss");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
int input_size = ctx.Attr<int>("input_size");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
float loss_weight_xy = ctx.Attr<float>("loss_weight_xy");
float loss_weight_wh = ctx.Attr<float>("loss_weight_wh");
......@@ -358,30 +360,46 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
&pred_w, &pred_h, an_num, class_num);
Tensor obj_mask, noobj_mask;
Tensor tx, ty, tw, th, tconf, tclass;
obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
Tensor tx, ty, tw, th, tweight, tconf, tclass;
obj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tweight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
&noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, input_size,
h, &obj_mask, &noobj_mask, &tx, &ty, &tw, &th, &tweight,
&tconf, &tclass);
Tensor obj_weight;
obj_weight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
auto obj_weight_t = EigenTensor<T, 4>::From(obj_weight);
auto obj_mask_t = EigenTensor<T, 4>::From(obj_mask);
auto tweight_t = EigenTensor<T, 4>::From(tweight);
obj_weight_t = obj_mask_t * tweight_t;
Tensor obj_mask_expand;
obj_mask_expand.mutable_data<int>({n, an_num, h, w, class_num},
ctx.GetPlace());
ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
T loss_x = CalcMSEWithMask<T>(pred_x, tx, obj_mask);
T loss_y = CalcMSEWithMask<T>(pred_y, ty, obj_mask);
T loss_w = CalcMSEWithMask<T>(pred_w, tw, obj_mask);
T loss_h = CalcMSEWithMask<T>(pred_h, th, obj_mask);
T loss_conf_target = CalcBCEWithMask<T>(pred_conf, tconf, obj_mask);
T loss_conf_notarget = CalcBCEWithMask<T>(pred_conf, tconf, noobj_mask);
T loss_class = CalcBCEWithMask<T>(pred_class, tclass, obj_mask_expand);
obj_mask_expand.mutable_data<T>({n, an_num, h, w, class_num},
ctx.GetPlace());
auto obj_mask_expand_t = EigenTensor<T, 5>::From(obj_mask_expand);
obj_mask_expand_t = obj_mask_t.reshape(Array5(n, an_num, h, w, 1))
.broadcast(Array5(1, 1, 1, 1, class_num));
T box_f = static_cast<T>(an_num * h * w);
T class_f = static_cast<T>(an_num * h * w * class_num);
T loss_x = CalcSCEWithWeight<T>(pred_x, tx, obj_weight, box_f);
T loss_y = CalcSCEWithWeight<T>(pred_y, ty, obj_weight, box_f);
T loss_w = CalcMSEWithWeight<T>(pred_w, tw, obj_weight, box_f);
T loss_h = CalcMSEWithWeight<T>(pred_h, th, obj_weight, box_f);
T loss_conf_target =
CalcSCEWithWeight<T>(pred_conf, tconf, obj_mask, box_f);
T loss_conf_notarget =
CalcSCEWithWeight<T>(pred_conf, tconf, noobj_mask, box_f);
T loss_class =
CalcSCEWithWeight<T>(pred_class, tclass, obj_mask_expand, class_f);
auto* loss_data = loss->mutable_data<T>({1}, ctx.GetPlace());
loss_data[0] = loss_weight_xy * (loss_x + loss_y) +
......@@ -405,6 +423,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
const T loss = output_grad->data<T>()[0];
int input_size = ctx.Attr<int>("input_size");
float loss_weight_xy = ctx.Attr<float>("loss_weight_xy");
float loss_weight_wh = ctx.Attr<float>("loss_weight_wh");
float loss_weight_conf_target = ctx.Attr<float>("loss_weight_conf_target");
......@@ -430,22 +449,33 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
&pred_w, &pred_h, an_num, class_num);
Tensor obj_mask, noobj_mask;
Tensor tx, ty, tw, th, tconf, tclass;
obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
Tensor tx, ty, tw, th, tweight, tconf, tclass;
obj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tweight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
&noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, input_size,
h, &obj_mask, &noobj_mask, &tx, &ty, &tw, &th, &tweight,
&tconf, &tclass);
Tensor obj_weight;
obj_weight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
auto obj_weight_t = EigenTensor<T, 4>::From(obj_weight);
auto obj_mask_t = EigenTensor<T, 4>::From(obj_mask);
auto tweight_t = EigenTensor<T, 4>::From(tweight);
obj_weight_t = obj_mask_t * tweight_t;
Tensor obj_mask_expand;
obj_mask_expand.mutable_data<int>({n, an_num, h, w, class_num},
ctx.GetPlace());
ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
obj_mask_expand.mutable_data<T>({n, an_num, h, w, class_num},
ctx.GetPlace());
auto obj_mask_expand_t = EigenTensor<T, 5>::From(obj_mask_expand);
obj_mask_expand_t = obj_mask_t.reshape(Array5(n, an_num, h, w, 1))
.broadcast(Array5(1, 1, 1, 1, class_num));
Tensor grad_x, grad_y, grad_w, grad_h;
Tensor grad_conf_target, grad_conf_notarget, grad_class;
......@@ -456,19 +486,18 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
grad_conf_target.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_conf_notarget.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
T obj_mf = CalcMaskPointNum<int>(obj_mask);
T noobj_mf = CalcMaskPointNum<int>(noobj_mask);
T obj_expand_mf = CalcMaskPointNum<int>(obj_mask_expand);
CalcMSEGradWithMask<T>(&grad_x, pred_x, tx, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_y, pred_y, ty, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_w, pred_w, tw, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_h, pred_h, th, obj_mask, obj_mf);
CalcBCEGradWithMask<T>(&grad_conf_target, pred_conf, tconf, obj_mask,
obj_mf);
CalcBCEGradWithMask<T>(&grad_conf_notarget, pred_conf, tconf, noobj_mask,
noobj_mf);
CalcBCEGradWithMask<T>(&grad_class, pred_class, tclass, obj_mask_expand,
obj_expand_mf);
T box_f = static_cast<T>(an_num * h * w);
T class_f = static_cast<T>(an_num * h * w * class_num);
CalcSCEGradWithWeight<T>(&grad_x, pred_x, tx, obj_weight, box_f);
CalcSCEGradWithWeight<T>(&grad_y, pred_y, ty, obj_weight, box_f);
CalcMSEGradWithWeight<T>(&grad_w, pred_w, tw, obj_weight, box_f);
CalcMSEGradWithWeight<T>(&grad_h, pred_h, th, obj_weight, box_f);
CalcSCEGradWithWeight<T>(&grad_conf_target, pred_conf, tconf, obj_mask,
box_f);
CalcSCEGradWithWeight<T>(&grad_conf_notarget, pred_conf, tconf, noobj_mask,
box_f);
CalcSCEGradWithWeight<T>(&grad_class, pred_class, tclass, obj_mask_expand,
class_f);
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
AddAllGradToInputGrad<T>(
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 192d2938
......@@ -415,6 +415,7 @@ def yolov3_loss(x,
anchors,
class_num,
ignore_thresh,
input_size,
loss_weight_xy=None,
loss_weight_wh=None,
loss_weight_conf_target=None,
......@@ -436,6 +437,7 @@ def yolov3_loss(x,
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
input_size (int): ${input_size_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
......@@ -490,6 +492,7 @@ def yolov3_loss(x,
"anchors": anchors,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
"input_size": input_size,
}
if loss_weight_xy is not None and isinstance(loss_weight_xy, float):
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 192d2938
......@@ -464,7 +464,7 @@ class TestYoloDetection(unittest.TestCase):
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13], 10,
0.5)
0.7, 416)
self.assertIsNotNone(loss)
......
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @ 192d2938
......@@ -16,31 +16,22 @@ from __future__ import division
import unittest
import numpy as np
from scipy.special import logit
from scipy.special import expit
from op_test import OpTest
from paddle.fluid import core
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
def mse(x, y, weight, num):
return ((y - x)**2 * weight).sum() / num
def mse(x, y, num):
return ((y - x)**2).sum() / num
def bce(x, y, mask):
x = x.reshape((-1))
y = y.reshape((-1))
mask = mask.reshape((-1))
error_sum = 0.0
count = 0
for i in range(x.shape[0]):
if mask[i] > 0:
error_sum += y[i] * np.log(x[i]) + (1 - y[i]) * np.log(1 - x[i])
count += 1
return error_sum / (-1.0 * count)
def sce(x, label, weight, num):
sigmoid_x = expit(x)
term1 = label * np.log(sigmoid_x)
term2 = (1.0 - label) * np.log(1.0 - sigmoid_x)
return ((-term1 - term2) * weight).sum() / num
def box_iou(box1, box2):
......@@ -66,11 +57,12 @@ def box_iou(box1, box2):
return inter_area / (b1_area + b2_area + inter_area)
def build_target(gtboxs, gtlabel, attrs, grid_size):
n, b, _ = gtboxs.shape
def build_target(gtboxes, gtlabel, attrs, grid_size):
n, b, _ = gtboxes.shape
ignore_thresh = attrs["ignore_thresh"]
anchors = attrs["anchors"]
class_num = attrs["class_num"]
input_size = attrs["input_size"]
an_num = len(anchors) // 2
obj_mask = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
noobj_mask = np.ones((n, an_num, grid_size, grid_size)).astype('float32')
......@@ -78,20 +70,21 @@ def build_target(gtboxs, gtlabel, attrs, grid_size):
ty = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tw = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
th = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tweight = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tconf = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tcls = np.zeros(
(n, an_num, grid_size, grid_size, class_num)).astype('float32')
for i in range(n):
for j in range(b):
if gtboxs[i, j, :].sum() == 0:
if gtboxes[i, j, :].sum() == 0:
continue
gt_label = gtlabel[i, j]
gx = gtboxs[i, j, 0] * grid_size
gy = gtboxs[i, j, 1] * grid_size
gw = gtboxs[i, j, 2] * grid_size
gh = gtboxs[i, j, 3] * grid_size
gx = gtboxes[i, j, 0] * grid_size
gy = gtboxes[i, j, 1] * grid_size
gw = gtboxes[i, j, 2] * input_size
gh = gtboxes[i, j, 3] * input_size
gi = int(gx)
gj = int(gy)
......@@ -115,10 +108,12 @@ def build_target(gtboxs, gtlabel, attrs, grid_size):
best_an_index])
th[i, best_an_index, gj, gi] = np.log(
gh / anchors[2 * best_an_index + 1])
tweight[i, best_an_index, gj, gi] = 2.0 - gtboxes[
i, j, 2] * gtboxes[i, j, 3]
tconf[i, best_an_index, gj, gi] = 1
tcls[i, best_an_index, gj, gi, gt_label] = 1
return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
return (tx, ty, tw, th, tweight, tconf, tcls, obj_mask, noobj_mask)
def YoloV3Loss(x, gtbox, gtlabel, attrs):
......@@ -126,27 +121,28 @@ def YoloV3Loss(x, gtbox, gtlabel, attrs):
an_num = len(attrs['anchors']) // 2
class_num = attrs["class_num"]
x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
pred_x = sigmoid(x[:, :, :, :, 0])
pred_y = sigmoid(x[:, :, :, :, 1])
pred_x = x[:, :, :, :, 0]
pred_y = x[:, :, :, :, 1]
pred_w = x[:, :, :, :, 2]
pred_h = x[:, :, :, :, 3]
pred_conf = sigmoid(x[:, :, :, :, 4])
pred_cls = sigmoid(x[:, :, :, :, 5:])
pred_conf = x[:, :, :, :, 4]
pred_cls = x[:, :, :, :, 5:]
tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
tx, ty, tw, th, tweight, tconf, tcls, obj_mask, noobj_mask = build_target(
gtbox, gtlabel, attrs, x.shape[2])
obj_weight = obj_mask * tweight
obj_mask_expand = np.tile(
np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
loss_x = mse(pred_x * obj_mask, tx * obj_mask, obj_mask.sum())
loss_y = mse(pred_y * obj_mask, ty * obj_mask, obj_mask.sum())
loss_w = mse(pred_w * obj_mask, tw * obj_mask, obj_mask.sum())
loss_h = mse(pred_h * obj_mask, th * obj_mask, obj_mask.sum())
loss_conf_target = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
loss_conf_notarget = bce(pred_conf * noobj_mask, tconf * noobj_mask,
noobj_mask)
loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
obj_mask_expand)
box_f = an_num * h * w
class_f = an_num * h * w * class_num
loss_x = sce(pred_x, tx, obj_weight, box_f)
loss_y = sce(pred_y, ty, obj_weight, box_f)
loss_w = mse(pred_w, tw, obj_weight, box_f)
loss_h = mse(pred_h, th, obj_weight, box_f)
loss_conf_target = sce(pred_conf, tconf, obj_mask, box_f)
loss_conf_notarget = sce(pred_conf, tconf, noobj_mask, box_f)
loss_class = sce(pred_cls, tcls, obj_mask_expand, class_f)
return attrs['loss_weight_xy'] * (loss_x + loss_y) \
+ attrs['loss_weight_wh'] * (loss_w + loss_h) \
......@@ -164,7 +160,7 @@ class TestYolov3LossOp(OpTest):
self.loss_weight_class = 1.0
self.initTestCase()
self.op_type = 'yolov3_loss'
x = np.random.random(size=self.x_shape).astype('float32')
x = logit(np.random.uniform(0, 1, self.x_shape).astype('float32'))
gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
gtlabel = np.random.randint(0, self.class_num,
self.gtbox_shape[:2]).astype('int32')
......@@ -173,6 +169,7 @@ class TestYolov3LossOp(OpTest):
"anchors": self.anchors,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"input_size": self.input_size,
"loss_weight_xy": self.loss_weight_xy,
"loss_weight_wh": self.loss_weight_wh,
"loss_weight_conf_target": self.loss_weight_conf_target,
......@@ -196,18 +193,19 @@ class TestYolov3LossOp(OpTest):
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.06)
max_relative_error=0.3)
def initTestCase(self):
self.anchors = [10, 13, 12, 12]
self.class_num = 10
self.ignore_thresh = 0.5
self.ignore_thresh = 0.7
self.input_size = 416
self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
self.gtbox_shape = (5, 10, 4)
self.loss_weight_xy = 2.5
self.loss_weight_xy = 1.4
self.loss_weight_wh = 0.8
self.loss_weight_conf_target = 1.5
self.loss_weight_conf_notarget = 0.5
self.loss_weight_conf_target = 1.1
self.loss_weight_conf_notarget = 0.9
self.loss_weight_class = 1.2
......
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