Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 f0804433 编写于 作者: D dengkaipeng
浏览文件
操作

add mixup score and label_smooth for yolov3_loss. test=develop

上级 4e8c03bd
隐藏空白更改
内联 并排
Showing with 76 addition and 26 deletion
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ f0804433
......@@ -72,6 +72,18 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater then 0.");
if (ctx->HasInput("GTScore")) {
auto dim_gtscore = ctx->GetInputDim("GTScore");
PADDLE_ENFORCE_EQ(dim_gtscore.size(), 2,
"Input(GTScore) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(
dim_gtscore[0], dim_gtbox[0],
"Input(GTBox) and Input(GTScore) dim[0] should be same");
PADDLE_ENFORCE_EQ(
dim_gtscore[1], dim_gtbox[1],
"Input(GTBox) and Input(GTScore) dim[1] should be same");
}
std::vector<int64_t> dim_out({dim_x[0]});
ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
......@@ -112,6 +124,11 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element should be an integer to indicate the "
"box class id.");
AddInput("GTScore",
"The score of GTLabel, This is a 2-D tensor in same shape "
"GTLabel, and score values should in range (0, 1). This "
"input is for GTLabel score can be not 1.0 in image mixup "
"augmentation.");
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [N]");
......@@ -143,6 +160,8 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.")
.SetDefault(0.7);
AddAttr<bool>("use_label_smooth", "bool,default True", "use label smooth")
.SetDefault(true);
AddComment(R"DOC(
This operator generates yolov3 loss based on given predict result and ground
truth boxes.
......@@ -240,6 +259,7 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetInput("X", Input("X"));
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput("GTScore", Input("GTScore"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetInput("ObjectnessMask", Output("ObjectnessMask"));
op->SetInput("GTMatchMask", Output("GTMatchMask"));
......@@ -249,6 +269,7 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("GTBox"), {});
op->SetOutput(framework::GradVarName("GTLabel"), {});
op->SetOutput(framework::GradVarName("GTScore"), {});
return std::unique_ptr<framework::OpDesc>(op);
}
};
......
paddle/fluid/operators/detection/yolov3_loss_op.h
浏览文件 @ f0804433
......@@ -37,8 +37,8 @@ static T SigmoidCrossEntropy(T x, T label) {
}
template <typename T>
static T L2Loss(T x, T y) {
return 0.5 * (y - x) * (y - x);
static T L1Loss(T x, T y) {
return std::abs(y - x);
}
template <typename T>
......@@ -47,8 +47,8 @@ static T SigmoidCrossEntropyGrad(T x, T label) {
}
template <typename T>
static T L2LossGrad(T x, T y) {
return x - y;
static T L1LossGrad(T x, T y) {
return x > y ? 1.0 : -1.0;
}
static int GetMaskIndex(std::vector<int> mask, int val) {
......@@ -121,47 +121,49 @@ template <typename T>
static void CalcBoxLocationLoss(T* loss, const T* input, Box<T> gt,
std::vector<int> anchors, int an_idx,
int box_idx, int gi, int gj, int grid_size,
int input_size, int stride) {
int input_size, int stride, T score) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
T scale = (2.0 - gt.w * gt.h) * score;
loss[0] += SigmoidCrossEntropy<T>(input[box_idx], tx) * scale;
loss[0] += SigmoidCrossEntropy<T>(input[box_idx + stride], ty) * scale;
loss[0] += L2Loss<T>(input[box_idx + 2 * stride], tw) * scale;
loss[0] += L2Loss<T>(input[box_idx + 3 * stride], th) * scale;
loss[0] += L1Loss<T>(input[box_idx + 2 * stride], tw) * scale;
loss[0] += L1Loss<T>(input[box_idx + 3 * stride], th) * scale;
}
template <typename T>
static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
Box<T> gt, std::vector<int> anchors,
int an_idx, int box_idx, int gi, int gj,
int grid_size, int input_size, int stride) {
int grid_size, int input_size, int stride,
T score) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
T scale = (2.0 - gt.w * gt.h) * score;
input_grad[box_idx] =
SigmoidCrossEntropyGrad<T>(input[box_idx], tx) * scale * loss;
input_grad[box_idx + stride] =
SigmoidCrossEntropyGrad<T>(input[box_idx + stride], ty) * scale * loss;
input_grad[box_idx + 2 * stride] =
L2LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
L1LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
input_grad[box_idx + 3 * stride] =
L2LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
L1LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
}
template <typename T>
static inline void CalcLabelLoss(T* loss, const T* input, const int index,
const int label, const int class_num,
const int stride) {
const int stride, const T pos, const T neg,
T score) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? 1.0 : 0.0);
loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? pos : neg) * score;
}
}
......@@ -169,11 +171,13 @@ template <typename T>
static inline void CalcLabelLossGrad(T* input_grad, const T loss,
const T* input, const int index,
const int label, const int class_num,
const int stride) {
const int stride, const T pos, const T neg,
T score) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
input_grad[index + i * stride] =
SigmoidCrossEntropyGrad<T>(pred, (i == label) ? 1.0 : 0.0) * loss;
SigmoidCrossEntropyGrad<T>(pred, (i == label) ? pos : neg) * score *
loss;
}
}
......@@ -188,8 +192,8 @@ static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
for (int l = 0; l < w; l++) {
T obj = objness[k * w + l];
if (obj > 1e-5) {
// positive sample: obj = 1
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0);
// positive sample: obj = mixup score
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0) * obj;
} else if (obj > -0.5) {
// negetive sample: obj = 0
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 0.0);
......@@ -215,7 +219,8 @@ static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
T obj = objness[k * w + l];
if (obj > 1e-5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * loss[i];
SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * obj *
loss[i];
} else if (obj > -0.5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 0.0) * loss[i];
......@@ -252,6 +257,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* gt_score = ctx.Input<Tensor>("GTScore");
auto* loss = ctx.Output<Tensor>("Loss");
auto* objness_mask = ctx.Output<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Output<Tensor>("GTMatchMask");
......@@ -260,6 +266,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
const int n = input->dims()[0];
const int h = input->dims()[2];
......@@ -272,9 +279,17 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
T label_pos = 1.0;
T label_neg = 0.0;
if (use_label_smooth) {
label_pos = 1.0 - 1.0 / static_cast<T>(class_num);
label_neg = 1.0 / static_cast<T>(class_num);
}
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
const T* gt_score_data = gt_score->data<T>();
T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
memset(loss_data, 0, loss->numel() * sizeof(T));
T* obj_mask_data =
......@@ -355,19 +370,20 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
int mask_idx = GetMaskIndex(anchor_mask, best_n);
gt_match_mask_data[i * b + t] = mask_idx;
if (mask_idx >= 0) {
T score = gt_score_data[i * b + t];
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLoss<T>(loss_data + i, input_data, gt, anchors, best_n,
box_idx, gi, gj, h, input_size, stride);
box_idx, gi, gj, h, input_size, stride, score);
int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
obj_mask_data[obj_idx] = 1.0;
obj_mask_data[obj_idx] = score;
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLoss<T>(loss_data + i, input_data, label_idx, label,
class_num, stride);
class_num, stride, label_pos, label_neg, score);
}
}
}
......@@ -384,6 +400,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* gt_score = ctx.Input<Tensor>("GTScore");
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
auto* objness_mask = ctx.Input<Tensor>("ObjectnessMask");
......@@ -392,6 +409,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
int class_num = ctx.Attr<int>("class_num");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
const int n = input_grad->dims()[0];
const int c = input_grad->dims()[1];
......@@ -404,9 +422,17 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
T label_pos = 1.0;
T label_neg = 0.0;
if (use_label_smooth) {
label_pos = 1.0 - 1.0 / static_cast<T>(class_num);
label_neg = 1.0 / static_cast<T>(class_num);
}
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
const T* gt_score_data = gt_score->data<T>();
const T* loss_grad_data = loss_grad->data<T>();
const T* obj_mask_data = objness_mask->data<T>();
const int* gt_match_mask_data = gt_match_mask->data<int>();
......@@ -418,21 +444,24 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
for (int t = 0; t < b; t++) {
int mask_idx = gt_match_mask_data[i * b + t];
if (mask_idx >= 0) {
T score = gt_score_data[i * b + t];
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
int gi = static_cast<int>(gt.x * w);
int gj = static_cast<int>(gt.y * h);
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLossGrad<T>(
input_grad_data, loss_grad_data[i], input_data, gt, anchors,
anchor_mask[mask_idx], box_idx, gi, gj, h, input_size, stride);
CalcBoxLocationLossGrad<T>(input_grad_data, loss_grad_data[i],
input_data, gt, anchors,
anchor_mask[mask_idx], box_idx, gi, gj, h,
input_size, stride, score);
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLossGrad<T>(input_grad_data, loss_grad_data[i], input_data,
label_idx, label, class_num, stride);
label_idx, label, class_num, stride, label_pos,
label_neg, score);
}
}
}
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或