enhance api. test=develop

paddle/fluid/API.spec

@@ -288,7 +288,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
 paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
 paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'anchors', 'class_num', 'ignore_thresh', 'lambda_xy', 'lambda_wh', 'lambda_conf_obj', 'lambda_conf_noobj', 'lambda_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
+paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
 paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
 paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
 paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))

paddle/fluid/operators/yolov3_loss_op.cc

@@ -25,11 +25,14 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
                    "Input(X) of Yolov3LossOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("GTBox"),
                    "Input(GTBox) of Yolov3LossOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
+                   "Input(GTLabel) of Yolov3LossOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Loss"),
                    "Output(Loss) of Yolov3LossOp should not be null.");
 
     auto dim_x = ctx->GetInputDim("X");
-    auto dim_gt = ctx->GetInputDim("GTBox");
+    auto dim_gtbox = ctx->GetInputDim("GTBox");
+    auto dim_gtlabel = ctx->GetInputDim("GTLabel");
     auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
     auto class_num = ctx->Attrs().Get<int>("class_num");
     PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
@@ -38,8 +41,15 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
                       "Input(X) dim[1] should be equal to (anchor_number * (5 "
                       "+ class_num)).");
-    PADDLE_ENFORCE_EQ(dim_gt.size(), 3, "Input(GTBox) should be a 3-D tensor");
-    PADDLE_ENFORCE_EQ(dim_gt[2], 5, "Input(GTBox) dim[2] should be 5");
+    PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
+                      "Input(GTBox) should be a 3-D tensor");
+    PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
+    PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
+                      "Input(GTBox) should be a 2-D tensor");
+    PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
+                      "Input(GTBox) and Input(GTLabel) dim[0] should be same");
+    PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
+                      "Input(GTBox) and Input(GTLabel) dim[1] should be same");
     PADDLE_ENFORCE_GT(anchors.size(), 0,
                       "Attr(anchors) length should be greater then 0.");
     PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
@@ -73,11 +83,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
              "The input tensor of ground truth boxes, "
              "This is a 3-D tensor with shape of [N, max_box_num, 5], "
              "max_box_num is the max number of boxes in each image, "
-             "In the third dimention, stores label, x, y, w, h, "
-             "label is an integer to specify box class, x, y is the "
-             "center cordinate of boxes and w, h is the width and height"
-             "and x, y, w, h should be divided by input image height to "
-             "scale to [0, 1].");
+             "In the third dimention, stores x, y, w, h coordinates, "
+             "x, y is the center cordinate of boxes and w, h is the "
+             "width and height and x, y, w, h should be divided by "
+             "input image height to scale to [0, 1].");
+    AddInput("GTLabel",
+             "The input tensor of ground truth label, "
+             "This is a 2-D tensor with shape of [N, max_box_num], "
+             "and each element shoudl be an integer to indicate the "
+             "box class id.");
     AddOutput("Loss",
               "The output yolov3 loss tensor, "
               "This is a 1-D tensor with shape of [1]");
@@ -88,19 +102,19 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
                               "it will be parsed pair by pair.");
     AddAttr<float>("ignore_thresh",
                    "The ignore threshold to ignore confidence loss.");
-    AddAttr<float>("lambda_xy", "The weight of x, y location loss.")
+    AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
         .SetDefault(1.0);
-    AddAttr<float>("lambda_wh", "The weight of w, h location loss.")
+    AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
         .SetDefault(1.0);
     AddAttr<float>(
-        "lambda_conf_obj",
+        "loss_weight_conf_target",
         "The weight of confidence score loss in locations with target object.")
         .SetDefault(1.0);
-    AddAttr<float>("lambda_conf_noobj",
+    AddAttr<float>("loss_weight_conf_notarget",
                    "The weight of confidence score loss in locations without "
                    "target object.")
         .SetDefault(1.0);
-    AddAttr<float>("lambda_class", "The weight of classification loss.")
+    AddAttr<float>("loss_weight_class", "The weight of classification loss.")
         .SetDefault(1.0);
     AddComment(R"DOC(
          This operator generate yolov3 loss by given predict result and ground
@@ -141,10 +155,10 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
          Final loss will be represented as follow.
 
          $$
-         loss = \lambda_{xy} * loss_{xy} + \lambda_{wh} * loss_{wh}
-              + \lambda_{conf_obj} * loss_{conf_obj}
-              + \lambda_{conf_noobj} * loss_{conf_noobj}
-              + \lambda_{class} * loss_{class}
+         loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+              + \loss_weight_{conf_target} * loss_{conf_target}
+              + \loss_weight_{conf_notarget} * loss_{conf_notarget}
+              + \loss_weight_{class} * loss_{class}
          $$
          )DOC");
   }
@@ -182,12 +196,14 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
     op->SetType("yolov3_loss_grad");
     op->SetInput("X", Input("X"));
     op->SetInput("GTBox", Input("GTBox"));
+    op->SetInput("GTLabel", Input("GTLabel"));
     op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
 
     op->SetAttrMap(Attrs());
 
     op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
     op->SetOutput(framework::GradVarName("GTBox"), {});
+    op->SetOutput(framework::GradVarName("GTLabel"), {});
     return std::unique_ptr<framework::OpDesc>(op);
   }
 };

paddle/fluid/operators/yolov3_loss_op.h

@@ -186,15 +186,17 @@ static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
 }
 
 template <typename T>
-static void PreProcessGTBox(const Tensor& gt_boxes, 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,
+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 n = gt_boxes.dims()[0];
-  const int b = gt_boxes.dims()[1];
+  const int n = gt_box.dims()[0];
+  const int b = gt_box.dims()[1];
   const int anchor_num = anchors.size() / 2;
-  auto gt_boxes_t = EigenTensor<T, 3>::From(gt_boxes);
+  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 tx_t = EigenTensor<T, 4>::From(*tx).setConstant(0.0);
@@ -206,28 +208,27 @@ static void PreProcessGTBox(const Tensor& gt_boxes, const float ignore_thresh,
 
   for (int i = 0; i < n; i++) {
     for (int j = 0; j < b; j++) {
-      if (isZero<T>(gt_boxes_t(i, j, 0)) && isZero<T>(gt_boxes_t(i, j, 1)) &&
-          isZero<T>(gt_boxes_t(i, j, 2)) && isZero<T>(gt_boxes_t(i, j, 3)) &&
-          isZero<T>(gt_boxes_t(i, j, 4))) {
+      if (isZero<T>(gt_box_t(i, j, 0)) && isZero<T>(gt_box_t(i, j, 1)) &&
+          isZero<T>(gt_box_t(i, j, 2)) && isZero<T>(gt_box_t(i, j, 3))) {
         continue;
       }
 
-      int gt_label = static_cast<int>(gt_boxes_t(i, j, 0));
-      T gx = gt_boxes_t(i, j, 1) * grid_size;
-      T gy = gt_boxes_t(i, j, 2) * grid_size;
-      T gw = gt_boxes_t(i, j, 3) * grid_size;
-      T gh = gt_boxes_t(i, j, 4) * grid_size;
+      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;
       int gi = static_cast<int>(gx);
       int gj = static_cast<int>(gy);
 
       T max_iou = static_cast<T>(0);
       T iou;
       int best_an_index = -1;
-      std::vector<T> gt_box({0, 0, gw, gh});
+      std::vector<T> gt_box_shape({0, 0, gw, gh});
       for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
         std::vector<T> anchor_shape({0, 0, static_cast<T>(anchors[2 * an_idx]),
                                      static_cast<T>(anchors[2 * an_idx + 1])});
-        iou = CalcBoxIoU<T>(gt_box, anchor_shape);
+        iou = CalcBoxIoU<T>(gt_box_shape, anchor_shape);
         if (iou > max_iou) {
           max_iou = iou;
           best_an_index = an_idx;
@@ -242,7 +243,7 @@ static void PreProcessGTBox(const Tensor& gt_boxes, const float ignore_thresh,
       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]);
-      tclass_t(i, best_an_index, gj, gi, gt_label) = 1;
+      tclass_t(i, best_an_index, gj, gi, cur_label) = 1;
       tconf_t(i, best_an_index, gj, gi) = 1;
     }
   }
@@ -267,10 +268,10 @@ static void AddAllGradToInputGrad(
     Tensor* grad, T loss, const Tensor& pred_x, const Tensor& pred_y,
     const Tensor& pred_conf, const Tensor& pred_class, const Tensor& grad_x,
     const Tensor& grad_y, const Tensor& grad_w, const Tensor& grad_h,
-    const Tensor& grad_conf_obj, const Tensor& grad_conf_noobj,
-    const Tensor& grad_class, const int class_num, const float lambda_xy,
-    const float lambda_wh, const float lambda_conf_obj,
-    const float lambda_conf_noobj, const float lambda_class) {
+    const Tensor& grad_conf_target, const Tensor& grad_conf_notarget,
+    const Tensor& grad_class, const int class_num, const float loss_weight_xy,
+    const float loss_weight_wh, const float loss_weight_conf_target,
+    const float loss_weight_conf_notarget, const float loss_weight_class) {
   const int n = pred_x.dims()[0];
   const int an_num = pred_x.dims()[1];
   const int h = pred_x.dims()[2];
@@ -285,8 +286,8 @@ static void AddAllGradToInputGrad(
   auto grad_y_t = EigenTensor<T, 4>::From(grad_y);
   auto grad_w_t = EigenTensor<T, 4>::From(grad_w);
   auto grad_h_t = EigenTensor<T, 4>::From(grad_h);
-  auto grad_conf_obj_t = EigenTensor<T, 4>::From(grad_conf_obj);
-  auto grad_conf_noobj_t = EigenTensor<T, 4>::From(grad_conf_noobj);
+  auto grad_conf_target_t = EigenTensor<T, 4>::From(grad_conf_target);
+  auto grad_conf_notarget_t = EigenTensor<T, 4>::From(grad_conf_notarget);
   auto grad_class_t = EigenTensor<T, 5>::From(grad_class);
 
   for (int i = 0; i < n; i++) {
@@ -295,25 +296,26 @@ static void AddAllGradToInputGrad(
         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 * lambda_xy;
+              (1.0 - pred_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 * lambda_xy;
+              (1.0 - pred_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 * lambda_wh;
+              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 * lambda_wh;
+              grad_h_t(i, j, k, l) * loss * loss_weight_wh;
           grad_t(i, j * attr_num + 4, k, l) =
-              grad_conf_obj_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
-              (1.0 - pred_conf_t(i, j, k, l)) * loss * lambda_conf_obj;
+              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_t(i, j * attr_num + 4, k, l) +=
-              grad_conf_noobj_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
-              (1.0 - pred_conf_t(i, j, k, l)) * loss * lambda_conf_noobj;
+              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 *
+              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 * lambda_class;
+                (1.0 - pred_class_t(i, j, k, l, c)) * loss * loss_weight_class;
           }
         }
       }
@@ -326,16 +328,18 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* input = ctx.Input<Tensor>("X");
-    auto* gt_boxes = ctx.Input<Tensor>("GTBox");
+    auto* gt_box = ctx.Input<Tensor>("GTBox");
+    auto* gt_label = ctx.Input<Tensor>("GTLabel");
     auto* loss = ctx.Output<Tensor>("Loss");
     auto anchors = ctx.Attr<std::vector<int>>("anchors");
     int class_num = ctx.Attr<int>("class_num");
     float ignore_thresh = ctx.Attr<float>("ignore_thresh");
-    float lambda_xy = ctx.Attr<float>("lambda_xy");
-    float lambda_wh = ctx.Attr<float>("lambda_wh");
-    float lambda_conf_obj = ctx.Attr<float>("lambda_conf_obj");
-    float lambda_conf_noobj = ctx.Attr<float>("lambda_conf_noobj");
-    float lambda_class = ctx.Attr<float>("lambda_class");
+    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");
+    float loss_weight_conf_notarget =
+        ctx.Attr<float>("loss_weight_conf_notarget");
+    float loss_weight_class = ctx.Attr<float>("loss_weight_class");
 
     const int n = input->dims()[0];
     const int h = input->dims()[2];
@@ -363,7 +367,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
     th.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_boxes, ignore_thresh, anchors, h, &obj_mask,
+    PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
                        &noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
 
     Tensor obj_mask_expand;
@@ -375,15 +379,16 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
     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_obj = CalcBCEWithMask<T>(pred_conf, tconf, obj_mask);
-    T loss_conf_noobj = CalcBCEWithMask<T>(pred_conf, tconf, noobj_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);
 
     auto* loss_data = loss->mutable_data<T>({1}, ctx.GetPlace());
-    loss_data[0] =
-        lambda_xy * (loss_x + loss_y) + lambda_wh * (loss_w + loss_h) +
-        lambda_conf_obj * loss_conf_obj + lambda_conf_noobj * loss_conf_noobj +
-        lambda_class * loss_class;
+    loss_data[0] = loss_weight_xy * (loss_x + loss_y) +
+                   loss_weight_wh * (loss_w + loss_h) +
+                   loss_weight_conf_target * loss_conf_target +
+                   loss_weight_conf_notarget * loss_conf_notarget +
+                   loss_weight_class * loss_class;
   }
 };
 
@@ -392,18 +397,20 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* input = ctx.Input<Tensor>("X");
-    auto* gt_boxes = ctx.Input<Tensor>("GTBox");
+    auto* gt_box = ctx.Input<Tensor>("GTBox");
+    auto* gt_label = ctx.Input<Tensor>("GTLabel");
     auto anchors = ctx.Attr<std::vector<int>>("anchors");
     int class_num = ctx.Attr<int>("class_num");
     float ignore_thresh = ctx.Attr<float>("ignore_thresh");
     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];
-    float lambda_xy = ctx.Attr<float>("lambda_xy");
-    float lambda_wh = ctx.Attr<float>("lambda_wh");
-    float lambda_conf_obj = ctx.Attr<float>("lambda_conf_obj");
-    float lambda_conf_noobj = ctx.Attr<float>("lambda_conf_noobj");
-    float lambda_class = ctx.Attr<float>("lambda_class");
+    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");
+    float loss_weight_conf_notarget =
+        ctx.Attr<float>("loss_weight_conf_notarget");
+    float loss_weight_class = ctx.Attr<float>("loss_weight_class");
 
     const int n = input->dims()[0];
     const int c = input->dims()[1];
@@ -432,7 +439,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
     th.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_boxes, ignore_thresh, anchors, h, &obj_mask,
+    PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
                        &noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
 
     Tensor obj_mask_expand;
@@ -441,13 +448,13 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
     ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
 
     Tensor grad_x, grad_y, grad_w, grad_h;
-    Tensor grad_conf_obj, grad_conf_noobj, grad_class;
+    Tensor grad_conf_target, grad_conf_notarget, grad_class;
     grad_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
     grad_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
     grad_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
     grad_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    grad_conf_obj.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    grad_conf_noobj.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    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);
@@ -456,8 +463,9 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
     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_obj, pred_conf, tconf, obj_mask, obj_mf);
-    CalcBCEGradWithMask<T>(&grad_conf_noobj, pred_conf, tconf, noobj_mask,
+    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);
@@ -465,8 +473,9 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
     input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
     AddAllGradToInputGrad<T>(
         input_grad, loss, pred_x, pred_y, pred_conf, pred_class, grad_x, grad_y,
-        grad_w, grad_h, grad_conf_obj, grad_conf_noobj, grad_class, class_num,
-        lambda_xy, lambda_wh, lambda_conf_obj, lambda_conf_noobj, lambda_class);
+        grad_w, grad_h, grad_conf_target, grad_conf_notarget, grad_class,
+        class_num, loss_weight_xy, loss_weight_wh, loss_weight_conf_target,
+        loss_weight_conf_notarget, loss_weight_class);
   }
 };
 

python/paddle/fluid/layers/detection.py

@@ -409,32 +409,36 @@ def polygon_box_transform(input, name=None):
 @templatedoc(op_type="yolov3_loss")
 def yolov3_loss(x,
                 gtbox,
+                gtlabel,
                 anchors,
                 class_num,
                 ignore_thresh,
-                lambda_xy=None,
-                lambda_wh=None,
-                lambda_conf_obj=None,
-                lambda_conf_noobj=None,
-                lambda_class=None,
+                loss_weight_xy=None,
+                loss_weight_wh=None,
+                loss_weight_conf_target=None,
+                loss_weight_conf_notarget=None,
+                loss_weight_class=None,
                 name=None):
     """
     ${comment}
 
     Args:
         x (Variable): ${x_comment}
-        gtbox (Variable): groud truth boxes, shoulb be in shape of [N, B, 5],
-                          in the third dimenstion, class_id, x, y, w, h should
-                          be stored and x, y, w, h should be relative valud of
-                          input image.
+        gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
+                          in the third dimenstion, x, y, w, h should be stored 
+                          and x, y, w, h should be relative value of input image.
+                          N is the batch number and B is the max box number in 
+                          an image.
+        gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
+                            of [N, B].
         anchors (list|tuple): ${anchors_comment}
         class_num (int): ${class_num_comment}
         ignore_thresh (float): ${ignore_thresh_comment}
-        lambda_xy (float|None): ${lambda_xy_comment}
-        lambda_wh (float|None): ${lambda_wh_comment}
-        lambda_conf_obj (float|None): ${lambda_conf_obj_comment}
-        lambda_conf_noobj (float|None): ${lambda_conf_noobj_comment}
-        lambda_class (float|None): ${lambda_class_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}
+        loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
+        loss_weight_class (float|None): ${loss_weight_class_comment}
         name (string): the name of yolov3 loss
 
     Returns:
@@ -443,6 +447,7 @@ def yolov3_loss(x,
     Raises:
         TypeError: Input x of yolov3_loss must be Variable
         TypeError: Input gtbox of yolov3_loss must be Variable"
+        TypeError: Input gtlabel of yolov3_loss must be Variable"
         TypeError: Attr anchors of yolov3_loss must be list or tuple
         TypeError: Attr class_num of yolov3_loss must be an integer
         TypeError: Attr ignore_thresh of yolov3_loss must be a float number
@@ -450,8 +455,9 @@ def yolov3_loss(x,
     Examples:
     .. code-block:: python
 
-        x = fluid.layers.data(name='x', shape=[10, 255, 13, 13], dtype='float32')
-        gtbox = fluid.layers.data(name='gtbox', shape=[10, 6, 5], dtype='float32')
+        x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
+        gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
+        gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
         anchors = [10, 13, 16, 30, 33, 23]
         loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
                                         anchors=anchors, ignore_thresh=0.5)
@@ -462,6 +468,8 @@ def yolov3_loss(x,
         raise TypeError("Input x of yolov3_loss must be Variable")
     if not isinstance(gtbox, Variable):
         raise TypeError("Input gtbox of yolov3_loss must be Variable")
+    if not isinstance(gtlabel, Variable):
+        raise TypeError("Input gtlabel of yolov3_loss must be Variable")
     if not isinstance(anchors, list) and not isinstance(anchors, tuple):
         raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
     if not isinstance(class_num, int):
@@ -482,21 +490,24 @@ def yolov3_loss(x,
         "ignore_thresh": ignore_thresh,
     }
 
-    if lambda_xy is not None and isinstance(lambda_xy, float):
-        self.attrs['lambda_xy'] = lambda_xy
-    if lambda_wh is not None and isinstance(lambda_wh, float):
-        self.attrs['lambda_wh'] = lambda_wh
-    if lambda_conf_obj is not None and isinstance(lambda_conf_obj, float):
-        self.attrs['lambda_conf_obj'] = lambda_conf_obj
-    if lambda_conf_noobj is not None and isinstance(lambda_conf_noobj, float):
-        self.attrs['lambda_conf_noobj'] = lambda_conf_noobj
-    if lambda_class is not None and isinstance(lambda_class, float):
-        self.attrs['lambda_class'] = lambda_class
+    if loss_weight_xy is not None and isinstance(loss_weight_xy, float):
+        self.attrs['loss_weight_xy'] = loss_weight_xy
+    if loss_weight_wh is not None and isinstance(loss_weight_wh, float):
+        self.attrs['loss_weight_wh'] = loss_weight_wh
+    if loss_weight_conf_target is not None and isinstance(
+            loss_weight_conf_target, float):
+        self.attrs['loss_weight_conf_target'] = loss_weight_conf_target
+    if loss_weight_conf_notarget is not None and isinstance(
+            loss_weight_conf_notarget, float):
+        self.attrs['loss_weight_conf_notarget'] = loss_weight_conf_notarget
+    if loss_weight_class is not None and isinstance(loss_weight_class, float):
+        self.attrs['loss_weight_class'] = loss_weight_class
 
     helper.append_op(
         type='yolov3_loss',
-        inputs={'X': x,
-                "GTBox": gtbox},
+        inputs={"X": x,
+                "GTBox": gtbox,
+                "GTLabel": gtlabel},
         outputs={'Loss': loss},
         attrs=attrs)
     return loss

python/paddle/fluid/tests/test_detection.py

@@ -366,5 +366,18 @@ class TestGenerateProposals(unittest.TestCase):
         print(rpn_rois.shape)
 
 
+class TestYoloDetection(unittest.TestCase):
+    def test_yolov3_loss(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
+            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)
+
+            self.assertIsNotNone(loss)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_layers.py

@@ -911,15 +911,6 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(data_1)
         print(str(program))
 
-    def test_yolov3_loss(self):
-        program = Program()
-        with program_guard(program):
-            x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
-            gtbox = layers.data(name='gtbox', shape=[10, 5], dtype='float32')
-            loss = layers.yolov3_loss(x, gtbox, [10, 13, 30, 13], 10, 0.5)
-
-            self.assertIsNotNone(loss)
-
     def test_bilinear_tensor_product_layer(self):
         program = Program()
         with program_guard(program):

python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py

@@ -66,7 +66,7 @@ def box_iou(box1, box2):
     return inter_area / (b1_area + b2_area + inter_area)
 
 
-def build_target(gtboxs, attrs, grid_size):
+def build_target(gtboxs, gtlabel, attrs, grid_size):
     n, b, _ = gtboxs.shape
     ignore_thresh = attrs["ignore_thresh"]
     anchors = attrs["anchors"]
@@ -87,11 +87,11 @@ def build_target(gtboxs, attrs, grid_size):
             if gtboxs[i, j, :].sum() == 0:
                 continue
 
-            gt_label = int(gtboxs[i, j, 0])
-            gx = gtboxs[i, j, 1] * grid_size
-            gy = gtboxs[i, j, 2] * grid_size
-            gw = gtboxs[i, j, 3] * grid_size
-            gh = gtboxs[i, j, 4] * grid_size
+            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
 
             gi = int(gx)
             gj = int(gy)
@@ -121,7 +121,7 @@ def build_target(gtboxs, attrs, grid_size):
     return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
 
 
-def YoloV3Loss(x, gtbox, attrs):
+def YoloV3Loss(x, gtbox, gtlabel, attrs):
     n, c, h, w = x.shape
     an_num = len(attrs['anchors']) // 2
     class_num = attrs["class_num"]
@@ -134,7 +134,7 @@ def YoloV3Loss(x, gtbox, attrs):
     pred_cls = sigmoid(x[:, :, :, :, 5:])
 
     tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
-        gtbox, attrs, x.shape[2])
+        gtbox, gtlabel, attrs, x.shape[2])
 
     obj_mask_expand = np.tile(
         np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
@@ -142,73 +142,73 @@ def YoloV3Loss(x, gtbox, attrs):
     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_obj = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
-    loss_conf_noobj = bce(pred_conf * noobj_mask, tconf * noobj_mask,
+    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)
 
-    return attrs['lambda_xy'] * (loss_x + loss_y) \
-            + attrs['lambda_wh'] * (loss_w + loss_h) \
-            + attrs['lambda_conf_obj'] * loss_conf_obj \
-            + attrs['lambda_conf_noobj'] * loss_conf_noobj \
-            + attrs['lambda_class'] * loss_class
+    return attrs['loss_weight_xy'] * (loss_x + loss_y) \
+            + attrs['loss_weight_wh'] * (loss_w + loss_h) \
+            + attrs['loss_weight_conf_target'] * loss_conf_target \
+            + attrs['loss_weight_conf_notarget'] * loss_conf_notarget \
+            + attrs['loss_weight_class'] * loss_class
 
 
 class TestYolov3LossOp(OpTest):
     def setUp(self):
-        self.lambda_xy = 1.0
-        self.lambda_wh = 1.0
-        self.lambda_conf_obj = 1.0
-        self.lambda_conf_noobj = 1.0
-        self.lambda_class = 1.0
+        self.loss_weight_xy = 1.0
+        self.loss_weight_wh = 1.0
+        self.loss_weight_conf_target = 1.0
+        self.loss_weight_conf_notarget = 1.0
+        self.loss_weight_class = 1.0
         self.initTestCase()
         self.op_type = 'yolov3_loss'
         x = np.random.random(size=self.x_shape).astype('float32')
         gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
-        gtbox[:, :, 0] = np.random.randint(0, self.class_num,
-                                           self.gtbox_shape[:2])
+        gtlabel = np.random.randint(0, self.class_num,
+                                    self.gtbox_shape[:2]).astype('int32')
 
         self.attrs = {
             "anchors": self.anchors,
             "class_num": self.class_num,
             "ignore_thresh": self.ignore_thresh,
-            "lambda_xy": self.lambda_xy,
-            "lambda_wh": self.lambda_wh,
-            "lambda_conf_obj": self.lambda_conf_obj,
-            "lambda_conf_noobj": self.lambda_conf_noobj,
-            "lambda_class": self.lambda_class,
+            "loss_weight_xy": self.loss_weight_xy,
+            "loss_weight_wh": self.loss_weight_wh,
+            "loss_weight_conf_target": self.loss_weight_conf_target,
+            "loss_weight_conf_notarget": self.loss_weight_conf_notarget,
+            "loss_weight_class": self.loss_weight_class,
         }
 
-        self.inputs = {'X': x, 'GTBox': gtbox}
+        self.inputs = {'X': x, 'GTBox': gtbox, 'GTLabel': gtlabel}
         self.outputs = {
-            'Loss':
-            np.array([YoloV3Loss(x, gtbox, self.attrs)]).astype('float32')
+            'Loss': np.array(
+                [YoloV3Loss(x, gtbox, gtlabel, self.attrs)]).astype('float32')
         }
 
     def test_check_output(self):
         place = core.CPUPlace()
         self.check_output_with_place(place, atol=1e-3)
 
-    # def test_check_grad_ignore_gtbox(self):
-    #     place = core.CPUPlace()
-    #     self.check_grad_with_place(
-    #         place, ['X'],
-    #         'Loss',
-    #         no_grad_set=set("GTBox"),
-    #         max_relative_error=0.06)
+    def test_check_grad_ignore_gtbox(self):
+        place = core.CPUPlace()
+        self.check_grad_with_place(
+            place, ['X'],
+            'Loss',
+            no_grad_set=set("GTBox"),
+            max_relative_error=0.06)
 
     def initTestCase(self):
         self.anchors = [10, 13, 12, 12]
         self.class_num = 10
         self.ignore_thresh = 0.5
         self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
-        self.gtbox_shape = (5, 5, 5)
-        self.lambda_xy = 2.5
-        self.lambda_wh = 0.8
-        self.lambda_conf_obj = 1.5
-        self.lambda_conf_noobj = 0.5
-        self.lambda_class = 1.2
+        self.gtbox_shape = (5, 10, 4)
+        self.loss_weight_xy = 2.5
+        self.loss_weight_wh = 0.8
+        self.loss_weight_conf_target = 1.5
+        self.loss_weight_conf_notarget = 0.5
+        self.loss_weight_class = 1.2
 
 
 if __name__ == "__main__":