use darknet loss and trick

paddle/fluid/API.spec

@@ -324,7 +324,7 @@ paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes',
 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', 'gtlabel', 'gtscore', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(True, None,))
+paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, 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/operators/yolov3_loss_op.cc

@@ -27,8 +27,6 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
                    "Input(GTBox) of Yolov3LossOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
                    "Input(GTLabel) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("GTScore"),
-                   "Input(GTScore) of Yolov3LossOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Loss"),
                    "Output(Loss) of Yolov3LossOp should not be null.");
     PADDLE_ENFORCE(
@@ -40,7 +38,6 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
     auto dim_x = ctx->GetInputDim("X");
     auto dim_gtbox = ctx->GetInputDim("GTBox");
     auto dim_gtlabel = ctx->GetInputDim("GTLabel");
-    auto dim_gtscore = ctx->GetInputDim("GTScore");
     auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
     int anchor_num = anchors.size() / 2;
     auto anchor_mask = ctx->Attrs().Get<std::vector<int>>("anchor_mask");
@@ -63,12 +60,6 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
                       "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_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");
     PADDLE_ENFORCE_GT(anchors.size(), 0,
                       "Attr(anchors) length should be greater then 0.");
     PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
@@ -121,11 +112,6 @@ 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]");
@@ -157,8 +143,6 @@ 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 generate yolov3 loss by given predict result and ground
          truth boxes.
@@ -245,7 +229,6 @@ 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"));
@@ -255,7 +238,6 @@ 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/yolov3_loss_op.h

@@ -36,11 +36,6 @@ static T SCE(T x, T label) {
   return (x > 0 ? x : 0.0) - x * label + std::log(1.0 + std::exp(-std::abs(x)));
 }
 
-template <typename T>
-static T L1Loss(T x, T y) {
-  return std::abs(y - x);
-}
-
 template <typename T>
 static T L2Loss(T x, T y) {
   return 0.5 * (y - x) * (y - x);
@@ -51,11 +46,6 @@ static T SCEGrad(T x, T label) {
   return 1.0 / (1.0 + std::exp(-x)) - label;
 }
 
-template <typename T>
-static T L1LossGrad(T x, T y) {
-  return x > y ? 1.0 : -1.0;
-}
-
 template <typename T>
 static T L2LossGrad(T x, T y) {
   return x - y;
@@ -131,13 +121,13 @@ 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, T score) {
+                                int input_size, int stride) {
   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) * score;
+  T scale = (2.0 - gt.w * gt.h);
   loss[0] += SCE<T>(input[box_idx], tx) * scale;
   loss[0] += SCE<T>(input[box_idx + stride], ty) * scale;
   loss[0] += L2Loss<T>(input[box_idx + 2 * stride], tw) * scale;
@@ -148,14 +138,13 @@ 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,
-                                    T score) {
+                                    int grid_size, int input_size, int stride) {
   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) * score;
+  T scale = (2.0 - gt.w * gt.h);
   input_grad[box_idx] = SCEGrad<T>(input[box_idx], tx) * scale * loss;
   input_grad[box_idx + stride] =
       SCEGrad<T>(input[box_idx + stride], ty) * scale * loss;
@@ -168,11 +157,10 @@ static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
 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 T pos, const T neg,
-                                 T score) {
+                                 const int stride) {
   for (int i = 0; i < class_num; i++) {
     T pred = input[index + i * stride];
-    loss[0] += SCE<T>(pred, (i == label) ? pos : neg) * score;
+    loss[0] += SCE<T>(pred, (i == label) ? 1.0 : 0.0);
   }
 }
 
@@ -180,12 +168,11 @@ 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 T pos, const T neg,
-                                     T score) {
+                                     const int stride) {
   for (int i = 0; i < class_num; i++) {
     T pred = input[index + i * stride];
     input_grad[index + i * stride] =
-        SCEGrad<T>(pred, (i == label) ? pos : neg) * score * loss;
+        SCEGrad<T>(pred, (i == label) ? 1.0 : 0.0) * loss;
   }
 }
 
@@ -201,7 +188,7 @@ static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
           T obj = objness[k * w + l];
           if (obj > 1e-5) {
             // positive sample: obj = mixup score
-            loss[i] += SCE<T>(input[k * w + l], 1.0) * obj;
+            loss[i] += SCE<T>(input[k * w + l], 1.0);
           } else if (obj > -0.5) {
             // negetive sample: obj = 0
             loss[i] += SCE<T>(input[k * w + l], 0.0);
@@ -226,8 +213,7 @@ static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
         for (int l = 0; l < w; l++) {
           T obj = objness[k * w + l];
           if (obj > 1e-5) {
-            input_grad[k * w + l] =
-                SCEGrad<T>(input[k * w + l], 1.0) * obj * loss[i];
+            input_grad[k * w + l] = SCEGrad<T>(input[k * w + l], 1.0) * loss[i];
           } else if (obj > -0.5) {
             input_grad[k * w + l] = SCEGrad<T>(input[k * w + l], 0.0) * loss[i];
           }
@@ -263,7 +249,6 @@ 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");
@@ -272,7 +257,6 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
     int class_num = ctx.Attr<int>("class_num");
     float ignore_thresh = ctx.Attr<float>("ignore_thresh");
     int downsample = ctx.Attr<int>("downsample");
-    bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
 
     const int n = input->dims()[0];
     const int h = input->dims()[2];
@@ -285,17 +269,9 @@ 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 =
@@ -376,20 +352,19 @@ 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, score);
+                                 box_idx, gi, gj, h, input_size, stride);
 
           int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
-          obj_mask_data[obj_idx] = score;
+          obj_mask_data[obj_idx] = 1.0;
 
           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, label_pos, label_neg, score);
+                           class_num, stride);
         }
       }
     }
@@ -406,7 +381,6 @@ 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");
@@ -415,7 +389,6 @@ 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 = ctx.Attr<int>("downsample");
-    bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
 
     const int n = input_grad->dims()[0];
     const int c = input_grad->dims()[1];
@@ -428,17 +401,9 @@ 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>();
@@ -450,24 +415,21 @@ 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, score);
+          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);
 
           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_pos,
-                               label_neg, score);
+                               label_idx, label, class_num, stride);
         }
       }
     }

python/paddle/fluid/layers/detection.py

@@ -412,13 +412,11 @@ def polygon_box_transform(input, name=None):
 def yolov3_loss(x,
                 gtbox,
                 gtlabel,
-                gtscore,
                 anchors,
                 anchor_mask,
                 class_num,
                 ignore_thresh,
                 downsample,
-                use_label_smooth=True,
                 name=None):
     """
     ${comment}
@@ -432,14 +430,11 @@ def yolov3_loss(x,
                           an image.
         gtlabel (Variable): class id of ground truth boxes, shoud be in shape
                             of [N, B].
-        gtscore (Variable): score of gtlabel, should be in same shape with gtlabel
-                            and score value in range (0, 1).
         anchors (list|tuple): ${anchors_comment}
         anchor_mask (list|tuple): ${anchor_mask_comment}
         class_num (int): ${class_num_comment}
         ignore_thresh (float): ${ignore_thresh_comment}
         downsample (int): ${downsample_comment}
-        use_label_smooth(bool): ${use_label_smooth_comment}
         name (string): the name of yolov3 loss
 
     Returns:
@@ -449,11 +444,9 @@ def yolov3_loss(x,
         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: Input gtscore 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
-        TypeError: Attr use_label_smooth of yolov3_loss must be a bool value
 
     Examples:
     .. code-block:: python
@@ -474,16 +467,12 @@ def yolov3_loss(x,
         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(gtscore, Variable):
-        raise TypeError("Input gtscore 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(anchor_mask, list) and not isinstance(anchor_mask, tuple):
         raise TypeError("Attr anchor_mask of yolov3_loss must be list or tuple")
     if not isinstance(class_num, int):
         raise TypeError("Attr class_num of yolov3_loss must be an integer")
-    if not isinstance(use_label_smooth, bool):
-        raise TypeError("Attr ues_label_smooth of yolov3 must be a bool value")
     if not isinstance(ignore_thresh, float):
         raise TypeError(
             "Attr ignore_thresh of yolov3_loss must be a float number")
@@ -503,7 +492,6 @@ def yolov3_loss(x,
         "class_num": class_num,
         "ignore_thresh": ignore_thresh,
         "downsample": downsample,
-        "use_label_smooth": use_label_smooth
     }
 
     helper.append_op(
@@ -512,7 +500,6 @@ def yolov3_loss(x,
             "X": x,
             "GTBox": gtbox,
             "GTLabel": gtlabel,
-            "GTScore": gtscore
         },
         outputs={
             'Loss': loss,

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

@@ -23,10 +23,6 @@ from op_test import OpTest
 from paddle.fluid import core
 
 
-def l1loss(x, y):
-    return abs(x - y)
-
-
 def l2loss(x, y):
     return 0.5 * (y - x) * (y - x)
 
@@ -70,7 +66,7 @@ def batch_xywh_box_iou(box1, box2):
     return inter_area / union
 
 
-def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
+def YOLOv3Loss(x, gtbox, gtlabel, attrs):
     n, c, h, w = x.shape
     b = gtbox.shape[1]
     anchors = attrs['anchors']
@@ -80,14 +76,10 @@ def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
     class_num = attrs["class_num"]
     ignore_thresh = attrs['ignore_thresh']
     downsample = attrs['downsample']
-    use_label_smooth = attrs['use_label_smooth']
     input_size = downsample * h
     x = x.reshape((n, mask_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
     loss = np.zeros((n)).astype('float32')
 
-    label_pos = 1.0 - 1.0 / class_num if use_label_smooth else 1.0
-    label_neg = 1.0 / class_num if use_label_smooth else 0.0
-
     pred_box = x[:, :, :, :, :4].copy()
     grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
     grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
@@ -146,22 +138,21 @@ def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
             ty = gtbox[i, j, 1] * w - gj
             tw = np.log(gtbox[i, j, 2] * input_size / mask_anchors[an_idx][0])
             th = np.log(gtbox[i, j, 3] * input_size / mask_anchors[an_idx][1])
-            scale = (2.0 - gtbox[i, j, 2] * gtbox[i, j, 3]) * gtscore[i, j]
+            scale = (2.0 - gtbox[i, j, 2] * gtbox[i, j, 3])
             loss[i] += sce(x[i, an_idx, gj, gi, 0], tx) * scale
             loss[i] += sce(x[i, an_idx, gj, gi, 1], ty) * scale
             loss[i] += l2loss(x[i, an_idx, gj, gi, 2], tw) * scale
             loss[i] += l2loss(x[i, an_idx, gj, gi, 3], th) * scale
 
-            objness[i, an_idx * h * w + gj * w + gi] = gtscore[i, j]
+            objness[i, an_idx * h * w + gj * w + gi] = 1.0
 
             for label_idx in range(class_num):
-                loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx], label_pos
-                               if label_idx == gtlabel[i, j] else
-                               label_neg) * gtscore[i, j]
+                loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx],
+                               float(label_idx == gtlabel[i, j]))
 
         for j in range(mask_num * h * w):
             if objness[i, j] > 0:
-                loss[i] += sce(pred_obj[i, j], 1.0) * objness[i, j]
+                loss[i] += sce(pred_obj[i, j], 1.0)
             elif objness[i, j] == 0:
                 loss[i] += sce(pred_obj[i, j], 0.0)
 
@@ -176,7 +167,6 @@ class TestYolov3LossOp(OpTest):
         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])
-        gtscore = np.random.random(self.gtbox_shape[:2]).astype('float32')
         gtmask = np.random.randint(0, 2, self.gtbox_shape[:2])
         gtbox = gtbox * gtmask[:, :, np.newaxis]
         gtlabel = gtlabel * gtmask
@@ -187,17 +177,14 @@ class TestYolov3LossOp(OpTest):
             "class_num": self.class_num,
             "ignore_thresh": self.ignore_thresh,
             "downsample": self.downsample,
-            "use_label_smooth": self.use_label_smooth,
         }
 
         self.inputs = {
             'X': x,
             'GTBox': gtbox.astype('float32'),
             'GTLabel': gtlabel.astype('int32'),
-            'GTScore': gtscore.astype('float32')
         }
-        loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, gtscore,
-                                               self.attrs)
+        loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, self.attrs)
         self.outputs = {
             'Loss': loss,
             'ObjectnessMask': objness,
@@ -213,7 +200,7 @@ class TestYolov3LossOp(OpTest):
         self.check_grad_with_place(
             place, ['X'],
             'Loss',
-            no_grad_set=set(["GTBox", "GTLabel", "GTScore"]),
+            no_grad_set=set(["GTBox", "GTLabel"]),
             max_relative_error=0.3)
 
     def initTestCase(self):
@@ -224,12 +211,6 @@ class TestYolov3LossOp(OpTest):
         self.downsample = 32
         self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
         self.gtbox_shape = (3, 5, 4)
-        self.use_label_smooth = True
-
-
-class TestYolov3LossWithoutLabelSmooth(TestYolov3LossOp):
-    def set_label_smooth(self):
-        self.use_label_smooth = False
 
 
 if __name__ == "__main__":