Merge pull request #14371 from heavengate/yolo_loss

Add YOLOv3 loss operator for YOLOv3 model

paddle/fluid/API.spec

@@ -299,6 +299,7 @@ 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', '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

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+   http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/fluid/operators/yolov3_loss_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class Yolov3LossOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "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_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.");
+    PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
+                      "Input(X) dim[3] and dim[4] should be euqal.");
+    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_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,
+                      "Attr(anchors) length should be even integer.");
+    PADDLE_ENFORCE_GT(class_num, 0,
+                      "Attr(class_num) should be an integer greater then 0.");
+
+    std::vector<int64_t> dim_out({1});
+    ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
+  }
+};
+
+class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "The input tensor of YOLO v3 loss operator, "
+             "This is a 4-D tensor with shape of [N, C, H, W]."
+             "H and W should be same, and the second dimention(C) stores"
+             "box locations, confidence score and classification one-hot"
+             "key of each anchor box");
+    AddInput("GTBox",
+             "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 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]");
+
+    AddAttr<int>("class_num", "The number of classes to predict.");
+    AddAttr<std::vector<int>>("anchors",
+                              "The anchor width and height, "
+                              "it will be parsed pair by pair.");
+    AddAttr<float>("ignore_thresh",
+                   "The ignore threshold to ignore confidence loss.");
+    AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
+        .SetDefault(1.0);
+    AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
+        .SetDefault(1.0);
+    AddAttr<float>(
+        "loss_weight_conf_target",
+        "The weight of confidence score loss in locations with target object.")
+        .SetDefault(1.0);
+    AddAttr<float>("loss_weight_conf_notarget",
+                   "The weight of confidence score loss in locations without "
+                   "target object.")
+        .SetDefault(1.0);
+    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
+         truth boxes.
+         
+         The output of previous network is in shape [N, C, H, W], while H and W
+         should be the same, specify the grid size, each grid point predict given
+         number boxes, this given number is specified by anchors, it should be 
+         half anchors length, which following will be represented as S. In the 
+         second dimention(the channel dimention), C should be S * (class_num + 5),
+         class_num is the box categoriy number of source dataset(such as coco), 
+         so in the second dimention, stores 4 box location coordinates x, y, w, h 
+         and confidence score of the box and class one-hot key of each anchor box.
+
+         While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
+         correspnd to:
+
+         $$
+         b_x = \sigma(t_x) + c_x
+         b_y = \sigma(t_y) + c_y
+         b_w = p_w e^{t_w}
+         b_h = p_h e^{t_h}
+         $$
+
+         While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
+         is specified by anchors.
+
+         As for confidence score, it is the logistic regression value of IoU between
+         anchor boxes and ground truth boxes, the score of the anchor box which has 
+         the max IoU should be 1, and if the anchor box has IoU bigger then ignore 
+         thresh, the confidence score loss of this anchor box will be ignored.
+
+         Therefore, the yolov3 loss consist of three major parts, box location loss,
+         confidence score loss, and classification loss. The MSE loss is used for 
+         box location, and binary cross entropy loss is used for confidence score 
+         loss and classification loss.
+
+         Final loss will be represented as follow.
+
+         $$
+         loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+              + \loss_weight_{conf_target} * loss_{conf_target}
+              + \loss_weight_{conf_notarget} * loss_{conf_notarget}
+              + \loss_weight_{class} * loss_{class}
+         $$
+         )DOC");
+  }
+};
+
+class Yolov3LossOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
+                   "Input(Loss@GRAD) should not be null");
+    auto dim_x = ctx->GetInputDim("X");
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
+  }
+};
+
+class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op = new framework::OpDesc();
+    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);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(yolov3_loss, ops::Yolov3LossOp, ops::Yolov3LossOpMaker,
+                  ops::Yolov3LossGradMaker);
+REGISTER_OPERATOR(yolov3_loss_grad, ops::Yolov3LossOpGrad);
+REGISTER_OP_CPU_KERNEL(yolov3_loss, ops::Yolov3LossKernel<float>,
+                       ops::Yolov3LossKernel<double>);
+REGISTER_OP_CPU_KERNEL(yolov3_loss_grad, ops::Yolov3LossGradKernel<float>,
+                       ops::Yolov3LossGradKernel<double>);

paddle/fluid/operators/yolov3_loss_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+   http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+#include <algorithm>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
+using Array5 = Eigen::DSizes<int64_t, 5>;
+
+template <typename T>
+static inline bool isZero(T x) {
+  return fabs(x) < 1e-6;
+}
+
+template <typename T>
+static inline T sigmoid(T x) {
+  return 1.0 / (exp(-1.0 * x) + 1.0);
+}
+
+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;
+    }
+  }
+  return count;
+}
+
+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;
+    }
+  }
+  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;
+    }
+  }
+}
+
+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);
+
+  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;
+    }
+  }
+  return (error_sum / points);
+}
+
+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;
+    }
+  }
+}
+
+template <typename T>
+static void CalcPredResult(const Tensor& input, Tensor* pred_conf,
+                           Tensor* pred_class, Tensor* pred_x, Tensor* pred_y,
+                           Tensor* pred_w, Tensor* pred_h, const int anchor_num,
+                           const int class_num) {
+  const int n = input.dims()[0];
+  const int h = input.dims()[2];
+  const int w = input.dims()[3];
+  const int box_attr_num = 5 + class_num;
+
+  auto input_t = EigenTensor<T, 4>::From(input);
+  auto pred_conf_t = EigenTensor<T, 4>::From(*pred_conf);
+  auto pred_class_t = EigenTensor<T, 5>::From(*pred_class);
+  auto pred_x_t = EigenTensor<T, 4>::From(*pred_x);
+  auto pred_y_t = EigenTensor<T, 4>::From(*pred_y);
+  auto pred_w_t = EigenTensor<T, 4>::From(*pred_w);
+  auto pred_h_t = EigenTensor<T, 4>::From(*pred_h);
+
+  for (int i = 0; i < n; i++) {
+    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_y_t(i, an_idx, j, k) =
+              sigmoid(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));
+
+          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));
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
+  T b1_x1 = box1[0] - box1[2] / 2;
+  T b1_x2 = box1[0] + box1[2] / 2;
+  T b1_y1 = box1[1] - box1[3] / 2;
+  T b1_y2 = box1[1] + box1[3] / 2;
+  T b2_x1 = box2[0] - box2[2] / 2;
+  T b2_x2 = box2[0] + box2[2] / 2;
+  T b2_y1 = box2[1] - box2[3] / 2;
+  T b2_y2 = box2[1] + box2[3] / 2;
+
+  T b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1);
+  T b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1);
+
+  T inter_rect_x1 = std::max(b1_x1, b2_x1);
+  T inter_rect_y1 = std::max(b1_y1, b2_y1);
+  T inter_rect_x2 = std::min(b1_x2, b2_x2);
+  T inter_rect_y2 = std::min(b1_y2, b2_y2);
+  T inter_area = std::max(inter_rect_x2 - inter_rect_x1, static_cast<T>(0.0)) *
+                 std::max(inter_rect_y2 - inter_rect_y1, static_cast<T>(0.0));
+
+  return inter_area / (b1_area + b2_area - inter_area);
+}
+
+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 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 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 tconf_t = EigenTensor<T, 4>::From(*tconf).setConstant(0.0);
+  auto tclass_t = EigenTensor<T, 5>::From(*tclass).setConstant(0.0);
+
+  for (int i = 0; i < n; i++) {
+    for (int j = 0; j < b; j++) {
+      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 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_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_shape, anchor_shape);
+        if (iou > max_iou) {
+          max_iou = iou;
+          best_an_index = an_idx;
+        }
+        if (iou > ignore_thresh) {
+          noobj_mask_t(i, an_idx, gj, gi) = 0;
+        }
+      }
+      obj_mask_t(i, best_an_index, gj, gi) = 1;
+      noobj_mask_t(i, best_an_index, gj, gi) = 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]);
+      tclass_t(i, best_an_index, gj, gi, cur_label) = 1;
+      tconf_t(i, best_an_index, gj, gi) = 1;
+    }
+  }
+}
+
+static void ExpandObjMaskByClassNum(Tensor* obj_mask_expand,
+                                    const Tensor& obj_mask) {
+  const int n = obj_mask_expand->dims()[0];
+  const int an_num = obj_mask_expand->dims()[1];
+  const int h = obj_mask_expand->dims()[2];
+  const int w = obj_mask_expand->dims()[3];
+  const int class_num = obj_mask_expand->dims()[4];
+  auto obj_mask_expand_t = EigenTensor<int, 5>::From(*obj_mask_expand);
+  auto obj_mask_t = EigenTensor<int, 4>::From(obj_mask);
+
+  obj_mask_expand_t = obj_mask_t.reshape(Array5(n, an_num, h, w, 1))
+                          .broadcast(Array5(1, 1, 1, 1, class_num));
+}
+
+template <typename T>
+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_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];
+  const int w = pred_x.dims()[3];
+  const int attr_num = class_num + 5;
+  auto grad_t = EigenTensor<T, 4>::From(*grad).setConstant(0.0);
+  auto pred_x_t = EigenTensor<T, 4>::From(pred_x);
+  auto pred_y_t = EigenTensor<T, 4>::From(pred_y);
+  auto pred_conf_t = EigenTensor<T, 4>::From(pred_conf);
+  auto pred_class_t = EigenTensor<T, 5>::From(pred_class);
+  auto grad_x_t = EigenTensor<T, 4>::From(grad_x);
+  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_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++) {
+    for (int j = 0; j < an_num; j++) {
+      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_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_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_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 *
+              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;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+class Yolov3LossKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    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 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];
+    const int w = input->dims()[3];
+    const int an_num = anchors.size() / 2;
+
+    Tensor pred_x, pred_y, pred_w, pred_h;
+    Tensor pred_conf, pred_class;
+    pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_conf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
+    CalcPredResult<T>(*input, &pred_conf, &pred_class, &pred_x, &pred_y,
+                      &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());
+    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());
+    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);
+
+    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);
+
+    auto* loss_data = loss->mutable_data<T>({1}, ctx.GetPlace());
+    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;
+  }
+};
+
+template <typename T>
+class Yolov3LossGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    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 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];
+    const int h = input->dims()[2];
+    const int w = input->dims()[3];
+    const int an_num = anchors.size() / 2;
+
+    Tensor pred_x, pred_y, pred_w, pred_h;
+    Tensor pred_conf, pred_class;
+    pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_conf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    pred_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
+    CalcPredResult<T>(*input, &pred_conf, &pred_class, &pred_x, &pred_y,
+                      &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());
+    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());
+    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);
+
+    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);
+
+    Tensor grad_x, grad_y, grad_w, grad_h;
+    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_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);
+
+    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_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);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/detection.py

@@ -20,6 +20,7 @@ from __future__ import print_function
 from .layer_function_generator import generate_layer_fn
 from .layer_function_generator import autodoc, templatedoc
 from ..layer_helper import LayerHelper
+from ..framework import Variable
 from . import tensor
 from . import nn
 from . import ops
@@ -46,6 +47,7 @@ __all__ = [
     'iou_similarity',
     'box_coder',
     'polygon_box_transform',
+    'yolov3_loss',
 ]
 
 
@@ -401,6 +403,113 @@ def polygon_box_transform(input, name=None):
     return output
 
 
+@templatedoc(op_type="yolov3_loss")
+def yolov3_loss(x,
+                gtbox,
+                gtlabel,
+                anchors,
+                class_num,
+                ignore_thresh,
+                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, 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}
+        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:
+        Variable: A 1-D tensor with shape [1], the value of yolov3 loss
+
+    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
+
+    Examples:
+    .. code-block:: python
+
+        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)
+    """
+    helper = LayerHelper('yolov3_loss', **locals())
+
+    if not isinstance(x, Variable):
+        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):
+        raise TypeError("Attr class_num of yolov3_loss must be an integer")
+    if not isinstance(ignore_thresh, float):
+        raise TypeError(
+            "Attr ignore_thresh of yolov3_loss must be a float number")
+
+    if name is None:
+        loss = helper.create_variable_for_type_inference(dtype=x.dtype)
+    else:
+        loss = helper.create_variable(
+            name=name, dtype=x.dtype, persistable=False)
+
+    attrs = {
+        "anchors": anchors,
+        "class_num": class_num,
+        "ignore_thresh": ignore_thresh,
+    }
+
+    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,
+                "GTLabel": gtlabel},
+        outputs={'Loss': loss},
+        attrs=attrs)
+    return loss
+
+
 @templatedoc()
 def detection_map(detect_res,
                   label,

python/paddle/fluid/tests/test_detection.py

@@ -388,5 +388,18 @@ 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_yolov3_loss_op.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import division
+
+import unittest
+import numpy as np
+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, 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 box_iou(box1, box2):
+    b1_x1 = box1[0] - box1[2] / 2
+    b1_x2 = box1[0] + box1[2] / 2
+    b1_y1 = box1[1] - box1[3] / 2
+    b1_y2 = box1[1] + box1[3] / 2
+    b2_x1 = box2[0] - box2[2] / 2
+    b2_x2 = box2[0] + box2[2] / 2
+    b2_y1 = box2[1] - box2[3] / 2
+    b2_y2 = box2[1] + box2[3] / 2
+
+    b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
+    b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
+
+    inter_rect_x1 = max(b1_x1, b2_x1)
+    inter_rect_y1 = max(b1_y1, b2_y1)
+    inter_rect_x2 = min(b1_x2, b2_x2)
+    inter_rect_y2 = min(b1_y2, b2_y2)
+    inter_area = max(inter_rect_x2 - inter_rect_x1, 0) * max(
+        inter_rect_y2 - inter_rect_y1, 0)
+
+    return inter_area / (b1_area + b2_area + inter_area)
+
+
+def build_target(gtboxs, gtlabel, attrs, grid_size):
+    n, b, _ = gtboxs.shape
+    ignore_thresh = attrs["ignore_thresh"]
+    anchors = attrs["anchors"]
+    class_num = attrs["class_num"]
+    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')
+    tx = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
+    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')
+    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:
+                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
+
+            gi = int(gx)
+            gj = int(gy)
+
+            gtbox = [0, 0, gw, gh]
+            max_iou = 0
+            for k in range(an_num):
+                anchor_box = [0, 0, anchors[2 * k], anchors[2 * k + 1]]
+                iou = box_iou(gtbox, anchor_box)
+                if iou > max_iou:
+                    max_iou = iou
+                    best_an_index = k
+                if iou > ignore_thresh:
+                    noobj_mask[i, best_an_index, gj, gi] = 0
+
+            obj_mask[i, best_an_index, gj, gi] = 1
+            noobj_mask[i, best_an_index, gj, gi] = 0
+            tx[i, best_an_index, gj, gi] = gx - gi
+            ty[i, best_an_index, gj, gi] = gy - gj
+            tw[i, best_an_index, gj, gi] = np.log(gw / anchors[2 *
+                                                               best_an_index])
+            th[i, best_an_index, gj, gi] = np.log(
+                gh / anchors[2 * best_an_index + 1])
+            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)
+
+
+def YoloV3Loss(x, gtbox, gtlabel, attrs):
+    n, c, h, w = x.shape
+    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_w = x[:, :, :, :, 2]
+    pred_h = x[:, :, :, :, 3]
+    pred_conf = sigmoid(x[:, :, :, :, 4])
+    pred_cls = sigmoid(x[:, :, :, :, 5:])
+
+    tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
+        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'])))
+    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)
+
+    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.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')
+        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,
+            "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, 'GTLabel': gtlabel}
+        self.outputs = {
+            '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", "GTLabel"]),
+            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, 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__":
+    unittest.main()