ignore pred overlap gt > 0.7. test=develop

paddle/fluid/operators/yolov3_loss_op.cc

@@ -35,12 +35,15 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
     auto dim_gtlabel = ctx->GetInputDim("GTLabel");
     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");
+    int mask_num = anchor_mask.size();
     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], anchor_num * (5 + class_num),
-                      "Input(X) dim[1] should be equal to (anchor_number * (5 "
+    PADDLE_ENFORCE_EQ(
+        dim_x[1], mask_num * (5 + class_num),
+        "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
         "+ class_num)).");
     PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
                       "Input(GTBox) should be a 3-D tensor");
@@ -55,6 +58,11 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
                       "Attr(anchors) length should be greater then 0.");
     PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
                       "Attr(anchors) length should be even integer.");
+    for (size_t i = 0; i < anchor_mask.size(); i++) {
+      PADDLE_ENFORCE_LT(
+          anchor_mask[i], anchor_num,
+          "Attr(anchor_mask) should not crossover Attr(anchors).");
+    }
     PADDLE_ENFORCE_GT(class_num, 0,
                       "Attr(class_num) should be an integer greater then 0.");
 
@@ -74,7 +82,7 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X",
-             "The input tensor of YOLO v3 loss operator, "
+             "The input tensor of YOLOv3 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"
@@ -99,13 +107,20 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
     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<int>("input_size",
-                 "The input size of YOLOv3 net, "
-                 "generally this is set as 320, 416 or 608.")
-        .SetDefault(406);
+                              "it will be parsed pair by pair.")
+        .SetDefault(std::vector<int>{});
+    AddAttr<std::vector<int>>("anchor_mask",
+                              "The mask index of anchors used in "
+                              "current YOLOv3 loss calculation.")
+        .SetDefault(std::vector<int>{});
+    AddAttr<int>("downsample",
+                 "The downsample ratio from network input to YOLOv3 loss "
+                 "input, so 32, 16, 8 should be set for the first, second, "
+                 "and thrid YOLOv3 loss operators.")
+        .SetDefault(32);
     AddAttr<float>("ignore_thresh",
-                   "The ignore threshold to ignore confidence loss.");
+                   "The ignore threshold to ignore confidence loss.")
+        .SetDefault(0.7);
     AddComment(R"DOC(
          This operator generate yolov3 loss by given predict result and ground
          truth boxes.

paddle/fluid/operators/yolov3_loss_op.h

@@ -321,6 +321,182 @@ static void CalcYolov3LossGrad(T* input_grad_data, const Tensor& loss_grad,
                  obj_mask_data, n, an_num, grid_num, class_num, class_num);
 }
 
+static int mask_index(std::vector<int> mask, int val) {
+  for (int i = 0; i < mask.size(); i++) {
+    if (mask[i] == val) {
+      return i;
+    }
+  }
+  return -1;
+}
+
+template <typename T>
+struct Box {
+  float x, y, w, h;
+};
+
+template <typename T>
+static inline T sigmoid(T x) {
+  return 1.0 / (1.0 + std::exp(-x));
+}
+
+template <typename T>
+static inline void sigmoid_arrray(T* arr, int len) {
+  for (int i = 0; i < len; i++) {
+    arr[i] = sigmoid(arr[i]);
+  }
+}
+
+template <typename T>
+static inline Box<T> get_yolo_box(const T* x, std::vector<int> anchors, int i,
+                                  int j, int an_idx, int grid_size,
+                                  int input_size, int index, int stride) {
+  Box<T> b;
+  b.x = (i + sigmoid<T>(x[index])) / grid_size;
+  b.y = (j + sigmoid<T>(x[index + stride])) / grid_size;
+  b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
+  b.h = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] / input_size;
+  return b;
+}
+
+template <typename T>
+static inline Box<T> get_gt_box(const T* gt, int batch, int max_boxes,
+                                int idx) {
+  Box<T> b;
+  b.x = gt[(batch * max_boxes + idx) * 4];
+  b.y = gt[(batch * max_boxes + idx) * 4 + 1];
+  b.w = gt[(batch * max_boxes + idx) * 4 + 2];
+  b.h = gt[(batch * max_boxes + idx) * 4 + 3];
+  return b;
+}
+
+template <typename T>
+static inline T overlap(T c1, T w1, T c2, T w2) {
+  T l1 = c1 - w1 / 2.0;
+  T l2 = c2 - w2 / 2.0;
+  T left = l1 > l2 ? l1 : l2;
+  T r1 = c1 + w1 / 2.0;
+  T r2 = c2 + w2 / 2.0;
+  T right = r1 < r2 ? r1 : r2;
+  return right - left;
+}
+
+template <typename T>
+static inline T box_iou(Box<T> b1, Box<T> b2) {
+  T w = overlap(b1.x, b1.w, b2.x, b2.w);
+  T h = overlap(b1.y, b1.h, b2.y, b2.h);
+  T inter_area = (w < 0 || h < 0) ? 0.0 : w * h;
+  T union_area = b1.w * b1.h + b2.w * b2.h - inter_area;
+  return inter_area / union_area;
+}
+
+static inline int entry_index(int batch, int an_idx, int hw_idx, int an_num,
+                              int an_stride, int stride, int entry) {
+  return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx;
+}
+
+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 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;
+  loss[0] += SCE<T>(input[box_idx], tx) * scale;
+  loss[0] += SCE<T>(input[box_idx + stride], ty) * scale;
+  loss[0] += L1Loss<T>(input[box_idx + 2 * stride], tw) * scale;
+  loss[0] += L1Loss<T>(input[box_idx + 3 * stride], th) * scale;
+}
+
+template <typename T>
+static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
+                                    Box<T> gt, std::vector<int> anchors,
+                                    int an_idx, int box_idx, int gi, int gj,
+                                    int grid_size, int input_size, int stride) {
+  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;
+  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;
+  input_grad[box_idx + 2 * stride] =
+      L1LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
+  input_grad[box_idx + 3 * stride] =
+      L1LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
+}
+
+template <typename T>
+static inline void CalcLabelLoss(T* loss, const T* input, const int index,
+                                 const int label, const int class_num,
+                                 const int stride) {
+  for (int i = 0; i < class_num; i++) {
+    loss[0] += SCE<T>(input[index + i * stride], (i == label) ? 1.0 : 0.0);
+  }
+}
+
+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) {
+  for (int i = 0; i < class_num; i++) {
+    input_grad[index + i * stride] =
+        SCEGrad<T>(input[index + i * stride], (i == label) ? 1.0 : 0.0) * loss;
+  }
+}
+
+template <typename T>
+static inline void CalcObjnessLoss(T* loss, const T* input, const int* objness,
+                                   const int n, const int an_num, const int h,
+                                   const int w, const int stride,
+                                   const int an_stride) {
+  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++) {
+          int obj = objness[k * w + l];
+          if (obj >= 0) {
+            loss[i] += SCE<T>(input[k * w + l], static_cast<T>(obj));
+          }
+        }
+      }
+      objness += stride;
+      input += an_stride;
+    }
+  }
+}
+
+template <typename T>
+static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
+                                       const T* input, const int* objness,
+                                       const int n, const int an_num,
+                                       const int h, const int w,
+                                       const int stride, const int an_stride) {
+  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++) {
+          int obj = objness[k * w + l];
+          if (obj >= 0) {
+            input_grad[k * w + l] =
+                SCEGrad<T>(input[k * w + l], static_cast<T>(obj)) * loss[i];
+          }
+        }
+      }
+      objness += stride;
+      input += an_stride;
+      input_grad += an_stride;
+    }
+  }
+}
+
 template <typename T>
 class Yolov3LossKernel : public framework::OpKernel<T> {
  public:
@@ -330,55 +506,158 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
     auto* gt_label = ctx.Input<Tensor>("GTLabel");
     auto* loss = ctx.Output<Tensor>("Loss");
     auto anchors = ctx.Attr<std::vector<int>>("anchors");
+    auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
     int class_num = ctx.Attr<int>("class_num");
-    int input_size = ctx.Attr<int>("input_size");
     float ignore_thresh = ctx.Attr<float>("ignore_thresh");
+    int downsample = ctx.Attr<int>("downsample");
 
     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;
+    const int mask_num = anchor_mask.size();
+    const int b = gt_box->dims()[1];
+    int input_size = downsample * h;
 
-    Tensor conf_mask, obj_mask;
-    Tensor tx, ty, tw, th, tweight, tconf, tclass;
-    conf_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    obj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tweight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
-
-    math::SetConstant<platform::CPUDeviceContext, T> constant;
-    constant(ctx.template device_context<platform::CPUDeviceContext>(),
-             &conf_mask, static_cast<T>(1.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(),
-             &obj_mask, static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tx,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &ty,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tw,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &th,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(),
-             &tweight, static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tconf,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tclass,
-             static_cast<T>(0.0));
-
-    PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, input_size,
-                       h, &conf_mask, &obj_mask, &tx, &ty, &tw, &th, &tweight,
-                       &tconf, &tclass);
-
+    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>();
     T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
-    memset(loss_data, 0, n * sizeof(T));
-    CalcYolov3Loss<T>(loss_data, *input, tx, ty, tw, th, tweight, tconf, tclass,
-                      conf_mask, obj_mask);
+    memset(loss_data, 0, n * sizeof(int));
+
+    Tensor objness;
+    int* objness_data =
+        objness.mutable_data<int>({n, mask_num, h, w}, ctx.GetPlace());
+    memset(objness_data, 0, objness.numel() * sizeof(int));
+
+    const int stride = h * w;
+    const int an_stride = (class_num + 5) * stride;
+
+    for (int i = 0; i < n; i++) {
+      for (int j = 0; j < mask_num; j++) {
+        for (int k = 0; k < h; k++) {
+          for (int l = 0; l < w; l++) {
+            int box_idx =
+                entry_index(i, j, k * w + l, mask_num, an_stride, stride, 0);
+            Box<T> pred =
+                get_yolo_box(input_data, anchors, l, k, anchor_mask[j], h,
+                             input_size, box_idx, stride);
+            T best_iou = 0;
+            // int best_t = 0;
+            for (int t = 0; t < b; t++) {
+              if (isZero<T>(gt_box_data[i * b * 4 + t * 4]) &&
+                  isZero<T>(gt_box_data[i * b * 4 + t * 4 + 1])) {
+                continue;
+              }
+              Box<T> gt = get_gt_box(gt_box_data, i, b, t);
+              T iou = box_iou(pred, gt);
+              if (iou > best_iou) {
+                best_iou = iou;
+                // best_t = t;
+              }
+            }
+
+            if (best_iou > ignore_thresh) {
+              int obj_idx = (i * mask_num + j) * stride + k * w + l;
+              objness_data[obj_idx] = -1;
+            }
+          }
+        }
+      }
+      for (int t = 0; t < b; t++) {
+        if (isZero<T>(gt_box_data[i * b * 4 + t * 4]) &&
+            isZero<T>(gt_box_data[i * b * 4 + t * 4 + 1])) {
+          continue;
+        }
+        Box<T> gt = get_gt_box(gt_box_data, i, b, t);
+        int gi = static_cast<int>(gt.x * w);
+        int gj = static_cast<int>(gt.y * h);
+        Box<T> gt_shift = gt;
+        gt_shift.x = 0.0;
+        gt_shift.y = 0.0;
+        T best_iou = 0.0;
+        int best_n = 0;
+        for (int an_idx = 0; an_idx < an_num; an_idx++) {
+          Box<T> an_box;
+          an_box.x = 0.0;
+          an_box.y = 0.0;
+          an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);
+          an_box.h = anchors[2 * an_idx + 1] / static_cast<T>(input_size);
+          float iou = box_iou<T>(an_box, gt_shift);
+          // TO DO: iou > 0.5 ?
+          if (iou > best_iou) {
+            best_iou = iou;
+            best_n = an_idx;
+          }
+        }
+
+        int mask_idx = mask_index(anchor_mask, best_n);
+        if (mask_idx >= 0) {
+          int box_idx = entry_index(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);
+
+          int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
+          objness_data[obj_idx] = 1;
+
+          int label = gt_label_data[i * b + t];
+          int label_idx = entry_index(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);
+        }
+      }
+    }
+
+    CalcObjnessLoss<T>(loss_data, input_data + 4 * stride, objness_data, n,
+                       mask_num, h, w, stride, an_stride);
+
+    // Tensor conf_mask, obj_mask;
+    // Tensor tx, ty, tw, th, tweight, tconf, tclass;
+    // conf_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // obj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tweight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
+    //
+    // math::SetConstant<platform::CPUDeviceContext, T> constant;
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    //          &conf_mask, static_cast<T>(1.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    //          &obj_mask, static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &tx,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &ty,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &tw,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &th,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    //          &tweight, static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    // &tconf,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    // &tclass,
+    //          static_cast<T>(0.0));
+    //
+    // PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors,
+    // input_size,
+    //                    h, &conf_mask, &obj_mask, &tx, &ty, &tw, &th,
+    //                    &tweight,
+    //                    &tconf, &tclass);
+    //
+    // T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
+    // memset(loss_data, 0, n * sizeof(T));
+    // CalcYolov3Loss<T>(loss_data, *input, tx, ty, tw, th, tweight, tconf,
+    // tclass,
+    //                   conf_mask, obj_mask);
   }
 };
 
@@ -389,59 +668,172 @@ 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* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
     auto anchors = ctx.Attr<std::vector<int>>("anchors");
+    auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
     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* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
-    int input_size = ctx.Attr<int>("input_size");
+    int downsample = ctx.Attr<int>("downsample");
 
     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 conf_mask, obj_mask;
-    Tensor tx, ty, tw, th, tweight, tconf, tclass;
-    conf_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    obj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tweight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
-    tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
-
-    math::SetConstant<platform::CPUDeviceContext, T> constant;
-    constant(ctx.template device_context<platform::CPUDeviceContext>(),
-             &conf_mask, static_cast<T>(1.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(),
-             &obj_mask, static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tx,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &ty,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tw,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &th,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(),
-             &tweight, static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tconf,
-             static_cast<T>(0.0));
-    constant(ctx.template device_context<platform::CPUDeviceContext>(), &tclass,
-             static_cast<T>(0.0));
-
-    PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, input_size,
-                       h, &conf_mask, &obj_mask, &tx, &ty, &tw, &th, &tweight,
-                       &tconf, &tclass);
-
+    const int mask_num = anchor_mask.size();
+    const int b = gt_box->dims()[1];
+    int input_size = downsample * h;
+
+    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* loss_grad_data = loss_grad->data<T>();
     T* input_grad_data =
         input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
-    CalcYolov3LossGrad<T>(input_grad_data, *loss_grad, *input, tx, ty, tw, th,
-                          tweight, tconf, tclass, conf_mask, obj_mask);
+    memset(input_grad_data, 0, input_grad->numel() * sizeof(T));
+
+    Tensor objness;
+    int* objness_data =
+        objness.mutable_data<int>({n, mask_num, h, w}, ctx.GetPlace());
+    memset(objness_data, 0, objness.numel() * sizeof(int));
+
+    const int stride = h * w;
+    const int an_stride = (class_num + 5) * stride;
+
+    for (int i = 0; i < n; i++) {
+      for (int j = 0; j < mask_num; j++) {
+        for (int k = 0; k < h; k++) {
+          for (int l = 0; l < w; l++) {
+            int box_idx =
+                entry_index(i, j, k * w + l, mask_num, an_stride, stride, 0);
+            Box<T> pred =
+                get_yolo_box(input_data, anchors, l, k, anchor_mask[j], h,
+                             input_size, box_idx, stride);
+            T best_iou = 0;
+            // int best_t = 0;
+            for (int t = 0; t < b; t++) {
+              if (isZero<T>(gt_box_data[i * b * 4 + t * 4]) &&
+                  isZero<T>(gt_box_data[i * b * 4 + t * 4 + 1])) {
+                continue;
+              }
+              Box<T> gt = get_gt_box(gt_box_data, i, b, t);
+              T iou = box_iou(pred, gt);
+              if (iou > best_iou) {
+                best_iou = iou;
+                // best_t = t;
+              }
+            }
+
+            if (best_iou > ignore_thresh) {
+              int obj_idx = (i * mask_num + j) * stride + k * w + l;
+              objness_data[obj_idx] = -1;
+            }
+          }
+        }
+      }
+      for (int t = 0; t < b; t++) {
+        if (isZero<T>(gt_box_data[i * b * 4 + t * 4]) &&
+            isZero<T>(gt_box_data[i * b * 4 + t * 4 + 1])) {
+          continue;
+        }
+        Box<T> gt = get_gt_box(gt_box_data, i, b, t);
+        int gi = static_cast<int>(gt.x * w);
+        int gj = static_cast<int>(gt.y * h);
+        Box<T> gt_shift = gt;
+        gt_shift.x = 0.0;
+        gt_shift.y = 0.0;
+        T best_iou = 0.0;
+        int best_n = 0;
+        for (int an_idx = 0; an_idx < an_num; an_idx++) {
+          Box<T> an_box;
+          an_box.x = 0.0;
+          an_box.y = 0.0;
+          an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);
+          an_box.h = anchors[2 * an_idx + 1] / static_cast<T>(input_size);
+          float iou = box_iou<T>(an_box, gt_shift);
+          // TO DO: iou > 0.5 ?
+          if (iou > best_iou) {
+            best_iou = iou;
+            best_n = an_idx;
+          }
+        }
+
+        int mask_idx = mask_index(anchor_mask, best_n);
+        if (mask_idx >= 0) {
+          int box_idx = entry_index(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, best_n, box_idx,
+                                     gi, gj, h, input_size, stride);
+
+          int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
+          objness_data[obj_idx] = 1;
+
+          int label = gt_label_data[i * b + t];
+          int label_idx = entry_index(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);
+        }
+      }
+    }
+
+    CalcObjnessLossGrad<T>(input_grad_data + 4 * stride, loss_grad_data,
+                           input_data + 4 * stride, objness_data, n, mask_num,
+                           h, w, stride, an_stride);
+
+    // 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 conf_mask, obj_mask;
+    // Tensor tx, ty, tw, th, tweight, tconf, tclass;
+    // conf_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // obj_mask.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tweight.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
+    // tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
+    //
+    // math::SetConstant<platform::CPUDeviceContext, T> constant;
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    //          &conf_mask, static_cast<T>(1.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    //          &obj_mask, static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &tx,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &ty,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &tw,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(), &th,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    //          &tweight, static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    // &tconf,
+    //          static_cast<T>(0.0));
+    // constant(ctx.template device_context<platform::CPUDeviceContext>(),
+    // &tclass,
+    //          static_cast<T>(0.0));
+    //
+    // PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors,
+    // input_size,
+    //                    h, &conf_mask, &obj_mask, &tx, &ty, &tw, &th,
+    //                    &tweight,
+    //                    &tconf, &tclass);
+    //
+    // T* input_grad_data =
+    //     input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    // CalcYolov3LossGrad<T>(input_grad_data, *loss_grad, *input, tx, ty, tw,
+    // th,
+    //                       tweight, tconf, tclass, conf_mask, obj_mask);
   }
 };
 

python/paddle/fluid/layers/detection.py

@@ -413,9 +413,10 @@ def yolov3_loss(x,
                 gtbox,
                 gtlabel,
                 anchors,
+                anchor_mask,
                 class_num,
                 ignore_thresh,
-                input_size,
+                downsample,
                 name=None):
     """
     ${comment}
@@ -430,9 +431,10 @@ def yolov3_loss(x,
         gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
                             of [N, B].
         anchors (list|tuple): ${anchors_comment}
+        anchor_mask (list|tuple): ${anchor_mask_comment}
         class_num (int): ${class_num_comment}
         ignore_thresh (float): ${ignore_thresh_comment}
-        input_size (int): ${input_size_comment}
+        downsample (int): ${downsample_comment}
         name (string): the name of yolov3 loss
 
     Returns:
@@ -452,7 +454,8 @@ def yolov3_loss(x,
         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]
+        anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
+        anchors = [0, 1, 2]
         loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
                                         anchors=anchors, ignore_thresh=0.5)
     """
@@ -466,6 +469,8 @@ def yolov3_loss(x,
         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(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(ignore_thresh, float):
@@ -480,9 +485,10 @@ def yolov3_loss(x,
 
     attrs = {
         "anchors": anchors,
+        "anchor_mask": anchor_mask,
         "class_num": class_num,
         "ignore_thresh": ignore_thresh,
-        "input_size": input_size,
+        "downsample": downsample,
     }
 
     helper.append_op(

python/paddle/fluid/tests/test_detection.py

@@ -463,8 +463,8 @@ class TestYoloDetection(unittest.TestCase):
             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,
-                                      0.7, 416)
+            loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13],
+                                      [0, 1], 10, 0.7, 32)
 
             self.assertIsNotNone(loss)
 

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

@@ -22,32 +22,42 @@ from op_test import OpTest
 
 from paddle.fluid import core
 
-
-def l1loss(x, y, weight):
-    n = x.shape[0]
-    x = x.reshape((n, -1))
-    y = y.reshape((n, -1))
-    weight = weight.reshape((n, -1))
-    return (np.abs(y - x) * weight).sum(axis=1)
+# def l1loss(x, y, weight):
+#     n = x.shape[0]
+#     x = x.reshape((n, -1))
+#     y = y.reshape((n, -1))
+#     weight = weight.reshape((n, -1))
+#     return (np.abs(y - x) * weight).sum(axis=1)
+#
+#
+# def mse(x, y, weight):
+#     n = x.shape[0]
+#     x = x.reshape((n, -1))
+#     y = y.reshape((n, -1))
+#     weight = weight.reshape((n, -1))
+#     return ((y - x)**2 * weight).sum(axis=1)
+#
+#
+# def sce(x, label, weight):
+#     n = x.shape[0]
+#     x = x.reshape((n, -1))
+#     label = label.reshape((n, -1))
+#     weight = weight.reshape((n, -1))
+#     sigmoid_x = expit(x)
+#     term1 = label * np.log(sigmoid_x)
+#     term2 = (1.0 - label) * np.log(1.0 - sigmoid_x)
+#     return ((-term1 - term2) * weight).sum(axis=1)
 
 
-def mse(x, y, weight):
-    n = x.shape[0]
-    x = x.reshape((n, -1))
-    y = y.reshape((n, -1))
-    weight = weight.reshape((n, -1))
-    return ((y - x)**2 * weight).sum(axis=1)
+def l1loss(x, y):
+    return abs(x - y)
 
 
-def sce(x, label, weight):
-    n = x.shape[0]
-    x = x.reshape((n, -1))
-    label = label.reshape((n, -1))
-    weight = weight.reshape((n, -1))
+def sce(x, label):
     sigmoid_x = expit(x)
     term1 = label * np.log(sigmoid_x)
     term2 = (1.0 - label) * np.log(1.0 - sigmoid_x)
-    return ((-term1 - term2) * weight).sum(axis=1)
+    return -term1 - term2
 
 
 def box_iou(box1, box2):
@@ -160,6 +170,121 @@ def YoloV3Loss(x, gtbox, gtlabel, attrs):
     return loss_x + loss_y + loss_w + loss_h + loss_obj + loss_class
 
 
+def sigmoid(x):
+    return 1.0 / (1.0 + np.exp(-1.0 * x))
+
+
+def batch_xywh_box_iou(box1, box2):
+    b1_left = box1[:, :, 0] - box1[:, :, 2] / 2
+    b1_right = box1[:, :, 0] + box1[:, :, 2] / 2
+    b1_top = box1[:, :, 1] - box1[:, :, 3] / 2
+    b1_bottom = box1[:, :, 1] + box1[:, :, 3] / 2
+
+    b2_left = box2[:, :, 0] - box2[:, :, 2] / 2
+    b2_right = box2[:, :, 0] + box2[:, :, 2] / 2
+    b2_top = box2[:, :, 1] - box2[:, :, 3] / 2
+    b2_bottom = box2[:, :, 1] + box2[:, :, 3] / 2
+
+    left = np.maximum(b1_left[:, :, np.newaxis], b2_left[:, np.newaxis, :])
+    right = np.minimum(b1_right[:, :, np.newaxis], b2_right[:, np.newaxis, :])
+    top = np.maximum(b1_top[:, :, np.newaxis], b2_top[:, np.newaxis, :])
+    bottom = np.minimum(b1_bottom[:, :, np.newaxis],
+                        b2_bottom[:, np.newaxis, :])
+
+    inter_w = np.clip(right - left, 0., 1.)
+    inter_h = np.clip(bottom - top, 0., 1.)
+    inter_area = inter_w * inter_h
+
+    b1_area = (b1_right - b1_left) * (b1_bottom - b1_top)
+    b2_area = (b2_right - b2_left) * (b2_bottom - b2_top)
+    union = b1_area[:, :, np.newaxis] + b2_area[:, np.newaxis, :] - inter_area
+
+    return inter_area / union
+
+
+def YOLOv3Loss(x, gtbox, gtlabel, attrs):
+    n, c, h, w = x.shape
+    b = gtbox.shape[1]
+    anchors = attrs['anchors']
+    an_num = len(anchors) // 2
+    anchor_mask = attrs['anchor_mask']
+    mask_num = len(anchor_mask)
+    class_num = attrs["class_num"]
+    ignore_thresh = attrs['ignore_thresh']
+    downsample = attrs['downsample']
+    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')
+
+    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))
+    pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0])) / w
+    pred_box[:, :, :, :, 1] = (grid_y + sigmoid(pred_box[:, :, :, :, 1])) / h
+
+    mask_anchors = []
+    for m in anchor_mask:
+        mask_anchors.append((anchors[2 * m], anchors[2 * m + 1]))
+    anchors_s = np.array(
+        [(an_w / input_size, an_h / input_size) for an_w, an_h in mask_anchors])
+    anchor_w = anchors_s[:, 0:1].reshape((1, mask_num, 1, 1))
+    anchor_h = anchors_s[:, 1:2].reshape((1, mask_num, 1, 1))
+    pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) * anchor_w
+    pred_box[:, :, :, :, 3] = np.exp(pred_box[:, :, :, :, 3]) * anchor_h
+
+    pred_box = pred_box.reshape((n, -1, 4))
+    pred_obj = x[:, :, :, :, 4].reshape((n, -1))
+    objness = np.zeros(pred_box.shape[:2])
+    ious = batch_xywh_box_iou(pred_box, gtbox)
+    ious_max = np.max(ious, axis=-1)
+    objness = np.where(ious_max > ignore_thresh, -np.ones_like(objness),
+                       objness)
+
+    gtbox_shift = gtbox.copy()
+    gtbox_shift[:, :, 0] = 0
+    gtbox_shift[:, :, 1] = 0
+
+    anchors = [(anchors[2 * i], anchors[2 * i + 1]) for i in range(0, an_num)]
+    anchors_s = np.array(
+        [(an_w / input_size, an_h / input_size) for an_w, an_h in anchors])
+    anchor_boxes = np.concatenate(
+        [np.zeros_like(anchors_s), anchors_s], axis=-1)
+    anchor_boxes = np.tile(anchor_boxes[np.newaxis, :, :], (n, 1, 1))
+    ious = batch_xywh_box_iou(gtbox_shift, anchor_boxes)
+    iou_matches = np.argmax(ious, axis=-1)
+    for i in range(n):
+        for j in range(b):
+            if gtbox[i, j, 2:].sum() == 0:
+                continue
+            if iou_matches[i, j] not in anchor_mask:
+                continue
+            an_idx = anchor_mask.index(iou_matches[i, j])
+            gi = int(gtbox[i, j, 0] * w)
+            gj = int(gtbox[i, j, 1] * h)
+
+            tx = gtbox[i, j, 0] * w - gi
+            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]
+            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] += l1loss(x[i, an_idx, gj, gi, 2], tw) * scale
+            loss[i] += l1loss(x[i, an_idx, gj, gi, 3], th) * scale
+
+            objness[i, an_idx * h * w + gj * w + gi] = 1
+
+            for label_idx in range(class_num):
+                loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx],
+                               int(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], objness[i, j])
+
+    return loss
+
+
 class TestYolov3LossOp(OpTest):
     def setUp(self):
         self.initTestCase()
@@ -171,13 +296,14 @@ class TestYolov3LossOp(OpTest):
 
         self.attrs = {
             "anchors": self.anchors,
+            "anchor_mask": self.anchor_mask,
             "class_num": self.class_num,
             "ignore_thresh": self.ignore_thresh,
-            "input_size": self.input_size,
+            "downsample": self.downsample,
         }
 
         self.inputs = {'X': x, 'GTBox': gtbox, 'GTLabel': gtlabel}
-        self.outputs = {'Loss': YoloV3Loss(x, gtbox, gtlabel, self.attrs)}
+        self.outputs = {'Loss': YOLOv3Loss(x, gtbox, gtlabel, self.attrs)}
 
     def test_check_output(self):
         place = core.CPUPlace()
@@ -189,15 +315,19 @@ class TestYolov3LossOp(OpTest):
             place, ['X'],
             'Loss',
             no_grad_set=set(["GTBox", "GTLabel"]),
-            max_relative_error=0.31)
+            max_relative_error=0.15)
 
     def initTestCase(self):
-        self.anchors = [12, 12]
+        self.anchors = [
+            10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198,
+            373, 326
+        ]
+        self.anchor_mask = [0, 1, 2]
         self.class_num = 5
-        self.ignore_thresh = 0.5
-        self.input_size = 416
-        self.x_shape = (1, len(self.anchors) // 2 * (5 + self.class_num), 3, 3)
-        self.gtbox_shape = (1, 5, 4)
+        self.ignore_thresh = 0.7
+        self.downsample = 32
+        self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
+        self.gtbox_shape = (3, 10, 4)
 
 
 if __name__ == "__main__":