add CPU kernel forward

paddle/fluid/operators/yolov3_loss_op.cc

@@ -27,18 +27,8 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
                    "Input(X) of Yolov3LossOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("GTBox"),
                    "Input(GTBox) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of Yolov3LossOp should not be null.");
-
-    // PADDLE_ENFORCE(ctx->HasAttr("img_height"),
-    //                "Attr(img_height) of Yolov3LossOp should not be null. ");
-    // PADDLE_ENFORCE(ctx->HasAttr("anchors"),
-    //                "Attr(anchor) of Yolov3LossOp should not be null.")
-    // PADDLE_ENFORCE(ctx->HasAttr("class_num"),
-    //                "Attr(class_num) of Yolov3LossOp should not be null.");
-    // PADDLE_ENFORCE(ctx->HasAttr(
-    //     "ignore_thresh",
-    //     "Attr(ignore_thresh) of Yolov3LossOp should not be null."));
+    PADDLE_ENFORCE(ctx->HasOutput("Loss"),
+                   "Output(Loss) of Yolov3LossOp should not be null.");
 
     auto dim_x = ctx->GetInputDim("X");
     auto dim_gt = ctx->GetInputDim("GTBox");
@@ -46,6 +36,14 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
     auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
     auto box_num = ctx->Attrs().Get<int>("box_num");
     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_gt.size(), 3, "Input(GTBox) should be a 3-D tensor");
+    PADDLE_ENFORCE_EQ(dim_gt[2], 5, "Input(GTBox) dim[2] should be 5");
     PADDLE_ENFORCE_GT(img_height, 0,
                       "Attr(img_height) value should be greater then 0");
     PADDLE_ENFORCE_GT(anchors.size(), 0,
@@ -56,14 +54,9 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
                       "Attr(box_num) should be an integer greater then 0.");
     PADDLE_ENFORCE_GT(class_num, 0,
                       "Attr(class_num) should be an integer greater then 0.");
-    PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
-                      "Input(X) dim[1] should be equal to (anchor_number * (5 "
-                      "+ class_num)).");
-    PADDLE_ENFORCE_EQ(dim_gt.size(), 3, "Input(GTBox) should be a 3-D tensor");
-    PADDLE_ENFORCE_EQ(dim_gt[2], 5, "Input(GTBox) dim[2] should be 5");
 
-    std::vector<int64_t> dim_out({dim_x[0], 1});
-    ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
+    std::vector<int64_t> dim_out({1});
+    ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
   }
 
  protected:
@@ -80,12 +73,31 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "The input tensor of bilinear interpolation, "
              "This is a 4-D tensor with shape of [N, C, H, W]");
-    AddOutput("Out",
-              "The output yolo loss tensor, "
-              "This is a 2-D tensor with shape of [N, 1]");
+    AddInput(
+        "GTBox",
+        "The input tensor of ground truth boxes, "
+        "This is a 3-D tensor with shape of [N, max_box_num, 5 + class_num], "
+        "max_box_num is the max number of boxes in each image, "
+        "class_num is the number of classes in data set. "
+        "In the third dimention, stores x, y, w, h, confidence, classes "
+        "one-hot key. "
+        "x, y is the center cordinate of boxes and w, h is the width and "
+        "height, "
+        "and all of them should be divided by input image height to scale to "
+        "[0, 1].");
+    AddOutput("Loss",
+              "The output yolov3 loss tensor, "
+              "This is a 1-D tensor with shape of [1]");
 
     AddAttr<int>("box_num", "The number of boxes generated in each grid.");
     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>("img_height",
+                 "The input image height after crop of yolov3 network.");
+    AddAttr<float>("ignore_thresh",
+                   "The ignore threshold to ignore confidence loss.");
     AddComment(R"DOC(
          This operator generate yolov3 loss by given predict result and ground
          truth boxes.
@@ -100,8 +112,8 @@ class Yolov3LossOpGrad : public framework::OperatorWithKernel {
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) 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);