fix doc statement. test=develop

paddle/fluid/operators/detection/yolo_box_op.cc

@@ -75,25 +75,25 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X",
              "The input tensor of YoloBox operator, "
-             "This is a 4-D tensor with shape of [N, C, H, W]."
-             "H and W should be same, and the second dimension(C) stores"
-             "box locations, confidence score and classification one-hot"
-             "keys of each anchor box. Generally, X should be the output"
+             "This is a 4-D tensor with shape of [N, C, H, W]. "
+             "H and W should be same, and the second dimension(C) stores "
+             "box locations, confidence score and classification one-hot "
+             "keys of each anchor box. Generally, X should be the output "
              "of YOLOv3 network.");
     AddInput("ImgSize",
              "The image size tensor of YoloBox operator, "
-             "This is a 2-D tensor with shape of [N, 2]. This tensor holds"
-             "height and width of each input image using for resize output"
+             "This is a 2-D tensor with shape of [N, 2]. This tensor holds "
+             "height and width of each input image using for resize output "
              "box in input image scale.");
     AddOutput("Boxes",
               "The output tensor of detection boxes of YoloBox operator, "
-              "This is a 3-D tensor with shape of [N, M, 4], N is the"
-              "batch num, M is output box number, and the 3rd dimension"
+              "This is a 3-D tensor with shape of [N, M, 4], N is the "
+              "batch num, M is output box number, and the 3rd dimension "
               "stores [xmin, ymin, xmax, ymax] coordinates of boxes.");
     AddOutput("Scores",
-              "The output tensor ofdetection boxes scores of YoloBox"
-              "operator, This is a 3-D tensor with shape of [N, M, C],"
-              "N is the batch num, M is output box number, C is the"
+              "The output tensor ofdetection boxes scores of YoloBox "
+              "operator, This is a 3-D tensor with shape of [N, M, C], "
+              "N is the batch num, M is output box number, C is the "
               "class number.");
 
     AddAttr<int>("class_num", "The number of classes to predict.");
@@ -107,30 +107,31 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
                  "and thrid YoloBox operators.")
         .SetDefault(32);
     AddAttr<float>("conf_thresh",
-                   "The confidence scores threshold of detection boxes."
-                   "boxes with confidence scores under threshold should"
+                   "The confidence scores threshold of detection boxes. "
+                   "Boxes with confidence scores under threshold should "
                    "be ignored.")
         .SetDefault(0.01);
     AddComment(R"DOC(
          This operator generate YOLO detection boxes from output of YOLOv3 network.
          
          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 dimension(the channel dimension), C should be S * (class_num + 5),
-         class_num is the box categoriy number of source dataset(such as coco), 
-         so in the second dimension, 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 :math:`tx, ty, tw, th`, the box 
-         predictions correspnd to:
+         should be the same, H and W specify the grid size, each grid point predict 
+         given number boxes, this given number, which following will be represented as S,
+         is specified by the number of anchors, In the second dimension(the channel
+         dimension), C should be equal to S * (class_num + 5), class_num is the object 
+         category number of source dataset(such as 80 in coco dataset), so in the 
+         second(channel) dimension, apart from 4 box location coordinates x, y, w, h, 
+         also includes confidence score of the box and class one-hot key of each anchor 
+         box.
+
+         Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box 
+         predictions should be as follows:
 
          $$
-         b_x = \sigma(t_x) + c_x
+         b_x = \\sigma(t_x) + c_x
          $$
          $$
-         b_y = \sigma(t_y) + c_y
+         b_y = \\sigma(t_y) + c_y
          $$
          $$
          b_w = p_w e^{t_w}
@@ -139,14 +140,14 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
          b_h = p_h e^{t_h}
          $$
 
-         While :math:`c_x, c_y` is the left top corner of current grid and 
-         :math:`p_w, p_h` is specified by anchors.
+         in the equation above, :math:`c_x, c_y` is the left top corner of current grid
+         and :math:`p_w, p_h` is specified by anchors.
 
-         The logistic scores of the 5rd channel of each anchor prediction boxes
-         represent the confidence score of each prediction scores, and the logistic
-         scores of the last class_num channels of each anchor prediction boxes 
-         represent the classifcation scores. Boxes with confidence scores less than
-         conf_thresh should be ignored, and box final scores is the product of 
+         The logistic regression value of the 5rd channel of each anchor prediction boxes
+         represent the confidence score of each prediction box, and the logistic
+         regression value of the last :attr:`class_num` channels of each anchor prediction 
+         boxes represent the classifcation scores. Boxes with confidence scores less than
+         :attr:`conf_thresh` should be ignored, and box final scores is the product of 
          confidence scores and classification scores.
 
          )DOC");

python/paddle/fluid/layers/detection.py

@@ -628,10 +628,12 @@ def yolo_box(x,
         class_num (int): ${class_num_comment}
         conf_thresh (float): ${conf_thresh_comment}
         downsample_ratio (int): ${downsample_ratio_comment}
-        name (string): the name of yolov3 loss
+        name (string): the name of yolo box layer
 
     Returns:
-        Variable: A 1-D tensor with shape [1], the value of yolov3 loss
+        Variable: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,
+        and a 3-D tensor with shape [N, M, C], the classification scores
+        of boxes.
 
     Raises:
         TypeError: Input x of yolov_box must be Variable
@@ -640,6 +642,7 @@ def yolo_box(x,
         TypeError: Attr conf_thresh of yolo box must be a float number
 
     Examples:
+
     .. code-block:: python
 
         x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')