fix doc. test=develop

paddle/fluid/API.spec

@@ -328,7 +328,7 @@ paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=Non
 paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e'))
 paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
 paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691'))
-paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691'))
+paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '170091cef6ebfcba6e54c55b496d0021'))
 paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
 paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0'))
 paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7bb011ec26bace2bc23235aa4a17647d'))

paddle/fluid/operators/detection/yolo_box_op.cc

@@ -83,7 +83,7 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
     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 "
+             "height and width of each input image used for resizing output "
              "box in input image scale.");
     AddOutput("Boxes",
               "The output tensor of detection boxes of YoloBox operator, "
@@ -117,9 +117,9 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
          The output of previous network is in shape [N, C, H, W], while H and W
          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 
+         is specified by the number of anchors. In the second dimension(the channel
+         dimension), C should be equal to S * (5 + class_num), class_num is the object 
+         category number of source dataset(such as 80 in coco dataset), so 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.
@@ -143,10 +143,10 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
          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 regression value of the 5rd channel of each anchor prediction boxes
-         represent the confidence score of each prediction box, and the logistic
+         The logistic regression value of the 5th channel of each anchor prediction boxes
+         represents 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
+         boxes represents 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.