refine doc, test=develop

paddle/fluid/API.spec

@@ -328,7 +328,7 @@ paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varar
 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.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', 'fa7008889611447edd1bac71dd42b558'))
+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', 'fdffe52577f7e74c090b030867fefc11'))
 paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', '9808534c12c5e739a10f73ebb0b4eafd'))
 paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'e0e95334fce92d16c2d9db6e7caffc47'))
 paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '98a5050bee8522fcea81aa795adaba51'))

python/paddle/fluid/layers/detection.py

@@ -2231,21 +2231,23 @@ def distribute_fpn_proposals(fpn_rois,
                              refer_scale,
                              name=None):
     """
-    Distribute all proposals into different fpn level, with respect to scale 
-    of the proposals, the referring scale and the referring level. Besides, to
-    restore the order of proposals, we return an array which indicates the 
-    original index of rois in current proposals. To compute fpn level for each 
-    roi, the formula is given as follows:
+    In Feature Pyramid Networks (FPN) models, it is needed to distribute all 
+    proposals into different FPN level, with respect to scale of the proposals,
+    the referring scale and the referring level. Besides, to restore the order
+    of proposals, we return an array which indicates the original index of rois
+    in current proposals. To compute FPN level for each roi, the formula is 
+    given as follows:
     
     .. math::
-        roi\_scale = \sqrt{BBoxArea(fpn\_roi)}
 
-        level = floor(\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
+        roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}
 
-    where BBoxArea is the area of each roi
+        level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
+
+    where BBoxArea is a function to compute the area of each roi.
 
     Args:
-        fpn_rois(variable): The input fpn_rois, the last dimension is 4.
+        fpn_rois(variable): The input fpn_rois, the second dimension is 4.
         min_level(int): The lowest level of FPN layer where the proposals come 
                         from.
         max_level(int): The highest level of FPN layer where the proposals