Polish english apis' doc (#20184)

* polish en api,test=develop,test=document_fix

* modified api.spec, test=develop,test=document_fix

* modified ops for arc,test=develop,test=document_fix

* update for 1.6, test=develop,test=document_fix

* update API.spec

* Update API.spec

test=develop,test=document_fix

* Update API.spec

test=develop,test=document_fix

paddle/fluid/API.spec

@@ -113,7 +113,7 @@ paddle.fluid.initializer.NormalInitializer ('paddle.fluid.initializer.NormalInit
 paddle.fluid.initializer.NormalInitializer.__init__ (ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.initializer.TruncatedNormalInitializer ('paddle.fluid.initializer.TruncatedNormalInitializer', ('document', 'b8e90aad6ee5687cb5f2b6fd404370d1'))
 paddle.fluid.initializer.TruncatedNormalInitializer.__init__ (ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.initializer.XavierInitializer ('paddle.fluid.initializer.XavierInitializer', ('document', '3d5676f1a5414aa0c815d793a795ccb3'))
+paddle.fluid.initializer.XavierInitializer ('paddle.fluid.initializer.XavierInitializer', ('document', 'c3b1953ac9b0bf6c0dac50a093b4ef04'))
 paddle.fluid.initializer.XavierInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.initializer.BilinearInitializer ('paddle.fluid.initializer.BilinearInitializer', ('document', '0abf3908cf6a520dcf822c481bd05767'))
 paddle.fluid.initializer.BilinearInitializer.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd389912dc079cbef432335a00017cec0'))
@@ -385,26 +385,26 @@ paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var']
 paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '5b552a1f0f7eb4dacb768a975ba15d08'))
 paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, 20, True, True, True, True, 'both')), ('document', '3130bed32922b9fd84ce2dea6250f635'))
 paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '3011dc695f490afdf504dc24f628319a'))
-paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bdc9a71908d3c9748532ff44c2f31034'))
-paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9a4c346630a042454f727ad5e0cffc11'))
-paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '92bec0a7fdec48ad78effdf30b02c6fa'))
-paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a4af62b6c6ce858c897f74a4f0f'))
-paddle.fluid.layers.atan (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2dde114018cbcaff9b24c566bf6704a5'))
-paddle.fluid.layers.tanh_shrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '000a76652c8e59e21e7fb6d87cc7a668'))
-paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e3dce5e892ce63cc9c6ed87a7e6206d5'))
-paddle.fluid.layers.rsqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0b90c858d4d71a58896537c1bd7acb09'))
-paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '503f4d5723bbe1b6c9f24058078709ed'))
-paddle.fluid.layers.ceil (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5602b78da33c4b0ccaea0374411de423'))
-paddle.fluid.layers.floor (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a0977ab14448ba472e5c2e152f42a818'))
-paddle.fluid.layers.cos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e94c8569179ffa3a0dca028a5b518dbf'))
-paddle.fluid.layers.acos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5c9a00178c5c28bb824f7d6c25060d3b'))
-paddle.fluid.layers.asin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '20d1d49fe4d13430a63c57fc4b29a677'))
-paddle.fluid.layers.sin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '4441e4e5e9934eb98760e31330e7a13c'))
-paddle.fluid.layers.round (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '40132ef34808ed621c63ed4fd886fd1c'))
-paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '578106495166d0fb65ade2bb51cdf926'))
-paddle.fluid.layers.square (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '728233aff902803f5f62e2d340c3bcbb'))
-paddle.fluid.layers.softplus (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '74c4e6dfbdfc3453301ea11d722ad3d6'))
-paddle.fluid.layers.softsign (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a70e9320b113ca33c1299bbc032f09d4'))
+paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6e5a9645817c781e936da11c20395909'))
+paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '846e66903386aadb31a68c8226ac4880'))
+paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f96f7fbb3af9816d9960c14737682ee5'))
+paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '78df12509dddc43baa9cd3d9bb051e8d'))
+paddle.fluid.layers.atan (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f9ad8c2d64119bf4025ffe371bfbc3ad'))
+paddle.fluid.layers.tanh_shrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1c9a12739f34385ecd94b03690fffe0b'))
+paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2bfcce8ed36f46973b384db8d229b6f5'))
+paddle.fluid.layers.rsqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '27b079cd8002e07dffb4334eba5b3c96'))
+paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '940e2c21a0dab0f035b71eae9d71adec'))
+paddle.fluid.layers.ceil (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a8e6a72192ea5f433a0a150de8152354'))
+paddle.fluid.layers.floor (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ad6c7fc037b433753729f47c7765034f'))
+paddle.fluid.layers.cos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '4f4b15408ae927112e8793441c165709'))
+paddle.fluid.layers.acos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '685761a55d82355ff3421e99b2bc710d'))
+paddle.fluid.layers.asin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9a2272c67f32c5e662dd96ed21d0b22a'))
+paddle.fluid.layers.sin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b5226c5b623ecc8bf893990e3b190f7d'))
+paddle.fluid.layers.round (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '16fdd1d5a03b9c3f8c436156c0cd508b'))
+paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '50b76cb575a1a09fe1b53ffc06a77e3f'))
+paddle.fluid.layers.square (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b283c4eb9a975d5289d4f53500aa50fa'))
+paddle.fluid.layers.softplus (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e619a20fa04d2d997b7757dd246eeb2'))
+paddle.fluid.layers.softsign (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bf02c7440088ebb934217c92f001a09d'))
 paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'alpha'], varargs=None, keywords=None, defaults=(None,)), ('document', '958c7bfdfb0b5e92af6ca4a90d24e5ef'))
 paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '386a4103d2884b2f1312ebc1e8ee6486'))
 paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '5ab9d5721a6734fe127069e4314e1309'))
@@ -427,10 +427,10 @@ paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes',
 paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e24478fd1fcf1727d4947fe14356b3d4'))
 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', '511d7033c0cfce1a5b88c04ad6e7ed5b'))
 paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2183f03c4f16712dcef6a474dbcefa24'))
-paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '400403175718d5a632402cdae88b01b8'))
-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', 'ed56ff21536ca5c8ad418d0cfaf6a7b9'))
+paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', 'df35e6510e8db0844320ec77dc8b7dc4'))
+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', '7f255b1f6a548f8fa78bbbc06285fc46'))
 paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ce2bfbd685f2a36eda400e00569908cb'))
-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', 'f6e333d76922c6e564413b4d216c245c'))
+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', 'c01ac2f1fced1ddd98574e71e877a6c2'))
 paddle.fluid.layers.multiclass_nms2 (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'return_index', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, False, None)), ('document', 'be156186ee7a2ee56ab30b964acb15e5'))
 paddle.fluid.layers.retinanet_detection_output (ArgSpec(args=['bboxes', 'scores', 'anchors', 'im_info', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0.05, 1000, 100, 0.3, 1.0)), ('document', '078d28607ce261a0cba2b965a79f6bb8'))
 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', '1f2b6bfb3027ea63ab86859391f45b03'))

python/paddle/fluid/initializer.py

@@ -496,10 +496,10 @@ class XavierInitializer(Initializer):
 
 
     Args:
-        uniform (bool): whether to use uniform or normal distribution
-        fan_in (float): fan_in for Xavier initialization. If None, it is
+        uniform (bool,default True): whether to use uniform ,if False use normal distribution
+        fan_in (float,default None): fan_in for Xavier initialization. If None, it is
                 inferred from the variable.
-        fan_out (float): fan_out for Xavier initialization. If None, it is
+        fan_out (float,default None): fan_out for Xavier initialization. If None, it is
                  inferred from the variable.
         seed (int): random seed
 
@@ -510,7 +510,7 @@ class XavierInitializer(Initializer):
         .. code-block:: python
 
             import paddle.fluid as fluid
-            queries = fluid.layers.data(name='x', shape=[1], dtype='float32')
+            queries = fluid.data(name='x', shape=[None,1], dtype='float32')
             fc = fluid.layers.fc(
                 input=queries, size=10,
                 param_attr=fluid.initializer.Xavier(uniform=False))

python/paddle/fluid/layers/detection.py

@@ -834,22 +834,24 @@ def yolov3_loss(x,
     ${comment}
 
     Args:
-        x (Variable): ${x_comment}
+        x (Variable): ${x_comment}The data type is float32 or float64. 
         gt_box (Variable): groud truth boxes, should be in shape of [N, B, 4],
                           in the third dimenstion, x, y, w, h should be stored. 
                           x,y is the center cordinate of boxes, w, h are the
                           width and height, x, y, w, h should be divided by 
                           input image height to scale to [0, 1].
                           N is the batch number and B is the max box number in 
-                          an image.
+                          an image.The data type is float32 or float64. 
         gt_label (Variable): class id of ground truth boxes, shoud be in shape
-                            of [N, B].
+                            of [N, B].The data type is int32. 
         anchors (list|tuple): ${anchors_comment}
         anchor_mask (list|tuple): ${anchor_mask_comment}
         class_num (int): ${class_num_comment}
         ignore_thresh (float): ${ignore_thresh_comment}
         downsample_ratio (int): ${downsample_ratio_comment}
-        name (string): the name of yolov3 loss. Default None.
+        name (string): The default value is None.  Normally there is no need 
+                       for user to set this property.  For more information, 
+                       please refer to :ref:`api_guide_Name`
         gt_score (Variable): mixup score of ground truth boxes, shoud be in shape
                             of [N, B]. Default None.
         use_label_smooth (bool): ${use_label_smooth_comment}
@@ -871,10 +873,10 @@ def yolov3_loss(x,
       .. code-block:: python
 
           import paddle.fluid as fluid
-          x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
-          gt_box = fluid.layers.data(name='gt_box', shape=[6, 4], dtype='float32')
-          gt_label = fluid.layers.data(name='gt_label', shape=[6], dtype='int32')
-          gt_score = fluid.layers.data(name='gt_score', shape=[6], dtype='float32')
+          x = fluid.data(name='x', shape=[None, 255, 13, 13], dtype='float32')
+          gt_box = fluid.data(name='gt_box', shape=[None, 6, 4], dtype='float32')
+          gt_label = fluid.data(name='gt_label', shape=[None, 6], dtype='int32')
+          gt_score = fluid.data(name='gt_score', shape=[None, 6], dtype='float32')
           anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
           anchor_mask = [0, 1, 2]
           loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label,
@@ -955,13 +957,15 @@ def yolo_box(x,
     ${comment}
 
     Args:
-        x (Variable): ${x_comment}
-        img_size (Variable): ${img_size_comment}
+        x (Variable): ${x_comment} The data type is float32 or float64. 
+        img_size (Variable): ${img_size_comment} The data type is int32. 
         anchors (list|tuple): ${anchors_comment}
         class_num (int): ${class_num_comment}
         conf_thresh (float): ${conf_thresh_comment}
         downsample_ratio (int): ${downsample_ratio_comment}
-        name (string): the name of yolo box layer. Default None.
+        name (string): The default value is None.  Normally there is no need 
+                       for user to set this property.  For more information, 
+                       please refer to :ref:`api_guide_Name`
 
     Returns:
         Variable: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,
@@ -979,8 +983,8 @@ def yolo_box(x,
     .. code-block:: python
 
         import paddle.fluid as fluid
-        x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
-        img_size = fluid.layers.data(name='img_size',shape=[2],dtype='int64')
+        x = fluid.data(name='x', shape=[None, 255, 13, 13], dtype='float32')
+        img_size = fluid.data(name='img_size',shape=[None, 2],dtype='int64')
         anchors = [10, 13, 16, 30, 33, 23]
         boxes,scores = fluid.layers.yolo_box(x=x, img_size=img_size, class_num=80, anchors=anchors, 
                                         conf_thresh=0.01, downsample_ratio=32)
@@ -2802,9 +2806,10 @@ def multiclass_nms(bboxes,
                            N is the batch size. Each bounding box has four
                            coordinate values and the layout is 
                            [xmin, ymin, xmax, ymax], when box size equals to 4.
+                           The data type is float32 or float64.
                            2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
                            M is the number of bounding boxes, C is the 
-                           class number   
+                           class number. The data type is float32 or float64.   
         scores (Variable): Two types of scores are supported:
                            1. (Tensor) A 3-D Tensor with shape [N, C, M]
                            represents the predicted confidence predictions.
@@ -2812,11 +2817,11 @@ def multiclass_nms(bboxes,
                            number of bounding boxes. For each category there 
                            are total M scores which corresponding M bounding
                            boxes. Please note, M is equal to the 2nd dimension
-                           of BBoxes.
+                           of BBoxes.The data type is float32 or float64. 
                            2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
                            M is the number of bbox, C is the class number.
                            In this case, input BBoxes should be the second
-                           case with shape [M, C, 4].
+                           case with shape [M, C, 4].The data type is float32 or float64. 
         background_label (int): The index of background label, the background 
                                 label will be ignored. If set to -1, then all
                                 categories will be considered. Default: 0
@@ -2834,7 +2839,7 @@ def multiclass_nms(bboxes,
         name(str): Name of the multiclass nms op. Default: None.
 
     Returns:
-        Out(Variable): A 2-D LoDTensor with shape [No, 6] represents the detections.
+        Variable: A 2-D LoDTensor with shape [No, 6] represents the detections.
              Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
              or A 2-D LoDTensor with shape [No, 10] represents the detections.
              Each row has 10 values: 
@@ -2851,9 +2856,9 @@ def multiclass_nms(bboxes,
 
 
             import paddle.fluid as fluid
-            boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
+            boxes = fluid.data(name='bboxes', shape=[None,81, 4],
                                       dtype='float32', lod_level=1)
-            scores = fluid.layers.data(name='scores', shape=[81],
+            scores = fluid.data(name='scores', shape=[None,81],
                                       dtype='float32', lod_level=1)
             out = fluid.layers.multiclass_nms(bboxes=boxes,
                                               scores=scores,

python/paddle/fluid/layers/layer_function_generator.py

@@ -261,7 +261,7 @@ Examples:
     .. code-block:: python
 
         import paddle.fluid as fluid
-        data = fluid.layers.data(name="input", shape=[32, 784])
+        data = fluid.layers.data(name="input", shape=[None, 32, 784])
         result = fluid.layers.%s(data)
 """ % op_type
     return func