cherry-pick update en doc, test=develop, test=document_fix (#20342)

2639c4f7 · wangguanzhong · GitHub · 42ea819e · 2639c4f7 · 2639c4f7
Showing with 50 addition and 55 deletion

paddle/fluid/API.spec paddle/fluid/API.spec +11 -11

python/paddle/fluid/layers/detection.py python/paddle/fluid/layers/detection.py +30 -35

python/paddle/fluid/layers/nn.py python/paddle/fluid/layers/nn.py +9 -9

未找到文件。
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -204,7 +204,7 @@ paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], va
 paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '95aa1972983f30fe9b5a3713e523e20f'))
 paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '214f1dfbe95a628600bbe99e836319cf'))
 paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', '49368d724023a66b41b0071be41c0ba5'))
-paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', 'dc2e2fa3d6e3d30de0a81e8ee70de733'))
+paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '3885fd76e122ac0563fa8369bcab7363'))
 paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '7e8e4bf1f0f8612961ed113e8af8f0c5'))
 paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1, 'NCHW')), ('document', 'd29d829607b5ff12924197a3ba296c89'))
 paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', 'bd97ebfe4bdf5110a5fcb8ecb626a447'))
@@ -232,7 +232,7 @@ paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, key
 paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.67, 1.7159, None)), ('document', 'd3f742178a7263adf5929153d104883d'))
 paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', '607d79ca873bee40eed1c79a96611591'))
 paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'e0dc7bc66cba939033bc028d7a62c5f4'))
-paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '1fadca6622c70bd33cc260817f4ff191'))
+paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cb417a61f701c937f33d057fe85203ab'))
 paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '49580538249a52c857fce75c94ad8af7'))
 paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', '1eb3009c69060299ec87949ee0d4b9ae'))
 paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '6455afd2498b00198f53f83d63d6c6a4'))
@@ -271,11 +271,11 @@ paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=
 paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '77ccf37b710c507dd97e03f08ce8bb29'))
 paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6e2fe8a322ec69811f6507d22acf8f9f'))
 paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ce33756573c572da67302499455dbcd'))
-paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1fc6e217c7a6128df31b806c1a8067ff'))
+paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a5f4917fda557ceb834168cdbec6d51b'))
 paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '597257fb94d0597c404a6a5c91ab5258'))
 paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', '784b7e36cea88493f9e37a41b10fbf4d'))
 paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)), ('document', '7637c974f2d749d359acae9062c4d96f'))
-paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '169882eb87fb693198e0153629134c22'))
+paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '406eee439e41988c8a0304186626a0dd'))
 paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '26decdea9376b6b9a0d3432d82ca207b'))
 paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f85b263b7b6698d000977529a28f202b'))
 paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65c8362e48810b8226e311c5d046db51'))
@@ -412,30 +412,30 @@ paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=N
 paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '0fdf82762fd0a5acb2578a72771b5b44'))
 paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', '7a484a0da5e993a7734867a3dfa86571'))
 paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'fd58078fdfffd899b91f992ba224628f'))
-paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '409c351dee8a4a4ea02771dc691b49cb'))
+paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '6f795f407a8e3a3ec3da52726c73405a'))
 paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'e9685f32d21bec8c013626c0254502c5'))
 paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta', 'return_index'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0, False)), ('document', '5485bcaceb0cde2695565a2ffd5bbd40'))
 paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)), ('document', '14d1eeae0f41b6792be43c1c0be0589b'))
 paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '651d98d51879dfa1bc1cd40391786a41'))
 paddle.fluid.layers.retinanet_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'gt_labels', 'is_crowd', 'im_info', 'num_classes', 'positive_overlap', 'negative_overlap'], varargs=None, keywords=None, defaults=(1, 0.5, 0.4)), ('document', 'fa1d1c9d5e0111684c0db705f86a2595'))
 paddle.fluid.layers.sigmoid_focal_loss (ArgSpec(args=['x', 'label', 'fg_num', 'gamma', 'alpha'], varargs=None, keywords=None, defaults=(2, 0.25)), ('document', 'aeac6aae100173b3fc7f102cf3023a3d'))
-paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', 'd25e5e90f9a342764f32b5cd48657148'))
+paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', 'a7778d4f557c60dca52321673667690d'))
 paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'a82016342789ba9d85737e405f824ff1'))
 paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', '69def376b42ef0681d0cc7f53a2dac4b'))
 paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', 'b7d707822b6af2a586bce608040235b1'))
 paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef'))
 paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99'))
-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', '1d5144c3856673d05c29c752c7c8f821'))
+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', 'e308ce1661cb722b220a6f482f85b9e4'))
 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.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '882c99ed2adad54f612a40275b881850'))
+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_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', 'be432c9b5f19ccba7aca38789ead29e4'))
-paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5203935538d06a6d47b8630ad80cb2b0'))
-paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '808fcca082e0040e2b77dbc53a0cf9d5'))
+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'))
+paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8874f917b4da34541efe427841a8f205'))
+paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ff4a651d65a9a9f9da71349ba6a2dc1f'))
 paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', 'b691b7be425e281bd36897b514b2b064'))
 paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'c36ac7125da977c2bd1b192bee301f75'))
 paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'eaf430c5a0380fb11bfe9a8922cd6295'))

--- a/python/paddle/fluid/layers/detection.py
+++ b/python/paddle/fluid/layers/detection.py
@@ -717,27 +717,23 @@ def box_coder(prior_box,
 
            import paddle.fluid as fluid
            # For encode
-            prior_box_encode = fluid.layers.data(name='prior_box_encode',
+            prior_box_encode = fluid.data(name='prior_box_encode',
                                  shape=[512, 4],
-                                  dtype='float32',
-                                  append_batch_size=False)
-            target_box_encode = fluid.layers.data(name='target_box_encode',
-                                   shape=[81,4],
-                                   dtype='float32',
-                                   append_batch_size=False)
+                                  dtype='float32')
+            target_box_encode = fluid.data(name='target_box_encode',
+                                   shape=[81, 4],
+                                   dtype='float32')
            output_encode = fluid.layers.box_coder(prior_box=prior_box_encode,
                                    prior_box_var=[0.1,0.1,0.2,0.2],
                                    target_box=target_box_encode,
                                    code_type="encode_center_size")
            # For decode
-            prior_box_decode = fluid.layers.data(name='prior_box_decode',
+            prior_box_decode = fluid.data(name='prior_box_decode',
                                  shape=[512, 4],
-                                  dtype='float32',
-                                  append_batch_size=False)
-            target_box_decode = fluid.layers.data(name='target_box_decode',
-                                   shape=[512,81,4],
-                                   dtype='float32',
-                                   append_batch_size=False)
+                                  dtype='float32')
+            target_box_decode = fluid.data(name='target_box_decode',
+                                   shape=[512, 81, 4],
+                                   dtype='float32')
            output_decode = fluid.layers.box_coder(prior_box=prior_box_decode,
                                    prior_box_var=[0.1,0.1,0.2,0.2],
                                    target_box=target_box_decode,
@@ -1173,8 +1169,8 @@ def bipartite_match(dist_matrix,
    Examples:

        >>> import paddle.fluid as fluid
-        >>> x = fluid.layers.data(name='x', shape=[4], dtype='float32')
-        >>> y = fluid.layers.data(name='y', shape=[4], dtype='float32')
+        >>> x = fluid.data(name='x', shape=[None, 4], dtype='float32')
+        >>> y = fluid.data(name='y', shape=[None, 4], dtype='float32')
        >>> iou = fluid.layers.iou_similarity(x=x, y=y)
        >>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
    """
@@ -2081,7 +2077,7 @@ def anchor_generator(input,
        .. code-block:: python

            import paddle.fluid as fluid
-            conv1 = fluid.layers.data(name='conv1', shape=[48, 16, 16], dtype='float32')
+            conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32')
            anchor, var = fluid.layers.anchor_generator(
                input=conv1,
                anchor_sizes=[64, 128, 256, 512],
@@ -2607,9 +2603,9 @@ def box_clip(input, im_info, name=None):
        .. code-block:: python
        
            import paddle.fluid as fluid
-            boxes = fluid.layers.data(
-                name='boxes', shape=[8, 4], dtype='float32', lod_level=1)
-            im_info = fluid.layers.data(name='im_info', shape=[3])
+            boxes = fluid.data(
+                name='boxes', shape=[None, 8, 4], dtype='float32', lod_level=1)
+            im_info = fluid.data(name='im_info', shape=[-1 ,3])
            out = fluid.layers.box_clip(
                input=boxes, im_info=im_info)
    """
@@ -3056,8 +3052,8 @@ def distribute_fpn_proposals(fpn_rois,
        .. code-block:: python

            import paddle.fluid as fluid
-            fpn_rois = fluid.layers.data(
-                name='data', shape=[4], dtype='float32', lod_level=1)
+            fpn_rois = fluid.data(
+                name='data', shape=[None, 4], dtype='float32', lod_level=1)
            multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
                fpn_rois=fpn_rois,
                min_level=2,
@@ -3118,15 +3114,14 @@ def box_decoder_and_assign(prior_box,
        .. code-block:: python

            import paddle.fluid as fluid
-            pb = fluid.layers.data(
-                name='prior_box', shape=[4], dtype='float32')
-            pbv = fluid.layers.data(
-                name='prior_box_var', shape=[4], 
-                dtype='float32', append_batch_size=False)
-            loc = fluid.layers.data(
-                name='target_box', shape=[4*81], dtype='float32')
-            scores = fluid.layers.data(
-                name='scores', shape=[81], dtype='float32')
+            pb = fluid.data(
+                name='prior_box', shape=[None, 4], dtype='float32')
+            pbv = fluid.data(
+                name='prior_box_var', shape=[4], dtype='float32')
+            loc = fluid.data(
+                name='target_box', shape=[None, 4*81], dtype='float32')
+            scores = fluid.data(
+                name='scores', shape=[None, 81], dtype='float32')
            decoded_box, output_assign_box = fluid.layers.box_decoder_and_assign(
                pb, pbv, loc, scores, 4.135)

@@ -3199,11 +3194,11 @@ def collect_fpn_proposals(multi_rois,
            multi_rois = []
            multi_scores = []
            for i in range(4):
-                multi_rois.append(fluid.layers.data(
-                    name='roi_'+str(i), shape=[4], dtype='float32', lod_level=1))
+                multi_rois.append(fluid.data(
+                    name='roi_'+str(i), shape=[None, 4], dtype='float32', lod_level=1))
            for i in range(4):
-                multi_scores.append(fluid.layers.data(
-                    name='score_'+str(i), shape=[1], dtype='float32', lod_level=1))
+                multi_scores.append(fluid.data(
+                    name='score_'+str(i), shape=[None, 1], dtype='float32', lod_level=1))

            fpn_rois = fluid.layers.collect_fpn_proposals(
                multi_rois=multi_rois, 

--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -8613,10 +8613,10 @@ def roi_align(input,
        .. code-block:: python

            import paddle.fluid as fluid
-            x = fluid.layers.data(
-                name='data', shape=[256, 32, 32], dtype='float32')
-            rois = fluid.layers.data(
-                name='rois', shape=[4], dtype='float32')
+            x = fluid.data(
+                name='data', shape=[None, 256, 32, 32], dtype='float32')
+            rois = fluid.data(
+                name='rois', shape=[None, 4], dtype='float32')
            align_out = fluid.layers.roi_align(input=x,
                                               rois=rois,
                                               pooled_height=7,
@@ -10980,7 +10980,7 @@ def prelu(x, mode, param_attr=None, name=None):

            import paddle.fluid as fluid
            from paddle.fluid.param_attr import ParamAttr
-            x = fluid.layers.data(name="x", shape=[5,10,10], dtype="float32")
+            x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32")
            mode = 'channel'
            output = fluid.layers.prelu(
                     x,mode,param_attr=ParamAttr(name='alpha'))
@@ -13041,8 +13041,8 @@ def clip_by_norm(x, max_norm, name=None):
        .. code-block:: python

            import paddle.fluid as fluid
-            input = fluid.layers.data(
-                name='data', shape=[1], dtype='float32')
+            input = fluid.data(
+                name='data', shape=[None, 1], dtype='float32')
            reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
    """

@@ -13256,9 +13256,9 @@ def maxout(x, groups, name=None):
        .. code-block:: python

            import paddle.fluid as fluid
-            input = fluid.layers.data(
+            input = fluid.data(
                name='data', 
-                shape=[256, 32, 32], 
+                shape=[None, 256, 32, 32], 
                dtype='float32')
            out = fluid.layers.maxout(input, groups=2)
    """