fix conflicts,test=develop (#17186)

paddle/fluid/API.spec

@@ -121,10 +121,10 @@ paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized',
 paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '35c6a241bcc1a1fc89508860d82ad62b'))
 paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'b4cbe1ac451005df6dad12e9ffdccca9'))
 paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2a1e9ea041ff4d6a9948bb8d03b743ea'))
-paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', 'aaba49c038ba927f0a8e45c0c9a686ab'))
+paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', '4aa9df890b47eb67d5442f04aaf9eeec'))
 paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'f568714a876425004aca4ea2d4a27701'))
 paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1feac48b843d679db82312dc85885f4'))
-paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '3ce01160ede80b1c26f776f8fef9340f'))
+paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '33134416fc27dd65a767e5f15116ee16'))
 paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '32b3c442da0f3df682b5fcac10468116'))
 paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', '5db30b8a74e8c93687943a3e8d221da0'))
 paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', '80641ee6810b1cdc3fd6e14fc89ecc9d'))
@@ -157,7 +157,7 @@ paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywo
 paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342'))
 paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '71df5136cf03b06c65027b692fe78f1a'))
 paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c9ab9e460ef0a1823249935a30e82c66'))
-paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', '35cbbdfa585d027bb490707c95a176b9'))
+paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'e3b6630ba43cb13dfeeb1601cb64d671'))
 paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bf1676268df8ef100b8ab01d51336b25'))
 paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '9044c7fe667b76cb2d9264f2db11f417'))
 paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '98247c59d1c9b40af6730001b2aea73d'))
@@ -177,7 +177,7 @@ paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=
 paddle.fluid.layers.flatten (ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'cb295c13cb957db85cd9609269d7784d'))
 paddle.fluid.layers.sequence_mask (ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)), ('document', '767cea598dee8e2b94f04110fa6b7e67'))
 paddle.fluid.layers.stack (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '43a9fef72d45df3bc58f52b93cdb61cb'))
-paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '7f4d46320cc077ca2e8db600c35f4030'))
+paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '3f3abdb795a5c2aad8c2312249551ce5'))
 paddle.fluid.layers.unstack (ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)), ('document', '98eb9d633116efcfc6f90c114bd44fd6'))
 paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', '2e49e97069beb57ee89d54ed088ae2da'))
 paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '117d3607d1ffa0571835bbaebc7857ff'))
@@ -213,7 +213,7 @@ paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None,
 paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'aba90d0cbb43185216000b82fd231734'))
 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'))
-paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ab84fdc6dc60f3ad9aa397e6007e3bf9'))
+paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '9f303c67538e468a36c5904a0a3aa110'))
 paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6f90d6ff76bf4f5e592332c1ef28494e'))
 paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '97bf4353bb046a5629308a38f98ac204'))
 paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5d16663e096d7f04954c70ce1cc5e195'))
@@ -233,7 +233,7 @@ paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'
 paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '2985a372ac897ea4e13aced7f930d6f8'))
 paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '46994d10276dd4cb803b4062b5d14329'))
 paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '132b6e74ff642a392bd6b14c10aedc65'))
-paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', 'b76ccca3735bea4a58a0dbf0d77c5393'))
+paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', '20992b20d19c2e5983f366150827b4a6'))
 paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', '94e2819b7c9715ea71b62e9c78f36b29'))
 paddle.fluid.layers.where (ArgSpec(args=['condition'], varargs=None, keywords=None, defaults=None), ('document', '3126e3039e752ce26077f1efaca355c6'))
 paddle.fluid.layers.sign (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'ccf6bb7912afd2818d24bc45461e807a'))
@@ -345,15 +345,15 @@ paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'ne
 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'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)), ('document', 'efae414c1137c7944d6174dd08c5347a'))
 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', '6d5028fd09d01ab82d296adc0ea95aee'))
 paddle.fluid.layers.detection_map (ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral')), ('document', '1467d91b50c22cd52103b4aa1ee9d0a1'))
-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', '1dddef3eb4b3cbd4df8e03ac480dbf97'))
+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', '1e164a56fe9376e18a56d22563d9f801'))
 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', '82b2aefeeb1b706bc4afec70928a259a'))
 paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'd1ddc75629fedee46f82e631e22c79dc'))
-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'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '87863717edeb7fe87a1268976cbc015d'))
-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', '57ab49f3f324f310b7eed322e7c1057a'))
+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'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '9c601df88b251f22e9311c52939948cd'))
+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', '032d0f4b7d8f6235ee5d91e473344f0e'))
-paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
+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', 'eb62b1ff7cc981f3483a62321a491f2e'))
 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', 'f332fb8c5bb581bd1a6b5be450a99990'))
 paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '04384378ff00a42ade8fabd52e27cbc5'))

python/paddle/fluid/layers/detection.py

@@ -142,19 +142,24 @@ def rpn_target_assign(bbox_pred,
     Examples:
         .. code-block:: python
 
-            bbox_pred = layers.data(name='bbox_pred', shape=[100, 4],
-                              append_batch_size=False, dtype='float32')
-            cls_logits = layers.data(name='cls_logits', shape=[100, 1],
-                              append_batch_size=False, dtype='float32')
-            anchor_box = layers.data(name='anchor_box', shape=[20, 4],
-                              append_batch_size=False, dtype='float32')
-            gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
-                             append_batch_size=False, dtype='float32')
-            loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
-                fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
-                                              cls_logits=cls_logits,
-                                              anchor_box=anchor_box,
-                                              gt_boxes=gt_boxes)
+            import paddle.fluid as fluid
+            bbox_pred = fluid.layers.data(name='bbox_pred', shape=[100, 4],
+                            append_batch_size=False, dtype='float32')
+            cls_logits = fluid.layers.data(name='cls_logits', shape=[100, 1],
+                            append_batch_size=False, dtype='float32')
+            anchor_box = fluid.layers.data(name='anchor_box', shape=[20, 4],
+                            append_batch_size=False, dtype='float32')
+            anchor_var = fluid.layers.data(name='anchor_var', shape=[20, 4],
+                            append_batch_size=False, dtype='float32')
+            gt_boxes = fluid.layers.data(name='gt_boxes', shape=[10, 4],
+                            append_batch_size=False, dtype='float32')
+            is_crowd = fluid.layers.data(name='is_crowd', shape=[1],
+                            append_batch_size=False, dtype='float32')
+            im_info = fluid.layers.data(name='im_infoss', shape=[1, 3],
+                            append_batch_size=False, dtype='float32')
+            loc_pred, score_pred, loc_target, score_target, bbox_inside_weight=
+                fluid.layers.rpn_target_assign(bbox_pred, cls_logits,
+                anchor_box, anchor_var, gt_boxes, is_crowd, im_info)
 
     """
 
@@ -503,6 +508,14 @@ def polygon_box_transform(input, name=None):
 
     Returns:
         output(${output_type}): ${output_comment}
+
+    Examples:
+        .. code-block:: python
+            
+            import paddle.fluid as fluid
+            input = fluid.layers.data(name='input', shape=[4, 10, 5, 5],
+                                      append_batch_size=False, dtype='float32')
+            out = fluid.layers.polygon_box_transform(input)
     """
     helper = LayerHelper("polygon_box_transform", **locals())
     if name is None:
@@ -1936,6 +1949,26 @@ def generate_proposal_labels(rpn_rois,
         bbox_reg_weights(list|tuple): Box regression weights.
         class_nums(int): Class number.
         use_random(bool): Use random sampling to choose foreground and background boxes.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle.fluid as fluid
+            rpn_rois = fluid.layers.data(name='rpn_rois', shape=[2, 4],
+                           append_batch_size=False, dtype='float32')
+            gt_classes = fluid.layers.data(name='gt_classes', shape=[8, 1],
+                           append_batch_size=False, dtype='float32')
+            is_crowd = fluid.layers.data(name='is_crowd', shape=[8, 1],
+                           append_batch_size=False, dtype='float32')
+            gt_boxes = fluid.layers.data(name='gt_boxes', shape=[8, 4],
+                           append_batch_size=False, dtype='float32')
+            im_info = fluid.layers.data(name='im_info', shape=[10, 3],
+                           append_batch_size=False, dtype='float32')
+            rois, labels_int32, bbox_targets, bbox_inside_weights,
+            bbox_outside_weights = fluid.layers.generate_proposal_labels(
+                           rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
+                           class_nums=10)
+
     """
 
     helper = LayerHelper('generate_proposal_labels', **locals())
@@ -2182,6 +2215,24 @@ def generate_proposals(scores,
             width < min_size. 0.1 by default.
         eta(float): Apply in adaptive NMS, if adaptive threshold > 0.5,
             adaptive_threshold = adaptive_threshold * eta in each iteration.
+
+    Examples:
+        .. code-block:: python
+        
+            import paddle.fluid as fluid
+            scores = fluid.layers.data(name='scores', shape=[2, 4, 5, 5],
+                         append_batch_size=False, dtype='float32')
+            bbox_deltas = fluid.layers.data(name='bbox_deltas', shape=[2, 16, 5, 5],
+                         append_batch_size=False, dtype='float32')
+            im_info = fluid.layers.data(name='im_info', shape=[2, 3],
+                         append_batch_size=False, dtype='float32')
+            anchors = fluid.layers.data(name='anchors', shape=[5, 5, 4, 4],
+                         append_batch_size=False, dtype='float32')
+            variances = fluid.layers.data(name='variances', shape=[5, 5, 10, 4],
+                         append_batch_size=False, dtype='float32')
+            rois, roi_probs = fluid.layers.generate_proposals(scores, bbox_deltas,
+                         im_info, anchors, variances)
+
     """
     helper = LayerHelper('generate_proposals', **locals())
 

python/paddle/fluid/layers/nn.py

@@ -5456,8 +5456,11 @@ def warpctc(input, label, blank=0, norm_by_times=False, use_cudnn=False):
 
         .. code-block:: python
 
-            label = fluid.layers.data(shape=[11, 8], dtype='float32', lod_level=1)
-            predict = fluid.layers.data(shape=[11, 1], dtype='float32')
+            import paddle.fluid as fluid
+            label = fluid.layers.data(name='label', shape=[11, 8],
+                                      dtype='float32', lod_level=1)
+            predict = fluid.layers.data(name='predict', shape=[11, 1],
+                                        dtype='float32')
             cost = fluid.layers.warpctc(input=predict, label=label)
 
     """
@@ -6070,8 +6073,12 @@ def im2sequence(input,
 
         .. code-block:: python
 
+            import paddle.fluid as fluid
+            data = fluid.layers.data(name='data', shape=[3, 32, 32],
+                                     dtype='float32')
             output = fluid.layers.im2sequence(
-                input=layer, stride=[1, 1], filter_size=[2, 2])
+                input=data, stride=[1, 1], filter_size=[2, 2])
+
 
     """
     assert not in_dygraph_mode(), (
@@ -8115,7 +8122,11 @@ def mean_iou(input, label, num_classes):
 
         .. code-block:: python
 
-            iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
+            import paddle.fluid as fluid
+            predict = fluid.layers.data(name='predict', shape=[3, 32, 32])
+            label = fluid.layers.data(name='label', shape=[1])
+            iou, wrongs, corrects = fluid.layers.mean_iou(predict, label,
+                                                          num_classes=5)
     """
     helper = LayerHelper('mean_iou', **locals())
     dtype = helper.input_dtype()
@@ -8549,8 +8560,11 @@ def pad2d(input,
     Examples:
         .. code-block:: python
 
-          data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
-          result = fluid.layers.pad2d(input=data, padding=[1,2,3,4], mode='reflect')
+          import paddle.fluid as fluid
+          data = fluid.layers.data(name='data', shape=[3, 32, 32],
+                                   dtype='float32')
+          result = fluid.layers.pad2d(input=data, paddings=[1, 2, 3, 4],
+                                      mode='reflect')
     """
 
     helper = LayerHelper('pad2d', **locals())
@@ -10233,6 +10247,20 @@ def affine_channel(x,
 
     Returns:
         out (Variable): A tensor of the same shape and data layout with x.
+
+    Examples:
+        .. code-block:: python
+            
+            import paddle.fluid as fluid
+            data = fluid.layers.data(name='data', shape=[3, 32, 32],
+                                     dtype='float32')
+            input_scale = fluid.layers.create_parameter(shape=[3],
+                                     dtype="float32")
+            input_bias = fluid.layers.create_parameter(shape=[3],
+                                     dtype="float32")
+            out = fluid.layers.affine_channel(data,scale=input_scale,
+                                     bias=input_bias)
+
     """
     helper = LayerHelper("affine_channel", **locals())
 
@@ -11476,8 +11504,12 @@ def fsp_matrix(x, y):
 
         .. code-block:: python
 
-            feature_map_0 = fluid.layers.conv2d(x)
-            feature_map_1 = fluid.layers.conv2d(feature_map_0)
+            import paddle.fluid as fluid
+            data = fluid.layers.data(name='data', shape=[3, 32, 32])
+            feature_map_0 = fluid.layers.conv2d(data, num_filters=2,
+                                                filter_size=3)
+            feature_map_1 = fluid.layers.conv2d(feature_map_0, num_filters=2,
+                                                filter_size=1)
             loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1)
 
     """