test=develop (#17322)

paddle/fluid/API.spec

@@ -115,7 +115,7 @@ paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, ke
 paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6'))
 paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', 'f1dd22f7351f7f9853212958e0d8aa7a'))
 paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6'))
-paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbf2adbd79683dc93db03454dfa18c2'))
+paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2bc3a59efa9d52b628a6255422d9f0e8'))
 paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571'))
 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'))
@@ -142,12 +142,12 @@ paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=Non
 paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbd62da391b1df984a1909d069a759b2'))
 paddle.fluid.layers.lod_reset (ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f122194c562bd674f6ecdccf33785f99'))
 paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', '330241f0bc57e9d16973ec322a6aef71'))
-paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '2f28153bdd2d5ea6f7bad5867bd03eeb'))
-paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', 'd2e1f45fef51b2c214e3f2aa8976c46c'))
+paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '2f189f8ef61f1c23779e1593b78755c0'))
+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', '70c113658102a11cc5d8e3d45145737a'))
 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', 'c317aa595deb31649083c8faa91cdb97'))
 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', '3d8f4891c1d5e890a4e574371027dd35'))
-paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '1ba0508d573f65feecf3564dce22aa1d'))
+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'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', 'f1bc5eb7198175d2b79197a681d98b43'))
 paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '099b9f051e6247ae661e4a7b4fd3f89a'))
 paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', '746bf58fdb1bd475f8c5f996b05b0e52'))
@@ -160,7 +160,7 @@ paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], va
 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'))
-paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '883104791204d3127e24234bb630b2e7'))
+paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'ddf9837ee83e549119210a3d714d5f44'))
 paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c542e39ac6add24a6bef6e79bf5617e2'))
 paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '99b3fee0daee04911d2bee8871b26435'))
 paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '463258ee9f8b60760eb1e26357cc9bfa'))
@@ -203,14 +203,14 @@ paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs
 paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0eae3f726a4afe590757552fa3ced012'))
 paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b0daaa3fa4a0aa62f9b58c43d959eb25'))
 paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cd1c8cf31e040427d4e05711044caeb6'))
-paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b020b7aab59719be98a4ae229a76deba'))
+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', 'a1ea0bc5a926f427458c4254ca022749'))
 paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd638d915195ce86a8d7963b81110d4c8'))
 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', 'ccd37fa6b53f074adbfb732d738c4c2d'))
 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', '180c284317ea45ef89a460d8d79c0b72'))
 paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '71426e02d240d0daedae81a02ca1c191'))
 paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a9221eaef53884a00654e028551b78e2'))
-paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '51def402b8910e163cbace9d0c0526ed'))
+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', '77a6d80aa5551ca70324fc975c44507f'))
 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.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6f90d6ff76bf4f5e592332c1ef28494e'))
@@ -225,7 +225,7 @@ paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len',
 paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2fa6782d43d02ae64482d21235a82949'))
 paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'fe4481fb31363b09cfdd228fc6776ddf'))
 paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb'))
-paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1546136806fef5c08f6918544bd9151d'))
+paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '42d5155374f69786300d90d751956998'))
 paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99'))
 paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '431a4301c35032166ec029f7432c80a7'))
 paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '776d536cac47c89073abc7ee524d5aec'))
@@ -343,7 +343,7 @@ paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box',
 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.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', '5761f9ed83654314416e24372b33bb84'))
+paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', '9307c12b1d4e554279b9708f787cd019'))
 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_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', 'f73706a65468e9ca3e0bee4a31521b0a'))
@@ -432,8 +432,8 @@ paddle.fluid.transpiler.RoundRobin.__init__ (ArgSpec(args=['self', 'pserver_endp
 paddle.fluid.transpiler.RoundRobin.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.transpiler.RoundRobin.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ 
-paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', 'e0f67f35abf27f666f81003113b90244'))
-paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', '48c434dd7bb827f69d90e5135d77470f'))
+paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', '13f01ff80e8dfbd3427d90cf49bc62eb'))
+paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', 'd6a1e527b53f5cc15594fee307dfc5cf'))
 paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '6486b2595300fc3305b5a1f0ac363dce'))
 paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', '921714c9bfb351b41403418265393203'))
 paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '5178bc1b4d302192597a5efbae13d902'))

python/paddle/fluid/layers/detection.py

@@ -1820,7 +1820,7 @@ def roi_perspective_transform(input,
                           coordinates, and (x3, y3) is the bottom right coordinates, 
                           and (x4, y4) is the bottom left coordinates.
         transformed_height (integer): The height of transformed output.
-        transformed_height (integer): The width of transformed output.
+        transformed_width (integer): The width of transformed output.
         spatial_scale (float): Spatial scale factor to scale ROI coords. Default: 1.0
 
     Returns:
@@ -1830,7 +1830,10 @@ def roi_perspective_transform(input,
     Examples:
         .. code-block:: python
 
-            out = fluid.layers.roi_perspective_transform(input, rois, 7, 7, 1.0)
+            import paddle.fluid as fluid
+            x = fluid.layers.data(name='x', shape=[256, 28, 28], dtype='float32')
+            rois = fluid.layers.data(name='rois', shape=[8], lod_level=1, dtype='float32')
+            out = fluid.layers.roi_perspective_transform(x, rois, 7, 7, 1.0)
     """
     helper = LayerHelper('roi_perspective_transform', **locals())
     dtype = helper.input_dtype()

python/paddle/fluid/layers/nn.py

@@ -5305,8 +5305,8 @@ def ctc_greedy_decoder(input, blank, name=None):
     Examples:
         .. code-block:: python
 
+            import paddle.fluid as fluid
             x = fluid.layers.data(name='x', shape=[8], dtype='float32')
-
             cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
     """
     helper = LayerHelper("ctc_greedy_decoder", **locals())
@@ -6899,6 +6899,8 @@ def pad(x, paddings, pad_value=0., name=None):
         .. code-block:: python
 
             # x is a rank 2 tensor variable.
+            import paddle.fluid as fluid
+            x = fluid.layers.data(name='data', shape=[224], dtype='float32')
             out = fluid.layers.pad(
                 x=x, paddings=[0, 1, 1, 2], pad_value=0.)
     """
@@ -6978,6 +6980,9 @@ def pad_constant_like(x, y, pad_value=0., name=None):
 
             # x is a rank 4 tensor variable, x.shape = (2, 3, 2, 3)
             # y is a rank 4 tensor variable, y.shape = (1, 3, 1, 3)
+            import paddle.fluid as fluid
+            x = fluid.layers.data(name='x', shape=[2,3,2,3], dtype='float32')
+            y = fluid.layers.data(name='y', shape=[1,3,1,3], dtype='float32')
             out = fluid.layers.pad_constant_like(x=x, y=y, pad_value=0.)
             # out is a rank 4 tensor variable, and out.shape = [2, 3 ,2 , 3]
     """
@@ -7176,8 +7181,11 @@ def dice_loss(input, label, epsilon=0.00001):
     Examples:
         .. code-block:: python
 
+            import paddle.fluid as fluid
+            x = fluid.layers.data(name='data', shape = [3, 224, 224, 2], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[3, 224, 224, 1], dtype='float32')
             predictions = fluid.layers.softmax(x)
-            loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
+            loss = fluid.layers.dice_loss(input=predictions, label=label)
     """
     label = one_hot(label, depth=input.shape[-1])
     reduce_dim = list(range(1, len(input.shape)))
@@ -8048,9 +8056,9 @@ def crop(x, shape=None, offsets=None, name=None):
             is suitable for the case that the output shape may be changed each
             iteration. If a list/tupe of integer, it's length must be the same
             as the rank of `x`
-        offsets (Variable|list/tuple of integer|None): Specifies the copping
+        offsets (Variable|list/tuple of integer|None): Specifies the cropping
             offsets at each dimension. It can be a Variable or or a list/tupe
-            of integer. If a tensor Variable, it's rank must be the same as `x`.
+            of integers. If a tensor Variable, it's rank must be the same as `x`.
             This way is suitable for the case that the offsets may be changed
             each iteration. If a list/tupe of integer, it's length must be the
             same as the rank of `x`. If None, the offsets are 0 at each
@@ -8068,6 +8076,7 @@ def crop(x, shape=None, offsets=None, name=None):
 
         .. code-block:: python
 
+            import paddle.fluid as fluid
             x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
             y = fluid.layers.data(name="y", shape=[2, 3], dtype="float32")
             crop = fluid.layers.crop(x, shape=y)
@@ -8192,6 +8201,7 @@ def affine_grid(theta, out_shape, name=None):
 
         .. code-block:: python
 
+            import paddle.fluid as fluid
             theta = fluid.layers.data(name="x", shape=[2, 3], dtype="float32")
             out_shape = fluid.layers.data(name="y", shape=[-1], dtype="float32")
             data = fluid.layers.affine_grid(theta, out_shape)
@@ -9698,6 +9708,7 @@ def clip(x, min, max, name=None):
     Examples:
         .. code-block:: python
 
+            import paddle.fluid as fluid
             input = fluid.layers.data(
                 name='data', shape=[1], dtype='float32')
             reward = fluid.layers.clip(x=input, min=-1.0, max=1.0)
@@ -10925,7 +10936,11 @@ def psroi_pool(input,
 
     Args:
         input (Variable): ${x_comment}
-        rois (Variable): ROIs (Regions of Interest) to pool over.
+        rois (Variable): ROIs (Regions of Interest) to pool over.It should be
+                         a 2-D LoDTensor of shape (num_rois, 4), the lod level
+                         is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
+                         the top left coordinates, and (x2, y2) is the bottom
+                         right coordinates.
         output_channels (integer): ${output_channels_comment}
         spatial_scale (float): ${spatial_scale_comment} Default: 1.0
         pooled_height (integer): ${pooled_height_comment} Default: 1
@@ -10938,7 +10953,10 @@ def psroi_pool(input,
     Examples:
         .. code-block:: python
 
-            pool_out = fluid.layers.psroi_pool(input=x, rois=rois, 490, 1.0, 7, 7)
+            import paddle.fluid as fluid
+            x = fluid.layers.data(name='x', shape=[490, 28, 28], dtype='float32')
+            rois = fluid.layers.data(name='rois', shape=[4], lod_level=1, dtype='float32')
+            pool_out = fluid.layers.psroi_pool(x, rois, 10, 1.0, 7, 7)
     """
     helper = LayerHelper('psroi_pool', **locals())
     # check attrs

python/paddle/fluid/nets.py

@@ -100,6 +100,7 @@ def simple_img_conv_pool(input,
     Examples:
         .. code-block:: python
 
+            import paddle.fluid as fluid
             img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
             conv_pool = fluid.nets.simple_img_conv_pool(input=img,
                                                         filter_size=5,
@@ -278,10 +279,11 @@ def sequence_conv_pool(input,
     Examples:
         .. code-block:: python
 
-            input_dim = len(word_dict)
+            import paddle.fluid as fluid
+            input_dim = 100 #len(word_dict)
             emb_dim = 128
             hid_dim = 512
-            data = fluid.layers.data( ame="words", shape=[1], dtype="int64", lod_level=1)
+            data = fluid.layers.data(name="words", shape=[1], dtype="int64", lod_level=1)
             emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
             seq_conv = fluid.nets.sequence_conv_pool(input=emb,
                                                      num_filters=hid_dim,