Fix type error and fluid.layers.data (#1498)

doc/fluid/api_cn/layers_cn/detection_output_cn.rst

@@ -35,14 +35,10 @@ detection_output
 .. code-block:: python
     
     import paddle.fluid as fluid
-    pb = fluid.layers.data(name='prior_box', shape=[10, 4],
-             append_batch_size=False, dtype='float32')
-    pbv = fluid.layers.data(name='prior_box_var', shape=[10, 4],
-              append_batch_size=False, dtype='float32')
-    loc = fluid.layers.data(name='target_box', shape=[2, 21, 4],
-              append_batch_size=False, dtype='float32')
-    scores = fluid.layers.data(name='scores', shape=[2, 21, 10],
-              append_batch_size=False, dtype='float32')
+    pb = fluid.data(name='prior_box', shape=[10, 4], dtype='float32')
+    pbv = fluid.data(name='prior_box_var', shape=[10, 4], dtype='float32')
+    loc = fluid.data(name='target_box', shape=[2, 21, 4], dtype='float32')
+    scores = fluid.data(name='scores', shape=[2, 21, 10], dtype='float32')
     nmsed_outs = fluid.layers.detection_output(scores=scores,
                            loc=loc,
                            prior_box=pb,

doc/fluid/api_cn/layers_cn/generate_mask_labels_cn.rst

@@ -59,18 +59,17 @@ generate_mask_labels
     
     import paddle.fluid as fluid
 
-    im_info = fluid.layers.data(name="im_info", shape=[3],
-        dtype="float32")
-    gt_classes = fluid.layers.data(name="gt_classes", shape=[1],
+    im_info = fluid.data(name="im_info", shape=[None, 3], dtype="float32")
+    gt_classes = fluid.data(name="gt_classes", shape=[None, 1],
         dtype="float32", lod_level=1)
-    is_crowd = fluid.layers.data(name="is_crowd", shape=[1],
+    is_crowd = fluid.data(name="is_crowd", shape=[None, 1],
         dtype="float32", lod_level=1)
-    gt_masks = fluid.layers.data(name="gt_masks", shape=[2],
+    gt_masks = fluid.data(name="gt_masks", shape=[None, 2],
         dtype="float32", lod_level=3)
     # rois, roi_labels 可以是fluid.layers.generate_proposal_labels的输出
-    rois = fluid.layers.data(name="rois", shape=[4],
+    rois = fluid.data(name="rois", shape=[None, 4],
         dtype="float32", lod_level=1)
-    roi_labels = fluid.layers.data(name="roi_labels", shape=[1],
+    roi_labels = fluid.data(name="roi_labels", shape=[None, 1],
         dtype="int32", lod_level=1)
     mask_rois, mask_index, mask_int32 = fluid.layers.generate_mask_labels(
         im_info=im_info,

doc/fluid/api_cn/layers_cn/multi_box_head_cn.rst

@@ -25,7 +25,7 @@ multi_box_head
 
         - **num_classes** (int) - 类别数。
         - **aspect_ratios**  (list(float) | tuple(float) | list(list(float)) | tuple(tuple(float)) - 候选框的宽高比， ``aspect_ratios`` 和 ``input`` 的个数必须相等。如果每个特征层提取先验框的 ``aspect_ratio`` 多余一个，写成嵌套的list，例如[[2., 3.]]。
-        - **min_ratio** (int）- 先验框的长度和 ``base_size`` 的最小比率，注意，这里是百分比，加入比率为0.2，这里应该给20.0。默认值: None。
+        - **min_ratio** (int）- 先验框的长度和 ``base_size`` 的最小比率，注意，这里是百分比，假如比率为0.2，这里应该给20.0。默认值: None。
         - **max_ratio** (int）- 先验框的长度和 ``base_size`` 的最大比率，注意事项同 ``min_ratio`` 。默认值: None。
         - **min_sizes** (list(float) | tuple(float) | None）- 每层提取的先验框的最小长度，如果输入个数len(inputs)<= 2，则必须设置 ``min_sizes`` ，并且 ``min_sizes`` 的个数应等于len(inputs)。默认值：None。
         - **max_sizes** (list | tuple | None）- 每层提取的先验框的最大长度，如果len(inputs）<= 2，则必须设置 ``max_sizes`` ，并且 ``min_sizes`` 的长度应等于len(inputs)。默认值：None。
@@ -56,13 +56,13 @@ multi_box_head
         
         import paddle.fluid as fluid
      
-        images = fluid.layers.data(name='data', shape=[3, 300, 300], dtype='float32')
-        conv1 = fluid.layers.data(name='conv1', shape=[512, 19, 19], dtype='float32')
-        conv2 = fluid.layers.data(name='conv2', shape=[1024, 10, 10], dtype='float32')
-        conv3 = fluid.layers.data(name='conv3', shape=[512, 5, 5], dtype='float32')
-        conv4 = fluid.layers.data(name='conv4', shape=[256, 3, 3], dtype='float32')
-        conv5 = fluid.layers.data(name='conv5', shape=[256, 2, 2], dtype='float32')
-        conv6 = fluid.layers.data(name='conv6', shape=[128, 1, 1], dtype='float32')
+        images = fluid.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
+        conv1 = fluid.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
+        conv2 = fluid.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
+        conv3 = fluid.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
+        conv4 = fluid.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
+        conv5 = fluid.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
+        conv6 = fluid.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')
         
         mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
           inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
@@ -83,13 +83,13 @@ multi_box_head
         
         import paddle.fluid as fluid
      
-        images = fluid.layers.data(name='data', shape=[3, 300, 300], dtype='float32')
-        conv1 = fluid.layers.data(name='conv1', shape=[512, 19, 19], dtype='float32')
-        conv2 = fluid.layers.data(name='conv2', shape=[1024, 10, 10], dtype='float32')
-        conv3 = fluid.layers.data(name='conv3', shape=[512, 5, 5], dtype='float32')
-        conv4 = fluid.layers.data(name='conv4', shape=[256, 3, 3], dtype='float32')
-        conv5 = fluid.layers.data(name='conv5', shape=[256, 2, 2], dtype='float32')
-        conv6 = fluid.layers.data(name='conv6', shape=[128, 1, 1], dtype='float32')
+        images = fluid.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
+        conv1 = fluid.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
+        conv2 = fluid.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
+        conv3 = fluid.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
+        conv4 = fluid.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
+        conv5 = fluid.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
+        conv6 = fluid.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')
         
         mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
           inputs=[conv1, conv2, conv3, conv4, conv5, conv6],

doc/fluid/api_cn/layers_cn/random_crop_cn.rst

@@ -21,16 +21,15 @@ random_crop
 ..  code-block:: python
 
    import paddle.fluid as fluid
-   img = fluid.layers.data("img", [3, 256, 256])
+   img = fluid.data("img", [None, 3, 256, 256])
    # cropped_img的shape: [-1, 3, 224, 224]
    cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
    
    # cropped_img2的shape: [-1, 2, 224, 224]
    # cropped_img2 = fluid.layers.random_crop(img, shape=[2，224, 224])
    
-
-   # cropped_img2的shape: [-1, 3, 128, 224]
-   # cropped_img2 = fluid.layers.random_crop(img, shape=[128, 224])
+   # cropped_img3的shape: [-1, 3, 128, 224]
+   # cropped_img3 = fluid.layers.random_crop(img, shape=[128, 224])
 
 
 

doc/fluid/api_cn/layers_cn/target_assign_cn.rst

@@ -47,17 +47,15 @@ neg_indices中的第i个实例的索引称作neg_indice，则对于第i个实例
 .. code-block:: python
 
         import paddle.fluid as fluid
-        x = fluid.layers.data(
+        x = fluid.data(
             name='x',
             shape=[4, 20, 4],
             dtype='float',
-            lod_level=1,
-            append_batch_size=False)
-        matched_id = fluid.layers.data(
+            lod_level=1)
+        matched_id = fluid.data(
             name='indices',
             shape=[8, 20],
-            dtype='int32',
-            append_batch_size=False)
+            dtype='int32')
         trg, trg_weight = fluid.layers.target_assign(
             x,
             matched_id,