add cn doc for crop_tensor and modified cn doc for crop, test=document_preview (#1159)

add cn doc for crop_tensor
modified cn doc for crop

doc/fluid/api_cn/layers_cn.rst

@@ -61,6 +61,7 @@ fluid.layers
     layers_cn/create_tensor_cn.rst
     layers_cn/crf_decoding_cn.rst
     layers_cn/crop_cn.rst
+    layers_cn/crop_tensor_cn.rst
     layers_cn/cross_entropy_cn.rst
     layers_cn/ctc_greedy_decoder_cn.rst
     layers_cn/cumsum_cn.rst

doc/fluid/api_cn/layers_cn/crop_cn.rst

@@ -7,6 +7,8 @@ crop
 
 根据偏移量（offsets）和形状（shape），裁剪输入张量。
 
+**注意:** 此功能已被弃用，它将在以后的版本中被删除。更新说明：使用 `fluid.layers.crop_tensor <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/layers_cn/nn_cn.html#crop_tensor>`_ 替代。
+
 **样例**：
 
 ::

doc/fluid/api_cn/layers_cn/crop_tensor_cn.rst

+.. _cn_api_fluid_layers_crop_tensor:
+
+crop_tensor
+-------------------------------
+
+.. py:function:: paddle.fluid.layers.crop_tensor(x, shape=None, offsets=None, name=None)
+
+根据偏移量（offsets）和形状（shape），裁剪输入张量。
+
+**样例**：
+
+::
+
+    * Case 1:
+        Given
+            X = [[0, 1, 2, 0, 0]
+                 [0, 3, 4, 0, 0]
+                 [0, 0, 0, 0, 0]],
+        and
+            shape = [2, 2],
+            offsets = [0, 1],
+        output is:
+            Out = [[1, 2],
+                   [3, 4]].
+    * Case 2:
+        Given
+                X =  [[[0, 1, 2, 3]
+                       [0, 5, 6, 7]
+                       [0, 0, 0, 0]],
+
+                      [[0, 3, 4, 5]
+                       [0, 6, 7, 8]
+                       [0, 0, 0, 0]]].
+            and
+                shape = [2, 2, 3],
+                offsets = [0, 0, 1],
+            output is:
+                Out = [[[1, 2, 3]
+                        [5, 6, 7]],
+
+                       [[3, 4, 5]
+                        [6, 7, 8]]].
+
+参数:
+  - **x** (Variable): 输入张量。
+  - **shape** (Variable|list|tuple of integer) - 输出张量的形状由参数shape指定，它可以是一个1-D的变量/列表/整数元组。如果是1-D的变量，它的秩必须与x相同。如果是列表或整数元组，则其长度必须与x的秩相同。当它是列表时，每一个元素可以是整数或者shape为[1]的变量。含有变量的方式适用于每次迭代时需要改变输出形状的情况。列表和元组中只有第一个元素可以被设置为-1，这意味着输出的第一维大小与输入相同。
+  - **offsets** (Variable|list|tuple of integer|None) - 指定每个维度上的裁剪的偏移量。它可以是一个1-D的变量/列表/整数元组。如果是1-D的变量，它的秩必须与x相同。如果是列表或整数元组，则其长度必须与x的秩相同。当它是列表时，每一个元素可以是整数或者shape为[1]的变量。含有变量的方式适用于每次迭代的偏移量（offset）都可能改变的情况。如果offsets=None，则每个维度的偏移量为0。
+  - **name** (str|None) - 该层的名称(可选)。如果设置为None，该层将被自动命名。
+
+返回: 裁剪张量。
+
+返回类型: 变量（Variable）
+
+抛出异常: 如果形状不是列表、元组或变量，抛出ValueError
+
+抛出异常: 如果偏移量不是None、列表、元组或变量，抛出ValueError
+
+**代码示例**:
+
+..  code-block:: python
+    
+    import paddle.fluid as fluid
+    x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
+    # x.shape = [-1, 3, 5], where -1 indicates batch size, and it will get the exact value in runtime.
+
+    # shape is a 1-D tensor variable
+    crop_shape = fluid.layers.data(name="crop_shape", shape=[3], dtype="int32", append_batch_size=False)
+    crop0 = fluid.layers.crop_tensor(x, shape=crop_shape)
+    # crop0.shape = [-1, -1, -1], it means crop0.shape[0] = x.shape[0] in runtime.
+
+    # or shape is a list in which each element is a constant
+    crop1 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3])
+    # crop1.shape = [-1, 2, 3]
+
+    # or shape is a list in which each element is a constant or variable
+    y = fluid.layers.data(name="y", shape=[3, 8, 8], dtype="float32")
+    dim1 = fluid.layers.data(name="dim1", shape=[1], dtype="int32", append_batch_size=False)
+    crop2 = fluid.layers.crop_tensor(y, shape=[-1, 3, dim1, 4])
+    # crop2.shape = [-1, 3, -1, 4]
+
+    # offsets is a 1-D tensor variable
+    crop_offsets = fluid.layers.data(name="crop_offsets", shape=[3], dtype="int32", append_batch_size=False)
+    crop3 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3], offsets=crop_offsets)
+    # crop3.shape = [-1, 2, 3]
+
+    # offsets is a list in which each element is a constant or variable
+    offsets_var =  fluid.layers.data(name="dim1", shape=[1], dtype="int32", append_batch_size=False)
+    crop4 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3], offsets=[0, 1, offsets_var])
+    # crop4.shape = [-1, 2, 3]
+
+
+
+
+
+
+
+
+