Merge pull request #16525 from zhoukunsheng/rank

Rank

paddle/fluid/API.spec

@@ -206,6 +206,7 @@ paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'sha
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860'))
 paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a'))
 paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8'))
+paddle.fluid.layers.rank (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'ee1386c42ecc8f424fe3fb21862fefc2'))
 paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cdcf20c494c92060d10feb9374532f42'))
 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'))

python/paddle/fluid/layers/nn.py

@@ -161,6 +161,7 @@ __all__ = [
     'sum',
     'slice',
     'shape',
+    'rank',
     'logical_and',
     'logical_or',
     'logical_xor',
@@ -9339,6 +9340,32 @@ def shape(input):
     return out
 
 
+def rank(input):
+    """
+    **Rank Layer**
+
+    Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
+
+    Args:
+        input (Variable): The input variable.
+
+    Returns:
+        Variable: The rank of the input variable.
+
+    Examples:
+        .. code-block:: python
+
+            input = layers.data(
+                name="input", shape=[3, 100, 100], dtype="float32")
+            rank = layers.rank(input) # 4
+    """
+
+    ndims = len(input.shape)
+    out = assign(np.array(ndims, 'int32'))
+
+    return out
+
+
 def _elementwise_op(helper):
     op_type = helper.layer_type
     x = helper.kwargs.get('x', None)