Add python wrapper for ctc_evaluator

python/paddle/v2/fluid/layers/nn.py

@@ -50,6 +50,7 @@ __all__ = [
     'sequence_last_step',
     'dropout',
     'split',
+    'greedy_ctc_evaluator',
 ]
 
 
@@ -1547,13 +1548,13 @@ def split(input, num_or_sections, dim=-1):
 
     Args:
         input (Variable): The input variable which is a Tensor or LoDTensor.
-        num_or_sections (int|list): If :attr:`num_or_sections` is an integer, 
-            then the integer indicates the number of equal sized sub-tensors 
-            that the tensor will be divided into. If :attr:`num_or_sections` 
-            is a list of integers, the length of list indicates the number of 
-            sub-tensors and the integers indicate the sizes of sub-tensors' 
+        num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
+            then the integer indicates the number of equal sized sub-tensors
+            that the tensor will be divided into. If :attr:`num_or_sections`
+            is a list of integers, the length of list indicates the number of
+            sub-tensors and the integers indicate the sizes of sub-tensors'
             :attr:`dim` dimension orderly.
-        dim (int): The dimension along which to split. If :math:`dim < 0`, the 
+        dim (int): The dimension along which to split. If :math:`dim < 0`, the
             dimension to split along is :math:`rank(input) + dim`.
 
     Returns:
@@ -1597,3 +1598,39 @@ def split(input, num_or_sections, dim=-1):
             'axis': dim
         })
     return outs
+
+
+def greedy_ctc_evaluator(input, label, blank, normalized=False, name=None):
+    """
+    """
+
+    helper = LayerHelper("greedy_ctc_evalutor", **locals())
+    # top 1 op
+    topk_out = helper.create_tmp_variable(dtype=input.dtype)
+    topk_indices = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="top_k",
+        inputs={"X": [input]},
+        outputs={"Out": [topk_out],
+                 "Indices": [topk_indices]},
+        attrs={"k": 1})
+
+    # ctc align op
+    ctc_out = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="ctc_align",
+        inputs={"Input": [topk_indices]},
+        outputs={"Out": [ctc_out]},
+        attrs={"merge_repeated": True,
+               "blank": blank})
+
+    # edit distance op
+    edit_distance_out = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="edit_distance",
+        inputs={"Hyps": [ctc_out],
+                "Refs": [label]},
+        outputs={"Out": [edit_distance_out]},
+        attrs={"normalized": normalized})
+
+    return edit_distance_out