Add EditDistance to evaluator.py

python/paddle/v2/fluid/evaluator.py

@@ -218,21 +218,23 @@ class EditDistance(Evaluator):
             raise ValueError("You can only invoke Evaluator in root block")
 
         self.total_error = self.create_state(
-            dtype='int64', shape=[1], suffix='total')
-        self.batch_num = 0
+            dtype='float32', shape=[1], suffix='total')
+        self.batch_num = self.create_state(
+            dtype='float32', shape=[1], suffix='total')
         error = layers.edit_distance(input=input, label=label)
-        mean_error = layers.mean(input=error)
+        error = layers.cast(x=error, dtype='float32')
+        mean_error = layers.mean(x=error)
         layers.sums(input=[self.total_error, mean_error], out=self.total_error)
+        const1 = layers.fill_constant(shape=[1], value=1.0, dtype="float32")
+        layers.sums(input=[self.batch_num, const1], out=self.batch_num)
         self.metrics.append(mean_error)
 
     def eval(self, executor, eval_program=None):
-        self.batch_num += 1
         if eval_program is None:
             eval_program = Program()
         block = eval_program.current_block()
         with program_guard(main_program=eval_program):
             total_error = _clone_var_(block, self.total_error)
-            batch_num = layers.fill_constant(
-                shape=[1], value=self.batch_num, dtype="float32")
+            batch_num = _clone_var_(block, self.batch_num)
             out = layers.elementwise_div(x=total_error, y=batch_num)
         return np.array(executor.run(eval_program, fetch_list=[out])[0])