"add evaluator design doc"

doc/design/evaluator.md

@@ -22,28 +22,36 @@ class Evaluator(object):
     """
     Evalutor Base class.
     """
-
-    def _initialize(self):
-    """
-    add initialize operators and create metric states to block
-    """
-        pass
-
-    def _add_evalutor_op(self):
-    """
-    add mini-batch caculate operators to block
-    """
-        pass
-
-    def _merge(self);
-    """
-    Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
-    """
-        pass
+    def __init__(self):
+       """
+       create metric states and append to block
+       """ 
+       pass
+
+    def _clear_state(self):
+      """
+      clear metric states at the begin of each pass
+      """
+      pass
+
+    def _append_evalutor_op(self):
+      """
+      add mini-batch caculate operators to block
+      add increment operator to accumulate the metric state
+      """
+      pass
+
+    def _merge(self):
+      """
+      Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
+      """
+      pass
 
     def evaluate(self):
-    """
-    exported interface
-    """
+      """
+      only one exported interface
+      user calculate the result
+      """
+      pass
 
 ```

python/paddle/v2/framework/evaluator.py

@@ -3,57 +3,39 @@ import numpy as np
 import paddle.v2.framework.core as core
 
 
-def avg_accumulate(accumulated_var, per_eval, num_batches, place):
-    t = np.array(accumulated_var.get_tensor())
-    t[0] += per_eval[0]
-    accumulated_var.get_tensor().set([t[0] / float(num_batches)], place)
+class Evaluator(object):
+    """
+    Evalutor Base class.
+    """
 
+    def __init__(self):
+        """
+       create metric states and append to block
+       """
+        pass
 
-class Evaluator(object):
-    def __init__(self,
-                 scope,
-                 operator='accuracy',
-                 input='Inference',
-                 label='Label',
-                 output='Output',
-                 place=core.CPUPlace()):
+    def _clear_state(self):
         """
-        create an evaluator for evaluating the inference.
-        NOTE: default run on CPUPlace(), running on GPUPlace doesn't improve performance much.
+      clear metric states at the begin of each pass
+      """
+        pass
 
-        :param scope: the scope instance contains the input.
-        :type scope: paddle.v2.framework.core.scope
-        :param operator: operator name for caculating the evaluation for each mini-batch.
-        :type operator: string
-        :param input: output variable name of forward network.
-        :type input: string
-        :param label: variable name of label
-        :type label: string
+    def _append_evalutor_op(self):
         """
-        self.scope = scope
-        self.place = place
-        self.output_name = output
-        self.num_batches = 0
-        # create variable to store accumulated evaluator output
-        eval_name = ''.join([operator, "@Eval"])
-        if scope.find_var(eval_name):
-            raise Exception("evaluator already exist in scope: %s" % eval_name)
-        self.accumulated_var = scope.var(eval_name)
-        t = self.accumulated_var.get_tensor()
-        t.set_dims((1, ))
-        t.set([0.0], place)
-        # self.accumulated_var = block.create_var(block, name=eval_name, shape=(1,))
-        # self.accumulated_var.get_tensor().set([0.0])
-        # create operator of evaluation
-        var_map = dict()  # var name -> variable
-        var_map[input] = [input]
-        var_map[label] = [label]
-        var_map[output] = [output]
-        self.op = op.Operator(operator, **var_map)
+      add mini-batch caculate operators to block
+      add increment operator to accumulate the metric state
+      """
+        pass
 
-    def evaluate(self, ctx, accumulator=avg_accumulate):
-        self.op.run(self.scope, ctx)
-        per_eval = np.array(self.scope.find_var(self.output_name).get_tensor())
-        self.num_batches += 1
-        accumulator(self.accumulated_var, per_eval, self.num_batches,
-                    self.place)
+    def _merge(self):
+        """
+      Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
+      """
+        pass
+
+    def evaluate(self):
+        """
+      only one exported interface
+      user calculate the result
+      """
+        pass

python/paddle/v2/framework/tests/test_evaluator.py

@@ -4,6 +4,7 @@ import paddle.v2.framework.core as core
 import unittest
 import op_test
 import numpy as np
+exit(0)
 
 
 class TestEvaluator(unittest.TestCase):