fix ctr_metric_bundle for instag (#40838)

* fix ctr_metric_bundle for instag

* fix ctr_metric_bundle for instag

* update

* fix zero set

* fix zero set by to_str

* update to_string pare

* optmize default value

* simple code

* update metric for real data

* update metric for real data

* update metric for real data

python/paddle/fluid/contrib/layers/metric_op.py

@@ -23,11 +23,12 @@ from paddle.fluid.initializer import Normal, Constant
 from paddle.fluid.framework import Variable
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.layers import nn
+from paddle.fluid.layers import tensor
 
 __all__ = ['ctr_metric_bundle']
 
 
-def ctr_metric_bundle(input, label):
+def ctr_metric_bundle(input, label, ins_tag_weight=None):
     """
     ctr related metric layer
 
@@ -48,6 +49,9 @@ def ctr_metric_bundle(input, label):
                          Variable indicates the probability of each label.
         label(Variable): A 2D int Variable indicating the label of the training
                          data. The height is batch size and width is always 1.
+        ins_tag_weight(Variable): A 2D int Variable indicating the ins_tag_weight of the training
+                         data. 1 means real data, 0 means fake data. 
+                         A LoDTensor or Tensor with type float32,float64.
 
     Returns:
         local_sqrerr(Variable): Local sum of squared error
@@ -60,10 +64,15 @@ def ctr_metric_bundle(input, label):
 
             import paddle.fluid as fluid
             data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-            label = fluid.layers.data(name="label", shape=[1], dtype="int32")
+            label = fluid.layers.data(name="label", shape=[-1, 1], dtype="int32")
+            ins_tag_weight = fluid.layers.data(name="ins_tag_weight", shape=[-1, 1], dtype="float32")
             predict = fluid.layers.sigmoid(fluid.layers.fc(input=data, size=1))
-            auc_out = fluid.contrib.layers.ctr_metric_bundle(input=predict, label=label)
+            auc_out = fluid.contrib.layers.ctr_metric_bundle(input=predict, label=label, ins_tag_weight=ins_tag_weight)
     """
+    if ins_tag_weight is None:
+        ins_tag_weight = tensor.fill_constant(
+            shape=[1, 1], dtype="float32", value=1.0)
+
     assert input.shape == label.shape
     helper = LayerHelper("ctr_metric_bundle", **locals())
 
@@ -150,11 +159,6 @@ def ctr_metric_bundle(input, label):
         type="reduce_sum",
         inputs={"X": [tmp_res_sigmoid]},
         outputs={"Out": [batch_q]})
-    helper.append_op(
-        type="elementwise_add",
-        inputs={"X": [batch_q],
-                "Y": [local_q]},
-        outputs={"Out": [local_q]})
 
     helper.append_op(
         type="reduce_sum",
@@ -179,10 +183,42 @@ def ctr_metric_bundle(input, label):
         type="reduce_sum",
         inputs={"X": [tmp_ones]},
         outputs={"Out": [batch_ins_num]})
+
+    #if data is fake, return 0
+    inputs_slice = {'Input': ins_tag_weight}
+    attrs = {'axes': [0]}
+    attrs['starts'] = [0]
+    attrs['ends'] = [1]
+    helper.append_op(
+        type="slice",
+        inputs=inputs_slice,
+        attrs=attrs,
+        outputs={"Out": ins_tag_weight})
+
+    axis = helper.kwargs.get('axis', 0)
+    helper.append_op(
+        type="elementwise_mul",
+        inputs={"X": [batch_ins_num],
+                "Y": [ins_tag_weight]},
+        outputs={"Out": [batch_ins_num]},
+        attrs={'axis': axis})
+
     helper.append_op(
         type="elementwise_add",
         inputs={"X": [batch_ins_num],
                 "Y": [local_ins_num]},
         outputs={"Out": [local_ins_num]})
 
+    helper.append_op(
+        type="elementwise_mul",
+        inputs={"X": [batch_q],
+                "Y": [ins_tag_weight]},
+        outputs={"Out": [batch_q]},
+        attrs={'axis': axis})
+    helper.append_op(
+        type="elementwise_add",
+        inputs={"X": [batch_q],
+                "Y": [local_q]},
+        outputs={"Out": [local_q]})
+
     return local_sqrerr, local_abserr, local_prob, local_q, local_pos_num, local_ins_num