fix auc layer and add check for auc op (#12954)

* fix auc layer and add check for auc op

* use input to check if states are inited

* optimize code

paddle/fluid/operators/auc_op.h

@@ -60,6 +60,20 @@ class AucKernel : public framework::OpKernel<T> {
     const T* inference_data = predict->data<T>();
     const auto* label_data = label->data<int64_t>();
 
+    // check if states are inited.
+    auto* tp_in = ctx.Input<Tensor>("TP");
+    auto* fp_in = ctx.Input<Tensor>("FP");
+    auto* tn_in = ctx.Input<Tensor>("TN");
+    auto* fn_in = ctx.Input<Tensor>("FN");
+    PADDLE_ENFORCE(tp_in->IsInitialized(), "true_positive is not inited!");
+    PADDLE_ENFORCE(fp_in->IsInitialized(), "false_negative is not inited!");
+    PADDLE_ENFORCE(tn_in->IsInitialized(), "true_negative is not inited!");
+    PADDLE_ENFORCE(fn_in->IsInitialized(), "false_positive is not inited!");
+    PADDLE_ENFORCE_EQ(tp_in->numel(), num_thresholds, "");
+    PADDLE_ENFORCE_EQ(fp_in->numel(), num_thresholds, "");
+    PADDLE_ENFORCE_EQ(tn_in->numel(), num_thresholds, "");
+    PADDLE_ENFORCE_EQ(fn_in->numel(), num_thresholds, "");
+
     auto* tp_data = true_positive->mutable_data<int64_t>(ctx.GetPlace());
     auto* fn_data = false_negative->mutable_data<int64_t>(ctx.GetPlace());
     auto* tn_data = true_negative->mutable_data<int64_t>(ctx.GetPlace());

python/paddle/fluid/layers/metric_op.py

@@ -119,10 +119,14 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
     helper = LayerHelper("auc", **locals())
     auc_out = helper.create_tmp_variable(dtype="float64")
     # make tp, tn, fp, fn persistable, so that can accumulate all batches.
-    tp = helper.create_global_variable(persistable=True, dtype='int64')
-    tn = helper.create_global_variable(persistable=True, dtype='int64')
-    fp = helper.create_global_variable(persistable=True, dtype='int64')
-    fn = helper.create_global_variable(persistable=True, dtype='int64')
+    tp = helper.create_global_variable(
+        persistable=True, dtype='int64', shape=[num_thresholds])
+    tn = helper.create_global_variable(
+        persistable=True, dtype='int64', shape=[num_thresholds])
+    fp = helper.create_global_variable(
+        persistable=True, dtype='int64', shape=[num_thresholds])
+    fn = helper.create_global_variable(
+        persistable=True, dtype='int64', shape=[num_thresholds])
     for var in [tp, tn, fp, fn]:
         helper.set_variable_initializer(
             var, Constant(