fix auc instag (#40672)

* fix auc instag

* update ins_tag_w default

* update ins_tag_weight default style

* update ins_tag_weight default style

* optimize

* update by reviews of zwh

* optmize default value

* fix auc_op order

paddle/fluid/operators/metrics/auc_op.cc

@@ -25,6 +25,8 @@ class AucOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext *ctx) const override {
     OP_INOUT_CHECK(ctx->HasInput("Predict"), "Input", "Predict", "Auc");
     OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "Auc");
+    OP_INOUT_CHECK(ctx->HasInput("InsTagWeight"), "Input", "InsTagWeight",
+                   "Auc");
     auto predict_width = ctx->GetInputDim("Predict")[1];
     if (ctx->IsRuntime()) {
       PADDLE_ENFORCE_LE(predict_width, 2,
@@ -83,10 +85,11 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Label",
              "A 2D int tensor indicating the label of the training data. "
              "shape: [batch_size, 1]");
-
     // TODO(typhoonzero): support weight input
     AddInput("StatPos", "Statistic value when label = 1");
     AddInput("StatNeg", "Statistic value when label = 0");
+    AddInput("InsTagWeight",
+             "(Tensor) instag weight, 1 means real data, 0 means false data");
 
     AddOutput("AUC",
               "A scalar representing the "

paddle/fluid/operators/metrics/auc_op.cu

@@ -112,6 +112,12 @@ class AucCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     auto *predict = ctx.Input<Tensor>("Predict");
     auto *label = ctx.Input<Tensor>("Label");
+    auto *ins_tag_weight = ctx.Input<Tensor>("InsTagWeight");
+    const auto *ins_tag_weight_value = ins_tag_weight->data<float>();
+    bool is_fake_data = 0;
+    if (ins_tag_weight_value[0] == 0) {
+      is_fake_data = 1;
+    }
 
     int num_thresholds = ctx.Attr<int>("num_thresholds");
     int slide_steps = ctx.Attr<int>("slide_steps");
@@ -145,8 +151,12 @@ class AucCUDAKernel : public framework::OpKernel<T> {
                  cudaMemcpyDeviceToDevice);
     }
 
+    if (slide_steps == 0 && is_fake_data) {
+      return;
+    }
+
     statAuc(ctx, label, predict, num_thresholds, slide_steps, origin_stat_pos,
-            origin_stat_neg);
+            origin_stat_neg, is_fake_data);
     int sum_offset = slide_steps * (num_thresholds + 1);
     auto stream =
         ctx.template device_context<platform::CUDADeviceContext>().stream();
@@ -165,8 +175,8 @@ class AucCUDAKernel : public framework::OpKernel<T> {
                              const framework::Tensor *label,
                              const framework::Tensor *predict,
                              const int num_thresholds, const int slide_steps,
-                             int64_t *origin_stat_pos,
-                             int64_t *origin_stat_neg) {
+                             int64_t *origin_stat_pos, int64_t *origin_stat_neg,
+                             const bool is_fake_data) {
     size_t batch_size = predict->dims()[0];
     size_t inference_width = predict->dims()[1];
     const T *inference_data = predict->data<T>();
@@ -200,11 +210,13 @@ class AucCUDAKernel : public framework::OpKernel<T> {
                     PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
         label_data, inference_data, inference_width, num_thresholds,
         origin_stat_pos, origin_stat_neg, batch_size, slide_steps);
+    if (!is_fake_data) {
       UpdateSumDataKernel<<<(bucket_length + PADDLE_CUDA_NUM_THREADS - 1) /
                                 PADDLE_CUDA_NUM_THREADS,
                             PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
           origin_stat_pos, origin_stat_neg, bucket_length, slide_steps);
     }
+  }
 };
 
 }  // namespace operators

paddle/fluid/operators/metrics/auc_op.h

@@ -29,7 +29,12 @@ class AucKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     auto *predict = ctx.Input<Tensor>("Predict");
     auto *label = ctx.Input<Tensor>("Label");
-
+    auto *ins_tag_weight = ctx.Input<Tensor>("InsTagWeight");
+    const auto *ins_tag_weight_value = ins_tag_weight->data<float>();
+    bool is_fake_data = 0;
+    if (ins_tag_weight_value[0] == 0) {
+      is_fake_data = 1;
+    }
     int num_thresholds = ctx.Attr<int>("num_thresholds");
     int slide_steps = ctx.Attr<int>("slide_steps");
 
@@ -60,8 +65,13 @@ class AucKernel : public framework::OpKernel<T> {
               (slide_steps > 0 ? 1 : 0)) *
                  sizeof(int64_t));
     }
+
+    // when calculate global_auc && is fake data, just do nothing
+    if (slide_steps == 0 && is_fake_data) {
+      return;
+    }
     statAuc(label, predict, num_thresholds, slide_steps, origin_stat_pos,
-            origin_stat_neg);
+            origin_stat_neg, is_fake_data);
 
     int sum_offset = slide_steps * (num_thresholds + 1);
     calcAuc(origin_stat_pos + sum_offset, origin_stat_neg + sum_offset,
@@ -81,8 +91,8 @@ class AucKernel : public framework::OpKernel<T> {
   inline static void statAuc(const framework::Tensor *label,
                              const framework::Tensor *predict,
                              const int num_thresholds, const int slide_steps,
-                             int64_t *origin_stat_pos,
-                             int64_t *origin_stat_neg) {
+                             int64_t *origin_stat_pos, int64_t *origin_stat_neg,
+                             const bool is_fake_data) {
     size_t batch_size = predict->dims()[0];
     size_t inference_width = predict->dims()[1];
     const T *inference_data = predict->data<T>();
@@ -148,6 +158,7 @@ class AucKernel : public framework::OpKernel<T> {
         origin_stat_neg[cur_step_begin + binIdx] += 1;
       }
     }
+    if (!is_fake_data) {
       for (int i = 0; i < bucket_length; ++i) {
         origin_stat_pos[sum_step_begin + i] +=
             origin_stat_pos[cur_step_begin + i];
@@ -155,6 +166,7 @@ class AucKernel : public framework::OpKernel<T> {
             origin_stat_neg[cur_step_begin + i];
       }
     }
+  }
 
   inline static void calcAuc(const int64_t *stat_pos, const int64_t *stat_neg,
                              int num_thresholds, double *auc) {

python/paddle/fluid/layers/metric_op.py

@@ -24,6 +24,7 @@ from ..framework import Variable, in_dygraph_mode, _varbase_creator
 from .. import core
 from ..param_attr import ParamAttr
 from . import nn
+from . import tensor
 from ..data_feeder import check_variable_and_dtype
 
 __all__ = ['accuracy', 'auc']
@@ -113,7 +114,8 @@ def auc(input,
         curve='ROC',
         num_thresholds=2**12 - 1,
         topk=1,
-        slide_steps=1):
+        slide_steps=1,
+        ins_tag_weight=None):
     """
     **Area Under the Curve (AUC) Layer**
 
@@ -143,7 +145,9 @@ def auc(input,
                              the roc curve. Default 200.
         topk(int): only topk number of prediction output will be used for auc.
         slide_steps: when calc batch auc, we can not only use step currently but the previous steps can be used. slide_steps=1 means use the current step, slide_steps=3 means use current step and the previous second steps, slide_steps=0 use all of the steps.
-
+        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:
         Variable: A tuple representing the current AUC.
@@ -159,6 +163,7 @@ def auc(input,
 
             data = fluid.data(name="input", shape=[-1, 32,32], dtype="float32")
             label = fluid.data(name="label", shape=[-1], dtype="int")
+            ins_tag_weight = fluid.data(name="ins_tag_weight", shape=[-1], dtype="float32")
             fc_out = fluid.layers.fc(input=data, size=2)
             predict = fluid.layers.softmax(input=fc_out)
             result=fluid.layers.auc(input=predict, label=label)
@@ -169,14 +174,22 @@ def auc(input,
             exe.run(fluid.default_startup_program())
             x = np.random.rand(3,32,32).astype("float32")
             y = np.array([1,0,1])
-            output= exe.run(feed={"input": x,"label": y},
+            z = np.array([1,1,1]) #this means real data
+            output= exe.run(feed={"input": x,"label": y, "ins_tag_weight": z},
                              fetch_list=[result[0]])
             print(output)
             #[array([0.5])]
     """
     helper = LayerHelper("auc", **locals())
+
+    if ins_tag_weight is None:
+        ins_tag_weight = tensor.fill_constant(
+            shape=[1, 1], dtype="float32", value=1.0)
+
     check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'auc')
     check_variable_and_dtype(label, 'label', ['int32', 'int64'], 'auc')
+    check_variable_and_dtype(ins_tag_weight, 'ins_tag_weight',
+                             ['float32', 'float64'], 'auc')
     auc_out = helper.create_variable_for_type_inference(dtype="float64")
     batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
     # make tp, tn, fp, fn persistable, so that can accumulate all batches.
@@ -215,7 +228,8 @@ def auc(input,
             "Predict": [input],
             "Label": [label],
             "StatPos": [batch_stat_pos],
-            "StatNeg": [batch_stat_neg]
+            "StatNeg": [batch_stat_neg],
+            "InsTagWeight": [ins_tag_weight]
         },
         attrs={
             "curve": curve,
@@ -234,7 +248,8 @@ def auc(input,
             "Predict": [input],
             "Label": [label],
             "StatPos": [stat_pos],
-            "StatNeg": [stat_neg]
+            "StatNeg": [stat_neg],
+            "InsTagWeight": [ins_tag_weight]
         },
         attrs={
             "curve": curve,