Fix 堆栈溢出 (stack overflow) of case3: paddle.metric.accuracy

paddle/phi/kernels/cpu/accuracy_kernel.cc

@@ -35,10 +35,33 @@ void AccuracyRawKernel(const Context& dev_ctx,
   const int64_t* indices_data = indices.data<int64_t>();
   const int64_t* label_data = label.data<int64_t>();
 
+  PADDLE_ENFORCE_EQ(
+      inference.dims().size(),
+      2,
+      phi::errors::InvalidArgument(
+          "Rank(Input) of AccuracyOp must be 2, with shape "
+          "[sample_number, class_dim], But received rank(Input) is %d",
+          inference.dims().size()));
+
   size_t num_samples = inference.dims()[0];
   size_t class_dim = inference.dims()[1];
   *accuracy_data = 0.0f;
 
+  PADDLE_ENFORCE_GT(label.dims().size(),
+                    0,
+                    phi::errors::InvalidArgument(
+                        "Rank(Label) of AccuracyOp must greater than 0, "
+                        "But received rank(Label) is %d",
+                        label.dims().size()));
+
+  PADDLE_ENFORCE_GE(
+      label.dims()[0],
+      inference.dims()[0],
+      phi::errors::InvalidArgument("num_samples(%d) of Label should less than "
+                                   "or equal to num_samples(%d) of Input",
+                                   label.dims()[0],
+                                   num_samples));
+
   if (num_samples == 0) {
     return;
   }

paddle/phi/kernels/gpu/accuracy_kernel.cu

@@ -82,6 +82,14 @@ void AccuracyRawKernel(const Context& dev_ctx,
   const int64_t* indices_data = indices.data<int64_t>();
   const int64_t* label_data = label.data<int64_t>();
 
+  PADDLE_ENFORCE_EQ(
+      inference.dims().size(),
+      2,
+      phi::errors::InvalidArgument(
+          "Rank(Input) of AccuracyOp must be 2, with shape "
+          "[sample_number, class_dim], But received rank(Input) is %d",
+          inference.dims().size()));
+
   int* correct_data = dev_ctx.template Alloc<int>(correct);
   int* total_data = dev_ctx.template Alloc<int>(total);
   T* accuracy_data = dev_ctx.template Alloc<T>(accuracy);
@@ -91,6 +99,21 @@ void AccuracyRawKernel(const Context& dev_ctx,
   auto stream = dev_ctx.stream();
   phi::backends::gpu::GpuMemsetAsync(accuracy_data, 0, sizeof(T), stream);
 
+  PADDLE_ENFORCE_GT(label.dims().size(),
+                    0,
+                    phi::errors::InvalidArgument(
+                        "Rank(Label) of AccuracyOp must greater than 0, "
+                        "But received rank(Label) is %d",
+                        label.dims().size()));
+
+  PADDLE_ENFORCE_GE(
+      label.dims()[0],
+      inference.dims()[0],
+      phi::errors::InvalidArgument("num_samples(%d) of Label should less than "
+                                   "or equal to num_samples(%d) of Input",
+                                   label.dims()[0],
+                                   num_samples));
+
   if (num_samples == 0) {
     return;
   }

python/paddle/fluid/tests/unittests/test_accuracy_op.py

@@ -60,7 +60,7 @@ def test_check_output(self):
 
 
 class TestAccuracyOpError(unittest.TestCase):
-    def test_errors(self):
+    def test_type_errors(self):
         with program_guard(Program(), Program()):
             # The input type of accuracy_op must be Variable.
             x1 = fluid.create_lod_tensor(
@@ -75,12 +75,27 @@ def test_errors(self):
             x2 = paddle.static.data(name='x2', shape=[-1, 4], dtype="int32")
             self.assertRaises(TypeError, paddle.static.accuracy, x2, label)
             self.assertRaises(TypeError, paddle.metric.accuracy, x2, label)
+
             x3 = paddle.static.data(
                 name='input', shape=[-1, 2], dtype="float16"
             )
             paddle.static.accuracy(input=x3, label=label)
             paddle.metric.accuracy(input=x3, label=label)
 
+    def test_value_errors(self):
+        with program_guard(Program(), Program()):
+            paddle.disable_static()
+
+            # The input rank of accuracy_op must be 2.
+            with self.assertRaises(ValueError):
+                x3 = paddle.to_tensor([0.1], dtype='float32')
+                label3 = paddle.to_tensor(
+                    np.reshape([0], [1, 1]), dtype='int32'
+                )
+                paddle.metric.accuracy(x3, label3)
+
+            paddle.enable_static()
+
 
 class TestAccuracyAPI1(unittest.TestCase):
     def setUp(self):