From d49daff06f89a7854c039e6dd21be18d4e852160 Mon Sep 17 00:00:00 2001
From: HydrogenSulfate <490868991@qq.com>
Date: Sun, 26 Dec 2021 20:03:02 +0800
Subject: [PATCH] restore test for min,max labels

---
 .../unittests/test_cross_entropy_loss.py      | 28 +++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py b/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py
index 29676fcff12..adf11e815fa 100644
--- a/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py
+++ b/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py
@@ -1465,6 +1465,34 @@ class TestCrossEntropyFAPIError(unittest.TestCase):
 
             self.assertRaises(ValueError, test_WeightLength_NotEqual)
 
+            def test_LabelValue_ExceedMax():
+                input_data = paddle.rand(shape=[20, 100])
+                label_data = paddle.randint(
+                    0, 100, shape=[20, 1], dtype="int64")  # hard label
+                label_data[0] = 100
+                weight_data = paddle.rand([100])  # provide weight
+                paddle.nn.functional.cross_entropy(
+                    input=input_data,
+                    label=label_data,
+                    weight=weight_data,
+                    ignore_index=-100)
+
+            self.assertRaises(ValueError, test_LabelValue_ExceedMax)
+
+            def test_LabelValue_ExceedMin():
+                input_data = paddle.rand(shape=[20, 100])
+                label_data = paddle.randint(
+                    0, 100, shape=[20, 1], dtype="int64")  # hard label
+                label_data[0] = -1
+                weight_data = paddle.rand([100])  # provide weight
+                paddle.nn.functional.cross_entropy(
+                    input=input_data,
+                    label=label_data,
+                    weight=weight_data,
+                    ignore_index=-100)
+
+            self.assertRaises(ValueError, test_LabelValue_ExceedMin)
+
             def static_test_WeightLength_NotEqual():
                 input_np = np.random.random([2, 4]).astype('float32')
                 label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
-- 
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