add static label check

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")
+                label_data[0] = 100
+                weight_data = paddle.rand([100])
+                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")
+                label_data[0] = -1
+                weight_data = paddle.rand([100])
+                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)

python/paddle/nn/functional/loss.py

@@ -1710,6 +1710,22 @@ def cross_entropy(input,
                     ignore_weight_mask,
                     dtype=label.dtype) * label  # ignored position will be 0
 
+                if len(paddle.nonzero(valid_label < 0)) > 0:
+                    invalid_label = paddle.gather_nd(
+                        valid_label, paddle.nonzero(valid_label < 0))
+                    raise ValueError(
+                        "Target({}) is out of class_dimension's lower bound({})".
+                        format(invalid_label[0], 0))
+                # TODO: Temporarily use paddle.nonzero instead of paddle.max 
+                # to detect and find out possible illegal label values
+                if len(paddle.nonzero(valid_label >= input.shape[axis])) > 0:
+                    invalid_label = paddle.gather_nd(
+                        valid_label,
+                        paddle.nonzero(valid_label >= input.shape[axis]))
+                    raise ValueError(
+                        "Target({}) is out of class_dimension's upper bound({})".
+                        format(invalid_label[0], input.shape[axis] - 1))
+
                 ignore_weight_mask = paddle.cast(
                     ignore_weight_mask, out.dtype)  # convert from 0 to 0.0
 
@@ -1833,6 +1849,22 @@ def cross_entropy(input,
                 ignore_weight_mask,
                 dtype=label.dtype) * label  # ignored position will be 0
 
+            if len(paddle.nonzero(valid_label < 0)) > 0:
+                invalid_label = paddle.gather_nd(
+                    valid_label, paddle.nonzero(valid_label < 0))
+                raise ValueError(
+                    "Target({}) is out of class_dimension's lower bound({})".
+                    format(invalid_label[0], 0))
+            # TODO: Temporarily use paddle.nonzero instead of paddle.max 
+            # to detect and find out possible illegal label values
+            if len(paddle.nonzero(valid_label >= input.shape[axis])) > 0:
+                invalid_label = paddle.gather_nd(
+                    valid_label,
+                    paddle.nonzero(valid_label >= input.shape[axis]))
+                raise ValueError(
+                    "Target({}) is out of class_dimension's upper bound({})".
+                    format(invalid_label[0], input.shape[axis] - 1))
+
             ignore_weight_mask = paddle.cast(ignore_weight_mask,
                                              out.dtype)  # convert from 0 to 0.0