fix bug (#35482)

paddle/fluid/operators/margin_cross_entropy_op.cu

@@ -323,6 +323,11 @@ class MarginCrossEntropyOpCUDAKernel : public framework::OpKernel<T> {
           T><<<NumBlocks(N), threads, 0, dev_ctx.stream()>>>(
           logits_ptr, labels->data<LabelT>(), margin1, margin2, margin3, rank,
           nranks, N, D, class_interval.data<int>());
+    } else {
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "margin_cross_entropy label type noly support int32 and int64, "
+          "but got %s",
+          label_type));
     }
 
     // scale by s

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

@@ -138,7 +138,7 @@ class TestClassCenterSampleV2(unittest.TestCase):
             label = paddle.static.data(
                 name='label', shape=[self.batch_size], dtype=self.dtype)
             remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(
-                label, self.num_classes, self.num_samples, seed=self.seed)
+                label, self.num_classes, self.num_samples)
 
             remapped_label_np, sampled_class_center_np = class_center_sample_numpy(
                 label_np, [self.num_classes], self.num_samples)
@@ -163,7 +163,7 @@ class TestClassCenterSampleV2(unittest.TestCase):
             label = paddle.to_tensor(label_np, dtype=self.dtype)
 
             remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(
-                label, self.num_classes, self.num_samples, seed=self.seed)
+                label, self.num_classes, self.num_samples)
 
             remapped_label_np, sampled_class_center_np = class_center_sample_numpy(
                 label_np, [self.num_classes], self.num_samples)
@@ -210,13 +210,41 @@ class TestClassCenterSampleAPIError(unittest.TestCase):
                     label = paddle.to_tensor(label_np)
 
                     remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(
-                        label,
-                        self.num_classes,
-                        self.num_samples,
-                        seed=self.seed)
+                        label, self.num_classes, self.num_samples)
 
         self.assertRaises(ValueError, test_num_samples)
 
 
+class TestClassCenterSampleAPIError1(unittest.TestCase):
+    def setUp(self):
+        self.initParams()
+        np.random.seed(self.seed)
+        self.places = [paddle.fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(paddle.fluid.CUDAPlace(0))
+
+    def initParams(self):
+        self.batch_size = 5
+        self.num_samples = 5
+        self.num_classes = 10
+        self.seed = 2021
+        self.init_dtype()
+
+    def init_dtype(self):
+        self.dtype = np.int64
+
+    def test_dynamic_errors(self):
+        def test_empty_label():
+            for place in self.places:
+                with paddle.fluid.dygraph.guard(place):
+                    label = paddle.to_tensor(np.array([], dtype=self.dtype))
+
+                    remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(
+                        label, self.num_classes, self.num_samples)
+                    print(remapped_label, sampled_class_index)
+
+        self.assertRaises(ValueError, test_empty_label)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -378,7 +378,30 @@ class TestMarginCrossEntropyOpAPIError(unittest.TestCase):
                         return_softmax=True,
                         reduction=None)
 
+        def test_label_type():
+            for place in self.places:
+                with paddle.fluid.dygraph.guard(place):
+                    labels_np = np.random.uniform(
+                        0, self.num_class,
+                        (self.batch_dim, 1)).astype(self.dtype)
+                    logits_np = np.random.uniform(
+                        -0.99, 0.99,
+                        [self.batch_dim, self.num_class]).astype(self.dtype)
+                    labels = paddle.to_tensor(labels_np)
+                    logits = paddle.to_tensor(logits_np)
+
+                    loss, softmax = paddle.nn.functional.margin_cross_entropy(
+                        logits,
+                        labels,
+                        margin1=self.margin1,
+                        margin2=self.margin2,
+                        margin3=self.margin3,
+                        scale=self.scale,
+                        return_softmax=True,
+                        reduction=None)
+
         self.assertRaises(ValueError, test_dim)
+        self.assertRaises(NotImplementedError, test_label_type)
 
 
 if __name__ == '__main__':

python/paddle/nn/functional/common.py

@@ -1584,7 +1584,7 @@ def label_smooth(label, prior_dist=None, epsilon=0.1, name=None):
     return smooth_label
 
 
-def class_center_sample(label, num_classes, num_samples, group=None, seed=None):
+def class_center_sample(label, num_classes, num_samples, group=None):
     """
     Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers.
     The process of sampling subset class centers is straightforward: 
@@ -1605,13 +1605,12 @@ def class_center_sample(label, num_classes, num_samples, group=None, seed=None):
         The API supports CPU, single GPU and multi GPU.
 
     Args:
-    	label (Tensor): 1-D tensor with shape [N], each label in [0, num_classes)
-    	num_classes (int): A positive integer to specify the number of classes at local rank.
+        label (Tensor): 1-D tensor with shape [N], each label in [0, num_classes)
+        num_classes (int): A positive integer to specify the number of classes at local rank.
             Note that num_classes of each GPU can be different.
-    	num_samples (int): A positive integer to specify the number of class center to sample.
+        num_samples (int): A positive integer to specify the number of class center to sample.
         group (Group, optional): The abstract representation of group.
             See paddle.distributed.collective.Group. Default is ``None``.
-        seed (int, optional): Random seed. Default is ``None``.
 
     Returns:
         Tuple of two ``Tensor`` : (remapped_label, sampled_class_center), remapped label using sampled class center,
@@ -1702,6 +1701,19 @@ def class_center_sample(label, num_classes, num_samples, group=None, seed=None):
             'Expected num_samples less than or equal to {}, got num_samples {}'.
             format(num_classes, num_samples))
 
+    label_size = 1
+    for dim in list(label.shape):
+        label_size *= dim
+    if label_size < 1:
+        raise ValueError('Expected label_size > 0 \
+             (got label_size{})'.format(label_size))
+
+    label_dims = len(list(label.shape))
+    if label_dims != 1:
+        raise ValueError('Expected label_dims == 1 \
+             (got label_dims{})'.format(label_dims))
+
+    seed = None
     if (seed is None or seed == 0) and default_main_program().random_seed != 0:
         seed = default_main_program().random_seed
 

python/paddle/nn/functional/loss.py

@@ -1120,13 +1120,13 @@ def margin_cross_entropy(logits,
         The API supports model parallel and single GPU. And logits.shape[-1] can be different at each rank.
 
     Args:
-    	logits (Tensor): shape[N, local_num_classes], the output of the normalized X multiply the normalized W.
+        logits (Tensor): shape[N, local_num_classes], the output of the normalized X multiply the normalized W.
                 The logits is shard_logits when using model parallel.
-    	label (Tensor): shape[N] or shape[N, 1], the groud truth label.
-    	margin1 (float, optional): m1 of margin loss, default value is `1.0`.
-    	margin2 (float, optional): m2 of margin loss, default value is `0.5`.
-    	margin3 (float, optional): m3 of margin loss, default value is `0.0`.
-    	scale (float, optional): s of margin loss, default value is `64.0`.
+        label (Tensor): shape[N] or shape[N, 1], the groud truth label.
+        margin1 (float, optional): m1 of margin loss, default value is `1.0`.
+        margin2 (float, optional): m2 of margin loss, default value is `0.5`.
+        margin3 (float, optional): m3 of margin loss, default value is `0.0`.
+        scale (float, optional): s of margin loss, default value is `64.0`.
         group (Group, optional): The abstract representation of group, see paddle.distributed.collective.Group.
             Default `None`.
         return_softmax (bool, optional): Whether return softmax probability. Default value is `False`.