Support squeezed label as input in paddle.metric.Accuracy

python/paddle/metric/metrics.py

@@ -244,6 +244,7 @@ def compute(self, pred, label, *args):
             Tensor: Correct mask, a tensor with shape [batch_size, topk].
         """
         pred = paddle.argsort(pred, descending=True)[:, :self.maxk]
+        label = paddle.reshape(label, (-1, 1))
         correct = pred == label
         return paddle.cast(correct, dtype='float32')
 

python/paddle/tests/test_metrics.py

@@ -28,6 +28,7 @@
 def accuracy(pred, label, topk=(1, )):
     maxk = max(topk)
     pred = np.argsort(pred)[:, ::-1][:, :maxk]
+    label = label.reshape(-1, 1)
     correct = (pred == np.repeat(label, maxk, 1))
 
     batch_size = label.shape[0]
@@ -47,21 +48,27 @@ def convert_to_one_hot(y, C):
 
 
 class TestAccuracy(unittest.TestCase):
-    def test_acc(self):
+    def test_acc(self, squeeze_y=False):
         paddle.disable_static()
 
         x = paddle.to_tensor(
             np.array([[0.1, 0.2, 0.3, 0.4], [0.1, 0.4, 0.3, 0.2],
                       [0.1, 0.2, 0.4, 0.3], [0.1, 0.2, 0.3, 0.4]]))
-        y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
+
+        y = np.array([[0], [1], [2], [3]])
+        if squeeze_y:
+            y = y.squeeze()
+
+        y = paddle.to_tensor(y)
 
         m = paddle.metric.Accuracy(name='my_acc')
 
         # check name
         self.assertEqual(m.name(), ['my_acc'])
 
         correct = m.compute(x, y)
-        # check results
+        # check shape and results
+        self.assertEqual(correct.shape, [4, 1])
         self.assertEqual(m.update(correct), 0.75)
         self.assertEqual(m.accumulate(), 0.75)
 
@@ -80,19 +87,25 @@ def test_acc(self):
         self.assertEqual(m.count[0], 0.0)
         paddle.enable_static()
 
+    def test_1d_label(self):
+        self.test_acc(True)
+
 
 class TestAccuracyDynamic(unittest.TestCase):
     def setUp(self):
         self.topk = (1, )
         self.class_num = 5
         self.sample_num = 1000
         self.name = None
+        self.squeeze_label = False
 
     def random_pred_label(self):
         label = np.random.randint(0, self.class_num,
                                   (self.sample_num, 1)).astype('int64')
         pred = np.random.randint(0, self.class_num,
                                  (self.sample_num, 1)).astype('int32')
+        if self.squeeze_label:
+            label = label.squeeze()
         pred_one_hot = convert_to_one_hot(pred, self.class_num)
         pred_one_hot = pred_one_hot.astype('float32')
 
@@ -123,9 +136,14 @@ def setUp(self):
         self.class_num = 10
         self.sample_num = 1000
         self.name = "accuracy"
+        self.squeeze_label = True
 
 
 class TestAccuracyStatic(TestAccuracyDynamic):
+    def setUp(self):
+        super().setUp()
+        self.squeeze_label = True
+
     def test_main(self):
         main_prog = fluid.Program()
         startup_prog = fluid.Program()
@@ -164,6 +182,7 @@ def setUp(self):
         self.class_num = 10
         self.sample_num = 100
         self.name = "accuracy"
+        self.squeeze_label = False
 
 
 class TestPrecision(unittest.TestCase):

python/paddle/vision/datasets/cifar.py

@@ -53,7 +53,9 @@ class Cifar10(Dataset):
             :attr:`data_file` is not set. Default True
 
     Returns:
-        Dataset: instance of cifar-10 dataset
+        Dataset: instance of cifar-10 dataset. If transform is None, the shape
+            of each data iterm is [3, 32, 32], and default dtype is float32.
+            The dtype of label is int64.
 
     Examples: