Enhance paddle.metric.Accuracy (#29125)

python/paddle/metric/metrics.py

@@ -246,16 +246,27 @@ class Accuracy(Metric):
         Compute the top-k (maxinum value in `topk`) indices.
 
         Args:
-            pred (Tensor): The predicted value is a Tensor wit type
-                float32 or float64.
-            label (Tensor): The ground truth value is a 2D Tensor, its
-                shape is [batch_size, 1] and type is int64.
-
+            pred (Tensor): The predicted value is a Tensor with dtype
+                float32 or float64. Shape is [batch_size, d0, ..., dN].
+            label (Tensor): The ground truth value is Tensor with dtype
+                int64. Shape is [batch_size, d0, ..., 1], or
+                [batch_size, d0, ..., num_classes] in one hot representation.
+                
         Return:
             Tensor: Correct mask, a tensor with shape [batch_size, topk].
         """
-        pred = paddle.argsort(pred, descending=True)[:, :self.maxk]
-        label = paddle.reshape(label, (-1, 1))
+        pred = paddle.argsort(pred, descending=True)
+        pred = paddle.slice(
+            pred, axes=[len(pred.shape) - 1], starts=[0], ends=[self.maxk])
+        if (len(label.shape) == 1) or \
+           (len(label.shape) == 2 and label.shape[-1] == 1):
+            # In static mode, the real label data shape may be different
+            # from shape defined by paddle.static.InputSpec in model
+            # building, reshape to the right shape.
+            label = paddle.reshape(label, (-1, 1))
+        elif label.shape[-1] != 1:
+            # one-hot label
+            label = paddle.argmax(label, axis=-1, keepdim=True)
         correct = pred == label
         return paddle.cast(correct, dtype='float32')
 
@@ -273,10 +284,10 @@ class Accuracy(Metric):
         """
         if isinstance(correct, paddle.Tensor):
             correct = correct.numpy()
+        num_samples = np.prod(np.array(correct.shape[:-1]))
         accs = []
         for i, k in enumerate(self.topk):
-            num_corrects = correct[:, :k].sum()
-            num_samples = len(correct)
+            num_corrects = correct[..., :k].sum()
             accs.append(float(num_corrects) / num_samples)
             self.total[i] += num_corrects
             self.count[i] += num_samples

python/paddle/tests/test_metrics.py

@@ -25,17 +25,28 @@ import paddle.fluid as fluid
 from paddle.hapi.model import to_list
 
 
+def one_hot(x, n_class):
+    res = np.eye(n_class)[np.array(x).reshape(-1)]
+    res = res.reshape(list(x.shape) + [n_class])
+    return res
+
+
 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))
+    pred = np.argsort(pred)[..., ::-1][..., :maxk]
+    if len(label.shape) == 1:
+        label = label.reshape(-1, 1)
+    elif label.shape[-1] != 1:
+        label = np.argmax(label, axis=-1)
+        label = label[..., np.newaxis]
+    correct = (pred == np.repeat(label, maxk, -1))
+
+    total = np.prod(np.array(label.shape[:-1]))
 
-    batch_size = label.shape[0]
     res = []
     for k in topk:
-        correct_k = correct[:, :k].sum()
-        res.append(float(correct_k) / batch_size)
+        correct_k = correct[..., :k].sum()
+        res.append(float(correct_k) / total)
     return res
 
 
@@ -49,8 +60,6 @@ def convert_to_one_hot(y, C):
 
 class TestAccuracy(unittest.TestCase):
     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]]))
@@ -85,11 +94,36 @@ class TestAccuracy(unittest.TestCase):
         m.reset()
         self.assertEqual(m.total[0], 0.0)
         self.assertEqual(m.count[0], 0.0)
-        paddle.enable_static()
 
     def test_1d_label(self):
         self.test_acc(True)
 
+    def compare(self, x_np, y_np, k=(1, )):
+        x = paddle.to_tensor(x_np)
+        y = paddle.to_tensor(y_np)
+
+        m = paddle.metric.Accuracy(name='my_acc', topk=k)
+        correct = m.compute(x, y)
+
+        acc_np = accuracy(x_np, y_np, k)
+        acc_np = acc_np[0] if len(acc_np) == 1 else acc_np
+
+        # check shape and results
+        self.assertEqual(correct.shape, list(x_np.shape)[:-1] + [max(k)])
+        self.assertEqual(m.update(correct), acc_np)
+        self.assertEqual(m.accumulate(), acc_np)
+
+    def test_3d(self):
+        x_np = np.random.rand(2, 3, 4)
+        y_np = np.random.randint(4, size=(2, 3, 1))
+        self.compare(x_np, y_np)
+
+    def test_one_hot(self):
+        x_np = np.random.rand(2, 3, 4)
+        y_np = np.random.randint(4, size=(2, 3))
+        y_one_hot_np = one_hot(y_np, 4)
+        self.compare(x_np, y_one_hot_np, (1, 2))
+
 
 class TestAccuracyDynamic(unittest.TestCase):
     def setUp(self):
@@ -148,6 +182,8 @@ class TestAccuracyStatic(TestAccuracyDynamic):
         self.squeeze_label = True
 
     def test_main(self):
+        paddle.enable_static()
+
         main_prog = fluid.Program()
         startup_prog = fluid.Program()
         main_prog.random_seed = 1024
@@ -178,6 +214,8 @@ class TestAccuracyStatic(TestAccuracyDynamic):
             assert np.sum(acc.total) == 0
             assert np.sum(acc.count) == 0
 
+        paddle.disable_static()
+
 
 class TestAccuracyStaticMultiTopk(TestAccuracyStatic):
     def setUp(self):
@@ -190,7 +228,6 @@ class TestAccuracyStaticMultiTopk(TestAccuracyStatic):
 
 class TestPrecision(unittest.TestCase):
     def test_1d(self):
-        paddle.disable_static()
 
         x = np.array([0.1, 0.5, 0.6, 0.7])
         y = np.array([1, 0, 1, 1])
@@ -206,11 +243,7 @@ class TestPrecision(unittest.TestCase):
         r = m.accumulate()
         self.assertAlmostEqual(r, 4. / 6.)
 
-        paddle.enable_static()
-
     def test_2d(self):
-        paddle.disable_static()
-
         x = np.array([0.1, 0.5, 0.6, 0.7]).reshape(-1, 1)
         y = np.array([1, 0, 1, 1]).reshape(-1, 1)
 
@@ -231,13 +264,9 @@ class TestPrecision(unittest.TestCase):
         self.assertEqual(m.fp, 0.0)
         self.assertEqual(m.accumulate(), 0.0)
 
-        paddle.enable_static()
-
 
 class TestRecall(unittest.TestCase):
     def test_1d(self):
-        paddle.disable_static()
-
         x = np.array([0.1, 0.5, 0.6, 0.7])
         y = np.array([1, 0, 1, 1])
 
@@ -257,12 +286,10 @@ class TestRecall(unittest.TestCase):
         self.assertEqual(m.tp, 0.0)
         self.assertEqual(m.fn, 0.0)
         self.assertEqual(m.accumulate(), 0.0)
-        paddle.enable_static()
 
 
 class TestAuc(unittest.TestCase):
     def test_auc_numpy(self):
-        paddle.disable_static()
         x = np.array([[0.78, 0.22], [0.62, 0.38], [0.55, 0.45], [0.30, 0.70],
                       [0.14, 0.86], [0.59, 0.41], [0.91, 0.08], [0.16, 0.84]])
         y = np.array([[0], [1], [1], [0], [1], [0], [0], [1]])
@@ -274,10 +301,7 @@ class TestAuc(unittest.TestCase):
         m.reset()
         self.assertEqual(m.accumulate(), 0.0)
 
-        paddle.enable_static()
-
     def test_auc_tensor(self):
-        paddle.disable_static()
         x = paddle.to_tensor(
             np.array([[0.78, 0.22], [0.62, 0.38], [0.55, 0.45], [0.30, 0.70],
                       [0.14, 0.86], [0.59, 0.41], [0.91, 0.08], [0.16, 0.84]]))
@@ -290,8 +314,6 @@ class TestAuc(unittest.TestCase):
         m.reset()
         self.assertEqual(m.accumulate(), 0.0)
 
-        paddle.enable_static()
-
 
 if __name__ == '__main__':
     unittest.main()