Fix syncbn

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

@@ -248,7 +248,7 @@ def test_convert(self):
                         isinstance(model[idx], paddle.nn.SyncBatchNorm), True)
 
 
-class TestConvertSyncBatchNormCase2(unittest.TestCase):
+class TestConvertSyncBatchNormCast1(unittest.TestCase):
     def test_convert(self):
         if not core.is_compiled_with_cuda():
             return
@@ -277,5 +277,70 @@ def forward(self, x):
         self.assertEqual(len(compare_model.sublayers()), len(model.sublayers()))
 
 
+class TestConvertSyncBatchNormCase2(unittest.TestCase):
+    def test_convert(self):
+        if not core.is_compiled_with_cuda():
+            return
+
+        with fluid.dygraph.guard(fluid.CUDAPlace(0)):
+
+            class SyBNNet(paddle.nn.Layer):
+                def __init__(self, in_ch=3, out_ch=3, dirate=1):
+                    super(SyBNNet, self).__init__()
+                    self.bn_s1 = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(
+                        paddle.nn.BatchNorm3D(
+                            out_ch,
+                            weight_attr=paddle.ParamAttr(
+                                regularizer=paddle.regularizer.L2Decay(0.))))
+                    self.bn_s2 = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(
+                        paddle.nn.BatchNorm3D(
+                            out_ch, data_format='NDHWC'))
+
+                def forward(self, x):
+                    x = self.bn_s1(x)
+                    out = paddle.sum(paddle.abs(self.bn_s2(x)))
+                    return out
+
+            class BNNet(paddle.nn.Layer):
+                def __init__(self, in_ch=3, out_ch=3, dirate=1):
+                    super(BNNet, self).__init__()
+                    self.bn_s1 = paddle.nn.BatchNorm3D(
+                        out_ch,
+                        weight_attr=paddle.ParamAttr(
+                            regularizer=paddle.regularizer.L2Decay(0.)))
+                    self.bn_s2 = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(
+                        paddle.nn.BatchNorm3D(
+                            out_ch, data_format='NDHWC'))
+
+                def forward(self, x):
+                    x = self.bn_s1(x)
+                    out = paddle.sum(paddle.abs(self.bn_s2(x)))
+                    return out
+
+            bn_model = BNNet()
+            sybn_model = SyBNNet()
+            np.random.seed(10)
+            data = np.random.random([3, 3, 3, 3, 3]).astype('float32')
+            x = paddle.to_tensor(data)
+            bn_out = bn_model(x)
+            sybn_out = sybn_model(x)
+            self.assertTrue(
+                np.allclose(bn_out.numpy(), sybn_out.numpy()),
+                "Output has diff. \n" + "\nBN     " + str(bn_out.numpy()) + "\n"
+                + "Sync BN " + str(sybn_out.numpy()))
+
+
+class TestDygraphSyncBatchNormDataFormatError(unittest.TestCase):
+    def test_errors(self):
+        if not core.is_compiled_with_cuda():
+            return
+
+        with fluid.dygraph.guard(fluid.CUDAPlace(0)):
+            my_sync_batch_norm = paddle.nn.SyncBatchNorm(10, data_format='CN')
+            data = np.random.random([3, 3, 3]).astype('float32')
+            x = paddle.to_tensor(data)
+            self.assertRaises(ValueError, my_sync_batch_norm, x)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/layer/norm.py

@@ -1057,7 +1057,18 @@ def __init__(self,
               self).__init__(num_features, momentum, epsilon, weight_attr,
                              bias_attr, data_format, None, name)
 
+    def _check_data_format(self):
+        if self._data_format in ['NCHW', 'NCDHW', 'NC', 'NCL']:
+            self._data_format = 'NCHW'
+        elif self._data_format in ["NHWC", "NDHWC", 'NLC']:
+            self._data_format = 'NHWC'
+        else:
+            raise ValueError(
+                'expected \'NCDHW\', \'NDHWC\', \'NCL\', \'NLC\', \'NC\', \'NCHW\', \'NHWC\' for data_format'
+            )
+
     def forward(self, x):
+        self._check_data_format()
         # create output
         # mean and mean_out share the same memory
         mean_out = self._mean
@@ -1142,11 +1153,12 @@ def convert_sync_batchnorm(cls, layer):
         """
         layer_output = layer
         if isinstance(layer, _BatchNormBase):
-            if layer._weight_attr != None and not isinstance(layer._weight_attr,
-                                                             bool):
+            if layer._weight_attr != None and not isinstance(
+                    layer._weight_attr,
+                    bool) and layer._weight_attr.name != None:
                 layer._weight_attr.name = layer._weight_attr.name + '_sync'
-            if layer._bias_attr != None and not isinstance(layer._weight_attr,
-                                                           bool):
+            if layer._bias_attr != None and not isinstance(
+                    layer._bias_attr, bool) and layer._bias_attr.name != None:
                 layer._bias_attr.name = layer._bias_attr.name + '_sync'
 
             layer_output = SyncBatchNorm(layer._num_features, layer._momentum,