【fluid api clear】Move batch norm1

python/paddle/fluid/contrib/layers/nn.py

@@ -1963,8 +1963,11 @@ def fused_bn_add_act(
     Examples:
             .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
 
+            paddle.enable_static()
+            # required: gpu
             def build_program(main_program, startup_program):
                 with fluid.program_guard(main_program, startup_program):
                     x = fluid.layers.data(name='x', shape=[1, 28, 28], dtype='float32')
@@ -1987,7 +1990,7 @@ def build_program(main_program, startup_program):
                         act=None,
                         bias_attr=False,
                         data_format='NHWC')
-                    bn = fluid.layers.batch_norm(
+                    bn = paddle.static.nn.batch_norm(
                         input=conv1_1,
                         act=None,
                         data_layout='NHWC')

python/paddle/fluid/contrib/slim/tests/test_graph.py

@@ -37,7 +37,7 @@ def conv_block():
         pool_stride=2,
         act="relu",
     )
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,

python/paddle/fluid/contrib/slim/tests/test_quantization_mkldnn_pass.py

@@ -37,7 +37,7 @@ def conv_net(img, label):
         pool_stride=2,
         act="relu",
     )
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,

python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py

@@ -57,7 +57,7 @@ def conv_bn_layer(
             act=None,
             bias_attr=bias_attr,
         )
-        return fluid.layers.batch_norm(input=tmp, act=act)
+        return paddle.static.nn.batch_norm(input=tmp, act=act)
 
     data = fluid.layers.data(
         name='image',
@@ -102,7 +102,7 @@ def conv_net(img, label, quant_skip_pattern):
         pool_type='max',
         act="relu",
     )
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
@@ -712,7 +712,7 @@ def conv_bn_layer(
             act=None,
             bias_attr=bias_attr,
         )
-        return fluid.layers.batch_norm(input=tmp, act=act)
+        return paddle.static.nn.batch_norm(input=tmp, act=act)
 
     data1 = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
     data2 = fluid.layers.data(

python/paddle/fluid/contrib/slim/tests/test_quantization_scale_pass.py

@@ -43,7 +43,7 @@ def conv_net(img, label):
         pool_type='max',
         act="relu",
     )
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,

python/paddle/fluid/contrib/slim/tests/test_user_defined_quantization.py

@@ -45,7 +45,7 @@ def conv_net(img, label):
         pool_type='max',
         act="relu",
     )
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,

python/paddle/fluid/contrib/tests/test_image_classification_fp16.py

@@ -41,7 +41,7 @@ def conv_bn_layer(
             act=None,
             bias_attr=bias_attr,
         )
-        return fluid.layers.batch_norm(input=tmp, act=act)
+        return paddle.static.nn.batch_norm(input=tmp, act=act)
 
     def shortcut(input, ch_in, ch_out, stride):
         if ch_in != ch_out:
@@ -97,7 +97,7 @@ def conv_block(input, num_filter, groups, dropouts):
 
     drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
     fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
-    bn = fluid.layers.batch_norm(input=fc1, act='relu')
+    bn = paddle.static.nn.batch_norm(input=fc1, act='relu')
     drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
     fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
     return fc2

python/paddle/fluid/contrib/tests/test_multi_precision_fp16_train.py

@@ -59,7 +59,7 @@ def conv_bn_layer(
             act=None,
             bias_attr=bias_attr,
         )
-        return fluid.layers.batch_norm(input=tmp, act=act)
+        return paddle.static.nn.batch_norm(input=tmp, act=act)
 
     def shortcut(input, ch_in, ch_out, stride):
         if ch_in != ch_out:

python/paddle/fluid/contrib/tests/test_quantize_transpiler.py

@@ -48,7 +48,7 @@ def conv_bn_layer(
             act=None,
             bias_attr=bias_attr,
         )
-        return fluid.layers.batch_norm(input=tmp, act=act)
+        return paddle.static.nn.batch_norm(input=tmp, act=act)
 
     data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
@@ -72,7 +72,7 @@ def conv_net(img, label):
         pool_stride=2,
         act="relu",
     )
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,