remove nets.py in fluid

python/paddle/fluid/tests/book/notest_understand_sentiment.py

@@ -23,21 +23,23 @@
 import paddle
 import paddle.fluid as fluid
 
+from ..unittests import nets
+
 
 def convolution_net(
     data, label, input_dim, class_dim=2, emb_dim=32, hid_dim=32
 ):
     emb = fluid.layers.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True
     )
-    conv_3 = fluid.nets.sequence_conv_pool(
+    conv_3 = nets.sequence_conv_pool(
         input=emb,
         num_filters=hid_dim,
         filter_size=3,
         act="tanh",
         pool_type="sqrt",
     )
-    conv_4 = fluid.nets.sequence_conv_pool(
+    conv_4 = nets.sequence_conv_pool(
         input=emb,
         num_filters=hid_dim,
         filter_size=4,

python/paddle/fluid/tests/book/test_image_classification.py

@@ -23,6 +23,7 @@
 
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 
 paddle.enable_static()
 
@@ -74,7 +75,7 @@ def layer_warp(block_func, input, ch_in, ch_out, count, stride):
 
 def vgg16_bn_drop(input):
     def conv_block(input, num_filter, groups, dropouts):
-        return fluid.nets.img_conv_group(
+        return nets.img_conv_group(
             input=input,
             pool_size=2,
             pool_stride=2,

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -22,6 +22,7 @@
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+import paddle.fluid.tests.unittests.nets as nets
 
 paddle.enable_static()
 
@@ -45,7 +46,7 @@ def mlp(img, label):
 
 
 def conv_net(img, label):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=img,
         filter_size=5,
         num_filters=20,
@@ -54,7 +55,7 @@ def conv_net(img, label):
         act="relu",
     )
     conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -23,10 +23,11 @@
 import paddle.fluid as fluid
 import paddle.fluid.framework as framework
 import paddle.fluid.layers as layers
-import paddle.fluid.nets as nets
 from paddle.fluid.executor import Executor
 from paddle.fluid.optimizer import SGDOptimizer
 
+from ..unittests import nets
+
 paddle.enable_static()
 
 IS_SPARSE = True

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

@@ -19,6 +19,7 @@
 import paddle
 import paddle.distributed.fleet as fleet
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 
 paddle.enable_static()
 
@@ -31,7 +32,7 @@
 
 
 def cnn_model(data):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=data,
         filter_size=5,
         num_filters=20,
@@ -42,7 +43,7 @@ def cnn_model(data):
             initializer=paddle.nn.initializer.Constant(value=0.01)
         ),
     )
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

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

@@ -20,6 +20,7 @@
 import paddle.distributed.fleet as fleet
 import paddle.distributed.fleet.base.role_maker as role_maker
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 
 paddle.enable_static()
 
@@ -32,7 +33,7 @@
 
 
 def cnn_model(data):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=data,
         filter_size=5,
         num_filters=20,
@@ -43,7 +44,7 @@ def cnn_model(data):
             initializer=paddle.nn.initializer.Constant(value=0.01)
         ),
     )
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

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

@@ -20,6 +20,7 @@
 import paddle.distributed.fleet as fleet
 import paddle.distributed.fleet.base.role_maker as role_maker
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 
 paddle.enable_static()
 
@@ -32,7 +33,7 @@
 
 
 def cnn_model(data):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=data,
         filter_size=5,
         num_filters=20,
@@ -43,7 +44,7 @@ def cnn_model(data):
             initializer=paddle.nn.initializer.Constant(value=0.01)
         ),
     )
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

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

@@ -18,6 +18,7 @@
 
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 from paddle.incubate.distributed.fleet.collective import fleet
 
 paddle.enable_static()
@@ -31,7 +32,7 @@
 
 
 def cnn_model(data):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=data,
         filter_size=5,
         num_filters=20,
@@ -42,7 +43,7 @@ def cnn_model(data):
             initializer=paddle.nn.initializer.Constant(value=0.01)
         ),
     )
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

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

@@ -18,6 +18,7 @@
 
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 
 paddle.enable_static()
 
@@ -30,7 +31,7 @@
 
 
 def cnn_model(data):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=data,
         filter_size=5,
         num_filters=20,
@@ -41,7 +42,7 @@ def cnn_model(data):
             initializer=paddle.nn.initializer.Constant(value=0.01)
         ),
     )
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

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

@@ -22,6 +22,8 @@
 import paddle
 import paddle.fluid as fluid
 
+from . import nets
+
 DTYPE = "float32"
 VOCAB_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/imdb.vocab'
 VOCAB_MD5 = '23c86a0533c0151b6f12fa52b106dcc2'
@@ -63,7 +65,7 @@ def conv_net(
         ),
     )
 
-    conv_3 = fluid.nets.sequence_conv_pool(
+    conv_3 = nets.sequence_conv_pool(
         input=emb,
         num_filters=num_filters,
         filter_size=window_size,

python/paddle/fluid/nets.py → python/paddle/fluid/tests/unittests/nets.py

@@ -13,15 +13,12 @@
 # limitations under the License.
 
 import paddle
-from . import layers
-from .data_feeder import check_variable_and_dtype, convert_dtype
-from ..utils import deprecated
-import paddle
+
+from ...data_feeder import check_variable_and_dtype, convert_dtype
 
 __all__ = [
     "simple_img_conv_pool",
     "sequence_conv_pool",
-    "glu",
     "scaled_dot_product_attention",
     "img_conv_group",
 ]
@@ -356,60 +353,11 @@ def sequence_conv_pool(
     return pool_out
 
 
-@deprecated(since="2.0.0", update_to="paddle.nn.functional.glu")
-def glu(input, dim=-1):
-    r"""
-        :api_attr: Static Graph
-
-    The Gated Linear Units(GLU) composed by :ref:`api_fluid_layers_split` ,
-    :ref:`api_fluid_layers_sigmoid`  and :ref:`api_fluid_layers_elementwise_mul` .
-    Specifically, GLU will plit the input into two equal-sized parts,
-    :math:`a` and :math:`b`, along the given dimension and then compute as
-    following:
-
-        .. math::
-
-            {GLU}(a, b)= a \otimes \sigma(b)
-
-    Refer to `Language Modeling with Gated Convolutional Networks
-    <https://arxiv.org/pdf/1612.08083.pdf>`_.
-
-    Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
-                          The supported data types include float32, float64
-                          and float16 (only for GPU).
-        dim (int, optional): The dimension along which to split. If :math:`dim < 0`, the
-            dimension to split along is :math:`rank(input) + dim`. Default -1.
-
-    Returns:
-        Variable: Variable with half the size and same data type of input.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            import paddle
-            paddle.enable_static()
-
-            data = fluid.data(
-                name="words", shape=[-1, 6, 3, 9], dtype="float32")
-            # shape of output: [-1, 3, 3, 9]
-            output = fluid.nets.glu(input=data, dim=1)
-    """
-    check_variable_and_dtype(
-        input, 'input', ['float16', 'float32', 'float64'], "glu"
-    )
-    a, b = paddle.split(input, num_or_sections=2, axis=dim)
-    act_b = paddle.nn.functional.sigmoid(x=b)
-    out = paddle.multiply(x=a, y=act_b)
-    return out
-
-
 def scaled_dot_product_attention(
     queries, keys, values, num_heads=1, dropout_rate=0.0
 ):
     r"""
-	:api_attr: Static Graph
+    :api_attr: Static Graph
 
     This interface Multi-Head Attention using scaled dot product.
     Attention mechanism can be seen as mapping a query and a set of key-value

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

@@ -20,6 +20,7 @@
 
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 
 BATCH_SIZE = 64
 
@@ -40,7 +41,7 @@ def convolutional_neural_network(use_py_reader):
                 use_double_buffer=False,
             )
 
-        conv_pool_1 = fluid.nets.simple_img_conv_pool(
+        conv_pool_1 = nets.simple_img_conv_pool(
             input=img,
             filter_size=5,
             num_filters=20,
@@ -49,7 +50,7 @@ def convolutional_neural_network(use_py_reader):
             act="relu",
         )
         conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
-        conv_pool_2 = fluid.nets.simple_img_conv_pool(
+        conv_pool_2 = nets.simple_img_conv_pool(
             input=conv_pool_1,
             filter_size=5,
             num_filters=50,

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

@@ -18,6 +18,7 @@
 
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.tests.unittests.nets as nets
 from paddle.fluid import core
 
 SEED = 1
@@ -29,15 +30,15 @@
 # random seed must set before configuring the network.
 # fluid.default_startup_program().random_seed = SEED
 def cnn_model(data):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+    conv_pool_1 = nets.simple_img_conv_pool(
         input=data,
         filter_size=5,
         num_filters=20,
         pool_size=2,
         pool_stride=2,
         act="relu",
     )
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,

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

@@ -41,7 +41,7 @@ def setUp(self):
     def check_identity(self, place):
         with dg.guard(place):
             x_var = dg.to_variable(self.x)
-            y_var = fluid.nets.glu(x_var, self.dim)
+            y_var = paddle.nn.functional.glu(x_var, self.dim)
             y_np = y_var.numpy()
 
         np.testing.assert_allclose(y_np, self.out)

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

@@ -16,7 +16,7 @@
 
 import paddle
 import paddle.fluid as fluid
-import paddle.fluid.nets as nets
+import paddle.fluid.tests.unittests.nets as nets
 from paddle.fluid.framework import Program
 
 

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

@@ -23,7 +23,7 @@
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
-import paddle.fluid.nets as nets
+import paddle.fluid.tests.unittests.nets as nets
 import paddle.nn.functional as F
 from paddle.fluid import core
 from paddle.fluid.dygraph import base, to_variable