remove_leaky_relu in nn.py under fluid (#47901)

python/paddle/fluid/layers/nn.py

@@ -121,7 +121,6 @@ __all__ = [
     'pow',
     'prelu',
     'brelu',
-    'leaky_relu',
     'flatten',
     'pad2d',
     'unique',
@@ -8238,33 +8237,6 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
     return out
 
 
-@deprecated(since="2.0.0", update_to="paddle.nn.functional.leaky_relu")
-@templatedoc()
-def leaky_relu(x, alpha=0.02, name=None):
-    """
-    ${comment}
-    Args:
-        x(${x_type}): ${x_comment}
-        alpha(${alpha_type}|0.02): ${alpha_comment}
-        name(str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
-
-    Returns:
-        output(${out_type}): ${out_comment}
-
-    Examples:
-
-        .. code-block:: python
-
-            import paddle
-
-            x = paddle.to_tensor([[-1, 2], [3, -4]], dtype='float32')
-            y = paddle.fluid.layers.leaky_relu(x, alpha=0.1)
-            print(y) # [[-0.1, 2], [3, -0.4]]
-
-    """
-    return paddle.nn.functional.leaky_relu(x, alpha, name)
-
-
 def flatten(x, axis=1, name=None):
     r"""
     **Flatten op**

python/paddle/fluid/tests/unittests/dygraph_to_static/darknet.py

@@ -65,7 +65,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
         out = self.conv(inputs)
         out = self.batch_norm(out)
         if self.act == 'leaky':
-            out = fluid.layers.leaky_relu(x=out, alpha=0.1)
+            out = paddle.nn.functional.leaky_relu(out, 0.1)
         return out
 
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_cycle_gan.py

@@ -396,7 +396,7 @@ class conv2d(fluid.dygraph.Layer):
         if self.norm:
             conv = self.bn(conv)
         if self.relu:
-            conv = fluid.layers.leaky_relu(conv, alpha=self.relufactor)
+            conv = paddle.nn.functional.leaky_relu(conv, self.relufactor)
         return conv
 
 
@@ -468,7 +468,7 @@ class DeConv2D(fluid.dygraph.Layer):
         if self.norm:
             conv = self.bn(conv)
         if self.relu:
-            conv = fluid.layers.leaky_relu(conv, alpha=self.relufactor)
+            conv = paddle.nn.functional.leaky_relu(conv, self.relufactor)
         return conv
 
 

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_elt_act_fuse_pass.py

@@ -84,7 +84,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_LeakyRelu(
     def set_params(self):
         self.operand = fluid.layers.elementwise_add
         self.act_alpha = 0.2
-        self.act = fluid.layers.leaky_relu
+        self.act = paddle.nn.functional.leaky_relu
 
 
 class ElementwiseActivationMkldnnFusePassTest_Add_Swish(
@@ -184,7 +184,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_LeakyRelu(
     def set_params(self):
         self.operand = fluid.layers.elementwise_sub
         self.act_alpha = 0.2
-        self.act = fluid.layers.leaky_relu
+        self.act = paddle.nn.functional.leaky_relu
 
 
 class ElementwiseActivationMkldnnFusePassTest_Sub_Swish(
@@ -276,7 +276,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_LeakyRelu(
     def set_params(self):
         self.operand = fluid.layers.elementwise_mul
         self.act_alpha = 0.2
-        self.act = fluid.layers.leaky_relu
+        self.act = paddle.nn.functional.leaky_relu
 
 
 class ElementwiseActivationMkldnnFusePassTest_Mul_Swish(

python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py

@@ -67,7 +67,7 @@ class TensorRTSubgraphPassActivationTest(InferencePassTest):
 
 class TensorRTSubgraphPassLeakyReluTest(TensorRTSubgraphPassActivationTest):
     def append_act(self, x):
-        return fluid.layers.leaky_relu(x)
+        return paddle.nn.functional.leaky_relu(x)
 
 
 class TensorRTSubgraphPassRelu6Test(TensorRTSubgraphPassActivationTest):

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

@@ -217,7 +217,7 @@ class TestLeakyReluDoubleGradCheck(unittest.TestCase):
         x = layers.data('x', shape, False, dtype)
         x.persistable = True
 
-        y = layers.leaky_relu(x, alpha=alpha)
+        y = paddle.nn.functional.leaky_relu(x, alpha)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         x_arr[np.abs(x_arr) < 0.005] = 0.02
 

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

@@ -1689,7 +1689,6 @@ class TestLeakyRelu_ZeroDim(TestLeakyRelu):
 
 class TestLeakyReluAPI(unittest.TestCase):
     # test paddle.nn.LeakyReLU, paddle.nn.functional.leaky_relu,
-    # fluid.layers.leaky_relu
     def setUp(self):
         np.random.seed(1024)
         self.x_np = np.random.uniform(-1, 1, [10, 12]).astype('float32')

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

@@ -365,7 +365,7 @@ class TestDygraphDoubleGrad(TestCase):
         x.stop_gradient = False
 
         alpha = 0.2
-        y = fluid.layers.leaky_relu(x, alpha=alpha)
+        y = paddle.nn.functional.leaky_relu(x, alpha)
         y = y * y
         z = y * y
 

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

@@ -163,7 +163,7 @@ class Conv2DLayer(fluid.dygraph.Layer):
             conv = self._norm(conv)
 
         if self.relufactor is not None:
-            conv = fluid.layers.leaky_relu(conv, alpha=self.relufactor)
+            conv = paddle.nn.functional.leaky_relu(conv, self.relufactor)
 
         return conv
 
@@ -205,7 +205,7 @@ class Deconv2DLayer(fluid.dygraph.Layer):
             deconv = self._norm(deconv)
 
         if self.relufactor is not None:
-            deconv = fluid.layers.leaky_relu(deconv, alpha=self.relufactor)
+            deconv = paddle.nn.functional.leaky_relu(deconv, self.relufactor)
 
         return deconv
 

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

@@ -78,7 +78,7 @@ class TestInplaceANBOpTraining(unittest.TestCase):
                         in_place=inplace,
                     )
                     if activation == 'leaky_relu':
-                        bn = fluid.layers.leaky_relu(bn, alpha)
+                        bn = paddle.nn.functional.leaky_relu(bn, alpha)
                     if activation == 'elu':
                         bn = paddle.nn.functional.elu(bn, alpha)
 

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

@@ -164,7 +164,7 @@ class TestDygraphDoubleGrad(TestCase):
         x.stop_gradient = False
 
         alpha = 0.2
-        y = fluid.layers.leaky_relu(x, alpha=alpha)
+        y = paddle.nn.functional.leaky_relu(x, alpha)
         y = y * y
         z = y * y