refine APIs: brelu, hardsigmoid, hardswish, maxout (#27658)

paddle/fluid/operators/maxout_op.cc

@@ -83,6 +83,18 @@ class MaxOutOp : public framework::OperatorWithKernel {
                                      "Attr(groups) of Op(maxout) should be "
                                      "larger than 1. But received %d.",
                                      groups));
+    PADDLE_ENFORCE_EQ(
+        axis == 1 || axis == -1 || axis == 3, true,
+        platform::errors::InvalidArgument(
+            "axis only supported 1, -1 or 3, but recevied axis is: %d", axis));
+    PADDLE_ENFORCE_EQ(in_x_dims.size(), 4,
+                      platform::errors::InvalidArgument(
+                          "x's dims should be 4, but received x's dims is: %d",
+                          in_x_dims.size()));
+
+    if (axis < 0) {
+      axis += in_x_dims.size();
+    }
     PADDLE_ENFORCE_EQ(
         in_x_dims[axis] % groups, 0,
         platform::errors::InvalidArgument(

paddle/fluid/operators/maxout_op.h

@@ -31,6 +31,9 @@ class MaxOutKernel : public framework::OpKernel<T> {
     Tensor* out = context.Output<Tensor>("Out");
     int groups = context.template Attr<int>("groups");
     int axis = context.template Attr<int>("axis");
+    if (axis < 0) {
+      axis += in_x->dims().size();
+    }
 
     math::MaxOutFunctor<DeviceContext, T> maxout_forward;
     maxout_forward(context.template device_context<DeviceContext>(), *in_x, out,
@@ -49,6 +52,10 @@ class MaxOutGradKernel : public framework::OpKernel<T> {
     Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
     int groups = context.template Attr<int>("groups");
     int axis = context.template Attr<int>("axis");
+    if (axis < 0) {
+      axis += in_x->dims().size();
+    }
+
     auto& device_ctx = context.template device_context<DeviceContext>();
     math::SetConstant<DeviceContext, T> zero;
     if (in_x_grad) {

python/paddle/fluid/layers/nn.py

@@ -9592,10 +9592,6 @@ def stanh(x, scale_a=0.67, scale_b=1.7159, name=None):
 @templatedoc()
 def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
     """
-    :alias_main: paddle.nn.functional.hard_sigmoid
-	:alias: paddle.nn.functional.hard_sigmoid,paddle.nn.functional.activation.hard_sigmoid
-	:old_api: paddle.fluid.layers.hard_sigmoid
-
     ${comment}
     Parameters:
         x (${x_type}): ${x_comment}
@@ -9613,9 +9609,15 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
+
             data = fluid.layers.fill_constant(shape=[3, 2], value=0.5, dtype='float32') # [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]
             result = fluid.layers.hard_sigmoid(data) # [[0.6, 0.6], [0.6, 0.6], [0.6, 0.6]]
     """
+    if in_dygraph_mode():
+        return core.ops.hard_sigmoid(x, 'slope', slope, 'offset', offset)
+
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                              'hard_sigmoid')
 
@@ -9802,10 +9804,6 @@ def prelu(x, mode, param_attr=None, name=None):
 @templatedoc()
 def brelu(x, t_min=0.0, t_max=24.0, name=None):
     """
-    :alias_main: paddle.nn.functional.brelu
-	:alias: paddle.nn.functional.brelu,paddle.nn.functional.activation.brelu
-	:old_api: paddle.fluid.layers.brelu
-
     ${comment}
     Args:
         x(${x_type}): ${x_comment}
@@ -9821,7 +9819,9 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
     .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
             import numpy as np
+            paddle.enable_static()
 
             input_brelu = np.array([[-1,6],[1,15.6]])
             with fluid.dygraph.guard():
@@ -9831,6 +9831,9 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
                 #[[ 1.  6.]
                 #[ 1. 10.]]
     """
+    if in_dygraph_mode():
+        return core.ops.brelu(x, 't_min', t_min, 't_max', t_max)
+
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'brelu')
 
     helper = LayerHelper('brelu', **locals())
@@ -12564,13 +12567,10 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
     return out
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.maxout")
 @templatedoc()
 def maxout(x, groups, name=None, axis=1):
     """
-    :alias_main: paddle.nn.functional.maxout
-	:alias: paddle.nn.functional.maxout,paddle.nn.functional.activation.maxout
-	:old_api: paddle.fluid.layers.maxout
-
     ${comment}
 
     Args:
@@ -12592,31 +12592,16 @@ def maxout(x, groups, name=None, axis=1):
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
+            paddle.enable_static()
+
             input = fluid.data(
                 name='data',
                 shape=[None, 256, 32, 32],
                 dtype='float32')
             out = fluid.layers.maxout(input, groups=2)
     """
-    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'maxout')
-
-    helper = LayerHelper("maxout", **locals())
-    if axis not in [1, -1, 3]:
-        raise ValueError(
-            "Attr(axis) should be 1 when data format is NCHW, -1 or 3 when data format is NHWC. Received "
-            "Attr(axis): %s." % str(axis))
-    if axis == -1:
-        axis = 3
-
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-
-    helper.append_op(
-        type="maxout",
-        inputs={"X": x},
-        attrs={"groups": groups,
-               "axis": axis},
-        outputs={"Out": out})
-    return out
+    return paddle.nn.functional.maxout(**locals())
 
 
 def space_to_depth(x, blocksize, name=None):
@@ -14877,10 +14862,6 @@ def shard_index(input, index_num, nshards, shard_id, ignore_value=-1):
 @templatedoc()
 def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
     """
-    :alias_main: paddle.nn.functional.hard_swish
-	:alias: paddle.nn.functional.hard_swish,paddle.nn.functional.activation.hard_swish
-	:old_api: paddle.fluid.layers.hard_swish
-
     This operator implements the hard_swish activation function.
     Hard_swish is proposed in MobileNetV3, and performs better in computational stability and efficiency compared to swish function.
     For more details please refer to: https://arxiv.org/pdf/1905.02244.pdf
@@ -14911,7 +14892,9 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
     .. code-block:: python
 
         import paddle.fluid as fluid
+        import paddle
         import numpy as np
+        paddle.enable_static()
 
         DATATYPE='float32'
 
@@ -14926,6 +14909,10 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
         out, = exe.run(feed={'x':x_data}, fetch_list=[y.name])
         print(out)  # [[0.66666667, 1.66666667,3., 4.]]
     """
+    if in_dygraph_mode():
+        return core.ops.hard_swish(x, 'threshold', threshold, 'scale', scale,
+                                   'offset', offset)
+
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                              'hard_swish')
 

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

@@ -1657,21 +1657,6 @@ class TestLayer(LayerTest):
         with self.assertRaises(TypeError):
             layers.eye(num_rows=3, batch_shape=[-1])
 
-    def test_hard_swish(self):
-        with self.static_graph():
-            t = layers.data(name='t', shape=[3, 3], dtype='float32')
-            ret = layers.hard_swish(t)
-            static_ret = self.get_static_graph_result(
-                feed={'t': np.ones(
-                    [3, 3], dtype='float32')}, fetch_list=[ret])[0]
-
-        with self.dynamic_graph():
-            t = np.ones([3, 3], dtype='float32')
-            dy_ret = layers.hard_swish(base.to_variable(t))
-            dy_ret_rlt = dy_ret.numpy()
-
-        self.assertTrue(np.allclose(static_ret, dy_ret_rlt))
-
     def test_while_loop(self):
         with self.static_graph():
             i = layers.fill_constant(shape=[1], dtype='int64', value=0)
@@ -2563,13 +2548,6 @@ class TestBook(LayerTest):
             output = layers.l2_normalize(x, axis=1)
             return output
 
-    def make_maxout(self):
-        with program_guard(fluid.default_main_program(),
-                           fluid.default_startup_program()):
-            data = self._get_data(name='x', shape=[8, 6, 6], dtype="float32")
-            output = layers.maxout(x=data, groups=2)
-            return (output)
-
     def make_crop(self):
         with program_guard(fluid.default_main_program(),
                            fluid.default_startup_program()):
@@ -2656,13 +2634,6 @@ class TestBook(LayerTest):
                 name='prelu')
             return (out)
 
-    def make_brelu(self):
-        with program_guard(fluid.default_main_program(),
-                           fluid.default_startup_program()):
-            input = self._get_data(name="input", shape=[16], dtype="float32")
-            out = layers.brelu(input, t_min=1.0, t_max=20.0, name='brelu')
-            return (out)
-
     def make_soft_relu(self):
         with program_guard(fluid.default_main_program(),
                            fluid.default_startup_program()):

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

@@ -16,32 +16,43 @@ from __future__ import print_function
 
 import unittest
 import numpy as np
+import paddle
 import paddle.fluid as fluid
-from paddle.fluid import Program, program_guard
 import paddle.fluid.core as core
+import paddle.nn.functional as F
 from op_test import OpTest
 
+paddle.enable_static()
+np.random.seed(1)
 
-def maxout_forward_naive(input, groups, channel_axis):
-    s0, s1, s2, s3 = input.shape
-    if channel_axis == 3:
-        return np.ndarray([s0, s1, s2, s3 // groups, groups], \
-            buffer = input, dtype=input.dtype).max(axis=(4))
-    return np.ndarray([s0, s1 // groups, groups, s2, s3], \
-        buffer = input, dtype=input.dtype).max(axis=(2))
+
+def maxout_forward_naive(x, groups, channel_axis):
+    s0, s1, s2, s3 = x.shape
+    if channel_axis == 1:
+        return np.ndarray([s0, s1 // groups, groups, s2, s3], \
+            buffer = x, dtype=x.dtype).max(axis=2)
+    return np.ndarray([s0, s1, s2, s3 // groups, groups], \
+        buffer = x, dtype=x.dtype).max(axis=4)
 
 
 class TestMaxOutOp(OpTest):
     def setUp(self):
         self.op_type = "maxout"
-        self.init_test_case()
-        input = np.random.random(self.shape)
-        output = self.MaxOut_forward_naive(input, self.groups, self.axis)
+        self.dtype = 'float64'
+        self.shape = [3, 6, 2, 4]
+        self.groups = 2
+        self.axis = 1
+        self.set_attrs()
+
+        x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        out = maxout_forward_naive(x, self.groups, self.axis)
 
-        self.inputs = {'X': input}
+        self.inputs = {'X': x}
         self.attrs = {'groups': self.groups, 'axis': self.axis}
+        self.outputs = {'Out': out}
 
-        self.outputs = {'Out': output}
+    def set_attrs(self):
+        pass
 
     def test_check_output(self):
         self.check_output()
@@ -49,65 +60,89 @@ class TestMaxOutOp(OpTest):
     def test_check_grad(self):
         self.check_grad(['X'], 'Out')
 
-    def init_test_case(self):
-        self.MaxOut_forward_naive = maxout_forward_naive
-        self.shape = [100, 6, 2, 2]
-        self.groups = 2
-        self.axis = 1
-
 
-class TestMaxOutOpAxis(TestMaxOutOp):
-    def init_test_case(self):
-        self.MaxOut_forward_naive = maxout_forward_naive
-        self.shape = [100, 2, 2, 6]  # NHWC format
-        self.groups = 2
-        self.axis = 3
+class TestMaxOutOpAxis0(TestMaxOutOp):
+    def set_attrs(self):
+        self.axis = -1
 
 
-class TestMaxOutOpAxisAPI(unittest.TestCase):
-    def test_axis(self):
-        data1 = fluid.data(name='data1', shape=[3, 6, 2, 2], dtype='float32')
-        data2 = fluid.data(name='data2', shape=[3, 2, 2, 6], dtype='float32')
-        out1 = fluid.layers.maxout(data1, groups=2, axis=1)
-        out2 = fluid.layers.maxout(data2, groups=2, axis=-1)
-        data1_np = np.random.random((3, 6, 2, 2)).astype("float32")
-        data2_np = np.transpose(data1_np, [0, 2, 3, 1])
+class TestMaxOutOpAxis1(TestMaxOutOp):
+    def set_attrs(self):
+        self.axis = 3
 
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(fluid.default_main_program(),
-                          feed={"data1": data1_np,
-                                "data2": data2_np},
-                          fetch_list=[out1, out2],
-                          return_numpy=True)
 
-        self.assertTrue(
-            np.allclose(results[0], np.transpose(results[1], (0, 3, 1, 2))))
+class TestMaxOutOpFP32(TestMaxOutOp):
+    def set_attrs(self):
+        self.dtype = 'float32'
 
-    def test_exception(self):
-        input = fluid.data(name="input", shape=[2, 4, 6, 6], dtype="float32")
 
-        def _attr_axis():
-            out = fluid.layers.maxout(input, groups=2, axis=2)
+class TestMaxOutOpGroups(TestMaxOutOp):
+    def set_attrs(self):
+        self.groups = 3
 
-        self.assertRaises(ValueError, _attr_axis)
 
+class TestMaxoutAPI(unittest.TestCase):
+    # test paddle.nn.Maxout, paddle.nn.functional.maxout
+    def setUp(self):
+        self.x_np = np.random.uniform(-1, 1, [2, 6, 5, 4]).astype(np.float64)
+        self.groups = 2
+        self.axis = 1
+        self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def test_static_api(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
+            out1 = F.maxout(x, self.groups, self.axis)
+            m = paddle.nn.Maxout(self.groups, self.axis)
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = maxout_forward_naive(self.x_np, self.groups, self.axis)
+        for r in res:
+            self.assertTrue(np.allclose(out_ref, r))
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out1 = F.maxout(x, self.groups, self.axis)
+        m = paddle.nn.Maxout(self.groups, self.axis)
+        out2 = m(x)
+        out_ref = maxout_forward_naive(self.x_np, self.groups, self.axis)
+        for r in [out1, out2]:
+            self.assertTrue(np.allclose(out_ref, r.numpy()))
+
+        out3 = F.maxout(x, self.groups, -1)
+        out3_ref = maxout_forward_naive(self.x_np, self.groups, -1)
+        self.assertTrue(np.allclose(out3_ref, out3.numpy()))
+        paddle.enable_static()
+
+    def test_fluid_api(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
+            out = fluid.layers.maxout(x, groups=self.groups, axis=self.axis)
+            exe = fluid.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
+        out_ref = maxout_forward_naive(self.x_np, self.groups, self.axis)
+        self.assertTrue(np.allclose(out_ref, res[0]))
+
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out = paddle.fluid.layers.maxout(x, groups=self.groups, axis=self.axis)
+        self.assertTrue(np.allclose(out_ref, out.numpy()))
+        paddle.enable_static()
 
-class TestMaxOutOpError(unittest.TestCase):
     def test_errors(self):
-        with program_guard(Program()):
+        with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, fluid.layers.maxout, 1, 2)
+            self.assertRaises(TypeError, F.maxout, 1)
             # The input dtype must be float16, float32, float64.
-            x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
-            self.assertRaises(TypeError, fluid.layers.maxout, x_int32, 2)
-            # support the input dtype is float32
-            x_fp32 = fluid.data(name='x_fp32', shape=[12, 10], dtype='float32')
-            fluid.layers.maxout(x_fp32, 2)
+            x_int32 = paddle.data(
+                name='x_int32', shape=[2, 4, 6, 8], dtype='int32')
+            self.assertRaises(TypeError, F.maxout, x_int32)
+
+            x_float32 = paddle.data(name='x_float32', shape=[2, 4, 6, 8])
+            self.assertRaises(ValueError, F.maxout, x_float32, 2, 2)
 
 
 if __name__ == '__main__':

python/paddle/nn/__init__.py

@@ -55,6 +55,7 @@ from .layer.activation import ELU  #DEFINE_ALIAS
 from .layer.activation import GELU  #DEFINE_ALIAS
 from .layer.activation import Tanh  #DEFINE_ALIAS
 from .layer.activation import Hardshrink  #DEFINE_ALIAS
+from .layer.activation import Hardswish  #DEFINE_ALIAS
 from .layer.activation import Hardtanh  #DEFINE_ALIAS
 from .layer.activation import PReLU  #DEFINE_ALIAS
 from .layer.activation import ReLU  #DEFINE_ALIAS
@@ -62,6 +63,7 @@ from .layer.activation import ReLU6  #DEFINE_ALIAS
 from .layer.activation import SELU  #DEFINE_ALIAS
 from .layer.activation import LeakyReLU  #DEFINE_ALIAS
 from .layer.activation import Sigmoid  #DEFINE_ALIAS
+from .layer.activation import Hardsigmoid  #DEFINE_ALIAS
 from .layer.activation import LogSigmoid
 from .layer.activation import Softmax  #DEFINE_ALIAS
 from .layer.activation import Softplus  #DEFINE_ALIAS
@@ -70,6 +72,7 @@ from .layer.activation import Softsign  #DEFINE_ALIAS
 from .layer.activation import Tanhshrink  #DEFINE_ALIAS
 from .layer.activation import LogSoftmax  #DEFINE_ALIAS
 from .layer.activation import HSigmoid  #DEFINE_ALIAS
+from .layer.activation import Maxout  #DEFINE_ALIAS
 from .layer.common import BilinearTensorProduct  #DEFINE_ALIAS
 from .layer.common import Pool2D  #DEFINE_ALIAS
 from .layer.common import Pad2D  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -29,14 +29,13 @@ from . import pooling
 __all__ += pooling.__all__
 from . import loss
 __all__ += loss.__all__
-from .activation import brelu  #DEFINE_ALIAS
 from .activation import elu  #DEFINE_ALIAS
 from .activation import erf  #DEFINE_ALIAS
 from .activation import gelu  #DEFINE_ALIAS
 from .activation import hardshrink  #DEFINE_ALIAS
 from .activation import hardtanh  #DEFINE_ALIAS
-from .activation import hard_sigmoid  #DEFINE_ALIAS
-from .activation import hard_swish  #DEFINE_ALIAS
+from .activation import hardsigmoid  #DEFINE_ALIAS
+from .activation import hardswish  #DEFINE_ALIAS
 from .activation import hsigmoid  #DEFINE_ALIAS
 from .activation import leaky_relu  #DEFINE_ALIAS
 from .activation import log_sigmoid  #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -13,11 +13,7 @@
 # limitations under the License.
 
 # TODO: define activation functions of neural network
-from ...fluid.layers import brelu  #DEFINE_ALIAS
 from ...fluid.layers import erf  #DEFINE_ALIAS
-from ...fluid.layers import hard_sigmoid  #DEFINE_ALIAS
-from ...fluid.layers import hard_swish  #DEFINE_ALIAS
-from ...fluid.layers import maxout  #DEFINE_ALIAS
 from ...fluid.layers import soft_relu  #DEFINE_ALIAS
 from ...fluid.layers import swish  #DEFINE_ALIAS
 from ...fluid.layers import sigmoid  #DEFINE_ALIAS
@@ -25,14 +21,13 @@ from ...fluid.layers import thresholded_relu  #DEFINE_ALIAS
 from ...tensor.math import tanh  #DEFINE_ALIAS
 
 __all__ = [
-    'brelu',
     'elu',
     'erf',
     'gelu',
     'hardshrink',
     'hardtanh',
-    'hard_sigmoid',
-    'hard_swish',
+    'hardsigmoid',
+    'hardswish',
     'hsigmoid',
     'leaky_relu',
     'log_sigmoid',
@@ -75,10 +70,10 @@ def elu(x, alpha=1.0, name=None):
         alpha (float, optional): The 'alpha' value of the ELU formulation. Default is 1.0.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Returns:
         A Tensor with the same data type and shape as ``x`` .
-    
+
     Examples:
         .. code-block:: python
 
@@ -89,7 +84,7 @@ def elu(x, alpha=1.0, name=None):
             paddle.disable_static()
 
             x = paddle.to_tensor(np.array([[-1,6],[1,15.6]]))
-            out = F.elu(x, alpha=0.2) 
+            out = F.elu(x, alpha=0.2)
             # [[-0.12642411  6.        ]
             #  [ 1.          15.6      ]]
     """
@@ -123,16 +118,16 @@ def gelu(x, approximate=False, name=None):
     .. math::
 
         gelu(x) = 0.5 * x * (1 + erf(\\frac{x}{\\sqrt{2}}))
-    
+
     Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         approximate (bool, optional): Wether to enable approximation. Default is False.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Returns:
         A Tensor with the same data type and shape as ``x`` .
-    
+
     Examples:
         .. code-block:: python
 
@@ -265,6 +260,109 @@ def hardtanh(x, min=-1.0, max=1.0, name=None):
     return out
 
 
+def hardsigmoid(x, name=None):
+    """
+    hardsigmoid activation.
+
+    A 3-part piecewise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
+    which is much faster than sigmoid.
+
+    .. math::
+
+        hardsigmoid(x)=
+            \\left\\{
+            \\begin{aligned}
+            &0, & & \\text{if } x \\leq -3 \\\\
+            &1, & & \\text{if } x \\geq 3 \\\\
+            &x/6 + 1/2, & & \\text{otherwise}
+            \\end{aligned}
+            \\right.
+
+    Parameters:
+        x (Tensor): The input Tensor with data type float32, float64.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        A Tensor with the same data type and shape as ``x`` .
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn.functional as F
+
+            x = paddle.to_tensor([-4., 5., 1.])
+            out = F.hardsigmoid(x) # [0., 1., 0.666667]
+    """
+
+    if in_dygraph_mode():
+        return core.ops.hard_sigmoid(x, 'slope', 0.1666666666666667, 'offset',
+                                     0.5)
+
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                             'hardsigmoid')
+
+    helper = LayerHelper('hardsigmoid', **locals())
+    out = helper.create_variable_for_type_inference(x.dtype)
+    helper.append_op(
+        type='hard_sigmoid',
+        inputs={'X': x},
+        outputs={'Out': out},
+        attrs={'slope': 0.1666666666666667,
+               'offset': 0.5})
+    return out
+
+
+def hardswish(x, name=None):
+    """
+    hardswish activation
+
+    hardswish is proposed in MobileNetV3, and performs better in computational stability
+    and efficiency compared to swish function. For more details please refer
+    to: https://arxiv.org/pdf/1905.02244.pdf
+
+    .. math::
+
+        hardswish(x)=
+            \\left\\{
+            \\begin{aligned}
+            &0, & & \\text{if } x \\leq -3 \\\\
+            &x, & & \\text{if } x \\geq 3 \\\\
+            &\\frac{x(x+3)}{6}, & & \\text{otherwise}
+            \\end{aligned}
+            \\right.
+
+    Parameters:
+        x (Tensor): The input Tensor with data type float32, float64.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        A Tensor with the same data type and shape as ``x`` .
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn.functional as F
+
+            x = paddle.to_tensor([-4., 5., 1.])
+            out = F.hardswish(x) # [0., 5., 0.666667]
+    """
+
+    if in_dygraph_mode():
+        return core.ops.hard_swish(x)
+
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                             'hardswish')
+
+    helper = LayerHelper('hardswish', **locals())
+    out = helper.create_variable_for_type_inference(x.dtype)
+    helper.append_op(type='hard_swish', inputs={'X': x}, outputs={'Out': out})
+    return out
+
+
 def hsigmoid(input,
              label,
              weight,
@@ -489,7 +587,7 @@ def prelu(x, weight, name=None):
     assert len(weight.shape
                ) == 1, "The dim count of weight shape should be 1 in prelu()."
 
-    # NOTE(): The input of this API should be ``N,C,...`` format, 
+    # NOTE(): The input of this API should be ``N,C,...`` format,
     # which means x.shape[0] is batch_size and x.shape[0] is channel.
     mode = 'all'
     if weight.shape[0] > 1:
@@ -559,15 +657,15 @@ def log_sigmoid(x, name=None):
     .. math::
 
         log\\_sigmoid(x) = log \\frac{1}{1 + e^{-x}}
-    
+
     Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Returns:
         A Tensor with the same data type and shape as ``x`` .
-    
+
     Examples:
         .. code-block:: python
 
@@ -591,6 +689,81 @@ def log_sigmoid(x, name=None):
     return out
 
 
+def maxout(x, groups, axis=1, name=None):
+    """
+    maxout activation.
+
+    Assumed the input shape is (N, Ci, H, W).
+    The output shape is (N, Co, H, W).
+    Then Co = Ci/groups and the operator formula is as follows:
+
+    .. math::
+
+        &out_{si+j} = \\max_{k} x_{gsi + sk + j} \\\\
+        &g = groups \\\\
+        &s = \\frac{input.size}{num\\_channels} \\\\
+        &0 \\le i < \\frac{num\\_channels}{groups} \\\\
+        &0 \\le j < s \\\\
+        &0 \\le k < groups
+
+    Parameters:
+        x (Tensor): The input is 4-D Tensor with shape [N, C, H, W] or [N, H, W, C], the data type
+            of input is float32 or float64.
+        groups (int, optional): The groups number of maxout. `groups` specifies the
+            index of channel dimension where maxout will be performed. This must be
+            a factor of number of features. Default is 1.
+        axis (int, optional): The axis along which to perform maxout calculations.
+            It should be 1 when data format is NCHW, be -1 or 3 when data format
+            is NHWC. If ``axis`` < 0, it works the same way as :math:`axis + D` ,
+            where D is the dimensions of ``x`` . ``axis`` only supports 1, 3 or -1.
+            Default is 1.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        A Tensor with the same data type as ``x`` .
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn.functional as F
+
+            x = paddle.rand([1, 2, 3, 4])
+            # [[[[0.5002636  0.22272532 0.17402348 0.2874594 ]
+            #    [0.95313174 0.6228939  0.7129065  0.7087491 ]
+            #    [0.02879342 0.88725346 0.61093384 0.38833922]]
+            #   [[0.5231306  0.03807496 0.91661984 0.15602879]
+            #    [0.666127   0.616567   0.30741522 0.24044901]
+            #    [0.7142536  0.7351477  0.31588817 0.23782359]]]]
+            out = F.maxout(x, groups=2)
+            # [[[[0.5231306  0.22272532 0.91661984 0.2874594 ]
+            #    [0.95313174 0.6228939  0.7129065  0.7087491 ]
+            #    [0.7142536  0.88725346 0.61093384 0.38833922]]]]
+    """
+
+    if in_dygraph_mode():
+        return core.ops.maxout(x, 'groups', groups, 'axis', axis)
+
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'maxout')
+    if axis not in [1, -1, 3]:
+        raise ValueError(
+            "Attr(axis) should be 1 when data format is NCHW, -1 or 3 when data format is NHWC. Received "
+            "Attr(axis): %s." % str(axis))
+    if axis == -1:
+        axis = 3
+
+    helper = LayerHelper('maxout', **locals())
+    out = helper.create_variable_for_type_inference(x.dtype)
+    helper.append_op(
+        type='maxout',
+        inputs={'X': x},
+        outputs={'Out': out},
+        attrs={'groups': groups,
+               'axis': axis})
+    return out
+
+
 def relu6(x, name=None):
     """
     relu6 activation
@@ -778,7 +951,7 @@ def softmax(x, axis=-1, dtype=None, name=None):
             :math:`axis + D` . Default is -1.
         dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
             type of the output tensor. If dtype is specified, ``x`` is casted
-            to ``dtype`` before the operation is performed. This is useful for 
+            to ``dtype`` before the operation is performed. This is useful for
             preventing data type overflows. Supported dtype: float32, float64.
             If ``dtype`` is None, the output Tensor has the same dtype as x.
             Default is None.
@@ -1051,13 +1224,13 @@ def log_softmax(x, axis=-1, dtype=None, name=None):
             :math:`axis + D` . Default is -1.
         dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
             type of the output tensor. If dtype is specified, ``x`` is casted
-            to ``dtype`` before the operation is performed. This is useful for 
+            to ``dtype`` before the operation is performed. This is useful for
             preventing data type overflows. Supported dtype: float32, float64.
             If ``dtype`` is None, the output Tensor has the same dtype as x.
             Default is None.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
- 
+
     Returns:
         A Tensor with the same shape and data type (use ``dtype`` if it is
         specified) as x.

python/paddle/nn/layer/activation.py

@@ -18,6 +18,7 @@ __all__ = [
     'ELU',
     'GELU',
     'Hardshrink',
+    'Hardswish',
     'Tanh',
     'Hardtanh',
     'PReLU',
@@ -26,6 +27,7 @@ __all__ = [
     'SELU',
     'LeakyReLU',
     'Sigmoid',
+    'Hardsigmoid',
     'Softmax',
     'Softplus',
     'Softshrink',
@@ -33,6 +35,7 @@ __all__ = [
     'Tanhshrink',
     'LogSigmoid',
     'LogSoftmax',
+    'Maxout',
     'HSigmoid',
 ]
 
@@ -50,18 +53,18 @@ class ELU(layers.Layer):
     ELU Activation.
 
     .. math::
-    
+
         ELU(x) = max(0, x) + min(0, \\alpha * (e^{x}-1))
 
     Parameters:
         alpha (float, optional): The 'alpha' value of the ELU formulation. Default is 1.0.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -106,11 +109,11 @@ class GELU(layers.Layer):
         approximate (bool, optional): Wether to enable approximation. Default is False.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -120,7 +123,7 @@ class GELU(layers.Layer):
             paddle.disable_static()
 
             x = paddle.to_tensor(np.array([[-1, 0.5],[1, 1.5]]))
-            
+
             m = paddle.nn.GELU()
             out = m(x) # [-0.158655 0.345731 0.841345 1.39979]
 
@@ -184,6 +187,52 @@ class Hardshrink(layers.Layer):
         return F.hardshrink(x, self._threshold, self._name)
 
 
+class Hardswish(layers.Layer):
+    """
+    Hardswish activation
+
+    Hardswish is proposed in MobileNetV3, and performs better in computational stability
+    and efficiency compared to swish function. For more details please refer
+    to: https://arxiv.org/pdf/1905.02244.pdf
+
+    .. math::
+
+        Hardswish(x)=
+            \\left\\{
+            \\begin{aligned}
+            &0, & & \\text{if } x \\leq -3 \\\\
+            &x, & & \\text{if } x \\geq 3 \\\\
+            &\\frac{x(x+3)}{6}, & & \\text{otherwise}
+            \\end{aligned}
+            \\right.
+
+    Parameters:
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape:
+        - input: Tensor with any shape.
+        - output: Tensor with the same shape as input.
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle
+
+            x = paddle.to_tensor([-4., 5., 1.])
+            m = paddle.nn.Hardswish()
+            out = m(x) # [0., 5., 0.666667]
+    """
+
+    def __init__(self, name=None):
+        super(Hardswish, self).__init__()
+        self._name = name
+
+    def forward(self, x):
+        return F.hardswish(x, self._name)
+
+
 class Tanh(layers.Layer):
     """
     Tanh Activation.
@@ -240,11 +289,11 @@ class Hardtanh(layers.Layer):
         max (float, optional): The value of max for Hardtanh. Default is 1.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -274,7 +323,7 @@ class HSigmoid(layers.Layer):
 	:alias: paddle.nn.HSigmoid,paddle.nn.layer.HSigmoid,paddle.nn.layer.activation.HSigmoid
 
     Hierarchical Sigmoid Layer.
-    
+
     The hierarchical sigmoid organizes the classes into a complete binary tree to reduce the computational complexity
     and speed up the model training, especially the training of language model.
     Each leaf node of the complete binary tree represents a class(word) and each non-leaf node acts as a binary classifier.
@@ -309,7 +358,7 @@ class HSigmoid(layers.Layer):
             is set to False, no bias will be added. If it is set to None or one attribute of ParamAttr,
             hsigmoid will create a ParamAttr as bias_attr. If the Initializer of the bias_attr is not
             set, the bias is initialized zero. Default: None.
-        is_custom (bool, optional): Whether use custom binary tree. If it's True, `path_table` and 
+        is_custom (bool, optional): Whether use custom binary tree. If it's True, `path_table` and
             `path_code` should be passed to its forward method, otherwise `path_table` and `path_code`
             should not be passed to its forward method. Default: False.
         is_sparse (bool, optional): Whether use sparse updating instead of dense updating, if it's True, the
@@ -414,19 +463,19 @@ class PReLU(layers.Layer):
 
     Parameters:
         num_parameters (int, optional): Number of `weight` to learn. The supported values are:
-            1 - a single parameter `alpha` is used for all input channels; 
+            1 - a single parameter `alpha` is used for all input channels;
             Number of channels - a seperate `alpha` is used for each input channel.
             Default is 1.
         init (float, optional): Init value of learnable `weight`. Default is 0.25.
-        weight_attr(ParamAttr, optional): The parameter attribute for the learnable `weight`. 
+        weight_attr(ParamAttr, optional): The parameter attribute for the learnable `weight`.
             Default is None. For more information, please refer to :ref:`api_fluid_ParamAttr`.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Shape:
         - input: Tensor with any shape. Default dtype is float32.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -487,7 +536,7 @@ class ReLU(layers.Layer):
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -613,11 +662,11 @@ class LeakyReLU(layers.Layer):
             :math:`x < 0` . Default is 0.01.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -643,11 +692,11 @@ class LeakyReLU(layers.Layer):
 class Sigmoid(layers.Layer):
     """
     this interface is used to construct a callable object of the ``Sigmoid`` class. This layer calcluate the `sigmoid` of input x.
-    
+
     .. math::
 
         Sigmoid(x) = \frac{1}{1 + e^{-x}}
-    
+
     Parameters:
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
@@ -656,7 +705,7 @@ class Sigmoid(layers.Layer):
 
     Returns:
         A callable object of Sigmoid.
-    
+
     Examples:
 
         .. code-block:: python
@@ -680,6 +729,53 @@ class Sigmoid(layers.Layer):
         return F.sigmoid(x, self.name)
 
 
+class Hardsigmoid(layers.Layer):
+    """
+    This interface is used to construct a callable object of the ``Hardsigmoid`` class.
+    This layer calcluate the `hardsigmoid` of input x.
+
+    A 3-part piecewise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
+    which is much faster than sigmoid.
+
+    .. math::
+
+        Hardsigmoid(x)=
+            \\left\\{
+            \\begin{aligned}
+            &0, & & \\text{if } x \\leq -3 \\\\
+            &1, & & \\text{if } x \\geq 3 \\\\
+            &x/6 + 1/2, & & \\text{otherwise}
+            \\end{aligned}
+            \\right.
+
+    Parameters:
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape:
+        x: N-D tensor, available dtype is float32, float64.
+
+    Returns:
+        A callable object of Hardsigmoid.
+
+    Examples:
+
+        .. code-block:: python
+
+          import paddle
+
+          m = paddle.nn.Sigmoid()
+          x = paddle.to_tensor([-4., 5., 1.])
+          out = m(x) # [0., 1, 0.666667]
+    """
+
+    def __init__(self, name=None):
+        super(Hardsigmoid, self).__init__()
+        self.name = name
+
+    def forward(self, x):
+        return F.hardsigmoid(x, self.name)
+
+
 class Softplus(layers.Layer):
     """
     Softplus Activation
@@ -842,7 +938,7 @@ class Tanhshrink(layers.Layer):
 class LogSigmoid(layers.Layer):
     """
     LogSigmoid Activation.
-    
+
     .. math::
 
         LogSigmoid(x) = log \\frac{1}{1 + e^{-x}}
@@ -851,11 +947,11 @@ class LogSigmoid(layers.Layer):
         x (Tensor): The input Tensor with data type float32, or float64.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
-    
+
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
-    
+
     Examples:
         .. code-block:: python
 
@@ -961,7 +1057,7 @@ class Softmax(layers.Layer):
             :math:`axis + D` . Default is -1.
         dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
             type of the output tensor. If dtype is specified, ``x`` is casted
-            to ``dtype`` before the operation is performed. This is useful for 
+            to ``dtype`` before the operation is performed. This is useful for
             preventing data type overflows. Supported dtype: float32, float64.
             If ``dtype`` is None, the output Tensor has the same dtype as x.
             Default is None.
@@ -1013,7 +1109,7 @@ class LogSoftmax(layers.Layer):
 
     .. math::
 
-        Out[i, j] = log(softmax(x)) 
+        Out[i, j] = log(softmax(x))
                   = log(\\frac{\exp(X[i, j])}{\\sum_j(exp(X[i, j])})
 
     Parameters:
@@ -1023,7 +1119,7 @@ class LogSoftmax(layers.Layer):
             same way as :math:`axis + D` . Default is -1.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
- 
+
     Shape:
         - input: Tensor with any shape.
         - output: Tensor with the same shape as input.
@@ -1060,3 +1156,64 @@ class LogSoftmax(layers.Layer):
 
     def forward(self, x):
         return F.log_softmax(x, self._axis)
+
+
+class Maxout(layers.Layer):
+    """
+    Maxout Activation.
+
+    Assumed the input shape is (N, Ci, H, W).
+    The output shape is (N, Co, H, W).
+    Then Co = Ci/groups and the operator formula is as follows:
+
+    .. math::
+
+        &out_{si+j} = \max_{k} x_{gsi + sk + j} \\\\
+        &g = groups \\\\
+        &s = \\frac{input.size}{num\\_channels} \\\\
+        &0 \\le i < \\frac{num\\_channels}{groups} \\\\
+        &0 \\le j < s \\\\
+        &0 \\le k < groups
+
+    Parameters:
+        groups (int, optional): The groups number of maxout. `groups` specifies the
+            index of channel dimension where maxout will be performed. This must be
+            a factor of number of features. Default is 1.
+        axis (int, optional): The axis along which to perform maxout calculations.
+            It should be 1 when data format is NCHW, be -1 or 3 when data format
+            is NHWC. If ``axis`` < 0, it works the same way as :math:`axis + D` ,
+            where D is the dimensions of ``x`` . Default is 1.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape:
+        - input: :math:`(N, C_{in}, H_{in}, W_{in})`
+        - output: :math:`(N, C_{out}, H_{out}, W_{out})`
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            x = paddle.rand([1, 2, 3, 4])
+            # [[[[0.5002636  0.22272532 0.17402348 0.2874594 ]
+            #    [0.95313174 0.6228939  0.7129065  0.7087491 ]
+            #    [0.02879342 0.88725346 0.61093384 0.38833922]]
+            #   [[0.5231306  0.03807496 0.91661984 0.15602879]
+            #    [0.666127   0.616567   0.30741522 0.24044901]
+            #    [0.7142536  0.7351477  0.31588817 0.23782359]]]]
+            m = paddle.nn.Maxout(groups=2)
+            out = m(x)
+            # [[[[0.5231306  0.22272532 0.91661984 0.2874594 ]
+            #    [0.95313174 0.6228939  0.7129065  0.7087491 ]
+            #    [0.7142536  0.88725346 0.61093384 0.38833922]]]]
+    """
+
+    def __init__(self, groups, axis=1, name=None):
+        super(Maxout, self).__init__()
+        self._groups = groups
+        self._axis = axis
+        self._name = name
+
+    def forward(self, x):
+        return F.maxout(x, self._groups, self._axis, self._name)