add activation ops under paddle.nn and paddle.nn.functional: ReLU, LogSoftmax (#23258)

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

@@ -21,6 +21,8 @@ from op_test import OpTest
 from scipy.special import expit, erf
 import paddle
 import paddle.fluid as fluid
+import paddle.nn as nn
+import paddle.nn.functional as functional
 from paddle.fluid import compiler, Program, program_guard
 
 
@@ -759,9 +761,6 @@ class TestPow_factor_tensor(TestActivation):
         self.check_grad(['X'], 'Out')
 
     def test_api(self):
-        import paddle
-        import paddle.fluid as fluid
-
         input = np.random.uniform(1, 2, [11, 17]).astype("float32")
         x = fluid.layers.data(
             name="x", shape=[11, 17], append_batch_size=False, dtype="float32")
@@ -1003,5 +1002,74 @@ create_test_act_fp16_class(TestHardSigmoid)
 create_test_act_fp16_class(TestSwish)
 create_test_act_fp16_class(TestHardSwish)
 
+
+class TestNNReluAPI(unittest.TestCase):
+    def setUp(self):
+        self.init_data()
+
+    def init_data(self):
+        self.x_shape = [10, 12]
+        self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
+        self.y = self.ref_forward(self.x)
+
+    def ref_forward(self, x):
+        return np.maximum(x, 0)
+
+    def ref_backward(self, y, dy):
+        y_t = y.copy()
+        y_t[y_t > 0] = 1
+        return y_t * dy
+
+    def check_api(self, place=fluid.CPUPlace(), inplace=False):
+        main_program = Program()
+        myrelu = nn.ReLU(inplace)
+        with fluid.program_guard(main_program):
+            x = fluid.data(name='x', shape=self.x_shape)
+            x.stop_gradient = False
+            y = myrelu(x)
+            fluid.backward.append_backward(fluid.layers.mean(y))
+        exe = fluid.Executor(place)
+        out = exe.run(main_program,
+                      feed={'x': self.x},
+                      fetch_list=[y, y.grad_name, x.grad_name])
+        self.assertTrue(np.allclose(out[0], self.y))
+        self.assertTrue(np.allclose(out[2], self.ref_backward(self.y, out[1])))
+
+        with fluid.dygraph.guard(place):
+            x = fluid.dygraph.to_variable(self.x)
+            y = myrelu(x)
+        self.assertTrue(np.allclose(y.numpy(), self.y))
+
+    def test_check_api(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for place in places:
+            for inplace in [True, False]:
+                self.check_api(place, inplace)
+
+
+class TestNNFunctionalReluAPI(unittest.TestCase):
+    def setUp(self):
+        self.init_data()
+
+    def init_data(self):
+        self.x_shape = [10, 12]
+        self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
+        self.y = self.ref_forward(self.x)
+
+    def ref_forward(self, x):
+        return np.maximum(x, 0)
+
+    def test_check_api(self):
+        main_program = Program()
+        with fluid.program_guard(main_program):
+            x = fluid.data(name='x', shape=self.x_shape)
+            y = functional.relu(x)
+        exe = fluid.Executor(fluid.CPUPlace())
+        out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])
+        self.assertTrue(np.allclose(out[0], self.y))
+
+
 if __name__ == "__main__":
     unittest.main()

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.nn as nn
+import paddle.nn.functional as functional
+
+
+def stable_softmax(x):
+    shiftx = (x - np.max(x))
+    exps = np.exp(shiftx)
+    return exps / np.sum(exps)
+
+
+def ref_log_softmax(x, axis=None, dtype=None):
+    x_t = x.copy()
+    if dtype is not None:
+        x_t = x_t.astype(dtype)
+    if axis is None:
+        axis = -1
+    out = np.apply_along_axis(stable_softmax, axis, x_t)
+    return np.log(out)
+
+
+class TestNNLogSoftmaxAPI(unittest.TestCase):
+    def setUp(self):
+        self.init_data()
+
+    def init_data(self):
+        self.x_shape = [2, 3, 4, 5]
+        self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
+
+    def check_api(self, place=fluid.CPUPlace(), axis=None):
+        ref_out = ref_log_softmax(self.x, axis)
+
+        main_program = fluid.Program()
+        mylogsoftmax = nn.LogSoftmax(axis)
+        with fluid.program_guard(main_program):
+            x = fluid.data(name='x', shape=self.x_shape)
+            y = mylogsoftmax(x)
+        exe = fluid.Executor(place)
+        out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])
+        self.assertTrue(np.allclose(out[0], ref_out))
+
+        with fluid.dygraph.guard(place):
+            x = fluid.dygraph.to_variable(self.x)
+            y = mylogsoftmax(x)
+        self.assertTrue(np.allclose(y.numpy(), ref_out))
+
+    def test_check_api(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for place in places:
+            for axis in [None, 2]:
+                self.check_api(place, axis)
+
+
+class TestNNFunctionalLogSoftmaxAPI(unittest.TestCase):
+    def setUp(self):
+        self.init_data()
+
+    def init_data(self):
+        self.x_shape = [2, 3, 4, 5]
+        self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
+
+    def check_api(self, place=fluid.CPUPlace(), axis=None, dtype=None):
+        ref_out = ref_log_softmax(self.x, axis, dtype)
+        main_program = fluid.Program()
+        mylogsoftmax = nn.LogSoftmax(axis)
+        with fluid.program_guard(main_program):
+            x = fluid.data(name='x', shape=self.x_shape)
+            y = functional.log_softmax(x, axis, dtype)
+        exe = fluid.Executor(place)
+        out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])
+        self.assertTrue(np.allclose(out[0], ref_out))
+
+        with fluid.dygraph.guard(place):
+            x = fluid.dygraph.to_variable(self.x)
+            y = functional.log_softmax(x, axis, dtype)
+        self.assertTrue(np.allclose(y.numpy(), ref_out))
+
+    def test_check_api(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for place in places:
+            self.check_api(place, None, None)
+            self.check_api(place, None, np.float64)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -81,10 +81,10 @@ from .layer.conv import Conv2D, Conv2DTranspose, Conv3D, Conv3DTranspose  #DEFIN
 from .layer.norm import InstanceNorm  #DEFINE_ALIAS
 # from .layer.norm import SpectralNorm   #DEFINE_ALIAS
 # from .layer.activation import PReLU   #DEFINE_ALIAS
-# from .layer.activation import ReLU   #DEFINE_ALIAS
+from .layer.activation import ReLU  #DEFINE_ALIAS
 # from .layer.activation import Sigmoid   #DEFINE_ALIAS
 # from .layer.activation import Softmax   #DEFINE_ALIAS
-# from .layer.activation import LogSoftmax   #DEFINE_ALIAS
+from .layer.activation import LogSoftmax  #DEFINE_ALIAS
 # from .layer.rnn import RNNCell   #DEFINE_ALIAS
 # from .layer.rnn import GRUCell   #DEFINE_ALIAS
 # from .layer.rnn import LSTMCell   #DEFINE_ALIAS
@@ -189,7 +189,7 @@ from .functional.conv import conv3d_transpose  #DEFINE_ALIAS
 # from .functional.activation import logsigmoid   #DEFINE_ALIAS
 # from .functional.activation import maxout   #DEFINE_ALIAS
 # from .functional.activation import prelu   #DEFINE_ALIAS
-# from .functional.activation import relu   #DEFINE_ALIAS
+from .functional.activation import relu  #DEFINE_ALIAS
 # from .functional.activation import relu6   #DEFINE_ALIAS
 # from .functional.activation import selu   #DEFINE_ALIAS
 # from .functional.activation import sigmoid   #DEFINE_ALIAS
@@ -201,7 +201,7 @@ from .functional.conv import conv3d_transpose  #DEFINE_ALIAS
 # from .functional.activation import swish   #DEFINE_ALIAS
 # from .functional.activation import tanh_shrink   #DEFINE_ALIAS
 # from .functional.activation import thresholded_relu   #DEFINE_ALIAS
-# from .functional.activation import log_softmax   #DEFINE_ALIAS
+from .functional.activation import log_softmax  #DEFINE_ALIAS
 # from .functional.extension import add_position_encoding   #DEFINE_ALIAS
 # from .functional.extension import autoincreased_step_counter   #DEFINE_ALIAS
 # from .functional.extension import continuous_value_model   #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -102,6 +102,7 @@ from .conv import conv3d_transpose  #DEFINE_ALIAS
 # from .vision import space_to_depth   #DEFINE_ALIAS
 # from .vision import yolo_box   #DEFINE_ALIAS
 # from .vision import yolov3_loss   #DEFINE_ALIAS
+from . import activation
 # from .activation import brelu   #DEFINE_ALIAS
 # from .activation import elu   #DEFINE_ALIAS
 # from .activation import erf   #DEFINE_ALIAS
@@ -114,7 +115,7 @@ from .conv import conv3d_transpose  #DEFINE_ALIAS
 # from .activation import logsigmoid   #DEFINE_ALIAS
 # from .activation import maxout   #DEFINE_ALIAS
 # from .activation import prelu   #DEFINE_ALIAS
-# from .activation import relu   #DEFINE_ALIAS
+from .activation import relu  #DEFINE_ALIAS
 # from .activation import relu6   #DEFINE_ALIAS
 # from .activation import selu   #DEFINE_ALIAS
 # from .activation import sigmoid   #DEFINE_ALIAS
@@ -126,7 +127,7 @@ from .conv import conv3d_transpose  #DEFINE_ALIAS
 # from .activation import swish   #DEFINE_ALIAS
 # from .activation import tanh_shrink   #DEFINE_ALIAS
 # from .activation import thresholded_relu   #DEFINE_ALIAS
-# from .activation import log_softmax   #DEFINE_ALIAS
+from .activation import log_softmax  #DEFINE_ALIAS
 # from .extension import add_position_encoding   #DEFINE_ALIAS
 # from .extension import autoincreased_step_counter   #DEFINE_ALIAS
 # from .extension import continuous_value_model   #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -12,29 +12,167 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
+from ...fluid.layer_helper import LayerHelper
+from ...fluid.framework import in_dygraph_mode, convert_np_dtype_to_dtype_
+from ...fluid import core
+
 # TODO: define activation functions of neural network  
-# __all__ = ['brelu',
-#            'elu',
-#            'erf',
-#            'gelu',
-#            'hard_shrink',
-#            'hard_sigmoid',
-#            'hard_swish',
-#            'hsigmoid',
-#            'leaky_relu',
-#            'logsigmoid',
-#            'maxout',
-#            'prelu',
-#            'relu',
-#            'relu6',
-#            'selu',
-#            'sigmoid',
-#            'soft_relu',
-#            'softmax',
-#            'softplus',
-#            'softshrink',
-#            'softsign',
-#            'swish',
-#            'tanh_shrink',
-#            'thresholded_relu',
-#            'log_softmax']
+__all__ = [
+    # 'brelu',
+    # 'elu',
+    # 'erf',
+    # 'gelu',
+    # 'hard_shrink',
+    # 'hard_sigmoid',
+    # 'hard_swish',
+    # 'hsigmoid',
+    # 'leaky_relu',
+    # 'logsigmoid',
+    # 'maxout',
+    # 'prelu',
+    'relu',
+    # 'relu6',
+    # 'selu',
+    # 'sigmoid',
+    # 'soft_relu',
+    # 'softmax',
+    # 'softplus',
+    # 'softshrink',
+    # 'softsign',
+    # 'swish',
+    # 'tanh_shrink',
+    # 'thresholded_relu',
+    'log_softmax',
+]
+
+
+def relu(input, inplace=False, name=None):
+    """
+    ReLU Activation.
+
+    .. math:
+
+        out = max(x, 0)
+
+    Parameters:
+        input (Variable): The input variable. A multi-dimension Tensor with type float16, float32, or float64.
+        inplace (bool, optional): If inplace is True, the input and output of ``ReLU`` are the same variable.
+            Otherwise, the input and output of ``ReLU`` are different variables. Default: False. Note that if x is
+            more than one OPs' input, inplace must be False.
+        name (str, optional): 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 of relu operator, a Tensor with shape same as input
+
+    Examples:
+        .. code-block:: python
+
+          import paddle.fluid as fluid
+          import paddle.nn.functional as functional
+          import numpy as np
+
+          data = np.array([-2, 0, 1]).astype('float32')
+          with fluid.dygraph.guard():
+              data = fluid.dygraph.to_variable(data)
+              res = functional.relu(data)  # [0, 0, 1]
+    """
+
+    if in_dygraph_mode():
+        if inplace:
+            warnings.warn(
+                "Inplace on ReLU is not allowed and will be discarded in dygraph mode currently."
+            )
+        return core.ops.relu(input)
+
+    helper = LayerHelper('relu', **locals())
+
+    outs = input if inplace else helper.create_variable_for_type_inference(
+        input.dtype)
+    helper.append_op(type='relu', inputs={'X': [input]}, outputs={'Out': outs})
+    return outs
+
+
+def log_softmax(input, axis=None, dtype=None, name=None):
+    """
+    This operator implements the log_softmax layer. The calculation process is as follows:
+
+    .. math::
+
+        Out[i, j] = log(softmax(x)) 
+                  = log(\\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])})
+
+    Parameters:
+        input (Variable): The input variable. A multi-dimension Tensor with type float32, or float64.
+        axis (int, optional): The index of dimension to perform softmax calculations, it should be in
+            range :math:`[-1, rank-1]`, while :math:`rank` is the rank of input variable. Default: None. 
+            None and -1 means the last dimension.
+        dtype (np.dtype|core.VarDesc.VarType|str): The desired data type of returned tensor. If specified,
+            the input tensor is casted to dtype before the operation is performed. This is useful for
+            preventing data type overflows. Default: None. Supported dtype: float32 or float64
+        name (str, optional): 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:
+        Variable: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input``.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle.fluid as fluid
+          import paddle.nn.functional as F
+          import numpy as np
+
+          data = np.array([[[-2.0, 3.0, -4.0, 5.0],
+                            [3.0, -4.0, 5.0, -6.0],
+                            [-7.0, -8.0, 8.0, 9.0]],
+                           [[1.0, -2.0, -3.0, 4.0],
+                            [-5.0, 6.0, 7.0, -8.0],
+                            [6.0, 7.0, 8.0, 9.0]]]).astype('float32')
+          with fluid.dygraph.guard():
+              data = fluid.dygraph.to_variable(data)
+              res = F.log_softmax(data, -1)
+              # [[[ -7.1278396   -2.1278396   -9.127839    -0.12783948]
+              #   [ -2.1270514   -9.127051    -0.12705144 -11.127051  ]
+              #   [-16.313261   -17.313261    -1.3132617   -0.31326184]]
+              #  [[ -3.0518122   -6.051812    -7.051812    -0.051812  ]
+              #   [-12.313267    -1.3132664   -0.3132665  -15.313267  ]
+              #   [ -3.4401896   -2.4401896   -1.4401896   -0.44018966]]]
+    """
+
+    axis = -1 if axis is None else axis
+    dtype = convert_np_dtype_to_dtype_(dtype) if dtype is not None else dtype
+
+    if in_dygraph_mode():
+        outs_cast = input if dtype is None \
+            else core.ops.cast(input, 'in_dtype', input.dtype, 'out_dtype', dtype)
+        outs_softmax = core.ops.softmax(outs_cast, 'axis', axis, 'use_cudnn',
+                                        False)
+        return core.ops.log(outs_softmax)
+
+    helper = LayerHelper("log_softmax", **locals())
+
+    outs_cast = input
+    if dtype is not None:
+        outs_cast = helper.create_variable_for_type_inference(dtype)
+        helper.append_op(
+            type='cast',
+            inputs={'X': input},
+            outputs={'Out': outs_cast},
+            attrs={'in_dtype': input.dtype,
+                   'out_dtype': dtype})
+
+    outs_softmax = helper.create_variable_for_type_inference(outs_cast.dtype)
+    helper.append_op(
+        type='softmax',
+        inputs={'X': outs_cast},
+        outputs={'Out': outs_softmax},
+        attrs={'axis': axis,
+               'use_cudnn': False})
+
+    outs_log = helper.create_variable_for_type_inference(outs_softmax.dtype)
+    helper.append_op(
+        type='log', inputs={'X': outs_softmax}, outputs={'Out': outs_log})
+
+    return outs_log

python/paddle/nn/layer/__init__.py

@@ -14,10 +14,12 @@
 
 # TODO: define activation functions of neural network
 
+from . import activation
 from . import loss
 from . import conv
 from . import norm
 
+from .activation import *
 from .loss import *
 from .conv import *
 from .norm import *

python/paddle/nn/layer/activation.py

@@ -12,5 +12,108 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from ...fluid.dygraph import layers
+from ...fluid import core
+from ...fluid.framework import in_dygraph_mode
+from .. import functional
+
 # TODO: define activation functions of neural network  
-# __all__ = ['PReLU', 'ReLU', 'Sigmoid', 'Softmax', 'LogSoftmax']
+__all__ = [
+    # 'PReLU',
+    'ReLU',
+    # 'Sigmoid',
+    # 'Softmax',
+    'LogSoftmax',
+]
+
+
+class ReLU(layers.Layer):
+    """
+    ReLU Activation.
+
+    .. math:
+
+        out = max(x, 0)
+
+    Parameters:
+        inplace (bool, optional): If inplace is True, the input and output of 
+            ``ReLU`` are the same variable. Otherwise, the input and output of
+            ``ReLU`` are different variables. Default False. Note that if x is
+            more than one OPs' input, inplace must be False.
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+          import paddle.fluid as fluid
+          import paddle.nn as nn
+          import numpy as np
+
+          data = np.array([-2, 0, 1]).astype('float32')
+          my_relu = nn.ReLU()
+          with fluid.dygraph.guard():
+              data = fluid.dygraph.to_variable(data)
+              res = my_relu(data)  # [0, 0, 1]
+    """
+
+    def __init__(self, inplace=False):
+        super(ReLU, self).__init__()
+        self._inplace = inplace
+
+    def forward(self, input):
+        return functional.relu(input, self._inplace)
+
+
+class LogSoftmax(layers.Layer):
+    """
+    This operator implements the log_softmax layer. The calculation process is as follows:
+
+    .. math::
+
+        Out[i, j] = log(softmax(x)) 
+                  = log(\\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])})
+
+    Parameters:
+        axis (int, optional): The index of dimension to perform softmax calculations, it should be in
+            range :math:`[-1, rank-1]`, while :math:`rank` is the rank of input variable. Default: None. 
+            None and -1 means the last dimension.
+        dtype (np.dtype|core.VarDesc.VarType|str): The desired data type of returned tensor. If specified,
+            the input tensor is casted to dtype before the operation is performed. This is useful for
+            preventing data type overflows. Default: None. Supported dtype: float32 or float64
+ 
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+          import paddle.fluid as fluid
+          import paddle.nn as nn
+          import numpy as np
+
+          data = np.array([[[-2.0, 3.0, -4.0, 5.0],
+                            [3.0, -4.0, 5.0, -6.0],
+                            [-7.0, -8.0, 8.0, 9.0]],
+                           [[1.0, -2.0, -3.0, 4.0],
+                            [-5.0, 6.0, 7.0, -8.0],
+                            [6.0, 7.0, 8.0, 9.0]]]).astype('float32')
+          my_log_softnmax = nn.LogSoftmax()
+          with fluid.dygraph.guard():
+              data = fluid.dygraph.to_variable(data)
+              res = my_log_softnmax(data)
+              # [[[ -7.1278396   -2.1278396   -9.127839    -0.12783948]
+              #   [ -2.1270514   -9.127051    -0.12705144 -11.127051  ]
+              #   [-16.313261   -17.313261    -1.3132617   -0.31326184]]
+              #  [[ -3.0518122   -6.051812    -7.051812    -0.051812  ]
+              #   [-12.313267    -1.3132664   -0.3132665  -15.313267  ]
+              #   [ -3.4401896   -2.4401896   -1.4401896   -0.44018966]]]
+    """
+
+    def __init__(self, axis=None):
+        super(LogSoftmax, self).__init__()
+        self._axis = axis
+
+    def forward(self, input):
+        return functional.log_softmax(input, self._axis)

python/setup.py.in

@@ -105,8 +105,6 @@ write_version_py(filename='@PADDLE_BINARY_DIR@/python/paddle/version.py')
 
 
 packages=['paddle',
-          'paddle.nn',
-          'paddle.nn.layer',
           'paddle.libs',
           'paddle.utils',
           'paddle.dataset',