Add logsigmoid and reduce_logsumexp

mindspore/nn/layer/__init__.py

@@ -17,7 +17,7 @@ Layer.
 
 The high-level components(Cells) used to construct the neural network.
 """
-from . import activation, normalization, container, conv, lstm, basic, embedding, pooling, image, quant
+from . import activation, normalization, container, conv, lstm, basic, embedding, pooling, image, quant, math
 from .activation import *
 from .normalization import *
 from .container import *
@@ -28,6 +28,7 @@ from .embedding import *
 from .pooling import *
 from .image import *
 from .quant import *
+from .math import *
 
 __all__ = []
 __all__.extend(activation.__all__)
@@ -40,3 +41,4 @@ __all__.extend(embedding.__all__)
 __all__.extend(pooling.__all__)
 __all__.extend(image.__all__)
 __all__.extend(quant.__all__)
+__all__.extend(math.__all__)

mindspore/nn/layer/activation.py

@@ -35,6 +35,7 @@ __all__ = ['Softmax',
            'HSigmoid',
            'HSwish',
            'ELU',
+           'LogSigmoid',
            ]
 
 
@@ -476,6 +477,49 @@ class HSigmoid(Cell):
         return self.hsigmoid(x)
 
 
+class LogSigmoid(Cell):
+    r"""
+    Logsigmoid activation function.
+
+    Applies logsigmoid activation element-wise. The input is a Tensor with any valid shape.
+
+    Logsigmoid is defined as:
+
+    .. math::
+        \text{logsigmoid}(x_{i}) = log(\frac{1}{1 + \exp(-x_i)}),
+
+    where :math:`x_{i}` is the element of the input.
+
+    Inputs:
+        - **input_data** (Tensor) - The input of LogSigmoid.
+
+    Outputs:
+        Tensor, with the same type and shape as the `input_data`.
+
+    Examples:
+        >>> net = nn.LogSigmoid()
+        >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
+        >>> logsigmoid = net(input_x)
+        [-3.1326166e-01, -1.2692806e-01, -4.8587345e-02]
+
+    """
+    def __init__(self):
+        super(LogSigmoid, self).__init__()
+        self.mul = P.Mul()
+        self.exp = P.Exp()
+        self.add = P.TensorAdd()
+        self.rec = P.Reciprocal()
+        self.log = P.Log()
+
+    def construct(self, input_x):
+        neg_input = self.mul(input_x, -1)
+        exp_neg_input = self.exp(neg_input)
+        exp_neg_input_1 = self.add(exp_neg_input, 1)
+        rec_exp_neg_input_1 = self.rec(exp_neg_input_1)
+        ret = self.log(rec_exp_neg_input_1)
+        return ret
+
+
 _activation = {
     'softmax': Softmax,
     'logsoftmax': LogSoftmax,
@@ -488,6 +532,7 @@ _activation = {
     'leakyrelu': LeakyReLU,
     'hswish': HSwish,
     'hsigmoid': HSigmoid,
+    'logsigmoid': LogSigmoid,
 }
 
 

mindspore/nn/layer/math.py

@@ -0,0 +1,68 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""math"""
+from mindspore.ops import operations as P
+from ..cell import Cell
+from ..._checkparam import Validator as validator
+
+__all__ = ['ReduceLogSumExp']
+
+class ReduceLogSumExp(Cell):
+    r"""
+    Reduce a dimension of a tensor by calculating exponential for all elements in the dimension,
+    then calculate logarithm of the sum.
+
+    The dtype of the tensor to be reduced is number.
+
+    Args:
+        keep_dims (bool): If True, keep these reduced dimensions and the length is 1.
+                          If False, don't keep these dimensions.
+                          Default : False.
+
+    Inputs:
+        - **input_x** (Tensor[Number]) - The input tensor.
+        - **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
+          Only constant value is allowed.
+
+    Outputs:
+        Tensor, has the same dtype as the 'input_x'.
+
+        - If axis is (), and keep_dims is false,
+          the output is a 0-D tensor representing the sum of all elements in the input tensor.
+        - If axis is int, set as 2, and keep_dims is false,
+          the shape of output is :math:`(x_1, x_3, ..., x_R)`.
+        - If axis is tuple(int), set as (2, 3), and keep_dims is false,
+          the shape of output is :math:`(x_1, x_4, ..., x_R)`.
+
+    Examples:
+        >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
+        >>> op = P.ReduceLogSumExp(keep_dims=True)
+        >>> output = op(input_x, 1)
+    """
+
+    def __init__(self, axis, keep_dims=False):
+        super(ReduceLogSumExp, self).__init__()
+        validator.check_value_type('axis', axis, [int, list, tuple], self.cls_name)
+        validator.check_value_type('keep_dims', keep_dims, [bool], self.cls_name)
+        self.axis = axis
+        self.exp = P.Exp()
+        self.sum = P.ReduceSum(keep_dims)
+        self.log = P.Log()
+
+    def construct(self, input_x):
+        exp = self.exp(input_x)
+        sumexp = self.sum(exp, self.axis)
+        logsumexp = self.log(sumexp)
+        return logsumexp

tests/ut/python/ops/test_nn_ops.py

@@ -522,6 +522,16 @@ test_cases = [
         'desc_inputs': [Tensor(np.ones([1, 1, 3, 3], np.float32))],
         'desc_bprop': [Tensor(np.ones([1, 4, 2, 2], np.float32))],
         'skip': ['backward']}),
+    ('LogSigmoid', {
+        'block': nn.LogSigmoid(),
+        'desc_inputs': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
+        'desc_bprop': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    ('ReduceLogSumExp', {
+        'block': nn.ReduceLogSumExp((0, ), False),
+        'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
+        'desc_bprop': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
 ]
 
 test_cases_for_verify_exception = [
@@ -621,6 +631,20 @@ test_cases_for_verify_exception = [
         ),
         'desc_inputs': [Tensor(np.random.randn(32, 3, 112, 112).astype(np.float32).transpose(0, 3, 1, 2))],
     }),
+    ('ReduceLogsumexp_TypeError_1', {
+        'block': (
+            lambda _: nn.ReduceLogSumExp(axis=(0,), keep_dims=2),
+            {'exception': TypeError},
+        ),
+        'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
+    }),
+    ('ReduceLogsumexp_TypeError_2', {
+        'block': (
+            lambda _: nn.ReduceLogSumExp(axis=1.2, keep_dims=True),
+            {'exception': TypeError},
+        ),
+        'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
+    }),
 ]