paddle.nn.functional.logsigmoid -> log_sigmoid (#27277)

python/paddle/fluid/dygraph/math_op_patch.py

@@ -285,7 +285,7 @@ def monkey_patch_math_varbase():
         ('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None)),
         ('__array_ufunc__', None),
         ('sigmoid', _method_creator_('sigmoid', 'name=None')),
-        ('logsigmoid', _method_creator_('logsigmoid', 'name=None')),
+        ('log_sigmoid', _method_creator_('logsigmoid', 'name=None')),
         ('exp', _method_creator_('exp', 'name=None')),
         ('tanh', _method_creator_('tanh', 'name=None')),
         ('atan', _method_creator_('atan', 'name=None')),

python/paddle/fluid/layers/ops.py

@@ -20,7 +20,10 @@ from ..framework import convert_np_dtype_to_dtype_, Variable
 from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
 from paddle.utils import deprecated
 
-__deprecated_func_name__ = {'tanh_shrink': 'tanhshrink', }
+__deprecated_func_name__ = {
+    'tanh_shrink': 'tanhshrink',
+    'logsigmoid': 'log_sigmoid'
+}
 
 __activations_noattr__ = [
     'sigmoid',
@@ -106,7 +109,7 @@ Examples:
         paddle.disable_static()
 
         x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
-        out = F.logsigmoid(x)
+        out = F.log_sigmoid(x)
         print(out.numpy())
         # [-0.91301525 -0.79813887 -0.64439666 -0.55435524]
 

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

@@ -128,7 +128,7 @@ class TestLogSigmoid(TestActivation):
 
 
 class TestLogSigmoidAPI(unittest.TestCase):
-    # test paddle.nn.LogSigmoid, paddle.nn.functional.logsigmoid
+    # test paddle.nn.LogSigmoid, paddle.nn.functional.log_sigmoid
     def setUp(self):
         self.x_np = np.random.uniform(-1, 1, [11, 17]).astype('float32')
         self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
@@ -137,36 +137,45 @@ class TestLogSigmoidAPI(unittest.TestCase):
     def test_static_api(self):
         with paddle.static.program_guard(paddle.static.Program()):
             x = paddle.data('X', [11, 17])
-            out1 = F.logsigmoid(x)
+            out1 = F.log_sigmoid(x)
             m = paddle.nn.LogSigmoid()
             out2 = m(x)
             exe = paddle.static.Executor(self.place)
             res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
         out_ref = np.log(1 / (1 + np.exp(-self.x_np)))
         for r in res:
-            self.assertEqual(np.allclose(out_ref, r), True)
+            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.logsigmoid(x)
+        out1 = F.log_sigmoid(x)
         m = paddle.nn.LogSigmoid()
         out2 = m(x)
         out_ref = np.log(1 / (1 + np.exp(-self.x_np)))
         for r in [out1, out2]:
-            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+            self.assertTrue(np.allclose(out_ref, r.numpy()))
         paddle.enable_static()
 
+    def test_fluid_api(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', [11, 17])
+            out = paddle.fluid.layers.logsigmoid(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
+        out_ref = np.log(1 / (1 + np.exp(-self.x_np)))
+        self.assertTrue(np.allclose(out_ref, res[0]))
+
     def test_errors(self):
         with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, F.logsigmoid, 1)
+            self.assertRaises(TypeError, F.log_sigmoid, 1)
             # The input dtype must be float16, float32, float64.
             x_int32 = paddle.data(name='x_int32', shape=[11, 17], dtype='int32')
-            self.assertRaises(TypeError, F.logsigmoid, x_int32)
+            self.assertRaises(TypeError, F.log_sigmoid, x_int32)
             # support the input dtype is float16
             x_fp16 = paddle.data(name='x_fp16', shape=[11, 17], dtype='float16')
-            F.logsigmoid(x_fp16)
+            F.log_sigmoid(x_fp16)
 
 
 class TestTanh(TestActivation, TestParameter):

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

@@ -2677,13 +2677,6 @@ class TestBook(LayerTest):
             out = layers.sigmoid(input, name='sigmoid')
             return (out)
 
-    def make_logsigmoid(self):
-        with program_guard(fluid.default_main_program(),
-                           fluid.default_startup_program()):
-            input = self._get_data(name="input", shape=[16], dtype="float32")
-            out = layers.logsigmoid(input, name='logsigmoid')
-            return (out)
-
     def make_exp(self):
         with program_guard(fluid.default_main_program(),
                            fluid.default_startup_program()):

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

@@ -307,7 +307,7 @@ class TestMathOpPatchesVarBase(unittest.TestCase):
             np.array_equal(x.sigmoid().numpy(), fluid.layers.sigmoid(x).numpy(
             )))
         self.assertTrue(
-            np.array_equal(x.logsigmoid().numpy(),
+            np.array_equal(x.log_sigmoid().numpy(),
                            fluid.layers.logsigmoid(x).numpy()))
         self.assertTrue(np.array_equal(x.exp().numpy(), paddle.exp(x).numpy()))
         self.assertTrue(

python/paddle/nn/functional/__init__.py

@@ -39,7 +39,7 @@ from .activation import hard_sigmoid  #DEFINE_ALIAS
 from .activation import hard_swish  #DEFINE_ALIAS
 from .activation import hsigmoid  #DEFINE_ALIAS
 from .activation import leaky_relu  #DEFINE_ALIAS
-from .activation import logsigmoid  #DEFINE_ALIAS
+from .activation import log_sigmoid  #DEFINE_ALIAS
 from .activation import maxout  #DEFINE_ALIAS
 from .activation import prelu  #DEFINE_ALIAS
 from .activation import relu  #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -35,7 +35,7 @@ __all__ = [
     'hard_swish',
     'hsigmoid',
     'leaky_relu',
-    'logsigmoid',
+    'log_sigmoid',
     'maxout',
     'prelu',
     'relu',
@@ -552,13 +552,13 @@ def relu(x, name=None):
     return out
 
 
-def logsigmoid(x, name=None):
+def log_sigmoid(x, name=None):
     """
-    logsigmoid activation.
+    log_sigmoid activation.
 
     .. math::
 
-        logsigmoid(x) = log \\frac{1}{1 + e^{-x}}
+        log\\_sigmoid(x) = log \\frac{1}{1 + e^{-x}}
     
     Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
@@ -573,20 +573,19 @@ def logsigmoid(x, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
             paddle.disable_static()
 
-            x = paddle.to_tensor(np.array([1.0, 2.0, 3.0, 4.0]))
-            out = F.logsigmoid(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
+            x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
+            out = F.log_sigmoid(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
     """
 
     if in_dygraph_mode():
         return core.ops.logsigmoid(x)
 
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
-                             'logsigmoid')
-    helper = LayerHelper("logsigmoid", **locals())
+                             'log_sigmoid')
+    helper = LayerHelper("log_sigmoid", **locals())
     out = helper.create_variable_for_type_inference(x.dtype)
     helper.append_op(type='logsigmoid', inputs={'X': x}, outputs={'Out': out})
     return out

python/paddle/nn/layer/activation.py

@@ -860,11 +860,10 @@ class LogSigmoid(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
             paddle.disable_static()
 
-            x = paddle.to_tensor(np.array([1.0, 2.0, 3.0, 4.0]))
+            x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
             m = paddle.nn.LogSigmoid()
             out = m(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
     """
@@ -874,7 +873,7 @@ class LogSigmoid(layers.Layer):
         self._name = name
 
     def forward(self, x):
-        return F.logsigmoid(x, self._name)
+        return F.log_sigmoid(x, self._name)
 
 
 class Softmax(layers.Layer):