Add paddle.rad2deg and paddle.deg2rad (#37598)

python/paddle/__init__.py

@@ -223,6 +223,8 @@
 from .tensor.math import digamma  # noqa: F401
 from .tensor.math import neg  # noqa: F401
 from .tensor.math import lgamma  # noqa: F401
+from .tensor.math import rad2deg  # noqa: F401
+from .tensor.math import deg2rad  # noqa: F401
 from .tensor.math import diff  # noqa: F401
 
 from .tensor.random import multinomial  # noqa: F401
@@ -458,6 +460,8 @@
            'ceil',
            'atan',
            'atan2',
+           'rad2deg',
+           'deg2rad',
            'expand',
            'broadcast_to',
            'ones_like',

python/paddle/fluid/tests/unittests/test_deg2rad.py

@@ -0,0 +1,75 @@
+#   Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid import Program, program_guard
+from op_test import OpTest
+
+paddle.enable_static()
+
+
+class TestDeg2radAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_dtype = 'float64'
+        self.x_np = np.array(
+            [180.0, -180.0, 360.0, -360.0, 90.0, -90.0]).astype(np.float64)
+        self.x_shape = [6]
+        self.out_np = np.deg2rad(self.x_np)
+
+    def test_static_graph(self):
+        startup_program = fluid.Program()
+        train_program = fluid.Program()
+        with fluid.program_guard(startup_program, train_program):
+            x = fluid.data(name='input', dtype=self.x_dtype, shape=self.x_shape)
+            out = paddle.deg2rad(x)
+
+            place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            res = exe.run(fluid.default_main_program(),
+                          feed={'input': self.x_np},
+                          fetch_list=[out])
+            self.assertTrue((np.array(out[0]) == self.out_np).all())
+
+    def test_dygraph(self):
+        paddle.disable_static()
+        x1 = paddle.to_tensor([180.0, -180.0, 360.0, -360.0, 90.0, -90.0])
+        result1 = paddle.deg2rad(x1)
+        self.assertEqual(np.allclose(self.out_np, result1.numpy()), True)
+
+        paddle.enable_static()
+
+
+class TestDeg2radAPI2(TestDeg2radAPI):
+    # Test input data type is int
+    def setUp(self):
+        self.x_np = 180
+        self.x_shape = [1]
+        self.out_np = np.pi
+        self.x_dtype = 'int64'
+
+    def test_dygraph(self):
+        paddle.disable_static()
+
+        x2 = paddle.to_tensor(180)
+        result2 = paddle.deg2rad(x2)
+        self.assertEqual(np.allclose(np.pi, result2.numpy()), True)
+
+        paddle.enable_static()

python/paddle/fluid/tests/unittests/test_rad2deg.py

@@ -0,0 +1,92 @@
+#   Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid import Program, program_guard
+from op_test import OpTest
+
+paddle.enable_static()
+
+
+class TestRad2degAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_dtype = 'float64'
+        self.x_np = np.array(
+            [3.142, -3.142, 6.283, -6.283, 1.570, -1.570]).astype(np.float64)
+        self.x_shape = [6]
+        self.out_np = np.rad2deg(self.x_np)
+
+    def test_static_graph(self):
+        startup_program = fluid.Program()
+        train_program = fluid.Program()
+        with fluid.program_guard(startup_program, train_program):
+            x = fluid.data(name='input', dtype=self.x_dtype, shape=self.x_shape)
+            out = paddle.rad2deg(x)
+
+            place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            res = exe.run(fluid.default_main_program(),
+                          feed={'input': self.x_np},
+                          fetch_list=[out])
+            self.assertTrue((np.array(out[0]) == self.out_np).all())
+
+    def test_dygraph(self):
+        paddle.disable_static()
+        x1 = paddle.to_tensor([3.142, -3.142, 6.283, -6.283, 1.570, -1.570])
+        result1 = paddle.rad2deg(x1)
+        self.assertEqual(np.allclose(self.out_np, result1.numpy()), True)
+
+        paddle.enable_static()
+
+
+class TestRad2degAPI2(TestRad2degAPI):
+    def setUp(self):
+        self.x_np = np.pi / 2
+        self.x_shape = [1]
+        self.out_np = 90
+        self.x_dtype = 'float32'
+
+    def test_dygraph(self):
+        paddle.disable_static()
+
+        x2 = paddle.to_tensor(np.pi / 2)
+        result2 = paddle.rad2deg(x2)
+        self.assertEqual(np.allclose(90, result2.numpy()), True)
+
+        paddle.enable_static()
+
+
+class TestRad2degAPI3(TestRad2degAPI):
+    # Test input data type is int
+    def setUp(self):
+        self.x_np = 1
+        self.x_shape = [1]
+        self.out_np = 180 / np.pi
+        self.x_dtype = 'int64'
+
+    def test_dygraph(self):
+        paddle.disable_static()
+
+        x2 = paddle.to_tensor(1)
+        result2 = paddle.rad2deg(x2)
+        self.assertEqual(np.allclose(180 / np.pi, result2.numpy()), True)
+
+        paddle.enable_static()

python/paddle/tensor/__init__.py

@@ -189,6 +189,8 @@
 from .math import neg  # noqa: F401
 from .math import lgamma  # noqa: F401
 from .math import diagonal  # noqa: F401
+from .math import rad2deg  # noqa: F401
+from .math import deg2rad  # noqa: F401
 from .math import diff  # noqa: F401
 
 from .random import multinomial  # noqa: F401

python/paddle/tensor/math.py

@@ -2612,6 +2612,117 @@ def atan2(x, y, name=None):
                 type='atan2', inputs=inputs, outputs={'Out': out})
         return out
 
+def rad2deg(x, name=None):
+    """
+    Convert each of the elements of input x from angles in radians to degrees.
+    
+    Equation:
+        .. math::
+
+            rad2deg(x)=180/ \pi * x
+
+    Args:
+        x (Tensor): An N-D Tensor, the data type is float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        out (Tensor): An N-D Tensor, the shape and data type is the same with input (The output data type is float32 when the input data type is int).
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            
+            x1 = paddle.to_tensor([3.142, -3.142, 6.283, -6.283, 1.570, -1.570])
+            result1 = paddle.rad2deg(x1)
+            print(result1)
+            # Tensor(shape=[6], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #         [180.02334595, -180.02334595,  359.98937988, -359.98937988,
+            #           9.95437622 , -89.95437622])
+
+            x2 = paddle.to_tensor(np.pi/2)
+            result2 = paddle.rad2deg(x2)
+            print(result2)
+            # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #         [90.])
+                     
+            x3 = paddle.to_tensor(1)
+            result3 = paddle.rad2deg(x3)
+            print(result3)
+            # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #         [57.29578018])
+    """
+    rad2deg_scale = 180 / np.pi
+    if in_dygraph_mode():
+        if convert_dtype(x.dtype) in ['int32', 'int64']:
+            x = cast(x, dtype="float32")
+        return _C_ops.scale(x, 'scale', rad2deg_scale)
+    else:
+        check_variable_and_dtype(x, 'x', ['int32', 'int64', 'float32', 'float64'], 'rad2deg')
+        helper = LayerHelper('rad2deg', **locals())
+        out_cast = x
+        if convert_dtype(x.dtype) in ['int32', 'int64']:
+            out_cast = helper.create_variable_for_type_inference(dtype=paddle.float32)
+            helper.append_op(
+                    type='cast', inputs={'X':x}, outputs={'Out': out_cast}, attrs={'in_dtype': x.dtype,'out_dtype': paddle.float32})
+        out = helper.create_variable_for_type_inference(dtype=out_cast.dtype)
+        helper.append_op(
+            type='scale', inputs={'X':out_cast}, outputs={'Out': out}, attrs={'scale': rad2deg_scale})
+        return out
+
+def deg2rad(x, name=None):
+    """
+    Convert each of the elements of input x from degrees to angles in radians.
+    
+    Equation:
+        .. math::
+
+            deg2rad(x)=\pi * x / 180
+
+    Args:
+        x (Tensor): An N-D Tensor, the data type is float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        out (Tensor): An N-D Tensor, the shape and data type is the same with input (The output data type is float32 when the input data type is int).
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            
+            x1 = paddle.to_tensor([180.0, -180.0, 360.0, -360.0, 90.0, -90.0])
+            result1 = paddle.deg2rad(x1)
+            print(result1)
+            # Tensor(shape=[6], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #         [3.14159274, -3.14159274,  6.28318548, -6.28318548,  1.57079637,
+            #           -1.57079637])
+
+            x2 = paddle.to_tensor(180)
+            result2 = paddle.deg2rad(x2)
+            print(result2)
+            # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #         [3.14159274])
+    """
+    deg2rad_scale = np.pi / 180.0
+    if in_dygraph_mode():
+        if convert_dtype(x.dtype) in ['int32', 'int64']:
+            x = cast(x, dtype="float32")
+        return _C_ops.scale(x, 'scale', deg2rad_scale)
+    else:
+        check_variable_and_dtype(x, 'x', ['int32', 'int64', 'float32', 'float64'], 'deg2rad')
+        helper = LayerHelper('deg2rad', **locals())
+        out_cast = x
+        if convert_dtype(x.dtype) in ['int32', 'int64']:
+            out_cast = helper.create_variable_for_type_inference(dtype=paddle.float32)
+            helper.append_op(
+                    type='cast', inputs={'X':x}, outputs={'Out': out_cast}, attrs={'in_dtype': x.dtype,'out_dtype': paddle.float32})
+        out = helper.create_variable_for_type_inference(dtype=out_cast.dtype)
+        helper.append_op(
+            type='scale', inputs={'X':out_cast}, outputs={'Out': out}, attrs={'scale': deg2rad_scale})
+        return out
 
 def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
     r"""
@@ -2646,6 +2757,7 @@ def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
         .. code-block:: python
 
             import paddle
+
             x = paddle.to_tensor([1, 4, 5, 2])
             out = paddle.diff(x)
             print(out)
@@ -2667,8 +2779,6 @@ def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
             print(out)
             # out:
             # [[1, 1], [1, 1]]
-
-
     """
 
     if axis < 0: