add deg2rad unittest

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

@@ -27,6 +27,7 @@
 
 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]
@@ -36,7 +37,7 @@ 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='float64', shape=self.x_shape)
+            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(
@@ -61,6 +62,7 @@ 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()
@@ -70,3 +72,21 @@ def test_dygraph(self):
         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/math.py

@@ -2622,11 +2622,11 @@ def rad2deg(x, name=None):
             rad2deg(x)=180/ \pi * x
 
     Args:
-        x (Tensor): An N-D Tensor, the data type is float32, float64.
+        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.
+        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
@@ -2638,25 +2638,37 @@ def rad2deg(x, name=None):
             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])
+            #         [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.])
+            #         [90.])
+                     
+            x3 = paddle.to_tensor(1)
+            result2 = 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', ['float32', 'float64'], 'rad2deg')
+        check_variable_and_dtype(x, 'x', ['int32', 'int64', 'float32', 'float64'], 'rad2deg')
         helper = LayerHelper('rad2deg', **locals())
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
+        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':x}, outputs={'Out': out}, attrs={'scale': rad2deg_scale})
+            type='scale', inputs={'X':out_cast}, outputs={'Out': out}, attrs={'scale': rad2deg_scale})
         return out
 
 def deg2rad(x, name=None):
@@ -2669,11 +2681,11 @@ def deg2rad(x, name=None):
             deg2rad(x)=\pi * x / 180
 
     Args:
-        x (Tensor): An N-D Tensor, the data type is float32, float64.
+        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.
+        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
@@ -2685,25 +2697,29 @@ def deg2rad(x, name=None):
             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])
+            #         [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])
+            #         [3.14159274])
     """
-
     deg2rad_scale = np.pi / 180.0
     if in_dygraph_mode():
-        if convert_dtype(x.dtype) in ['int64']:
-            x = cast(x, dtype="float64")
+        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', ['float32', 'float64'], 'deg2rad')
+        check_variable_and_dtype(x, 'x', ['int32', 'int64', 'float32', 'float64'], 'deg2rad')
         helper = LayerHelper('deg2rad', **locals())
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
+        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':x}, outputs={'Out': out}, attrs={'scale': deg2rad_scale})
+            type='scale', inputs={'X':out_cast}, outputs={'Out': out}, attrs={'scale': deg2rad_scale})
         return out