# Copyright (c) 2021 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 numpy as np import unittest from op_test import OpTest import paddle import paddle.nn as nn import paddle.nn.functional as F import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid import compiler, Program, program_guard paddle.enable_static() np.random.seed(0) def atan2_grad(x1, x2, dout): dx1 = dout * x2 / (x1 * x1 + x2 * x2) dx2 = -dout * x1 / (x1 * x1 + x2 * x2) return dx1, dx2 class TestAtan2(OpTest): def setUp(self): self.op_type = "atan2" self.python_api = paddle.atan2 self.init_dtype() x1 = np.random.uniform(-1, -0.1, [15, 17]).astype(self.dtype) x2 = np.random.uniform(0.1, 1, [15, 17]).astype(self.dtype) out = np.arctan2(x1, x2) self.inputs = {'X1': x1, 'X2': x2} self.outputs = {'Out': out} def test_check_grad(self): self.check_grad(['X1', 'X2'], 'Out', check_eager=True) def test_check_output(self): self.check_output(check_eager=True) def init_dtype(self): self.dtype = np.float64 class TestAtan2_float(TestAtan2): def init_dtype(self): self.dtype = np.float32 def test_check_grad(self): if self.dtype not in [np.int32, np.int64]: self.check_grad( ['X1', 'X2'], 'Out', user_defined_grads=atan2_grad(self.inputs['X1'], self.inputs['X2'], 1 / self.inputs['X1'].size), check_eager=True) class TestAtan2_float16(TestAtan2_float): def init_dtype(self): self.dtype = np.float16 class TestAtan2_int32(TestAtan2_float): def init_dtype(self): self.dtype = np.int32 class TestAtan2_int64(TestAtan2_float): def init_dtype(self): self.dtype = np.int64 class TestAtan2API(unittest.TestCase): def init_dtype(self): self.dtype = 'float64' self.shape = [11, 17] def setUp(self): self.init_dtype() self.x1 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype) self.x2 = np.random.uniform(-1, -0.1, self.shape).astype(self.dtype) self.place = [paddle.CPUPlace()] if core.is_compiled_with_cuda(): self.place.append(paddle.CUDAPlace(0)) def test_static_api(self): paddle.enable_static() def run(place): with paddle.static.program_guard(paddle.static.Program()): X1 = paddle.fluid.data('X1', self.shape, dtype=self.dtype) X2 = paddle.fluid.data('X2', self.shape, dtype=self.dtype) out = paddle.atan2(X1, X2) exe = paddle.static.Executor(place) res = exe.run(feed={'X1': self.x1, 'X2': self.x2}) out_ref = np.arctan2(self.x1, self.x2) for r in res: self.assertEqual(np.allclose(out_ref, r), True) for place in self.place: run(place) def test_dygraph_api(self): def run(place): paddle.disable_static(place) X1 = paddle.to_tensor(self.x1) X2 = paddle.to_tensor(self.x2) out = paddle.atan2(X1, X2) out_ref = np.arctan2(self.x1, self.x2) self.assertEqual(np.allclose(out_ref, out.numpy()), True) paddle.enable_static() for place in self.place: run(place) if __name__ == '__main__': paddle.enable_static() unittest.main()