# Copyright (c) 2018 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 paddle import paddle.fluid as fluid import unittest import numpy as np class TestAllcloseLayer(unittest.TestCase): def allclose_check(self, use_cuda, dtype='float32'): a = fluid.data(name="a", shape=[2], dtype=dtype) b = fluid.data(name="b", shape=[2], dtype=dtype) result = paddle.allclose( a, b, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") result_nan = paddle.allclose( a, b, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") result_corner = paddle.allclose( a, b, rtol=0.01, atol=0.0, name="corner_case") place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) x = np.array([10000., 1e-07]).astype(dtype) y = np.array([10000.1, 1e-08]).astype(dtype) result_v, result_nan_v = exe.run(feed={'a': x, 'b': y}, fetch_list=[result, result_nan]) self.assertEqual(result_v[0], False) self.assertEqual(result_nan_v[0], False) x = np.array([10000., 1e-08]).astype(dtype) y = np.array([10000.1, 1e-09]).astype(dtype) result_v, result_nan_v = exe.run(feed={'a': x, 'b': y}, fetch_list=[result, result_nan]) self.assertEqual(result_v[0], True) self.assertEqual(result_nan_v[0], True) x = np.array([1.0, float('nan')]).astype(dtype) y = np.array([1.0, float('nan')]).astype(dtype) result_v, result_nan_v = exe.run(feed={'a': x, 'b': y}, fetch_list=[result, result_nan]) self.assertEqual(result_v[0], False) self.assertEqual(result_nan_v[0], True) # for corner case x = np.array([10.1, 10.1]).astype(dtype) y = np.array([10, 10]).astype(dtype) result_c, = exe.run(feed={'a': x, 'b': y}, fetch_list=[result_corner]) corner_res = (dtype == 'float64') self.assertEqual(result_c[0], corner_res) def test_allclose_cpu_fp32(self): main = fluid.Program() startup = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, startup): self.allclose_check(use_cuda=False, dtype='float32') def test_allclose_cpu_fp64(self): main = fluid.Program() startup = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, startup): self.allclose_check(use_cuda=False, dtype='float64') def test_allclose_gpu_fp32(self): if fluid.core.is_compiled_with_cuda(): main = fluid.Program() startup = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, startup): self.allclose_check(use_cuda=True, dtype='float32') def test_allclose_gpu_fp64(self): if fluid.core.is_compiled_with_cuda(): main = fluid.Program() startup = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, startup): self.allclose_check(use_cuda=True, dtype='float64') def test_dygraph_mode(self): x_1 = np.array([10000., 1e-07]).astype("float32") y_1 = np.array([10000.1, 1e-08]).astype("float32") x_2 = np.array([10000., 1e-08]).astype("float32") y_2 = np.array([10000.1, 1e-09]).astype("float32") x_3 = np.array([1.0, float('nan')]).astype("float32") y_3 = np.array([1.0, float('nan')]).astype("float32") x_4 = np.array([10.1]).astype("float32") y_4 = np.array([10]).astype("float32") x_5 = np.array([10.1]).astype("float64") y_5 = np.array([10]).astype("float64") with fluid.dygraph.guard(): x_v_1 = paddle.to_tensor(x_1) y_v_1 = paddle.to_tensor(y_1) ret_1 = paddle.allclose( x_v_1, y_v_1, rtol=1e-05, atol=1e-08, equal_nan=False, name='test_1') self.assertEqual(ret_1.numpy()[0], False) ret_1 = paddle.allclose( x_v_1, y_v_1, rtol=1e-05, atol=1e-08, equal_nan=True, name='test_2') self.assertEqual(ret_1.numpy()[0], False) x_v_2 = paddle.to_tensor(x_2) y_v_2 = paddle.to_tensor(y_2) ret_2 = paddle.allclose( x_v_2, y_v_2, rtol=1e-05, atol=1e-08, equal_nan=False, name='test_3') self.assertEqual(ret_2.numpy()[0], True) ret_2 = paddle.allclose( x_v_2, y_v_2, rtol=1e-05, atol=1e-08, equal_nan=True, name='test_4') self.assertEqual(ret_2.numpy()[0], True) x_v_3 = paddle.to_tensor(x_3) y_v_3 = paddle.to_tensor(y_3) ret_3 = paddle.allclose( x_v_3, y_v_3, rtol=1e-05, atol=1e-08, equal_nan=False, name='test_5') self.assertEqual(ret_3.numpy()[0], False) ret_3 = paddle.allclose( x_v_3, y_v_3, rtol=1e-05, atol=1e-08, equal_nan=True, name='test_6') self.assertEqual(ret_3.numpy()[0], True) # for corner case x_v_4 = paddle.to_tensor(x_4) y_v_4 = paddle.to_tensor(y_4) ret_4 = paddle.allclose( x_v_4, y_v_4, rtol=0.01, atol=0.0, name='test_7') self.assertEqual(ret_4.numpy()[0], False) x_v_5 = paddle.to_tensor(x_5) y_v_5 = paddle.to_tensor(y_5) ret_5 = paddle.allclose( x_v_5, y_v_5, rtol=0.01, atol=0.0, name='test_8') self.assertEqual(ret_5.numpy()[0], True) if __name__ == "__main__": unittest.main()