# 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. from __future__ import print_function import unittest import numpy as np import paddle import paddle.fluid.core as core from op_test import OpTest paddle.enable_static() class ApiFMinTest(unittest.TestCase): """ApiFMinTest""" def setUp(self): """setUp""" if core.is_compiled_with_cuda(): self.place = core.CUDAPlace(0) else: self.place = core.CPUPlace() self.input_x = np.random.rand(10, 15).astype("float32") self.input_y = np.random.rand(10, 15).astype("float32") self.input_z = np.random.rand(15).astype("float32") self.input_a = np.array([0, np.nan, np.nan]).astype('int64') self.input_b = np.array([2, np.inf, -np.inf]).astype('int64') self.input_c = np.array([4, 1, 3]).astype('int64') self.np_expected1 = np.fmin(self.input_x, self.input_y) self.np_expected2 = np.fmin(self.input_x, self.input_z) self.np_expected3 = np.fmin(self.input_a, self.input_c) self.np_expected4 = np.fmin(self.input_b, self.input_c) def test_static_api(self): """test_static_api""" paddle.enable_static() with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): data_x = paddle.static.data("x", shape=[10, 15], dtype="float32") data_y = paddle.static.data("y", shape=[10, 15], dtype="float32") result_fmin = paddle.fmin(data_x, data_y) exe = paddle.static.Executor(self.place) res, = exe.run(feed={ "x": self.input_x, "y": self.input_y }, fetch_list=[result_fmin]) np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05) with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): data_x = paddle.static.data("x", shape=[10, 15], dtype="float32") data_z = paddle.static.data("z", shape=[15], dtype="float32") result_fmin = paddle.fmin(data_x, data_z) exe = paddle.static.Executor(self.place) res, = exe.run(feed={ "x": self.input_x, "z": self.input_z }, fetch_list=[result_fmin]) np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05) with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): data_a = paddle.static.data("a", shape=[3], dtype="int64") data_c = paddle.static.data("c", shape=[3], dtype="int64") result_fmin = paddle.fmin(data_a, data_c) exe = paddle.static.Executor(self.place) res, = exe.run(feed={ "a": self.input_a, "c": self.input_c }, fetch_list=[result_fmin]) np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05) with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): data_b = paddle.static.data("b", shape=[3], dtype="int64") data_c = paddle.static.data("c", shape=[3], dtype="int64") result_fmin = paddle.fmin(data_b, data_c) exe = paddle.static.Executor(self.place) res, = exe.run(feed={ "b": self.input_b, "c": self.input_c }, fetch_list=[result_fmin]) np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05) def test_dynamic_api(self): """test_dynamic_api""" paddle.disable_static() x = paddle.to_tensor(self.input_x) y = paddle.to_tensor(self.input_y) z = paddle.to_tensor(self.input_z) a = paddle.to_tensor(self.input_a) b = paddle.to_tensor(self.input_b) c = paddle.to_tensor(self.input_c) res = paddle.fmin(x, y) res = res.numpy() np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05) # test broadcast res = paddle.fmin(x, z) res = res.numpy() np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05) res = paddle.fmin(a, c) res = res.numpy() np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05) res = paddle.fmin(b, c) res = res.numpy() np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05) class TestElementwiseFminOp(OpTest): """TestElementwiseFminOp""" def setUp(self): """setUp""" self.op_type = "elementwise_fmin" self.python_api = paddle.fmin # If x and y have the same value, the min() is not differentiable. # So we generate test data by the following method # to avoid them being too close to each other. x = np.random.uniform(0.1, 1, [13, 17]).astype("float64") sgn = np.random.choice([-1, 1], [13, 17]).astype("float64") y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float64") self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.fmin(self.inputs['X'], self.inputs['Y'])} def test_check_output(self): """test_check_output""" self.check_output(check_eager=True) def test_check_grad_normal(self): """test_check_grad_normal""" self.check_grad(['X', 'Y'], 'Out', check_eager=True) def test_check_grad_ingore_x(self): """test_check_grad_ingore_x""" self.check_grad(['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"), check_eager=True) def test_check_grad_ingore_y(self): """test_check_grad_ingore_y""" self.check_grad(['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'), check_eager=True) class TestElementwiseFmin2Op(OpTest): """TestElementwiseFmin2Op""" def setUp(self): """setUp""" self.op_type = "elementwise_fmin" self.python_api = paddle.fmin # If x and y have the same value, the min() is not differentiable. # So we generate test data by the following method # to avoid them being too close to each other. x = np.random.uniform(0.1, 1, [13, 17]).astype("float64") sgn = np.random.choice([-1, 1], [13, 17]).astype("float64") y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float64") y[2, 10:] = np.nan self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.fmin(self.inputs['X'], self.inputs['Y'])} def test_check_output(self): """test_check_output""" self.check_output(check_eager=True) def test_check_grad_normal(self): """test_check_grad_normal""" self.check_grad(['X', 'Y'], 'Out', check_eager=True) def test_check_grad_ingore_x(self): """test_check_grad_ingore_x""" self.check_grad(['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"), check_eager=True) def test_check_grad_ingore_y(self): """test_check_grad_ingore_y""" self.check_grad(['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'), check_eager=True) class TestElementwiseFmin3Op(OpTest): """TestElementwiseFmin2Op""" def setUp(self): """setUp""" self.op_type = "elementwise_fmin" self.python_api = paddle.fmin # If x and y have the same value, the min() is not differentiable. # So we generate test data by the following method # to avoid them being too close to each other. x = np.random.uniform(1, 1, [13, 17]).astype("float16") sgn = np.random.choice([-1, 1], [13, 17]).astype("float16") y = x + sgn * np.random.uniform(1, 1, [13, 17]).astype("float16") self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.fmin(self.inputs['X'], self.inputs['Y'])} def test_check_output(self): """test_check_output""" self.check_output(check_eager=True) def test_check_grad_normal(self): """test_check_grad_normal""" self.check_grad(['X', 'Y'], 'Out', check_eager=True) if __name__ == "__main__": paddle.enable_static() unittest.main()