# 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. from __future__ import print_function, division import unittest from decorator_helper import prog_scope import paddle import paddle.fluid as fluid import numpy import numpy as np class TestMathOpPatches(unittest.TestCase): def setUp(self): paddle.enable_static() @prog_scope() def test_add_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = a + 10 ab = fluid.layers.concat(input=[a, b], axis=1) c = ab + 10 d = ab + a # e = a + ab place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np, c_np, d_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b, c, d]) self.assertTrue(numpy.allclose(a_np + 10, b_np)) ab_np = numpy.concatenate([a_np, b_np], axis=1) self.assertTrue(numpy.allclose(ab_np + 10, c_np)) d_expected = ab_np + numpy.concatenate([a_np, a_np], axis=1) self.assertTrue(numpy.allclose(d_expected, d_np)) @prog_scope() def test_radd_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = 10 + a place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(a_np + 10, b_np)) @prog_scope() def test_sub_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = a - 10 place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(a_np - 10, b_np)) @prog_scope() def test_radd_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = 10 - a place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(10 - a_np, b_np)) @prog_scope() def test_mul_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = a * 10 place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(a_np * 10, b_np)) @prog_scope() def test_rmul_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = 10 * a place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(10 * a_np, b_np)) @prog_scope() def test_div_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = a / 10 place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(a_np / 10, b_np)) @prog_scope() def test_rdiv_scalar(self): a = fluid.layers.data(name="a", shape=[1]) b = 10 / a place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2 b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(10 / a_np, b_np)) @prog_scope() def test_div_two_tensor(self): a = fluid.layers.data(name="a", shape=[1]) b = fluid.layers.data(name="b", shape=[1]) c = a / b place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2 c_np = exe.run(fluid.default_main_program(), feed={"a": a_np, 'b': b_np}, fetch_list=[c]) self.assertTrue(numpy.allclose(a_np / b_np, c_np)) @prog_scope() def test_mul_two_tensor(self): a = fluid.layers.data(name="a", shape=[1]) b = fluid.layers.data(name="b", shape=[1]) c = a * b place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = numpy.random.random(size=[10, 1]).astype('float32') c_np = exe.run(fluid.default_main_program(), feed={"a": a_np, 'b': b_np}, fetch_list=[c]) self.assertTrue(numpy.allclose(a_np * b_np, c_np)) @prog_scope() def test_add_two_tensor(self): a = fluid.layers.data(name="a", shape=[1]) b = fluid.layers.data(name="b", shape=[1]) c = a + b place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = numpy.random.random(size=[10, 1]).astype('float32') c_np = exe.run(fluid.default_main_program(), feed={"a": a_np, 'b': b_np}, fetch_list=[c]) self.assertTrue(numpy.allclose(a_np + b_np, c_np)) @prog_scope() def test_sub_two_tensor(self): a = fluid.layers.data(name="a", shape=[1]) b = fluid.layers.data(name="b", shape=[1]) c = a - b place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.random(size=[10, 1]).astype('float32') b_np = numpy.random.random(size=[10, 1]).astype('float32') c_np = exe.run(fluid.default_main_program(), feed={"a": a_np, 'b': b_np}, fetch_list=[c]) self.assertTrue(numpy.allclose(a_np - b_np, c_np)) @prog_scope() def test_integer_div(self): a = fluid.layers.data(name="a", shape=[1], dtype='int64') b = a / 7 place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.array([3, 4, 10, 14, 9, 18]).astype('int64') b_np, = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) b_np_actual = (a_np / 7).astype('float32') self.assertTrue(numpy.allclose(b_np, b_np_actual)) @prog_scope() def test_equal(self): a = fluid.layers.data(name="a", shape=[1], dtype='float32') b = fluid.layers.data(name="b", shape=[1], dtype='float32') c = (a == b) place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.array([3, 4, 10, 14, 9, 18]).astype('float32') b_np = numpy.array([3, 4, 11, 15, 8, 18]).astype('float32') c_np, = exe.run(fluid.default_main_program(), feed={"a": a_np, "b": b_np}, fetch_list=[c]) self.assertTrue(numpy.array_equal(c_np, a_np == b_np)) self.assertEqual(c.dtype, fluid.core.VarDesc.VarType.BOOL) @prog_scope() def test_equal_and_cond(self): a = fluid.layers.data(name="a", shape=[1], dtype='float32') b = fluid.layers.data(name="b", shape=[1], dtype='float32') one = fluid.layers.ones(shape=[1], dtype='int32') zero = fluid.layers.zeros(shape=[1], dtype='int32') cond = (one == zero) c = fluid.layers.cond(cond, lambda: a + b, lambda: a - b) place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.array([3, 4, 10, 14, 9, 18]).astype('float') b_np = numpy.array([3, 4, 11, 15, 8, 18]).astype('float') c_np, = exe.run(fluid.default_main_program(), feed={"a": a_np, "b": b_np}, fetch_list=[c]) self.assertTrue(numpy.array_equal(c_np, a_np - b_np)) @prog_scope() def test_neg(self): a = fluid.layers.data(name="a", shape=[10, 1]) b = -a place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.uniform(-1, 1, size=[10, 1]).astype('float32') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(-a_np, b_np)) @prog_scope() def test_astype(self): a = fluid.layers.data(name="a", shape=[10, 1]) b = a.astype('float32') place = fluid.CPUPlace() exe = fluid.Executor(place) a_np = numpy.random.uniform(-1, 1, size=[10, 1]).astype('float64') b_np = exe.run(fluid.default_main_program(), feed={"a": a_np}, fetch_list=[b]) self.assertTrue(numpy.allclose(a_np.astype('float32'), b_np)) @prog_scope() def test_bitwise_and(self): x_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") y_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") out_np = x_np & y_np x = paddle.static.data(name="x", shape=[2, 3, 5], dtype="int32") y = paddle.static.data(name="y", shape=[2, 3, 5], dtype="int32") z = x & y exe = fluid.Executor() out = exe.run(fluid.default_main_program(), feed={"x": x_np, "y": y_np}, fetch_list=[z]) self.assertTrue(np.array_equal(out[0], out_np)) @prog_scope() def test_bitwise_or(self): x_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") y_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") out_np = x_np | y_np x = paddle.static.data(name="x", shape=[2, 3, 5], dtype="int32") y = paddle.static.data(name="y", shape=[2, 3, 5], dtype="int32") z = x | y exe = fluid.Executor() out = exe.run(fluid.default_main_program(), feed={"x": x_np, "y": y_np}, fetch_list=[z]) self.assertTrue(np.array_equal(out[0], out_np)) @prog_scope() def test_bitwise_xor(self): x_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") y_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") out_np = x_np ^ y_np x = paddle.static.data(name="x", shape=[2, 3, 5], dtype="int32") y = paddle.static.data(name="y", shape=[2, 3, 5], dtype="int32") z = x ^ y exe = fluid.Executor() out = exe.run(fluid.default_main_program(), feed={"x": x_np, "y": y_np}, fetch_list=[z]) self.assertTrue(np.array_equal(out[0], out_np)) @prog_scope() def test_bitwise_not(self): x_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") out_np = ~x_np x = paddle.static.data(name="x", shape=[2, 3, 5], dtype="int32") z = ~x exe = fluid.Executor() out = exe.run(fluid.default_main_program(), feed={"x": x_np}, fetch_list=[z]) self.assertTrue(np.array_equal(out[0], out_np)) if __name__ == '__main__': unittest.main()