# Copyright (c) 2020 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 unittest import numpy as np from eager_op_test import OpTest import paddle from paddle.fluid import core from paddle.static import Program, program_guard def check_randperm_out(n, data_np): assert isinstance( data_np, np.ndarray ), "The input data_np should be np.ndarray." gt_sorted = np.arange(n) out_sorted = np.sort(data_np) return list(gt_sorted == out_sorted) def error_msg(data_np): return ( "The sorted ground truth and sorted out should " + "be equal, out = " + str(data_np) ) def convert_dtype(dtype_str): dtype_str_list = ["int32", "int64", "float32", "float64"] dtype_num_list = [ core.VarDesc.VarType.INT32, core.VarDesc.VarType.INT64, core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP64, ] assert dtype_str in dtype_str_list, ( dtype_str + " should in " + str(dtype_str_list) ) return dtype_num_list[dtype_str_list.index(dtype_str)] class TestRandpermOp(OpTest): """Test randperm op.""" def setUp(self): self.op_type = "randperm" self.python_api = paddle.randperm self.n = 200 self.dtype = "int64" self.inputs = {} self.outputs = {"Out": np.zeros((self.n)).astype(self.dtype)} self.init_attrs() self.attrs = { "n": self.n, "dtype": convert_dtype(self.dtype), } def init_attrs(self): pass def test_check_output(self): self.check_output_customized(self.verify_output) def verify_output(self, outs): out_np = np.array(outs[0]) self.assertTrue( check_randperm_out(self.n, out_np), msg=error_msg(out_np) ) class TestRandpermOpN(TestRandpermOp): def init_attrs(self): self.n = 10000 class TestRandpermOpInt32(TestRandpermOp): def init_attrs(self): self.dtype = "int32" class TestRandpermOpFloat32(TestRandpermOp): def init_attrs(self): self.dtype = "float32" class TestRandpermOpFloat64(TestRandpermOp): def init_attrs(self): self.dtype = "float64" class TestRandpermOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): self.assertRaises(ValueError, paddle.randperm, -3) self.assertRaises(TypeError, paddle.randperm, 10, 'int8') class TestRandpermAPI(unittest.TestCase): def test_out(self): n = 10 place = ( paddle.CUDAPlace(0) if core.is_compiled_with_cuda() else paddle.CPUPlace() ) with program_guard(Program(), Program()): x1 = paddle.randperm(n) x2 = paddle.randperm(n, 'float32') exe = paddle.static.Executor(place) res = exe.run(fetch_list=[x1, x2]) self.assertEqual(res[0].dtype, np.int64) self.assertEqual(res[1].dtype, np.float32) self.assertTrue(check_randperm_out(n, res[0])) self.assertTrue(check_randperm_out(n, res[1])) class TestRandpermImperative(unittest.TestCase): def test_out(self): paddle.disable_static() n = 10 for dtype in ['int32', np.int64, 'float32', 'float64']: data_p = paddle.randperm(n, dtype) data_np = data_p.numpy() self.assertTrue( check_randperm_out(n, data_np), msg=error_msg(data_np) ) paddle.enable_static() class TestRandpermEager(unittest.TestCase): def test_out(self): paddle.disable_static() n = 10 for dtype in ['int32', np.int64, 'float32', 'float64']: data_p = paddle.randperm(n, dtype) data_np = data_p.numpy() self.assertTrue( check_randperm_out(n, data_np), msg=error_msg(data_np) ) paddle.enable_static() class TestRandomValue(unittest.TestCase): def test_fixed_random_number(self): # Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t' if not paddle.is_compiled_with_cuda(): return print("Test Fixed Random number on GPU------>") paddle.disable_static() paddle.set_device('gpu') paddle.seed(2021) x = paddle.randperm(30000, dtype='int32').numpy() expect = [ 24562, 8409, 9379, 10328, 20503, 18059, 9681, 21883, 11783, 27413, ] np.testing.assert_array_equal(x[0:10], expect) expect = [ 29477, 27100, 9643, 16637, 8605, 16892, 27767, 2724, 1612, 13096, ] np.testing.assert_array_equal(x[10000:10010], expect) expect = [ 298, 4104, 16479, 22714, 28684, 7510, 14667, 9950, 15940, 28343, ] np.testing.assert_array_equal(x[20000:20010], expect) x = paddle.randperm(30000, dtype='int64').numpy() expect = [ 6587, 1909, 5525, 23001, 6488, 14981, 14355, 3083, 29561, 8171, ] np.testing.assert_array_equal(x[0:10], expect) expect = [ 23460, 12394, 22501, 5427, 20185, 9100, 5127, 1651, 25806, 4818, ] np.testing.assert_array_equal(x[10000:10010], expect) expect = [5829, 4508, 16193, 24836, 8526, 242, 9984, 9243, 1977, 11839] np.testing.assert_array_equal(x[20000:20010], expect) x = paddle.randperm(30000, dtype='float32').numpy() expect = [ 5154.0, 10537.0, 14362.0, 29843.0, 27185.0, 28399.0, 27561.0, 4144.0, 22906.0, 10705.0, ] np.testing.assert_array_equal(x[0:10], expect) expect = [ 1958.0, 18414.0, 20090.0, 21910.0, 22746.0, 27346.0, 22347.0, 3002.0, 4564.0, 26991.0, ] np.testing.assert_array_equal(x[10000:10010], expect) expect = [ 25580.0, 12606.0, 553.0, 16387.0, 29536.0, 4241.0, 20946.0, 16899.0, 16339.0, 4662.0, ] np.testing.assert_array_equal(x[20000:20010], expect) x = paddle.randperm(30000, dtype='float64').numpy() expect = [ 19051.0, 2449.0, 21940.0, 11121.0, 282.0, 7330.0, 13747.0, 24321.0, 21147.0, 9163.0, ] np.testing.assert_array_equal(x[0:10], expect) expect = [ 15483.0, 1315.0, 5723.0, 20954.0, 13251.0, 25539.0, 5074.0, 1823.0, 14945.0, 17624.0, ] np.testing.assert_array_equal(x[10000:10010], expect) expect = [ 10516.0, 2552.0, 29970.0, 5941.0, 986.0, 8007.0, 24805.0, 26753.0, 12202.0, 21404.0, ] np.testing.assert_array_equal(x[20000:20010], expect) paddle.enable_static() if __name__ == "__main__": unittest.main()