# 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. from __future__ import print_function import paddle import paddle.fluid as fluid import unittest import numpy as np from op_test import OpTest class TestArangeOp(OpTest): def setUp(self): self.op_type = "range" self.init_config() self.inputs = { 'Start': np.array([self.case[0]]).astype(self.dtype), 'End': np.array([self.case[1]]).astype(self.dtype), 'Step': np.array([self.case[2]]).astype(self.dtype) } self.outputs = { 'Out': np.arange(self.case[0], self.case[1], self.case[2]).astype(self.dtype) } def init_config(self): self.dtype = np.float32 self.case = (0, 1, 0.2) def test_check_output(self): self.check_output() class TestFloatArangeOpCase0(TestArangeOp): def init_config(self): self.dtype = np.float32 self.case = (0, 5, 1) class TestInt32ArangeOpCase0(TestArangeOp): def init_config(self): self.dtype = np.int32 self.case = (0, 5, 2) class TestInt32ArangeOpCase1(TestArangeOp): def init_config(self): self.dtype = np.int32 self.case = (10, 1, -2) class TestInt32ArangeOpCase2(TestArangeOp): def init_config(self): self.dtype = np.int32 self.case = (-1, -10, -2) class TestArangeAPI(unittest.TestCase): def test_out(self): with fluid.program_guard(fluid.Program()): data = paddle.arange(0, 5, 1) place = fluid.CPUPlace() exe = fluid.Executor(place) result, = exe.run(fetch_list=[data]) expected_data = np.arange(0, 5, 1).astype(np.float32) self.assertEqual((result == expected_data).all(), True) with fluid.program_guard(fluid.Program()): data = paddle.arange(0.0, 5.0, 1.0, 'int32') place = fluid.CPUPlace() exe = fluid.Executor(place) result, = exe.run(fetch_list=[data]) expected_data = np.arange(0, 5, 1).astype(np.int32) self.assertEqual((result == expected_data).all(), True) if __name__ == "__main__": unittest.main()