test_arange.py

#   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()