test_max_op.py 4.8 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
# 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 unittest
import numpy as np
19
from op_test import OpTest, skip_check_grad_ci, check_out_dtype
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
import paddle
import paddle.fluid.core as core


class ApiMaxTest(unittest.TestCase):
    def setUp(self):
        if core.is_compiled_with_cuda():
            self.place = core.CUDAPlace(0)
        else:
            self.place = core.CPUPlace()

    def test_api(self):
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program(),
                                         paddle.static.Program()):
35
            data = paddle.static.data("data", shape=[10, 10], dtype="float32")
36 37 38 39 40 41 42 43
            result_max = paddle.max(x=data, axis=1)
            exe = paddle.static.Executor(self.place)
            input_data = np.random.rand(10, 10).astype(np.float32)
            res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
        self.assertEqual((res == np.max(input_data, axis=1)).all(), True)

        with paddle.static.program_guard(paddle.static.Program(),
                                         paddle.static.Program()):
44
            data = paddle.static.data("data", shape=[10, 10], dtype="int64")
45 46 47 48 49 50
            result_max = paddle.max(x=data, axis=0)
            exe = paddle.static.Executor(self.place)
            input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
            res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
        self.assertEqual((res == np.max(input_data, axis=0)).all(), True)

51 52
        with paddle.static.program_guard(paddle.static.Program(),
                                         paddle.static.Program()):
53
            data = paddle.static.data("data", shape=[10, 10], dtype="int64")
54 55 56 57 58 59
            result_max = paddle.max(x=data, axis=(0, 1))
            exe = paddle.static.Executor(self.place)
            input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
            res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
        self.assertEqual((res == np.max(input_data, axis=(0, 1))).all(), True)

60 61 62 63 64 65 66 67 68 69 70
    def test_errors(self):
        paddle.enable_static()

        def test_input_type():
            with paddle.static.program_guard(paddle.static.Program(),
                                             paddle.static.Program()):
                data = np.random.rand(10, 10)
                result_max = paddle.max(x=data, axis=0)

        self.assertRaises(TypeError, test_input_type)

71 72 73
        def test_axis_type():
            with paddle.static.program_guard(paddle.static.Program(),
                                             paddle.static.Program()):
74 75
                data = paddle.static.data("data", shape=[10, 10], dtype="int64")
                axis = paddle.static.data("axis", shape=[10, 10], dtype="int64")
76 77 78 79
                result_min = paddle.min(data, axis)

        self.assertRaises(TypeError, test_axis_type)

80 81 82
    def test_imperative_api(self):
        paddle.disable_static()
        np_x = np.array([10, 10]).astype('float64')
Z
Zhou Wei 已提交
83
        x = paddle.to_tensor(np_x)
84 85 86 87
        z = paddle.max(x, axis=0)
        np_z = z.numpy()
        z_expected = np.array(np.max(np_x, axis=0))
        self.assertEqual((np_z == z_expected).all(), True)
88

89 90 91 92 93 94 95 96 97 98 99 100
    def test_big_dimension(self):
        paddle.disable_static()
        x = paddle.rand(shape=[2, 2, 2, 2, 2, 2, 2])
        np_x = x.numpy()
        z1 = paddle.max(x, axis=-1)
        z2 = paddle.max(x, axis=6)
        np_z1 = z1.numpy()
        np_z2 = z2.numpy()
        z_expected = np.array(np.max(np_x, axis=6))
        self.assertEqual((np_z1 == z_expected).all(), True)
        self.assertEqual((np_z2 == z_expected).all(), True)

101 102 103 104 105 106 107 108 109
    def test_all_negative_axis(self):
        paddle.disable_static()
        x = paddle.rand(shape=[2, 2])
        np_x = x.numpy()
        z1 = paddle.max(x, axis=(-2, -1))
        np_z1 = z1.numpy()
        z_expected = np.array(np.max(np_x, axis=(0, 1)))
        self.assertEqual((np_z1 == z_expected).all(), True)

110 111 112 113 114 115 116 117 118 119 120 121 122

class TestOutDtype(unittest.TestCase):
    def test_max(self):
        api_fn = paddle.max
        shape = [10, 16]
        check_out_dtype(
            api_fn,
            in_specs=[(shape, )],
            expect_dtypes=['float32', 'float64', 'int32', 'int64'])


if __name__ == '__main__':
    unittest.main()