test_max_op.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 unittest
import numpy as np
from op_test import OpTest, skip_check_grad_ci
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()):
            data = paddle.nn.data("data", shape=[10, 10], dtype="float32")
            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()):
            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
            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)

        with paddle.static.program_guard(paddle.static.Program(),
                                         paddle.static.Program()):
            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
            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)

    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)

        def test_axis_type():
            with paddle.static.program_guard(paddle.static.Program(),
                                             paddle.static.Program()):
                data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
                axis = paddle.nn.data("axis", shape=[10, 10], dtype="int64")
                result_min = paddle.min(data, axis)

        self.assertRaises(TypeError, test_axis_type)

    def test_imperative_api(self):
        paddle.disable_static()
        np_x = np.array([10, 10]).astype('float64')
        x = paddle.to_variable(np_x)
        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)