test_reverse_op.py 6.0 KB
Newer Older
F
fengjiayi 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14
# Copyright (c) 2018 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.

15 16
from __future__ import print_function

F
fengjiayi 已提交
17 18
import unittest
import numpy as np
19
from op_test import OpTest
20
import paddle
21 22
import paddle.fluid as fluid
from paddle.fluid import core
F
fengjiayi 已提交
23 24 25 26


class TestReverseOp(OpTest):
    def initTestCase(self):
27
        self.x = np.random.random((3, 40)).astype('float64')
F
fengjiayi 已提交
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
        self.axis = [0]

    def setUp(self):
        self.initTestCase()
        self.op_type = "reverse"
        self.inputs = {"X": self.x}
        self.attrs = {'axis': self.axis}
        out = self.x
        for a in self.axis:
            out = np.flip(out, axis=a)
        self.outputs = {'Out': out}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestCase0(TestReverseOp):
    def initTestCase(self):
49
        self.x = np.random.random((3, 40)).astype('float64')
F
fengjiayi 已提交
50 51 52
        self.axis = [1]


53
class TestCase0_neg(TestReverseOp):
54 55 56 57 58
    def initTestCase(self):
        self.x = np.random.random((3, 40)).astype('float64')
        self.axis = [-1]


F
fengjiayi 已提交
59 60
class TestCase1(TestReverseOp):
    def initTestCase(self):
61
        self.x = np.random.random((3, 40)).astype('float64')
F
fengjiayi 已提交
62 63 64
        self.axis = [0, 1]


65
class TestCase1_neg(TestReverseOp):
66 67 68 69 70
    def initTestCase(self):
        self.x = np.random.random((3, 40)).astype('float64')
        self.axis = [0, -1]


F
fengjiayi 已提交
71 72
class TestCase2(TestReverseOp):
    def initTestCase(self):
73
        self.x = np.random.random((3, 4, 10)).astype('float64')
F
fengjiayi 已提交
74 75 76
        self.axis = [0, 2]


77
class TestCase2_neg(TestReverseOp):
78 79 80 81 82
    def initTestCase(self):
        self.x = np.random.random((3, 4, 10)).astype('float64')
        self.axis = [0, -2]


F
fengjiayi 已提交
83 84
class TestCase3(TestReverseOp):
    def initTestCase(self):
85
        self.x = np.random.random((3, 4, 10)).astype('float64')
F
fengjiayi 已提交
86 87 88
        self.axis = [1, 2]


89
class TestCase3_neg(TestReverseOp):
90 91 92 93 94
    def initTestCase(self):
        self.x = np.random.random((3, 4, 10)).astype('float64')
        self.axis = [-1, -2]


95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
class TestCase4(unittest.TestCase):
    def test_error(self):
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        train_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            label = fluid.layers.data(
                name="label", shape=[1, 1, 1, 1, 1, 1, 1, 1], dtype="int64")
            rev = fluid.layers.reverse(label, axis=[-1, -2])

        def _run_program():
            x = np.random.random(size=(10, 1, 1, 1, 1, 1, 1)).astype('int64')
            exe.run(train_program, feed={"label": x})

111
        self.assertRaises(IndexError, _run_program)
112 113


114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184
class TestReverseLoDTensorArray(unittest.TestCase):
    def setUp(self):
        self.shapes = [[5, 25], [5, 20], [5, 5]]
        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
        ) else fluid.CPUPlace()
        self.exe = fluid.Executor(self.place)

    def run_program(self, arr_len, axis=0):
        main_program = fluid.Program()

        with fluid.program_guard(main_program):
            inputs, inputs_data = [], []
            for i in range(arr_len):
                x = fluid.data("x%s" % i, self.shapes[i], dtype='float32')
                x.stop_gradient = False
                inputs.append(x)
                inputs_data.append(
                    np.random.random(self.shapes[i]).astype('float32'))

            tensor_array = fluid.layers.create_array(dtype='float32')
            for i in range(arr_len):
                idx = fluid.layers.array_length(tensor_array)
                fluid.layers.array_write(inputs[i], idx, tensor_array)

            reverse_array = fluid.layers.reverse(tensor_array, axis=axis)
            output, _ = fluid.layers.tensor_array_to_tensor(reverse_array)
            loss = fluid.layers.reduce_sum(output)
            fluid.backward.append_backward(loss)
            input_grads = list(
                map(main_program.global_block().var,
                    [x.name + "@GRAD" for x in inputs]))

            feed_dict = dict(zip([x.name for x in inputs], inputs_data))
            res = self.exe.run(main_program,
                               feed=feed_dict,
                               fetch_list=input_grads + [output.name])

            return np.hstack(inputs_data[::-1]), res

    def test_case1(self):
        gt, res = self.run_program(arr_len=3)
        self.check_output(gt, res)
        # test with tuple type of axis
        gt, res = self.run_program(arr_len=3, axis=(0, ))
        self.check_output(gt, res)

    def test_case2(self):
        gt, res = self.run_program(arr_len=1)
        self.check_output(gt, res)
        # test with list type of axis
        gt, res = self.run_program(arr_len=1, axis=[0])
        self.check_output(gt, res)

    def check_output(self, gt, res):
        arr_len = len(res) - 1
        reversed_array = res[-1]
        # check output
        self.assertTrue(np.array_equal(gt, reversed_array))
        # check grad
        for i in range(arr_len):
            self.assertTrue(np.array_equal(res[i], np.ones_like(res[i])))

    def test_raise_error(self):
        # The len(axis) should be 1 is input(X) is LoDTensorArray
        with self.assertRaises(Exception):
            self.run_program(arr_len=3, axis=[0, 1])
        # The value of axis should be 0 is input(X) is LoDTensorArray
        with self.assertRaises(Exception):
            self.run_program(arr_len=3, axis=1)


F
fengjiayi 已提交
185
if __name__ == '__main__':
186
    paddle.enable_static()
F
fengjiayi 已提交
187
    unittest.main()