test_expand_op.py 7.5 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# 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
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13 14
# 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

Y
yangyaming 已提交
17 18
import unittest
import numpy as np
19
from op_test import OpTest
L
liym27 已提交
20
import paddle.fluid as fluid
W
wangchaochaohu 已提交
21
from paddle.fluid import compiler, Program, program_guard
Y
yangyaming 已提交
22 23


L
liym27 已提交
24
# Situation 1: expand_times is a list(without tensor)
Y
yangyaming 已提交
25 26 27
class TestExpandOpRank1(OpTest):
    def setUp(self):
        self.op_type = "expand"
L
liym27 已提交
28 29 30 31 32
        self.init_data()

        self.inputs = {'X': np.random.random(self.ori_shape).astype("float32")}
        self.attrs = {'expand_times': self.expand_times}
        output = np.tile(self.inputs['X'], self.expand_times)
Y
yangyaming 已提交
33 34
        self.outputs = {'Out': output}

L
liym27 已提交
35 36 37 38
    def init_data(self):
        self.ori_shape = [12]
        self.expand_times = [2]

Y
yangyaming 已提交
39 40 41 42 43 44 45
    def test_check_output(self):
        self.check_output()

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


L
liym27 已提交
46 47 48 49
class TestExpandOpRank2_Corner(TestExpandOpRank1):
    def init_data(self):
        self.ori_shape = [12]
        self.expand_times = [2]
50 51


L
liym27 已提交
52 53 54 55
class TestExpandOpRank2(TestExpandOpRank1):
    def init_data(self):
        self.ori_shape = [12, 14]
        self.expand_times = [2, 3]
56 57


L
liym27 已提交
58 59 60 61
class TestExpandOpRank3_Corner(TestExpandOpRank1):
    def init_data(self):
        self.ori_shape = (2, 4, 5)
        self.expand_times = (1, 1, 1)
Y
yangyaming 已提交
62 63


L
liym27 已提交
64 65 66 67 68
class TestExpandOpRank3(TestExpandOpRank1):
    def init_data(self):
        self.ori_shape = (2, 4, 5)
        self.expand_times = (2, 1, 4)

Y
yangyaming 已提交
69

L
liym27 已提交
70 71 72 73
class TestExpandOpRank4(TestExpandOpRank1):
    def init_data(self):
        self.ori_shape = (2, 4, 5, 7)
        self.expand_times = (3, 2, 1, 2)
Y
yangyaming 已提交
74

L
liym27 已提交
75 76 77

# Situation 2: expand_times is a list(with tensor)
class TestExpandOpRank1_tensor_attr(OpTest):
78 79
    def setUp(self):
        self.op_type = "expand"
L
liym27 已提交
80 81 82 83 84 85
        self.init_data()
        expand_times_tensor = []
        for index, ele in enumerate(self.expand_times):
            expand_times_tensor.append(("x" + str(index), np.ones(
                (1)).astype('int32') * ele))

86
        self.inputs = {
L
liym27 已提交
87 88
            'X': np.random.random(self.ori_shape).astype("float32"),
            'expand_times_tensor': expand_times_tensor,
89
        }
L
liym27 已提交
90 91
        self.attrs = {"expand_times": self.infer_expand_times}
        output = np.tile(self.inputs['X'], self.expand_times)
92 93
        self.outputs = {'Out': output}

L
liym27 已提交
94 95 96 97 98
    def init_data(self):
        self.ori_shape = [12]
        self.expand_times = [2]
        self.infer_expand_times = [-1]

99 100 101 102 103 104 105
    def test_check_output(self):
        self.check_output()

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


L
liym27 已提交
106 107 108 109 110
class TestExpandOpRank2_Corner_tensor_attr(TestExpandOpRank1_tensor_attr):
    def init_data(self):
        self.ori_shape = [12, 14]
        self.expand_times = [1, 1]
        self.infer_expand_times = [1, -1]
Y
yangyaming 已提交
111 112


L
liym27 已提交
113 114 115 116 117
class TestExpandOpRank2_attr_tensor(TestExpandOpRank1_tensor_attr):
    def init_data(self):
        self.ori_shape = [12, 14]
        self.expand_times = [2, 3]
        self.infer_expand_times = [-1, 3]
Y
yangyaming 已提交
118 119


L
liym27 已提交
120 121
# Situation 3: expand_times is a tensor
class TestExpandOpRank1_tensor(OpTest):
122 123
    def setUp(self):
        self.op_type = "expand"
L
liym27 已提交
124 125
        self.init_data()

126
        self.inputs = {
L
liym27 已提交
127 128
            'X': np.random.random(self.ori_shape).astype("float32"),
            'ExpandTimes': np.array(self.expand_times).astype("int32"),
129 130
        }
        self.attrs = {}
L
liym27 已提交
131
        output = np.tile(self.inputs['X'], self.expand_times)
132 133
        self.outputs = {'Out': output}

L
liym27 已提交
134 135 136
    def init_data(self):
        self.ori_shape = [12]
        self.expand_times = [2]
Y
yangyaming 已提交
137 138 139 140 141 142 143 144

    def test_check_output(self):
        self.check_output()

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


L
liym27 已提交
145 146 147 148
class TestExpandOpRank2_tensor(TestExpandOpRank1_tensor):
    def init_data(self):
        self.ori_shape = [12, 14]
        self.expand_times = [2, 3]
Y
yangyaming 已提交
149 150


L
liym27 已提交
151
# Situation 4: input x is Integer
152 153 154
class TestExpandOpInteger(OpTest):
    def setUp(self):
        self.op_type = "expand"
J
jerrywgz 已提交
155 156 157 158
        self.inputs = {
            'X': np.random.randint(
                10, size=(2, 4, 5)).astype("int32")
        }
159 160 161 162 163 164 165 166
        self.attrs = {'expand_times': [2, 1, 4]}
        output = np.tile(self.inputs['X'], (2, 1, 4))
        self.outputs = {'Out': output}

    def test_check_output(self):
        self.check_output()


L
liym27 已提交
167
# Situation 5: input x is Bool
168 169 170
class TestExpandOpBoolean(OpTest):
    def setUp(self):
        self.op_type = "expand"
J
jerrywgz 已提交
171
        self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
172 173 174 175 176 177 178 179
        self.attrs = {'expand_times': [2, 1, 4]}
        output = np.tile(self.inputs['X'], (2, 1, 4))
        self.outputs = {'Out': output}

    def test_check_output(self):
        self.check_output()


W
wangchaochaohu 已提交
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209
# Situation 56: input x is Integer
class TestExpandOpInt64_t(OpTest):
    def setUp(self):
        self.op_type = "expand"
        self.inputs = {
            'X': np.random.randint(
                10, size=(2, 4, 5)).astype("int64")
        }
        self.attrs = {'expand_times': [2, 1, 4]}
        output = np.tile(self.inputs['X'], (2, 1, 4))
        self.outputs = {'Out': output}

    def test_check_output(self):
        self.check_output()


class TestExpandError(OpTest):
    def test_errors(self):
        with program_guard(Program(), Program()):
            x1 = fluid.create_lod_tensor(
                np.array([[-1]]), [[1]], fluid.CPUPlace())
            expand_times = [2, 2]
            self.assertRaises(TypeError, fluid.layers.expand, x1, expand_times)
            x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
            self.assertRaises(TypeError, fluid.layers.expand, x2, expand_times)
            x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
            x3.stop_gradient = True
            self.assertRaises(ValueError, fluid.layers.expand, x3, expand_times)


L
liym27 已提交
210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
# Test python API
class TestExpandAPI(OpTest):
    def test_api(self):
        input = np.random.random([12, 14]).astype("float32")
        x = fluid.layers.data(
            name='x', shape=[12, 14], append_batch_size=False, dtype="float32")

        positive_2 = fluid.layers.fill_constant([1], "int32", 2)
        expand_times = fluid.layers.data(
            name="expand_times", shape=[2], append_batch_size=False)

        out_1 = fluid.layers.expand(x, expand_times=[2, 3])
        out_2 = fluid.layers.expand(x, expand_times=[positive_2, 3])
        out_3 = fluid.layers.expand(x, expand_times=expand_times)

L
liym27 已提交
225 226
        g0 = fluid.backward.calc_gradient(out_2, x)

L
liym27 已提交
227 228 229 230 231 232 233 234 235 236 237 238 239
        exe = fluid.Executor(place=fluid.CPUPlace())
        res_1, res_2, res_3 = exe.run(fluid.default_main_program(),
                                      feed={
                                          "x": input,
                                          "expand_times":
                                          np.array([1, 3]).astype("int32")
                                      },
                                      fetch_list=[out_1, out_2, out_3])
        assert np.array_equal(res_1, np.tile(input, (2, 3)))
        assert np.array_equal(res_2, np.tile(input, (2, 3)))
        assert np.array_equal(res_3, np.tile(input, (1, 3)))


Y
yangyaming 已提交
240 241
if __name__ == "__main__":
    unittest.main()