1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
# 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.
import sys
sys.path.append("../")
import unittest
import paddle
import paddle.nn as nn
from paddle.vision.models import mobilenet_v1
from paddleslim.nas.ofa.convert_super import Convert, supernet
class TestConvertSuper(unittest.TestCase):
def setUp(self):
self.model = mobilenet_v1()
def test_convert(self):
sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
sp_model = Convert(sp_net_config).convert(self.model)
assert len(sp_model.sublayers()) == 151
class TestConvertSuper(unittest.TestCase):
def setUp(self):
class Model(nn.Layer):
def __init__(self):
super(Model, self).__init__()
self.fc = nn.Linear(
5,
10,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.XavierNormal()),
bias_attr=paddle.ParamAttr(
initializer=nn.initializer.Constant(value=0.0)))
def forward(self, inputs):
return self.fc(inputs)
self.model = Model()
def test_convert(self):
sp_net_config = supernet(expand_ratio=[1, 2, 4])
sp_model = Convert(sp_net_config).convert(self.model)
if __name__ == '__main__':
unittest.main()