Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
csdn_franckjun
Paddle
提交
caa2003a
P
Paddle
项目概览
csdn_franckjun
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
caa2003a
编写于
10月 13, 2021
作者:
F
fuqianya
提交者:
GitHub
10月 13, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[PaddlePaddle Hackathon] add AlexNet (#36058)
* add alexnet
上级
90457d8c
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
205 addition
and
3 deletion
+205
-3
python/paddle/tests/test_pretrained_model.py
python/paddle/tests/test_pretrained_model.py
+3
-1
python/paddle/tests/test_vision_models.py
python/paddle/tests/test_vision_models.py
+3
-1
python/paddle/vision/__init__.py
python/paddle/vision/__init__.py
+2
-0
python/paddle/vision/models/__init__.py
python/paddle/vision/models/__init__.py
+5
-1
python/paddle/vision/models/alexnet.py
python/paddle/vision/models/alexnet.py
+192
-0
未找到文件。
python/paddle/tests/test_pretrained_model.py
浏览文件 @
caa2003a
...
...
@@ -52,7 +52,9 @@ class TestPretrainedModel(unittest.TestCase):
np
.
testing
.
assert_allclose
(
res
[
'dygraph'
],
res
[
'static'
])
def
test_models
(
self
):
arches
=
[
'mobilenet_v1'
,
'mobilenet_v2'
,
'resnet18'
,
'vgg16'
]
arches
=
[
'mobilenet_v1'
,
'mobilenet_v2'
,
'resnet18'
,
'vgg16'
,
'alexnet'
]
for
arch
in
arches
:
self
.
infer
(
arch
)
...
...
python/paddle/tests/test_vision_models.py
浏览文件 @
caa2003a
...
...
@@ -11,7 +11,6 @@
# 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
unittest
import
numpy
as
np
...
...
@@ -71,6 +70,9 @@ class TestVisonModels(unittest.TestCase):
def
test_resnet152
(
self
):
self
.
models_infer
(
'resnet152'
)
def
test_alexnet
(
self
):
self
.
models_infer
(
'alexnet'
)
def
test_vgg16_num_classes
(
self
):
vgg16
=
models
.
__dict__
[
'vgg16'
](
pretrained
=
False
,
num_classes
=
10
)
...
...
python/paddle/vision/__init__.py
浏览文件 @
caa2003a
...
...
@@ -44,6 +44,8 @@ from .models import vgg13 # noqa: F401
from
.models
import
vgg16
# noqa: F401
from
.models
import
vgg19
# noqa: F401
from
.models
import
LeNet
# noqa: F401
from
.models
import
AlexNet
# noqa: F401
from
.models
import
alexnet
# noqa: F401
from
.transforms
import
BaseTransform
# noqa: F401
from
.transforms
import
Compose
# noqa: F401
from
.transforms
import
Resize
# noqa: F401
...
...
python/paddle/vision/models/__init__.py
浏览文件 @
caa2003a
...
...
@@ -28,6 +28,8 @@ from .vgg import vgg13 # noqa: F401
from
.vgg
import
vgg16
# noqa: F401
from
.vgg
import
vgg19
# noqa: F401
from
.lenet
import
LeNet
# noqa: F401
from
.alexnet
import
AlexNet
# noqa: F401
from
.alexnet
import
alexnet
# noqa: F401
__all__
=
[
#noqa
'ResNet'
,
...
...
@@ -45,5 +47,7 @@ __all__ = [ #noqa
'mobilenet_v1'
,
'MobileNetV2'
,
'mobilenet_v2'
,
'LeNet'
'LeNet'
,
'AlexNet'
,
'alexnet'
]
python/paddle/vision/models/alexnet.py
0 → 100644
浏览文件 @
caa2003a
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
math
import
paddle
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
from
paddle.nn
import
Linear
,
Dropout
,
ReLU
from
paddle.nn
import
Conv2D
,
MaxPool2D
from
paddle.nn.initializer
import
Uniform
from
paddle.fluid.param_attr
import
ParamAttr
from
paddle.utils.download
import
get_weights_path_from_url
model_urls
=
{
"alexnet"
:
(
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams"
,
"7f0f9f737132e02732d75a1459d98a43"
,
)
}
__all__
=
[]
class
ConvPoolLayer
(
nn
.
Layer
):
def
__init__
(
self
,
input_channels
,
output_channels
,
filter_size
,
stride
,
padding
,
stdv
,
groups
=
1
,
act
=
None
):
super
(
ConvPoolLayer
,
self
).
__init__
()
self
.
relu
=
ReLU
()
if
act
==
"relu"
else
None
self
.
_conv
=
Conv2D
(
in_channels
=
input_channels
,
out_channels
=
output_channels
,
kernel_size
=
filter_size
,
stride
=
stride
,
padding
=
padding
,
groups
=
groups
,
weight_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)))
self
.
_pool
=
MaxPool2D
(
kernel_size
=
3
,
stride
=
2
,
padding
=
0
)
def
forward
(
self
,
inputs
):
x
=
self
.
_conv
(
inputs
)
if
self
.
relu
is
not
None
:
x
=
self
.
relu
(
x
)
x
=
self
.
_pool
(
x
)
return
x
class
AlexNet
(
nn
.
Layer
):
"""AlexNet model from
`"ImageNet Classification with Deep Convolutional Neural Networks"
<https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf>`_
Args:
num_classes (int): Output dim of last fc layer. Default: 1000.
Examples:
.. code-block:: python
from paddle.vision.models import AlexNet
alexnet = AlexNet()
"""
def
__init__
(
self
,
num_classes
=
1000
):
super
(
AlexNet
,
self
).
__init__
()
self
.
num_classes
=
num_classes
stdv
=
1.0
/
math
.
sqrt
(
3
*
11
*
11
)
self
.
_conv1
=
ConvPoolLayer
(
3
,
64
,
11
,
4
,
2
,
stdv
,
act
=
"relu"
)
stdv
=
1.0
/
math
.
sqrt
(
64
*
5
*
5
)
self
.
_conv2
=
ConvPoolLayer
(
64
,
192
,
5
,
1
,
2
,
stdv
,
act
=
"relu"
)
stdv
=
1.0
/
math
.
sqrt
(
192
*
3
*
3
)
self
.
_conv3
=
Conv2D
(
192
,
384
,
3
,
stride
=
1
,
padding
=
1
,
weight_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)))
stdv
=
1.0
/
math
.
sqrt
(
384
*
3
*
3
)
self
.
_conv4
=
Conv2D
(
384
,
256
,
3
,
stride
=
1
,
padding
=
1
,
weight_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)))
stdv
=
1.0
/
math
.
sqrt
(
256
*
3
*
3
)
self
.
_conv5
=
ConvPoolLayer
(
256
,
256
,
3
,
1
,
1
,
stdv
,
act
=
"relu"
)
if
self
.
num_classes
>
0
:
stdv
=
1.0
/
math
.
sqrt
(
256
*
6
*
6
)
self
.
_drop1
=
Dropout
(
p
=
0.5
,
mode
=
"downscale_in_infer"
)
self
.
_fc6
=
Linear
(
in_features
=
256
*
6
*
6
,
out_features
=
4096
,
weight_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)))
self
.
_drop2
=
Dropout
(
p
=
0.5
,
mode
=
"downscale_in_infer"
)
self
.
_fc7
=
Linear
(
in_features
=
4096
,
out_features
=
4096
,
weight_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)))
self
.
_fc8
=
Linear
(
in_features
=
4096
,
out_features
=
num_classes
,
weight_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)),
bias_attr
=
ParamAttr
(
initializer
=
Uniform
(
-
stdv
,
stdv
)))
def
forward
(
self
,
inputs
):
x
=
self
.
_conv1
(
inputs
)
x
=
self
.
_conv2
(
x
)
x
=
self
.
_conv3
(
x
)
x
=
F
.
relu
(
x
)
x
=
self
.
_conv4
(
x
)
x
=
F
.
relu
(
x
)
x
=
self
.
_conv5
(
x
)
if
self
.
num_classes
>
0
:
x
=
paddle
.
flatten
(
x
,
start_axis
=
1
,
stop_axis
=-
1
)
x
=
self
.
_drop1
(
x
)
x
=
self
.
_fc6
(
x
)
x
=
F
.
relu
(
x
)
x
=
self
.
_drop2
(
x
)
x
=
self
.
_fc7
(
x
)
x
=
F
.
relu
(
x
)
x
=
self
.
_fc8
(
x
)
return
x
def
_alexnet
(
arch
,
pretrained
,
**
kwargs
):
model
=
AlexNet
(
**
kwargs
)
if
pretrained
:
assert
arch
in
model_urls
,
"{} model do not have a pretrained model now, you should set pretrained=False"
.
format
(
arch
)
weight_path
=
get_weights_path_from_url
(
model_urls
[
arch
][
0
],
model_urls
[
arch
][
1
])
param
=
paddle
.
load
(
weight_path
)
model
.
load_dict
(
param
)
return
model
def
alexnet
(
pretrained
=
False
,
**
kwargs
):
"""AlexNet model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
from paddle.vision.models import alexnet
# build model
model = alexnet()
# build model and load imagenet pretrained weight
# model = alexnet(pretrained=True)
"""
return
_alexnet
(
'alexnet'
,
pretrained
,
**
kwargs
)
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录