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体验新版 GitCode,发现更多精彩内容 >>
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f21594cc
编写于
4月 23, 2020
作者:
D
dyning
提交者:
GitHub
4月 23, 2020
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差异文件
Merge pull request #69 from littletomatodonkey/add_mv_lr
fix mv3 arch and faq doc
上级
ea7e9558
cbd94d72
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
57 addition
and
25 deletion
+57
-25
docs/zh_CN/faq.md
docs/zh_CN/faq.md
+6
-1
docs/zh_CN/models/Mobile.md
docs/zh_CN/models/Mobile.md
+1
-1
docs/zh_CN/tutorials/getting_started.md
docs/zh_CN/tutorials/getting_started.md
+2
-0
ppcls/modeling/architectures/mobilenet_v3.py
ppcls/modeling/architectures/mobilenet_v3.py
+48
-23
未找到文件。
docs/zh_CN/faq.md
浏览文件 @
f21594cc
...
...
@@ -12,4 +12,9 @@
>>
*
Q: 在配置文件的
`TRAIN`
字段中配置了
`mix`
的参数,为什么
`mixup`
的数据增广预处理没有生效呢?
*
A: 使用mixup时,数据预处理部分与模型输入部分均需要修改,因此还需要在配置文件中显式地配置
`use_mix: True`
,才能使得
`mixup`
生效。
\ No newline at end of file
*
A: 使用mixup时,数据预处理部分与模型输入部分均需要修改,因此还需要在配置文件中显式地配置
`use_mix: True`
,才能使得
`mixup`
生效。
>>
*
Q: 评估和预测时,已经指定了预训练模型所在文件夹的地址,但是仍然无法导入参数,这么为什么呢?
*
A: 加载预训练模型时,需要指定预训练模型的前缀,例如预训练模型参数所在的文件夹为
`output/ResNet50_vd/19`
,预训练模型参数的名称为
`output/ResNet50_vd/19/ppcls.pdparams`
,则
`pretrained_model`
参数需要指定为
`output/ResNet50_vd/19/ppcls`
,PaddleClas会自动补齐
`.pdparams`
的后缀。
docs/zh_CN/models/Mobile.md
浏览文件 @
f21594cc
...
...
@@ -32,7 +32,7 @@ MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络
| MobileNetV2_x2_0 | 0.752 | 0.926 | | | 2.320 | 11.130 |
| MobileNetV2_ssld | 0.7674 | 0.9339 | | | 0.600 | 3.440 |
| MobileNetV3_large_
<br>
x1_25 | 0.764 | 0.930 | 0.766 | | 0.714 | 7.440 |
| MobileNetV3_large_
<br>
x1_0 | 0.753 | 0.
75
3 | 0.752 | | 0.450 | 5.470 |
| MobileNetV3_large_
<br>
x1_0 | 0.753 | 0.
92
3 | 0.752 | | 0.450 | 5.470 |
| MobileNetV3_large_
<br>
x0_75 | 0.731 | 0.911 | 0.733 | | 0.296 | 3.910 |
| MobileNetV3_large_
<br>
x0_5 | 0.692 | 0.885 | 0.688 | | 0.138 | 2.670 |
| MobileNetV3_large_
<br>
x0_35 | 0.643 | 0.855 | 0.642 | | 0.077 | 2.100 |
...
...
docs/zh_CN/tutorials/getting_started.md
浏览文件 @
f21594cc
...
...
@@ -68,6 +68,8 @@ python tools/eval.py \
```
可以更改configs/eval.yaml中的
`ARCHITECTURE.name`
字段和pretrained_model字段来配置评估模型,也可以通过-o参数更新配置。
**注意:**
加载预训练模型时,需要指定预训练模型的前缀,例如预训练模型参数所在的文件夹为
`output/ResNet50_vd/19`
,预训练模型参数的名称为
`output/ResNet50_vd/19/ppcls.pdparams`
,则
`pretrained_model`
参数需要指定为
`output/ResNet50_vd/19/ppcls`
,PaddleClas会自动补齐
`.pdparams`
的后缀。
## 三、模型推理
PaddlePaddle提供三种方式进行预测推理,接下来介绍如何用预测引擎进行推理:
...
...
ppcls/modeling/architectures/mobilenet_v3.py
浏览文件 @
f21594cc
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
copyright (c) 2020 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
#
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.
#
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
paddle.fluid
as
fluid
from
paddle.fluid.initializer
import
MSRA
from
paddle.fluid.param_attr
import
ParamAttr
__all__
=
[
...
...
@@ -30,9 +29,19 @@ __all__ = [
class
MobileNetV3
():
def
__init__
(
self
,
scale
=
1.0
,
model_name
=
'small'
):
def
__init__
(
self
,
scale
=
1.0
,
model_name
=
'small'
,
lr_mult_list
=
[
1.0
,
1.0
,
1.0
,
1.0
,
1.0
]):
self
.
scale
=
scale
self
.
inplanes
=
16
self
.
lr_mult_list
=
lr_mult_list
assert
len
(
self
.
lr_mult_list
)
==
5
,
\
"lr_mult_list length in MobileNetV3 must be 5 but got {}!!"
.
format
(
len
(
self
.
lr_mult_list
))
self
.
curr_stage
=
0
if
model_name
==
"large"
:
self
.
cfg
=
[
# k, exp, c, se, nl, s,
...
...
@@ -54,6 +63,7 @@ class MobileNetV3():
]
self
.
cls_ch_squeeze
=
960
self
.
cls_ch_expand
=
1280
self
.
lr_interval
=
3
elif
model_name
==
"small"
:
self
.
cfg
=
[
# k, exp, c, se, nl, s,
...
...
@@ -71,9 +81,10 @@ class MobileNetV3():
]
self
.
cls_ch_squeeze
=
576
self
.
cls_ch_expand
=
1280
self
.
lr_interval
=
2
else
:
raise
NotImplementedError
(
"mode["
+
model_name
+
"_model] is not implemented!"
)
raise
NotImplementedError
(
"mode[{}_model] is not implemented!"
.
format
(
model_name
)
)
def
net
(
self
,
input
,
class_dim
=
1000
):
scale
=
self
.
scale
...
...
@@ -81,7 +92,7 @@ class MobileNetV3():
cfg
=
self
.
cfg
cls_ch_squeeze
=
self
.
cls_ch_squeeze
cls_ch_expand
=
self
.
cls_ch_expand
#conv1
#
conv1
conv
=
self
.
conv_bn_layer
(
input
,
filter_size
=
3
,
...
...
@@ -107,6 +118,7 @@ class MobileNetV3():
name
=
'conv'
+
str
(
i
+
2
))
inplanes
=
self
.
make_divisible
(
scale
*
layer_cfg
[
2
])
i
+=
1
self
.
curr_stage
=
i
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
...
...
@@ -149,6 +161,10 @@ class MobileNetV3():
name
=
None
,
use_cudnn
=
True
,
res_last_bn_init
=
False
):
lr_idx
=
self
.
curr_stage
//
self
.
lr_interval
lr_idx
=
min
(
lr_idx
,
len
(
self
.
lr_mult_list
)
-
1
)
lr_mult
=
self
.
lr_mult_list
[
lr_idx
]
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
num_filters
,
...
...
@@ -158,7 +174,8 @@ class MobileNetV3():
groups
=
num_groups
,
act
=
None
,
use_cudnn
=
use_cudnn
,
param_attr
=
ParamAttr
(
name
=
name
+
'_weights'
),
param_attr
=
ParamAttr
(
name
=
name
+
'_weights'
,
learning_rate
=
lr_mult
),
bias_attr
=
False
)
bn_name
=
name
+
'_bn'
bn
=
fluid
.
layers
.
batch_norm
(
...
...
@@ -189,6 +206,10 @@ class MobileNetV3():
return
new_v
def
se_block
(
self
,
input
,
num_out_filter
,
ratio
=
4
,
name
=
None
):
lr_idx
=
self
.
curr_stage
//
self
.
lr_interval
lr_idx
=
min
(
lr_idx
,
len
(
self
.
lr_mult_list
)
-
1
)
lr_mult
=
self
.
lr_mult_list
[
lr_idx
]
num_mid_filter
=
num_out_filter
//
ratio
pool
=
fluid
.
layers
.
pool2d
(
input
=
input
,
pool_type
=
'avg'
,
global_pooling
=
True
,
use_cudnn
=
False
)
...
...
@@ -197,15 +218,19 @@ class MobileNetV3():
filter_size
=
1
,
num_filters
=
num_mid_filter
,
act
=
'relu'
,
param_attr
=
ParamAttr
(
name
=
name
+
'_1_weights'
),
bias_attr
=
ParamAttr
(
name
=
name
+
'_1_offset'
))
param_attr
=
ParamAttr
(
name
=
name
+
'_1_weights'
,
learning_rate
=
lr_mult
),
bias_attr
=
ParamAttr
(
name
=
name
+
'_1_offset'
,
learning_rate
=
lr_mult
))
conv2
=
fluid
.
layers
.
conv2d
(
input
=
conv1
,
filter_size
=
1
,
num_filters
=
num_out_filter
,
act
=
'hard_sigmoid'
,
param_attr
=
ParamAttr
(
name
=
name
+
'_2_weights'
),
bias_attr
=
ParamAttr
(
name
=
name
+
'_2_offset'
))
param_attr
=
ParamAttr
(
name
=
name
+
'_2_weights'
,
learning_rate
=
lr_mult
),
bias_attr
=
ParamAttr
(
name
=
name
+
'_2_offset'
,
learning_rate
=
lr_mult
))
scale
=
fluid
.
layers
.
elementwise_mul
(
x
=
input
,
y
=
conv2
,
axis
=
0
)
return
scale
...
...
@@ -275,8 +300,8 @@ def MobileNetV3_small_x0_75():
return
model
def
MobileNetV3_small_x1_0
():
model
=
MobileNetV3
(
model_name
=
'small'
,
scale
=
1.0
)
def
MobileNetV3_small_x1_0
(
**
args
):
model
=
MobileNetV3
(
model_name
=
'small'
,
scale
=
1.0
,
**
args
)
return
model
...
...
@@ -300,8 +325,8 @@ def MobileNetV3_large_x0_75():
return
model
def
MobileNetV3_large_x1_0
():
model
=
MobileNetV3
(
model_name
=
'large'
,
scale
=
1.0
)
def
MobileNetV3_large_x1_0
(
**
args
):
model
=
MobileNetV3
(
model_name
=
'large'
,
scale
=
1.0
,
**
args
)
return
model
...
...
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