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70ccf385
编写于
9月 30, 2019
作者:
B
Bai Yifan
提交者:
whs
9月 30, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix scripts and docs (#3465)
上级
a324d22d
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
105 addition
and
64 deletion
+105
-64
PaddleSlim/classification/distillation/README.md
PaddleSlim/classification/distillation/README.md
+2
-0
PaddleSlim/classification/distillation/run.sh
PaddleSlim/classification/distillation/run.sh
+3
-31
PaddleSlim/classification/pruning/README.md
PaddleSlim/classification/pruning/README.md
+2
-0
PaddleSlim/classification/pruning/run.sh
PaddleSlim/classification/pruning/run.sh
+42
-11
PaddleSlim/classification/quantization/README.md
PaddleSlim/classification/quantization/README.md
+2
-0
PaddleSlim/classification/quantization/run.sh
PaddleSlim/classification/quantization/run.sh
+54
-22
未找到文件。
PaddleSlim/classification/distillation/README.md
浏览文件 @
70ccf385
...
...
@@ -5,6 +5,8 @@
## 概述
该示例使用PaddleSlim提供的
[
蒸馏策略
](
[https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-%E8%92%B8%E9%A6%8F](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-蒸馏
)
)对分类模型进行知识蒸馏。
>本文默认使用ILSVRC2012数据集,数据集存放在`models/PaddleSlim/data/`路径下, 可以参考[数据准备](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#数据准备)在执行训练脚本run.sh前配置好您的数据集
在阅读该示例前,建议您先了解以下内容:
-
[
分类模型的常规训练方法
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification
)
...
...
PaddleSlim/classification/distillation/run.sh
浏览文件 @
70ccf385
...
...
@@ -2,9 +2,6 @@
# download pretrain model
root_url
=
"http://paddle-imagenet-models-name.bj.bcebos.com"
MobileNetV1
=
"MobileNetV1_pretrained.tar"
MobileNetV2
=
"MobileNetV2_pretrained.tar"
ResNet34
=
"ResNet34_pretrained.tar"
ResNet50
=
"ResNet50_pretrained.tar"
pretrain_dir
=
'../pretrain'
...
...
@@ -14,15 +11,6 @@ fi
cd
${
pretrain_dir
}
if
[
!
-f
${
MobileNetV2
}
]
;
then
wget
${
root_url
}
/
${
MobileNetV2
}
tar
xf
${
MobileNetV2
}
fi
if
[
!
-f
${
ResNet34
}
]
;
then
wget
${
root_url
}
/
${
ResNet34
}
tar
xf
${
ResNet34
}
fi
if
[
!
-f
${
ResNet50
}
]
;
then
wget
${
root_url
}
/
${
ResNet50
}
tar
xf
${
ResNet50
}
...
...
@@ -62,7 +50,7 @@ for files in $(ls res50_*)
done
cd
-
# for mobilenet_v2 distillation
#
#
for mobilenet_v2 distillation
#cd ${pretrain_dir}/ResNet50_pretrained
#for files in $(ls res50_*)
# do mv $files ${files#*_}
...
...
@@ -86,7 +74,7 @@ cd -
#done
#cd -
# for resnet34 distillation
#
#
for resnet34 distillation
#cd ${pretrain_dir}/ResNet50_pretrained
#for files in $(ls res50_*)
# do mv $files ${files#*_}
...
...
@@ -96,16 +84,7 @@ cd -
#done
#cd -
#
#cd ${pretrain_dir}/ResNet34_pretrained
#for files in $(ls res34_*)
# do mv $files ${files#*_}
#done
#for files in $(ls *)
# do mv $files "res34_"$files
#done
#cd -
#
#python compress.py \
#python -u compress.py \
#--model "ResNet34" \
#--teacher_model "ResNet50" \
#--teacher_pretrained_model ../pretrain/ResNet50_pretrained \
...
...
@@ -118,10 +97,3 @@ cd -
# do mv $files ${files#*_}
#done
#cd -
#
#cd ${pretrain_dir}/ResNet34_pretrained
#for files in $(ls res34_*)
# do mv $files ${files#*_}
#done
#cd -
PaddleSlim/classification/pruning/README.md
浏览文件 @
70ccf385
...
...
@@ -5,6 +5,8 @@
## 概述
该示例使用PaddleSlim提供的
[
卷积通道剪裁压缩策略
](
https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86
)
对分类模型进行压缩。
>本文默认使用ILSVRC2012数据集,数据集存放在`models/PaddleSlim/data/`路径下, 可以参考[数据准备](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#数据准备)在执行训练脚本run.sh前配置好您的数据集
在阅读该示例前,建议您先了解以下内容:
-
[
分类模型的常规训练方法
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification
)
...
...
PaddleSlim/classification/pruning/run.sh
浏览文件 @
70ccf385
#!/usr/bin/env bash
export
CUDA_VISIBLE_DEVICES
=
0
nohup
python compress.py
\
# download pretrain model
root_url
=
"http://paddle-imagenet-models-name.bj.bcebos.com"
MobileNetV1
=
"MobileNetV1_pretrained.tar"
MobileNetV2
=
"MobileNetV2_pretrained.tar"
ResNet50
=
"ResNet50_pretrained.tar"
pretrain_dir
=
'../pretrain'
if
[
!
-d
${
pretrain_dir
}
]
;
then
mkdir
${
pretrain_dir
}
fi
cd
${
pretrain_dir
}
if
[
!
-f
${
MobileNetV1
}
]
;
then
wget
${
root_url
}
/
${
MobileNetV1
}
tar
xf
${
MobileNetV1
}
fi
if
[
!
-f
${
MobileNetV2
}
]
;
then
wget
${
root_url
}
/
${
MobileNetV2
}
tar
xf
${
MobileNetV2
}
fi
if
[
!
-f
${
ResNet50
}
]
;
then
wget
${
root_url
}
/
${
ResNet50
}
tar
xf
${
ResNet50
}
fi
cd
-
nohup
python
-u
compress.py
\
--model
"MobileNet"
\
--use_gpu
0
\
--batch_size
1
\
--use_gpu
1
\
--batch_size
256
\
--pretrained_model
../pretrain/MobileNetV1_pretrained
\
--config_file
"./configs/mobilenet_v1.yaml"
\
>
mobilenet_v1.log 2>&1 &
tailf mobilenet_v1.log
# for compression of mobilenet_v2
#nohup python compress.py \
#nohup python
-u
compress.py \
#--model "MobileNetV2" \
#--use_gpu
0
\
#--batch_size
1
\
#--use_gpu
1
\
#--batch_size
256
\
#--pretrained_model ../pretrain/MobileNetV2_pretrained \
#--config_file "./configs/mobilenet_v2.yaml" \
#> mobilenet_v2.log 2>&1 &
#tailf mobilenet_v2.log
# for compression of resnet50
#python compress.py \
#
#
for compression of resnet50
#python
-u
compress.py \
#--model "ResNet50" \
#--use_gpu
0
\
#--batch_size
1
\
#--use_gpu
1
\
#--batch_size
256
\
#--pretrained_model ../pretrain/ResNet50_pretrained \
#--config_file "./configs/resnet50.yaml" \
#> resnet50.log 2>&1 &
#tailf resnet50.log
PaddleSlim/classification/quantization/README.md
浏览文件 @
70ccf385
...
...
@@ -5,6 +5,8 @@
## 概述
该示例使用PaddleSlim提供的
[
量化压缩策略
](
https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#1-quantization-aware-training%E9%87%8F%E5%8C%96%E4%BB%8B%E7%BB%8D
)
对分类模型进行压缩。
>本文默认使用ILSVRC2012数据集,数据集存放在`models/PaddleSlim/data/`路径下, 可以参考[数据准备](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#数据准备)在执行训练脚本run.sh前配置好您的数据集
在阅读该示例前,建议您先了解以下内容:
-
[
分类模型的常规训练方法
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification
)
...
...
PaddleSlim/classification/quantization/run.sh
浏览文件 @
70ccf385
#!/usr/bin/env bash
#export CUDA_VISIBLE_DEVICES=0
# download pretrain model
root_url
=
"http://paddle-imagenet-models-name.bj.bcebos.com"
MobileNetV1
=
"MobileNetV1_pretrained.tar"
MobileNetV2
=
"MobileNetV2_pretrained.tar"
ResNet50
=
"ResNet50_pretrained.tar"
pretrain_dir
=
'../pretrain'
# for quantization for mobilenet_v1
#python compress.py \
# --model "MobileNet" \
# --use_gpu 1 \
# --batch_size 32 \
# --pretrained_model ../pretrain/MobileNetV1_pretrained \
# --config_file "./configs/mobilenet_v1.yaml" \
#> mobilenet_v1.log 2>&1 &
#tailf mobilenet_v1.log
if
[
!
-d
${
pretrain_dir
}
]
;
then
mkdir
${
pretrain_dir
}
fi
cd
${
pretrain_dir
}
if
[
!
-f
${
MobileNetV1
}
]
;
then
wget
${
root_url
}
/
${
MobileNetV1
}
tar
xf
${
MobileNetV1
}
fi
if
[
!
-f
${
MobileNetV2
}
]
;
then
wget
${
root_url
}
/
${
MobileNetV2
}
tar
xf
${
MobileNetV2
}
fi
if
[
!
-f
${
ResNet50
}
]
;
then
wget
${
root_url
}
/
${
ResNet50
}
tar
xf
${
ResNet50
}
fi
cd
-
# enable GC strategy
export
FLAGS_fast_eager_deletion_mode
=
1
export
FLAGS_eager_delete_tensor_gb
=
0.0
export
CUDA_VISIBLE_DEVICES
=
0
# for quantization of mobilenet_v2
# python compress.py \
## for quantization for mobilenet_v1
python
-u
compress.py
\
--model
"MobileNet"
\
--use_gpu
1
\
--batch_size
32
\
--pretrained_model
../pretrain/MobileNetV1_pretrained
\
--config_file
"./configs/mobilenet_v1.yaml"
\
>
mobilenet_v1.log 2>&1 &
tailf mobilenet_v1.log
## for quantization of mobilenet_v2
#python -u compress.py \
# --model "MobileNetV2" \
# --use_gpu 1 \
# --batch_size 32 \
...
...
@@ -22,14 +56,12 @@
# > mobilenet_v2.log 2>&1 &
#tailf mobilenet_v2.log
# for compression of resnet50
python compress.py
\
--model
"ResNet50"
\
--use_gpu
1
\
--batch_size
32
\
--pretrained_model
../pretrain/ResNet50_pretrained
\
--config_file
"./configs/resnet50.yaml"
\
>
resnet50.log 2>&1 &
tailf resnet50.log
#python -u compress.py \
# --model "ResNet50" \
# --use_gpu 1 \
# --batch_size 32 \
# --pretrained_model ../pretrain/ResNet50_pretrained \
# --config_file "./configs/resnet50.yaml" \
# > resnet50.log 2>&1 &
#tailf resnet50.log
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