From a892651fc294b16ff36c97ccb8477b83b8e69d3c Mon Sep 17 00:00:00 2001 From: Bin Lu Date: Wed, 2 Mar 2022 15:13:29 +0800 Subject: [PATCH] add tipc of amp train (#5474) * add tipc of amp train/inference * update test_train_amp_inference_python.md * update README.md * update test_train_amp_inference_python.md * update test_train_amp_inference_python.md * add test develop doc of amp * update test develop doc of amp * udpate tipc/train_amp_infer_python/README.md * update tipc/train_amp_infer_python/test_train_amp_infer_python.md * update ../tipc/train_amp_infer_python/test_train_amp_infer_python.md --- .../Step6/test_tipc/README.md | 4 +- .../train_amp_infer_python.txt | 32 ++ .../docs/test_train_amp_inference_python.md | 84 +++++ .../tipc/train_amp_infer_python/README.md | 45 ++- ..._linux_train_amp_infer_python_pipeline.png | Bin 0 -> 397038 bytes .../test_train_amp_infer_python.md | 344 +++++++++++++++++- 6 files changed, 501 insertions(+), 8 deletions(-) create mode 100644 tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt create mode 100644 tutorials/mobilenetv3_prod/Step6/test_tipc/docs/test_train_amp_inference_python.md create mode 100644 tutorials/tipc/train_amp_infer_python/images/test_linux_train_amp_infer_python_pipeline.png diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md b/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md index 3250cc6b..40652476 100644 --- a/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md +++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md @@ -17,7 +17,7 @@ | 算法论文 | 模型名称 | 模型类型 | 基础
训练预测 | 更多
训练方式 | 模型压缩 | 其他预测部署 | | :--- | :--- | :----: | :--------: | :---- | :---- | :---- | -| MobileNetV3 | mobilenet_v3_small | 分类 | 支持 | - | - | - | +| MobileNetV3 | mobilenet_v3_small | 分类 | 支持 | 混合精度
| - | - | ## 3. 测试工具简介 @@ -53,7 +53,7 @@ test_tipc - 更多训练方式测试(coming soon): - [Linux GPU/CPU 多机多卡训练推理测试] - - [Linux GPU/CPU 混合精度训练推理测试] + - [Linux GPU/CPU 混合精度训练推理测试](docs/test_train_amp_inference_python.md) - 更多部署方式测试(coming soon): - [Linux GPU/CPU PYTHON 服务化部署测试] diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt b/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt new file mode 100644 index 00000000..dc295e83 --- /dev/null +++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt @@ -0,0 +1,32 @@ +===========================train_params=========================== +model_name:mobilenet_v3_small +python:python3.7 +gpu_list:0|0,1 +use-gpu:True|True +--epochs:lite_train_lite_infer=5|whole_train_whole_infer=90 +--output-dir:./output/ +--batch-size:lite_train_lite_infer=4|whole_train_whole_infer=128 +--pretrained:null +train_model_name:latest.pdparams +--data-path:./lite_data +## +trainer:amp_train +amp_train:train.py --amp_level=O1 +# +===========================eval_params=========================== +eval:train.py --test-only +## +===========================infer_params=========================== +--save-inference-dir:./output/mobilenet_v3_small_infer/ +--pretrained: +norm_export:tools/export_model.py --model=mobilenet_v3_small +## +train_model:./pretrain_models/mobilenet_v3_small_pretrained.pdparams +infer_export:tools/export_model.py --model=mobilenet_v3_small +## +inference:deploy/inference_python/infer.py +--use-gpu:True|False +--batch-size:1 +--model-dir:./output/mobilenet_v3_small_infer/ +--img-path:./images/demo.jpg +--benchmark:False \ No newline at end of file diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/docs/test_train_amp_inference_python.md b/tutorials/mobilenetv3_prod/Step6/test_tipc/docs/test_train_amp_inference_python.md new file mode 100644 index 00000000..97851012 --- /dev/null +++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/docs/test_train_amp_inference_python.md @@ -0,0 +1,84 @@ +# Linux GPU/CPU 混合精度训练推理测试 + +Linux GPU/CPU 混合精度训练推理测试的主程序为`test_train_inference_python.sh`,可以测试基于Python的模型训练、评估、推理等基本功能。 + +## 1. 测试结论汇总 + +- 训练相关: + +| 算法名称 | 模型名称 | 单机单卡 | 单机多卡 | +| :----: | :----: | :----: | :----: | +| MobileNetV3 | mobilenet_v3_small | 混合精度训练 | 混合精度训练 | + + +- 推理相关: + +| 算法名称 | 模型名称 | device_CPU | device_GPU | batchsize | +| :----: | :----: | :----: | :----: | :----: | +| MobileNetV3 | mobilenet_v3_small | 支持 | 支持 | 1 | + + +## 2. 测试流程 + +### 2.1 准备数据 + +用于混合精度训练推理测试的数据位于`test_images/lite_data.tar`,直接解压即可(如果已经解压完成,则无需运行下面的命令)。 + +```bash +tar -xf test_images/lite_data.tar +``` + +### 2.2 准备环境 + + +- 安装PaddlePaddle:如果您已经安装了2.2或者以上版本的paddlepaddle,那么无需运行下面的命令安装paddlepaddle。 + ``` + # 需要安装2.2及以上版本的Paddle + # 安装GPU版本的Paddle + pip install paddlepaddle-gpu==2.2.0 + # 安装CPU版本的Paddle + pip install paddlepaddle==2.2.0 + ``` + +- 安装依赖 + ``` + pip3 install -r requirements.txt + ``` +- 安装AutoLog(规范化日志输出工具) + ``` + git clone https://github.com/LDOUBLEV/AutoLog + cd AutoLog + pip install -r requirements.txt + python setup.py bdist_wheel + pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl + cd ../ + ``` + +### 2.3 功能测试 + + +测试方法如下所示,希望测试不同的模型文件,只需更换为自己的参数配置文件,即可完成对应模型的测试。 + +```bash +bash test_tipc/test_train_inference_python.sh ${your_params_file} lite_train_lite_infer +``` + +以`mobilenet_v3_small`的`Linux GPU/CPU 混合精度训练推理测试`为例,命令如下所示。 + +```bash +bash test_tipc/prepare.sh test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt lite_train_lite_infer +``` + +```bash +bash test_tipc/test_train_inference_python.sh test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt lite_train_lite_infer +``` + +输出结果如下,表示命令运行成功。 + +```bash +Run successfully with command - python3.7 train.py --amp_level=O1 --output-dir=./log/mobilenet_v3_small/lite_train_lite_infer/norm_train_gpus_0 --epochs=5 --batch-size=4! +...... +Run successfully with command - python3.7 deploy/inference_python/infer.py --use-gpu=False --model-dir=./log/mobilenet_v3_small/lite_train_lite_infer/norm_train_gpus_0,1 --batch-size=1 --benchmark=False > ./log/mobilenet_v3_small/lite_train_lite_infer/python_infer_cpu_batchsize_1.log 2>&1 ! +``` + +如果运行失败,也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令,分析运行失败的原因。 diff --git a/tutorials/tipc/train_amp_infer_python/README.md b/tutorials/tipc/train_amp_infer_python/README.md index 7d16ddb3..da9a8a5a 100644 --- a/tutorials/tipc/train_amp_infer_python/README.md +++ b/tutorials/tipc/train_amp_infer_python/README.md @@ -23,7 +23,7 @@ -# 2. Linux GPU 混合精度训练功能与规范 +## 2. Linux GPU 混合精度训练功能与规范 @@ -61,6 +61,45 @@ Paddle 混合精度训练开发可以分为4个步骤,如下图所示。 -# 3. Linux GPU 混合精度训练功能测试与规范 +## 3. Linux GPU 混合精度训练功能测试与规范 -coming soon! + + +### 3.1 开发流程 + +混合精度训练推理测试开发的流程如下所示。 + +
+ +
+ +更多的介绍可以参考:[Linux GPU/CPU 混合精度训练推理测试开发规范](./test_train_amp_infer_python.md)。 + + + +### 3.2 核验点 + +#### 3.2.1 目录结构 + +如果您已经完成了[基础训练推理功能开发](../train_infer_python/README.md),那么只需要在原`test_tipc`目录的基础上,增加混合精度训练推理测试的配置文件和使用说明文档。 +否则,需要在repo根目录下面新建`test_tipc`文件夹,目录结构如下所示。 + +``` +test_tipc + |--configs # 配置目录 + | |--model_name # 您的模型名称 + | |--train_infer_python.txt # 基础训练推理测试配置文件 + | |--train_amp_infer_python.txt # 混合精度训练推理测试配置文件 + |--docs # 文档目录 + | |--test_train_inference_python.md # 基础训练推理测试说明文档 + | |--test_train_amp_inference_python.md # 混合精度训练推理测试说明文档 + |----README.md # TIPC说明文档 + |----test_train_inference_python.sh # TIPC基础训练、混合精度训练推理测试解析脚本,无需改动 + |----common_func.sh # TIPC基础训练、混合精度训练推理测试常用函数,无需改动 +``` + +#### 3.2.2 配置文件和测试文档 + +* `test_tipc/README.md` 文档中对该模型支持的的功能进行总体介绍。 +* `test_tipc/docs/test_train_amp_inference_python.md` 文档中对**Linux GPU/CPU 混合精度训练推理**的功能支持情况进行介绍。 +* 根据测试文档,基于配置文件,跑通训练推理全流程测试。 diff --git a/tutorials/tipc/train_amp_infer_python/images/test_linux_train_amp_infer_python_pipeline.png b/tutorials/tipc/train_amp_infer_python/images/test_linux_train_amp_infer_python_pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..c4f3e65c588e2ad785b4464a8cdf9523a28f751c GIT binary patch literal 397038 zcmb@tbyOYAn=K3kcXxLQ?s|f|yF-BB?s|f2a0?ndNP*LlhMPjZl&hizbSEnmEip>wBQAsU4$FJr0HC0|Mhwq2}c|OyCiQ9NRLIfPe7u7Mr#9#nxNI@d0Y5%SRs$Nf{Z`R}^0W z%Z+t}gzJb!(}}y8CxEjHsjLVOK>&5YwI zOAbOx$Z5voV$c6Dl7qh{AIdSpM>{YHNfrF#2{ny|N?HA&H`-t1-P38dD3~d{2In4t z)n)r$55#j*3uU|%(0vbT*#6XmF{h>x{Y5>KgsP@Nc!X{Q$#5Oy1Kl z0+FN*lRh)&ZfBgBE_))n?v&gnGcwcXqyD0yLpPCjo}`n9CH#AFm!gcL%52kn~Hpv4zjRJZn$ub zT$9t{l|sgQdzwxmI`A%1@EilybVYx-isd4H{3XZG7Cr#$#O@2FbdgNrLaD+oL)Je9 zThtzXJQ5x+Z@7E7(;Y1kHu3zi^*s$PzG-NUa4`omdwtUkTQNOF4xp7x5k_DQz;+!m zYhqH9`qEu;d+{03GZA*RCrf(;f?h4i%_r zY_G|V@2NfV4CkMJKKDRi-xRLvZ|uZ?UEq&=*e~?>ae;tLeR6Uld81v%}%%y1l&r0%9Cf++9=l(mnzt1lMnsd zXu=ckZ4l-Mu=%jQQ`p&3zr>vyXyK<2dBHWz+2qoI>l!ul#JQB}Z%gOR_+#Mx7s20y z89|so#@KcQFJm7m!z8WJN{1SE@UA?dT#;IQ3}OUObc-Q&Ib2)9vTPmM6*aL}VmLE; zXxlUyDnj(X2*S5aHP{XaYY52*mk9!i(umd)(h{Ya2`3Wv6fo&WGlDAR^f;V_o!O4a zk1%VaZzCIIMcBHth9@ZOF}}uF$Zp8WsP)N3D4kb1>ASH@KxdH z!>ykG$AAR{7Xjfsi9$2gPZcHA8x?(vv$IVLmy0U1_^#i7eq30o@YdQb`=(sLGoA6R z>|Gg$Q?k>V)0lIov*(dt7q@hNi7am=Z~5}9_D{N5kduIrrO?*`r0Mqsh6Q&8#zNN4 zx@~T4D`vZGN$s3%_pX*dkAGG>t-1KN1^*Ikt8c?|*<3iEvpxKBG;&0I_;O*j%-8DA zS02@6BT)_G50b`#t>>!iW7sLSqg#BBDdH*IDf20~(&Z(^w8p%~jTWAHR`ph$eV_Yk zBI@AwFisc~@B1!^xmaq zwQ{s_Y~j1mkbz6ruiHxJy!Vl&O{Sk$FA?H;_D*PVhbf~%Hn$TKs2TsS#8TQ$ifYE^VfQQqgd)a%Uv--0}aeNvR++f@jY97h*RI60R zG0rh{t&drqSu4)P`fGece4DnJwu{3a67xl?KNNqo|G=}vws>u!Wudrel&tZ(lhZr-?M zmBwJJX)EiV@E(T^|BYgqC7Jg!uHhanwSaoZHUj<~wa8t*R-NSE`bk8!Yk6yjp#QuS?5SoV6C!XtK<*ATr!S z?xtqXAGa&2)4K9Kig>gx3W_k`%UgD8gRXGvzs!upC&0_ z#Ebb3_yhF-AcjnGV6fq3AXv4#ZGHAj8NKhy9}BklI4 z6V|>z4}_+LizQvpcS{)~>bMtQ=HzS?{Q-FnhlTY8MVug|q12OwaT8@*vIlZEvI9No z{g*>C9w1}*?@snm-ILcBqbGUzc`CWTO(@&^o&|~>MyF_b>Avho8Sm{OQu{#tF!~Knx`gwK|>t9cpKN+jjtKfcf|Tf_B{_?7lLU)&o`b zb;~ZyZsc#Q1n?c(_-29UBjtJGJ7_w@Ec_LK@tul=3g5JQ8ZMb7UyG2Q6NMj%W7E5Z z7bVpG*S~6G^gq>9XqQS7MI7vz-mlvILHa=>a}fyNLRpH9OWq(mG4CkYUHIlb$k;t=&%r}k}y|nF+J3&Cp|UX zJwjl9Mp_uQ^ZKY*a<=_>r+iQ*_VPpH1^4Ny$`s;bOJ!qYXIJCJ)En}br!;Bm2GSq( z5}F)W#CFt;WJw=I%a67Dr>@2%b(FR+B6>Oad$i0lY2{;eJf0U_QQ0r}r;wEpJ*9O-}4KW+Y%BYi;n4+~_* z4=?_sj8yQCDQr@SANb<2PIfI?UFQCC8RJd%V8k9Wij;zbarv?PloB*BekK& z!8y(uGIGaM0vlaJAH zDDOVnQG-)<)0PTENCkTX#;(T1;E!8ZV;QGT1bO$S`4*+=C|XQzUt(!xb_y3XTGtF1 zTW2etC(!+`nc#9+UP_RM7TY8hdp#G%-!;l%SrU`hm>Iuyfjaxj@sxYEy^7|q6)??h zl|_m;ei_451XZf&Wz&KM4XS65evKZhs>E?AOPsE@Oa=U|?Jeu>qoYdN9K5oan=(s; z)YRLcT>xDh;obf6LT$Ec2IZOO_?QzIi{=~-4YhUE=fXAAtFRh(Rr0oZUG@^ze$|dL zDwrcez)#@EGd8W_RG|cAf+;^?xx3xHUg&2)sdf_ldye`njoqf;JG};#@UP7|cce09S(5 zqU{-Cxz}~!nS?m*QS23P*@8KE%Dt$8y^Z8I_?QB}W?anC?itndW;39PNb$T`sbE43 zOWjRVODYlBeSA@#vdgPqcC5lnhtFm|;ysKkfb*^8FS|%IZqM~ZUJHn(fL1> zFwP3$u-T!SqDHEY*VevKNV#piy%6C}S7fE(z8YB}HEhgKOz4{nE|l?(B!MsusxKAq!t6&2**-6E-vH>vpOUWiDJ+IY->b2{ z-UBJ{9c7*xH831?;NUn(pldiXy!YT;@O^kpD=FOXd|PT}c!dzL*UzD+%1@6USqOTO ziH9`*RGmrE+}<>iw&&>xp@SMz1aU^k`{&1k_do{k)8IEE0yCeF`X8mXj_7R+qDyP- z8I@r1=-nR{b%-O``0n>c<=%s7mvYasykf~Oq^0@b#3rA5$O#w zngcG<{DA%h$u

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HcmV?d00001 diff --git a/tutorials/tipc/train_amp_infer_python/test_train_amp_infer_python.md b/tutorials/tipc/train_amp_infer_python/test_train_amp_infer_python.md index 4c8213dc..1c187048 100644 --- a/tutorials/tipc/train_amp_infer_python/test_train_amp_infer_python.md +++ b/tutorials/tipc/train_amp_infer_python/test_train_amp_infer_python.md @@ -1,8 +1,346 @@ -# Linux GPU 混合精度训练推理测试开发文档 +# Linux GPU/CPU 混合精度训练推理测试开发文档 # 目录 - [1. 简介](#1) -- [2. 混合精度训练推理功能测试开发](#2---) -- [3. FAQ](#3) +- [2. 命令与配置文件解析](#2) + - [2.1 命令解析](#2.1) + - [2.2 配置文件和运行命令映射解析](#2.2) +- [3. 基本训练推理功能测试开发](#3) + - [2.1 准备待测试的命令](#3.1) + - [2.2 准备数据与环境](#3.2) + - [2.3 准备开发所需脚本](#3.3) + - [2.4 填写配置文件](#3.4) + - [2.5 验证配置正确性](#3.5) + - [2.6 撰写说明文档](#3.6) +- [4. FAQ](#4) + + +## 1. 简介 + +本文档主要关注Linux GPU/CPU 下模型的混合精度训练推理全流程功能测试,具体测试点如下: + +- 模型训练:单机单卡/多卡训练跑通 +- 模型动转静:保存静态图模型 +- 模型推理:推理过程跑通 + +为了一键跑通上述所有功能,本文档提供了`训推一体全流程`功能自动化测试工具,它包含3个脚本文件和1个配置文件,分别是: + +* `test_train_inference_python.sh`: 测试Linux上训练、模型动转静、推理功能的脚本,会对`train_amp_infer_python.txt`进行解析,得到具体的执行命令。**该脚本无需修改**。 +* `prepare.sh`: 准备测试需要的数据或需要的预训练模型。 +* `common_func.sh`: 在配置文件一些通用的函数,如配置文件的解析函数等,**该脚本无需修改**。 +* `train_amp_infer_python.txt`: 配置文件,其中的内容会被`test_train_inference_python.sh`解析成具体的执行命令字段。 + + + +## 2. 命令与配置文件解析 + + + +### 2.1 命令解析 + +模型训练、动转静、推理过程的运行命令差别很大,但是都可以拆解为3个部分: + +``` +python run_script set_configs +``` + +例如: + +* 对于通过配置文件传参的场景来说,`python3.7 train.py -c config.yaml -o epoch_num=120` + * `python`部分为`python3.7` + * `run_script`部分为`train.py` + * `set_configs`部分为`-c config.yaml -o epoch_num=120` +* 对于通过argparse传参的场景来说,`python3.7 train.py --data-path="./lite_data" --lr=0.1` + * `python`部分为`python3.7` + * `run_script`部分为`train.py` + * `set_configs`部分为`--data-path="./lite_data" --lr=0.1` + +其中,可修改参数`set_configs`一般通过`=`进行分隔,`=`前面的内容可以认为是key,后面的内容可以认为是value,那么通过给定配置文件模板,解析配置,得到其中的key和value,结合`python`和`run_script`,便可以组合出一条完整的命令。 + + + +### 2.2 配置文件和运行命令映射解析 + +完整的`train_amp_infer_python.txt`配置文件共有32行,包含5个方面的内容。 + +* 训练参数:第1~11行 +* 训练脚本配置:第13~14行 +* 评估脚本和配置:第16~17行(本部分无需关注,这里不再展开介绍) +* 模型导出脚本和配置:第19~25行 +* 模型Inference推理:第26~32行 + +具体内容见[train_amp_infer_python.txt](../../mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt)。 + +配置文件中主要有以下3种类型的字段。 + +* 一行内容以冒号为分隔符:该行可以被解析为`key:value`的格式,需要根据实际的含义修改该行内容,下面进行详细说明。 +* 一行内容为`======xxxxx=====`:该行内容为注释信息,无需修改。 +* 一行内容为`##`:该行内容表示段落分隔符,没有实际意义,无需修改。 + +#### 2.2.1 训练配置参数 + +在配置文件中,可以通过下面的方式配置一些常用的超参数,如:是否使用GPU、迭代轮数、batch-size、预训练模型路径等,下面给出了常用的训练配置以及需要修改的内容。 + +

+训练配置参数(点击以展开详细内容或者折叠) + + + +| 行号 | 参考内容 | 含义 | key是否需要修改 | value是否需要修改 | 修改内容 | +|----|-------------------------------------|---------------|-----------|-------------|----------------------------------| +| 2 | model_name:mobilenet_v3_small | 模型名字 | 否 | 是 | value修改为自己的模型名字 | +| 3 | python:python3.7 | python环境 | 否 | 是 | value修改为自己的python环境 | +| 4 | gpu_list:0 | gpu id | 否 | 是 | value修改为自己的GPU ID | +| 5 | use-gpu:True | 是否使用GPU | 是 | 是 | key修改为设置GPU的内容,value修改为设置GPU的值 | +| 6 | --epochs:lite_train_lite_infer=5 | 迭代的epoch数目 | 是 | 否 | key修改为代码中设置epoch数量的内容 | +| 7 | --output-dir:./output/ | 输出目录 | 是 | 否 | key修改为代码中设置输出路径的内容 | +| 8 | --batch-size:lite_train_lite_infer=4 | 训练的batch size | 是 | 否 | key修改为代码中设置batch size的内容 | +| 9 | --pretrained:null | 预训练模型 | 是 | 是 | 如果训练时指定了预训练模型,则key和value需要对应修改 | +| 10 | train_model_name:latest.pdparams | 训练结果的模型名字 | 否 | 是 | value需要修改为训练完成之后保存的模型名称,用于后续的动转静 | +| 11 | --data-path:./lite_data | 数据集路径 | 是 | 是 | key和value需要对应修改为指定的数据集路径 | + +
+ +以训练命令`python3.7 train.py --amp_level=O1 --device=gpu --epochs=2 --data-path=./lite_data`为例,总共包含4个超参数。 + +* 运行设备:`--device=gpu`,则需要修改为配置文件的第5行,`key`为`--device`, `value`为`gpu`,修改后内容为`--device:gpu` +* 迭代轮数:`--epochs=2`,则需要修改配置文件的第6行,修改后内容为`--epochs:lite_train_lite_infer=2`(`lite_train_lite_infer`为模式设置,表示少量数据训练,少量数据推理,此处无需修改) +* 数据路径:`--data-path=./lite_data`,则需要修改为配置文件的第11行,修改后内容为`--data-path:./lite_data` +* 学习率:`--amp_level=O1`,由于配置文件中不包含该项配置,因此可以将其和`train.py`字段(配置文件第14行)放在一起,具体会在2.2.2节详细说明。 + +#### 2.2.2 训练命令配置参数 + +下面给出了配置文件中的训练命令配置参数(点击以展开详细内容或者折叠) + +
+训练命令配置参数(点击以展开详细内容或者折叠) + + +| 行号 | 参考内容 | 含义 | key是否需要修改 | value是否需要修改 | 修改内容 | +|----|---------------------------------------------|-----------------|-----------|-------------|-------------------| +| 13 | trainer:amp_train | 训练方法 | 否 | 否 | - | +| 14 | amp_train:train.py --amp_level=O1 | norm_train的训练脚本 | 否 | 是 | value可以修改为自己的训练命令 | + +
+ +以训练命令`python3.7 train.py --amp_level=O1 --device=gpu --epochs=1 --data-path=./lite_data`为例,该命令为混合精度训练(非裁剪、量化、蒸馏等方式),因此 + +* 配置文件的第13行直接写`amp_train`即可。 +* 第14行配置`amp_train`的具体运行脚本/入口,即上述命令中的`train.py`因此配置文件的14行内容初步可以修改为`amp_train:train.py --amp_level=O1`,考虑到`--amp_level=O1`超参数无法在配置文件中配置,因此可以在这里添加,修改后内容为`amp_train:train.py --amp_level=O1` + +#### 2.2.3 模型动转静配置参数 + +下面给出了配置文件中的模型动转静配置参数。 + +**【注意】:** 在模型动转静过程中,为方便管理输入输出,程序会自动指定输入和输出目录,因此我们只需要提供可以配置输入输出目录的参数即可。 + +
+模型动转静配置参数(点击以展开详细内容或者折叠) + +| 行号 | 参考内容 | 含义 | key是否需要修改 | value是否需要修改 | 修改内容 | +|----|------------------|-----------|-----------|-------------|-------------------------| +| 20 | --save-inference-dir:./output/mobilenet_v3_small_infer/ | 动转静输出目录 | 是 | 否 | key修改为代码中设置输出目录的内容 | +| 21 | --pretrained: | 预训练模型路径 | 是 | 否 | key修改为代码中可以设置预训练模型路径的内容 | +| 22 | norm_export:tools/export_model.py | 模型动转静导出命令 | 否 | 是 | value修改为实际的动转静导出命令 | + +
+ +以模型动转静命令`python tools/export_model.py --pretrained=./mobilenet_v3_small_pretrained.pdparams --save-inference-dir="./mobilenet_v3_small_infer" --model=mobilenet_v3_small`为例。 + +* 预训练模型路径的配置为`--pretrained=./mobilenet_v3_small_pretrained.pdparams`,因此需要将21行修改为`--pretrained:` +* 动转静保存目录的配置为`--save-inference-dir="./mobilenet_v3_small_infer"`,因此需要将20行修改为`--save-inference-dir:` +* 模型导出命令为`tools/export_model.py`,考虑到也需要配置`--model=mobilenet_v3_small`,因此可以将22行修改为`norm_export:tools/export_model.py --model=mobilenet_v3_small` + +#### 2.2.4 模型推理配置参数 + +下面给出了配置文件中的模型推理配置参数。 + +
+模型推理配置参数(点击以展开详细内容或者折叠) + +| 行号 | 参考内容 | 含义 | key是否需要修改 | value是否需要修改 | 修改内容 | +|----|------------------|-----------------|-----------|-------------|-------------| +| 24 | train_model:./pretrain_models/mobilenet_v3_small_pretrained.pdparams | 训练模型保存路径 | 否 | 否 | | +| 25 | infer_export:tools/export_model.py --model=mobilenet_v3_small | 推理前是否需要导出 | 否 | 是 | value修改为和22行内容一致即可 | +| 27 | inference:deploy/inference_python/infer.py | 推理脚本 | 否 | 是 | value修改为自己的推理脚本 | +| 28 | --use-gpu:True|False | 是否使用GPU | 是 | 是 | key和value修改为GPU设置的参数和值 | +| 29 | --batch_size:1 | 推理batch size | 是 | 否 | key修改为代码中设置batch size的内容 | +| 30 | --model-dir:./output/mobilenet_v3_small_infer/ | 推理模型保存路径 | 是 | 否 | key修改为代码中可以设置inference model路径的内容 | +| 31 | --img-path:./images/demo.jpg | 图片路径或者图片文件夹路径 | 是 | 否 | key修改为代码中设置图片路径的内容 | +| 32 | --benchmark:False | 是否使用benchmark | 是 | 是 | key和value修改为规范化推理日志输出设置的参数和值 | + +
+ +以推理命令`python deploy/inference_python/infer.py --model-dir=./mobilenet_v3_small_infer/ --img-path=./images/demo.jpg`为例。 + +* 推理的入口脚本为`deploy/inference_python/infer.py`,因此27行需要修改为`inference:deploy/inference_python/infer.py`。 +* Inference模型路径配置为`--model-dir=./mobilenet_v3_small_infer/`,而运行该命令时,会直接基于已经生成的Inference模型,因此30行需要修改为`--model-dir:./mobilenet_v3_small_infer/` +* 测试图像路径配置为`--img-path=./images/demo.jpg`,因此31行修改为`--img-path:./images/demo.jpg` + + + +## 3. 混合精度训练推理功能测试开发 + +混合精度训练推理功能测试开发过程主要分为以下6个步骤。 + +
+ +
+ +其中设置了2个核验点,下面详细介绍开发过程。 + + + +### 3.1 准备待测试的命令 + +**【基本内容】** + +准备训练、模型动转静、模型推理的命令,后续会将这些命令按照[第2节](#2)所述内容,映射到配置文件中。 + +**【实战】** + +MobileNetV3的训练、动转静、推理示例运行命令如下所示。 + +```bash +# 模型训练 +python3.7 train.py --device=gpu --epochs=2 --data-path=./lite_data --amp_level=O1 +# 模型动转静 +python tools/export_model.py --pretrained=./mobilenet_v3_small_pretrained.pdparams --save-inference-dir="./mobilenet_v3_small_infer" --model=mobilenet_v3_small +# 推理 +python deploy/inference_python/infer.py --model-dir=./mobilenet_v3_small_infer/ --img-path=./images/demo.jpg +``` + + + +### 3.2 准备数据与环境 + +**【基本内容】** + +1. 数据集:为方便快速验证训练/评估/推理过程,需要准备一个小数据集(训练集和验证集各8~16张图像即可,压缩后数据大小建议在`20M`以内),放在`lite_data`文件夹下。 + + 相关文档可以参考[论文复现赛指南3.2章节](../../../docs/lwfx/ArticleReproduction_CV.md),代码可以参考`基于ImageNet准备小数据集的脚本`:[prepare.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/tipc/pipeline/Step2/prepare.py)。 + +2. 环境:安装好PaddlePaddle即可进行混合精度训练推理测试开发 + +**【注意事项】** + +* 为方便管理,建议在上传至github前,首先将lite_data文件夹压缩为tar包,直接上传tar包即可,在测试训练评估与推理过程时,可以首先对数据进行解压。 + * 压缩命令: `tar -zcf lite_data.tar lite_data` + * 解压命令: `tar -xf lite_data.tar` + + + + +### 3.3 准备开发所需脚本 + +**【基本内容】** + +在repo中新建`test_tipc`目录,将文件 [common_func.sh](../../mobilenetv3_prod/Step6/test_tipc/common_func.sh) , [prepare.sh](../../mobilenetv3_prod/Step6/test_tipc/prepare.sh) 和 [test_train_inference_python.sh](../../mobilenetv3_prod/Step6/test_tipc/test_train_inference_python.sh) 分别拷贝到`test_tipc`目录中。 + + +**【注意事项】** + +* 上述3个脚本文件无需改动,在实际使用时,直接修改配置文件即可。 + + + +### 3.4 填写配置文件 + +**【基本内容】** + +在repo的`test_tipc/`目录中新建`configs/model_name`,将文件 [train_amp_infer_python.txt](../../mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt) 拷贝到该目录中,其中`model_name`需要修改为您自己的模型名称。 + +**【实战】** + +配置文件的含义解析可以参考 [2.2节配置文件解析](#2.2) 部分。 + +mobilenet_v3_small的测试开发配置文件可以参考:[train_amp_infer_python.txt](../../mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt)。 + + + +### 3.5 验证配置正确性 + +**【基本内容】** + +基于修改完的配置,运行 + +```bash +bash test_tipc/prepare.sh ${your_params_file} lite_train_lite_infer +bash test_tipc/test_train_inference_python.sh ${your_params_file} lite_train_lite_infer +``` + +**【注意事项】** + +如果运行失败,会输出具体的报错命令,可以根据输出的报错命令排查下配置文件的问题并修改,示例报错如下所示。 + +``` +Run failed with command - python3.7 tools/export_model.py --model=mobilenet_v3_small --pretrained=./log/mobilenet_v3_small/lite_train_lite_infer/norm_train_gpus_0/latest --save-inference-dir=./log/mobilenet_v3_small/lite_train_lite_infer/norm_train_gpus_0! +``` + +**【实战】** + +以mobilenet_v3_small的`Linux GPU/CPU 混合精度训练推理功能测试` 为例,命令如下所示。 + +```bash +bash test_tipc/test_train_inference_python.sh test_tipc/configs/mobilenet_v3_small/train_amp_infer_python.txt lite_train_lite_infer +``` + +输出结果如下,表示命令运行成功。 + +```bash +Run successfully with command - python3.7 --amp_level=O1 train.py --output-dir=./log/mobilenet_v3_small/lite_train_lite_infer/norm_train_gpus_0 --epochs=5 --batch-size=4! +...... +Run successfully with command - python3.7 deploy/inference_python/infer.py --use-gpu=False --model-dir=./log/mobilenet_v3_small/lite_train_lite_infer/norm_train_gpus_0,1 --batch-size=1 --benchmark=False > ./log/mobilenet_v3_small/lite_train_lite_infer/python_infer_cpu_batchsize_1.log 2>&1 ! +``` + +**【核验】** + +基于修改后的配置文件,测试通过,全部命令成功 + + + +### 3.6 撰写说明文档 + +**【基本内容】** + +撰写TIPC功能总览和测试流程说明文档,分别为 + +1. TIPC功能总览文档:test_tipc/README.md +2. Linux GPU/CPU 混合精度训练推理功能测试说明文档:test_tipc/docs/test_amp_train_infer_python.md + +2个文档模板分别位于下述位置,可以直接拷贝到自己的repo中,根据自己的模型进行修改。 + +1. [README.md](../../mobilenetv3_prod/Step6/test_tipc/README.md) +2. [test_train_amp_inference_python.md](../../mobilenetv3_prod/Step6/test_tipc/docs/test_train_amp_inference_python.md) + +**【实战】** + +mobilenet_v3_small中`test_tipc`文档如下所示。 + +1. TIPC功能总览文档:[README.md](../../mobilenetv3_prod/Step6/test_tipc/README.md) +2. Linux GPU/CPU 混合精度训练推理测试说明文档:[test_train_amp_inference_python.md](../../mobilenetv3_prod/Step6/test_tipc/docs/test_train_amp_inference_python.md) + +**【核验】** + +repo中最终目录结构如下所示。 + +``` +test_tipc + |--configs # 配置目录 + | |--model_name # 您的模型名称 + | |--train_amp_infer_python.txt # 混合精度训练推理测试配置文件 + |--docs # 文档目录 + | |--test_train_amp_inference_python.md # 混合精度训练推理测试说明文档 + |----README.md # TIPC说明文档 + |----prepare.sh # TIPC混合精度训练推理测试数据准备脚本 + |----test_train_inference_python.sh # TIPC混合精度训练推理测试解析脚本,无需改动 + |----common_func.sh # TIPC混合精度训练推理测试常用函数,无需改动 +``` + +基于`test_train_amp_inference_python.md`文档,跑通`Linux GPU/CPU 混合精度训练推理功能测试`流程。 + + + +## 4. FAQ -- GitLab