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HydrogenSulfate 2022-06-28 06:54:50 +00:00
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157
ppcls/configs/ImageNet/PPHGNet/PPHGNet_small_pact.yaml
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# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 600
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
use_dali: False
# model architecture
Arch:
name: PPHGNet_small
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.5
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00004
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m7-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.2
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 16
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 236
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 16
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 236
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

105
test_tipc/docs/test_train_ptq_inference_python.md 100644
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# Linux GPU/CPU KL离线量化推理测试
Linux GPU/CPU KL离线量化推理测试的主程序为`test_ptq_inference_python.sh`，可以测试基于Python的模型KL离线量化推理等基本功能。
## 1. 测试结论汇总
- KL离线量化：
| 算法名称 | 模型名称 | CPU |
| :-------------: | :-------------------------------------: | :----------: |
| MobileNetV3 | MobileNetV3_large_x1_0 | KL离线量化 |
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5 | KL离线量化 |
| PPHGNet | PPHGNet_small | KL离线量化 |
| PPHGNet | PPHGNet_tiny | KL离线量化 |
| PPLCNet | PPLCNet_x0_25 | KL离线量化 |
| PPLCNet | PPLCNet_x0_35 | KL离线量化 |
| PPLCNet | PPLCNet_x0_5 | KL离线量化 |
| PPLCNet | PPLCNet_x0_75 | KL离线量化 |
| PPLCNet | PPLCNet_x1_0 | KL离线量化 |
| PPLCNet | PPLCNet_x1_5 | KL离线量化 |
| PPLCNet | PPLCNet_x2_0 | KL离线量化 |
| PPLCNet | PPLCNet_x2_5 | KL离线量化 |
| PPLCNetV2 | PPLCNetV2_base | KL离线量化 |
| ResNet | ResNet50 | KL离线量化 |
| ResNet | ResNet50_vd | KL离线量化 |
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | KL离线量化 |
- 推理相关：
| 算法名称 | 模型名称 | CPU |
| :-------------: | :-------------------------------------: | :----------: |
| MobileNetV3 | MobileNetV3_large_x1_0 | KL离线量化 |
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5 | KL离线量化 |
| PPHGNet | PPHGNet_small | KL离线量化 |
| PPHGNet | PPHGNet_tiny | KL离线量化 |
| PPLCNet | PPLCNet_x0_25 | KL离线量化 |
| PPLCNet | PPLCNet_x0_35 | KL离线量化 |
| PPLCNet | PPLCNet_x0_5 | KL离线量化 |
| PPLCNet | PPLCNet_x0_75 | KL离线量化 |
| PPLCNet | PPLCNet_x1_0 | KL离线量化 |
| PPLCNet | PPLCNet_x1_5 | KL离线量化 |
| PPLCNet | PPLCNet_x2_0 | KL离线量化 |
| PPLCNet | PPLCNet_x2_5 | KL离线量化 |
| PPLCNetV2 | PPLCNetV2_base | KL离线量化 |
| ResNet | ResNet50 | KL离线量化 |
| ResNet | ResNet50_vd | KL离线量化 |
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | KL离线量化 |
## 2. 测试流程
一下测试流程以 MobileNetV3_large_x1_0 模型为例。
### 2.1 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。
```bash
# 需要安装2.2及以上版本的Paddle
# 安装GPU版本的Paddle
python3.7 -m pip install paddlepaddle-gpu==2.2.0
# 安装CPU版本的Paddle
python3.7 -m pip install paddlepaddle==2.2.0
```
- 安装PaddleSlim
```bash
python3.7 -m pip install paddleslim==2.2.0
```
- 安装依赖
```bash
python3.7 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
```
- 安装AutoLog（规范化日志输出工具）
```bash
python3.7 -m pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
```
### 2.2 准备数据和模型
```bash
bash test_tipc/prepare.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
```
离线量化的操作流程，可参考[文档](../../deploy/slim/README.md)。
### 2.3 功能测试
以`MobileNetV3_large_x1_0`的`Linux GPU/CPU KL离线量化训练推理测试`为例，命令如下所示。
```bash
bash test_tipc/test_ptq_inference_python.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
```
输出结果如下，表示命令运行成功。
```log
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./MobileNetV3_large_x1_0_infer!
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=True -o Global.use_tensorrt=False -o Global.use_fp16=False -o Global.inference_model_dir=.././MobileNetV3_large_x1_0_infer//quant_post_static_model -o Global.batch_size=1 -o Global.infer_imgs=../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg -o Global.benchmark=False > .././test_tipc/output/MobileNetV3_large_x1_0/whole_infer/infer_gpu_usetrt_False_precision_False_batchsize_1.log 2>&1 !
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=False -o Global.enable_mkldnn=False -o Global.cpu_num_threads=1 -o Global.inference_model_dir=.././MobileNetV3_large_x1_0_infer//quant_post_static_model -o Global.batch_size=1 -o Global.infer_imgs=../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg -o Global.benchmark=False > .././test_tipc/output/MobileNetV3_large_x1_0/whole_infer/infer_cpu_usemkldnn_False_threads_1_batchsize_1.log 2>&1 !
```
同时，测试过程中的日志保存在`PaddleClas/test_tipc/output/MobileNetV3_large_x1_0/whole_infer`下。
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。

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test_tipc/test_ptq_inference_python.sh 100644
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#!/bin/bash
FILENAME=$1
source test_tipc/common_func.sh
# MODE be one of ['whole_infer']
MODE=$2
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
# The training params
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
gpu_list=$(func_parser_value "${lines[3]}")
train_use_gpu_key=$(func_parser_key "${lines[4]}")
train_use_gpu_value=$(func_parser_value "${lines[4]}")
autocast_list=$(func_parser_value "${lines[5]}")
autocast_key=$(func_parser_key "${lines[5]}")
epoch_key=$(func_parser_key "${lines[6]}")
epoch_num=$(func_parser_params "${lines[6]}")
save_model_key=$(func_parser_key "${lines[7]}")
train_batch_key=$(func_parser_key "${lines[8]}")
train_batch_value=$(func_parser_value "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
pretrain_model_value=$(func_parser_value "${lines[9]}")
train_model_name=$(func_parser_value "${lines[10]}")
train_infer_img_dir=$(func_parser_value "${lines[11]}")
train_param_key1=$(func_parser_key "${lines[12]}")
train_param_value1=$(func_parser_value "${lines[12]}")
trainer_list=$(func_parser_value "${lines[14]}")
trainer_norm=$(func_parser_key "${lines[15]}")
norm_trainer=$(func_parser_value "${lines[15]}")
pact_key=$(func_parser_key "${lines[16]}")
pact_trainer=$(func_parser_value "${lines[16]}")
fpgm_key=$(func_parser_key "${lines[17]}")
fpgm_trainer=$(func_parser_value "${lines[17]}")
distill_key=$(func_parser_key "${lines[18]}")
distill_trainer=$(func_parser_value "${lines[18]}")
to_static_key=$(func_parser_key "${lines[19]}")
to_static_trainer=$(func_parser_value "${lines[19]}")
trainer_key2=$(func_parser_key "${lines[20]}")
trainer_value2=$(func_parser_value "${lines[20]}")
eval_py=$(func_parser_value "${lines[23]}")
eval_key1=$(func_parser_key "${lines[24]}")
eval_value1=$(func_parser_value "${lines[24]}")
save_infer_key=$(func_parser_key "${lines[27]}")
export_weight=$(func_parser_key "${lines[28]}")
norm_export=$(func_parser_value "${lines[29]}")
pact_export=$(func_parser_value "${lines[30]}")
fpgm_export=$(func_parser_value "${lines[31]}")
distill_export=$(func_parser_value "${lines[32]}")
kl_quant_cmd_key=$(func_parser_key "${lines[33]}")
kl_quant_cmd_value=$(func_parser_value "${lines[33]}")
export_key2=$(func_parser_key "${lines[34]}")
export_value2=$(func_parser_value "${lines[34]}")
# parser inference model
infer_model_dir_list=$(func_parser_value "${lines[36]}")
infer_export_flag=$(func_parser_value "${lines[37]}")
infer_is_quant=$(func_parser_value "${lines[38]}")
# parser inference
inference_py=$(func_parser_value "${lines[39]}")
use_gpu_key=$(func_parser_key "${lines[40]}")
use_gpu_list=$(func_parser_value "${lines[40]}")
use_mkldnn_key=$(func_parser_key "${lines[41]}")
use_mkldnn_list=$(func_parser_value "${lines[41]}")
cpu_threads_key=$(func_parser_key "${lines[42]}")
cpu_threads_list=$(func_parser_value "${lines[42]}")
batch_size_key=$(func_parser_key "${lines[43]}")
batch_size_list=$(func_parser_value "${lines[43]}")
use_trt_key=$(func_parser_key "${lines[44]}")
use_trt_list=$(func_parser_value "${lines[44]}")
precision_key=$(func_parser_key "${lines[45]}")
precision_list=$(func_parser_value "${lines[45]}")
infer_model_key=$(func_parser_key "${lines[46]}")
image_dir_key=$(func_parser_key "${lines[47]}")
infer_img_dir=$(func_parser_value "${lines[47]}")
save_log_key=$(func_parser_key "${lines[48]}")
benchmark_key=$(func_parser_key "${lines[49]}")
benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
if [ ! $epoch_num ]; then
epoch_num=2
fi
if [[ $MODE = 'benchmark_train' ]]; then
epoch_num=1
fi
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log"
function func_inference() {
IFS='|'
_python=$1
_script=$2
_model_dir=$3
_log_path=$4
_img_dir=$5
_flag_quant=$6
# inference
for use_gpu in ${use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
for threads in ${cpu_threads_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "../${status_log}" "${model_name}"
done
done
done
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
for use_trt in ${use_trt_list[*]}; do
for precision in ${precision_list[*]}; do
if [ ${precision} = "True" ] && [ ${use_trt} = "False" ]; then
continue
fi
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${precision_key}" "${precision}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "../${status_log}" "${model_name}"
done
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
}
# for kl_quant
if [ ${kl_quant_cmd_value} != "null" ] && [ ${kl_quant_cmd_value} != "False" ]; then
echo "kl_quant"
command="${python} ${kl_quant_cmd_value}"
echo ${command}
eval $command
last_status=${PIPESTATUS[0]}
status_check $last_status "${command}" "${status_log}" "${model_name}"
cd ${infer_model_dir_list}/quant_post_static_model
ln -s __model__ inference.pdmodel
ln -s __params__ inference.pdiparams
cd ../../deploy
is_quant=True
func_inference "${python}" "${inference_py}" "../${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
cd ..
fi