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rename tipc config

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WenmuZhou
2021-11-13 06:48:43 +00:00
parent dd350242fb
commit 538321e9d9
15 changed files with 232 additions and 389 deletions
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2
recognition/arcface_paddle/deploy/pdserving/config.yml
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@@ -41,5 +41,5 @@ op:
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
devices: "0"
ir_optim: True

7
recognition/arcface_paddle/deploy/pdserving/pipeline_http_client.py
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@@ -18,6 +18,11 @@ import json
import base64
import os
import argparse
parser = argparse.ArgumentParser(description="args for paddleserving")
parser.add_argument("--image_dir", type=str, default="./imgs")
args = parser.parse_args()
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
@@ -25,7 +30,7 @@ def cv2_to_base64(image):
url = "http://127.0.0.1:9998/ArcFace/prediction"
test_img_dir = "imgs"
test_img_dir = args.image_dir
for idx, img_file in enumerate(os.listdir(test_img_dir)):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()

7
recognition/arcface_paddle/deploy/pdserving/pipeline_rpc_client.py
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@@ -27,11 +27,16 @@ client = PipelineClient()
client.connect(['127.0.0.1:18091'])
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
import argparse
parser = argparse.ArgumentParser(description="args for paddleserving")
parser.add_argument("--image_dir", type=str, default="../../doc/imgs/")
args = parser.parse_args()
test_img_dir = args.image_dir
test_img_dir = "imgs"
for idx, img_file in enumerate(os.listdir(test_img_dir)):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()

132
recognition/arcface_paddle/test_tipc/compare_results.py
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@@ -1,132 +0,0 @@
import numpy as np
import os
import subprocess
import json
import argparse
import glob
def init_args():
parser = argparse.ArgumentParser()
# params for testing assert allclose
parser.add_argument("--atol", type=float, default=1e-3)
parser.add_argument("--rtol", type=float, default=1e-3)
parser.add_argument("--gt_file", type=str, default="")
parser.add_argument("--log_file", type=str, default="")
parser.add_argument("--precision", type=str, default="fp32")
return parser
def parse_args():
parser = init_args()
return parser.parse_args()
def run_shell_command(cmd):
p = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
out, err = p.communicate()
if p.returncode == 0:
return out.decode('utf-8')
else:
return None
def parser_results_from_log_by_name(log_path, names_list):
if not os.path.exists(log_path):
raise ValueError("The log file {} does not exists!".format(log_path))
if names_list is None or len(names_list) < 1:
return []
parser_results = {}
for name in names_list:
cmd = "grep {} {}".format(name, log_path)
outs = run_shell_command(cmd)
outs = outs.split("\n")[0]
result = outs.split("{}".format(name))[-1]
result = json.loads(result)
parser_results[name] = result
return parser_results
def load_gt_from_file(gt_file):
if not os.path.exists(gt_file):
raise ValueError("The log file {} does not exists!".format(gt_file))
with open(gt_file, 'r') as f:
data = f.readlines()
f.close()
parser_gt = {}
for line in data:
image_name, result = line.strip("\n").split("\t")
image_name = image_name.split('/')[-1]
result = json.loads(result)
parser_gt[image_name] = result
return parser_gt
def load_gt_from_txts(gt_file):
gt_list = glob.glob(gt_file)
gt_collection = {}
for gt_f in gt_list:
gt_dict = load_gt_from_file(gt_f)
basename = os.path.basename(gt_f)
if "fp32" in basename:
gt_collection["fp32"] = [gt_dict, gt_f]
elif "fp16" in basename:
gt_collection["fp16"] = [gt_dict, gt_f]
elif "int8" in basename:
gt_collection["int8"] = [gt_dict, gt_f]
else:
continue
return gt_collection
def collect_predict_from_logs(log_path, key_list):
log_list = glob.glob(log_path)
pred_collection = {}
for log_f in log_list:
pred_dict = parser_results_from_log_by_name(log_f, key_list)
key = os.path.basename(log_f)
pred_collection[key] = pred_dict
return pred_collection
def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7):
for k in dict_x:
np.testing.assert_allclose(
np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol)
if __name__ == "__main__":
# Usage:
# python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt --log_file=./tests/output/infer_*.log
args = parse_args()
gt_collection = load_gt_from_txts(args.gt_file)
key_list = gt_collection["fp32"][0].keys()
# print(gt_collection,key_list)
pred_collection = collect_predict_from_logs(args.log_file, key_list)
for filename in pred_collection.keys():
if "fp32" in filename:
gt_dict, gt_filename = gt_collection["fp32"]
elif "fp16" in filename:
gt_dict, gt_filename = gt_collection["fp16"]
elif "int8" in filename:
gt_dict, gt_filename = gt_collection["int8"]
else:
continue
pred_dict = pred_collection[filename]
try:
testing_assert_allclose(
gt_dict, pred_dict, atol=args.atol, rtol=args.rtol)
print(
"Assert allclose passed! The results of {} and {} are consistent!".
format(filename, gt_filename))
except Exception as E:
print(E)
raise ValueError(
"The results of {} and the results of {} are inconsistent!".
format(filename, gt_filename))

17
recognition/arcface_paddle/test_tipc/configs/ms1mv2_mobileface/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt Normal file
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@@ -0,0 +1,17 @@
===========================serving_params===========================
model_name:arcfae_mobileface
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/MobileFaceNet_128_serving/
--serving_client:./deploy/pdserving/MobileFaceNet_128_client/
serving_dir:./deploy/pdserving
web_service:web_service.py --config=config.yml --opt op.ArcFace.concurrency=1
op.ArcFace.local_service_conf.devices:null|0
op.ArcFace.local_service_conf.use_mkldnn:True|False
op.ArcFace.local_service_conf.thread_num:1|6
op.ArcFace.local_service_conf.use_trt:False|True
op.ArcFace.local_service_conf.precision:fp32
pipline:pipeline_http_client.py --image_dir=./imgs

25
recognition/arcface_paddle/test_tipc/configs/mobileface.txt → recognition/arcface_paddle/test_tipc/configs/ms1mv2_mobileface/train_infer_python.txt
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@@ -8,12 +8,12 @@ null:null
--output:./output/
--batch_size:2
--checkpoint_dir:null
train_model_name:lite_train_lite_infer=MobileFaceNet_128/0|lite_train_whole_infer=MobileFaceNet_128/0|whole_infer=MobileFaceNet_128/0|whole_train_whole_infer=MobileFaceNet_128/24
train_model_name:MobileFaceNet_128/0
train_infer_img_dir:./MS1M_v2/images
fp16:Trule|False
null:null
##
trainer:norm_train
norm_train:tools/train.py --config_file=configs/ms1mv2_mobileface.py --is_static=False --embedding_size=128 --fp16=False --dataset=MS1M_v2 --data_dir=MS1M_v2/ --label_file=MS1M_v2/label.txt --num_classes=85742 --log_interval_step=1
norm_train:tools/train.py --config_file=configs/ms1mv2_mobileface.py --is_static=False --fp16=False --embedding_size=128 --fp16=False --dataset=MS1M_v2 --data_dir=MS1M_v2/ --label_file=MS1M_v2/label.txt --num_classes=85742 --log_interval_step=1
pact_train:null
fpgm_train:null
distill_train:null
@@ -48,21 +48,4 @@ inference:tools/inference.py --export_type=paddle --benchmark=True
--image_path:MS1M_v2/images
null:null
--benchmark:null
null:null
===========================serving_params===========================
model_name:ocr_det
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/MobileFaceNet_128_serving/
--serving_client:./deploy/pdserving/MobileFaceNet_128_client/
serving_dir:./deploy/pdserving
web_service:web_service.py --config=config.yml --opt op.ArcFace.concurrency=1
op.ArcFace.local_service_conf.devices:null|0
op.ArcFace.local_service_conf.use_mkldnn:True|False
op.ArcFacedet.local_service_conf.thread_num:1|6
op.ArcFace.local_service_conf.use_trt:False|True
op.ArcFace.local_service_conf.precision:fp32
pipline:pipeline_http_client.py --image_dir=./imgs
null:null

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4
recognition/arcface_paddle/test_tipc/docs/test_serving.md
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@@ -16,10 +16,10 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试
先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ms1mv2_mobileface.txt "serving_infer"
bash test_tipc/prepare.sh ./test_tipc/configs/ms1mv2_mobileface/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt "serving_infer"
# 用法:
bash test_tipc/test_serving.sh ./test_tipc/configs/ms1mv2_mobileface.txt
bash test_tipc/test_serving.sh ./test_tipc/configs//ms1mv2_mobileface/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
```
#### 运行结果

38
recognition/arcface_paddle/test_tipc/docs/test_train_inference_python.md
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@@ -21,7 +21,7 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
## 2. 测试流程
运行环境配置请参考[文档](./install.md)的内容配置tipc的运行环境。
运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
### 2.1 安装依赖
- 安装PaddlePaddle >= 2.2
@@ -38,12 +38,12 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
`test_train_inference_python.sh`包含5种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
`test_train_inference_python.sh`包含5种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，本文档只测试lite_train_lite_infer一种模式：
- 模式1：lite_train_lite_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```shell
bash test_tipc/prepare.sh ./test_tipc/configs/mobileface.txt 'lite_train_lite_infer'
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/mobileface.txt 'lite_train_lite_infer'
bash test_tipc/prepare.sh ./test_tipc/configs/ms1mv2_mobileface/train_infer_python.txt 'lite_train_lite_infer'
bash test_tipc/test_train_inference_python.sh ./test_tipc/ms1mv2_mobileface/train_infer_python.txt 'lite_train_lite_infer'
```
运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
@@ -73,34 +73,6 @@ Run failed with command - python3.7 tools/validation.py --is_static=False --back
可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。
### 2.3 精度测试
使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
- 提取日志中的预测坐标；
- 从本地文件中提取保存好的坐标结果；
- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
#### 使用方式
运行命令：
```shell
python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
```
参数介绍：
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入
- atol: 设置的绝对误差
- rtol: 设置的相对误差
#### 运行结果
正常运行效果如下图：
<img src="compare_right.png" width="1000">
出现不一致结果时的运行输出：
<img src="compare_wrong.png" width="1000">
## 3. 更多教程
本文档为功能测试用，更丰富的训练预测使用教程请参考：
[模型训练与预测](https://github.com/WenmuZhou/insightface/blob/master/recognition/arcface_paddle/README_cn.md)
[模型训练与预测](../../README_cn.md)

2
recognition/arcface_paddle/test_tipc/prepare.sh
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@@ -32,7 +32,7 @@ elif [ ${MODE} = "serving_infer" ];then
${python_name} -m pip install install paddle_serving_server_gpu-0.0.0.post101-py3-none-any.whl
${python_name} -m pip install paddle_serving_client==0.6.3
${python_name} -m pip install paddle-serving-app==0.6.3
${python_name} -m pip install -U werkzeug
${python_name} -m pip install werkzeug==2.0.2
rm -rf ./inference

20
recognition/arcface_paddle/test_tipc/readme.md
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@@ -32,17 +32,22 @@
```shell
test_tipc/
├── configs/ # 配置文件目录
├── ms1mv2_mobileface.py # 测试mobile版识别模型训练的py文件
├── ms1mv2_mobileface.txt # 测试mobile版识别模型训练的参数配置文件
├── ms1mv2_mobileface # ms1mv2_mobileface 模型的测试配置文件目录
├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt # 测试Linux上python serving预测的配置文件
└── train_infer_python.txt # 测试Linux上python训练预测（基础训练预测）的配置文件
├── ...
├── results/ # 预先保存的预测结果，用于和实际预测结果进行精读比对
├── python_MobileFaceNet_128_fp32.txt # 预存的mobile版识别模型python预测fp32精度的结果
├── ...
├── data/ # 存放 TIPC 测试数据的目录
├── small_dataset.tar # 用于训练的小数据集 (10张图片)
├── small_lfw.bin # 用于评估的小数据集 (20张图片)
├── docs/ # 存放 TIPC 测试数据的目录
├── install.md # 安装 TIPC 所需环境的文档
├── test_train_inference_python.md # 测试Linux上python训练预测的文档
├── test_serving.md # 测试Linux上python serving预测的文档
├── prepare.sh # 完成test_*.sh运行所需要的数据和模型下载
├── test_serving.sh # 测试python训练预测的主程序
├── test_train_inference_python.sh # 测试python训练预测的主程序
├── compare_results.py # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内
├── common_func.sh # 通用shell脚本函数
└── readme.md # 使用文档
└── readme.md # TIPC使用文档
```
### 测试流程
@@ -53,7 +58,6 @@ test_tipc/
1. 运行prepare.sh准备测试所需数据和模型；
2. 运行要测试的功能对应的测试脚本`test_*.sh`，产出log，由log可以看到不同配置是否运行成功；
3. 用`compare_results.py`对比log中的预测结果和预存在results目录下的结果，判断预测精度是否符合预期（在误差范围内）。
其中，有1个测试主程序，功能如下：
- `test_train_inference_python.sh`：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。

49
recognition/arcface_paddle/test_tipc/results/python_MobileFaceNet_128_fp32.txt
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@@ -1,49 +0,0 @@
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130
recognition/arcface_paddle/test_tipc/test_serving.sh
View File

@@ -2,7 +2,7 @@
source test_tipc/common_func.sh
FILENAME=$1
dataline=$(awk 'NR==52, NR==68{print}' $FILENAME)
dataline=$(awk 'NR==1, NR==18{print}' $FILENAME)
# parser params
IFS=$'\n'
@@ -35,6 +35,8 @@ web_use_trt_list=$(func_parser_value "${lines[14]}")
web_precision_key=$(func_parser_key "${lines[15]}")
web_precision_list=$(func_parser_value "${lines[15]}")
pipeline_py=$(func_parser_value "${lines[16]}")
image_dir_key=$(func_parser_key "${lines[17]}")
image_dir_value=$(func_parser_value "${lines[17]}")
LOG_PATH="../../test_tipc/output"
mkdir -p ./test_tipc/output
@@ -51,67 +53,98 @@ function func_serving(){
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $trans_model_cmd
cd ${serving_dir_value}
echo $PWD
unset https_proxy
unset http_proxy
for use_gpu in ${web_use_gpu_list[*]}; do
echo ${ues_gpu}
if [ ${use_gpu} = "null" ]; then
for use_mkldnn in ${web_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ]; then
continue
fi
for threads in ${web_cpu_threads_list[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py} > ${_save_log_path} 2>&1 "
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
elif [ ${use_gpu} = "0" ]; then
for use_trt in ${web_use_trt_list[*]}; do
for precision in ${web_precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
_save_log_path="${LOG_PATH}/server_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${web_precision_key}" "${precision}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
for python in ${python[*]}; do
if [ ${python} = "cpp"]; then
for use_gpu in ${web_use_gpu_list[*]}; do
if [ ${use_gpu} = "null" ]; then
web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py} > ${_save_log_path} 2>&1"
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
else
web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
eval $web_service_cmd
sleep 2s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
status_check $last_status "${pipeline_cmd}" "${status_log}"
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
fi
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
# python serving
for use_gpu in ${web_use_gpu_list[*]}; do
echo ${ues_gpu}
if [ ${use_gpu} = "null" ]; then
for use_mkldnn in ${web_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ]; then
continue
fi
for threads in ${web_cpu_threads_list[*]}; do
set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
echo $web_service_cmd
sleep 2s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} ${set_image_dir} > ${_save_log_path} 2>&1 "
echo $pipeline_cmd
last_status=${PIPESTATUS[0]}
echo "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
sleep 2s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
elif [ ${use_gpu} = "0" ]; then
for use_trt in ${web_use_trt_list[*]}; do
for precision in ${web_precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${web_precision_key}" "${precision}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
echo $web_service_cmd
sleep 2s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} ${set_image_dir}> ${_save_log_path} 2>&1"
echo $pipeline_cmd
last_status=${PIPESTATUS[0]}
echo "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
sleep 2s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
fi
done
}
@@ -127,6 +160,7 @@ fi
set CUDA_VISIBLE_DEVICES
eval $env
echo "################### run test ###################"
export Count=0

188
recognition/arcface_paddle/test_tipc/test_train_inference_python.sh
View File

@@ -1,10 +1,8 @@
#!/bin/bash
source test_tipc/common_func.sh
unset GREP_OPTIONS
FILENAME=$1
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer']
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
MODE=$2
dataline=$(awk 'NR==1, NR==51{print}' $FILENAME)
@@ -28,7 +26,7 @@ train_batch_key=$(func_parser_key "${lines[8]}")
train_batch_value=$(func_parser_params "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
pretrain_model_value=$(func_parser_value "${lines[9]}")
train_model_name=$(func_parser_params "${lines[10]}")
train_model_name=$(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 "${lines[12]}")
@@ -92,7 +90,7 @@ infer_value1=$(func_parser_value "${lines[50]}")
# parser klquant_infer
if [ ${MODE} = "klquant_whole_infer" ]; then
dataline=$(awk 'NR==82, NR==98{print}' $FILENAME)
dataline=$(awk 'NR==85 NR==101{print}' $FILENAME)
lines=(${dataline})
# parser inference model
infer_model_dir_list=$(func_parser_value "${lines[1]}")
@@ -160,7 +158,7 @@ function func_inference(){
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} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${use_gpu_key}=${use_gpu} --model_file=${_model_dir}/${model_name}.pdmodel --params_file=${_model_dir}/${model_name}.pdiparams ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 "
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} --model_file=${_model_dir}/${model_name}.pdmodel --params_file=${_model_dir}/${model_name}.pdiparams > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
@@ -190,7 +188,7 @@ function func_inference(){
set_precision=$(func_set_params "${precision_key}" "${precision}")
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
command="${_python} ${_script} ${set_tensorrt} ${set_precision} ${use_gpu_key}=${use_gpu} --model_file=${_model_dir}/${model_name}.pdmodel --params_file=${_model_dir}/${model_name}.pdiparams ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} --model_file=${_model_dir}/${model_name}.pdmodel --params_file=${_model_dir}/${model_name}.pdiparams > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
@@ -246,7 +244,7 @@ else
export Count=0
USE_GPU_KEY=(${train_use_gpu_value})
for gpu in ${gpu_list[*]}; do
use_gpu=${USE_GPU_KEY[Count]}
train_use_gpu=${USE_GPU_KEY[Count]}
Count=$(($Count + 1))
ips=""
if [ ${gpu} = "-1" ];then
@@ -272,97 +270,103 @@ else
set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
else
set_amp_config=" "
fi
fi
for trainer in ${trainer_list[*]}; do
for use_fp16 in ${train_param_value1[*]}; do
flag_quant=False
if [ ${trainer} = ${pact_key} ]; then
run_train=${pact_trainer}
run_export=${pact_export}
flag_quant=True
elif [ ${trainer} = "${fpgm_key}" ]; then
run_train=${fpgm_trainer}
run_export=${fpgm_export}
elif [ ${trainer} = "${distill_key}" ]; then
run_train=${distill_trainer}
run_export=${distill_export}
elif [ ${trainer} = ${trainer_key1} ]; then
run_train=${trainer_value1}
run_export=${export_value1}
elif [[ ${trainer} = ${trainer_key2} ]]; then
run_train=${trainer_value2}
run_export=${export_value2}
else
run_train=${norm_trainer}
run_export=${norm_export}
fi
flag_quant=False
if [ ${trainer} = ${pact_key} ]; then
run_train=${pact_trainer}
run_export=${pact_export}
flag_quant=True
elif [ ${trainer} = "${fpgm_key}" ]; then
run_train=${fpgm_trainer}
run_export=${fpgm_export}
elif [ ${trainer} = "${distill_key}" ]; then
run_train=${distill_trainer}
run_export=${distill_export}
elif [ ${trainer} = ${trainer_key1} ]; then
run_train=${trainer_value1}
run_export=${export_value1}
elif [[ ${trainer} = ${trainer_key2} ]]; then
run_train=${trainer_value2}
run_export=${export_value2}
else
run_train=${norm_trainer}
run_export=${norm_export}
fi
if [ ${run_train} = "null" ]; then
continue
fi
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_fp16_${use_fp16}"
# load pretrain from norm training if current trainer is pact or fpgm trainer
if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
set_pretrain="${load_norm_train_model}"
fi
if [ ${run_train} = "null" ]; then
continue
fi
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
if [ ${#ips} -le 26 ];then
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
nodes=1
else
IFS=","
ips_array=(${ips})
IFS="|"
nodes=${#ips_array[@]}
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
fi
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} --fp16=${use_fp16}"
elif [ ${#ips} -le 26 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} --fp16=${use_fp16}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} --fp16=${use_fp16}"
fi
# run train
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
# load pretrain from norm training if current trainer is pact or fpgm trainer
if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
set_pretrain="${load_norm_train_model}"
fi
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ]; then
load_norm_train_model=${set_eval_pretrain}
fi
# run eval
if [ ${eval_py} != "null" ]; then
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
eval $eval_cmd
status_check $? "${eval_cmd}" "${status_log}"
fi
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
elif [ ${#ips} -le 26 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
fi
# run train
eval "unset CUDA_VISIBLE_DEVICES"
eval $cmd
status_check $? "${cmd}" "${status_log}"
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
load_norm_train_model=${set_eval_pretrain}
fi
# run eval
if [ ${eval_py} != "null" ]; then
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
eval $eval_cmd
status_check $? "${eval_cmd}" "${status_log}"
fi
# run export model
if [ ${run_export} != "null" ]; then
# run export model
if [ ${run_export} != "null" ]; then
# run export model
save_infer_path="${save_log}"
set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
save_infer_path="${save_log}"
set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
#run inference
eval $env
save_infer_path="${save_log}"
if [ ${inference_dir} != "null" ] && [ ${inference_dir} != '##' ]; then
infer_model_dir="${save_infer_path}/${inference_dir}"
else
infer_model_dir=${save_infer_path}
fi
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
eval "unset CUDA_VISIBLE_DEVICES"
#run inference
eval $env
save_infer_path="${save_log}"
if [ ${inference_dir} != "null" ] && [ ${inference_dir} != '##' ]; then
infer_model_dir="${save_infer_path}/${inference_dir}"
else
infer_model_dir=${save_infer_path}
fi
done
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
eval "unset CUDA_VISIBLE_DEVICES"
fi
done # done with: for trainer in ${trainer_list[*]}; do
done # done with: for autocast in ${autocast_list[*]}; do
done # done with: for gpu in ${gpu_list[*]}; do
fi # end if [ ${MODE} = "infer" ]; then
fi # end if [ ${MODE} = "infer" ]; then