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PaddleClas
提交 d25d7446 编写于 作者: W weishengyu
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Merge branch 'develop' into release/2.3

上级 b5401606 b5cf03e7
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Showing with 32883 addition and 5412 deletion
.gitignore
浏览文件 @ d25d7446
......@@ -11,3 +11,4 @@ _build/
build/
log/
nohup.out
.DS_Store
README_ch.md
浏览文件 @ d25d7446
......@@ -53,6 +53,7 @@ PP-ShiTu图像识别快速体验：[点击这里](./docs/zh_CN/quick_start/quick
- 图像分类快速体验
- [尝鲜版](./docs/zh_CN/quick_start/quick_start_classification_new_user.md)
- [进阶版](./docs/zh_CN/quick_start/quick_start_classification_professional.md)
- [多标签分类](./docs/zh_CN/quick_start/quick_start_multilabel_classification.md)
- [PP-ShiTu图像识别系统介绍](#图像识别系统介绍)
- [主体检测](./docs/zh_CN/image_recognition_pipeline/mainbody_detection.md)
- [特征提取](./docs/zh_CN/image_recognition_pipeline/feature_extraction.md)
......@@ -70,7 +71,7 @@ PP-ShiTu图像识别快速体验：[点击这里](./docs/zh_CN/quick_start/quick
- [模型导出](./docs/zh_CN/inference_deployment/export_model.md)
- Python/C++ 预测引擎
- [基于Python预测引擎预测推理](./docs/zh_CN/inference_deployment/python_deploy.md)
- [基于C++预测引擎预测推理](./docs/zh_CN/inference_deployment/cpp_deploy.md)(当前只支持图像分类任务，图像识别更新中)
- [基于C++分类预测引擎预测推理](./docs/zh_CN/inference_deployment/cpp_deploy.md)、[基于C++的PP-ShiTu预测引擎预测推理](deploy/cpp_shitu/readme.md)
- 服务化部署
- [Paddle Serving服务化部署(推荐)](./docs/zh_CN/inference_deployment/paddle_serving_deploy.md)
- [Hub serving服务化部署](./docs/zh_CN/inference_deployment/paddle_hub_serving_deploy.md)
......
README_en.md
浏览文件 @ d25d7446
......@@ -68,7 +68,7 @@ Quick experience of image recognition：[Link](./docs/en/tutorials/quick_start_r
- [Feature Learning](./docs/en/tutorials/getting_started_retrieval_en.md)
- Inference Model Prediction
- [Python Inference](./docs/en/inference.md)
- [C++ Inference](./deploy/cpp/readme_en.md)(only support classification for now, recognition coming soon)
- [C++ Classfication Inference](./deploy/cpp/readme_en.md)， [C++ PP-ShiTu Inference](deploy/cpp_shitu/readme_en.md)
- Model Deploy (only support classification for now, recognition coming soon)
- [Hub Serving Deployment](./deploy/hubserving/readme_en.md)
- [Mobile Deployment](./deploy/lite/readme_en.md)
......
__init__.py
浏览文件 @ d25d7446
......@@ -14,3 +14,4 @@
__all__ = ['PaddleClas']
from .paddleclas import PaddleClas
from ppcls.arch.backbone import *
benchmark/prepare_data.sh
浏览文件 @ d25d7446
#!/bin/bash
dataset_url=$1
package_check_list=(imageio tqdm Cython pycocotools tb_paddle scipy pandas wget h5py sklearn opencv-python visualdl)
for package in ${package_check_list[@]}; do
if python -c "import ${package}" >/dev/null 2>&1; then
echo "${package} have already installed"
else
echo "${package} NOT FOUND"
pip install ${package}
echo "${package} installed"
fi
done
cd dataset
rm -rf ILSVRC2012
wget -nc ${dataset_url}
......
benchmark/run_all.sh
浏览文件 @ d25d7446
......@@ -5,20 +5,26 @@
# pip install ...
# 2 拷贝该模型需要数据、预训练模型
# 3 批量运行（如不方便批量，1，2需放到单个模型中）
model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 EfficientNetB0 ShuffleNetV2_x1_0 DenseNet121 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base)
log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # LOG_PATH_INDEX_DIR 后续QA设置参数
model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 ShuffleNetV2_x1_0 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base) # benchmark 监控模型列表
#model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 EfficientNetB0 ShuffleNetV2_x1_0 DenseNet121 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base) # 该脚本支持列表
fp_item_list=(fp32)
bs_list=(32 64 96 128)
#bs_list=(32 64 96 128)
for model_mode in ${model_mode_list[@]}; do
for fp_item in ${fp_item_list[@]}; do
if [ ${model_mode} = MobileNetV3_large_x1_0 ] || [ ${model_mode} = ShuffleNetV2_x1_0 ]; then
bs_list=(256)
else
bs_list=(64)
fi
for bs_item in ${bs_list[@]};do
echo "index is speed, 1gpus, begin, ${model_name}"
run_mode=sp
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/clas_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}_1gpus 2>&1 # (5min)
sleep 10
echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
run_mode=mp
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode}| tee ${log_path}/clas_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}_8gpus8p 2>&1
sleep 10
done
done
......
benchmark/run_benchmark.sh
浏览文件 @ d25d7446
......@@ -6,27 +6,37 @@ function _set_params(){
run_mode=${1:-"sp"} # 单卡sp|多卡mp
batch_size=${2:-"64"}
fp_item=${3:-"fp32"} # fp32|fp16
epochs=${4:-"10"} # 可选，如果需要修改代码提前中断
model_name=${5:-"model_name"}
run_log_path="${TRAIN_LOG_DIR:-$(pwd)}/benchmark" # TRAIN_LOG_DIR 后续QA设置该参数
epochs=${4:-"2"} # 可选，如果需要修改代码提前中断
model_item=${5:-"model_item"}
run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
index=1
mission_name="图像分类" # 模型所属任务名称，具体可参考scripts/config.ini （必填）
direction_id=0 # 任务所属方向，0：CV，1：NLP，2：Rec。 (必填)
skip_steps=8 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
keyword_loss="loss:" #选填
model_mode=-1 # 解析日志，具体参考scripts/analysis.py. (必填)
ips_unit="images/s"
base_batch_size=$batch_size
# 以下不用修改
device=${CUDA_VISIBLE_DEVICES//,/ }
arr=(${device})
num_gpu_devices=${#arr[*]}
log_file=${run_log_path}/clas_${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
log_file=${run_log_path}/clas_${model_item}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
model_name=${model_item}_bs${batch_size}_${fp_item} # model_item 用于yml匹配，model_name用于入库
}
function _train(){
echo "Train on ${num_gpu_devices} GPUs"
echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
if [ ${fp_item} = "fp32" ];then
model_config=`find ppcls/configs/ImageNet -name ${model_name}.yaml`
model_config=`find ppcls/configs/ImageNet -name ${model_item}.yaml`
else
model_config=`find ppcls/configs/ImageNet -name ${model_name}_fp16.yaml`
model_config=`find ppcls/configs/ImageNet -name ${model_item}_fp16.yaml`
fi
train_cmd="-c ${model_config} -o DataLoader.Train.sampler.batch_size=${batch_size} -o Global.epochs=${epochs}"
train_cmd="-c ${model_config} -o DataLoader.Train.sampler.batch_size=${batch_size} -o Global.epochs=${epochs} -o Global.eval_during_train=False -o Global.print_batch_step=2"
case ${run_mode} in
sp) train_cmd="python -u tools/train.py ${train_cmd}" ;;
mp)
......@@ -36,7 +46,7 @@ function _train(){
esac
rm -rf mylog
# 以下不用修改
timeout 15m ${train_cmd} > ${log_file} 2>&1
timeout 5m ${train_cmd} > ${log_file} 2>&1
if [ $? -ne 0 ];then
echo -e "${model_name}, FAIL"
export job_fail_flag=1
......@@ -51,6 +61,8 @@ function _train(){
cp mylog/workerlog.0 ${log_file}
fi
}
source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
_set_params $@
_train
_run
#_train
deploy/configs/build_cartoon.yaml 已删除 100644 → 0
浏览文件 @ b5401606
Global:
rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
batch_size: 32
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
RecPreProcess:
transform_ops:
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_cartoon/index/"
image_root: "./recognition_demo_data_v1.1/gallery_cartoon/"
data_file: "./recognition_demo_data_v1.1/gallery_cartoon/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 2048
deploy/configs/build_drinks.yaml 已删除 100644 → 0
浏览文件 @ b5401606
Global:
rec_inference_model_dir: "./models/general_PPLCNet_x2_5_lite_v1.0_infer"
batch_size: 32
use_gpu: True
enable_mkldnn: False
cpu_num_threads: 10
enable_benchmark: False
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
RecPreProcess:
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./drink_dataset/index"
image_root: "./drink_dataset"
data_file: "./drink_dataset/drink_label.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: " "
dist_type: "IP"
embedding_size: 512
deploy/configs/build_logo.yaml 已删除 100644 → 0
浏览文件 @ b5401606
Global:
rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
batch_size: 32
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
RecPreProcess:
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_logo/index/"
image_root: "./recognition_demo_data_v1.1/gallery_logo/"
data_file: "./recognition_demo_data_v1.1/gallery_logo/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
deploy/configs/build_product.yaml 已删除 100644 → 0
浏览文件 @ b5401606
Global:
rec_inference_model_dir: "./models/product_ResNet50_vd_aliproduct_v1.0_infer"
batch_size: 32
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
RecPreProcess:
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_product/index"
image_root: "./recognition_demo_data_v1.1/gallery_product/"
data_file: "./recognition_demo_data_v1.1/gallery_product/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
deploy/configs/build_product_binary.yaml 已删除 100644 → 0
浏览文件 @ b5401606
Global:
rec_inference_model_dir: "./models/product_MV3_x1_0_aliproduct_bin_v1.0_infer"
batch_size: 32
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
RecPreProcess:
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
RecPostProcess:
main_indicator: Binarize
Binarize:
method: "round"
# indexing engine config
IndexProcess:
index_method: "Flat" # supported: HNSW32, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_product/index_binary"
image_root: "./recognition_demo_data_v1.1/gallery_product/"
data_file: "./recognition_demo_data_v1.1/gallery_product/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "hamming"
embedding_size: 512
deploy/configs/build_vehicle.yaml 已删除 100644 → 0
浏览文件 @ b5401606
Global:
rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
batch_size: 32
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
RecPreProcess:
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_vehicle/index/"
image_root: "./recognition_demo_data_v1.1/gallery_vehicle/"
data_file: "./recognition_demo_data_v1.1/gallery_vehicle/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
deploy/configs/inference_cartoon.yaml
浏览文件 @ d25d7446
......@@ -50,7 +50,16 @@ RecPreProcess:
RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_cartoon/index/"
image_root: "./recognition_demo_data_v1.1/gallery_cartoon/"
data_file: "./recognition_demo_data_v1.1/gallery_cartoon/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 2048
batch_size: 32
return_k: 5
score_thres: 0.5
score_thres: 0.5
\ No newline at end of file
deploy/configs/inference_cls.yaml
浏览文件 @ d25d7446
......@@ -11,6 +11,7 @@ Global:
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
PreProcess:
transform_ops:
- ResizeImage:
......@@ -24,10 +25,11 @@ PreProcess:
order: ''
channel_num: 3
- ToCHWImage:
PostProcess:
main_indicator: Topk
Topk:
topk: 5
class_id_map_file: "../ppcls/utils/imagenet1k_label_list.txt"
SavePreLabel:
save_dir: ./pre_label/
save_dir: ./pre_label/
\ No newline at end of file
deploy/configs/inference_cls_ch4.yaml
浏览文件 @ d25d7446
......@@ -11,6 +11,7 @@ Global:
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
PreProcess:
transform_ops:
- ResizeImage:
......@@ -24,10 +25,11 @@ PreProcess:
order: ''
channel_num: 4
- ToCHWImage:
PostProcess:
main_indicator: Topk
Topk:
topk: 5
class_id_map_file: "../ppcls/utils/imagenet1k_label_list.txt"
SavePreLabel:
save_dir: ./pre_label/
save_dir: ./pre_label/
\ No newline at end of file
deploy/configs/inference_multilabel_cls.yaml → deploy/configs/inference_cls_multilabel.yaml
浏览文件 @ d25d7446
......@@ -11,6 +11,7 @@ Global:
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
PreProcess:
transform_ops:
- ResizeImage:
......@@ -24,10 +25,11 @@ PreProcess:
order: ''
channel_num: 3
- ToCHWImage:
PostProcess:
main_indicator: MultiLabelTopk
MultiLabelTopk:
topk: 5
class_id_map_file: None
SavePreLabel:
save_dir: ./pre_label/
save_dir: ./pre_label/
\ No newline at end of file
deploy/configs/inference_det.yaml
浏览文件 @ d25d7446
......@@ -30,4 +30,5 @@ DetPreProcess:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
- DetPermute: {}
DetPostProcess: {}
DetPostProcess: {}
\ No newline at end of file
deploy/configs/inference_drink.yaml
浏览文件 @ d25d7446
Global:
infer_imgs: "./recognition_demo_data_v1.2/test_drinks/hongniu_1.jpg"
infer_imgs: "./drink_dataset_v1.0/test_images/hongniu_1.jpg"
det_inference_model_dir: "./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer"
rec_inference_model_dir: "./models/general_PPLCNet_x2_5_lite_v1.0_infer"
rec_nms_thresold: 0.05
batch_size: 1
image_shape: [3, 640, 640]
threshold: 0.2
......@@ -11,11 +11,10 @@ Global:
labe_list:
- foreground
# inference engine config
use_gpu: True
enable_mkldnn: False
cpu_num_threads: 10
enable_benchmark: True
enable_benchmark: False
use_fp16: False
ir_optim: True
use_tensorrt: False
......@@ -33,6 +32,7 @@ DetPreProcess:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
- DetPermute: {}
DetPostProcess: {}
RecPreProcess:
......@@ -50,6 +50,14 @@ RecPostProcess: null
# indexing engine config
IndexProcess:
index_dir: "./drink_dataset/index"
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./drink_dataset_v1.0/gallery"
image_root: "./drink_dataset_v1.0/index"
data_file: "./drink_dataset_v1.0/gallery/drink_label.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: " "
dist_type: "IP"
embedding_size: 512
batch_size: 32
return_k: 5
score_thres: 0.4
score_thres: 0.4
\ No newline at end of file
deploy/configs/inference_general.yaml
浏览文件 @ d25d7446
......@@ -3,7 +3,7 @@ Global:
det_inference_model_dir: "./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer"
rec_inference_model_dir: "./models/general_PPLCNet_x2_5_lite_v1.0_infer"
rec_nms_thresold: 0.05
batch_size: 1
image_shape: [3, 640, 640]
threshold: 0.2
......@@ -11,7 +11,6 @@ Global:
labe_list:
- foreground
# inference engine config
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
......@@ -33,6 +32,7 @@ DetPreProcess:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
- DetPermute: {}
DetPostProcess: {}
RecPreProcess:
......@@ -50,6 +50,14 @@ RecPostProcess: null
# indexing engine config
IndexProcess:
index_dir: "./drink_dataset_v1.0/index/"
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
image_root: "./drink_dataset_v1.0/gallery/"
index_dir: "./drink_dataset_v1.0/index"
data_file: "./drink_dataset_v1.0/gallery/drink_label.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
batch_size: 32
return_k: 5
score_thres: 0.5
score_thres: 0.5
\ No newline at end of file
deploy/configs/inference_product_binary.yaml → deploy/configs/inference_general_binary.yaml
浏览文件 @ d25d7446
Global:
infer_imgs: "./recognition_demo_data_v1.1/test_product/daoxiangcunjinzhubing_6.jpg"
det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer"
rec_inference_model_dir: "./models/product_MV3_x1_0_aliproduct_bin_v1.0_infer"
infer_imgs: "./drink_dataset_v1.0/test_images/001.jpeg"
det_inference_model_dir: "./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer"
rec_inference_model_dir: "./models/general_PPLCNet_x2_5_lite_binary_v1.0_infer"
rec_nms_thresold: 0.05
batch_size: 1
......@@ -11,7 +11,6 @@ Global:
labe_list:
- foreground
# inference engine config
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
......@@ -49,12 +48,18 @@ RecPreProcess:
RecPostProcess:
main_indicator: Binarize
Binarize:
method: "round"
method: "sign"
# indexing engine config
IndexProcess:
binary_index: true
index_dir: "./recognition_demo_data_v1.1/gallery_product/index_binary"
index_method: "Flat" # supported: HNSW32, Flat
image_root: "./drink_dataset_v1.0/gallery/"
index_dir: "./drink_dataset_v1.0/index_bin"
data_file: "./drink_dataset_v1.0/gallery/drink_label.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "hamming"
embedding_size: 512
batch_size: 32
return_k: 5
score_thres: 0
hamming_radius: 100
deploy/configs/inference_logo.yaml
浏览文件 @ d25d7446
......@@ -11,7 +11,6 @@ Global:
labe_list:
- foreground
# inference engine config
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
......@@ -33,6 +32,7 @@ DetPreProcess:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
- DetPermute: {}
DetPostProcess: {}
RecPreProcess:
......@@ -50,6 +50,14 @@ RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_logo/index/"
image_root: "./recognition_demo_data_v1.1/gallery_logo/"
data_file: "./recognition_demo_data_v1.1/gallery_logo/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
batch_size: 32
return_k: 5
score_thres: 0.5
score_thres: 0.5
\ No newline at end of file
deploy/configs/inference_product.yaml
浏览文件 @ d25d7446
......@@ -33,6 +33,7 @@ DetPreProcess:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
- DetPermute: {}
DetPostProcess: {}
RecPreProcess:
......@@ -50,6 +51,14 @@ RecPostProcess: null
# indexing engine config
IndexProcess:
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_product/index"
image_root: "./recognition_demo_data_v1.1/gallery_product/"
data_file: "./recognition_demo_data_v1.1/gallery_product/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
batch_size: 32
return_k: 5
score_thres: 0.5
score_thres: 0.5
\ No newline at end of file
deploy/configs/inference_rec.yaml
浏览文件 @ d25d7446
......@@ -2,13 +2,6 @@ Global:
infer_imgs: "./images/wangzai.jpg"
rec_inference_model_dir: "./models/product_ResNet50_vd_aliproduct_v1.0_infer"
batch_size: 1
image_shape: [3, 640, 640]
threshold: 0.5
max_det_results: 1
labe_list:
- foreground
# inference engine config
use_gpu: False
enable_mkldnn: True
cpu_num_threads: 10
......@@ -32,4 +25,4 @@ RecPreProcess:
order: ''
- ToCHWImage:
RecPostProcess: null
RecPostProcess: null
\ No newline at end of file
deploy/configs/inference_vehicle.yaml
浏览文件 @ d25d7446
......@@ -11,7 +11,6 @@ Global:
labe_list:
- foreground
# inference engine config
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
......@@ -36,7 +35,6 @@ DetPreProcess:
DetPostProcess: {}
RecPreProcess:
transform_ops:
- ResizeImage:
......@@ -52,6 +50,14 @@ RecPostProcess: null
# indexing engine config
IndexProcess:
index_dir: "./recognition_demo_data_v1.1/gallery_vehicle/index/"
return_k: 5
score_thres: 0.5
index_method: "HNSW32" # supported: HNSW32, IVF, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_vehicle/index/"
image_root: "./recognition_demo_data_v1.1/gallery_vehicle/"
data_file: "./recognition_demo_data_v1.1/gallery_vehicle/data_file.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "IP"
embedding_size: 512
batch_size: 32
return_k: 5
score_thres: 0.5
\ No newline at end of file
deploy/cpp/CMakeLists.txt
浏览文件 @ d25d7446
......@@ -14,6 +14,11 @@ SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
set(DEMO_NAME "clas_system")
include(external-cmake/yaml-cpp.cmake)
include_directories("${CMAKE_SOURCE_DIR}/")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
macro(safe_set_static_flag)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
......@@ -61,7 +66,7 @@ if (WIN32)
add_definitions(-DSTATIC_LIB)
endif()
else()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 -std=c++11")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O3 -std=c++11")
set(CMAKE_STATIC_LIBRARY_PREFIX "")
endif()
message("flags" ${CMAKE_CXX_FLAGS})
......@@ -153,7 +158,7 @@ endif(WITH_STATIC_LIB)
if (NOT WIN32)
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf z xxhash
glog gflags protobuf z xxhash yaml-cpp
)
if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
set(DEPS ${DEPS} snappystream)
......@@ -164,7 +169,7 @@ if (NOT WIN32)
else()
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags_static libprotobuf xxhash)
glog gflags_static libprotobuf xxhash libyaml-cppmt)
set(DEPS ${DEPS} libcmt shlwapi)
if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
set(DEPS ${DEPS} snappy)
......@@ -204,6 +209,7 @@ include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
AUX_SOURCE_DIRECTORY(./src SRCS)
add_executable(${DEMO_NAME} ${SRCS})
ADD_DEPENDENCIES(${DEMO_NAME} ext-yaml-cpp)
target_link_libraries(${DEMO_NAME} ${DEPS})
......
deploy/cpp/external-cmake/yaml-cpp.cmake 0 → 100644
浏览文件 @ d25d7446
find_package(Git REQUIRED)
include(ExternalProject)
message("${CMAKE_BUILD_TYPE}")
ExternalProject_Add(
ext-yaml-cpp
URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
URL_MD5 9542d6de397d1fbd649ed468cb5850e6
CMAKE_ARGS
-DYAML_CPP_BUILD_TESTS=OFF
-DYAML_CPP_BUILD_TOOLS=OFF
-DYAML_CPP_INSTALL=OFF
-DYAML_CPP_BUILD_CONTRIB=OFF
-DMSVC_SHARED_RT=OFF
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
-DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
# Disable install step
INSTALL_COMMAND ""
LOG_DOWNLOAD ON
LOG_BUILD 1
)
deploy/cpp/include/cls.h
浏览文件 @ d25d7446
......@@ -28,64 +28,63 @@
#include <fstream>
#include <numeric>
#include "include/cls_config.h"
#include <include/preprocess_op.h>
using namespace paddle_infer;
namespace PaddleClas {
class Classifier {
public:
explicit Classifier(const std::string &model_path,
const std::string &params_path, const bool &use_gpu,
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const bool &use_tensorrt,
const bool &use_fp16, const int &resize_short_size,
const int &crop_size) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->resize_short_size_ = resize_short_size;
this->crop_size_ = crop_size;
LoadModel(model_path, params_path);
}
// Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path);
// Run predictor
double Run(cv::Mat &img, std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
bool use_gpu_ = false;
int gpu_id_ = 0;
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
bool is_scale_ = true;
int resize_short_size_ = 256;
int crop_size_ = 224;
// pre-process
ResizeImg resize_op_;
Normalize normalize_op_;
Permute permute_op_;
CenterCropImg crop_op_;
};
class Classifier {
public:
explicit Classifier(const ClsConfig &config) {
this->use_gpu_ = config.use_gpu;
this->gpu_id_ = config.gpu_id;
this->gpu_mem_ = config.gpu_mem;
this->cpu_math_library_num_threads_ = config.cpu_threads;
this->use_fp16_ = config.use_fp16;
this->use_mkldnn_ = config.use_mkldnn;
this->use_tensorrt_ = config.use_tensorrt;
this->mean_ = config.mean;
this->std_ = config.std;
this->resize_short_size_ = config.resize_short_size;
this->scale_ = config.scale;
this->crop_size_ = config.crop_size;
this->ir_optim_ = config.ir_optim;
LoadModel(config.cls_model_path, config.cls_params_path);
}
// Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path);
// Run predictor
void Run(cv::Mat &img, std::vector<float> &out_data, std::vector<int> &idx,
std::vector<double> &times);
private:
std::shared_ptr <Predictor> predictor_;
bool use_gpu_ = false;
int gpu_id_ = 0;
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
bool ir_optim_ = true;
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
float scale_ = 0.00392157;
int resize_short_size_ = 256;
int crop_size_ = 224;
// pre-process
ResizeImg resize_op_;
Normalize normalize_op_;
Permute permute_op_;
CenterCropImg crop_op_;
};
} // namespace PaddleClas
deploy/cpp/include/cls_config.h
浏览文件 @ d25d7446
......@@ -14,6 +14,14 @@
#pragma once
#ifdef WIN32
#define OS_PATH_SEP "\\"
#else
#define OS_PATH_SEP "/"
#endif
#include "include/utility.h"
#include "yaml-cpp/yaml.h"
#include <iomanip>
#include <iostream>
#include <map>
......@@ -21,70 +29,105 @@
#include <string>
#include <vector>
#include "include/utility.h"
namespace PaddleClas {
class ClsConfig {
public:
explicit ClsConfig(const std::string &config_file) {
config_map_ = LoadConfig(config_file);
this->use_gpu = bool(stoi(config_map_["use_gpu"]));
this->gpu_id = stoi(config_map_["gpu_id"]);
this->gpu_mem = stoi(config_map_["gpu_mem"]);
this->cpu_threads = stoi(config_map_["cpu_threads"]);
this->use_mkldnn = bool(stoi(config_map_["use_mkldnn"]));
this->use_tensorrt = bool(stoi(config_map_["use_tensorrt"]));
this->use_fp16 = bool(stoi(config_map_["use_fp16"]));
this->cls_model_path.assign(config_map_["cls_model_path"]);
this->cls_params_path.assign(config_map_["cls_params_path"]);
this->resize_short_size = stoi(config_map_["resize_short_size"]);
this->crop_size = stoi(config_map_["crop_size"]);
this->benchmark = bool(stoi(config_map_["benchmark"]));
}
bool use_gpu = false;
int gpu_id = 0;
int gpu_mem = 4000;
int cpu_threads = 1;
bool use_mkldnn = false;
bool use_tensorrt = false;
bool use_fp16 = false;
bool benchmark = false;
std::string cls_model_path;
std::string cls_params_path;
int resize_short_size = 256;
int crop_size = 224;
void PrintConfigInfo();
private:
// Load configuration
std::map<std::string, std::string> LoadConfig(const std::string &config_file);
std::vector<std::string> split(const std::string &str,
const std::string &delim);
std::map<std::string, std::string> config_map_;
};
class ClsConfig {
public:
explicit ClsConfig(const std::string &path) {
ReadYamlConfig(path);
this->infer_imgs =
this->config_file["Global"]["infer_imgs"].as<std::string>();
this->batch_size = this->config_file["Global"]["batch_size"].as<int>();
this->use_gpu = this->config_file["Global"]["use_gpu"].as<bool>();
if (this->config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id = this->config_file["Global"]["gpu_id"].as<int>();
else
this->gpu_id = 0;
this->gpu_mem = this->config_file["Global"]["gpu_mem"].as<int>();
this->cpu_threads =
this->config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn = this->config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt = this->config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16 = this->config_file["Global"]["use_fp16"].as<bool>();
this->enable_benchmark =
this->config_file["Global"]["enable_benchmark"].as<bool>();
this->ir_optim = this->config_file["Global"]["ir_optim"].as<bool>();
this->enable_profile =
this->config_file["Global"]["enable_profile"].as<bool>();
this->cls_model_path =
this->config_file["Global"]["inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdmodel";
this->cls_params_path =
this->config_file["Global"]["inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdiparams";
this->resize_short_size =
this->config_file["PreProcess"]["transform_ops"][0]["ResizeImage"]
["resize_short"]
.as<int>();
this->crop_size =
this->config_file["PreProcess"]["transform_ops"][1]["CropImage"]["size"]
.as<int>();
this->scale = this->config_file["PreProcess"]["transform_ops"][2]
["NormalizeImage"]["scale"]
.as<float>();
this->mean = this->config_file["PreProcess"]["transform_ops"][2]
["NormalizeImage"]["mean"]
.as < std::vector < float >> ();
this->std = this->config_file["PreProcess"]["transform_ops"][2]
["NormalizeImage"]["std"]
.as < std::vector < float >> ();
if (this->config_file["Global"]["benchmark"].IsDefined())
this->benchmark = this->config_file["Global"]["benchmark"].as<bool>();
else
this->benchmark = false;
if (this->config_file["PostProcess"]["Topk"]["topk"].IsDefined())
this->topk = this->config_file["PostProcess"]["Topk"]["topk"].as<int>();
if (this->config_file["PostProcess"]["Topk"]["class_id_map_file"]
.IsDefined())
this->class_id_map_path =
this->config_file["PostProcess"]["Topk"]["class_id_map_file"]
.as<std::string>();
if (this->config_file["PostProcess"]["SavePreLabel"]["save_dir"]
.IsDefined())
this->label_save_dir =
this->config_file["PostProcess"]["SavePreLabel"]["save_dir"]
.as<std::string>();
ReadLabelMap();
}
YAML::Node config_file;
bool use_gpu = false;
int gpu_id = 0;
int gpu_mem = 4000;
int cpu_threads = 1;
bool use_mkldnn = false;
bool use_tensorrt = false;
bool use_fp16 = false;
bool benchmark = false;
int batch_size = 1;
bool enable_benchmark = false;
bool ir_optim = true;
bool enable_profile = false;
std::string cls_model_path;
std::string cls_params_path;
std::string infer_imgs;
int resize_short_size = 256;
int crop_size = 224;
float scale = 0.00392157;
std::vector<float> mean = {0.485, 0.456, 0.406};
std::vector<float> std = {0.229, 0.224, 0.225};
int topk = 5;
std::string class_id_map_path;
std::map<int, std::string> id_map;
std::string label_save_dir;
void PrintConfigInfo();
void ReadLabelMap();
void ReadYamlConfig(const std::string &path);
};
} // namespace PaddleClas
deploy/cpp/include/preprocess_op.h
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// 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
......@@ -31,26 +31,26 @@ using namespace std;
namespace PaddleClas {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale = true);
};
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float &scale);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
};
} // namespace PaddleClas
\ No newline at end of file
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
};
} // namespace PaddleClas
deploy/cpp/include/utility.h
浏览文件 @ d25d7446
......@@ -32,15 +32,15 @@
namespace PaddleClas {
class Utility {
public:
static std::vector<std::string> ReadDict(const std::string &path);
// template <class ForwardIterator>
// inline static size_t argmax(ForwardIterator first, ForwardIterator last)
// {
// return std::distance(first, std::max_element(first, last));
// }
};
class Utility {
public:
static std::vector <std::string> ReadDict(const std::string &path);
// template <class ForwardIterator>
// inline static size_t argmax(ForwardIterator first, ForwardIterator last)
// {
// return std::distance(first, std::max_element(first, last));
// }
};
} // namespace PaddleClas
\ No newline at end of file
deploy/cpp/readme.md
浏览文件 @ d25d7446
......@@ -122,12 +122,11 @@ build/paddle_inference_install_dir/
* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id1)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本，注意必须选择`develop`版本。
以`ubuntu14.04_cuda9.0_cudnn7_avx_mkl`的`develop`版本为例，使用下述命令下载并解压：
以`2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2`的`develop`版本为例，使用下述命令下载并解压：
```shell
wget https://paddle-inference-lib.bj.bcebos.com/latest-gpu-cuda9-cudnn7-avx-mkl/paddle_inference.tgz
wget https://paddle-inference-lib.bj.bcebos.com/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddle_inference.tgz
tar -xvf paddle_inference.tgz
```
......@@ -143,10 +142,10 @@ tar -xvf paddle_inference.tgz
```
inference/
|--cls_infer.pdmodel
|--cls_infer.pdiparams
|--inference.pdmodel
|--inference.pdiparams
```
**注意**：上述文件中，`cls_infer.pdmodel`文件存储了模型结构信息，`cls_infer.pdiparams`文件存储了模型参数信息。注意两个文件的路径需要与配置文件`tools/config.txt`中的`cls_model_path`和`cls_params_path`参数对应一致。
**注意**：上述文件中，`inference.pdmodel`文件存储了模型结构信息，`inference.pdiparams`文件存储了模型参数信息。模型目录可以随意设置，但是模型名字不能修改。
### 2.2 编译PaddleClas C++预测demo
......@@ -183,6 +182,7 @@ cmake .. \
-DCUDA_LIB=${CUDA_LIB_DIR} \
make -j
cd ..
```
上述命令中，
......@@ -200,31 +200,26 @@ make -j
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
### 运行demo
* 首先修改`tools/config.txt`中对应字段：
* use_gpu：是否使用GPU；
* gpu_id：使用的GPU卡号；
* gpu_mem：显存；
* cpu_math_library_num_threads：底层科学计算库所用线程的数量；
* use_mkldnn：是否使用MKLDNN加速；
* use_tensorrt: 是否使用tensorRT进行加速；
* use_fp16：是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效；
* cls_model_path：预测模型结构文件路径；
* cls_params_path：预测模型参数文件路径；
* resize_short_size：预处理时图像缩放大小；
* crop_size：预处理时图像裁剪后的大小。
### 2.3 运行demo
#### 2.3.1 设置配置文件
```shell
cp ../configs/inference_cls.yaml tools/
```
根据[python预测推理](../../docs/zh_CN/inference_deployment/python_deploy.md)的`图像分类推理`部分修改好`tools`目录下`inference_cls.yaml`文件。`yaml`文件的参数说明详见[python预测推理](../../docs/zh_CN/inference_deployment/python_deploy.md)。
* 然后修改`tools/run.sh`：
* `./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG`
* 上述命令中分别为：编译得到的可执行文件`clas_system`；运行时的配置文件`config.txt`；待预测的图像。
请根据实际存放文件，修改好`Global.infer_imgs`、`Global.inference_model_dir`、`PostProcess.Topk.topk`、`PostProcess.Topk.class_id_map_file`等参数。
* 最后执行以下命令，完成对一幅图像的分类。
#### 2.3.2 执行
```shell
sh tools/run.sh
./build/clas_system -c tools/inference_cls.yaml
# or
./build/clas_system -config tools/inference_cls.yaml
```
* 最终屏幕上会输出结果，如下图所示。
最终屏幕上会输出结果，如下图所示。
<div align="center">
<img src="./docs/imgs/cpp_infer_result.png" width="600">
......
deploy/cpp/src/cls.cpp
浏览文件 @ d25d7446
......@@ -12,102 +12,105 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <algorithm>
#include <include/cls.h>
#include <numeric>
namespace PaddleClas {
void Classifier::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 20, 1, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
void Classifier::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 20, 1, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
}
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(this->ir_optim_);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
double Classifier::Run(cv::Mat &img, std::vector<double> *times) {
cv::Mat srcimg;
cv::Mat resize_img;
img.copyTo(srcimg);
auto preprocess_start = std::chrono::system_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_size_);
this->crop_op_.Run(resize_img, this->crop_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
this->is_scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto preprocess_end = std::chrono::system_clock::now();
auto infer_start = std::chrono::system_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
std::vector<float> out_data;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::system_clock::now();
auto postprocess_start = std::chrono::system_clock::now();
int maxPosition =
max_element(out_data.begin(), out_data.end()) - out_data.begin();
auto postprocess_end = std::chrono::system_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count() * 1000);
times->push_back(inference_cost_time);
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
std::cout << "result: " << std::endl;
std::cout << "\tclass id: " << maxPosition << std::endl;
std::cout << std::fixed << std::setprecision(10)
<< "\tscore: " << double(out_data[maxPosition]) << std::endl;
return inference_cost_time;
}
void Classifier::Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<int> &idx, std::vector<double> &times) {
cv::Mat srcimg;
cv::Mat resize_img;
img.copyTo(srcimg);
auto preprocess_start = std::chrono::system_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_size_);
this->crop_op_.Run(resize_img, this->crop_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto preprocess_end = std::chrono::system_clock::now();
auto infer_start = std::chrono::system_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
idx.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::system_clock::now();
auto postprocess_start = std::chrono::system_clock::now();
// int maxPosition =
// max_element(out_data.begin(), out_data.end()) - out_data.begin();
iota(idx.begin(), idx.end(), 0);
stable_sort(idx.begin(), idx.end(), [&out_data](int i1, int i2) {
return out_data[i1] > out_data[i2];
});
auto postprocess_end = std::chrono::system_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
times[0] = double(preprocess_diff.count() * 1000);
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count() * 1000);
times[1] = inference_cost_time;
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times[2] = double(postprocess_diff.count() * 1000);
/* std::cout << "result: " << std::endl; */
/* std::cout << "\tclass id: " << maxPosition << std::endl; */
/* std::cout << std::fixed << std::setprecision(10) */
/* << "\tscore: " << double(out_data[maxPosition]) << std::endl; */
}
} // namespace PaddleClas
deploy/cpp/src/cls_config.cpp
浏览文件 @ d25d7446
......@@ -13,52 +13,40 @@
// limitations under the License.
#include <include/cls_config.h>
#include <ostream>
namespace PaddleClas {
std::vector<std::string> ClsConfig::split(const std::string &str,
const std::string &delim) {
std::vector<std::string> res;
if ("" == str)
return res;
char *strs = new char[str.length() + 1];
std::strcpy(strs, str.c_str());
char *d = new char[delim.length() + 1];
std::strcpy(d, delim.c_str());
char *p = std::strtok(strs, d);
while (p) {
std::string s = p;
res.push_back(s);
p = std::strtok(NULL, d);
}
return res;
}
void ClsConfig::PrintConfigInfo() {
std::cout << "=======Paddle Class inference config======" << std::endl;
std::cout << this->config_file << std::endl;
std::cout << "=======End of Paddle Class inference config======" << std::endl;
}
std::map<std::string, std::string>
ClsConfig::LoadConfig(const std::string &config_path) {
auto config = Utility::ReadDict(config_path);
void ClsConfig::ReadYamlConfig(const std::string &path) {
std::map<std::string, std::string> dict;
for (int i = 0; i < config.size(); i++) {
// pass for empty line or comment
if (config[i].size() <= 1 || config[i][0] == '#') {
continue;
try {
this->config_file = YAML::LoadFile(path);
} catch (YAML::BadFile &e) {
std::cout << "Something wrong in yaml file, please check yaml file"
<< std::endl;
exit(1);
}
}
std::vector<std::string> res = split(config[i], " ");
dict[res[0]] = res[1];
}
return dict;
}
void ClsConfig::PrintConfigInfo() {
std::cout << "=======Paddle Class inference config======" << std::endl;
for (auto iter = config_map_.begin(); iter != config_map_.end(); iter++) {
std::cout << iter->first << " : " << iter->second << std::endl;
}
std::cout << "=======End of Paddle Class inference config======" << std::endl;
}
} // namespace PaddleClas
\ No newline at end of file
void ClsConfig::ReadLabelMap() {
if (this->class_id_map_path.empty()) {
std::cout << "The Class Label file dose not input" << std::endl;
return;
}
std::ifstream in(this->class_id_map_path);
std::string line;
if (in) {
while (getline(in, line)) {
int split_flag = line.find_first_of(" ");
this->id_map[std::stoi(line.substr(0, split_flag))] =
line.substr(split_flag + 1, line.size());
}
}
}
}; // namespace PaddleClas
deploy/cpp/src/main.cpp
浏览文件 @ d25d7446
......@@ -22,11 +22,13 @@
#include <ostream>
#include <vector>
#include <algorithm>
#include <cstring>
#include <fstream>
#include <numeric>
#include <auto_log/autolog.h>
#include <gflags/gflags.h>
#include <include/cls.h>
#include <include/cls_config.h>
......@@ -34,74 +36,102 @@ using namespace std;
using namespace cv;
using namespace PaddleClas;
int main(int argc, char **argv) {
if (argc < 3) {
std::cerr << "[ERROR] usage: " << argv[0]
<< " configure_filepath image_path\n";
exit(1);
}
ClsConfig config(argv[1]);
config.PrintConfigInfo();
std::string path(argv[2]);
DEFINE_string(config,
"", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");
std::vector<std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector<cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
int main(int argc, char **argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
std::string yaml_path = "";
if (FLAGS_config == "" && FLAGS_c == "") {
std::cerr << "[ERROR] usage: " << std::endl
<< argv[0] << " -c $yaml_path" << std::endl
<< "or:" << std::endl
<< argv[0] << " -config $yaml_path" << std::endl;
exit(1);
} else if (FLAGS_config != "") {
yaml_path = FLAGS_config;
} else {
yaml_path = FLAGS_c;
}
} else {
img_files_list.push_back(path);
}
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
Classifier classifier(config.cls_model_path, config.cls_params_path,
config.use_gpu, config.gpu_id, config.gpu_mem,
config.cpu_threads, config.use_mkldnn,
config.use_tensorrt, config.use_fp16,
config.resize_short_size, config.crop_size);
double elapsed_time = 0.0;
std::vector<double> cls_times;
int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
for (int idx = 0; idx < img_files_list.size(); ++idx) {
std::string img_path = img_files_list[idx];
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(-1);
ClsConfig config(yaml_path);
config.PrintConfigInfo();
std::string path(config.infer_imgs);
std::vector <std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector <cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
}
} else {
img_files_list.push_back(path);
}
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
double run_time = classifier.Run(srcimg, &cls_times);
if (idx >= warmup_iter) {
elapsed_time += run_time;
std::cout << "Current image path: " << img_path << std::endl;
std::cout << "Current time cost: " << run_time << " s, "
<< "average time cost in all: "
<< elapsed_time / (idx + 1 - warmup_iter) << " s." << std::endl;
} else {
std::cout << "Current time cost: " << run_time << " s." << std::endl;
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
Classifier classifier(config);
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> cls_times_total = {0, 0, 0};
double infer_time;
std::vector<float> out_data;
std::vector<int> result_index;
int warmup_iter = 5;
bool label_output_equal_flag = true;
for (int idx = 0; idx < img_files_list.size(); ++idx) {
std::string img_path = img_files_list[idx];
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(-1);
}
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
classifier.Run(srcimg, out_data, result_index, cls_times);
if (label_output_equal_flag and out_data.size() != config.id_map.size()) {
std::cout << "Warning: the label size is not equal to output size!"
<< std::endl;
label_output_equal_flag = false;
}
int max_len = std::min(config.topk, int(out_data.size()));
std::cout << "Current image path: " << img_path << std::endl;
infer_time = cls_times[0] + cls_times[1] + cls_times[2];
std::cout << "Current total inferen time cost: " << infer_time << " ms."
<< std::endl;
for (int i = 0; i < max_len; ++i) {
printf("\tTop%d: class_id: %d, score: %.4f, ", i + 1, result_index[i],
out_data[result_index[i]]);
if (label_output_equal_flag)
printf("label: %s\n", config.id_map[result_index[i]].c_str());
}
if (idx >= warmup_iter) {
for (int i = 0; i < cls_times.size(); ++i)
cls_times_total[i] += cls_times[i];
}
}
}
if (img_files_list.size() > warmup_iter) {
std::string presion = "fp32";
infer_time = cls_times_total[0] + cls_times_total[1] + cls_times_total[2];
std::cout << "average time cost in all: "
<< infer_time / (img_files_list.size() - warmup_iter) << " ms."
<< std::endl;
}
if (config.use_fp16)
presion = "fp16";
if (config.benchmark) {
AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
config.use_mkldnn, config.cpu_threads, 1,
"1, 3, 224, 224", presion, cls_times,
img_files_list.size());
autolog.report();
}
return 0;
std::string presion = "fp32";
if (config.use_fp16)
presion = "fp16";
if (config.benchmark) {
AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
config.use_mkldnn, config.cpu_threads, 1,
"1, 3, 224, 224", presion, cls_times_total,
img_files_list.size());
autolog.report();
}
return 0;
}
deploy/cpp/src/preprocess_op.cpp
浏览文件 @ d25d7446
......@@ -32,59 +32,57 @@
namespace PaddleClas {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale) {
double e = 1.0;
if (is_scale) {
e /= 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) * scale[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) * scale[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) * scale[2];
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float &scale) {
if (scale) {
(*im).convertTo(*im, CV_32FC3, scale);
}
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size) {
int w = img.cols;
int h = img.rows;
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size) {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
int resize_h = round(float(h) * ratio);
int resize_w = round(float(w) * ratio);
int resize_h = round(float(h) * ratio);
int resize_w = round(float(w) * ratio);
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace PaddleClas
\ No newline at end of file
} // namespace PaddleClas
deploy/cpp/src/utility.cpp
浏览文件 @ d25d7446
......@@ -20,20 +20,20 @@
namespace PaddleClas {
std::vector<std::string> Utility::ReadDict(const std::string &path) {
std::ifstream in(path);
std::string line;
std::vector<std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
std::vector <std::string> Utility::ReadDict(const std::string &path) {
std::ifstream in(path);
std::string line;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
}
} else {
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
}
return m_vec;
}
} else {
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
}
return m_vec;
}
} // namespace PaddleClas
\ No newline at end of file
deploy/cpp/tools/build.sh
浏览文件 @ d25d7446
OPENCV_DIR=/work/project/project/cpp_infer/opencv-3.4.7/opencv3
LIB_DIR=/work/project/project/cpp_infer/paddle_inference/
OPENCV_DIR=/work/project/project/test/opencv-3.4.7/opencv3
LIB_DIR=/work/project/project/test/paddle_inference/
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
......@@ -18,3 +18,4 @@ cmake .. \
-DCUDA_LIB=${CUDA_LIB_DIR} \
make -j
cd ..
deploy/cpp/tools/config.txt 已删除 100755 → 0
浏览文件 @ b5401606
# model load config
use_gpu 0
gpu_id 0
gpu_mem 4000
cpu_threads 10
use_mkldnn 1
use_tensorrt 0
use_fp16 0
# cls config
cls_model_path /PaddleClas/inference/cls_infer.pdmodel
cls_params_path /PaddleClas/inference/cls_infer.pdiparams
resize_short_size 256
crop_size 224
# for log env info
benchmark 0
deploy/cpp/tools/run.sh 已删除 100755 → 0
浏览文件 @ b5401606
./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG
deploy/cpp_shitu/CMakeLists.txt 0 → 100755
浏览文件 @ d25d7446
project(pp_shitu CXX C)
cmake_minimum_required(VERSION 3.14)
option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON)
option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." OFF)
option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." ON)
option(WITH_TENSORRT "Compile demo with TensorRT." OFF)
option(FAISS_WITH_MKL "Faiss Compile demo with MKL." OFF)
SET(PADDLE_LIB "" CACHE PATH "Location of libraries")
SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
SET(CUDA_LIB "" CACHE PATH "Location of libraries")
SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
SET(FAISS_DIR "" CACHE PATH "Location of libraries")
set(DEMO_NAME "pp_shitu")
if (FAISS_WITH_MKL)
SET(BLAS_NAME "mklml_intel")
else()
SET(BLAS_NAME "openblas")
endif()
include(external-cmake/yaml-cpp.cmake)
include_directories("${CMAKE_SOURCE_DIR}/")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
macro(safe_set_static_flag)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
if(${flag_var} MATCHES "/MD")
string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
endif(${flag_var} MATCHES "/MD")
endforeach(flag_var)
endmacro()
if (WITH_MKL)
ADD_DEFINITIONS(-DUSE_MKL)
endif()
if(NOT DEFINED FAISS_DIR)
message(FATAL_ERROR "please set FAISS_DIR with -DFAISS_DIR=/path/faiss")
endif()
if(NOT DEFINED PADDLE_LIB)
message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
endif()
if(NOT DEFINED OPENCV_DIR)
message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
endif()
if (WIN32)
include_directories("${FAISS_DIR}/include")
link_directories("${FAISS_DIR}/lib")
find_package(faiss REQUIRED PATHS ${FAISS_DIR}/share/faiss/ NO_DEFAULT_PATH)
else()
find_package(faiss REQUIRED PATHS ${FAISS_DIR}/share/faiss NO_DEFAULT_PATH)
include_directories("${FAISS_DIR}/include")
link_directories("${FAISS_DIR}/lib")
endif()
if (WIN32)
include_directories("${PADDLE_LIB}/paddle/fluid/inference")
include_directories("${PADDLE_LIB}/paddle/include")
link_directories("${PADDLE_LIB}/paddle/fluid/inference")
find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
else ()
find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
include_directories("${PADDLE_LIB}/paddle/include")
link_directories("${PADDLE_LIB}/paddle/lib")
endif ()
include_directories(${OpenCV_INCLUDE_DIRS})
if (WIN32)
add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
if (WITH_STATIC_LIB)
safe_set_static_flag()
add_definitions(-DSTATIC_LIB)
endif()
else()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O3 -std=c++11")
set(CMAKE_STATIC_LIBRARY_PREFIX "")
endif()
message("flags" ${CMAKE_CXX_FLAGS})
if (WITH_GPU)
if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
endif()
if (NOT WIN32)
if (NOT DEFINED CUDNN_LIB)
message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
endif()
endif(NOT WIN32)
endif()
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
include_directories("${PADDLE_LIB}/third_party/boost")
include_directories("${PADDLE_LIB}/third_party/eigen3")
include_directories("${CMAKE_SOURCE_DIR}/")
if (NOT WIN32)
if (WITH_TENSORRT AND WITH_GPU)
include_directories("${TENSORRT_DIR}/include")
link_directories("${TENSORRT_DIR}/lib")
endif()
endif(NOT WIN32)
link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
link_directories("${PADDLE_LIB}/paddle/lib")
if(WITH_MKL)
include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
if (WIN32)
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.lib
${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.lib)
else ()
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
execute_process(COMMAND cp -r ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
endif ()
set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
if(EXISTS ${MKLDNN_PATH})
include_directories("${MKLDNN_PATH}/include")
if (WIN32)
set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
else ()
set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
endif ()
endif()
else()
if (WIN32)
set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/openblas${CMAKE_STATIC_LIBRARY_SUFFIX})
else ()
set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
endif ()
endif()
# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
if(WITH_STATIC_LIB)
if(WIN32)
set(DEPS
${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
else()
if(WIN32)
set(DEPS
${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
endif(WITH_STATIC_LIB)
if (NOT WIN32)
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf z xxhash yaml-cpp
)
if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
set(DEPS ${DEPS} snappystream)
endif()
if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
set(DEPS ${DEPS} snappy)
endif()
else()
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags_static libprotobuf xxhash libyaml-cppmt)
set(DEPS ${DEPS} libcmt shlwapi)
if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
set(DEPS ${DEPS} snappy)
endif()
if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
set(DEPS ${DEPS} snappystream)
endif()
endif(NOT WIN32)
if(WITH_GPU)
if(NOT WIN32)
if (WITH_TENSORRT)
set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
else()
set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
endif()
if (NOT WIN32)
set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
set(DEPS ${DEPS} ${EXTERNAL_LIB})
endif()
set(DEPS ${DEPS} ${OpenCV_LIBS})
include(FetchContent)
include(external-cmake/auto-log.cmake)
include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
AUX_SOURCE_DIRECTORY(./src SRCS)
add_executable(${DEMO_NAME} ${SRCS})
ADD_DEPENDENCIES(${DEMO_NAME} ext-yaml-cpp)
target_link_libraries(${DEMO_NAME} ${DEPS})
target_link_libraries(${DEMO_NAME} ${FAISS_DIR}/lib/libfaiss${CMAKE_STATIC_LIBRARY_SUFFIX})
target_link_libraries(${DEMO_NAME} ${BLAS_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX})
if (WIN32 AND WITH_MKL)
add_custom_command(TARGET ${DEMO_NAME} POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
)
endif()
deploy/cpp_shitu/external-cmake/auto-log.cmake 0 → 100644
浏览文件 @ d25d7446
find_package(Git REQUIRED)
include(FetchContent)
set(FETCHCONTENT_BASE_DIR "${CMAKE_CURRENT_BINARY_DIR}/third-party")
FetchContent_Declare(
extern_Autolog
PREFIX autolog
GIT_REPOSITORY https://github.com/LDOUBLEV/AutoLog.git
GIT_TAG main
)
FetchContent_MakeAvailable(extern_Autolog)
deploy/cpp_shitu/external-cmake/yaml-cpp.cmake 0 → 100644
浏览文件 @ d25d7446
find_package(Git REQUIRED)
include(ExternalProject)
message("${CMAKE_BUILD_TYPE}")
ExternalProject_Add(
ext-yaml-cpp
URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
URL_MD5 9542d6de397d1fbd649ed468cb5850e6
CMAKE_ARGS
-DYAML_CPP_BUILD_TESTS=OFF
-DYAML_CPP_BUILD_TOOLS=OFF
-DYAML_CPP_INSTALL=OFF
-DYAML_CPP_BUILD_CONTRIB=OFF
-DMSVC_SHARED_RT=OFF
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
-DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
# Disable install step
INSTALL_COMMAND ""
LOG_DOWNLOAD ON
LOG_BUILD 1
)
deploy/cpp_shitu/include/feature_extracter.h 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_inference_api.h"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
#include <include/preprocess_op.h>
#include <include/yaml_config.h>
using namespace paddle_infer;
namespace Feature {
class FeatureExtracter {
public:
explicit FeatureExtracter(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
else
this->gpu_id_ = 0;
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->cls_model_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdmodel";
this->cls_params_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdiparams";
this->resize_size_ =
config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
.as<int>();
this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]["NormalizeImage"]["scale"].as<float>();
this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["mean"]
.as < std::vector < float >> ();
this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["std"]
.as < std::vector < float >> ();
if (config_file["Global"]["rec_feature_normlize"].IsDefined())
this->feature_norm =
config_file["Global"]["rec_feature_normlize"].as<bool>();
LoadModel(cls_model_path_, cls_params_path_);
}
// Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path);
// Run predictor
void Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times);
void FeatureNorm(std::vector<float> &feature);
std::shared_ptr <Predictor> predictor_;
private:
bool use_gpu_ = false;
int gpu_id_ = 0;
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool feature_norm = true;
bool use_fp16_ = false;
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
float scale_ = 0.00392157;
int resize_size_ = 224;
int resize_short_ = 224;
std::string cls_model_path_;
std::string cls_params_path_;
// pre-process
ResizeImg resize_op_;
Normalize normalize_op_;
Permute permute_op_;
};
} // namespace Feature
deploy/cpp_shitu/include/nms.h 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
// This code is adpated from opencv(https://github.com/opencv/opencv)
#include <algorithm>
#include <include/object_detector.h>
template<typename T>
static inline bool SortScorePairDescend(const std::pair<float, T> &pair1,
const std::pair<float, T> &pair2) {
return pair1.first > pair2.first;
}
float RectOverlap(const Detection::ObjectResult &a,
const Detection::ObjectResult &b) {
float Aa = (a.rect[2] - a.rect[0] + 1) * (a.rect[3] - a.rect[1] + 1);
float Ab = (b.rect[2] - b.rect[0] + 1) * (b.rect[3] - b.rect[1] + 1);
int iou_w = max(min(a.rect[2], b.rect[2]) - max(a.rect[0], b.rect[0]) + 1, 0);
int iou_h = max(min(a.rect[3], b.rect[3]) - max(a.rect[1], b.rect[1]) + 1, 0);
float Aab = iou_w * iou_h;
return Aab / (Aa + Ab - Aab);
}
// Get max scores with corresponding indices.
// scores: a set of scores.
// threshold: only consider scores higher than the threshold.
// top_k: if -1, keep all; otherwise, keep at most top_k.
// score_index_vec: store the sorted (score, index) pair.
inline void
GetMaxScoreIndex(const std::vector <Detection::ObjectResult> &det_result,
const float threshold,
std::vector <std::pair<float, int>> &score_index_vec) {
// Generate index score pairs.
for (size_t i = 0; i < det_result.size(); ++i) {
if (det_result[i].confidence > threshold) {
score_index_vec.push_back(std::make_pair(det_result[i].confidence, i));
}
}
// Sort the score pair according to the scores in descending order
std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
SortScorePairDescend<int>);
// // Keep top_k scores if needed.
// if (top_k > 0 && top_k < (int)score_index_vec.size())
// {
// score_index_vec.resize(top_k);
// }
}
void NMSBoxes(const std::vector <Detection::ObjectResult> det_result,
const float score_threshold, const float nms_threshold,
std::vector<int> &indices) {
int a = 1;
// Get top_k scores (with corresponding indices).
std::vector <std::pair<float, int>> score_index_vec;
GetMaxScoreIndex(det_result, score_threshold, score_index_vec);
// Do nms
indices.clear();
for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second;
bool keep = true;
for (int k = 0; k < (int) indices.size() && keep; ++k) {
const int kept_idx = indices[k];
float overlap = RectOverlap(det_result[idx], det_result[kept_idx]);
keep = overlap <= nms_threshold;
}
if (keep)
indices.push_back(idx);
}
}
deploy/cpp_shitu/include/object_detector.h 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <ctime>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "paddle_inference_api.h" // NOLINT
#include "include/preprocess_op_det.h"
#include "include/yaml_config.h"
using namespace paddle_infer;
namespace Detection {
// Object Detection Result
struct ObjectResult {
// Rectangle coordinates of detected object: left, right, top, down
std::vector<int> rect;
// Class id of detected object
int class_id;
// Confidence of detected object
float confidence;
};
// Generate visualization colormap for each class
std::vector<int> GenerateColorMap(int num_class);
// Visualiztion Detection Result
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector <ObjectResult> &results,
const std::vector <std::string> &lables,
const std::vector<int> &colormap, const bool is_rbox);
class ObjectDetector {
public:
explicit ObjectDetector(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->model_dir_ =
config_file["Global"]["det_inference_model_dir"].as<std::string>();
this->threshold_ = config_file["Global"]["threshold"].as<float>();
this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
this->image_shape_ =
config_file["Global"]["image_shape"].as < std::vector < int >> ();
this->label_list_ =
config_file["Global"]["labe_list"].as < std::vector < std::string >> ();
this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
LoadModel(model_dir_, batch_size_, run_mode);
}
// Load Paddle inference model
void LoadModel(const std::string &model_dir, const int batch_size = 1,
const std::string &run_mode = "fluid");
// Run predictor
void Predict(const std::vector <cv::Mat> imgs, const int warmup = 0,
const int repeats = 1,
std::vector <ObjectResult> *result = nullptr,
std::vector<int> *bbox_num = nullptr,
std::vector<double> *times = nullptr);
const std::vector <std::string> &GetLabelList() const {
return this->label_list_;
}
const float &GetThreshold() const { return this->threshold_; }
private:
bool use_gpu_ = true;
int gpu_id_ = 0;
int gpu_mem_ = 800;
int cpu_math_library_num_threads_ = 6;
std::string run_mode = "fluid";
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool batch_size_ = 1;
bool use_fp16_ = false;
std::string model_dir_;
float threshold_ = 0.5;
float max_det_results_ = 5;
std::vector<int> image_shape_ = {3, 640, 640};
std::vector <std::string> label_list_;
bool ir_optim_ = true;
bool det_permute_ = true;
bool det_postprocess_ = true;
int min_subgraph_size_ = 30;
bool use_dynamic_shape_ = false;
int trt_min_shape_ = 1;
int trt_max_shape_ = 1280;
int trt_opt_shape_ = 640;
bool trt_calib_mode_ = false;
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat &image_mat);
// Postprocess result
void Postprocess(const std::vector <cv::Mat> mats,
std::vector <ObjectResult> *result, std::vector<int> bbox_num,
bool is_rbox);
std::shared_ptr <Predictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int> out_bbox_num_data_;
};
} // namespace Detection
deploy/cpp_shitu/include/preprocess_op.h 0 → 100644
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
using namespace std;
namespace Feature {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
};
} // namespace Feature
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <glog/logging.h>
#include <yaml-cpp/yaml.h>
#include <iostream>
#include <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
namespace Detection {
// Object for storing all preprocessed data
class ImageBlob {
public:
// image width and height
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
std::vector<float> im_data_;
// in net data shape(after pad)
std::vector<float> in_net_shape_;
// Evaluation image width and height
// std::vector<float> eval_im_size_f_;
// Scale factor for image size to origin image size
std::vector<float> scale_factor_;
};
// Abstraction of preprocessing opration class
class PreprocessOp {
public:
virtual void Init(const YAML::Node &item) = 0;
virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
};
class InitInfo : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class NormalizeImage : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
mean_ = item["mean"].as < std::vector < float >> ();
scale_ = item["std"].as < std::vector < float >> ();
is_scale_ = item["is_scale"].as<bool>();
}
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
// CHW or HWC
std::vector<float> mean_;
std::vector<float> scale_;
bool is_scale_;
};
class Permute : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class Resize : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
interp_ = item["interp"].as<int>();
// max_size_ = item["target_size"].as<int>();
keep_ratio_ = item["keep_ratio"].as<bool>();
target_size_ = item["target_size"].as < std::vector < int >> ();
}
// Compute best resize scale for x-dimension, y-dimension
std::pair<double, double> GenerateScale(const cv::Mat &im);
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
int interp_ = 2;
bool keep_ratio_;
std::vector<int> target_size_;
std::vector<int> in_net_shape_;
};
// Models with FPN need input shape % stride == 0
class PadStride : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
stride_ = item["stride"].as<int>();
}
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
int stride_;
};
class Preprocessor {
public:
void Init(const YAML::Node &config_node) {
// initialize image info at first
ops_["InitInfo"] = std::make_shared<InitInfo>();
for (int i = 0; i < config_node.size(); ++i) {
if (config_node[i]["DetResize"].IsDefined()) {
ops_["Resize"] = std::make_shared<Resize>();
ops_["Resize"]->Init(config_node[i]["DetResize"]);
}
if (config_node[i]["DetNormalizeImage"].IsDefined()) {
ops_["NormalizeImage"] = std::make_shared<NormalizeImage>();
ops_["NormalizeImage"]->Init(config_node[i]["DetNormalizeImage"]);
}
if (config_node[i]["DetPermute"].IsDefined()) {
ops_["Permute"] = std::make_shared<Permute>();
ops_["Permute"]->Init(config_node[i]["DetPermute"]);
}
if (config_node[i]["DetPadStrid"].IsDefined()) {
ops_["PadStride"] = std::make_shared<PadStride>();
ops_["PadStride"]->Init(config_node[i]["DetPadStrid"]);
}
}
}
void Run(cv::Mat *im, ImageBlob *data);
public:
static const std::vector <std::string> RUN_ORDER;
private:
std::unordered_map <std::string, std::shared_ptr<PreprocessOp>> ops_;
};
} // namespace Detection
deploy/cpp_shitu/include/vector_search.h 0 → 100644
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#ifdef WIN32
#define OS_PATH_SEP "\\"
#else
#define OS_PATH_SEP "/"
#endif
#include "yaml-cpp/yaml.h"
#include <cstring>
#include <faiss/Index.h>
#include <faiss/index_io.h>
#include <map>
struct SearchResult {
std::vector <faiss::Index::idx_t> I;
std::vector<float> D;
int return_k;
};
class VectorSearch {
public:
explicit VectorSearch(const YAML::Node &config_file) {
// IndexProcess
this->index_dir =
config_file["IndexProcess"]["index_dir"].as<std::string>();
this->return_k = config_file["IndexProcess"]["return_k"].as<int>();
this->score_thres = config_file["IndexProcess"]["score_thres"].as<float>();
this->max_query_number =
config_file["Global"]["max_det_results"].as<int>() + 1;
LoadIdMap();
LoadIndexFile();
this->I.resize(this->return_k * this->max_query_number);
this->D.resize(this->return_k * this->max_query_number);
};
void LoadIdMap();
void LoadIndexFile();
const SearchResult &Search(float *feature, int query_number);
const std::string &GetLabel(faiss::Index::idx_t ind);
const float &GetThreshold() { return this->score_thres; }
private:
std::string index_dir;
int return_k = 5;
float score_thres = 0.5;
std::map<long int, std::string> id_map;
faiss::Index *index;
int max_query_number = 6;
std::vector<float> D;
std::vector <faiss::Index::idx_t> I;
SearchResult sr;
};
deploy/cpp_shitu/include/yaml_config.h 0 → 100644
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#ifdef WIN32
#define OS_PATH_SEP "\\"
#else
#define OS_PATH_SEP "/"
#endif
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <stdlib.h>
#include <vector>
#include <algorithm>
#include <cstring>
#include <fstream>
#include <map>
#include <numeric>
#include <regex>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "yaml-cpp/yaml.h"
class YamlConfig {
public:
explicit YamlConfig(const std::string &path) {
config_file = ReadYamlConfig(path);
}
static std::vector <std::string> ReadDict(const std::string &path);
static std::map<int, std::string> ReadIndexId(const std::string &path);
static YAML::Node ReadYamlConfig(const std::string &path);
void PrintConfigInfo();
YAML::Node config_file;
};
deploy/cpp_shitu/readme.md 0 → 100644
浏览文件 @ d25d7446
# 服务器端C++预测
本教程将介绍在服务器端部署PP-ShiTU的详细步骤。
## 目录
- [1.准备环境](#1)
- [1.1 升级cmake](#1.1)
- [1.2 编译opencv库](#1.2)
- [1.3 下载或者编译Paddle预测库](#1.3)
- [1.3.1 预测库源码编译](#1.3.1)
- [1.3.2 直接下载安装](#1.3.2)
- [1.4 安装faiss库](#1.4)
- [2.代码编译](#2)
- [3.运行demo](#3)
- [4.使用自己模型](#4)
<a name="1"></a>
## 1. 准备环境
### 运行准备
- Linux环境，推荐使用ubuntu docker。
<a name="1.1"></a>
### 1.1 升级cmake
由于依赖库编译需要较高版本的cmake，因此，第一步首先将cmake升级。
- 下载最新版本cmake
```shell
# 当前版本最新为3.22.0，根据实际情况自行下载，建议最新版本
wget https://github.com/Kitware/CMake/releases/download/v3.22.0/cmake-3.22.0.tar.gz
tar xf cmake-3.22.0.tar.gz
```
最终可以在当前目录下看到`cmake-3.22.0/`的文件夹。
- 编译cmake，首先设置came源码路径(`root_path`)以及安装路径(`install_path`)，`root_path`为下载的came源码路径，`install_path`为came的安装路径。在本例中，源码路径即为当前目录下的`cmake-3.22.0/`。
```shell
cd ./cmake-3.22.0
export root_path=$PWD
export install_path=${root_path}/cmake
```
- 然后在cmake源码路径下，按照下面的方式进行编译
```shell
./bootstrap --prefix=${install_path}
make -j
make install
```
- 设置环境变量
```shell
export PATH=${install_path}/bin:$PATH
#检查是否正常使用
cmake --version
```
此时，cmake就可以使用了
<a name="1.2"></a>
### 1.2 编译opencv库
* 首先需要从opencv官网上下载在Linux环境下源码编译的包，以3.4.7版本为例，下载及解压缩命令如下：
```
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xvf 3.4.7.tar.gz
```
最终可以在当前目录下看到`opencv-3.4.7/`的文件夹。
* 编译opencv，首先设置opencv源码路径(`root_path`)以及安装路径(`install_path`)，`root_path`为下载的opencv源码路径，`install_path`为opencv的安装路径。在本例中，源码路径即为当前目录下的`opencv-3.4.7/`。
```shell
cd ./opencv-3.4.7
export root_path=$PWD
export install_path=${root_path}/opencv3
```
* 然后在opencv源码路径下，按照下面的方式进行编译。
```shell
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
```
* `make install`完成之后，会在该文件夹下生成opencv头文件和库文件，用于后面的PaddleClas代码编译。
以opencv3.4.7版本为例，最终在安装路径下的文件结构如下所示。**注意**：不同的opencv版本，下述的文件结构可能不同。
```
opencv3/
|-- bin
|-- include
|-- lib64
|-- share
```
<a name="1.3"></a>
### 1.3 下载或者编译Paddle预测库
* 有2种方式获取Paddle预测库，下面进行详细介绍。
<a name="1.3.1"></a>
#### 1.3.1 预测库源码编译
* 如果希望获取最新预测库特性，可以从Paddle github上克隆最新代码，源码编译预测库。
* 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)的说明，从github上获取Paddle代码，然后进行编译，生成最新的预测库。使用git获取代码方法如下。
```shell
git clone https://github.com/PaddlePaddle/Paddle.git
```
* 进入Paddle目录后，使用如下方法编译。
```shell
rm -rf build
mkdir build
cd build
cmake .. \
-DWITH_CONTRIB=OFF \
-DWITH_MKL=ON \
-DWITH_MKLDNN=ON \
-DWITH_TESTING=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_INFERENCE_API_TEST=OFF \
-DON_INFER=ON \
-DWITH_PYTHON=ON
make -j
make inference_lib_dist
```
更多编译参数选项可以参考[Paddle C++预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
```
build/paddle_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
|-- version.txt
```
其中`paddle`就是之后进行C++预测时所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
<a name="1.3.2"></a>
#### 1.3.2 直接下载安装
* [Paddle预测库官网](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本，注意必须选择`develop`版本。
以`https://paddle-inference-lib.bj.bcebos.com/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddle_inference.tgz`的`develop`版本为例，使用下述命令下载并解压：
```shell
wget https://paddle-inference-lib.bj.bcebos.com/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddle_inference.tgz
tar -xvf paddle_inference.tgz
```
最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
<a name="1.4"></a>
### 1.4 安装faiss库
```shell
# 下载 faiss
git clone https://github.com/facebookresearch/faiss.git
cd faiss
export faiss_install_path=$PWD/faiss_install
cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}
make -C build -j faiss
make -C build install
```
在安装`faiss`前，请安装`openblas`，`ubuntu`系统中安装命令如下：
```shell
apt-get install libopenblas-dev
```
注意本教程以安装faiss cpu版本为例，安装时请参考[faiss](https://github.com/facebookresearch/faiss)官网文档，根据需求自行安装。
<a name="2"></a>
## 2. 代码编译
编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。同时，编译过程中需要下载编译`yaml-cpp`等C++库，请保持联网环境。
```shell
sh tools/build.sh
```
具体地，`tools/build.sh`中内容如下，请根据具体路径修改。
```shell
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
cd ..
```
上述命令中，
* `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）；
* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
* `CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；
* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中为`/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT需要结合GPU使用。
* `FAISS_DIR`是faiss的安装地址
* `FAISS_WITH_MKL`是指在编译faiss的过程中，是否使用了mkldnn，本文档中编译faiss，没有使用，而使用了openblas，故设置为`OFF`，若使用了mkldnn，则为`ON`.
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`pp_shitu`的可执行文件。
<a name="3"></a>
## 3. 运行demo
- 请参考[识别快速开始文档](../../docs/zh_CN/quick_start/quick_start_recognition.md)，下载好相应的 轻量级通用主体检测模型、轻量级通用识别模型及瓶装饮料测试数据并解压。
```shell
mkdir models
cd models
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
cd ..
mkdir data
cd data
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
tar -xf drink_dataset_v1.0.tar
cd ..
```
- 将相应的yaml文件拷到当前文件夹下
```shell
cp ../configs/inference_drink.yaml .
```
- 将`inference_drink.yaml`中的相对路径，改成基于本目录的路径或者绝对路径。涉及到的参数有
- Global.infer_imgs ：此参数可以是具体的图像地址，也可以是图像集所在的目录
- Global.det_inference_model_dir ： 检测模型存储目录
- Global.rec_inference_model_dir ： 识别模型存储目录
- IndexProcess.index_dir ： 检索库的存储目录，在示例中，检索库在下载的demo数据中。
- 字典转换
由于python的检索库的字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此进行转换
```shell
python tools/transform_id_map.py -c inference_drink.yaml
```
转换成功后，在`IndexProcess.index_dir`目录下生成`id_map.txt`，方便c++ 读取。
- 执行程序
```shell
./build/pp_shitu -c inference_drink.yaml
# or
./build/pp_shitu -config inference_drink.yaml
```
若对图像集进行检索，则可能得到，如下结果。注意，此结果只做展示，具体以实际运行结果为准。
同时，需注意的是，由于opencv 版本问题，会导致图像在预处理的过程中，resize产生细微差别，导致python 和c++结果，轻微不同，如bbox相差几个像素，检索结果小数点后3位diff等。但不会改变最终检索label。
![](../../docs/images/quick_start/shitu_c++_result.png)
<a name="4"></a>
## 4. 使用自己模型
使用自己训练的模型，可以参考[模型导出](../../docs/zh_CN/inference_deployment/export_model.md)，导出`inference model`，用于模型预测。
同时注意修改`yaml`文件中具体参数。
deploy/cpp_shitu/readme_en.md 0 → 100644
浏览文件 @ d25d7446
# C++ Inference on Server
This tutorial details the steps to deploy PP-ShiTU on the server side.
## Catalogue
- [1.Prepare the Environment](#1)
- [1.1 Update cmake](#1.1)
- [1.2 Compile opencv Library](#1.2)
- [1.3 Download or Compile Paddle Inference Library](#1.3)
- [1.3.1 Compile the Source of Inference Library](#1.3.1)
- [1.3.2 Direct Download and Installation](#1.3.2)
- [1.4 Install faiss Library](#1.4)
- [2.Code Compilation](#2)
- [3.Run the demo](#3)
- [4.Use Your Own Model](#4)
<a name="1"></a>
## 1. Prepare the Environment
### Environment Preparation
- Linux environment, ubuntu docker recommended.
<a name="1.1"></a>
### 1.1 Update cmake
The first step is to upgrade `cmake` considering the requirements of the dependency library compilation.
- Download the latest version of cmake
```
# The latest version is 3.22.0, please download the appropriate one, the latest recommended.
wget https://github.com/Kitware/CMake/releases/download/v3.22.0/cmake-3.22.0.tar.gz
tar xf cmake-3.22.0.tar.gz
```
You can find `cmake-3.22.0/` folder in the current directory.
- To compile cmake, first set the source path of `cmake` (`root_path`) and installation path (`install_path`). In this example, the source path is `cmake-3.22.0/` in the current directory.
```shell
cd ./cmake-3.22.0
export root_path=$PWD
export install_path=${root_path}/cmake
```
- Then compile under the source path as follows:
```shell
./bootstrap --prefix=${install_path}
make -j
make install
```
- Set environment variables
```shell
export PATH=${install_path}/bin:$PATH
#Check its well functioning
cmake --version
```
cmake is now ready for use.
<a name="1.2"></a>
### 1.2 Compile opencv Library
- First, download the package for source compilation in Linux environment from the official website of opencv. Taking version 3.4.7 as an example, follow the command below to download and unzip it:
```shell
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xvf 3.4.7.tar.gz
```
You can find`opencv-3.4.7/`folder in the current directory.
- To compile opencv, first set the source path of opencv(`root_path`) and installation path (`install_path`). In this example, the source path is`opencv-3.4.7/`in the current directory.
```
cd ./opencv-3.4.7
export root_path=$PWD
export install_path=${root_path}/opencv3
```
- Then compile under the source path as follows:
```shell
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
```
- After `make install` is done, opencv header and library files will be generated in this folder for later compilation of PaddleClas code.
For opencv version 3.4.7, the final file structure under the installation path is shown below. **Note**: The following file structure may vary for different opencv versions.
```
opencv3/
|-- bin
|-- include
|-- lib64
|-- share
```
<a name="1.3"></a>
### 1.3 Download or Compile Paddle Inference Library
- Here we detail 2 ways to obtain Paddle inference library.
<a name="1.3.1"></a>
#### 1.3.1 Compile the Source of Inference Library
- To obtain the latest features of the inference library, you can clone the latest code from Paddle github and compile the source code of the library.
- Please refer to the website of [Paddle Inference Library](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_ and_install_lib_cn.html#id16) to get Paddle code from github and then compile it to generate the latest inference library. The method to obtain the code using git is as follows.
```shell
git clone https://github.com/PaddlePaddle/Paddle.git
```
- Adopt the following method to compile after entering Paddle directory.
```shell
rm -rf build
mkdir build
cd build
cmake .. \
-DWITH_CONTRIB=OFF \
-DWITH_MKL=ON \
-DWITH_MKLDNN=ON \
-DWITH_TESTING=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_INFERENCE_API_TEST=OFF \
-DON_INFER=ON \
-DWITH_PYTHON=ON
make -j
make inference_lib_dist
```
See the official website of [Paddle C++ Inference Library](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16) for more compilation parameters.
- The following files and folders can be found generated under `build/paddle_inference_install_dir/` after compilation.
```
build/paddle_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
|-- version.txt
```
`paddle` is the Paddle library needed for later C++ inference, and `version.txt` contains the version information of the current inference library.
<a name="1.3.2"></a>
#### 1.3.2 Direct Download and Installation
- The Linux inference library of different cuda versions are available on the official website of [Paddle Inference Library ](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html), where you can choose the appropriate version. Note that you must select the `develop` version.
For the `develop` version of `https://paddle-inference-lib.bj.bcebos.com/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddle_inference.tgz`, use the following command to download and unzip it:
```shell
wget https://paddle-inference-lib.bj.bcebos.com/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddle_inference.tgz
tar -xvf paddle_inference.tgz
```
The subfolder `paddle_inference/` will finally be created in the current folder.
<a name="1.4"></a>
### 1.4 Install faiss Library
```shell
# Download faiss
git clone https://github.com/facebookresearch/faiss.git
cd faiss
export faiss_install_path=$PWD/faiss_install
cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}
make -C build -j faiss
make -C build install
```
Please install `openblas` before `faiss`, the installation command in `ubuntu` system is as follows:
```
apt-get install libopenblas-dev
```
Note that this tutorial installs the cpu version of faiss as an example, please install it as your need by referring to the official documents of [faiss](https://github.com/facebookresearch/faiss).
<a name="2"></a>
## 2. Code Compilation
The command is as follows, where the address of Paddle C++ inference library, opencv and other dependency libraries need to be replaced with the actual address on your own machine. Also, you need to download and compile `yaml-cpp` and other C++ libraries during the compilation, so please keep the network unblocked.
```shell
sh tools/build.sh
```
Specifically, the contents of `tools/build.sh` are as follows, please modify according to the specific path.
```shell
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
cd ..
```
In the above commands:
- `OPENCV_DIR` is the address of the opencv compilation and installation (in this case, the path of the `opencv-3.4.7/opencv3` folder).
- `LIB_DIR` is the path of the downloaded Paddle inference library (`paddle_inference` folder), or the generated Paddle inference library after compilation (`build/paddle_inference_install_dir` folder).
- `CUDA_LIB_DIR` is path of the cuda library file, which in docker is `/usr/local/cuda/lib64`.
- `CUDNN_LIB_DIR` is the path of the cudnn library file, which in docker is `/usr/lib/x86_64-linux-gnu/` .
- `TENSORRT_DIR` is the path of the tensorrt library file, which in docker is `/usr/local/TensorRT6-cuda10.0-cudnn7/`. TensorRT needs to be used in combination with GPU.
- `FAISS_DIR` is the installation path of faiss.
- `FAISS_WITH_MKL` means whether mkldnn is used during the compilation of faiss. The compilation in this document employs openbals instead of mkldnn, so it is set to `OFF`, otherwise it is `ON`.
A `build` folder will be created in the current path after the compilation, which generates an executable file named `pp_shitu`.
<a name="3"></a>
## 3. Run the demo
- Please refer to the [Quick Start of Recognition](../../docs/en/quick_start/quick_start_recognition_en.md), download the corresponding Lightweight Generic Mainbody Detection Model, Lightweight Generic Recognition Model, and the beverage test data and unzip them.
```shell
mkdir models
cd models
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
cd ..
mkdir data
cd data
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
tar -xf drink_dataset_v1.0.tar
cd ..
```
- Copy the corresponding yaml file to the current folder
```
cp ../configs/inference_drink.yaml .
```
- Change the relative path in `inference_drink.yaml` to a path based on this directory or an absolute path. The parameters involved are:
- Global.infer_imgs: This parameter can be a specific image path or the directory where the image set is located
- Global.det_inference_model_dir: The directory where detection models are saved.
- Global.rec_inference_model_dir: The directory where recognition models are saved.
- IndexProcess.index_dir: The storage directory of the retrieval library, in the exmple, the retrieval library is in the downloaded demo data.
- Transform the ID-Label Map Dictionary
The id-label map dictionary in python is serialized using `pickle`, which make it hard to read for C++, so the transformation is needed:
```
python tools/transform_id_map.py -c inference_drink.yaml
```
`id_map.txt` is generated in `IndexProcess.index_dir` directory for convenience of C++ reading.
- Execute the program
```shell
./build/pp_shitu -c inference_drink.yaml
# or
./build/pp_shitu -config inference_drink.yaml
```
The following results can be obtained after searching the image set.
At the same time, it should be noticed that a slight difference may occur during the pre-processing of the image due to the version of opencv, resulting in a minor discrepancy in python and c++ results, such as a few pixels for bbox, 3 decimal places for retrieval results, etc. But it has no impact on the final search label.
[![img](../../docs/images/quick_start/shitu_c++_result.png)](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/images/quick_start/shitu_c++_result.png)
<a name="4"></a>
## 4. Use Your Own Model
You can also use your self-trained models. Please refer to [model export](../../docs/en/inference_deployment/export_model_en.md) to export ` inference model` for model inference.
Mind modifying the specific parameters in the `yaml` file.
deploy/cpp_shitu/src/feature_extracter.cpp 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include <cmath>
#include <include/feature_extracter.h>
#include <numeric>
namespace Feature {
void FeatureExtracter::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 20, 1, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
}
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times) {
cv::Mat resize_img;
std::vector<double> time;
auto preprocess_start = std::chrono::steady_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_,
this->resize_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto preprocess_end = std::chrono::steady_clock::now();
auto infer_start = std::chrono::steady_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
if (this->feature_norm)
FeatureNorm(out_data);
auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
time.push_back(double(preprocess_diff.count()) * 1000);
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count() * 1000);
time.push_back(inference_cost_time);
// std::chrono::duration<float> postprocess_diff =
// postprocess_end - postprocess_start;
time.push_back(0);
// std::cout << "result: " << std::endl;
// std::cout << "\tclass id: " << maxPosition << std::endl;
// std::cout << std::fixed << std::setprecision(10)
// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
times[0] += time[0];
times[1] += time[1];
times[2] += time[2];
}
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
float featuer_sqrt = std::sqrt(std::inner_product(
featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
for (int i = 0; i < featuer.size(); ++i)
featuer[i] /= featuer_sqrt;
}
} // namespace Feature
deploy/cpp_shitu/src/main.cpp 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <opencv2/core/utils/filesystem.hpp>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
#include <auto_log/autolog.h>
#include <gflags/gflags.h>
#include <include/feature_extracter.h>
#include <include/nms.h>
#include <include/object_detector.h>
#include <include/vector_search.h>
#include <include/yaml_config.h>
using namespace std;
using namespace cv;
DEFINE_string(config,
"", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");
void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
const std::vector <std::string> &all_img_paths,
const int batch_size, Detection::ObjectDetector *det,
std::vector <Detection::ObjectResult> &im_result,
std::vector<int> &im_bbox_num, std::vector<double> &det_t,
const bool visual_det = false,
const bool run_benchmark = false,
const std::string &output_dir = "output") {
int steps = ceil(float(all_img_paths.size()) / batch_size);
// printf("total images = %d, batch_size = %d, total steps = %d\n",
// all_img_paths.size(), batch_size, steps);
for (int idx = 0; idx < steps; idx++) {
int left_image_cnt = all_img_paths.size() - idx * batch_size;
if (left_image_cnt > batch_size) {
left_image_cnt = batch_size;
}
// for (int bs = 0; bs < left_image_cnt; bs++) {
// std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
// cv::Mat im = cv::imread(image_file_path, 1);
// batch_imgs.insert(batch_imgs.end(), im);
// }
// Store all detected result
std::vector <Detection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
bool is_rbox = false;
if (run_benchmark) {
det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times);
} else {
det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times);
// get labels and colormap
auto labels = det->GetLabelList();
auto colormap = Detection::GenerateColorMap(labels.size());
int item_start_idx = 0;
for (int i = 0; i < left_image_cnt; i++) {
cv::Mat im = batch_imgs[i];
int detect_num = 0;
for (int j = 0; j < bbox_num[i]; j++) {
Detection::ObjectResult item = result[item_start_idx + j];
if (item.confidence < det->GetThreshold() || item.class_id == -1) {
continue;
}
detect_num += 1;
im_result.push_back(item);
if (visual_det) {
if (item.rect.size() > 6) {
is_rbox = true;
printf(
"class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3], item.rect[4], item.rect[5],
item.rect[6], item.rect[7]);
} else {
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3]);
}
}
}
im_bbox_num.push_back(detect_num);
item_start_idx = item_start_idx + bbox_num[i];
// Visualization result
if (visual_det) {
std::cout << all_img_paths.at(idx * batch_size + i)
<< " The number of detected box: " << detect_num
<< std::endl;
cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels,
colormap, is_rbox);
std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95);
std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP;
}
std::string image_file_path = all_img_paths.at(idx * batch_size + i);
output_path +=
image_file_path.substr(image_file_path.find_last_of('/') + 1);
cv::imwrite(output_path, vis_img, compression_params);
printf("Visualized output saved as %s\n", output_path.c_str());
}
}
}
det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
}
}
void PrintResult(std::string &img_path,
std::vector <Detection::ObjectResult> &det_result,
std::vector<int> &indeices, VectorSearch &vector_search,
SearchResult &search_result) {
printf("%s:\n", img_path.c_str());
for (int i = 0; i < indeices.size(); ++i) {
int t = indeices[i];
printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
det_result[t].rect[0], det_result[t].rect[1], det_result[t].rect[2],
det_result[t].rect[3], det_result[t].confidence,
vector_search.GetLabel(search_result.I[search_result.return_k * t])
.c_str());
}
}
int main(int argc, char **argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
std::string yaml_path = "";
if (FLAGS_config == "" && FLAGS_c == "") {
std::cerr << "[ERROR] usage: " << std::endl
<< argv[0] << " -c $yaml_path" << std::endl
<< "or:" << std::endl
<< argv[0] << " -config $yaml_path" << std::endl;
exit(1);
} else if (FLAGS_config != "") {
yaml_path = FLAGS_config;
} else {
yaml_path = FLAGS_c;
}
YamlConfig config(yaml_path);
config.PrintConfigInfo();
// initialize detector, rec_Model, vector_search
Feature::FeatureExtracter feature_extracter(config.config_file);
Detection::ObjectDetector detector(config.config_file);
VectorSearch searcher(config.config_file);
// config
const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
bool visual_det = false;
if (config.config_file["Global"]["visual_det"].IsDefined()) {
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
}
bool benchmark = false;
if (config.config_file["Global"]["benchmark"].IsDefined()) {
benchmark = config.config_file["Global"]["benchmark"].as<bool>();
}
int max_det_results = 5;
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
}
float rec_nms_thresold = 0.05;
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
rec_nms_thresold =
config.config_file["Global"]["rec_nms_thresold"].as<float>();
}
// load image_file_path
std::string path =
config.config_file["Global"]["infer_imgs"].as<std::string>();
std::vector <std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector <cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
}
} else {
img_files_list.push_back(path);
}
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
// for time log
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> det_times = {0, 0, 0};
std::vector<double> search_times = {0, 0, 0};
int instance_num = 0;
// for read images
std::vector <cv::Mat> batch_imgs;
std::vector <std::string> img_paths;
// for detection
std::vector <Detection::ObjectResult> det_result;
std::vector<int> det_bbox_num;
// for vector search
std::vector<float> features;
std::vector<float> feature;
// for nms
std::vector<int> indeices;
int warmup_iter = img_files_list.size() > 5 ? 5 : img_files_list.size();
if (benchmark) {
img_files_list.insert(img_files_list.begin(), img_files_list.begin(),
img_files_list.begin() + warmup_iter);
}
for (int idx = 0; idx < img_files_list.size(); ++idx) {
std::string img_path = img_files_list[idx];
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(-1);
}
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
batch_imgs.push_back(srcimg);
img_paths.push_back(img_path);
// step1: get all detection results
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
det_bbox_num, det_times, visual_det, false);
// select max_det_results bbox
if (det_result.size() > max_det_results) {
det_result.resize(max_det_results);
}
instance_num += det_result.size();
// step2: add the whole image for recognition to improve recall
Detection::ObjectResult result_whole_img = {
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
det_result.push_back(result_whole_img);
det_bbox_num[0] = det_result.size() + 1;
// step3: extract feature for all boxes in an inmage
SearchResult search_result;
for (int j = 0; j < det_result.size(); ++j) {
int w = det_result[j].rect[2] - det_result[j].rect[0];
int h = det_result[j].rect[3] - det_result[j].rect[1];
cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h);
cv::Mat crop_img = srcimg(rect);
feature_extracter.Run(crop_img, feature, cls_times);
features.insert(features.end(), feature.begin(), feature.end());
}
// step4: get search result
auto search_start = std::chrono::steady_clock::now();
search_result = searcher.Search(features.data(), det_result.size());
auto search_end = std::chrono::steady_clock::now();
// nms for search result
for (int i = 0; i < det_result.size(); ++i) {
det_result[i].confidence = search_result.D[search_result.return_k * i];
}
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
auto nms_end = std::chrono::steady_clock::now();
std::chrono::duration<float> search_diff = search_end - search_start;
search_times[1] += double(search_diff.count() * 1000);
std::chrono::duration<float> nms_diff = nms_end - search_end;
search_times[2] += double(nms_diff.count() * 1000);
// print result
if (not benchmark or (benchmark and idx >= warmup_iter))
PrintResult(img_path, det_result, indeices, searcher, search_result);
// for postprocess
batch_imgs.clear();
img_paths.clear();
det_bbox_num.clear();
det_result.clear();
feature.clear();
features.clear();
indeices.clear();
if (benchmark and warmup_iter == idx + 1) {
det_times = {0, 0, 0};
cls_times = {0, 0, 0};
search_times = {0, 0, 0};
instance_num = 0;
}
}
if (benchmark) {
std::string presion = "fp32";
if (config.config_file["Global"]["use_fp16"].IsDefined() and
config.config_file["Global"]["use_fp16"].as<bool>())
presion = "fp16";
bool use_gpu = config.config_file["Global"]["use_gpu"].as<bool>();
bool use_tensorrt = config.config_file["Global"]["use_tensorrt"].as<bool>();
bool enable_mkldnn =
config.config_file["Global"]["enable_mkldnn"].as<bool>();
int cpu_num_threads =
config.config_file["Global"]["cpu_num_threads"].as<int>();
int batch_size = config.config_file["Global"]["batch_size"].as<int>();
std::vector<int> shape =
config.config_file["Global"]["image_shape"].as < std::vector < int >> ();
std::string det_shape = std::to_string(shape[0]);
for (int i = 1; i < shape.size(); ++i)
det_shape = det_shape + ", " + std::to_string(shape[i]);
AutoLogger autolog_det("Det", use_gpu, use_tensorrt, enable_mkldnn,
cpu_num_threads, batch_size, det_shape, presion,
det_times, img_files_list.size() - warmup_iter);
autolog_det.report();
AutoLogger autolog_rec("Rec", use_gpu, use_tensorrt, enable_mkldnn,
cpu_num_threads, batch_size, "3, 224, 224", presion,
cls_times, instance_num);
autolog_rec.report();
AutoLogger autolog_search("Search", false, use_tensorrt, enable_mkldnn,
cpu_num_threads, batch_size, "dynamic", presion,
search_times, instance_num);
autolog_search.report();
}
return 0;
}
deploy/cpp_shitu/src/object_detector.cpp 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include <sstream>
// for setprecision
#include "include/object_detector.h"
#include <chrono>
#include <iomanip>
using namespace paddle_infer;
namespace Detection {
// Load Model and create model predictor
void ObjectDetector::LoadModel(const std::string &model_dir,
const int batch_size,
const std::string &run_mode) {
paddle_infer::Config config;
std::string prog_file = model_dir + OS_PATH_SEP + "inference.pdmodel";
std::string params_file = model_dir + OS_PATH_SEP + "inference.pdiparams";
config.SetModel(prog_file, params_file);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
config.SwitchIrOptim(this->ir_optim_);
// // use tensorrt
// if (run_mode != "fluid") {
// auto precision = paddle_infer::Config::Precision::kFloat32;
// if (run_mode == "trt_fp32") {
// precision = paddle_infer::Config::Precision::kFloat32;
// }
// else if (run_mode == "trt_fp16") {
// precision = paddle_infer::Config::Precision::kHalf;
// }
// else if (run_mode == "trt_int8") {
// precision = paddle_infer::Config::Precision::kInt8;
// } else {
// printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or
// 'trt_int8'");
// }
// set tensorrt
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 30, batch_size, this->min_subgraph_size_,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, this->trt_calib_mode_);
// set use dynamic shape
if (this->use_dynamic_shape_) {
// set DynamicShsape for image tensor
const std::vector<int> min_input_shape = {1, 3, this->trt_min_shape_,
this->trt_min_shape_};
const std::vector<int> max_input_shape = {1, 3, this->trt_max_shape_,
this->trt_max_shape_};
const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_,
this->trt_opt_shape_};
const std::map <std::string, std::vector<int>> map_min_input_shape = {
{"image", min_input_shape}};
const std::map <std::string, std::vector<int>> map_max_input_shape = {
{"image", max_input_shape}};
const std::map <std::string, std::vector<int>> map_opt_input_shape = {
{"image", opt_input_shape}};
config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape,
map_opt_input_shape);
std::cout << "TensorRT dynamic shape enabled" << std::endl;
}
}
// } else if (this->device_ == "XPU"){
// config.EnableXpu(10*1024*1024);
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
config.SwitchIrOptim(this->ir_optim_);
config.DisableGlogInfo();
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
}
// Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector <ObjectResult> &results,
const std::vector <std::string> &lables,
const std::vector<int> &colormap,
const bool is_rbox = false) {
cv::Mat vis_img = img.clone();
for (int i = 0; i < results.size(); ++i) {
// Configure color and text size
std::ostringstream oss;
oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
oss << lables[results[i].class_id] << " ";
oss << results[i].confidence;
std::string text = oss.str();
int c1 = colormap[3 * results[i].class_id + 0];
int c2 = colormap[3 * results[i].class_id + 1];
int c3 = colormap[3 * results[i].class_id + 2];
cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
double font_scale = 0.5f;
float thickness = 0.5;
cv::Size text_size =
cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
cv::Point origin;
if (is_rbox) {
// Draw object, text, and background
for (int k = 0; k < 4; k++) {
cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
results[i].rect[(k * 2 + 1) % 8]);
cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
results[i].rect[(k * 2 + 3) % 8]);
cv::line(vis_img, pt1, pt2, roi_color, 2);
}
} else {
int w = results[i].rect[2] - results[i].rect[0];
int h = results[i].rect[3] - results[i].rect[1];
cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
// Draw roi object, text, and background
cv::rectangle(vis_img, roi, roi_color, 2);
}
origin.x = results[i].rect[0];
origin.y = results[i].rect[1];
// Configure text background
cv::Rect text_back =
cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height,
text_size.width, text_size.height);
// Draw text, and background
cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img, text, origin, font_face, font_scale,
cv::Scalar(255, 255, 255), thickness);
}
return vis_img;
}
void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
// Clone the image : keep the original mat for postprocess
cv::Mat im = ori_im.clone();
cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
preprocessor_.Run(&im, &inputs_);
}
void ObjectDetector::Postprocess(const std::vector <cv::Mat> mats,
std::vector <ObjectResult> *result,
std::vector<int> bbox_num,
bool is_rbox = false) {
result->clear();
int start_idx = 0;
for (int im_id = 0; im_id < mats.size(); im_id++) {
cv::Mat raw_mat = mats[im_id];
int rh = 1;
int rw = 1;
// if (config_.arch_ == "Face") {
// rh = raw_mat.rows;
// rw = raw_mat.cols;
// }
for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) {
if (is_rbox) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
// Confidence score
float score = output_data_[1 + j * 10];
int x1 = (output_data_[2 + j * 10] * rw);
int y1 = (output_data_[3 + j * 10] * rh);
int x2 = (output_data_[4 + j * 10] * rw);
int y2 = (output_data_[5 + j * 10] * rh);
int x3 = (output_data_[6 + j * 10] * rw);
int y3 = (output_data_[7 + j * 10] * rh);
int x4 = (output_data_[8 + j * 10] * rw);
int y4 = (output_data_[9 + j * 10] * rh);
ObjectResult result_item;
result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
} else {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
// Confidence score
float score = output_data_[1 + j * 6];
int xmin = (output_data_[2 + j * 6] * rw);
int ymin = (output_data_[3 + j * 6] * rh);
int xmax = (output_data_[4 + j * 6] * rw);
int ymax = (output_data_[5 + j * 6] * rh);
int wd = xmax - xmin;
int hd = ymax - ymin;
ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
}
}
start_idx += bbox_num[im_id];
}
}
void ObjectDetector::Predict(const std::vector <cv::Mat> imgs, const int warmup,
const int repeats,
std::vector <ObjectResult> *result,
std::vector<int> *bbox_num,
std::vector<double> *times) {
auto preprocess_start = std::chrono::steady_clock::now();
int batch_size = imgs.size();
// in_data_batch
std::vector<float> in_data_all;
std::vector<float> im_shape_all(batch_size * 2);
std::vector<float> scale_factor_all(batch_size * 2);
// Preprocess image
for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
cv::Mat im = imgs.at(bs_idx);
Preprocess(im);
im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
// TODO: reduce cost time
in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(),
inputs_.im_data_.end());
}
// Prepare input tensor
auto input_names = predictor_->GetInputNames();
for (const auto &tensor_name : input_names) {
auto in_tensor = predictor_->GetInputHandle(tensor_name);
if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({batch_size, 3, rh, rw});
in_tensor->CopyFromCpu(in_data_all.data());
} else if (tensor_name == "im_shape") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(im_shape_all.data());
} else if (tensor_name == "scale_factor") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(scale_factor_all.data());
}
}
auto preprocess_end = std::chrono::steady_clock::now();
// Run predictor
// warmup
for (int i = 0; i < warmup; i++) {
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
}
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
bool is_rbox = false;
auto inference_start = std::chrono::steady_clock::now();
for (int i = 0; i < repeats; i++) {
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
}
is_rbox = output_shape[output_shape.size() - 1] % 10 == 0;
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
// Postprocessing result
result->clear();
Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
bbox_num->clear();
for (int k = 0; k < out_bbox_num_data_.size(); k++) {
int tmp = out_bbox_num_data_[k];
bbox_num->push_back(tmp);
}
auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = inference_end - inference_start;
times->push_back(double(inference_diff.count() / repeats * 1000));
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
}
std::vector<int> GenerateColorMap(int num_class) {
auto colormap = std::vector<int>(3 * num_class, 0);
for (int i = 0; i < num_class; ++i) {
int j = 0;
int lab = i;
while (lab) {
colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
++j;
lab >>= 3;
}
}
return colormap;
}
} // namespace Detection
deploy/cpp_shitu/src/preprocess_op.cpp 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_api.h"
#include "paddle_inference_api.h"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <math.h>
#include <numeric>
#include <include/preprocess_op.h>
namespace Feature {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
if (size > 0) {
resize_h = size;
resize_w = size;
} else {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace Feature
deploy/cpp_shitu/src/preprocess_op_det.cpp 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include <string>
#include <vector>
#include "include/preprocess_op_det.h"
namespace Detection {
void InitInfo::Run(cv::Mat *im, ImageBlob *data) {
data->im_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
data->scale_factor_ = {1., 1.};
data->in_net_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
}
void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
double e = 1.0;
if (is_scale_) {
e /= 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
}
}
}
void Permute::Run(cv::Mat *im, ImageBlob *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
(data->im_data_).resize(rc * rh * rw);
float *base = (data->im_data_).data();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
}
}
void Resize::Run(cv::Mat *im, ImageBlob *data) {
auto resize_scale = GenerateScale(*im);
data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
cv::resize(*im, *im, cv::Size(), resize_scale.first, resize_scale.second,
interp_);
data->im_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
data->scale_factor_ = {
resize_scale.second, resize_scale.first,
};
}
std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
std::pair<double, double> resize_scale;
int origin_w = im.cols;
int origin_h = im.rows;
if (keep_ratio_) {
int im_size_max = std::max(origin_w, origin_h);
int im_size_min = std::min(origin_w, origin_h);
int target_size_max =
*std::max_element(target_size_.begin(), target_size_.end());
int target_size_min =
*std::min_element(target_size_.begin(), target_size_.end());
double scale_min =
static_cast<double>(target_size_min) / static_cast<double>(im_size_min);
double scale_max =
static_cast<double>(target_size_max) / static_cast<double>(im_size_max);
double scale_ratio = std::min(scale_min, scale_max);
resize_scale = {scale_ratio, scale_ratio};
} else {
resize_scale.first =
static_cast<double>(target_size_[1]) / static_cast<double>(origin_w);
resize_scale.second =
static_cast<double>(target_size_[0]) / static_cast<double>(origin_h);
}
return resize_scale;
}
void PadStride::Run(cv::Mat *im, ImageBlob *data) {
if (stride_ <= 0) {
return;
}
int rc = im->channels();
int rh = im->rows;
int rw = im->cols;
int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
cv::copyMakeBorder(*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT,
cv::Scalar(0));
data->in_net_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
}
// Preprocessor op running order
const std::vector <std::string> Preprocessor::RUN_ORDER = {
"InitInfo", "Resize", "NormalizeImage", "PadStride", "Permute"};
void Preprocessor::Run(cv::Mat *im, ImageBlob *data) {
for (const auto &name : RUN_ORDER) {
if (ops_.find(name) != ops_.end()) {
ops_[name]->Run(im, data);
}
}
}
} // namespace Detection
deploy/cpp_shitu/src/vector_search.cpp 0 → 100644
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include "include/vector_search.h"
#include <cstdio>
#include <faiss/index_factory.h>
#include <faiss/index_io.h>
#include <fstream>
#include <iostream>
#include <regex>
void VectorSearch::LoadIndexFile() {
std::string file_path = this->index_dir + OS_PATH_SEP + "vector.index";
const char *fname = file_path.c_str();
this->index = faiss::read_index(fname, 0);
}
void VectorSearch::LoadIdMap() {
std::string file_path = this->index_dir + OS_PATH_SEP + "id_map.txt";
std::ifstream in(file_path);
std::string line;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 2) {
std::cout << "The number of element for each line in : " << file_path
<< "must be 2, exit the program..." << std::endl;
exit(1);
} else
this->id_map.insert(std::pair<long int, std::string>(
std::stol(v[0], nullptr, 10), v[1]));
}
}
}
const SearchResult &VectorSearch::Search(float *feature, int query_number) {
this->D.resize(this->return_k * query_number);
this->I.resize(this->return_k * query_number);
this->index->search(query_number, feature, return_k, D.data(), I.data());
this->sr.return_k = this->return_k;
this->sr.D = this->D;
this->sr.I = this->I;
return this->sr;
}
const std::string &VectorSearch::GetLabel(faiss::Index::idx_t ind) {
return this->id_map.at(ind);
}
deploy/cpp_shitu/src/yaml_config.cpp 0 → 100644
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include <iostream>
#include <ostream>
#include <vector>
#include <include/yaml_config.h>
std::vector <std::string> YamlConfig::ReadDict(const std::string &path) {
std::ifstream in(path);
std::string line;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
}
} else {
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
}
return m_vec;
}
std::map<int, std::string> YamlConfig::ReadIndexId(const std::string &path) {
std::ifstream in(path);
std::string line;
std::map<int, std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 3) {
std::cout << "The number of element for each line in : " << path
<< "must be 3, exit the program..." << std::endl;
exit(1);
} else
m_vec.insert(std::pair<int, std::string>(stoi(v[0]), v[2]));
}
}
return m_vec;
}
YAML::Node YamlConfig::ReadYamlConfig(const std::string &path) {
YAML::Node config;
try {
config = YAML::LoadFile(path);
} catch (YAML::BadFile &e) {
std::cout << "Something wrong in yaml file, please check yaml file"
<< std::endl;
exit(1);
}
return config;
}
void YamlConfig::PrintConfigInfo() {
std::cout << this->config_file << std::endl;
// for (YAML::const_iterator
// it=config_file.begin();it!=config_file.end();++it)
// {
// std::cout << it->as<std::string>() << "\n";
// }
}
deploy/cpp_shitu/tools/build.sh 0 → 100755
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OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
cd ..
deploy/cpp_shitu/tools/transform_id_map.py 0 → 100644
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import argparse
import os
import pickle
import yaml
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, required=True)
args = parser.parse_args()
return args
def main():
args = parse_args()
with open(args.config) as fd:
config = yaml.load(fd.read(), yaml.FullLoader)
index_dir = ""
try:
index_dir = config["IndexProcess"]["index_dir"]
except Exception as e:
print("The IndexProcess.index_dir in config_file dose not exist")
exit(1)
id_map_path = os.path.join(index_dir, "id_map.pkl")
assert os.path.exists(
id_map_path), "The id_map file dose not exist: {}".format(id_map_path)
with open(id_map_path, "rb") as fd:
ids = pickle.load(fd)
with open(os.path.join(index_dir, "id_map.txt"), "w") as fd:
for k, v in ids.items():
v = v.split("\t")[1]
fd.write(str(k) + " " + v + "\n")
print('Transform id_map sucess')
if __name__ == "__main__":
main()
deploy/lite_shitu/Makefile 0 → 100644
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ARM_ABI = arm8#[arm7/arm8]
export ARM_ABI
ifeq ($(ARM_ABI), arm8)
ARM_PLAT=arm64-v8a
else
ARM_PLAT=armeabi-v7a
endif
${info ARM_ABI: ${ARM_ABI}}
${info ARM_PLAT: ${ARM_PLAT}; option[arm7/arm8]}
include ../Makefile.def
LITE_ROOT=../../../
${info LITE_ROOT: $(abspath ${LITE_ROOT})}
THIRD_PARTY_DIR=third_party
${info THIRD_PARTY_DIR: $(abspath ${THIRD_PARTY_DIR})}
OPENCV_VERSION=opencv4.1.0
OPENCV_LIBS = ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/libs/libopencv_imgcodecs.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/libs/libopencv_imgproc.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/libs/libopencv_core.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libtegra_hal.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibjpeg-turbo.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibwebp.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibpng.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibjasper.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/liblibtiff.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libIlmImf.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libtbb.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/3rdparty/libs/libcpufeatures.a
LITE_LIBS = -L${LITE_ROOT}/cxx/lib/ -lpaddle_light_api_shared
###############################################################
# How to use one of static libaray: #
# `libpaddle_api_full_bundled.a` #
# `libpaddle_api_light_bundled.a` #
###############################################################
# Note: default use lite's shared library. #
###############################################################
# 1. Comment above line using `libpaddle_light_api_shared.so`
# 2. Undo comment below line using `libpaddle_api_light_bundled.a`
# LITE_LIBS = ${LITE_ROOT}/cxx/lib/libpaddle_api_light_bundled.a
CXX_LIBS = $(LITE_LIBS) ${OPENCV_LIBS} $(SYSTEM_LIBS)
LOCAL_DIRSRCS=$(wildcard src/*.cc)
LOCAL_SRCS=$(notdir $(LOCAL_DIRSRCS))
LOCAL_OBJS=$(patsubst %.cpp, %.o, $(patsubst %.cc, %.o, $(LOCAL_SRCS)))
JSON_OBJS = json_reader.o json_value.o json_writer.o
pp_shitu: $(LOCAL_OBJS) $(JSON_OBJS) fetch_opencv
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) $(LOCAL_OBJS) $(JSON_OBJS) -o pp_shitu $(CXX_LIBS) $(LDFLAGS)
fetch_opencv:
@ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR}
@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
(echo "fetch opencv libs" && \
wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz)
@ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \
tar -zxf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
fetch_json_code:
@ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR}
@ test -e ${THIRD_PARTY_DIR}/jsoncpp_code.tar.gz || \
(echo "fetch jsoncpp_code.tar.gz" && \
wget -P ${THIRD_PARTY_DIR} https://bj.bcebos.com/v1/paddledet/deploy/jsoncpp_code.tar.gz )
@ test -d ${THIRD_PARTY_DIR}/jsoncpp_code || \
tar -zxf ${THIRD_PARTY_DIR}/jsoncpp_code.tar.gz -C ${THIRD_PARTY_DIR}
LOCAL_INCLUDES = -I./ -Iinclude
OPENCV_INCLUDE = -I${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PLAT}/include
JSON_INCLUDE = -I${THIRD_PARTY_DIR}/jsoncpp_code/include
CXX_INCLUDES = ${LOCAL_INCLUDES} ${INCLUDES} ${OPENCV_INCLUDE} ${JSON_INCLUDE} -I$(LITE_ROOT)/cxx/include
$(LOCAL_OBJS): %.o: src/%.cc fetch_opencv fetch_json_code
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -c $< -o $@
$(JSON_OBJS): %.o: ${THIRD_PARTY_DIR}/jsoncpp_code/%.cpp fetch_json_code
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -c $< -o $@
.PHONY: clean fetch_opencv fetch_json_code
clean:
rm -rf $(LOCAL_OBJS) $(JSON_OBJS)
rm -f pp_shitu
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deploy/lite_shitu/generate_json_config.py 0 → 100644
浏览文件 @ d25d7446
import argparse
import json
import os
import yaml
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--yaml_path', type=str, default='../configs/inference_drink.yaml')
parser.add_argument(
'--img_dir',
type=str,
default=None,
help='The dir path for inference images')
parser.add_argument(
'--img_path',
type=str,
default=None,
help='The dir path for inference images')
parser.add_argument(
'--det_model_path',
type=str,
default='./det.nb',
help="The model path for mainbody detection")
parser.add_argument(
'--rec_model_path',
type=str,
default='./rec.nb',
help="The rec model path")
parser.add_argument(
'--rec_label_path',
type=str,
default='./label.txt',
help='The rec model label')
parser.add_argument(
'--arch',
type=str,
default='PicoDet',
help='The model structure for detection model')
parser.add_argument(
'--fpn-stride',
type=list,
default=[8, 16, 32, 64],
help="The fpn strid for detection model")
parser.add_argument(
'--keep_top_k',
type=int,
default=100,
help='The params for nms(postprocess for detection)')
parser.add_argument(
'--nms-name',
type=str,
default='MultiClassNMS',
help='The nms name for postprocess of detection model')
parser.add_argument(
'--nms_threshold',
type=float,
default=0.5,
help='The nms nms_threshold for detection postprocess')
parser.add_argument(
'--nms_top_k',
type=int,
default=1000,
help='The nms_top_k in postprocess of detection model')
parser.add_argument(
'--score_threshold',
type=float,
default=0.3,
help='The score_threshold for postprocess of detection')
args = parser.parse_args()
return args
def main():
args = parse_args()
config_yaml = yaml.safe_load(open(args.yaml_path))
config_json = {}
config_json["Global"] = {}
config_json["Global"][
"infer_imgs"] = args.img_path if args.img_path else config_yaml[
"Global"]["infer_imgs"]
if args.img_dir is not None:
config_json["Global"]["infer_imgs_dir"] = args.img_dir
config_json["Global"]["infer_imgs"] = None
else:
config_json["Global"][
"infer_imgs"] = args.img_path if args.img_path else config_yaml[
"Global"]["infer_imgs"]
config_json["Global"]["batch_size"] = config_yaml["Global"]["batch_size"]
config_json["Global"]["cpu_num_threads"] = min(
config_yaml["Global"]["cpu_num_threads"], 4)
config_json["Global"]["image_shape"] = config_yaml["Global"]["image_shape"]
config_json["Global"]["det_model_path"] = args.det_model_path
config_json["Global"]["rec_model_path"] = args.rec_model_path
config_json["Global"]["rec_label_path"] = args.rec_label_path
config_json["Global"]["label_list"] = config_yaml["Global"]["labe_list"]
config_json["Global"]["rec_nms_thresold"] = config_yaml["Global"][
"rec_nms_thresold"]
config_json["Global"]["max_det_results"] = config_yaml["Global"][
"max_det_results"]
config_json["Global"]["det_fpn_stride"] = args.fpn_stride
config_json["Global"]["det_arch"] = args.arch
config_json["Global"]["return_k"] = config_yaml["IndexProcess"]["return_k"]
# config_json["DetPreProcess"] = config_yaml["DetPreProcess"]
config_json["DetPreProcess"] = {}
config_json["DetPreProcess"]["transform_ops"] = []
for x in config_yaml["DetPreProcess"]["transform_ops"]:
k = list(x.keys())[0]
y = x[k]
y['type'] = k
config_json["DetPreProcess"]["transform_ops"].append(y)
config_json["DetPostProcess"] = {
"keep_top_k": args.keep_top_k,
"name": args.nms_name,
"nms_threshold": args.nms_threshold,
"nms_top_k": args.nms_top_k,
"score_threshold": args.score_threshold
}
# config_json["RecPreProcess"] = config_yaml["RecPreProcess"]
config_json["RecPreProcess"] = {}
config_json["RecPreProcess"]["transform_ops"] = []
for x in config_yaml["RecPreProcess"]["transform_ops"]:
k = list(x.keys())[0]
y = x[k]
if y is not None:
y["type"] = k
config_json["RecPreProcess"]["transform_ops"].append(y)
with open('shitu_config.json', 'w') as fd:
json.dump(config_json, fd, indent=4)
if __name__ == '__main__':
main()
deploy/lite_shitu/include/config_parser.h 0 → 100644
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deploy/lite_shitu/include/object_detector.h 0 → 100644
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deploy/lite_shitu/include/picodet_postprocess.h 0 → 100644
浏览文件 @ d25d7446
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
#include <string>
#include <vector>
#include <memory>
#include <utility>
#include <ctime>
#include <numeric>
#include "include/utils.h"
namespace PPShiTu {
void PicoDetPostProcess(std::vector<PPShiTu::ObjectResult>* results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold = 0.3,
float nms_threshold = 0.5,
int num_class = 80,
int reg_max = 7);
} // namespace PPShiTu
deploy/lite_shitu/include/preprocess_op.h 0 → 100644
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deploy/lite_shitu/include/recognition.h 0 → 100644
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deploy/lite_shitu/include/utils.h 0 → 100644
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deploy/lite_shitu/src/config_parser.cc 0 → 100644
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deploy/lite_shitu/src/main.cc 0 → 100644
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deploy/lite_shitu/src/object_detector.cc 0 → 100644
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deploy/lite_shitu/src/picodet_postprocess.cc 0 → 100644
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deploy/lite_shitu/src/preprocess_op.cc 0 → 100644
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deploy/lite_shitu/src/recognition.cc 0 → 100644
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deploy/lite_shitu/src/utils.cc 0 → 100644
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deploy/paddle2onnx/readme.md 0 → 100644
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deploy/paddleserving/readme.md 已删除 100644 → 0
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deploy/paddleserving/readme.md 0 → 120000
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deploy/paddleserving/recognition/run_cpp_serving.sh 0 → 100644
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deploy/paddleserving/run_cpp_serving.sh 0 → 100644
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deploy/paddleserving/test_cpp_serving_cient.py 0 → 100644
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deploy/python/predict_cls.py
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deploy/python/predict_det.py
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deploy/python/predict_rec.py
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