Merge remote-tracking branch 'origin/dygraph' into dygraph
Leif
commit
84ce34bd3f
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@ -1,5 +1,5 @@
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# PaddleOCR DB/EAST 算法训练benchmark测试
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# PaddleOCR DB/EAST/PSE 算法训练benchmark测试
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PaddleOCR/benchmark目录下的文件用于获取并分析训练日志。
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训练采用icdar2015数据集，包括1000张训练图像和500张测试图像。模型配置采用resnet18_vd作为backbone，分别训练batch_size=8和batch_size=16的情况。
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@ -18,7 +18,7 @@ run_det.sh 执行方式如下:
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```
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# cd PaddleOCR/
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bash benchmark/run_det.sh
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bash benchmark/run_det.sh
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```
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以DB为例，将得到四个日志文件，如下：
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@ -28,7 +28,3 @@ det_res18_db_v2.0_sp_bs8_fp32_1
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det_res18_db_v2.0_mp_bs16_fp32_1
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det_res18_db_v2.0_mp_bs8_fp32_1
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```
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@ -6,7 +6,7 @@ function _set_params(){
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run_mode=${1:-"sp"} # 单卡sp|多卡mp
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batch_size=${2:-"64"}
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fp_item=${3:-"fp32"} # fp32|fp16
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max_iter=${4:-"500"} # 可选，如果需要修改代码提前中断
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max_iter=${4:-"10"} # 可选，如果需要修改代码提前中断
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model_name=${5:-"model_name"}
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run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
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@ -20,7 +20,7 @@ function _train(){
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echo "Train on ${num_gpu_devices} GPUs"
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echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
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train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} "
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train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} Global.eval_batch_step=[0,20000] Global.print_batch_step=2"
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case ${run_mode} in
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sp)
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train_cmd="python3.7 tools/train.py "${train_cmd}""
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@ -39,18 +39,24 @@ function _train(){
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echo -e "${model_name}, SUCCESS"
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export job_fail_flag=0
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fi
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kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'`
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if [ $run_mode = "mp" -a -d mylog ]; then
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rm ${log_file}
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cp mylog/workerlog.0 ${log_file}
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fi
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}
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# run log analysis
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analysis_cmd="python3.7 benchmark/analysis.py --filename ${log_file} --mission_name ${model_name} --run_mode ${mode} --direction_id 0 --keyword 'ips:' --base_batch_size ${batch_szie} --skip_steps 1 --gpu_num ${num_gpu_devices} --index 1 --model_mode=-1 --ips_unit=samples/sec"
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function _analysis_log(){
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analysis_cmd="python3.7 benchmark/analysis.py --filename ${log_file} --mission_name ${model_name} --run_mode ${run_mode} --direction_id 0 --keyword 'ips:' --base_batch_size ${batch_size} --skip_steps 1 --gpu_num ${num_gpu_devices} --index 1 --model_mode=-1 --ips_unit=samples/sec"
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eval $analysis_cmd
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}
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function _kill_process(){
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kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'`
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}
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_set_params $@
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_train
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_analysis_log
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_kill_process
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@ -3,11 +3,11 @@
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# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
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python3.7 -m pip install -r requirements.txt
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# 2 拷贝该模型需要数据、预训练模型
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wget -c -p ./tain_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../
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wget -c -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
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wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../
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wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
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# 3 批量运行（如不方便批量，1，2需放到单个模型中）
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model_mode_list=(det_res18_db_v2.0 det_r50_vd_east)
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model_mode_list=(det_res18_db_v2.0 det_r50_vd_east det_r50_vd_pse)
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fp_item_list=(fp32)
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bs_list=(8 16)
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for model_mode in ${model_mode_list[@]}; do
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@ -15,11 +15,11 @@ for model_mode in ${model_mode_list[@]}; do
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for bs_item in ${bs_list[@]}; do
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echo "index is speed, 1gpus, begin, ${model_name}"
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run_mode=sp
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CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
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CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} # (5min)
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sleep 60
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echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
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run_mode=mp
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode}
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sleep 60
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done
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done
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@ -17,7 +17,7 @@ Global:
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character_dict_path: ppocr/utils/EN_symbol_dict.txt
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max_text_length: 25
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infer_mode: False
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use_space_char: True
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use_space_char: False
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save_res_path: ./output/rec/predicts_nrtr.txt
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Optimizer:
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|
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@ -12,12 +12,14 @@
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|||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
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||||
#include "paddle_api.h" // NOLINT
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#include <chrono>
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#include "paddle_api.h" // NOLINT
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#include "paddle_place.h"
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#include "cls_process.h"
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#include "crnn_process.h"
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#include "db_post_process.h"
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#include "AutoLog/auto_log/lite_autolog.h"
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using namespace paddle::lite_api; // NOLINT
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using namespace std;
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@ -27,7 +29,7 @@ void NeonMeanScale(const float *din, float *dout, int size,
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const std::vector<float> mean,
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const std::vector<float> scale) {
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if (mean.size() != 3 || scale.size() != 3) {
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std::cerr << "[ERROR] mean or scale size must equal to 3\n";
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std::cerr << "[ERROR] mean or scale size must equal to 3" << std::endl;
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exit(1);
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}
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float32x4_t vmean0 = vdupq_n_f32(mean[0]);
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@ -159,7 +161,8 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
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std::vector<float> &rec_text_score,
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std::vector<std::string> charactor_dict,
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std::shared_ptr<PaddlePredictor> predictor_cls,
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int use_direction_classify) {
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int use_direction_classify,
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std::vector<double> *times) {
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std::vector<float> mean = {0.5f, 0.5f, 0.5f};
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std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
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@ -226,14 +229,15 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
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|||
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std::vector<std::vector<std::vector<int>>>
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RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
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std::map<std::string, double> Config) {
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std::map<std::string, double> Config, std::vector<double> *times) {
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// Read img
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int max_side_len = int(Config["max_side_len"]);
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int det_db_use_dilate = int(Config["det_db_use_dilate"]);
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cv::Mat srcimg;
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img.copyTo(srcimg);
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auto preprocess_start = std::chrono::steady_clock::now();
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std::vector<float> ratio_hw;
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img = DetResizeImg(img, max_side_len, ratio_hw);
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cv::Mat img_fp;
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@ -248,8 +252,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
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std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
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const float *dimg = reinterpret_cast<const float *>(img_fp.data);
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NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
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auto preprocess_end = std::chrono::steady_clock::now();
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// Run predictor
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auto inference_start = std::chrono::steady_clock::now();
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predictor->Run();
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|
||||
// Get output and post process
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|
|
@ -257,8 +263,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
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std::move(predictor->GetOutput(0)));
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auto *outptr = output_tensor->data<float>();
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auto shape_out = output_tensor->shape();
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auto inference_end = std::chrono::steady_clock::now();
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// Save output
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auto postprocess_start = std::chrono::steady_clock::now();
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float pred[shape_out[2] * shape_out[3]];
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unsigned char cbuf[shape_out[2] * shape_out[3]];
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@ -287,14 +295,35 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
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|||
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||||
std::vector<std::vector<std::vector<int>>> filter_boxes =
|
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FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg);
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auto postprocess_end = std::chrono::steady_clock::now();
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||||
|
||||
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
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times->push_back(double(preprocess_diff.count() * 1000));
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std::chrono::duration<float> inference_diff = inference_end - inference_start;
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times->push_back(double(inference_diff.count() * 1000));
|
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std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
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times->push_back(double(postprocess_diff.count() * 1000));
|
||||
|
||||
return filter_boxes;
|
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}
|
||||
|
||||
std::shared_ptr<PaddlePredictor> loadModel(std::string model_file) {
|
||||
std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, std::string power_mode, int num_threads) {
|
||||
MobileConfig config;
|
||||
config.set_model_from_file(model_file);
|
||||
|
||||
if (power_mode == "LITE_POWER_HIGH"){
|
||||
config.set_power_mode(LITE_POWER_HIGH);
|
||||
} else {
|
||||
if (power_mode == "LITE_POWER_LOW") {
|
||||
config.set_power_mode(LITE_POWER_HIGH);
|
||||
} else {
|
||||
std::cerr << "Only support LITE_POWER_HIGH or LITE_POWER_HIGH." << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
config.set_threads(num_threads);
|
||||
|
||||
std::shared_ptr<PaddlePredictor> predictor =
|
||||
CreatePaddlePredictor<MobileConfig>(config);
|
||||
return predictor;
|
||||
|
|
@ -354,60 +383,255 @@ std::map<std::string, double> LoadConfigTxt(std::string config_path) {
|
|||
return dict;
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
if (argc < 5) {
|
||||
std::cerr << "[ERROR] usage: " << argv[0]
|
||||
<< " det_model_file cls_model_file rec_model_file image_path "
|
||||
"charactor_dict\n";
|
||||
void check_params(int argc, char **argv) {
|
||||
if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) {
|
||||
std::cerr << "Please choose one mode of [det, rec, system] !" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
std::string det_model_file = argv[1];
|
||||
std::string rec_model_file = argv[2];
|
||||
std::string cls_model_file = argv[3];
|
||||
std::string img_path = argv[4];
|
||||
std::string dict_path = argv[5];
|
||||
if (strcmp(argv[1], "det") == 0) {
|
||||
if (argc < 9){
|
||||
std::cerr << "[ERROR] usage:" << argv[0]
|
||||
<< " det det_model num_threads batchsize power_mode img_dir det_config lite_benchmark_value" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
if (strcmp(argv[1], "rec") == 0) {
|
||||
if (argc < 9){
|
||||
std::cerr << "[ERROR] usage:" << argv[0]
|
||||
<< " rec rec_model num_threads batchsize power_mode img_dir key_txt lite_benchmark_value" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
if (strcmp(argv[1], "system") == 0) {
|
||||
if (argc < 12){
|
||||
std::cerr << "[ERROR] usage:" << argv[0]
|
||||
<< " system det_model rec_model clas_model num_threads batchsize power_mode img_dir det_config key_txt lite_benchmark_value" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void system(char **argv){
|
||||
std::string det_model_file = argv[2];
|
||||
std::string rec_model_file = argv[3];
|
||||
std::string cls_model_file = argv[4];
|
||||
std::string precision = argv[5];
|
||||
std::string num_threads = argv[6];
|
||||
std::string batchsize = argv[7];
|
||||
std::string power_mode = argv[8];
|
||||
std::string img_dir = argv[9];
|
||||
std::string det_config_path = argv[10];
|
||||
std::string dict_path = argv[11];
|
||||
|
||||
if (strcmp(argv[5], "FP32") != 0 && strcmp(argv[5], "INT8") != 0) {
|
||||
std::cerr << "Only support FP32 or INT8." << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
std::vector<cv::String> cv_all_img_names;
|
||||
cv::glob(img_dir, cv_all_img_names);
|
||||
|
||||
//// load config from txt file
|
||||
auto Config = LoadConfigTxt("./config.txt");
|
||||
auto Config = LoadConfigTxt(det_config_path);
|
||||
int use_direction_classify = int(Config["use_direction_classify"]);
|
||||
|
||||
auto start = std::chrono::system_clock::now();
|
||||
|
||||
auto det_predictor = loadModel(det_model_file);
|
||||
auto rec_predictor = loadModel(rec_model_file);
|
||||
auto cls_predictor = loadModel(cls_model_file);
|
||||
|
||||
auto charactor_dict = ReadDict(dict_path);
|
||||
charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
|
||||
charactor_dict.push_back(" ");
|
||||
|
||||
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
|
||||
auto boxes = RunDetModel(det_predictor, srcimg, Config);
|
||||
auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads));
|
||||
auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads));
|
||||
auto cls_predictor = loadModel(cls_model_file, power_mode, std::stoi(num_threads));
|
||||
|
||||
std::vector<std::string> rec_text;
|
||||
std::vector<float> rec_text_score;
|
||||
for (int i = 0; i < cv_all_img_names.size(); ++i) {
|
||||
std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
|
||||
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
|
||||
|
||||
RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
|
||||
charactor_dict, cls_predictor, use_direction_classify);
|
||||
if (!srcimg.data) {
|
||||
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
auto end = std::chrono::system_clock::now();
|
||||
auto duration =
|
||||
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
|
||||
std::vector<double> det_times;
|
||||
auto boxes = RunDetModel(det_predictor, srcimg, Config, &det_times);
|
||||
|
||||
std::vector<std::string> rec_text;
|
||||
std::vector<float> rec_text_score;
|
||||
|
||||
std::vector<double> rec_times;
|
||||
RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
|
||||
charactor_dict, cls_predictor, use_direction_classify, &rec_times);
|
||||
|
||||
//// visualization
|
||||
auto img_vis = Visualization(srcimg, boxes);
|
||||
|
||||
//// print recognized text
|
||||
for (int i = 0; i < rec_text.size(); i++) {
|
||||
std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//// visualization
|
||||
auto img_vis = Visualization(srcimg, boxes);
|
||||
void det(int argc, char **argv) {
|
||||
std::string det_model_file = argv[2];
|
||||
std::string precision = argv[3];
|
||||
std::string num_threads = argv[4];
|
||||
std::string batchsize = argv[5];
|
||||
std::string power_mode = argv[6];
|
||||
std::string img_dir = argv[7];
|
||||
std::string det_config_path = argv[8];
|
||||
|
||||
//// print recognized text
|
||||
for (int i = 0; i < rec_text.size(); i++) {
|
||||
std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
|
||||
<< std::endl;
|
||||
if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) {
|
||||
std::cerr << "Only support FP32 or INT8." << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
std::cout << "花费了"
|
||||
<< double(duration.count()) *
|
||||
std::chrono::microseconds::period::num /
|
||||
std::chrono::microseconds::period::den
|
||||
<< "秒" << std::endl;
|
||||
std::vector<cv::String> cv_all_img_names;
|
||||
cv::glob(img_dir, cv_all_img_names);
|
||||
|
||||
//// load config from txt file
|
||||
auto Config = LoadConfigTxt(det_config_path);
|
||||
|
||||
auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads));
|
||||
|
||||
std::vector<double> time_info = {0, 0, 0};
|
||||
for (int i = 0; i < cv_all_img_names.size(); ++i) {
|
||||
std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
|
||||
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
|
||||
|
||||
if (!srcimg.data) {
|
||||
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
std::vector<double> times;
|
||||
auto boxes = RunDetModel(det_predictor, srcimg, Config, ×);
|
||||
|
||||
//// visualization
|
||||
auto img_vis = Visualization(srcimg, boxes);
|
||||
std::cout << boxes.size() << " bboxes have detected:" << std::endl;
|
||||
|
||||
// for (int i=0; i<boxes.size(); i++){
|
||||
// std::cout << "The " << i << " box:" << std::endl;
|
||||
// for (int j=0; j<4; j++){
|
||||
// for (int k=0; k<2; k++){
|
||||
// std::cout << boxes[i][j][k] << "\t";
|
||||
// }
|
||||
// }
|
||||
// std::cout << std::endl;
|
||||
// }
|
||||
time_info[0] += times[0];
|
||||
time_info[1] += times[1];
|
||||
time_info[2] += times[2];
|
||||
}
|
||||
|
||||
if (strcmp(argv[9], "True") == 0) {
|
||||
AutoLogger autolog(det_model_file,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
std::stoi(num_threads),
|
||||
std::stoi(batchsize),
|
||||
"dynamic",
|
||||
precision,
|
||||
power_mode,
|
||||
time_info,
|
||||
cv_all_img_names.size());
|
||||
autolog.report();
|
||||
}
|
||||
}
|
||||
|
||||
void rec(int argc, char **argv) {
|
||||
std::string rec_model_file = argv[2];
|
||||
std::string precision = argv[3];
|
||||
std::string num_threads = argv[4];
|
||||
std::string batchsize = argv[5];
|
||||
std::string power_mode = argv[6];
|
||||
std::string img_dir = argv[7];
|
||||
std::string dict_path = argv[8];
|
||||
|
||||
if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) {
|
||||
std::cerr << "Only support FP32 or INT8." << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
std::vector<cv::String> cv_all_img_names;
|
||||
cv::glob(img_dir, cv_all_img_names);
|
||||
|
||||
auto charactor_dict = ReadDict(dict_path);
|
||||
charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
|
||||
charactor_dict.push_back(" ");
|
||||
|
||||
auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads));
|
||||
|
||||
std::shared_ptr<PaddlePredictor> cls_predictor;
|
||||
|
||||
std::vector<double> time_info = {0, 0, 0};
|
||||
for (int i = 0; i < cv_all_img_names.size(); ++i) {
|
||||
std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
|
||||
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
|
||||
|
||||
if (!srcimg.data) {
|
||||
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
int width = srcimg.cols;
|
||||
int height = srcimg.rows;
|
||||
std::vector<int> upper_left = {0, 0};
|
||||
std::vector<int> upper_right = {width, 0};
|
||||
std::vector<int> lower_right = {width, height};
|
||||
std::vector<int> lower_left = {0, height};
|
||||
std::vector<std::vector<int>> box = {upper_left, upper_right, lower_right, lower_left};
|
||||
std::vector<std::vector<std::vector<int>>> boxes = {box};
|
||||
|
||||
std::vector<std::string> rec_text;
|
||||
std::vector<float> rec_text_score;
|
||||
std::vector<double> times;
|
||||
RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
|
||||
charactor_dict, cls_predictor, 0, ×);
|
||||
|
||||
//// print recognized text
|
||||
for (int i = 0; i < rec_text.size(); i++) {
|
||||
std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
// TODO: support autolog
|
||||
if (strcmp(argv[9], "True") == 0) {
|
||||
AutoLogger autolog(rec_model_file,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
std::stoi(num_threads),
|
||||
std::stoi(batchsize),
|
||||
"dynamic",
|
||||
precision,
|
||||
power_mode,
|
||||
time_info,
|
||||
cv_all_img_names.size());
|
||||
autolog.report();
|
||||
}
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
check_params(argc, argv);
|
||||
std::cout << "mode: " << argv[1] << endl;
|
||||
|
||||
if (strcmp(argv[1], "system") == 0) {
|
||||
system(argv);
|
||||
}
|
||||
|
||||
if (strcmp(argv[1], "det") == 0) {
|
||||
det(argc, argv);
|
||||
}
|
||||
|
||||
if (strcmp(argv[1], "rec") == 0) {
|
||||
rec(argc, argv);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ C-CTC Loss是CTC Loss + Center Loss的简称。 其中Center Loss出自论文 <
|
|||
|
||||
以配置文件`configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml`为例， center提取命令如下所示:
|
||||
```
|
||||
python tools/export_center.py -c configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml -o Global.pretrained_model: "./output/rec_mobile_pp-OCRv2/best_accuracy"
|
||||
python tools/export_center.py -c configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml -o Global.pretrained_model="./output/rec_mobile_pp-OCRv2/best_accuracy"
|
||||
```
|
||||
运行完后，会在PaddleOCR主目录下生成`train_center.pkl`.
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ class NRTRLoss(nn.Layer):
|
|||
log_prb = F.log_softmax(pred, axis=1)
|
||||
non_pad_mask = paddle.not_equal(
|
||||
tgt, paddle.zeros(
|
||||
tgt.shape, dtype='int64'))
|
||||
tgt.shape, dtype=tgt.dtype))
|
||||
loss = -(one_hot * log_prb).sum(axis=1)
|
||||
loss = loss.masked_select(non_pad_mask).mean()
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -29,10 +29,7 @@ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, Di
|
|||
TableLabelDecode, NRTRLabelDecode, SARLabelDecode , SEEDLabelDecode
|
||||
from .cls_postprocess import ClsPostProcess
|
||||
from .pg_postprocess import PGPostProcess
|
||||
|
||||
if platform.system() != "Windows":
|
||||
# pse is not support in Windows
|
||||
from .pse_postprocess import PSEPostProcess
|
||||
from .pse_postprocess import PSEPostProcess
|
||||
|
||||
|
||||
def build_post_process(config, global_config=None):
|
||||
|
|
|
|||
|
|
@ -17,7 +17,12 @@ import subprocess
|
|||
|
||||
python_path = sys.executable
|
||||
|
||||
if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;{} setup.py build_ext --inplace;cd -'.format(python_path), shell=True) != 0:
|
||||
raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__))))
|
||||
ori_path = os.getcwd()
|
||||
os.chdir('ppocr/postprocess/pse_postprocess/pse')
|
||||
if subprocess.call(
|
||||
'{} setup.py build_ext --inplace'.format(python_path), shell=True) != 0:
|
||||
raise RuntimeError('Cannot compile pse: {}'.format(
|
||||
os.path.dirname(os.path.realpath(__file__))))
|
||||
os.chdir(ori_path)
|
||||
|
||||
from .pse import pse
|
||||
from .pse import pse
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ def run_shell_command(cmd):
|
|||
else:
|
||||
return None
|
||||
|
||||
|
||||
def parser_results_from_log_by_name(log_path, names_list):
|
||||
if not os.path.exists(log_path):
|
||||
raise ValueError("The log file {} does not exists!".format(log_path))
|
||||
|
|
@ -52,6 +53,7 @@ def parser_results_from_log_by_name(log_path, names_list):
|
|||
parser_results[name] = result
|
||||
return parser_results
|
||||
|
||||
|
||||
def load_gt_from_file(gt_file):
|
||||
if not os.path.exists(gt_file):
|
||||
raise ValueError("The log file {} does not exists!".format(gt_file))
|
||||
|
|
@ -1,21 +1,21 @@
|
|||
===========================train_params===========================
|
||||
model_name:ocr_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:null
|
||||
Global.epoch_num:lite_train_infer=1|whole_train_infer=300
|
||||
gpu_list:0|0,1|10.21.226.181,10.21.226.133;0,1
|
||||
Global.use_gpu:True|True|True
|
||||
Global.auto_cast:fp32|amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train|pact_train|fpgm_train
|
||||
norm_train:tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
pact_train:deploy/slim/quantization/quant.py -c tests/configs/det_mv3_db.yml -o
|
||||
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
|
||||
norm_train:tools/train.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/det_mv3_db.yml -o
|
||||
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
|
|
@ -27,13 +27,13 @@ null:null
|
|||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c tests/configs/det_mv3_db.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_mv3_db.yml -o
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py -c tests/configs/det_mv3_db.yml -o
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_db.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/det_mv3_db.yml -o
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/det_mv3_db.yml -o
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
inference_dir:null
|
||||
train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
|
||||
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
|
||||
infer_quant:False
|
||||
|
|
@ -98,3 +98,13 @@ null:null
|
|||
--benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================lite_params===========================
|
||||
inference:./ocr_db_crnn det
|
||||
infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb
|
||||
--cpu_threads:1|4
|
||||
--batch_size:1
|
||||
--power_mode:LITE_POWER_HIGH|LITE_POWER_LOW
|
||||
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg
|
||||
--config_dir:./config.txt
|
||||
--rec_dict_dir:./ppocr_keys_v1.txt
|
||||
--benchmark:True
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPOCRv2_ocr_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:fp32
|
||||
Global.epoch_num:lite_train_infer=1|whole_train_infer=500
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train|pact_train
|
||||
norm_train:tools/train.py -c configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml -o
|
||||
pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml -o
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml -o
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:Student
|
||||
infer_model:./inference/ch_PP-OCRv2_det_infer/
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
|
Before Width: | Height: | Size: 49 KiB After Width: | Height: | Size: 49 KiB |
|
Before Width: | Height: | Size: 63 KiB After Width: | Height: | Size: 63 KiB |
|
Before Width: | Height: | Size: 33 KiB After Width: | Height: | Size: 33 KiB |
|
Before Width: | Height: | Size: 64 KiB After Width: | Height: | Size: 64 KiB |
|
Before Width: | Height: | Size: 138 KiB After Width: | Height: | Size: 138 KiB |
|
|
@ -0,0 +1,121 @@
|
|||
## 1. 环境准备
|
||||
|
||||
本教程适用于PTDN目录下基础功能测试的运行环境搭建。
|
||||
|
||||
推荐环境：
|
||||
- CUDA 10.1/10.2
|
||||
- CUDNN 7.6/cudnn8.1
|
||||
- TensorRT 6.1.0.5 / 7.1 / 7.2
|
||||
|
||||
环境配置可以选择docker镜像安装，或者在本地环境Python搭建环境。推荐使用docker镜像安装，避免不必要的环境配置。
|
||||
|
||||
## 2. Docker 镜像安装
|
||||
|
||||
推荐docker镜像安装，按照如下命令创建镜像，当前目录映射到镜像中的`/paddle`目录下
|
||||
```
|
||||
nvidia-docker run --name paddle -it -v $PWD:/paddle paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
|
||||
cd /paddle
|
||||
|
||||
# 安装带TRT的paddle
|
||||
pip3.7 install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl
|
||||
```
|
||||
|
||||
## 3 Python 环境构建
|
||||
|
||||
非docker环境下，环境配置比较灵活，推荐环境组合配置：
|
||||
- CUDA10.1 + CUDNN7.6 + TensorRT 6
|
||||
- CUDA10.2 + CUDNN8.1 + TensorRT 7
|
||||
- CUDA11.1 + CUDNN8.1 + TensorRT 7
|
||||
|
||||
下面以 CUDA10.2 + CUDNN8.1 + TensorRT 7 配置为例，介绍环境配置的流程。
|
||||
|
||||
### 3.1 安装CUDNN
|
||||
|
||||
如果当前环境满足CUDNN版本的要求，可以跳过此步骤。
|
||||
|
||||
以CUDNN8.1 安装安装为例，安装步骤如下，首先下载CUDNN，从[Nvidia官网](https://developer.nvidia.com/rdp/cudnn-archive)下载CUDNN8.1版本，下载符合当前系统版本的三个deb文件，分别是：
|
||||
- cuDNN Runtime Library ，如：libcudnn8_8.1.0.77-1+cuda10.2_amd64.deb
|
||||
- cuDNN Developer Library ，如：libcudnn8-dev_8.1.0.77-1+cuda10.2_amd64.deb
|
||||
- cuDNN Code Samples，如：libcudnn8-samples_8.1.0.77-1+cuda10.2_amd64.deb
|
||||
|
||||
deb安装可以参考[官方文档](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb)，安装方式如下
|
||||
```
|
||||
# x.x.x表示下载的版本号
|
||||
# $HOME为工作目录
|
||||
sudo dpkg -i libcudnn8_x.x.x-1+cudax.x_arm64.deb
|
||||
sudo dpkg -i libcudnn8-dev_8.x.x.x-1+cudax.x_arm64.deb
|
||||
sudo dpkg -i libcudnn8-samples_8.x.x.x-1+cudax.x_arm64.deb
|
||||
|
||||
# 验证是否正确安装
|
||||
cp -r /usr/src/cudnn_samples_v8/ $HOME
|
||||
cd $HOME/cudnn_samples_v8/mnistCUDNN
|
||||
|
||||
# 编译
|
||||
make clean && make
|
||||
./mnistCUDNN
|
||||
```
|
||||
如果运行mnistCUDNN完后提示运行成功，则表示安装成功。如果运行后出现freeimage相关的报错，需要按照提示安装freeimage库:
|
||||
```
|
||||
sudo apt-get install libfreeimage-dev
|
||||
sudo apt-get install libfreeimage
|
||||
```
|
||||
|
||||
### 3.2 安装TensorRT
|
||||
|
||||
首先，从[Nvidia官网TensorRT板块](https://developer.nvidia.com/tensorrt-getting-started)下载TensorRT，这里选择7.1.3.4版本的TensorRT，注意选择适合自己系统版本和CUDA版本的TensorRT，另外建议下载TAR package的安装包。
|
||||
|
||||
以Ubuntu16.04+CUDA10.2为例，下载并解压后可以参考[官方文档](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-713/install-guide/index.html#installing-tar)的安装步骤，按照如下步骤安装:
|
||||
```
|
||||
# 以下安装命令中 '${version}' 为下载的TensorRT版本，如7.1.3.4
|
||||
# 设置环境变量，<TensorRT-${version}/lib> 为解压后的TensorRT的lib目录
|
||||
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>
|
||||
|
||||
# 安装TensorRT
|
||||
cd TensorRT-${version}/python
|
||||
pip3.7 install tensorrt-*-cp3x-none-linux_x86_64.whl
|
||||
|
||||
# 安装graphsurgeon
|
||||
cd TensorRT-${version}/graphsurgeon
|
||||
```
|
||||
|
||||
|
||||
### 3.3 安装PaddlePaddle
|
||||
|
||||
下载支持TensorRT版本的Paddle安装包，注意安装包的TensorRT版本需要与本地TensorRT一致，下载[链接](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#python)
|
||||
选择下载 linux-cuda10.2-trt7-gcc8.2 Python3.7版本的Paddle：
|
||||
```
|
||||
# 从下载链接中可以看到是paddle2.1.1-cuda10.2-cudnn8.1版本
|
||||
wget https://paddle-wheel.bj.bcebos.com/with-trt/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddlepaddle_gpu-2.1.1-cp37-cp37m-linux_x86_64.whl
|
||||
pip3.7 install -U paddlepaddle_gpu-2.1.1-cp37-cp37m-linux_x86_64.whl
|
||||
```
|
||||
|
||||
## 4. 安装PaddleOCR依赖
|
||||
```
|
||||
# 安装AutoLog
|
||||
git clone https://github.com/LDOUBLEV/AutoLog
|
||||
cd AutoLog
|
||||
pip3.7 install -r requirements.txt
|
||||
python3.7 setup.py bdist_wheel
|
||||
pip3.7 install ./dist/auto_log-1.0.0-py3-none-any.whl
|
||||
|
||||
# 下载OCR代码
|
||||
cd ../
|
||||
git clone https://github.com/PaddlePaddle/PaddleOCR
|
||||
|
||||
```
|
||||
|
||||
安装PaddleOCR依赖：
|
||||
```
|
||||
cd PaddleOCR
|
||||
pip3.7 install -r requirements.txt
|
||||
```
|
||||
|
||||
## FAQ :
|
||||
Q. You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found. Ignore this if TensorRT is not needed.
|
||||
|
||||
A. 问题一般是当前安装paddle版本带TRT，但是本地环境找不到TensorRT的预测库，需要下载TensorRT库，解压后设置环境变量LD_LIBRARY_PATH;
|
||||
如：
|
||||
```
|
||||
export LD_LIBRARY_PATH=/usr/local/python3.7.0/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/paddle/package/TensorRT-6.0.1.5/lib
|
||||
```
|
||||
或者问题是下载的TensorRT版本和当前paddle中编译的TRT版本不匹配，需要下载版本相符的TensorRT重新安装。
|
||||
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|
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|
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|
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|
After Width: | Height: | Size: 74 KiB |
|
|
@ -0,0 +1,128 @@
|
|||
# 安卓手机通过Termux连接电脑
|
||||
|
||||
由于通过adb方式连接手机后，很多linux命令无法运行，自动化测试受阻，所以此处特此介绍另外一种通过Termux的连接方式，不仅可以运行大部分linux命令，方便开发者在手机上在线调试，甚至还可以多实现台机器同时连接手机。Termux不是真实的Linux环境，但是Termux可以安装真实的Linux，而且不会损失性能，与此同时，Termux不需要root。在配置Termux之前，请确保电脑已经安装adb工具，安装方式请参考[Lite端部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md) 。在运行以下命令后确保可以显示安卓设备信息。
|
||||
|
||||
```
|
||||
adb devices
|
||||
```
|
||||
连接成功信息提示：
|
||||
```
|
||||
List of devices attached
|
||||
744be294 device
|
||||
```
|
||||
|
||||
## 1.安卓手机安装termux app
|
||||
|
||||
### 1.1 下载termux apk文件
|
||||
|
||||
由于目前该app目前各大商城暂无，所以可以直接下载如下apk文件。
|
||||
|
||||
打开电脑终端，执行以下命令：
|
||||
|
||||
```
|
||||
wget http://10.12.121.133:8911/cuicheng01/fullchain/termux-v1.0.3.apk
|
||||
```
|
||||
|
||||
### 1.2 安装termux到手机上
|
||||
|
||||
在手机端的开发者模式下，允许USB调试，允许USB安装。在电脑终端，执行如下命令，将termux app安装到手机上：
|
||||
|
||||
```
|
||||
adb install termux-v1.0.3.apk
|
||||
```
|
||||
|
||||
此处需要手机端确认安装，点击确认。
|
||||
|
||||
### 1.3 验证是否安装成功
|
||||
|
||||
打开手机，检验termux是否安装成功，如果没有，重新执行1.2，如果有相应的app，点击进入，会有如下显示。
|
||||
|
||||
<img src="termux.jpg" width="300" height = "300">
|
||||
|
||||
接下来的配置环境需要在手机上此终端运行相关命令。
|
||||
|
||||
## 2.手机端配置termux
|
||||
|
||||
首先将手机联网，最好可以连接外网，部分的配置需要外网。打开Termux终端，执行以下命令安装基础件`proot`，并使用`termux-chroot`命令可以模拟 root 环境与标准的 Linux 目录结构。
|
||||
|
||||
```
|
||||
pkg i -y proot
|
||||
termux-chroot
|
||||
```
|
||||
|
||||
Termux 默认只能访问自身内部的数据，如果要访问手机中其它的数据，输入下面的命令后，手机弹出对请求权限的窗口，允许即可（方便对部分运行出的结果在手机端可视化）。
|
||||
|
||||
```
|
||||
termux-setup-storage
|
||||
```
|
||||
|
||||
### 2.1 配置SSH
|
||||
|
||||
作为 Linux 终端或者服务器，必须有SSH。不管你是 SSH 连接到 Termux还是使用Termux去连其它主机，都需要先安装openssh。如果安装失败，请重复执行命令。
|
||||
|
||||
```
|
||||
pkg i -y openssh
|
||||
```
|
||||
|
||||
启动 SSH 服务端，默认端口号为8022
|
||||
|
||||
```
|
||||
sshd
|
||||
```
|
||||
|
||||
|
||||
### 2.2 电脑通过SSH方式连接手机
|
||||
|
||||
1.保证手机和电脑处于同一局域网下
|
||||
手机端分别输入以下命令获得ip地址和当前用户：
|
||||
|
||||
```
|
||||
# 获取ip地址
|
||||
ifconfig
|
||||
|
||||
# 获取当前用户
|
||||
whoami
|
||||
```
|
||||
|
||||
如获取到的ip地址和当前用户分别是`172.24.162.117`和`u0_a374`。
|
||||
|
||||
2.电脑端通过SSH连接手机
|
||||
|
||||
```
|
||||
#默认端口号为8022
|
||||
ssh u0_a374@172.24.162.117 -p 8022
|
||||
```
|
||||
|
||||
3.运行ls命令后，会有如下显示：
|
||||
|
||||
```
|
||||
ls
|
||||
```
|
||||
|
||||
<img src="ssh_termux_ls.png" width="800">
|
||||
|
||||
|
||||
### 2.3 通过scp传输数据
|
||||
|
||||
1.在当前目录上新建test目录
|
||||
|
||||
```
|
||||
mkdir test
|
||||
```
|
||||
|
||||
2.测试scp功能
|
||||
|
||||
将电脑中的某个文件拷贝到手机上：
|
||||
```
|
||||
scp -P 8022 test.txt u0_a374@172.24.162.117:/home/storage/test
|
||||
```
|
||||
|
||||
3.手机端查看
|
||||
|
||||
打开手机终端，在`/home/storage/test`下查看是否存在`test.txt`
|
||||
|
||||
|
||||
## 3. 更多教程
|
||||
|
||||
本教程可以完成Termux基本配置，更多关于Termux的用法，请参考：[Termux高级终端安装使用配置教程](https://www.sqlsec.com/2018/05/termux.html)。
|
||||
|
||||
|
Before Width: | Height: | Size: 224 KiB After Width: | Height: | Size: 224 KiB |
|
|
@ -6,7 +6,7 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于
|
|||
|
||||
基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的C++预测功能汇总如下：
|
||||
|
||||
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
|
||||
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
|
||||
| ---- | ---- | ---- | :----: | :----: | :----: |
|
||||
| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
|
||||
| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
|
||||
|
|
@ -15,17 +15,17 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于
|
|||
|
||||
## 2. 测试流程
|
||||
### 2.1 功能测试
|
||||
先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```tests/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
|
||||
先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
|
||||
|
||||
```shell
|
||||
bash tests/prepare.sh ./tests/configs/ppocr_det_mobile_params.txt "cpp_infer"
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "cpp_infer"
|
||||
|
||||
# 用法1:
|
||||
bash tests/test_inference_cpp.sh ./tests/configs/ppocr_det_mobile_params.txt
|
||||
bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
|
||||
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
|
||||
bash tests/test_inference_cpp.sh ./tests/configs/ppocr_det_mobile_params.txt '1'
|
||||
bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt '1'
|
||||
```
|
||||
|
||||
|
||||
|
||||
### 2.2 精度测试
|
||||
|
||||
|
|
@ -37,12 +37,12 @@ bash tests/test_inference_cpp.sh ./tests/configs/ppocr_det_mobile_params.txt '1'
|
|||
#### 使用方式
|
||||
运行命令：
|
||||
```shell
|
||||
python3.7 tests/compare_results.py --gt_file=./tests/results/cpp_*.txt --log_file=./tests/output/cpp_*.log --atol=1e-3 --rtol=1e-3
|
||||
python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/cpp_*.txt --log_file=./test_tipc/output/cpp_*.log --atol=1e-3 --rtol=1e-3
|
||||
```
|
||||
|
||||
参数介绍：
|
||||
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在tests/result/ 文件夹下
|
||||
- log_file: 指向运行tests/test.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持infer_*.log格式传入
|
||||
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
|
||||
- log_file: 指向运行test_tipc/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入
|
||||
- atol: 设置的绝对误差
|
||||
- rtol: 设置的相对误差
|
||||
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
# Lite预测功能测试
|
||||
|
||||
Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预测库的模型推理功能。
|
||||
|
||||
## 1. 测试结论汇总
|
||||
|
||||
目前Lite端的样本间支持以方式的组合：
|
||||
|
||||
**字段说明：**
|
||||
- 输入设置：包括C++预测、python预测、java预测
|
||||
- 模型类型：包括正常模型（FP32）和量化模型（FP16）
|
||||
- batch-size：包括1和4
|
||||
- predictor数量：包括多predictor预测和单predictor预测
|
||||
- 功耗模式：包括高性能模式（LITE_POWER_HIGH）和省电模式（LITE_POWER_LOW）
|
||||
- 预测库来源：包括下载方式和编译方式，其中编译方式分为以下目标硬件：(1)ARM CPU;(2)Linux XPU;(3)OpenCL GPU;(4)Metal GPU
|
||||
|
||||
| 模型类型 | batch-size | predictor数量 | 功耗模式 | 预测库来源 | 支持语言 |
|
||||
| :----: | :----: | :----: | :----: | :----: | :----: |
|
||||
| 正常模型/量化模型 | 1 | 1 | 高性能模式/省电模式 | 下载方式 | C++预测 |
|
||||
|
||||
|
||||
## 2. 测试流程
|
||||
|
||||
### 2.1 功能测试
|
||||
|
||||
先运行`prepare.sh`准备数据和模型，模型和数据会打包到test_lite.tar中，将test_lite.tar上传到手机上，解压后进`入test_lite`目录中，然后运行`test_lite.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
|
||||
|
||||
```shell
|
||||
|
||||
# 数据和模型准备
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "lite_infer"
|
||||
|
||||
# 手机端测试:
|
||||
bash test_lite.sh ppocr_det_mobile_params.txt
|
||||
|
||||
```
|
||||
|
||||
**注意**：由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
|
||||
|
||||
#### 运行结果
|
||||
|
||||
各测试的运行情况会打印在 `./output/` 中：
|
||||
运行成功时会输出：
|
||||
|
||||
```
|
||||
Run successfully with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1!
|
||||
Run successfully with command xxx
|
||||
...
|
||||
```
|
||||
|
||||
运行失败时会输出：
|
||||
|
||||
```
|
||||
Run failed with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1!
|
||||
Run failed with command xxx
|
||||
...
|
||||
```
|
||||
|
||||
在./output/文件夹下，会存在如下日志，每一个日志都是不同配置下的log结果：
|
||||
|
||||
<img src="lite_log.png" width="1000">
|
||||
|
||||
在每一个log中，都会调用autolog打印如下信息：
|
||||
|
||||
<img src="lite_auto_log.png" width="1000">
|
||||
|
||||
|
||||
|
||||
## 3. 更多教程
|
||||
|
||||
本文档为功能测试用，更详细的Lite端预测使用教程请参考：[Lite端部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)。
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
# PaddleServing预测功能测试
|
||||
|
||||
PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试基于PaddleServing的部署功能。
|
||||
|
||||
## 1. 测试结论汇总
|
||||
|
||||
基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的C++预测功能汇总如下：
|
||||
|
||||
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
|
||||
| ---- | ---- | ---- | :----: | :----: | :----: |
|
||||
| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
|
||||
| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
|
||||
| 量化模型 | GPU | 1/6 | int8 | - | - |
|
||||
| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
|
||||
|
||||
## 2. 测试流程
|
||||
### 2.1 功能测试
|
||||
先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
|
||||
|
||||
```shell
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "serving_infer"
|
||||
|
||||
# 用法:
|
||||
bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
|
||||
```
|
||||
|
||||
#### 运行结果
|
||||
|
||||
各测试的运行情况会打印在 `test_tipc/output/results_serving.log` 中：
|
||||
运行成功时会输出：
|
||||
|
||||
```
|
||||
Run successfully with command - python3.7 pipeline_http_client.py --image_dir=../../doc/imgs > ../../tests/output/server_infer_cpu_usemkldnn_True_threads_1_batchsize_1.log 2>&1 !
|
||||
Run successfully with command - xxxxx
|
||||
...
|
||||
```
|
||||
|
||||
运行失败时会输出：
|
||||
|
||||
```
|
||||
Run failed with command - python3.7 pipeline_http_client.py --image_dir=../../doc/imgs > ../../tests/output/server_infer_cpu_usemkldnn_True_threads_1_batchsize_1.log 2>&1 !
|
||||
Run failed with command - python3.7 pipeline_http_client.py --image_dir=../../doc/imgs > ../../tests/output/server_infer_cpu_usemkldnn_True_threads_6_batchsize_1.log 2>&1 !
|
||||
Run failed with command - xxxxx
|
||||
...
|
||||
```
|
||||
|
||||
详细的预测结果会存在 test_tipc/output/ 文件夹下，例如`server_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log`中会返回检测框的坐标:
|
||||
|
||||
```
|
||||
{'err_no': 0, 'err_msg': '', 'key': ['dt_boxes'], 'value': ['[[[ 78. 642.]\n [409. 640.]\n [409. 657.]\n
|
||||
[ 78. 659.]]\n\n [[ 75. 614.]\n [211. 614.]\n [211. 635.]\n [ 75. 635.]]\n\n
|
||||
[[103. 554.]\n [135. 554.]\n [135. 575.]\n [103. 575.]]\n\n [[ 75. 531.]\n
|
||||
[347. 531.]\n [347. 549.]\n [ 75. 549.] ]\n\n [[ 76. 503.]\n [309. 498.]\n
|
||||
[309. 521.]\n [ 76. 526.]]\n\n [[163. 462.]\n [317. 462.]\n [317. 493.]\n
|
||||
[163. 493.]]\n\n [[324. 431.]\n [414. 431.]\n [414. 452.]\n [324. 452.]]\n\n
|
||||
[[ 76. 412.]\n [208. 408.]\n [209. 424.]\n [ 76. 428.]]\n\n [[307. 409.]\n
|
||||
[428. 409.]\n [428. 426.]\n [307 . 426.]]\n\n [[ 74. 385.]\n [217. 382.]\n
|
||||
[217. 400.]\n [ 74. 403.]]\n\n [[308. 381.]\n [427. 380.]\n [427. 400.]\n
|
||||
[308. 401.]]\n\n [[ 74. 363.]\n [195. 362.]\n [195. 378.]\n [ 74. 379.]]\n\n
|
||||
[[303. 359.]\n [423. 357.]\n [423. 375.]\n [303. 377.]]\n\n [[ 70. 336.]\n
|
||||
[239. 334.]\n [239. 354.]\ n [ 70. 356.]]\n\n [[ 70. 312.]\n [204. 310.]\n
|
||||
[204. 327.]\n [ 70. 330.]]\n\n [[303. 308.]\n [419. 306.]\n [419. 326.]\n
|
||||
[303. 328.]]\n\n [[113. 2 72.]\n [246. 270.]\n [247. 299.]\n [113. 301.]]\n\n
|
||||
[[361. 269.]\n [384. 269.]\n [384. 296.]\n [361. 296.]]\n\n [[ 70. 250.]\n
|
||||
[243. 246.]\n [243. 265.]\n [ 70. 269.]]\n\n [[ 65. 221.]\n [187. 220.]\n
|
||||
[187. 240.]\n [ 65. 241.]]\n\n [[337. 216.]\n [382. 216.]\n [382. 240.]\n
|
||||
[337. 240.]]\n\n [ [ 65. 196.]\n [247. 193.]\n [247. 213.]\n [ 65. 216.]]\n\n
|
||||
[[296. 197.]\n [423. 191.]\n [424. 209.]\n [296. 215.]]\n\n [[ 65. 167.]\n [244. 167.]\n
|
||||
[244. 186.]\n [ 65. 186.]]\n\n [[ 67. 139.]\n [290. 139.]\n [290. 159.]\n [ 67. 159.]]\n\n
|
||||
[[ 68. 113.]\n [410. 113.]\n [410. 128.]\n [ 68. 129.] ]\n\n [[277. 87.]\n [416. 87.]\n
|
||||
[416. 108.]\n [277. 108.]]\n\n [[ 79. 28.]\n [132. 28.]\n [132. 62.]\n [ 79. 62.]]\n\n
|
||||
[[163. 17.]\n [410. 14.]\n [410. 50.]\n [163. 53.]]]']}
|
||||
```
|
||||
|
||||
|
||||
## 3. 更多教程
|
||||
|
||||
本文档为功能测试用，更详细的Serving预测使用教程请参考：[PPOCR 服务化部署](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/pdserving/README_CN.md)
|
||||
|
|
@ -19,7 +19,7 @@
|
|||
|
||||
- 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下，
|
||||
|
||||
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
|
||||
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
|
||||
| ---- | ---- | ---- | :----: | :----: | :----: |
|
||||
| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
|
||||
| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
|
||||
|
|
@ -46,42 +46,42 @@
|
|||
|
||||
|
||||
### 2.2 功能测试
|
||||
先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```tests/output```目录下生成`python_infer_*.log`格式的日志文件。
|
||||
先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
|
||||
|
||||
|
||||
`test_train_inference_python.sh`包含5种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
|
||||
|
||||
- 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
|
||||
- 模式1：lite_train_lite_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
|
||||
```shell
|
||||
bash tests/prepare.sh ./tests/configs/ppocr_det_mobile_params.txt 'lite_train_infer'
|
||||
bash tests/test_train_inference_python.sh ./tests/configs/ppocr_det_mobile_params.txt 'lite_train_infer'
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_lite_infer'
|
||||
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_lite_infer'
|
||||
```
|
||||
|
||||
- 模式2：whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
|
||||
- 模式2：lite_train_whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
|
||||
```shell
|
||||
bash tests/prepare.sh ./tests/configs/ppocr_det_mobile_params.txt 'whole_infer'
|
||||
bash tests/test_train_inference_python.sh ./tests/configs/ppocr_det_mobile_params.txt 'whole_infer'
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_whole_infer'
|
||||
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_whole_infer'
|
||||
```
|
||||
|
||||
- 模式3：infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
|
||||
- 模式3：whole_infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
|
||||
```shell
|
||||
bash tests/prepare.sh ./tests/configs/ppocr_det_mobile_params.txt 'infer'
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer'
|
||||
# 用法1:
|
||||
bash tests/test_train_inference_python.sh ./tests/configs/ppocr_det_mobile_params.txt 'infer'
|
||||
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer'
|
||||
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
|
||||
bash tests/test_train_inference_python.sh ./tests/configs/ppocr_det_mobile_params.txt 'infer' '1'
|
||||
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' '1'
|
||||
```
|
||||
|
||||
- 模式4：whole_train_infer，CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
|
||||
- 模式4：whole_train_whole_infer，CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
|
||||
```shell
|
||||
bash tests/prepare.sh ./tests/configs/ppocr_det_mobile_params.txt 'whole_train_infer'
|
||||
bash tests/test_train_inference_python.sh ./tests/configs/ppocr_det_mobile_params.txt 'whole_train_infer'
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_whole_infer'
|
||||
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_whole_infer'
|
||||
```
|
||||
|
||||
- 模式5：klquant_infer，测试离线量化；
|
||||
- 模式5：klquant_whole_infer，测试离线量化；
|
||||
```shell
|
||||
bash tests/prepare.sh ./tests/configs/ppocr_det_mobile_params.txt 'klquant_infer'
|
||||
bash tests/test_train_inference_python.sh tests/configs/ppocr_det_mobile_params.txt 'klquant_infer'
|
||||
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_whole_infer'
|
||||
bash test_tipc/test_train_inference_python.sh test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_whole_infer'
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -95,12 +95,12 @@ bash tests/test_train_inference_python.sh tests/configs/ppocr_det_mobile_params.
|
|||
#### 使用方式
|
||||
运行命令：
|
||||
```shell
|
||||
python3.7 tests/compare_results.py --gt_file=./tests/results/python_*.txt --log_file=./tests/output/python_*.log --atol=1e-3 --rtol=1e-3
|
||||
python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
|
||||
```
|
||||
|
||||
参数介绍：
|
||||
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在tests/result/ 文件夹下
|
||||
- log_file: 指向运行tests/test.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持infer_*.log格式传入
|
||||
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
|
||||
- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入
|
||||
- atol: 设置的绝对误差
|
||||
- rtol: 设置的相对误差
|
||||
|
||||
|
|
@ -1,8 +1,9 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer',
|
||||
# 'cpp_infer', 'serving_infer', 'klquant_infer']
|
||||
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',
|
||||
# 'whole_infer', 'klquant_whole_infer',
|
||||
# 'cpp_infer', 'serving_infer', 'lite_infer']
|
||||
|
||||
MODE=$2
|
||||
|
||||
|
|
@ -34,10 +35,14 @@ trainer_list=$(func_parser_value "${lines[14]}")
|
|||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
|
||||
MODE=$2
|
||||
|
||||
if [ ${MODE} = "lite_train_infer" ];then
|
||||
if [ ${MODE} = "lite_train_lite_infer" ];then
|
||||
# pretrain lite train data
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
|
||||
if [ ${model_name} == "PPOCRv2_ocr_det" ]; then
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar
|
||||
cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
|
||||
fi
|
||||
cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
|
||||
rm -rf ./train_data/icdar2015
|
||||
rm -rf ./train_data/ic15_data
|
||||
|
|
@ -50,14 +55,18 @@ if [ ${MODE} = "lite_train_infer" ];then
|
|||
ln -s ./icdar2015_lite ./icdar2015
|
||||
cd ../
|
||||
cd ./inference && tar xf rec_inference.tar && cd ../
|
||||
elif [ ${MODE} = "whole_train_infer" ];then
|
||||
elif [ ${MODE} = "whole_train_whole_infer" ];then
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
rm -rf ./train_data/icdar2015
|
||||
rm -rf ./train_data/ic15_data
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
|
||||
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
|
||||
cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
|
||||
elif [ ${MODE} = "whole_infer" ];then
|
||||
if [ ${model_name} == "PPOCRv2_ocr_det" ]; then
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar
|
||||
cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
|
||||
fi
|
||||
elif [ ${MODE} = "lite_train_whole_infer" ];then
|
||||
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
|
||||
rm -rf ./train_data/icdar2015
|
||||
rm -rf ./train_data/ic15_data
|
||||
|
|
@ -66,7 +75,11 @@ elif [ ${MODE} = "whole_infer" ];then
|
|||
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
|
||||
ln -s ./icdar2015_infer ./icdar2015
|
||||
cd ../
|
||||
elif [ ${MODE} = "infer" ];then
|
||||
if [ ${model_name} == "PPOCRv2_ocr_det" ]; then
|
||||
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar
|
||||
cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
|
||||
fi
|
||||
elif [ ${MODE} = "whole_infer" ];then
|
||||
if [ ${model_name} = "ocr_det" ]; then
|
||||
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
|
||||
rm -rf ./train_data/icdar2015
|
||||
|
|
@ -100,13 +113,29 @@ elif [ ${MODE} = "infer" ];then
|
|||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../
|
||||
fi
|
||||
elif [ ${MODE} = "klquant_infer" ];then
|
||||
|
||||
elif [ ${model_name} = "PPOCRv2_ocr_det" ]; then
|
||||
eval_model_name="ch_PP-OCRv2_det_infer"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
|
||||
fi
|
||||
|
||||
if [ ${MODE} = "klquant_whole_infer" ]; then
|
||||
if [ ${model_name} = "ocr_det" ]; then
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
|
||||
fi
|
||||
elif [ ${MODE} = "cpp_infer" ];then
|
||||
if [ ${model_name} = "PPOCRv2_ocr_det" ]; then
|
||||
eval_model_name="ch_PP-OCRv2_det_infer"
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
|
||||
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ ${MODE} = "cpp_infer" ];then
|
||||
if [ ${model_name} = "ocr_det" ]; then
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
|
|
@ -136,3 +165,37 @@ if [ ${MODE} = "serving_infer" ];then
|
|||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar
|
||||
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar && cd ../
|
||||
fi
|
||||
|
||||
|
||||
if [ ${MODE} = "lite_infer" ];then
|
||||
# prepare lite nb model and test data
|
||||
current_dir=${PWD}
|
||||
wget -nc -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb
|
||||
wget -nc -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb
|
||||
wget -nc -P ./test_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
|
||||
cd ./test_data && tar -xf icdar2015_lite.tar && rm icdar2015_lite.tar && cd ../
|
||||
# prepare lite env
|
||||
export http_proxy=http://172.19.57.45:3128
|
||||
export https_proxy=http://172.19.57.45:3128
|
||||
paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
|
||||
paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
|
||||
paddlelite_file=inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv
|
||||
wget ${paddlelite_url}
|
||||
tar -xf ${paddlelite_zipfile}
|
||||
mkdir -p ${paddlelite_file}/demo/cxx/ocr/test_lite
|
||||
mv models test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
|
||||
cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
|
||||
cp ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
|
||||
cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
|
||||
cp PTDN/configs/ppocr_det_mobile_params.txt PTDN/test_lite.sh PTDN/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
|
||||
cd ${paddlelite_file}/demo/cxx/ocr/
|
||||
git clone https://github.com/LDOUBLEV/AutoLog.git
|
||||
unset http_proxy
|
||||
unset https_proxy
|
||||
make -j
|
||||
sleep 1
|
||||
make -j
|
||||
cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
|
||||
tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
|
||||
fi
|
||||
|
||||
|
|
@ -1,9 +1,9 @@
|
|||
|
||||
# 推理部署导航
|
||||
# 飞桨训推一体认证
|
||||
|
||||
## 1. 简介
|
||||
|
||||
飞桨除了基本的模型训练和预测，还提供了支持多端多平台的高性能推理部署工具。本文档提供了PaddleOCR中所有模型的推理部署导航PTDN（Paddle Train Deploy Navigation），方便用户查阅每种模型的推理部署打通情况，并可以进行一键测试。
|
||||
飞桨除了基本的模型训练和预测，还提供了支持多端多平台的高性能推理部署工具。本文档提供了PaddleOCR中所有模型的飞桨训推一体认证 (Training and Inference Pipeline Certification(TIPC)) 信息和测试工具，方便用户查阅每种模型的训练推理部署打通情况，并可以进行一键测试。
|
||||
|
||||
<div align="center">
|
||||
<img src="docs/guide.png" width="1000">
|
||||
|
|
@ -15,20 +15,23 @@
|
|||
|
||||
**字段说明：**
|
||||
- 基础训练预测：包括模型训练、Paddle Inference Python预测。
|
||||
- 其他：包括Paddle Inference C++预测、Paddle Serving部署、Paddle-Lite部署等。
|
||||
- 更多训练方式：包括多机多卡、混合精度。
|
||||
- 模型压缩：包括裁剪、离线/在线量化、蒸馏。
|
||||
- 其他预测部署：包括Paddle Inference C++预测、Paddle Serving部署、Paddle-Lite部署等。
|
||||
|
||||
更详细的mkldnn、Tensorrt等预测加速相关功能的支持情况可以查看各测试工具的[更多教程](#more)。
|
||||
|
||||
| 算法论文 | 模型名称 | 模型类型 | 基础训练预测 | 其他 |
|
||||
| :--- | :--- | :----: | :--------: | :---- |
|
||||
| DB |ch_ppocr_mobile_v2.0_det | 检测 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| DB |ch_ppocr_server_v2.0_det | 检测 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| 算法论文 | 模型名称 | 模型类型 | 基础<br>训练预测 | 更多<br>训练方式 | 模型压缩 | 其他预测部署 |
|
||||
| :--- | :--- | :----: | :--------: | :---- | :---- | :---- |
|
||||
| DB |ch_ppocr_mobile_v2.0_det | 检测 | 支持 | 多机多卡 <br> 混合精度 | FPGM裁剪 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| DB |ch_ppocr_server_v2.0_det | 检测 | 支持 | 多机多卡 <br> 混合精度 | FPGM裁剪 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| DB |ch_PP-OCRv2_det | 检测 |
|
||||
| CRNN |ch_ppocr_mobile_v2.0_rec | 识别 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| CRNN |ch_ppocr_server_v2.0_rec | 识别 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| CRNN |ch_ppocr_mobile_v2.0_rec | 识别 | 支持 | 多机多卡 <br> 混合精度 | PACT量化 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| CRNN |ch_ppocr_server_v2.0_rec | 识别 | 支持 | 多机多卡 <br> 混合精度 | PACT量化 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| CRNN |ch_PP-OCRv2_rec | 识别 |
|
||||
| PP-OCR |ch_ppocr_mobile_v2.0 | 检测+识别 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| PP-OCR |ch_ppocr_server_v2.0 | 检测+识别 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
|PP-OCRv2|ch_PP-OCRv2 | 检测+识别 | 支持 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| PP-OCR |ch_ppocr_mobile_v2.0 | 检测+识别 | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
| PP-OCR |ch_ppocr_server_v2.0 | 检测+识别 | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
|
||||
|PP-OCRv2|ch_PP-OCRv2 | 检测+识别 |
|
||||
| DB |det_mv3_db_v2.0 | 检测 |
|
||||
| DB |det_r50_vd_db_v2.0 | 检测 |
|
||||
| EAST |det_mv3_east_v2.0 | 检测 |
|
||||
|
|
@ -55,7 +58,7 @@
|
|||
### 目录介绍
|
||||
|
||||
```shell
|
||||
PTDN/
|
||||
test_tipc/
|
||||
├── configs/ # 配置文件目录
|
||||
├── det_mv3_db.yml # 测试mobile版ppocr检测模型训练的yml文件
|
||||
├── det_r50_vd_db.yml # 测试server版ppocr检测模型训练的yml文件
|
||||
|
|
@ -66,7 +69,7 @@ PTDN/
|
|||
├── ppocr_sys_server_params.txt # 测试server版ppocr检测+识别模型串联的参数配置文件
|
||||
├── ppocr_det_server_params.txt # 测试server版ppocr检测模型的参数配置文件
|
||||
├── ppocr_rec_server_params.txt # 测试server版ppocr识别模型的参数配置文件
|
||||
├── ...
|
||||
├── ...
|
||||
├── results/ # 预先保存的预测结果，用于和实际预测结果进行精读比对
|
||||
├── python_ppocr_det_mobile_results_fp32.txt # 预存的mobile版ppocr检测模型python预测fp32精度的结果
|
||||
├── python_ppocr_det_mobile_results_fp16.txt # 预存的mobile版ppocr检测模型python预测fp16精度的结果
|
||||
|
|
@ -98,6 +101,8 @@ PTDN/
|
|||
- `test_serving.sh`：测试基于Paddle Serving的服务化部署功能。
|
||||
- `test_lite.sh`：测试基于Paddle-Lite的端侧预测部署功能。
|
||||
|
||||
<a name="more"></a>
|
||||
#### 更多教程
|
||||
各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
|
||||
[test_train_inference_python 使用](docs/test_train_inference_python.md)
|
||||
[test_inference_cpp 使用](docs/test_inference_cpp.md)
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
#!/bin/bash
|
||||
source tests/common_func.sh
|
||||
source test_tipc/common_func.sh
|
||||
|
||||
FILENAME=$1
|
||||
dataline=$(awk 'NR==52, NR==66{print}' $FILENAME)
|
||||
|
|
@ -35,7 +35,7 @@ cpp_benchmark_key=$(func_parser_key "${lines[14]}")
|
|||
cpp_benchmark_value=$(func_parser_value "${lines[14]}")
|
||||
|
||||
|
||||
LOG_PATH="./tests/output"
|
||||
LOG_PATH="./test_tipc/output"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results_cpp.log"
|
||||
|
||||
|
|
@ -0,0 +1,69 @@
|
|||
#!/bin/bash
|
||||
source ./common_func.sh
|
||||
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
|
||||
|
||||
FILENAME=$1
|
||||
dataline=$(awk 'NR==101, NR==110{print}' $FILENAME)
|
||||
echo $dataline
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
|
||||
# parser lite inference
|
||||
lite_inference_cmd=$(func_parser_value "${lines[1]}")
|
||||
lite_model_dir_list=$(func_parser_value "${lines[2]}")
|
||||
lite_cpu_threads_list=$(func_parser_value "${lines[3]}")
|
||||
lite_batch_size_list=$(func_parser_value "${lines[4]}")
|
||||
lite_power_mode_list=$(func_parser_value "${lines[5]}")
|
||||
lite_infer_img_dir_list=$(func_parser_value "${lines[6]}")
|
||||
lite_config_dir=$(func_parser_value "${lines[7]}")
|
||||
lite_rec_dict_dir=$(func_parser_value "${lines[8]}")
|
||||
lite_benchmark_value=$(func_parser_value "${lines[9]}")
|
||||
|
||||
LOG_PATH="./output"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results.log"
|
||||
|
||||
|
||||
function func_lite(){
|
||||
IFS='|'
|
||||
_script=$1
|
||||
_lite_model=$2
|
||||
_log_path=$3
|
||||
_img_dir=$4
|
||||
_config=$5
|
||||
if [[ $lite_model =~ "slim" ]]; then
|
||||
precision="INT8"
|
||||
else
|
||||
precision="FP32"
|
||||
fi
|
||||
is_single_img=$(echo $_img_dir | grep -E ".jpg|.jpeg|.png|.JPEG|.JPG")
|
||||
if [[ "$is_single_img" != "" ]]; then
|
||||
single_img="True"
|
||||
else
|
||||
single_img="False"
|
||||
fi
|
||||
|
||||
# lite inference
|
||||
for num_threads in ${lite_cpu_threads_list[*]}; do
|
||||
for power_mode in ${lite_power_mode_list[*]}; do
|
||||
for batchsize in ${lite_batch_size_list[*]}; do
|
||||
model_name=$(echo $lite_model | awk -F "/" '{print $NF}')
|
||||
_save_log_path="${_log_path}/lite_${model_name}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}_powermode_${power_mode}_singleimg_${single_img}.log"
|
||||
command="${_script} ${lite_model} ${precision} ${num_threads} ${batchsize} ${power_mode} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
|
||||
eval ${command}
|
||||
status_check $? "${command}" "${status_log}"
|
||||
done
|
||||
done
|
||||
done
|
||||
}
|
||||
|
||||
|
||||
echo "################### run test ###################"
|
||||
IFS="|"
|
||||
for lite_model in ${lite_model_dir_list[*]}; do
|
||||
#run lite inference
|
||||
for img_dir in ${lite_infer_img_dir_list[*]}; do
|
||||
func_lite "${lite_inference_cmd}" "${lite_model}" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
|
||||
done
|
||||
done
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
#!/bin/bash
|
||||
source tests/common_func.sh
|
||||
source test_tipc/common_func.sh
|
||||
|
||||
FILENAME=$1
|
||||
dataline=$(awk 'NR==67, NR==83{print}' $FILENAME)
|
||||
|
|
@ -36,8 +36,8 @@ web_precision_key=$(func_parser_key "${lines[15]}")
|
|||
web_precision_list=$(func_parser_value "${lines[15]}")
|
||||
pipeline_py=$(func_parser_value "${lines[16]}")
|
||||
|
||||
LOG_PATH="../../tests/output"
|
||||
mkdir -p ./tests/output
|
||||
LOG_PATH="../../test_tipc/output"
|
||||
mkdir -p ./test_tipc/output
|
||||
status_log="${LOG_PATH}/results_serving.log"
|
||||
|
||||
function func_serving(){
|
||||
|
|
@ -1,8 +1,8 @@
|
|||
#!/bin/bash
|
||||
source tests/common_func.sh
|
||||
source test_tipc/common_func.sh
|
||||
|
||||
FILENAME=$1
|
||||
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'klquant_infer']
|
||||
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(awk 'NR==1, NR==51{print}' $FILENAME)
|
||||
|
|
@ -59,6 +59,7 @@ export_key1=$(func_parser_key "${lines[33]}")
|
|||
export_value1=$(func_parser_value "${lines[33]}")
|
||||
export_key2=$(func_parser_key "${lines[34]}")
|
||||
export_value2=$(func_parser_value "${lines[34]}")
|
||||
inference_dir=$(func_parser_value "${lines[35]}")
|
||||
|
||||
# parser inference model
|
||||
infer_model_dir_list=$(func_parser_value "${lines[36]}")
|
||||
|
|
@ -88,7 +89,7 @@ infer_key1=$(func_parser_key "${lines[50]}")
|
|||
infer_value1=$(func_parser_value "${lines[50]}")
|
||||
|
||||
# parser klquant_infer
|
||||
if [ ${MODE} = "klquant_infer" ]; then
|
||||
if [ ${MODE} = "klquant_whole_infer" ]; then
|
||||
dataline=$(awk 'NR==82, NR==98{print}' $FILENAME)
|
||||
lines=(${dataline})
|
||||
# parser inference model
|
||||
|
|
@ -119,7 +120,7 @@ if [ ${MODE} = "klquant_infer" ]; then
|
|||
infer_value1=$(func_parser_value "${lines[15]}")
|
||||
fi
|
||||
|
||||
LOG_PATH="./tests/output"
|
||||
LOG_PATH="./test_tipc/output"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results_python.log"
|
||||
|
||||
|
|
@ -202,7 +203,7 @@ function func_inference(){
|
|||
done
|
||||
}
|
||||
|
||||
if [ ${MODE} = "infer" ] || [ ${MODE} = "klquant_infer" ]; then
|
||||
if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
|
||||
GPUID=$3
|
||||
if [ ${#GPUID} -le 0 ];then
|
||||
env=" "
|
||||
|
|
@ -245,6 +246,7 @@ else
|
|||
for gpu in ${gpu_list[*]}; do
|
||||
use_gpu=${USE_GPU_KEY[Count]}
|
||||
Count=$(($Count + 1))
|
||||
ips=""
|
||||
if [ ${gpu} = "-1" ];then
|
||||
env=""
|
||||
elif [ ${#gpu} -le 1 ];then
|
||||
|
|
@ -264,6 +266,11 @@ else
|
|||
env=" "
|
||||
fi
|
||||
for autocast in ${autocast_list[*]}; do
|
||||
if [ ${autocast} = "amp" ]; then
|
||||
set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
|
||||
else
|
||||
set_amp_config=" "
|
||||
fi
|
||||
for trainer in ${trainer_list[*]}; do
|
||||
flag_quant=False
|
||||
if [ ${trainer} = ${pact_key} ]; then
|
||||
|
|
@ -290,7 +297,6 @@ else
|
|||
if [ ${run_train} = "null" ]; then
|
||||
continue
|
||||
fi
|
||||
|
||||
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
|
||||
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
|
||||
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
|
||||
|
|
@ -306,11 +312,11 @@ else
|
|||
|
||||
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
|
||||
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
|
||||
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
|
||||
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
|
||||
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
|
||||
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
|
||||
elif [ ${#ips} -le 26 ];then # train with multi-gpu
|
||||
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
|
||||
else # train with multi-machine
|
||||
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
|
||||
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${set_use_gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
|
||||
fi
|
||||
# run train
|
||||
eval "unset CUDA_VISIBLE_DEVICES"
|
||||
|
|
@ -342,7 +348,13 @@ else
|
|||
#run inference
|
||||
eval $env
|
||||
save_infer_path="${save_log}"
|
||||
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
|
||||
if [ ${inference_dir} != "null" ] && [ ${inference_dir} != '##' ]; then
|
||||
infer_model_dir="${save_infer_path}/${inference_dir}"
|
||||
else
|
||||
infer_model_dir=${save_infer_path}
|
||||
fi
|
||||
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
|
||||
|
||||
eval "unset CUDA_VISIBLE_DEVICES"
|
||||
fi
|
||||
done # done with: for trainer in ${trainer_list[*]}; do
|
||||
|
|
@ -159,7 +159,8 @@ def train(config,
|
|||
eval_class,
|
||||
pre_best_model_dict,
|
||||
logger,
|
||||
vdl_writer=None):
|
||||
vdl_writer=None,
|
||||
scaler=None):
|
||||
cal_metric_during_train = config['Global'].get('cal_metric_during_train',
|
||||
False)
|
||||
log_smooth_window = config['Global']['log_smooth_window']
|
||||
|
|
@ -211,33 +212,49 @@ def train(config,
|
|||
for epoch in range(start_epoch, epoch_num + 1):
|
||||
train_dataloader = build_dataloader(
|
||||
config, 'Train', device, logger, seed=epoch)
|
||||
train_batch_cost = 0.0
|
||||
train_reader_cost = 0.0
|
||||
batch_sum = 0
|
||||
batch_start = time.time()
|
||||
train_run_cost = 0.0
|
||||
total_samples = 0
|
||||
reader_start = time.time()
|
||||
max_iter = len(train_dataloader) - 1 if platform.system(
|
||||
) == "Windows" else len(train_dataloader)
|
||||
for idx, batch in enumerate(train_dataloader):
|
||||
profiler.add_profiler_step(profiler_options)
|
||||
train_reader_cost += time.time() - batch_start
|
||||
train_reader_cost += time.time() - reader_start
|
||||
if idx >= max_iter:
|
||||
break
|
||||
lr = optimizer.get_lr()
|
||||
images = batch[0]
|
||||
if use_srn:
|
||||
model_average = True
|
||||
if model_type == 'table' or extra_input:
|
||||
preds = model(images, data=batch[1:])
|
||||
|
||||
train_start = time.time()
|
||||
# use amp
|
||||
if scaler:
|
||||
with paddle.amp.auto_cast():
|
||||
if model_type == 'table' or extra_input:
|
||||
preds = model(images, data=batch[1:])
|
||||
else:
|
||||
preds = model(images)
|
||||
else:
|
||||
preds = model(images)
|
||||
if model_type == 'table' or extra_input:
|
||||
preds = model(images, data=batch[1:])
|
||||
else:
|
||||
preds = model(images)
|
||||
loss = loss_class(preds, batch)
|
||||
avg_loss = loss['loss']
|
||||
avg_loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
if scaler:
|
||||
scaled_avg_loss = scaler.scale(avg_loss)
|
||||
scaled_avg_loss.backward()
|
||||
scaler.minimize(optimizer, scaled_avg_loss)
|
||||
else:
|
||||
avg_loss.backward()
|
||||
optimizer.step()
|
||||
optimizer.clear_grad()
|
||||
|
||||
train_batch_cost += time.time() - batch_start
|
||||
batch_sum += len(images)
|
||||
train_run_cost += time.time() - train_start
|
||||
total_samples += len(images)
|
||||
|
||||
if not isinstance(lr_scheduler, float):
|
||||
lr_scheduler.step()
|
||||
|
|
@ -268,12 +285,13 @@ def train(config,
|
|||
logs = train_stats.log()
|
||||
strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
|
||||
epoch, epoch_num, global_step, logs, train_reader_cost /
|
||||
print_batch_step, train_batch_cost / print_batch_step,
|
||||
batch_sum, batch_sum / train_batch_cost)
|
||||
print_batch_step, (train_reader_cost + train_run_cost) /
|
||||
print_batch_step, total_samples,
|
||||
total_samples / (train_reader_cost + train_run_cost))
|
||||
logger.info(strs)
|
||||
train_batch_cost = 0.0
|
||||
train_reader_cost = 0.0
|
||||
batch_sum = 0
|
||||
train_run_cost = 0.0
|
||||
total_samples = 0
|
||||
# eval
|
||||
if global_step > start_eval_step and \
|
||||
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
|
||||
|
|
@ -326,7 +344,7 @@ def train(config,
|
|||
global_step)
|
||||
global_step += 1
|
||||
optimizer.clear_grad()
|
||||
batch_start = time.time()
|
||||
reader_start = time.time()
|
||||
if dist.get_rank() == 0:
|
||||
save_model(
|
||||
model,
|
||||
|
|
@ -367,7 +385,11 @@ def eval(model,
|
|||
with paddle.no_grad():
|
||||
total_frame = 0.0
|
||||
total_time = 0.0
|
||||
pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
|
||||
pbar = tqdm(
|
||||
total=len(valid_dataloader),
|
||||
desc='eval model:',
|
||||
position=0,
|
||||
leave=True)
|
||||
max_iter = len(valid_dataloader) - 1 if platform.system(
|
||||
) == "Windows" else len(valid_dataloader)
|
||||
for idx, batch in enumerate(valid_dataloader):
|
||||
|
|
@ -436,8 +458,6 @@ def get_center(model, eval_dataloader, post_process_class):
|
|||
|
||||
batch = [item.numpy() for item in batch]
|
||||
# Obtain usable results from post-processing methods
|
||||
total_time += time.time() - start
|
||||
# Evaluate the results of the current batch
|
||||
post_result = post_process_class(preds, batch[1])
|
||||
|
||||
#update char_center
|
||||
|
|
@ -480,11 +500,6 @@ def preprocess(is_train=False):
|
|||
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
|
||||
'SEED'
|
||||
]
|
||||
windows_not_support_list = ['PSE']
|
||||
if platform.system() == "Windows" and alg in windows_not_support_list:
|
||||
logger.warning('{} is not support in Windows now'.format(
|
||||
windows_not_support_list))
|
||||
sys.exit()
|
||||
|
||||
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
|
||||
device = paddle.set_device(device)
|
||||
|
|
|
|||
|
|
@ -102,10 +102,27 @@ def main(config, device, logger, vdl_writer):
|
|||
if valid_dataloader is not None:
|
||||
logger.info('valid dataloader has {} iters'.format(
|
||||
len(valid_dataloader)))
|
||||
|
||||
use_amp = config["Global"].get("use_amp", False)
|
||||
if use_amp:
|
||||
AMP_RELATED_FLAGS_SETTING = {
|
||||
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
|
||||
'FLAGS_max_inplace_grad_add': 8,
|
||||
}
|
||||
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
|
||||
scale_loss = config["Global"].get("scale_loss", 1.0)
|
||||
use_dynamic_loss_scaling = config["Global"].get(
|
||||
"use_dynamic_loss_scaling", False)
|
||||
scaler = paddle.amp.GradScaler(
|
||||
init_loss_scaling=scale_loss,
|
||||
use_dynamic_loss_scaling=use_dynamic_loss_scaling)
|
||||
else:
|
||||
scaler = None
|
||||
|
||||
# start train
|
||||
program.train(config, train_dataloader, valid_dataloader, device, model,
|
||||
loss_class, optimizer, lr_scheduler, post_process_class,
|
||||
eval_class, pre_best_model_dict, logger, vdl_writer)
|
||||
eval_class, pre_best_model_dict, logger, vdl_writer, scaler)
|
||||
|
||||
|
||||
def test_reader(config, device, logger):
|
||||
|
|
|
|||
Loading…
Reference in New Issue