diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 4c37a6955799a65bc192b211095288ff8a120461..daa9b8d4611f1a24c66f0244facbba31de2729e6 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -21,9 +21,10 @@ ``` # 下载超轻量中文检测模型： -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar -tar xf ch_ppocr_mobile_v2.0_det_infer.tar -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar +tar xf ch_PP-OCRv2_det_infer.tar +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/" + ``` 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下： @@ -41,13 +42,13 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_m 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216： ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216 ``` 如果想使用CPU进行预测，执行命令如下 ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --use_gpu=False ``` @@ -64,9 +65,9 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di ``` # 下载超轻量中文识别模型： -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar -tar xf ch_ppocr_mobile_v2.0_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar +tar xf ch_PP-OCRv2_rec_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" ``` ![](../imgs_words/ch/word_4.jpg) @@ -81,10 +82,9 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) ### 2.2 多语言模型的推理 -如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果， -需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： - +如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： ``` +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` @@ -125,14 +125,13 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982] ```shell # 使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true # 不使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false # 使用多进程 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` 执行命令后，识别结果图像如下： ![](../imgs_results/system_res_00018069.jpg) - diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index fa3b1c88713f01e8e411cf95d107b4b58dd7f4e1..1cef2cee97c8814164e45581cfe76c52e8525379 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -9,7 +9,7 @@ This article introduces the use of the Python inference engine for the PP-OCR mo - [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE) - [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION) - [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE) - + - [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE) - [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION) @@ -22,10 +22,10 @@ The default configuration is based on the inference setting of the DB text detec ``` # download DB text detection inference model -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar -tar xf ch_ppocr_mobile_v2.0_det_infer.tar -# predict -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar +tar xf ch_PP-OCRv2_det_infer.tar +# run inference +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/" ``` The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows: @@ -42,12 +42,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216: ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216 ``` If you want to use the CPU for prediction, execute the command as follows ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --use_gpu=False ``` @@ -62,9 +62,10 @@ For lightweight Chinese recognition model inference, you can execute the followi ``` # download CRNN text recognition inference model -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar -tar xf ch_ppocr_mobile_v2.0_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar +tar xf ch_PP-OCRv2_rec_infer.tar +# run inference +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" ``` ![](../imgs_words_en/word_10.png) @@ -78,10 +79,12 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658) ### 2. Multilingaul Model Inference -If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, +If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: ``` +wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar + python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` ![](../imgs_words/korean/1.jpg) @@ -120,13 +123,13 @@ When performing prediction, you need to specify the path of a single image or a ```shell # use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true # not use use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false # use multi-process -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` diff --git a/tests/compare_results.py b/tests/compare_results.py deleted file mode 100644 index 1c3fe4ea951aef122728a7aed7fc4ecaf8e7607e..0000000000000000000000000000000000000000 --- a/tests/compare_results.py +++ /dev/null @@ -1,133 +0,0 @@ -import numpy as np -import os -import subprocess -import json -import argparse -import glob - - -def init_args(): - parser = argparse.ArgumentParser() - # params for testing assert allclose - parser.add_argument("--atol", type=float, default=1e-3) - parser.add_argument("--rtol", type=float, default=1e-3) - parser.add_argument("--gt_file", type=str, default="") - parser.add_argument("--log_file", type=str, default="") - parser.add_argument("--precision", type=str, default="fp32") - return parser - - -def parse_args(): - parser = init_args() - return parser.parse_args() - - -def run_shell_command(cmd): - p = subprocess.Popen( - cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) - out, err = p.communicate() - - if p.returncode == 0: - return out.decode('utf-8') - else: - return None - - -def parser_results_from_log_by_name(log_path, names_list): - if not os.path.exists(log_path): - raise ValueError("The log file {} does not exists!".format(log_path)) - - if names_list is None or len(names_list) < 1: - return [] - - parser_results = {} - for name in names_list: - cmd = "grep {} {}".format(name, log_path) - outs = run_shell_command(cmd) - outs = outs.split("\n")[0] - result = outs.split("{}".format(name))[-1] - result = json.loads(result) - parser_results[name] = result - return parser_results - - -def load_gt_from_file(gt_file): - if not os.path.exists(gt_file): - raise ValueError("The log file {} does not exists!".format(gt_file)) - with open(gt_file, 'r') as f: - data = f.readlines() - f.close() - parser_gt = {} - for line in data: - image_name, result = line.strip("\n").split("\t") - result = json.loads(result) - parser_gt[image_name] = result - return parser_gt - - -def load_gt_from_txts(gt_file): - gt_list = glob.glob(gt_file) - gt_collection = {} - for gt_f in gt_list: - gt_dict = load_gt_from_file(gt_f) - basename = os.path.basename(gt_f) - if "fp32" in basename: - gt_collection["fp32"] = [gt_dict, gt_f] - elif "fp16" in basename: - gt_collection["fp16"] = [gt_dict, gt_f] - elif "int8" in basename: - gt_collection["int8"] = [gt_dict, gt_f] - else: - continue - return gt_collection - - -def collect_predict_from_logs(log_path, key_list): - log_list = glob.glob(log_path) - pred_collection = {} - for log_f in log_list: - pred_dict = parser_results_from_log_by_name(log_f, key_list) - key = os.path.basename(log_f) - pred_collection[key] = pred_dict - - return pred_collection - - -def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7): - for k in dict_x: - np.testing.assert_allclose( - np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol) - - -if __name__ == "__main__": - # Usage: - # python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt --log_file=./tests/output/infer_*.log - - args = parse_args() - - gt_collection = load_gt_from_txts(args.gt_file) - key_list = gt_collection["fp32"][0].keys() - - pred_collection = collect_predict_from_logs(args.log_file, key_list) - for filename in pred_collection.keys(): - if "fp32" in filename: - gt_dict, gt_filename = gt_collection["fp32"] - elif "fp16" in filename: - gt_dict, gt_filename = gt_collection["fp16"] - elif "int8" in filename: - gt_dict, gt_filename = gt_collection["int8"] - else: - continue - pred_dict = pred_collection[filename] - - try: - testing_assert_allclose( - gt_dict, pred_dict, atol=args.atol, rtol=args.rtol) - print( - "Assert allclose passed! The results of {} and {} are consistent!". - format(filename, gt_filename)) - except Exception as E: - print(E) - raise ValueError( - "The results of {} and the results of {} are inconsistent!". - format(filename, gt_filename)) diff --git a/tests/ocr_det_params.txt b/tests/ocr_det_params.txt deleted file mode 100644 index 73b12cec9c4f8a8745f34b00222f89ba68ff9d5f..0000000000000000000000000000000000000000 --- a/tests/ocr_det_params.txt +++ /dev/null @@ -1,67 +0,0 @@ -===========================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 -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/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained -pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o -fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/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 -## -===========================eval_params=========================== -eval:tools/eval.py -c configs/det/det_mv3_db.yml -o -null:null -## -===========================infer_params=========================== -Global.save_inference_dir:./output/ -Global.pretrained_model: -norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o -quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o -fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o -distill_export:null -export1:null -export2:null -## -infer_model:./inference/ch_ppocr_mobile_v2.0_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/ ---save_log_path:null ---benchmark:True -null:null -===========================cpp_infer_params=========================== -use_opencv:True -infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ -infer_quant:False -inference:./deploy/cpp_infer/build/ppocr det ---use_gpu:True|False ---enable_mkldnn:True|False ---cpu_threads:1|6 ---rec_batch_num:1 ---use_tensorrt:False|True ---precision:fp32|fp16 ---det_model_dir: ---image_dir:./inference/ch_det_data_50/all-sum-510/ ---save_log_path:null ---benchmark:True - diff --git a/tests/ocr_det_server_params.txt b/tests/ocr_det_server_params.txt deleted file mode 100644 index 0835cfffa62dd879d092bf829d782e767b9680ad..0000000000000000000000000000000000000000 --- a/tests/ocr_det_server_params.txt +++ /dev/null @@ -1,52 +0,0 @@ -===========================train_params=========================== -model_name:ocr_server_det -python:python3.7 -gpu_list:0|0,1 -Global.use_gpu:True|True -Global.auto_cast:null -Global.epoch_num:lite_train_infer=2|whole_train_infer=300 -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/det_r50_vd_db.yml -o Global.pretrained_model="" -pact_train:null -fpgm_train:null -distill_train:null -null:null -null:null -## -===========================eval_params=========================== -eval:tools/eval.py -c configs/det/det_mv3_db.yml -o -null:null -## -===========================infer_params=========================== -Global.save_inference_dir:./output/ -Global.pretrained_model: -norm_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o -quant_export:null -fpgm_export:null -distill_export:null -export1:null -export2:null -## -infer_model:./inference/ch_ppocr_server_v2.0_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/ ---save_log_path:null ---benchmark:True -null:null - diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt deleted file mode 100644 index 71d12f90b3bda128c3f6047c6740911dac417954..0000000000000000000000000000000000000000 --- a/tests/ocr_rec_params.txt +++ /dev/null @@ -1,51 +0,0 @@ -===========================train_params=========================== -model_name:ocr_rec -python:python3.7 -gpu_list:0|2,3 -Global.use_gpu:True|True -Global.auto_cast:null -Global.epoch_num:lite_train_infer=2|whole_train_infer=300 -Global.save_model_dir:./output/ -Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128 -Global.pretrained_model:null -train_model_name:latest -train_infer_img_dir:./train_data/ic15_data/train -null:null -## -trainer:norm_train|pact_train -norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o -pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o -fpgm_train:null -distill_train:null -null:null -null:null -## -===========================eval_params=========================== -eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o -null:null -## -===========================infer_params=========================== -Global.save_inference_dir:./output/ -Global.pretrained_model: -norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o -quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o -fpgm_export:null -distill_export:null -export1:null -export2:null -## -infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/ -infer_export:null -infer_quant:False -inference:tools/infer/predict_rec.py ---use_gpu:True|False ---enable_mkldnn:True|False ---cpu_threads:1|6 ---rec_batch_num:1 ---use_tensorrt:True|False ---precision:fp32|fp16|int8 ---rec_model_dir: ---image_dir:./inference/rec_inference ---save_log_path:./test/output/ ---benchmark:True -null:null diff --git a/tests/prepare.sh b/tests/prepare.sh deleted file mode 100644 index 5da74d949f5543f50a4dc61b3093aa11c0caabf6..0000000000000000000000000000000000000000 --- a/tests/prepare.sh +++ /dev/null @@ -1,152 +0,0 @@ -#!/bin/bash -FILENAME=$1 -# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer'] -MODE=$2 - -dataline=$(cat ${FILENAME}) - -# parser params -IFS=$'\n' -lines=(${dataline}) -function func_parser_key(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[0]} - echo ${tmp} -} -function func_parser_value(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[1]} - echo ${tmp} -} -IFS=$'\n' -# The training params -model_name=$(func_parser_value "${lines[1]}") - -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 - # 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 - cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../ - 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_lite.tar - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos - wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle - - cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar - ln -s ./icdar2015_lite ./icdar2015 - cd ../ -elif [ ${MODE} = "whole_train_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 - 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_infer.tar - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar - cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar - ln -s ./icdar2015_infer ./icdar2015 - cd ../ -elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then - if [ ${model_name} = "ocr_det" ]; then - eval_model_name="ch_ppocr_mobile_v2.0_det_infer" - rm -rf ./train_data/icdar2015 - 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 - cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ocr_server_det" ]; then - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_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_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ - else - rm -rf ./train_data/ic15_data - eval_model_name="ch_ppocr_mobile_v2.0_rec_infer" - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar - cd ./inference && tar xf ${eval_model_name}.tar && tar xf ic15_data.tar && cd ../ - fi -fi - -if [ ${MODE} = "cpp_infer" ];then - cd deploy/cpp_infer - use_opencv=$(func_parser_value "${lines[52]}") - if [ ${use_opencv} = "True" ]; then - echo "################### build opencv ###################" - rm -rf 3.4.7.tar.gz opencv-3.4.7/ - wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz - tar -xf 3.4.7.tar.gz - - cd opencv-3.4.7/ - install_path=$(pwd)/opencv-3.4.7/opencv3 - - 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 - cd ../ - echo "################### build opencv finished ###################" - fi - - - echo "################### build PaddleOCR demo ####################" - if [ ${use_opencv} = "True" ]; then - OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/ - else - OPENCV_DIR='' - fi - LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/ - CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`) - CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`) - - 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 \ - -DWITH_TENSORRT=OFF \ - -DOPENCV_DIR=${OPENCV_DIR} \ - -DCUDNN_LIB=${CUDNN_LIB_DIR} \ - -DCUDA_LIB=${CUDA_LIB_DIR} \ - -DTENSORRT_DIR=${TENSORRT_DIR} \ - - make -j - echo "################### build PaddleOCR demo finished ###################" -fi \ No newline at end of file diff --git a/tests/readme.md b/tests/readme.md deleted file mode 100644 index 1c5e0faee90cad9709b6e4d517cbf7830aa2bb8e..0000000000000000000000000000000000000000 --- a/tests/readme.md +++ /dev/null @@ -1,58 +0,0 @@ - -# 介绍 - -test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。 - -# 安装依赖 -- 安装PaddlePaddle >= 2.0 -- 安装PaddleOCR依赖 - ``` - pip3 install -r ../requirements.txt - ``` -- 安装autolog - ``` - git clone https://github.com/LDOUBLEV/AutoLog - cd AutoLog - pip3 install -r requirements.txt - python3 setup.py bdist_wheel - pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl - cd ../ - ``` - -# 目录介绍 - -```bash -tests/ -├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件 -├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件 -└── prepare.sh # 完成test.sh运行所需要的数据和模型下载 -└── test.sh # 根据 -``` - -# 使用方法 -test.sh包含四种运行模式，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； -``` -bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer' -bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_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； -``` -bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer' -bash tests/test.sh ./tests/ocr_det_params.txt '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; -``` -bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer' -用法1: -bash tests/test.sh ./tests/ocr_det_params.txt 'infer' -用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 -bash tests/test.sh ./tests/ocr_det_params.txt '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 -``` -bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer' -bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer' -``` diff --git a/tests/results/det_results_gpu_fp32.txt b/tests/results/det_results_gpu_fp32.txt deleted file mode 100644 index 28af26d0b2178802baf45c58586d38f9eeffe820..0000000000000000000000000000000000000000 --- a/tests/results/det_results_gpu_fp32.txt +++ /dev/null @@ -1,49 +0,0 @@ -00008790.jpg [[[209, 406], [280, 406], [280, 419], [209, 419]], [[60, 398], [105, 398], [105, 411], [60, 411]], [[198, 389], [291, 389], [291, 402], [198, 402]], [[162, 391], [173, 391], [173, 401], [162, 401]], [[35, 380], [133, 380], [133, 393], [35, 393]], [[199, 371], [292, 371], [292, 384], [199, 384]], [[218, 310], [272, 310], [272, 324], [218, 324]], [[162, 305], [172, 305], [172, 314], [162, 314]], [[371, 302], [436, 302], [436, 316], [371, 316]], [[31, 302], [134, 301], [134, 315], [31, 316]], [[223, 292], [269, 292], [269, 306], [223, 306]], [[60, 225], 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116], [88, 116], [88, 131], [62, 131]], [[19, 121], [33, 111], [43, 124], [29, 134]], [[491, 107], [523, 107], [523, 113], [491, 113]], [[449, 107], [477, 107], [477, 113], [449, 113]], [[420, 107], [436, 107], [436, 113], [420, 113]], [[295, 107], [319, 107], [319, 114], [295, 114]], [[220, 107], [242, 107], [242, 113], [220, 113]], [[176, 107], [203, 107], [203, 113], [176, 113]], [[145, 107], [161, 107], [161, 114], [145, 114]], [[334, 105], [372, 105], [372, 114], [334, 114]], [[63, 106], [86, 106], [86, 113], [63, 113]], [[483, 89], [522, 89], [522, 99], [483, 99]], [[331, 88], [380, 88], [380, 99], [331, 99]], [[276, 88], [325, 88], [325, 99], [276, 99]], [[214, 88], [246, 88], [246, 99], [214, 99]], [[411, 86], [474, 86], [474, 100], [411, 100]], [[6, 86], [102, 86], [102, 100], [6, 100]], [[415, 66], [461, 66], [461, 77], [415, 77]], [[288, 66], [333, 66], [333, 77], [288, 77]], [[157, 64], [206, 64], [206, 78], [157, 78]], [[416, 48], [523, 49], [523, 63], [415, 62]], [[288, 49], [375, 49], [375, 63], [288, 63]], [[159, 49], [269, 49], [269, 62], [159, 62]], [[24, 53], [36, 46], [45, 59], [33, 67]], [[416, 36], [481, 36], [481, 46], [416, 46]], [[25, 38], [39, 32], [46, 46], [33, 52]], [[157, 34], [205, 34], [205, 47], [157, 47]], [[412, 4], [527, 4], [527, 17], [412, 17]], [[146, 4], [345, 2], [345, 15], [146, 17]]] -test_add_20.jpg [[[31, 346], [605, 346], [605, 370], [31, 370]], [[217, 294], [510, 294], [510, 322], [217, 322]], [[473, 271], [525, 271], [525, 286], [473, 286]], [[220, 267], [287, 267], [287, 286], [220, 286]], [[219, 239], [484, 239], [484, 263], [219, 263]], [[221, 217], [303, 217], [303, 234], [221, 234]], [[402, 192], [417, 192], [417, 205], [402, 205]], [[222, 187], [341, 187], [341, 207], [222, 207]], [[221, 162], [287, 162], [287, 180], [221, 180]], [[375, 122], [475, 124], [475, 146], [375, 143]], [[222, 124], [356, 122], [356, 143], [222, 146]], [[218, 81], [352, 84], [352, 116], [218, 113]], [[440, 35], [605, 35], [605, 60], [440, 60]], [[72, 16], [398, 16], [398, 44], [72, 44]]] -test_add_3.jpg [[[169, 327], [337, 326], [337, 341], [169, 342]], [[170, 288], [307, 290], [307, 312], [170, 310]], [[171, 221], [323, 221], [323, 234], [171, 234]], [[340, 221], [449, 217], [449, 231], [341, 234]], [[169, 201], [372, 201], [372, 214], [169, 214]], [[170, 183], [418, 183], [418, 196], [170, 196]], [[170, 149], [416, 149], [416, 163], [170, 163]], [[171, 119], [418, 119], [418, 140], [171, 140]], [[326, 64], [478, 64], [478, 91], [326, 91]], [[173, 64], [306, 60], [306, 89], [174, 93]]] -test_add_4.png [] -test_add_5.png [[[48, 164], [108, 164], [108, 174], [48, 174]], [[52, 121], [169, 121], [169, 134], [52, 134]], [[50, 102], [165, 102], [165, 118], [50, 118]], [[52, 83], [164, 83], [164, 100], [52, 100]], [[51, 68], [166, 68], [166, 84], [51, 84]], [[51, 50], [145, 47], [145, 64], [52, 67]]] -test_add_6.jpg [[[123, 223], [219, 227], [218, 251], [122, 247]], [[172, 172], [186, 186], [172, 200], [158, 186]]] -test_add_7.jpg [[[48, 938], [174, 936], [174, 962], [48, 964]], [[227, 873], [629, 876], [628, 953], [226, 949]], [[56, 745], [638, 745], [638, 790], [56, 790]], [[150, 674], [545, 678], [544, 721], [150, 718]], [[73, 504], [633, 504], [633, 601], [73, 601]], [[59, 270], [655, 279], [652, 441], [56, 432]], [[513, 193], [553, 193], [553, 223], [513, 223]], [[61, 175], [532, 175], [532, 239], [61, 239]], [[533, 178], [642, 178], [642, 236], [533, 236]]] -test_add_8.jpg [[[251, 586], [454, 580], [454, 606], [252, 613]], [[107, 533], [457, 527], [457, 560], [108, 566]], [[336, 494], [384, 494], [384, 507], [336, 507]], [[27, 307], [355, 297], [356, 320], [28, 330]], [[22, 259], [445, 251], [445, 274], [23, 282]], [[78, 209], [445, 205], [445, 225], [78, 229]], [[160, 23], [319, 30], [317, 79], [158, 72]]] -test_add_9.png [[[266, 687], [486, 687], [486, 696], [266, 696]], [[196, 668], [554, 668], [554, 681], [196, 681]], [[154, 596], [597, 596], [597, 606], [154, 606]], [[215, 578], [541, 578], [541, 588], [215, 588]], [[134, 560], [615, 560], [615, 570], [134, 570]], [[85, 543], [665, 543], [665, 553], [85, 553]], [[96, 522], [653, 522], [653, 535], [96, 535]], [[362, 449], [389, 449], [389, 460], [362, 460]], [[238, 376], [513, 376], [513, 389], [238, 389]], [[177, 356], [574, 356], [574, 368], [177, 368]], [[344, 281], [408, 283], [407, 297], [343, 294]], [[257, 205], [493, 205], [493, 219], [257, 219]]] diff --git a/tests/test.sh b/tests/test.sh deleted file mode 100644 index 484d55735368fa7ae341d63e9cb01439f511e2fe..0000000000000000000000000000000000000000 --- a/tests/test.sh +++ /dev/null @@ -1,476 +0,0 @@ -#!/bin/bash -FILENAME=$1 -# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer'] -MODE=$2 - -dataline=$(cat ${FILENAME}) - -# parser params -IFS=$'\n' -lines=(${dataline}) - -function func_parser_key(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[0]} - echo ${tmp} -} -function func_parser_value(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[1]} - echo ${tmp} -} -function func_set_params(){ - key=$1 - value=$2 - if [ ${key} = "null" ];then - echo " " - elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then - echo " " - else - echo "${key}=${value}" - fi -} -function func_parser_params(){ - strs=$1 - IFS=":" - array=(${strs}) - key=${array[0]} - tmp=${array[1]} - IFS="|" - res="" - for _params in ${tmp[*]}; do - IFS="=" - array=(${_params}) - mode=${array[0]} - value=${array[1]} - if [[ ${mode} = ${MODE} ]]; then - IFS="|" - #echo $(func_set_params "${mode}" "${value}") - echo $value - break - fi - IFS="|" - done - echo ${res} -} -function status_check(){ - last_status=$1 # the exit code - run_command=$2 - run_log=$3 - if [ $last_status -eq 0 ]; then - echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log} - else - echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log} - fi -} - -IFS=$'\n' -# The training params -model_name=$(func_parser_value "${lines[1]}") -python=$(func_parser_value "${lines[2]}") -gpu_list=$(func_parser_value "${lines[3]}") -train_use_gpu_key=$(func_parser_key "${lines[4]}") -train_use_gpu_value=$(func_parser_value "${lines[4]}") -autocast_list=$(func_parser_value "${lines[5]}") -autocast_key=$(func_parser_key "${lines[5]}") -epoch_key=$(func_parser_key "${lines[6]}") -epoch_num=$(func_parser_params "${lines[6]}") -save_model_key=$(func_parser_key "${lines[7]}") -train_batch_key=$(func_parser_key "${lines[8]}") -train_batch_value=$(func_parser_params "${lines[8]}") -pretrain_model_key=$(func_parser_key "${lines[9]}") -pretrain_model_value=$(func_parser_value "${lines[9]}") -train_model_name=$(func_parser_value "${lines[10]}") -train_infer_img_dir=$(func_parser_value "${lines[11]}") -train_param_key1=$(func_parser_key "${lines[12]}") -train_param_value1=$(func_parser_value "${lines[12]}") - -trainer_list=$(func_parser_value "${lines[14]}") -trainer_norm=$(func_parser_key "${lines[15]}") -norm_trainer=$(func_parser_value "${lines[15]}") -pact_key=$(func_parser_key "${lines[16]}") -pact_trainer=$(func_parser_value "${lines[16]}") -fpgm_key=$(func_parser_key "${lines[17]}") -fpgm_trainer=$(func_parser_value "${lines[17]}") -distill_key=$(func_parser_key "${lines[18]}") -distill_trainer=$(func_parser_value "${lines[18]}") -trainer_key1=$(func_parser_key "${lines[19]}") -trainer_value1=$(func_parser_value "${lines[19]}") -trainer_key2=$(func_parser_key "${lines[20]}") -trainer_value2=$(func_parser_value "${lines[20]}") - -eval_py=$(func_parser_value "${lines[23]}") -eval_key1=$(func_parser_key "${lines[24]}") -eval_value1=$(func_parser_value "${lines[24]}") - -save_infer_key=$(func_parser_key "${lines[27]}") -export_weight=$(func_parser_key "${lines[28]}") -norm_export=$(func_parser_value "${lines[29]}") -pact_export=$(func_parser_value "${lines[30]}") -fpgm_export=$(func_parser_value "${lines[31]}") -distill_export=$(func_parser_value "${lines[32]}") -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]}") - -# parser inference model -infer_model_dir_list=$(func_parser_value "${lines[36]}") -infer_export_list=$(func_parser_value "${lines[37]}") -infer_is_quant=$(func_parser_value "${lines[38]}") -# parser inference -inference_py=$(func_parser_value "${lines[39]}") -use_gpu_key=$(func_parser_key "${lines[40]}") -use_gpu_list=$(func_parser_value "${lines[40]}") -use_mkldnn_key=$(func_parser_key "${lines[41]}") -use_mkldnn_list=$(func_parser_value "${lines[41]}") -cpu_threads_key=$(func_parser_key "${lines[42]}") -cpu_threads_list=$(func_parser_value "${lines[42]}") -batch_size_key=$(func_parser_key "${lines[43]}") -batch_size_list=$(func_parser_value "${lines[43]}") -use_trt_key=$(func_parser_key "${lines[44]}") -use_trt_list=$(func_parser_value "${lines[44]}") -precision_key=$(func_parser_key "${lines[45]}") -precision_list=$(func_parser_value "${lines[45]}") -infer_model_key=$(func_parser_key "${lines[46]}") -image_dir_key=$(func_parser_key "${lines[47]}") -infer_img_dir=$(func_parser_value "${lines[47]}") -save_log_key=$(func_parser_key "${lines[48]}") -benchmark_key=$(func_parser_key "${lines[49]}") -benchmark_value=$(func_parser_value "${lines[49]}") -infer_key1=$(func_parser_key "${lines[50]}") -infer_value1=$(func_parser_value "${lines[50]}") - -if [ ${MODE} = "cpp_infer" ]; then - # parser cpp inference model - cpp_infer_model_dir_list=$(func_parser_value "${lines[53]}") - cpp_infer_is_quant=$(func_parser_value "${lines[54]}") - # parser cpp inference - inference_cmd=$(func_parser_value "${lines[55]}") - cpp_use_gpu_key=$(func_parser_key "${lines[56]}") - cpp_use_gpu_list=$(func_parser_value "${lines[56]}") - cpp_use_mkldnn_key=$(func_parser_key "${lines[57]}") - cpp_use_mkldnn_list=$(func_parser_value "${lines[57]}") - cpp_cpu_threads_key=$(func_parser_key "${lines[58]}") - cpp_cpu_threads_list=$(func_parser_value "${lines[58]}") - cpp_batch_size_key=$(func_parser_key "${lines[59]}") - cpp_batch_size_list=$(func_parser_value "${lines[59]}") - cpp_use_trt_key=$(func_parser_key "${lines[60]}") - cpp_use_trt_list=$(func_parser_value "${lines[60]}") - cpp_precision_key=$(func_parser_key "${lines[61]}") - cpp_precision_list=$(func_parser_value "${lines[61]}") - cpp_infer_model_key=$(func_parser_key "${lines[62]}") - cpp_image_dir_key=$(func_parser_key "${lines[63]}") - cpp_infer_img_dir=$(func_parser_value "${lines[63]}") - cpp_save_log_key=$(func_parser_key "${lines[64]}") - cpp_benchmark_key=$(func_parser_key "${lines[65]}") - cpp_benchmark_value=$(func_parser_value "${lines[65]}") -fi - - -LOG_PATH="./tests/output" -mkdir -p ${LOG_PATH} -status_log="${LOG_PATH}/results.log" - - -function func_inference(){ - IFS='|' - _python=$1 - _script=$2 - _model_dir=$3 - _log_path=$4 - _img_dir=$5 - _flag_quant=$6 - # inference - for use_gpu in ${use_gpu_list[*]}; do - if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then - for use_mkldnn in ${use_mkldnn_list[*]}; do - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - continue - fi - for threads in ${cpu_threads_list[*]}; do - for batch_size in ${batch_size_list[*]}; do - _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}") - set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}") - set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}") - set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") - set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") - command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - done - done - done - elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then - for use_trt in ${use_trt_list[*]}; do - for precision in ${precision_list[*]}; do - if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then - continue - fi - if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then - continue - fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then - continue - fi - for batch_size in ${batch_size_list[*]}; do - _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}") - set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}") - set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}") - set_precision=$(func_set_params "${precision_key}" "${precision}") - set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") - set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") - command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - - done - done - done - else - echo "Does not support hardware other than CPU and GPU Currently!" - fi - done -} - -function func_cpp_inference(){ - IFS='|' - _script=$1 - _model_dir=$2 - _log_path=$3 - _img_dir=$4 - _flag_quant=$5 - # inference - for use_gpu in ${cpp_use_gpu_list[*]}; do - if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then - for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - continue - fi - for threads in ${cpp_cpu_threads_list[*]}; do - for batch_size in ${cpp_batch_size_list[*]}; do - _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}") - set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}") - set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}") - set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}") - command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - done - done - done - elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then - for use_trt in ${cpp_use_trt_list[*]}; do - for precision in ${cpp_precision_list[*]}; do - if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then - continue - fi - if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then - continue - fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then - continue - fi - for batch_size in ${cpp_batch_size_list[*]}; do - _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}") - set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}") - set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}") - set_precision=$(func_set_params "${cpp_precision_key}" "${precision}") - set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}") - command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - - done - done - done - else - echo "Does not support hardware other than CPU and GPU Currently!" - fi - done -} - -if [ ${MODE} = "infer" ]; then - GPUID=$3 - if [ ${#GPUID} -le 0 ];then - env=" " - else - env="export CUDA_VISIBLE_DEVICES=${GPUID}" - fi - # set CUDA_VISIBLE_DEVICES - eval $env - export Count=0 - IFS="|" - infer_run_exports=(${infer_export_list}) - infer_quant_flag=(${infer_is_quant}) - for infer_model in ${infer_model_dir_list[*]}; do - # run export - if [ ${infer_run_exports[Count]} != "null" ];then - save_infer_dir=$(dirname $infer_model) - set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") - set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") - export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}" - eval $export_cmd - status_export=$? - if [ ${status_export} = 0 ];then - status_check $status_export "${export_cmd}" "${status_log}" - fi - else - save_infer_dir=${infer_model} - fi - #run inference - is_quant=${infer_quant_flag[Count]} - func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant} - Count=$(($Count + 1)) - done - -elif [ ${MODE} = "cpp_infer" ]; then - GPUID=$3 - if [ ${#GPUID} -le 0 ];then - env=" " - else - env="export CUDA_VISIBLE_DEVICES=${GPUID}" - fi - # set CUDA_VISIBLE_DEVICES - eval $env - export Count=0 - IFS="|" - infer_quant_flag=(${cpp_infer_is_quant}) - for infer_model in ${cpp_infer_model_dir_list[*]}; do - #run inference - is_quant=${infer_quant_flag[Count]} - func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant} - Count=$(($Count + 1)) - done - -else - IFS="|" - export Count=0 - USE_GPU_KEY=(${train_use_gpu_value}) - for gpu in ${gpu_list[*]}; do - use_gpu=${USE_GPU_KEY[Count]} - Count=$(($Count + 1)) - if [ ${gpu} = "-1" ];then - env="" - elif [ ${#gpu} -le 1 ];then - env="export CUDA_VISIBLE_DEVICES=${gpu}" - eval ${env} - elif [ ${#gpu} -le 15 ];then - IFS="," - array=(${gpu}) - env="export CUDA_VISIBLE_DEVICES=${array[0]}" - IFS="|" - else - IFS=";" - array=(${gpu}) - ips=${array[0]} - gpu=${array[1]} - IFS="|" - env=" " - fi - for autocast in ${autocast_list[*]}; do - for trainer in ${trainer_list[*]}; do - flag_quant=False - if [ ${trainer} = ${pact_key} ]; then - run_train=${pact_trainer} - run_export=${pact_export} - flag_quant=True - elif [ ${trainer} = "${fpgm_key}" ]; then - run_train=${fpgm_trainer} - run_export=${fpgm_export} - elif [ ${trainer} = "${distill_key}" ]; then - run_train=${distill_trainer} - run_export=${distill_export} - elif [ ${trainer} = ${trainer_key1} ]; then - run_train=${trainer_value1} - run_export=${export_value1} - elif [[ ${trainer} = ${trainer_key2} ]]; then - run_train=${trainer_value2} - run_export=${export_value2} - else - run_train=${norm_trainer} - run_export=${norm_export} - fi - - if [ ${run_train} = "null" ]; then - continue - fi - - set_autocast=$(func_set_params "${autocast_key}" "${autocast}") - set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}") - set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") - set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}") - set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}") - set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}") - save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}" - - # load pretrain from norm training if current trainer is pact or fpgm trainer - if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then - set_pretrain="${load_norm_train_model}" - fi - - 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} ${set_train_params1}" - else # train with multi-machine - cmd="${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}" - fi - # run train - eval "unset CUDA_VISIBLE_DEVICES" - eval $cmd - status_check $? "${cmd}" "${status_log}" - - set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") - # save norm trained models to set pretrain for pact training and fpgm training - if [ ${trainer} = ${trainer_norm} ]; then - load_norm_train_model=${set_eval_pretrain} - fi - # run eval - if [ ${eval_py} != "null" ]; then - set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}") - eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" - eval $eval_cmd - status_check $? "${eval_cmd}" "${status_log}" - fi - # run export model - if [ ${run_export} != "null" ]; then - # run export model - save_infer_path="${save_log}" - set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}") - set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}") - export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}" - eval $export_cmd - status_check $? "${export_cmd}" "${status_log}" - - #run inference - eval $env - save_infer_path="${save_log}" - func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" - eval "unset CUDA_VISIBLE_DEVICES" - fi - done # done with: for trainer in ${trainer_list[*]}; do - done # done with: for autocast in ${autocast_list[*]}; do - done # done with: for gpu in ${gpu_list[*]}; do -fi # end if [ ${MODE} = "infer" ]; then diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 7f60773c3e76aa4bf66caeb29dc2968be49cc51a..466f824c29d5493f56e56bc3243fc907aec24d60 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -236,11 +236,11 @@ def create_predictor(args, mode, logger): max_input_shape.update(max_pact_shape) opt_input_shape.update(opt_pact_shape) elif mode == "rec": - min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]} + min_input_shape = {"x": [1, 3, 32, 10]} max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]} opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]} elif mode == "cls": - min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]} + min_input_shape = {"x": [1, 3, 48, 10]} max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]} opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]} else: